From c91a70b501aa92dfae514414d98bad04e0d17b48 Mon Sep 17 00:00:00 2001 From: dzalkind Date: Wed, 6 Dec 2023 13:05:41 -0700 Subject: [PATCH 001/114] Cast raft_cases, so it's easier to save as an input yaml --- weis/frequency/raft_wrapper.py | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/weis/frequency/raft_wrapper.py b/weis/frequency/raft_wrapper.py index f2a5a1ad6..190b4b59b 100644 --- a/weis/frequency/raft_wrapper.py +++ b/weis/frequency/raft_wrapper.py @@ -45,7 +45,7 @@ def setup(self): members_opt = {} members_opt['nmembers'] = len(weis_opt["floating"]["members"]["name"]) members_opt['npts'] = weis_opt["floating"]["members"]["n_height"] - members_opt['npts_lfill'] = members_opt['npts_rho_fill'] = [m-1 for m in members_opt['npts']] + members_opt['npts_lfill'] = members_opt['npts_rho_fill'] = [int(m-1) for m in members_opt['npts']] members_opt['ncaps'] = weis_opt["floating"]["members"]["n_bulkheads"] members_opt['nreps'] = [0]*members_opt['nmembers'] members_opt['shape'] = ['circ']*members_opt['nmembers'] @@ -323,15 +323,15 @@ def compute(self, inputs, outputs, discrete_inputs, discrete_outputs): turbStr = f'{dlc_generator.wind_speed_class}{turb_class}_{wind_type}' opStr = 'operating' if icase.turbine_status.lower() == 'operating' else 'parked' waveStr = 'JONSWAP' #icase.wave_spectrum - raft_cases[i] = [icase.URef, - icase.wind_heading, - turbStr, - opStr, - icase.yaw_misalign, - waveStr, - max(1.0, icase.wave_period), - max(1.0, icase.wave_height), - icase.wave_heading] + raft_cases[i] = [float(icase.URef), + float(icase.wind_heading), + str(turbStr), + str(opStr), + float(icase.yaw_misalign), + str(waveStr), + float(max(1.0, icase.wave_period)), + float(max(1.0, icase.wave_height)), + float(icase.wave_heading)] discrete_outputs['raft_dlcs'] = raft_cases discrete_outputs['raft_dlcs_keys'] = ['wind_speed', 'wind_heading', 'turbulence', 'turbine_status', 'yaw_misalign', 'wave_spectrum', From 08fa5c0598ec473bd857ef0e0b9154d03dd308d6 Mon Sep 17 00:00:00 2001 From: dzalkind Date: Wed, 6 Dec 2023 13:11:43 -0700 Subject: [PATCH 002/114] Add IEA-22 optimization inputs --- .../IEA-22-280-RWT-Semi.yaml | 1077 +++++++++++++++++ .../IEA-22-280-RWT-Semi_ROSCO.yaml | 93 ++ .../analysis_options_control_tune.yaml | 53 + .../analysis_options_raft_ptfm_opt.yaml | 129 ++ .../analysis_options_single_run.yaml | 16 + .../driver_weis_raft_opt.py | 29 + .../modeling_options_openfast.yaml | 142 +++ .../modeling_options_openfast_nowisdem.yaml | 54 + .../modeling_options_raft.yaml | 90 ++ 9 files changed, 1683 insertions(+) create mode 100644 examples/17_IEA22_Optimization/IEA-22-280-RWT-Semi.yaml create mode 100644 examples/17_IEA22_Optimization/IEA-22-280-RWT-Semi_ROSCO.yaml create mode 100644 examples/17_IEA22_Optimization/analysis_options_control_tune.yaml create mode 100644 examples/17_IEA22_Optimization/analysis_options_raft_ptfm_opt.yaml create mode 100644 examples/17_IEA22_Optimization/analysis_options_single_run.yaml create mode 100644 examples/17_IEA22_Optimization/driver_weis_raft_opt.py create mode 100644 examples/17_IEA22_Optimization/modeling_options_openfast.yaml create mode 100644 examples/17_IEA22_Optimization/modeling_options_openfast_nowisdem.yaml create mode 100644 examples/17_IEA22_Optimization/modeling_options_raft.yaml diff --git a/examples/17_IEA22_Optimization/IEA-22-280-RWT-Semi.yaml b/examples/17_IEA22_Optimization/IEA-22-280-RWT-Semi.yaml new file mode 100644 index 000000000..f28ff70c6 --- /dev/null +++ b/examples/17_IEA22_Optimization/IEA-22-280-RWT-Semi.yaml @@ -0,0 +1,1077 @@ +name: IEA 22MW Offshore Wind Turbine in Floating Configuration +assembly: {turbine_class: I, turbulence_class: B, drivetrain: direct_drive, rotor_orientation: Upwind, number_of_blades: 3, hub_height: 170.0, rotor_diameter: 282.3766, rated_power: 22000000.0, lifetime: 25.0} +components: + blade: + outer_shape_bem: + airfoil_position: + grid: 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0.032632696059638595, 0.032082184545250386, 0.0317749381932823, 0.03161726722873818, 0.03154450419541661, 0.03151523768079876, 0.03150421337620152, 0.03150073462141164, 0.03150025020706162, 0.03150004927231167, 0.03150014528066387, 0.03149970050312757, 0.0314996837421847, 0.03150025236418264, 0.03150041966863019, 0.031499911576540196] + outfitting_factor: 1.0 + floating_platform: + transition_piece_mass: 100000.0 + transition_piece_cost: 100000.0 + joints: + - name: main_keel + location: [0.0, 0.0, -25.0] + transition: false + cylindrical: false + - name: main_freeboard + location: [0.0, 0.0, 15.0] + transition: true + cylindrical: false + - name: col1_keel + location: [65.0, 3.14159265, -25.0] + cylindrical: true + transition: false + - name: col1_freeboard + location: [65.0, 3.14159265, 15.0] + cylindrical: true + transition: false + - name: col2_keel + location: [65.0, 1.0471976, -25.0] + cylindrical: true + transition: false + - name: col2_freeboard + location: [65.0, 1.0471975, 15.0] + cylindrical: true + transition: false + - name: col3_keel + location: [65.0, -1.0471976, -25.0] + cylindrical: true + transition: false + - name: col3_freeboard + location: [65.0, -1.0471976, 15.0] + cylindrical: true + transition: false + - name: anchor1 + location: [837.8, 3.14159265, -200.0] + cylindrical: true + transition: false + - name: anchor2 + location: [837.8, 1.0471976, -200.0] + cylindrical: true + transition: false + - name: anchor3 + location: [837.8, -1.0471976, -200.0] + cylindrical: true + transition: false + members: + - name: main_column + joint1: main_keel + joint2: main_freeboard + Ca: 1.0 + Cd: 0.8 + outer_shape: + shape: circular + outer_diameter: + grid: [0.0, 1.0] + values: [12.0, 12.0] + internal_structure: + layers: + - name: main_twall + material: steel + thickness: + grid: [0.0, 1.0] + values: [0.05, 0.05] + bulkhead: + material: steel + thickness: + grid: [0.0, 0.1, 0.25, 1.0] + values: [0.05, 0.05, 0.05, 0.05] + ballasts: + - variable_flag: true + grid: [0.0, 0.25] + outfitting_factor: 1.0 + axial_joints: + - {name: main_upper_pontoon, grid: 0.987} + - {name: main_lower_pontoon, grid: 0.2} + Cp: 0.0 + - name: column1 + joint1: col1_keel + joint2: col1_freeboard + Ca: 1.0 + Cd: 0.8 + outer_shape: &id004 + shape: circular + outer_diameter: + grid: [0.0, 1.0] + values: [12.5, 12.5] + internal_structure: &id005 + layers: + - name: col_twall + material: steel + thickness: + grid: [0.0, 1.0] + values: [0.05, 0.05] + bulkhead: + material: steel + thickness: + grid: [0.0, 0.35, 1.0] + values: [0.05, 0.05, 0.05] + ring_stiffeners: {material: steel, flange_thickness: 0.1, flange_width: 0.2, web_height: 0.2, web_thickness: 0.1, spacing: 0.083333} + longitudinal_stiffeners: {material: steel, flange_thickness: 0.1, flange_width: 0.2, web_height: 0.2, web_thickness: 0.1, spacing: 0.52359} + ballasts: + - variable_flag: true + grid: [0.0, 0.35] + outfitting_factor: 1.0 + axial_joints: + - {name: col1_upper_pontoon, grid: 0.987} + - {name: col1_lower_pontoon, grid: 0.2} + - {name: col1_fairlead, grid: 0.1714} + Cp: 0.0 + - name: column2 + joint1: col2_keel + joint2: col2_freeboard + Ca: 1.0 + Cd: 0.8 + outer_shape: *id004 + internal_structure: *id005 + axial_joints: + - {name: col2_upper_pontoon, grid: 0.987} + - {name: col2_lower_pontoon, grid: 0.2} + - {name: col2_fairlead, grid: 0.1714} + Cp: 0.0 + - name: column3 + joint1: col3_keel + joint2: col3_freeboard + Ca: 1.0 + Cd: 0.8 + outer_shape: *id004 + internal_structure: *id005 + axial_joints: + - {name: col3_upper_pontoon, grid: 0.987} + - {name: col3_lower_pontoon, grid: 0.2} + - {name: col3_fairlead, grid: 0.1714} + Cp: 0.0 + - name: Y_pontoon_upper1 + joint1: main_upper_pontoon + joint2: col1_upper_pontoon + Ca: 1.0 + Cd: 0.8 + outer_shape: &id006 + shape: circular + outer_diameter: + grid: [0.0, 1.0] + values: [0.91, 0.91] + internal_structure: &id007 + layers: + - name: pontoon_upper_twall + material: steel + thickness: + grid: [0.0, 1.0] + values: [0.02, 0.02] + outfitting_factor: 1.0 + bulkhead: + material: steel + thickness: + grid: [0.0, 1.0] + values: [0.02, 0.02] + Cp: 0.0 + - name: Y_pontoon_upper2 + joint1: main_upper_pontoon + joint2: col2_upper_pontoon + Ca: 1.0 + Cd: 0.8 + outer_shape: *id006 + internal_structure: *id007 + Cp: 0.0 + - name: Y_pontoon_upper3 + joint1: main_upper_pontoon + joint2: col3_upper_pontoon + Ca: 1.0 + Cd: 0.8 + outer_shape: *id006 + internal_structure: *id007 + Cp: 0.0 + - name: Y_pontoon_lower1 + joint1: main_lower_pontoon + joint2: col1_lower_pontoon + Ca: 1.0 + Cd: 0.8 + outer_shape: &id008 + shape: circular + outer_diameter: + grid: [0.0, 1.0] + values: [10.0, 10.0] + internal_structure: &id009 + layers: + - name: pontoon_lower_twall + material: steel + thickness: + grid: [0.0, 1.0] + values: [0.04, 0.04] + ballasts: + - variable_flag: true + grid: [0.0, 1.0] + outfitting_factor: 1.0 + bulkhead: + material: steel + thickness: + grid: [0.0, 1.0] + values: [0.02, 0.02] + Cp: 0.0 + - name: Y_pontoon_lower2 + joint1: main_lower_pontoon + joint2: col2_lower_pontoon + outer_shape: *id008 + internal_structure: *id009 + Ca: 1.0 + Cd: 0.8 + Cp: 0.0 + - name: Y_pontoon_lower3 + joint1: main_lower_pontoon + joint2: col3_lower_pontoon + outer_shape: *id008 + internal_structure: *id009 + Ca: 1.0 + Cd: 0.8 + Cp: 0.0 + mooring: + nodes: + - {name: line1_anchor, node_type: fixed, joint: anchor1, anchor_type: drag_embedment, fairlead_type: rigid, node_mass: 0.0, node_volume: 0.0, drag_area: 0.0, added_mass: 0.0} + - {name: line2_anchor, node_type: fixed, joint: anchor2, anchor_type: drag_embedment, fairlead_type: rigid, node_mass: 0.0, node_volume: 0.0, drag_area: 0.0, added_mass: 0.0} + - {name: line3_anchor, node_type: fixed, joint: anchor3, anchor_type: drag_embedment, fairlead_type: rigid, node_mass: 0.0, node_volume: 0.0, drag_area: 0.0, added_mass: 0.0} + - {name: line1_vessel, node_type: vessel, joint: col1_fairlead, fairlead_type: rigid, anchor_type: none, node_mass: 0.0, node_volume: 0.0, drag_area: 0.0, added_mass: 0.0} + - {name: line2_vessel, node_type: vessel, joint: col2_fairlead, fairlead_type: rigid, anchor_type: none, node_mass: 0.0, node_volume: 0.0, drag_area: 0.0, added_mass: 0.0} + - {name: line3_vessel, joint: col3_fairlead, node_type: vessel, fairlead_type: rigid, anchor_type: none, node_mass: 0.0, node_volume: 0.0, drag_area: 0.0, added_mass: 0.0} + lines: + - {name: line1, node1: line1_anchor, node2: line1_vessel, line_type: main, unstretched_length: 837.0} + - {name: line2, node1: line2_anchor, node2: line2_vessel, line_type: main, unstretched_length: 837.0} + - {name: line3, node1: line3_anchor, node2: line3_vessel, line_type: main, unstretched_length: 837.0} + line_types: + - {name: main, diameter: 0.225, type: chain, transverse_added_mass: 1.0, tangential_added_mass: 0.0, transverse_drag: 1.6, tangential_drag: 0.1, damping: 0.0} + anchor_types: + - {name: drag_embedment, type: drag_embedment} +airfoils: + - name: FFA-W3-211 + coordinates: + x: [1.0, 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30% fully turbulent, 360 deg extrapolated using AirfoilPreppy, no 3D correction. 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airfoil data for 10 MW sized rotor, computed using EllipSys2D v16.0, 70% free transition, 30% fully turbulent, 360 deg extrapolated using AirfoilPreppy, no 3D correction. 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-0.12434, -0.09369, -0.06267, -0.0314, 0.0] + relative_thickness: 1.0 + aerodynamic_center: 0.5 + polars: + - configuration: Default + re: 3000000.0 + c_l: + grid: [-3.14, 3.14] + values: [0.0001, 0.0001] + c_d: + grid: [-3.14, 3.14] + values: [0.35, 0.35] + c_m: + grid: [-3.14, 3.14] + values: [-0.0001, -0.0001] +materials: + - {name: gelcoat, rho: 1235.0, E: 3440000000.0, G: 1323000000.0, nu: 0.3, orth: 0, ply_t: 0.0005, waste: 0.25, unit_cost: 7.23, Xt: 74, Xc: 87, alpha: 0.0, S: 21260000.0, GIc: 303, GIIc: 3446, alp0: 53} + - {name: Adhesive, orth: 0.0, rho: 1100.0, E: 4560000.0, G: 1450000.0, nu: 0.3, alpha: 0.0, Xt: 61510000.0, Xc: 65360000.0, S: 36610000.0, GIc: 0.0, GIIc: 0.0, alp0: 0.0, ply_t: 0.001, waste: 0.25, unit_cost: 7.23} + - {name: steel, rho: 7850.0, E: 200000000000.0, G: 79300000000.0, nu: 0.265, orth: 0, GIc: 0, GIIc: 0, alp0: 0, Xt: 450000000.0, Xc: 450000000.0, S: 0, Xy: 345000000.0, m: 3, unit_cost: 0.7} + - {name: steel_drive, description: Steel of the drivetrain ASTM 4140 40Cr1Mo28, source: http://www.matweb.com/search/DataSheet.aspx?MatGUID=38108bfd64c44b4c9c6a02af78d5b6c6, orth: 0, rho: 7850, alpha: 0.0, E: 205000000000.0, nu: 0.3, G: 80000000000.0, GIc: 0, GIIc: 0, alp0: 0, Xt: 814000000.0, Xc: 814000000.0, S: 0, Xy: 485000000.0, m: 3, unit_cost: 0.9} + - {name: cast_iron, description: Cast iron for hub and nacelle components, source: TODO, orth: 0, rho: 7200, alpha: 0.0, E: 118000000000.0, nu: 0.3, G: 47600000000.0, GIc: 0, GIIc: 0, alp0: 0, Xt: 310000000.0, Xc: 310000000.0, S: 0, Xy: 265000000.0, m: 3, unit_cost: 0.5} + - name: glass_uniax + description: Vectorply E-LT-5500, Epikote MGS RIMR 135/Epicure MGS RIMH 1366 epoxy + source: MSU composites database 3D property tests, desrcibed in https://doi.org/10.5194/wes-7-19-2022 + orth: 1.0 + rho: 1940.0 + E: [43700000000.0, 16500000000.0, 15450000000.0] + G: [3265000000.0, 3495000000.0, 3480000000.0] + nu: [0.262, 0.264, 0.35] + Xt: [640230000.0, 38100000.0, 0.0] + Xc: [370690000.0, 82180000.0, 0] + S: [30170000.0, 18970000.0, 6210000.0] + m: 10 + GIc: 61.0 + GIIc: 101.0 + alp0: 50.0 + fvf: 0.57 + fwf: 0.7450682696347697 + ply_t: 0.005 + unit_cost: 1.87 + waste: 0.05 + fiber_density: 2535.5 + area_density_dry: 7.227162215457267 + component_id: 5 + - name: carbon_uniax + description: Baseline industry CFRP tested by Sandia National Labs within the BAR Project + source: Described in https://doi.org/10.5194/wes-7-19-2022 and https://energy.sandia.gov/download/45350/, RVE analysis. Nominal E11 is 157.6 GPa, but it is reduced to 140 GPa to be more in line with the actual properties seen in real blades. + orth: 1.0 + rho: 1600.0 + E: [140000000000.0, 9100000000.0, 9100000000.0] + G: [4131000000.0, 4131000000.0, 2689000000.0] + nu: [0.3133, 0.3133, 0.47066] + Xt: [1285200000.0, 38100000.0, 0.0] + Xc: [878210000.0, 82180000.0, 0.0] + S: [30170000.0, 18970000.0, 6210000.0] + m: 16.1 + GIc: 0.0 + GIIc: 0.0 + alp0: 0.0 + fvf: 0.6923076923076923 + fwf: 0.7788461538461539 + ply_t: 0.005 + unit_cost: 20.08 + waste: 0.05 + fiber_density: 1800.0 + area_density_dry: 6.230769230769231 + component_id: 4 + - name: glass_biax + description: "(+-45)6 PPG-Devold DB810- E05\u2014a fabric infused with Epikote MGS RIMR 135/Epicure RIMH 1366 epoxy resin" + source: Described in https://doi.org/10.5194/wes-7-19-2022 + orth: 1.0 + rho: 1940.0 + E: [11023100000.0, 11023100000.0, 16047700000.0] + G: [13231400000.0, 3487480000.0, 3487480000.0] + nu: [0.688074, 0.117173, 0.117173] + Xt: [46210000.0, 46210000.0, 46210000.0] + Xc: [70690000.0, 70690000.0, 70690000.0] + S: [124460000.0, 18970000.0, 6210000.0] + m: 10 + GIc: 41.0 + GIIc: 170.0 + alp0: 45.0 + fvf: 0.57 + fwf: 0.7450682696347697 + ply_t: 0.001 + waste: 0.15 + unit_cost: 3.0 + fiber_density: 2535.5 + area_density_dry: 1.4454324430914534 + component_id: 3 + roll_mass: 181.4368 + - name: glass_triax + description: "((\xB145)(0)2)s Saertex U14EU920-00940-T1300 and Saertex VU-90079-00830-01270\u2014fabrics infused with Vantico TDT 177-155." + source: Described in https://doi.org/10.5194/wes-7-19-2022 + orth: 1.0 + rho: 1940.0 + E: [28211400000.0, 16238800000.0, 15835500000.0] + G: [8248220000.0, 3491240000.0, 3491240000.0] + nu: [0.497511, 0.18091, 0.27481] + Xt: [435630000.0, 76440000.0, 0] + Xc: [343100000.0, 174710000.0, 0] + S: [85060000.0, 18970000.0, 6210000.0] + m: 10 + GIc: 41.0 + GIIc: 170.0 + alp0: 45.0 + fvf: 0.57 + fwf: 0.7450682696347697 + ply_t: 0.001 + unit_cost: 2.86 + waste: 0.15 + fiber_density: 2535.5 + area_density_dry: 1.4454324430914534 + component_id: 2 + roll_mass: 181.4368 + - {name: medium_density_foam, description: "Airex C70.130 PVC Foam, source 'https://www.3accorematerials.com/uploads/documents/TDS-AIREX-C70-E_1106.pdf'", source: Described in https://doi.org/10.5194/wes-7-19-2022, orth: 0.0, rho: 130.0, E: 142500000.0, G: 54000000.0, nu: 0.3194, Xt: 2083000.0, Xc: 1563000.0, S: 1250000.0, GIc: 303.0, GIIc: 3446.0, alp0: 53.0, component_id: 1, waste: 0.2, unit_cost: 13.0} + - {name: resin, description: epoxy, source: Described in https://doi.org/10.5194/wes-7-19-2022, E: 1000000.0, nu: 0.3, G: 312500.0, GIc: 0.0, GIIc: 0.0, alp0: 0.0, Xt: 0.0, Xc: 0.0, S: 0.0, rho: 1150.0, alpha: 0.0, orth: 0.0, unit_cost: 3.63} + - {name: slurry, description: fixed/permanent ballast, E: 1.0, nu: 0.3, G: 1.0, Xt: 0, Xc: 0, S: 0, rho: 5000.0, alpha: 0.0, unit_cost: 1.0, orth: 0.0} +control: + supervisory: {Vin: 3.0, Vout: 25.0, maxTS: 105.0} + torque: {control_type: tsr_tracking, tsr: 9.56, VS_minspd: 0.20682151636132806, VS_maxspd: 0.7500028861670033, VS_zeta: 1.0, VS_omega: 0.2, max_torque_rate: 1500000.0} + pitch: {PC_zeta: 1.0, PC_omega: 0.2, ps_percent: 0.8, max_pitch: 1.57, max_pitch_rate: 0.03490658503988659, min_pitch: -0.024434609527920613} + setpoint_smooth: {ss_vsgain: 1, ss_pcgain: 0.001} + shutdown: {limit_type: gen_speed, limit_value: 2.0} +environment: {air_density: 1.225, air_dyn_viscosity: 1.81e-05, weib_shape_parameter: 2.0, air_speed_sound: 340.0, shear_exp: 0.12, water_density: 1025.0, water_dyn_viscosity: 0.0013351, soil_shear_modulus: 140000000.0, soil_poisson: 0.4, water_depth: 200.0, significant_wave_height: 4.52, significant_wave_period: 9.45, gravity: 9.80665, air_pressure: 103500.0, air_vapor_pressure: 1700.0, V_mean: 0.0} +bos: {plant_turbine_spacing: 7, plant_row_spacing: 7, commissioning_pct: 0.01, decommissioning_pct: 0.15, distance_to_substation: 1.0, distance_to_interconnection: 8.5, interconnect_voltage: 130.0, distance_to_site: 115.0, distance_to_landfall: 50.0, port_cost_per_month: 2000000.0, site_auction_price: 100000000.0, site_assessment_plan_cost: 1000000.0, site_assessment_cost: 25000000.0, construction_operations_plan_cost: 2500000.0, boem_review_cost: 0.0, design_install_plan_cost: 2500000.0} +costs: {wake_loss_factor: 0.15, fixed_charge_rate: 0.058, bos_per_kW: 4050, opex_per_kW: 118.0, turbine_number: 27.0, labor_rate: 58.8, painting_rate: 30.0, blade_mass_cost_coeff: 13.291168594347853, hub_mass_cost_coeff: 3.9, pitch_system_mass_cost_coeff: 22.1, spinner_mass_cost_coeff: 11.1, lss_mass_cost_coeff: 11.9, bearing_mass_cost_coeff: 4.5, gearbox_mass_cost_coeff: 12.9, hss_mass_cost_coeff: 6.8, generator_mass_cost_coeff: 23.247612965626796, bedplate_mass_cost_coeff: 2.9, yaw_mass_cost_coeff: 8.3, converter_mass_cost_coeff: 18.8, transformer_mass_cost_coeff: 18.8, hvac_mass_cost_coeff: 362.3333772011725, cover_mass_cost_coeff: 16.60090142324545, elec_connec_machine_rating_cost_coeff: 41.85, platforms_mass_cost_coeff: 17.1, tower_mass_cost_coeff: 2.7071858084019347, controls_machine_rating_cost_coeff: 21.15, crane_cost: 12000.0, electricity_price: 0.04, reserve_margin_price: 120.0, capacity_credit: 0.0, benchmark_price: 0.071} diff --git a/examples/17_IEA22_Optimization/IEA-22-280-RWT-Semi_ROSCO.yaml b/examples/17_IEA22_Optimization/IEA-22-280-RWT-Semi_ROSCO.yaml new file mode 100644 index 000000000..6ef76104b --- /dev/null +++ b/examples/17_IEA22_Optimization/IEA-22-280-RWT-Semi_ROSCO.yaml @@ -0,0 +1,93 @@ +path_params: {FAST_InputFile: IEA-22-280-RWT-Semi.fst, FAST_directory: ., rotor_performance_filename: ../IEA-22-280-RWT/IEA-22-280-RWT_Cp_Ct_Cq.txt} +controller_params: + flag: true + PS_Mode: 3 + ps_percent: 0.8 + Fl_Mode: 1 + zeta_pc: [1.4662448526479779, 2.030062512228778, 1.8130068579395848] + omega_pc: [0.11468384902891134, 0.12082643062647291, 0.3959805145442727] + U_pc: [12, 18, 24] + zeta_vs: 0.85 + omega_vs: 0.12 + twr_freq: 1.4799073778145184 + ptfm_freq: 0.2717484204906427 + Kp_float: -0.1477808226207514 + PA_Mode: 2 + PA_CornerFreq: 6.2831 + PA_Damping: 0.707 + DISCON: + PS_BldPitchMin_N: 60 + PS_WindSpeeds: [3.0, 3.293602652413793, 3.5872053048275863, 3.8808079572413794, 4.1744106096551725, 4.468013262068966, 4.761615914482759, 5.055218566896551, 5.348821219310345, 5.642423871724137, 5.936026524137931, 6.2296291765517235, 6.523231828965517, 6.81683448137931, 7.110437133793103, 7.404039786206896, 7.697642438620689, 7.991245091034482, 8.284847743448275, 8.578450395862069, 8.872053048275863, 9.165655700689655, 9.459258353103447, 9.752861005517241, 10.046463657931033, 10.340066310344827, 10.63366896275862, 10.927271615172412, 11.220874267586206, 11.51447692, 11.963994356, 12.413511792, 12.863029228, 13.312546664, 13.7620641, 14.211581536, 14.661098972, 15.110616408, 15.560133844, 16.00965128, 16.459168716, 16.908686152, 17.358203588, 17.807721024, 18.25723846, 18.706755896, 19.156273331999998, 19.605790768, 20.055308204, 20.50482564, 20.954343076, 21.403860512, 21.853377948000002, 22.302895384, 22.75241282, 23.201930255999997, 23.651447691999998, 24.100965128, 24.550482564, 25.0] + PS_BldPitchMin: [0.03939744826098132, 0.032811574332451486, 0.02622570040392165, 0.019639826475391812, 0.015362459291068742, 0.012662723393672927, 0.009962987496277111, 0.00732906718494601, 0.004979279352572738, 0.002629491520199481, 0.0002797036878262096, -0.0019006143278835366, -0.004033718947680466, -0.006166823567477394, -0.008232603284809691, -0.010186721278838627, -0.011385463477866837, -0.012217452345534272, -0.013651941026173463, -0.015827130794335113, -0.019982926316381596, -0.020555580662001055, -0.018360735976724184, -0.007154098747523962, 0.005385506738315432, 0.017291962538253533, 0.028185701390066556, 0.03786773368900956, 0.05005083534741778, 0.06437912330611562, 0.07459494522693336, 0.08425396493270616, 0.09347708832365398, 0.1024416723404767, 0.11112678992474039, 0.11965993850921552, 0.12799419633516185, 0.136216696214104, 0.14429704932705778, 0.1522740196985694, 0.1601596436844651, 0.1679319530484481, 0.17566224304735262, 0.1832552793235788, 0.1908399713479313, 0.19829531409286574, 0.20572642769823524, 0.21308434293945888, 0.2203745817295131, 0.227658266361481, 0.23481586485432457, 0.24198542779266807, 0.24906497695501842, 0.2561111393627153, 0.26314707558516237, 0.2700754574713929, 0.2770271078594649, 0.2838904779955082, 0.2907256102836033, 0.29756564415039394] + Echo: 0 + DLL_FileName: unused + DLL_InFile: unused + DLL_ProcName: DISCON + PF_Offsets: [0, 0, 0] + CC_Group_N: 0 + CC_GroupIndex: [0] + CC_ActTau: 20 + StC_Group_N: 0 + StC_GroupIndex: [0] + AWC_Mode: 0 + AWC_NumModes: 1 + AWC_n: [1] + AWC_harmonic: [1] + AWC_freq: [0.05] + AWC_amp: [1.0] + AWC_clockangle: [0] + LoggingLevel: 2 + F_LPFType: 1 + F_NotchType: 0 + IPC_ControlMode: 0 + VS_ControlMode: 2 + PC_ControlMode: 1 + Y_ControlMode: 0 + SS_Mode: 1 + WE_Mode: 2 + SD_Mode: 0 + TD_Mode: 0 + Flp_Mode: 0 + PwC_Mode: 0 + ZMQ_Mode: 0 + Ext_Mode: 0 + interp_type: sigma + max_pitch: 1.57 + min_pitch: -0.07 + vs_minspd: 0.0 + ss_vsgain: 1.0 + ss_pcgain: 0.001 + sd_maxpit: 0.6981 + flp_maxpit: 0.1745 + WS_GS_n: 60 + PC_GS_n: 30 + tune_Fl: false + max_torque_factor: 1.1 + IPC_Kp1p: 0.0 + IPC_Kp2p: 0.0 + IPC_Ki1p: 0.0 + IPC_Ki2p: 0.0 + IPC_Vramp: [0.0, 0.0] + filter_params: {f_we_cornerfreq: 0.20944, f_fl_highpassfreq: 0.01042, f_ss_cornerfreq: 0.6283, f_yawerr: 0.17952, f_sd_cornerfreq: 0.41888} + open_loop: {flag: false, filename: unused, OL_Ind_Breakpoint: 1, OL_Ind_BldPitch: 0, OL_Ind_GenTq: 0, OL_Ind_YawRate: 0} + linmodel_tuning: + type: none + linfile_path: none + lintune_outpath: lintune_outfiles + load_parallel: false + stability_margin: 0.1 + omega_pc: {} + omega_flp: 0.0 + zeta_flp: 0.0 + PF_Mode: 0 + AWC_Mode: 0 + CC_Mode: 0 + StC_Mode: 0 +turbine_params: {rotor_inertia: 749154917.0, rated_rotor_speed: 0.739436, v_min: 3.0, v_rated: 11.51447692, v_max: 25.0, max_pitch_rate: 0.03490659, max_torque_rate: 4500000.0, rated_power: 22000000.0, bld_edgewise_freq: 3.6531113561725572, bld_flapwise_freq: 2.7716172770411616, TSR_operational: 9.15} +linmodel_tuning: + type: none + linfile_path: none + lintune_outpath: lintune_outfiles + load_parallel: false + stability_margin: 0.1 + omega_pc: {} diff --git a/examples/17_IEA22_Optimization/analysis_options_control_tune.yaml b/examples/17_IEA22_Optimization/analysis_options_control_tune.yaml new file mode 100644 index 000000000..a6f1ae2e9 --- /dev/null +++ b/examples/17_IEA22_Optimization/analysis_options_control_tune.yaml @@ -0,0 +1,53 @@ +general: + folder_output: 42_UpdatePS_080 + fname_output: IEA-22-280-RWT + +design_variables: + control: + servo: + pitch_control: + omega: + flag: True + min: 0.1 + max: 0.5 + zeta: + flag: True + min: 0.1 + max: 3.0 + Kp_float: + flag: True + min: -0.25 + max: 0 + ptfm_freq: + flag: True + max: 4.0 + +merit_figure: DEL_TwrBsMyt # Merit figure of the optimization problem. The options are 'AEP' - 'LCOE' - 'Cp' - 'blade_mass' - 'blade_tip_deflection' + +constraints: + control: + rotor_overspeed: + flag: True + min: 0.0 + max: 0.25 + +driver: + optimization: + flag: True + tol: 1.e-2 # Optimality tolerance + max_major_iter: 2 # Maximum number of major design iterations (SNOPT) + max_minor_iter: 100 # Maximum number of minor design iterations (SNOPT) + max_iter: 40 # Maximum number of iterations (SLSQP) + solver: LN_COBYLA # Optimization solver. Other options are 'SLSQP' - 'CONMIN' + step_size: 1.e-3 # Step size for finite differencing + form: forward # Finite differencing mode, either forward or central + design_of_experiments: + flag: False # Flag to enable design of experiments + run_parallel: False # Flag to run using parallel processing + generator: LatinHypercube # Type of input generator. (Uniform) + num_samples: 8 # number of samples for (Uniform only) + criterion: center + +recorder: + flag: True # Flag to activate OpenMDAO recorder + file_name: log_opt.sql # Name of OpenMDAO recorder diff --git a/examples/17_IEA22_Optimization/analysis_options_raft_ptfm_opt.yaml b/examples/17_IEA22_Optimization/analysis_options_raft_ptfm_opt.yaml new file mode 100644 index 000000000..81210e051 --- /dev/null +++ b/examples/17_IEA22_Optimization/analysis_options_raft_ptfm_opt.yaml @@ -0,0 +1,129 @@ +general: + folder_output: 32_DesignRound1 + fname_output: IEA-22-280-RWT + +design_variables: + tower: + outer_diameter: + flag: False + lower_bound: 4.0 + upper_bound: 10.0 + layer_thickness: + flag: False + lower_bound: 4.e-3 + upper_bound: 2.e-1 + floating: + joints: + flag: True + z_coordinate: # draft + - names: [main_keel, col1_keel, col2_keel, col3_keel] + lower_bound: -30.0 + upper_bound: -12.0 + # - names: [col1_freeboard, col2_freeboard, col3_freeboard] + # lower_bound: 10.0 + # upper_bound: 20.0 + r_coordinate: + - names: [col1_keel, col1_freeboard, col2_keel, col2_freeboard, col3_keel, col3_freeboard] + lower_bound: 35.0 + upper_bound: 70.0 + members: + flag: True + groups: + - names: [column1, column2, column3] + diameter: + lower_bound: 12.0 + upper_bound: 16.0 + constant: True + # ballast: + # lower_bound: 0 + # upper_bound: 1000 + # - names: [main_column] + # ballast: + # lower_bound: 0 + # upper_bound: 1000 + # - names: [Y_pontoon_lower1, Y_pontoon_lower2, Y_pontoon_lower3] + # diameter: + # lower_bound: 10.0 + # upper_bound: 10.8 + # constant: True +constraints: + tower: + height_constraint: + flag: False + lower_bound: 1.e-2 + upper_bound: 1.e-2 + stress: + flag: False + global_buckling: + flag: False + shell_buckling: + flag: False + d_to_t: + flag: False + lower_bound: 120.0 + upper_bound: 2000.0 + taper: + flag: False + lower_bound: 0.2 + slope: + flag: False + frequency_1: + flag: False + lower_bound: 0.37 + floating: + survival_heel: + upper_bound: 0.17453292519943295 # 10 deg + metacentric_height: + flag: True + lower_bound: 15.0 + pitch_period: + flag: True + lower_bound: 20. # +/- 10% + upper_bound: 80. + heave_period: + flag: True + lower_bound: 16. # +/- 10% + upper_bound: 80. + fixed_ballast_capacity: + flag: True + variable_ballast_capacity: + flag: True + buoyancy: + flag: False + stress: + flag: False + global_buckling: + flag: False + shell_buckling: + flag: False + mooring_heel: + flag: False + freeboard_margin: # keep freeboard from being submerged below water during survival_heel, largest wave + flag: True + draft_margin: # keep draft from raising above water line during survival_heel, largest wave + flag: True + fairlead_depth: # keep fairlead above bottom trough of largest wave + flag: True + control: + Max_PtfmPitch: + flag: True + max: 5.5 + Std_PtfmPitch: + flag: True + max: 0.39 # Same as IEA-15MW with same DLCs + nacelle_acceleration: + flag: True + max: 0.25 + +merit_figure: structural_mass + +driver: + optimization: + flag: True # Flag to enable optimization + tol: 1.e-3 # Optimality tolerance + max_iter: 1000 # Maximum number of iterations (SLSQP) + solver: LN_COBYLA # Optimization solver. Other options are 'SLSQP' - 'CONMIN' + +recorder: + flag: True # Flag to activate OpenMDAO recorder + file_name: log_opt.sql # Name of OpenMDAO recorder diff --git a/examples/17_IEA22_Optimization/analysis_options_single_run.yaml b/examples/17_IEA22_Optimization/analysis_options_single_run.yaml new file mode 100644 index 000000000..b4b1eb829 --- /dev/null +++ b/examples/17_IEA22_Optimization/analysis_options_single_run.yaml @@ -0,0 +1,16 @@ +general: + folder_output: 38_DLCs_0 + fname_output: IEA-22-280-RWT + +merit_figure: structural_mass + +driver: + optimization: + flag: False # Flag to enable optimization + tol: 1.e-3 # Optimality tolerance + max_iter: 1000 # Maximum number of iterations (SLSQP) + solver: LN_COBYLA # Optimization solver. Other options are 'SLSQP' - 'CONMIN' + +recorder: + flag: True # Flag to activate OpenMDAO recorder + file_name: log_opt.sql # Name of OpenMDAO recorder diff --git a/examples/17_IEA22_Optimization/driver_weis_raft_opt.py b/examples/17_IEA22_Optimization/driver_weis_raft_opt.py new file mode 100644 index 000000000..e9769cbdb --- /dev/null +++ b/examples/17_IEA22_Optimization/driver_weis_raft_opt.py @@ -0,0 +1,29 @@ +#!/usr/bin/env python3 +import os +import shutil +from weis.glue_code.runWEIS import run_weis +import wisdem.inputs as sch +import numpy as np +from wisdem.commonse.mpi_tools import MPI + + +## File management +mydir = os.path.dirname(os.path.realpath(__file__)) # get path to this file + +fname_wt_input = mydir + os.sep + 'IEA-22-280-RWT-Semi.yaml' +# fname_wt_input = '/Users/dzalkind/Projects/IEA-22MW/FloaterIEA22/33_Round_DesRo1/IEA-22-280-RWT.yaml' +# fname_wt_input = os.path.join(mydir,'11_Lower_Pontoon_Stiffeners','IEA-22-280-RWT.yaml') +fname_modeling_options = mydir + os.sep + "modeling_options_raft.yaml" +fname_analysis_options = mydir + os.sep + "analysis_options_raft_ptfm_opt.yaml" +wt_opt, modeling_options, analysis_options = run_weis(fname_wt_input, fname_modeling_options, fname_analysis_options) + + +if MPI: + rank = MPI.COMM_WORLD.Get_rank() +else: + rank = 0 +if rank == 0: + # shutil.copyfile(os.path.join(analysis_options['general']['folder_output'],analysis_options['general']['fname_output']+'.yaml'), fname_wt_input) + print("Tower mass (kg) =", wt_opt["towerse.tower_mass"]) + print("Floating platform mass (kg) =", wt_opt["floatingse.platform_mass"]) + diff --git a/examples/17_IEA22_Optimization/modeling_options_openfast.yaml b/examples/17_IEA22_Optimization/modeling_options_openfast.yaml new file mode 100644 index 000000000..48589842b --- /dev/null +++ b/examples/17_IEA22_Optimization/modeling_options_openfast.yaml @@ -0,0 +1,142 @@ +General: + verbosity: False # When set to True, the code prints to screen many infos + openfast_configuration: + OF_run_fst: IEA_22_Semi + # OF_run_dir: outputs/IEA_w_TMD_33 + save_iterations: True + save_timeseries: True + use_exe: True + allow_fails: True + fail_value: 9999 +WISDEM: + RotorSE: + flag: True + n_pitch_perf_surfaces: 20 + n_tsr_perf_surfaces: 20 + spar_cap_ss: spar_cap_ss + spar_cap_ps: spar_cap_ps + # te_ss: TE_reinforcement_SS + # te_ps: TE_reinforcement_PS + peak_thrust_shaving: True + thrust_shaving_coeff: 0.8 + TowerSE: + flag: True + wind: PowerWind # Wind used + gamma_f: 1.35 # Safety factor for fatigue loads + gamma_m: 1.3 # Safety factor for material properties + gamma_n: 1.0 # Safety factor for ... + gamma_b: 1.1 # Safety factor for ... + gamma_fatigue: 1.755 # Safety factor for fatigue loads + buckling_method: dnvgl # Buckling code type [eurocode or dnvgl] + buckling_length: 15 # Buckling parameter + frame3dd: + shear: True + geom: True + tol: 1e-9 + DriveSE: + flag: True + FloatingSE: + flag: True + symmetric_moorings: True + gamma_f: 1.35 # Safety factor for fatigue loads + gamma_m: 1.3 # Safety factor for material properties + gamma_n: 1.0 # Safety factor for ... + gamma_b: 1.1 # Safety factor for ... + gamma_fatigue: 1.755 # Safety factor for fatigue loads + rank_and_file: True + BOS: + flag: True + +Level3: # Options for WEIS fidelity level 3 = nonlinear time domain + flag: True + simulation: + DT: 0.01 + CompElast: 1 + CompInflow: 1 + CompAero: 2 + CompServo: 1 + CompHydro: 1 + CompSub: 0 + CompMooring: 3 + CompIce: 0 + OutFileFmt: 3 + NumCrctn: 1 + ElastoDyn: + FlapDOF1: True + FlapDOF2: True + EdgeDOF: True + TeetDOF: False + DrTrDOF: False + GenDOF: True + YawDOF: False + TwFADOF1 : True + TwFADOF2 : True + TwSSDOF1 : True + TwSSDOF2 : True + PtfmSgDOF: True + PtfmSwDOF: True + PtfmHvDOF: True + PtfmRDOF : True + PtfmPDOF : True + PtfmYDOF : True + # HydroDyn: + # WvLowCOff: 0.15708 + # WvHiCOff: 3.2 + # WaveSeed1: 123456789 + # AddBQuad1: [9.23e5, 0.0, 0.0, 0.0, -8.92e6, 0.0] + # AddBQuad2: [0.0, 9.23e5, 0.0, 8.92e6, 0.0, 0.0] + # AddBQuad3: [0.0, 0.0, 2.3e6, 0.0, 0.0, 0.0] + # AddBQuad4: [0.0, 8.92e6, 0.0, 1.68e10, 0.0, 0.0] + # AddBQuad5: [-8.92e6, 0.0, 0.0, 0.0, 1.68e10, 0.0] + # AddBQuad6: [0.0, 0.0, 0.0, 0.0, 0.0, 4.8e10] + # PotMod: 1 + # WaveMod: 0 + # PotFile: examples/01_aeroelasticse/OpenFAST_models/IEA-15-240-RWT/IEA-15-240-RWT-UMaineSemi/HydroData/IEA-15-240-RWT-UMaineSemi +# Level1: +# flag: True +# potential_model_override: 0 +# trim_ballast: 2 +# heave_tol: 1 +# save_designs: True +ROSCO: + flag: True + # SD_Mode: 0 + PS_Mode: 3 + ps_percent: 0.9 + # F_LPFType: 2 + # F_NotchType: 2 + # Fl_Mode: 2 + tuning_yaml: IEA-22-280-RWT-Semi_ROSCO.yaml + zeta_pc: [1] + omega_pc: [0.1] + U_pc: [12] + zeta_vs: 0.85 # Torque controller desired damping ratio [-] + omega_vs: 0.12 + # twr_freq: 3.2 + # ptfm_freq: 0.2 + # Kp_float: -10 +DLC_driver: + metocean_conditions: + wind_speed: [4., 6., 8., 10., 12., 14., 16., 18., 20., 22., 24.] + wave_height_NSS: [0.83, 0.88, 0.94, 1.03, 1.16, 1.34, 1.57, 1.86, 2.22, 2.62, 3.07] + wave_period_NSS: [6.9, 6.96, 7.02, 7.12, 7.25, 7.43, 7.66, 7.94, 8.27, 8.63, 9.01] + wave_height_SSS: [6.3, 8, 8, 8.1, 8.5, 8.5, 9.8, 9.8, 9.8, 9.8, 9.9] + wave_period_SSS: [11.5, 12.7, 12.7, 12.8, 13.1, 13.1, 14.1, 14.1, 14.1, 14.1, 14.1] + wave_height_1: 6.98 + wave_period_1: 11.7 + wave_height_50: 10.68 + wave_period_50: 14.2 + DLCs: + - DLC: "1.1" + n_seeds: 6 + # - DLC: "1.6" + # n_seeds: 6 + # # wind_speed: [6,10,14] + # # analysis_time: 1. + # # transient_time: 0. + # # turbulent_wind: + # # HubHt: 160 + # # GridHeight: 300 + # # GridWidth: 300 + # - DLC: "6.1" + # n_seeds: 6 \ No newline at end of file diff --git a/examples/17_IEA22_Optimization/modeling_options_openfast_nowisdem.yaml b/examples/17_IEA22_Optimization/modeling_options_openfast_nowisdem.yaml new file mode 100644 index 000000000..2a2ffeb2a --- /dev/null +++ b/examples/17_IEA22_Optimization/modeling_options_openfast_nowisdem.yaml @@ -0,0 +1,54 @@ +General: + verbosity: False # When set to True, the code prints to screen many infos + openfast_configuration: + OF_run_fst: IEA22_Semi + # OF_run_dir: outputs/3_WEIS_setup + save_timeseries: True + save_iterations: True + use_exe: True + +WISDEM: + RotorSE: + flag: False + n_pitch_perf_surfaces: 5 + n_tsr_perf_surfaces: 5 + spar_cap_ss: Spar_Cap_SS + spar_cap_ps: Spar_Cap_PS + te_ss: TE_reinforcement_SS + te_ps: TE_reinforcement_PS + TowerSE: + flag: False + DriveSE: + flag: False + FloatingSE: + flag: False + BOS: + flag: False + +Level3: # Options for WEIS fidelity level 3 = nonlinear time domain + flag: True + from_openfast: True + openfast_file: IEA-22-280-RWT-Semi.fst + openfast_dir: ../IEA-22-280-RWT/OpenFAST/IEA-22-280-RWT-Semi/ # This should point to IEA repo + # AeroDyn: + # WakeMod: 2 + +ROSCO: + flag: True + tuning_yaml: IEA-22-280-RWT-Semi_ROSCO.yaml + +DLC_driver: + DLCs: + - DLC: "1.1" + wind_speed: [16] + n_seeds: 1 + analysis_time: 600. + transient_time: 0.0 + # turbulent_wind: + # HubHt: 142 + # RefHt: 142 + # GridHeight: 220 + # GridWidth: 220 + + + diff --git a/examples/17_IEA22_Optimization/modeling_options_raft.yaml b/examples/17_IEA22_Optimization/modeling_options_raft.yaml new file mode 100644 index 000000000..1f243ee7e --- /dev/null +++ b/examples/17_IEA22_Optimization/modeling_options_raft.yaml @@ -0,0 +1,90 @@ +General: + verbosity: False # When set to True, the code prints to screen many infos + openfast_configuration: + OF_run_fst: IEA_w_TMD + OF_run_dir: outputs/IEA_w_TMD_33 + save_iterations: True + save_timeseries: True + use_exe: True + allow_fails: True + fail_value: 9999 +WISDEM: + RotorSE: + flag: True + n_pitch_perf_surfaces: 5 + n_tsr_perf_surfaces: 5 + spar_cap_ss: spar_cap_ss + spar_cap_ps: spar_cap_ps + # te_ss: TE_reinforcement_SS + # te_ps: TE_reinforcement_PS + peak_thrust_shaving: True + thrust_shaving_coeff: 0.8 + TowerSE: + flag: True + wind: PowerWind # Wind used + gamma_f: 1.35 # Safety factor for fatigue loads + gamma_m: 1.3 # Safety factor for material properties + gamma_n: 1.0 # Safety factor for ... + gamma_b: 1.1 # Safety factor for ... + gamma_fatigue: 1.755 # Safety factor for fatigue loads + buckling_method: dnvgl # Buckling code type [eurocode or dnvgl] + buckling_length: 15 # Buckling parameter + frame3dd: + shear: True + geom: True + tol: 1e-9 + DriveSE: + flag: True + FloatingSE: + flag: True + symmetric_moorings: True + gamma_f: 1.35 # Safety factor for fatigue loads + gamma_m: 1.3 # Safety factor for material properties + gamma_n: 1.0 # Safety factor for ... + gamma_b: 1.1 # Safety factor for ... + gamma_fatigue: 1.755 # Safety factor for fatigue loads + BOS: + flag: True +Level1: + flag: True + potential_model_override: 0 + trim_ballast: 2 + heave_tol: 1 + save_designs: True +ROSCO: + flag: True + SD_Mode: 0 + PS_Mode: 1 + ps_percent: 0.85 + F_LPFType: 2 + F_NotchType: 2 + Fl_Mode: 2 + tuning_yaml: IEA-22-280-RWT-Semi_ROSCO.yaml + zeta_pc: [1] + omega_pc: [0.2] + U_pc: [12] + zeta_vs: 0.85 # Torque controller desired damping ratio [-] + omega_vs: 0.12 + twr_freq: 3.2 + ptfm_freq: 0.2 + Kp_float: -10 +DLC_driver: + metocean_conditions: + wind_speed: [4., 6., 8., 10., 12., 14., 16., 18., 20., 22., 24.] + wave_height_NSS: [0.83, 0.88, 0.94, 1.03, 1.16, 1.34, 1.57, 1.86, 2.22, 2.62, 3.07] + wave_period_NSS: [6.9, 6.96, 7.02, 7.12, 7.25, 7.43, 7.66, 7.94, 8.27, 8.63, 9.01] + wave_height_SSS: [6.3, 8, 8, 8.1, 8.5, 8.5, 9.8, 9.8, 9.8, 9.8, 9.9] + wave_period_SSS: [11.5, 12.7, 12.7, 12.8, 13.1, 13.1, 14.1, 14.1, 14.1, 14.1, 14.1] + wave_height_1: 6.98 + wave_period_1: 11.7 + wave_height_50: 10.68 + wave_period_50: 14.2 + DLCs: + - DLC: "1.6" + analysis_time: 600. + transient_time: 120. + # turbulent_wind: + # HubHt: 160 + # GridHeight: 300 + # GridWidth: 300 + - DLC: "6.1" \ No newline at end of file From 15a71c1153e7a036fde03addd5e17eecda0807eb Mon Sep 17 00:00:00 2001 From: pibo Date: Thu, 14 Dec 2023 15:00:41 -0700 Subject: [PATCH 003/114] fix bug with DT_Out, which is a number, nto a str --- weis/aeroelasticse/openmdao_openfast.py | 2 ++ weis/inputs/modeling_schema.yaml | 4 ++-- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/weis/aeroelasticse/openmdao_openfast.py b/weis/aeroelasticse/openmdao_openfast.py index 2e4f7faad..f3aab3a9d 100644 --- a/weis/aeroelasticse/openmdao_openfast.py +++ b/weis/aeroelasticse/openmdao_openfast.py @@ -856,6 +856,8 @@ def load_FAST_model_opts(self,fst_vt,modeling_options={}): fst_vt['AeroDyn15']['OLAF'] = {} if fst_vt['ElastoDyn']['DT'] == 0.: fst_vt['ElastoDyn']['DT'] = 'Default' + if fst_vt['Fst']['DT_Out'] == 0.: + fst_vt['Fst']['DT_Out'] = 'Default' return fst_vt diff --git a/weis/inputs/modeling_schema.yaml b/weis/inputs/modeling_schema.yaml index 47e2c0cb7..92fc6e8c4 100644 --- a/weis/inputs/modeling_schema.yaml +++ b/weis/inputs/modeling_schema.yaml @@ -360,8 +360,8 @@ properties: units: s description: Amount of time between creating checkpoint files for potential restart (s) DT_Out: - type: string - default: default + type: number + default: 0 description: Time step for tabular output (s) (or 'default') OutFileFmt: type: integer From 9de0ee7f5e7f8080e4234e36ed84b3e0ddb52d6d Mon Sep 17 00:00:00 2001 From: dzalkind Date: Mon, 18 Dec 2023 17:20:25 -0700 Subject: [PATCH 004/114] Flush and close OF input files, should fix kestrel issue --- weis/aeroelasticse/FAST_writer.py | 42 +++++++++++++++++++++++++++++++ 1 file changed, 42 insertions(+) diff --git a/weis/aeroelasticse/FAST_writer.py b/weis/aeroelasticse/FAST_writer.py index 8f88398b5..2f2742bf7 100644 --- a/weis/aeroelasticse/FAST_writer.py +++ b/weis/aeroelasticse/FAST_writer.py @@ -271,6 +271,8 @@ def write_MainInput(self): f.write('{!s:<22} {:<11} {:}'.format(self.fst_vt['Fst']['VTK_fields'], 'VTK_fields', '- Write mesh fields to VTK data files? (flag) {true/false} [unused if WrVTK=0]\n')) f.write('{:<22} {:<11} {:}'.format(self.fst_vt['Fst']['VTK_fps'], 'VTK_fps', '- Frame rate for VTK output (frames per second){will use closest integer multiple of DT} [used only if WrVTK=2]\n')) + f.flush() + os.fsync(f) f.close() def write_ElastoDyn(self): @@ -430,6 +432,8 @@ def write_ElastoDyn(self): f.write('END of input file (the word "END" must appear in the first 3 columns of this last OutList line)\n') f.write('---------------------------------------------------------------------------------------\n') + f.flush() + os.fsync(f) f.close() def write_ElastoDynBlade(self): @@ -481,6 +485,8 @@ def write_ElastoDynBlade(self): f.write('{:<22} {:<11} {:}'.format(self.fst_vt['ElastoDynBlade']['BldEdgSh'][3], 'BldEdgSh(5)', '- , coeff of x^5\n')) f.write('{:<22} {:<11} {:}'.format(self.fst_vt['ElastoDynBlade']['BldEdgSh'][4], 'BldEdgSh(6)', '- , coeff of x^6\n')) + f.flush() + os.fsync(f) f.close() def write_ElastoDynTower(self): @@ -537,6 +543,8 @@ def write_ElastoDynTower(self): f.write('{:<22} {:<11} {:}'.format(self.fst_vt['ElastoDynTower']['TwSSM2Sh'][3], 'TwSSM2Sh(5)', '- , coefficient of x^5 term\n')) f.write('{:<22} {:<11} {:}'.format(self.fst_vt['ElastoDynTower']['TwSSM2Sh'][4], 'TwSSM2Sh(6)', '- , coefficient of x^6 term\n')) + f.flush() + os.fsync(f) f.close() def write_BeamDyn(self): @@ -620,6 +628,8 @@ def write_BeamDyn(self): f.write('END of input file (the word "END" must appear in the first 3 columns of this last OutList line)\n') f.write('---------------------------------------------------------------------------------------') + f.flush() + os.fsync(f) f.close() # f.write('{:<22} {:<11} {:}'.format(self.fst_vt['BeamDyn'][''], '', '\n')) @@ -739,6 +749,8 @@ def write_InflowWind(self): f.write('END of input file (the word "END" must appear in the first 3 columns of this last OutList line)\n') f.write('---------------------------------------------------------------------------------------\n') + f.flush() + os.fsync(f) f.close() def write_AeroDyn15(self): @@ -868,6 +880,8 @@ def write_AeroDyn15(self): f.write('END of input file (the word "END" must appear in the first 3 columns of this last OutList line)\n') f.write('---------------------------------------------------------------------------------------\n') + f.flush() + os.fsync(f) f.close() def write_AeroDyn15Blade(self): @@ -894,6 +908,8 @@ def write_AeroDyn15Blade(self): for Spn, CrvAC, SwpAC, CrvAng, Twist, Chord, AFID in zip(BlSpn, BlCrvAC, BlSwpAC, BlCrvAng, BlTwist, BlChord, BlAFID): f.write('{: 2.15e} {: 2.15e} {: 2.15e} {: 2.15e} {: 2.15e} {: 2.15e} {: 8d}\n'.format(Spn, CrvAC, SwpAC, CrvAng, Twist, Chord, int(AFID))) + f.flush() + os.fsync(f) f.close() def write_AeroDyn15Polar(self): @@ -1038,6 +1054,8 @@ def write_AeroDyn15Polar(self): for row in polar: f.write(' '.join(['{: 2.14e}'.format(val) for val in row])+'\n') + f.flush() + os.fsync(f) f.close() def write_AeroDyn15Coord(self): @@ -1064,6 +1082,9 @@ def write_AeroDyn15Coord(self): f.write('! x/c y/c\n') for row in coord: f.write(' '.join(['{: 2.14e}'.format(val) for val in row])+'\n') + + f.flush() + os.fsync(f) f.close() def write_AeroDyn14(self): @@ -1139,6 +1160,8 @@ def write_AeroDyn14(self): for r, t, dr, c, a, p in zip(rnodes, twist, drnodes, chord, nfoil, prnelm): f.write('{: 2.15e}\t{: 2.15e}\t{: 2.15e}\t{: 2.15e}\t{:5}\t{:}\n'.format(r, t, dr, c, a, p)) + f.flush() + os.fsync(f) f.close() def write_AeroDyn14Tower(self): @@ -1162,6 +1185,8 @@ def write_AeroDyn14Tower(self): for Re, CD in zip(self.fst_vt['AeroDynTower']['TwrRe'], self.fst_vt['AeroDynTower']['TwrCD']): f.write('% 2.15e' %Re + ' '.join(['% 2.15e'%cdi for cdi in CD]) + '\n') + f.flush() + os.fsync(f) f.close() def write_AeroDyn14Polar(self, filename, a_i): @@ -1192,6 +1217,8 @@ def write_AeroDyn14Polar(self, filename, a_i): for a, cl, cd in zip(param['alpha'], param['cl'], param['cd']): f.write('{: 6e} {: 6e} {: 6e}\n'.format(a, cl, cd)) + f.flush() + os.fsync(f) f.close() def write_OLAF(self): @@ -1248,6 +1275,8 @@ def write_OLAF(self): f.write('--------------------------- ADVANCED OPTIONS --------------------------------------------------\n') f.write('===============================================================================================\n') + f.flush() + os.fsync(f) f.close() def write_ServoDyn(self): @@ -1376,6 +1405,8 @@ def write_ServoDyn(self): f.write('END of input file (the word "END" must appear in the first 3 columns of this last OutList line)\n') f.write('---------------------------------------------------------------------------------------\n') + f.flush() + os.fsync(f) f.close() def write_DISCON_in(self): @@ -1716,6 +1747,9 @@ def write_HydroDyn(self): f.write('END of output channels and end of file. (the word "END" must appear in the first 3 columns of this line)\n') + + f.flush() + os.fsync(f) f.close() def write_SubDyn(self): @@ -1924,6 +1958,8 @@ def write_SubDyn(self): for i in range(len(channel_list)): f.write('"' + channel_list[i] + '"\n') f.write('END of output channels and end of file. (the word "END" must appear in the first 3 columns of this line)\n') + f.flush() + os.fsync(f) f.close() def write_MAP(self): @@ -1985,6 +2021,8 @@ def write_MAP(self): f.write('{:<11s}'.format('(-)')+'\n') f.write("\n".join(self.fst_vt['MAP']['Option']).strip() + '\n') + f.flush() + os.fsync(f) f.close() def write_MoorDyn(self): @@ -2067,6 +2105,8 @@ def write_MoorDyn(self): f.write('END\n') f.write('------------------------- need this line --------------------------------------\n') + f.flush() + os.fsync(f) f.close() @@ -2169,6 +2209,8 @@ def write_StC(self,StC_vt,StC_filename): f.write('{!s:<22} {:<11} {:}'.format(StC_vt['PrescribedForcesFile'], 'PrescribedForcesFile', '- Time series force and moment (7 columns of time, FX, FY, FZ, MX, MY, MZ)\n')) f.write('-------------------------------------------------------------------------------\n') + f.flush() + os.fsync(f) f.close() if __name__=="__main__": From 24ea956d1a1c19b0f0fab28537176b8aaf94f54e Mon Sep 17 00:00:00 2001 From: dzalkind Date: Mon, 18 Dec 2023 17:20:25 -0700 Subject: [PATCH 005/114] Flush and close OF input files, seems to fix kestrel issue --- weis/aeroelasticse/FAST_writer.py | 42 +++++++++++++++++++++++++++++++ 1 file changed, 42 insertions(+) diff --git a/weis/aeroelasticse/FAST_writer.py b/weis/aeroelasticse/FAST_writer.py index 8f88398b5..2f2742bf7 100644 --- a/weis/aeroelasticse/FAST_writer.py +++ b/weis/aeroelasticse/FAST_writer.py @@ -271,6 +271,8 @@ def write_MainInput(self): f.write('{!s:<22} {:<11} {:}'.format(self.fst_vt['Fst']['VTK_fields'], 'VTK_fields', '- Write mesh fields to VTK data files? (flag) {true/false} [unused if WrVTK=0]\n')) f.write('{:<22} {:<11} {:}'.format(self.fst_vt['Fst']['VTK_fps'], 'VTK_fps', '- Frame rate for VTK output (frames per second){will use closest integer multiple of DT} [used only if WrVTK=2]\n')) + f.flush() + os.fsync(f) f.close() def write_ElastoDyn(self): @@ -430,6 +432,8 @@ def write_ElastoDyn(self): f.write('END of input file (the word "END" must appear in the first 3 columns of this last OutList line)\n') f.write('---------------------------------------------------------------------------------------\n') + f.flush() + os.fsync(f) f.close() def write_ElastoDynBlade(self): @@ -481,6 +485,8 @@ def write_ElastoDynBlade(self): f.write('{:<22} {:<11} {:}'.format(self.fst_vt['ElastoDynBlade']['BldEdgSh'][3], 'BldEdgSh(5)', '- , coeff of x^5\n')) f.write('{:<22} {:<11} {:}'.format(self.fst_vt['ElastoDynBlade']['BldEdgSh'][4], 'BldEdgSh(6)', '- , coeff of x^6\n')) + f.flush() + os.fsync(f) f.close() def write_ElastoDynTower(self): @@ -537,6 +543,8 @@ def write_ElastoDynTower(self): f.write('{:<22} {:<11} {:}'.format(self.fst_vt['ElastoDynTower']['TwSSM2Sh'][3], 'TwSSM2Sh(5)', '- , coefficient of x^5 term\n')) f.write('{:<22} {:<11} {:}'.format(self.fst_vt['ElastoDynTower']['TwSSM2Sh'][4], 'TwSSM2Sh(6)', '- , coefficient of x^6 term\n')) + f.flush() + os.fsync(f) f.close() def write_BeamDyn(self): @@ -620,6 +628,8 @@ def write_BeamDyn(self): f.write('END of input file (the word "END" must appear in the first 3 columns of this last OutList line)\n') f.write('---------------------------------------------------------------------------------------') + f.flush() + os.fsync(f) f.close() # f.write('{:<22} {:<11} {:}'.format(self.fst_vt['BeamDyn'][''], '', '\n')) @@ -739,6 +749,8 @@ def write_InflowWind(self): f.write('END of input file (the word "END" must appear in the first 3 columns of this last OutList line)\n') f.write('---------------------------------------------------------------------------------------\n') + f.flush() + os.fsync(f) f.close() def write_AeroDyn15(self): @@ -868,6 +880,8 @@ def write_AeroDyn15(self): f.write('END of input file (the word "END" must appear in the first 3 columns of this last OutList line)\n') f.write('---------------------------------------------------------------------------------------\n') + f.flush() + os.fsync(f) f.close() def write_AeroDyn15Blade(self): @@ -894,6 +908,8 @@ def write_AeroDyn15Blade(self): for Spn, CrvAC, SwpAC, CrvAng, Twist, Chord, AFID in zip(BlSpn, BlCrvAC, BlSwpAC, BlCrvAng, BlTwist, BlChord, BlAFID): f.write('{: 2.15e} {: 2.15e} {: 2.15e} {: 2.15e} {: 2.15e} {: 2.15e} {: 8d}\n'.format(Spn, CrvAC, SwpAC, CrvAng, Twist, Chord, int(AFID))) + f.flush() + os.fsync(f) f.close() def write_AeroDyn15Polar(self): @@ -1038,6 +1054,8 @@ def write_AeroDyn15Polar(self): for row in polar: f.write(' '.join(['{: 2.14e}'.format(val) for val in row])+'\n') + f.flush() + os.fsync(f) f.close() def write_AeroDyn15Coord(self): @@ -1064,6 +1082,9 @@ def write_AeroDyn15Coord(self): f.write('! x/c y/c\n') for row in coord: f.write(' '.join(['{: 2.14e}'.format(val) for val in row])+'\n') + + f.flush() + os.fsync(f) f.close() def write_AeroDyn14(self): @@ -1139,6 +1160,8 @@ def write_AeroDyn14(self): for r, t, dr, c, a, p in zip(rnodes, twist, drnodes, chord, nfoil, prnelm): f.write('{: 2.15e}\t{: 2.15e}\t{: 2.15e}\t{: 2.15e}\t{:5}\t{:}\n'.format(r, t, dr, c, a, p)) + f.flush() + os.fsync(f) f.close() def write_AeroDyn14Tower(self): @@ -1162,6 +1185,8 @@ def write_AeroDyn14Tower(self): for Re, CD in zip(self.fst_vt['AeroDynTower']['TwrRe'], self.fst_vt['AeroDynTower']['TwrCD']): f.write('% 2.15e' %Re + ' '.join(['% 2.15e'%cdi for cdi in CD]) + '\n') + f.flush() + os.fsync(f) f.close() def write_AeroDyn14Polar(self, filename, a_i): @@ -1192,6 +1217,8 @@ def write_AeroDyn14Polar(self, filename, a_i): for a, cl, cd in zip(param['alpha'], param['cl'], param['cd']): f.write('{: 6e} {: 6e} {: 6e}\n'.format(a, cl, cd)) + f.flush() + os.fsync(f) f.close() def write_OLAF(self): @@ -1248,6 +1275,8 @@ def write_OLAF(self): f.write('--------------------------- ADVANCED OPTIONS --------------------------------------------------\n') f.write('===============================================================================================\n') + f.flush() + os.fsync(f) f.close() def write_ServoDyn(self): @@ -1376,6 +1405,8 @@ def write_ServoDyn(self): f.write('END of input file (the word "END" must appear in the first 3 columns of this last OutList line)\n') f.write('---------------------------------------------------------------------------------------\n') + f.flush() + os.fsync(f) f.close() def write_DISCON_in(self): @@ -1716,6 +1747,9 @@ def write_HydroDyn(self): f.write('END of output channels and end of file. (the word "END" must appear in the first 3 columns of this line)\n') + + f.flush() + os.fsync(f) f.close() def write_SubDyn(self): @@ -1924,6 +1958,8 @@ def write_SubDyn(self): for i in range(len(channel_list)): f.write('"' + channel_list[i] + '"\n') f.write('END of output channels and end of file. (the word "END" must appear in the first 3 columns of this line)\n') + f.flush() + os.fsync(f) f.close() def write_MAP(self): @@ -1985,6 +2021,8 @@ def write_MAP(self): f.write('{:<11s}'.format('(-)')+'\n') f.write("\n".join(self.fst_vt['MAP']['Option']).strip() + '\n') + f.flush() + os.fsync(f) f.close() def write_MoorDyn(self): @@ -2067,6 +2105,8 @@ def write_MoorDyn(self): f.write('END\n') f.write('------------------------- need this line --------------------------------------\n') + f.flush() + os.fsync(f) f.close() @@ -2169,6 +2209,8 @@ def write_StC(self,StC_vt,StC_filename): f.write('{!s:<22} {:<11} {:}'.format(StC_vt['PrescribedForcesFile'], 'PrescribedForcesFile', '- Time series force and moment (7 columns of time, FX, FY, FZ, MX, MY, MZ)\n')) f.write('-------------------------------------------------------------------------------\n') + f.flush() + os.fsync(f) f.close() if __name__=="__main__": From ddf70b6ec0b5c9a70e70ad7bdcb35dad5469b444 Mon Sep 17 00:00:00 2001 From: dzalkind Date: Tue, 19 Dec 2023 13:37:52 -0700 Subject: [PATCH 006/114] Remove numpy from lists of dicts --- weis/aeroelasticse/FileTools.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/weis/aeroelasticse/FileTools.py b/weis/aeroelasticse/FileTools.py index 2262f10e4..498c733c9 100644 --- a/weis/aeroelasticse/FileTools.py +++ b/weis/aeroelasticse/FileTools.py @@ -22,6 +22,8 @@ def get_dict(vartree, branch): return reduce(operator.getitem, branch, vartree) def loop_dict(vartree, branch): + if type(vartree) is not dict: + return fst_vt for var in vartree.keys(): branch_i = copy.copy(branch) branch_i.append(var) @@ -38,6 +40,9 @@ def loop_dict(vartree, branch): get_dict(fst_vt, branch_i[:-1])[branch_i[-1]] = bool(get_dict(fst_vt, branch_i[:-1])[branch_i[-1]]) elif data_type in [np.ndarray]: get_dict(fst_vt, branch_i[:-1])[branch_i[-1]] = get_dict(fst_vt, branch_i[:-1])[branch_i[-1]].tolist() + elif data_type in [list,tuple]: + for item in get_dict(fst_vt, branch_i[:-1])[branch_i[-1]]: + remove_numpy(item) # set fast variables to update values loop_dict(fst_vt, []) From 4f54643ca28428de1009adf41918cc03ad63a9c2 Mon Sep 17 00:00:00 2001 From: dzalkind Date: Wed, 20 Dec 2023 09:15:15 -0700 Subject: [PATCH 007/114] Give a cross sectional HD member property to OF at each joint location --- weis/aeroelasticse/openmdao_openfast.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/weis/aeroelasticse/openmdao_openfast.py b/weis/aeroelasticse/openmdao_openfast.py index f3aab3a9d..f3ab72db8 100644 --- a/weis/aeroelasticse/openmdao_openfast.py +++ b/weis/aeroelasticse/openmdao_openfast.py @@ -1384,21 +1384,21 @@ def update_FAST_model(self, fst_vt, inputs, discrete_inputs): # Store data n_joints = joints_xyz.shape[0] n_members = N1.shape[0] + ijoints = np.arange( n_joints, dtype=np.int_ ) + 1 imembers = np.arange( n_members, dtype=np.int_ ) + 1 fst_vt['HydroDyn']['NJoints'] = n_joints - fst_vt['HydroDyn']['JointID'] = 1 + np.arange( n_joints, dtype=np.int_) + fst_vt['HydroDyn']['JointID'] = ijoints fst_vt['HydroDyn']['Jointxi'] = joints_xyz[:,0] fst_vt['HydroDyn']['Jointyi'] = joints_xyz[:,1] fst_vt['HydroDyn']['Jointzi'] = joints_xyz[:,2] - fst_vt['HydroDyn']['NPropSets'] = n_members - fst_vt['HydroDyn']['PropSetID'] = imembers + fst_vt['HydroDyn']['NPropSets'] = n_joints # each joint has a cross section + fst_vt['HydroDyn']['PropSetID'] = ijoints fst_vt['HydroDyn']['PropD'] = d_coarse fst_vt['HydroDyn']['PropThck'] = t_coarse fst_vt['HydroDyn']['NMembers'] = n_members fst_vt['HydroDyn']['MemberID'] = imembers - fst_vt['HydroDyn']['MJointID1'] = N1 - fst_vt['HydroDyn']['MJointID2'] = N2 - fst_vt['HydroDyn']['MPropSetID1'] = fst_vt['HydroDyn']['MPropSetID2'] = imembers + fst_vt['HydroDyn']['MJointID1'] = fst_vt['HydroDyn']['MPropSetID1'] = N1 + fst_vt['HydroDyn']['MJointID2'] = fst_vt['HydroDyn']['MPropSetID2'] = N2 fst_vt['HydroDyn']['MDivSize'] = 0.5*np.ones( fst_vt['HydroDyn']['NMembers'] ) fst_vt['HydroDyn']['MCoefMod'] = np.ones( fst_vt['HydroDyn']['NMembers'], dtype=np.int_) fst_vt['HydroDyn']['JointAxID'] = np.ones( fst_vt['HydroDyn']['NJoints'], dtype=np.int_) From 900a88d8204d9aba620776731875c7ca270e8a47 Mon Sep 17 00:00:00 2001 From: dzalkind Date: Wed, 20 Dec 2023 09:15:15 -0700 Subject: [PATCH 008/114] Give a cross sectional HD member property to OF at each joint location --- weis/aeroelasticse/openmdao_openfast.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/weis/aeroelasticse/openmdao_openfast.py b/weis/aeroelasticse/openmdao_openfast.py index f3aab3a9d..f3ab72db8 100644 --- a/weis/aeroelasticse/openmdao_openfast.py +++ b/weis/aeroelasticse/openmdao_openfast.py @@ -1384,21 +1384,21 @@ def update_FAST_model(self, fst_vt, inputs, discrete_inputs): # Store data n_joints = joints_xyz.shape[0] n_members = N1.shape[0] + ijoints = np.arange( n_joints, dtype=np.int_ ) + 1 imembers = np.arange( n_members, dtype=np.int_ ) + 1 fst_vt['HydroDyn']['NJoints'] = n_joints - fst_vt['HydroDyn']['JointID'] = 1 + np.arange( n_joints, dtype=np.int_) + fst_vt['HydroDyn']['JointID'] = ijoints fst_vt['HydroDyn']['Jointxi'] = joints_xyz[:,0] fst_vt['HydroDyn']['Jointyi'] = joints_xyz[:,1] fst_vt['HydroDyn']['Jointzi'] = joints_xyz[:,2] - fst_vt['HydroDyn']['NPropSets'] = n_members - fst_vt['HydroDyn']['PropSetID'] = imembers + fst_vt['HydroDyn']['NPropSets'] = n_joints # each joint has a cross section + fst_vt['HydroDyn']['PropSetID'] = ijoints fst_vt['HydroDyn']['PropD'] = d_coarse fst_vt['HydroDyn']['PropThck'] = t_coarse fst_vt['HydroDyn']['NMembers'] = n_members fst_vt['HydroDyn']['MemberID'] = imembers - fst_vt['HydroDyn']['MJointID1'] = N1 - fst_vt['HydroDyn']['MJointID2'] = N2 - fst_vt['HydroDyn']['MPropSetID1'] = fst_vt['HydroDyn']['MPropSetID2'] = imembers + fst_vt['HydroDyn']['MJointID1'] = fst_vt['HydroDyn']['MPropSetID1'] = N1 + fst_vt['HydroDyn']['MJointID2'] = fst_vt['HydroDyn']['MPropSetID2'] = N2 fst_vt['HydroDyn']['MDivSize'] = 0.5*np.ones( fst_vt['HydroDyn']['NMembers'] ) fst_vt['HydroDyn']['MCoefMod'] = np.ones( fst_vt['HydroDyn']['NMembers'], dtype=np.int_) fst_vt['HydroDyn']['JointAxID'] = np.ones( fst_vt['HydroDyn']['NJoints'], dtype=np.int_) From bcc010e83ee4893207048622bf3639bb32b34f12 Mon Sep 17 00:00:00 2001 From: dzalkind Date: Wed, 20 Dec 2023 09:16:19 -0700 Subject: [PATCH 009/114] Clean up comment around member cross section properties --- weis/aeroelasticse/openmdao_openfast.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/weis/aeroelasticse/openmdao_openfast.py b/weis/aeroelasticse/openmdao_openfast.py index f3ab72db8..99701a17c 100644 --- a/weis/aeroelasticse/openmdao_openfast.py +++ b/weis/aeroelasticse/openmdao_openfast.py @@ -1338,9 +1338,6 @@ def update_FAST_model(self, fst_vt, inputs, discrete_inputs): nk = joints_xyz.shape[0] N1 = np.append(N1, nk + inode_range + 1) N2 = np.append(N2, nk + inode_range + 2) - # Check this for semi-subs, I changed to this at one point to: - # d_coarse = np.append(d_coarse, np.mean(id_coarse)) # OpenFAST only wants one thickness - # t_coarse = np.append(t_coarse, np.mean(it_coarse)) # OpenFAST only wants one thickness d_coarse = np.append(d_coarse, id_coarse) t_coarse = np.append(t_coarse, it_coarse) joints_xyz = np.append(joints_xyz, inode_xyz, axis=0) From 88c58c51b7c3ad0a4c4371c8a12bd1acd4b04b6b Mon Sep 17 00:00:00 2001 From: dzalkind Date: Wed, 20 Dec 2023 13:59:09 -0700 Subject: [PATCH 010/114] Add documentation for constraints that can come from RAFT/OF --- weis/inputs/analysis_schema.yaml | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/weis/inputs/analysis_schema.yaml b/weis/inputs/analysis_schema.yaml index d187b824b..f2cc3843d 100644 --- a/weis/inputs/analysis_schema.yaml +++ b/weis/inputs/analysis_schema.yaml @@ -360,7 +360,7 @@ properties: max: *flapminmax rotor_overspeed: type: object - description: (Maximum rotor speed / rated rotor speed) - 1 + description: (Maximum rotor speed / rated rotor speed) - 1. Can be computed in both RAFT and OpenFAST. The higher fidelity option will be used when active. default: {} properties: flag: *flag @@ -372,7 +372,7 @@ properties: max: *rotor_overspeed Max_PtfmPitch: type: object - description: Maximum platform pitch displacement + description: Maximum platform pitch displacement over all cases. Can be computed in both RAFT and OpenFAST. The higher fidelity option will be used when active. default: {} properties: flag: *flag @@ -384,7 +384,7 @@ properties: unit: deg Std_PtfmPitch: type: object - description: Maximum platform pitch standard deviation + description: Maximum platform pitch standard deviation over all cases. Can be computed in both RAFT and OpenFAST. The higher fidelity option will be used when active. default: {} properties: flag: *flag @@ -420,7 +420,7 @@ properties: unit: kN*m nacelle_acceleration: type: object - description: Maximum Nacelle IMU accelleration magnitude, i.e., sqrt(NcIMUTAxs^2 + NcIMUTAys^2 + NcIMUTAzs^2) + description: Maximum Nacelle IMU accelleration magnitude, i.e., sqrt(NcIMUTAxs^2 + NcIMUTAys^2 + NcIMUTAzs^2). Can be computed in both RAFT and OpenFAST. The higher fidelity option will be used when active. default: {} properties: flag: *flag @@ -461,6 +461,7 @@ properties: Max_Offset: type: object default: {} + description: Maximum combined surge/sway offset. Can be computed in both RAFT and OpenFAST. The higher fidelity option will be used when active. properties: flag: *flag max: From a8edbed43605027e707cb48b3a3b4fba6ca22c0b Mon Sep 17 00:00:00 2001 From: dzalkind Date: Wed, 20 Dec 2023 13:59:32 -0700 Subject: [PATCH 011/114] Ensure some level is enable for rotor_overspeed merit figure --- weis/glue_code/gc_PoseOptimization.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/weis/glue_code/gc_PoseOptimization.py b/weis/glue_code/gc_PoseOptimization.py index 59bee9183..b48537470 100644 --- a/weis/glue_code/gc_PoseOptimization.py +++ b/weis/glue_code/gc_PoseOptimization.py @@ -89,6 +89,8 @@ def set_objective(self, wt_opt): wt_opt.model.add_objective('aeroelastic.DEL_TwrBsMyt', ref=1.e4) elif self.opt['merit_figure'] == 'rotor_overspeed': + if not any(self.level_flags): + raise Exception('Please turn on the call to OpenFAST or RAFT if you are trying to optimize rotor overspeed constraints.') wt_opt.model.add_objective(f'{self.floating_solve_component}.rotor_overspeed') elif self.opt['merit_figure'] == 'Std_PtfmPitch': From a2c54245edf34332a0eb57b98204b7fc0a09cf58 Mon Sep 17 00:00:00 2001 From: Garrett Barter Date: Thu, 21 Dec 2023 16:13:34 -0700 Subject: [PATCH 012/114] fix coveralls issue in CI --- .github/workflows/CI_WEIS.yml | 21 +++++++-------------- 1 file changed, 7 insertions(+), 14 deletions(-) diff --git a/.github/workflows/CI_WEIS.yml b/.github/workflows/CI_WEIS.yml index 6ddd6005d..dd36df048 100644 --- a/.github/workflows/CI_WEIS.yml +++ b/.github/workflows/CI_WEIS.yml @@ -39,15 +39,6 @@ jobs: activate-environment: test auto-activate-base: false - #extra-specs: | - # python=${{ matrix.python-version }} - # mpi4py - # petsc4py - # openmpi - # sel(osx): compilers - # sel(win): m2w64-toolchain - # sel(win): libpython` - # Install dependencies of WISDEM specific to mac # libgcc openblas libopenblas - name: Add dependencies mac specific if: contains( matrix.os, 'mac') @@ -97,10 +88,12 @@ jobs: # Run coveralls - name: Run coveralls - if: contains( matrix.os, 'ubuntu') + if: contains( matrix.os, 'ubuntu') && contains( matrix.python-version, '3.10') + uses: coverallsapp/github-action@v2 # This also works, https://github.com/AndreMiras/coveralls-python-action #uses: AndreMiras/coveralls-python-action@develop - env: - GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} - run: | - coveralls --service=github + #env: + # GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + #run: | + # coveralls --service=github + From 126800bcd0aaf6b16b255fbec0a4c540bf22ee16 Mon Sep 17 00:00:00 2001 From: dzalkind Date: Fri, 22 Dec 2023 10:57:51 -0700 Subject: [PATCH 013/114] Make wave_seeds input consistent --- docs/inputs/analysis_schema.rst | 176 ++++++++++++++------ docs/inputs/analysis_schema_wisdem.rst | 20 +-- docs/inputs/modeling_schema.rst | 213 +++++++++++++++++++------ weis/dlc_driver/dlc_generator.py | 2 +- weis/inputs/modeling_schema.yaml | 2 +- 5 files changed, 303 insertions(+), 110 deletions(-) diff --git a/docs/inputs/analysis_schema.rst b/docs/inputs/analysis_schema.rst index 6381c5232..f2cc3843d 100644 --- a/docs/inputs/analysis_schema.rst +++ b/docs/inputs/analysis_schema.rst @@ -36,27 +36,27 @@ properties: type: boolean default: False description: Activates as a design variable or constraint - minimum: + min: type: number maximum: 1.0 minimum: 0.1 - default: 1.0 - maximum: + default: 0.5 + max: type: number maximum: 1.0 minimum: 0.1 - default: 1.0 + default: 0.98 te_flap_ext: type: object default: {} properties: flag: *flag - minimum: + min: type: number maximum: 1.0 minimum: 0.0 default: 0.01 - maximum: + max: type: number maximum: 1.0 minimum: 0.0 @@ -188,36 +188,84 @@ properties: type: object default: {} properties: - flag: *flag - omega_min: &omega_min - type: number - default: 0.1 - minimum: 0.0 - maximum: 10.0 - unit: none - omega_max: &omega_max - type: number - default: 0.7 - minimum: 0.0 - maximum: 10.0 - unit: none - zeta_min: &zeta_min - type: number - default: 0.4 - minimum: 0.0 - maximum: 10.0 - unit: none - zeta_max: &zeta_max - type: number - default: 1.5 - minimum: 0.0 - maximum: 10.0 - unit: none + flp_kp_norm: + type: object + default: {} + properties: + flag: *flag + min: + type: number + default: 0.01 + minimum: 0.0 + maximum: 10.0 + unit: none + max: + type: number + default: 5.0 + minimum: 0.0 + maximum: 10.0 + unit: none + flp_tau: + type: object + default: {} + properties: + flag: *flag + min: + type: number + default: 5 + minimum: 0.0 + maximum: 100.0 + unit: none + max: + type: number + default: 30 + minimum: 0.0 + maximum: 100.0 + unit: none ipc_control: type: object default: {} properties: - flag: *flag + Kp: + type: object + default: {} + properties: + flag: *flag + min: + type: number + default: 0.0 + minimum: 0.0 + maximum: 1.e+3 + unit: s + max: + type: number + default: 0.0 + minimum: 0.0 + maximum: 1.e+3 + unit: s + ref: &ipc_ref + type: number + default: 1.e-8 + minimum: 1.e-10 + maximum: 1.e-5 + Ki: + type: object + default: {} + properties: + flag: *flag + min: + type: number + default: 0.0 + minimum: 0.0 + maximum: 1.e+3 + unit: none + max: + type: number + default: 1.e-7 + minimum: 0.0 + maximum: 1.e+3 + unit: none + ref: *ipc_ref TMDs: type: object description: Design variables associated with TMDs @@ -289,7 +337,6 @@ properties: lower_bound: *bound upper_bound: *bound initial: *initial - constraints: # GB: These all need gammas or safety factors type: object @@ -313,7 +360,7 @@ properties: max: *flapminmax rotor_overspeed: type: object - description: (Maximum rotor speed / rated rotor speed) - 1 + description: (Maximum rotor speed / rated rotor speed) - 1. Can be computed in both RAFT and OpenFAST. The higher fidelity option will be used when active. default: {} properties: flag: *flag @@ -325,7 +372,7 @@ properties: max: *rotor_overspeed Max_PtfmPitch: type: object - description: Maximum platform pitch displacement + description: Maximum platform pitch displacement over all cases. Can be computed in both RAFT and OpenFAST. The higher fidelity option will be used when active. default: {} properties: flag: *flag @@ -337,7 +384,7 @@ properties: unit: deg Std_PtfmPitch: type: object - description: Maximum platform pitch standard deviation + description: Maximum platform pitch standard deviation over all cases. Can be computed in both RAFT and OpenFAST. The higher fidelity option will be used when active. default: {} properties: flag: *flag @@ -347,9 +394,33 @@ properties: minimum: 0.0 maximum: 30.0 unit: deg + Max_TwrBsMyt: + type: object + description: Maximum platform pitch displacement + default: {} + properties: + flag: *flag + max: + type: number + default: 1.e+5 + minimum: 0.0 + maximum: 1.e+8 + unit: kN*m + DEL_TwrBsMyt: + type: object + description: Maximum platform pitch displacement + default: {} + properties: + flag: *flag + max: + type: number + default: 1.e+5 + minimum: 0.0 + maximum: 1.e+8 + unit: kN*m nacelle_acceleration: type: object - description: Maximum Nacelle IMU accelleration magnitude, i.e., sqrt(NcIMUTAxs^2 + NcIMUTAys^2 + NcIMUTAzs^2) + description: Maximum Nacelle IMU accelleration magnitude, i.e., sqrt(NcIMUTAxs^2 + NcIMUTAys^2 + NcIMUTAzs^2). Can be computed in both RAFT and OpenFAST. The higher fidelity option will be used when active. default: {} properties: flag: *flag @@ -383,6 +454,22 @@ properties: minimum: 0.0 maximum: 30.0 unit: deg/s + floating: + type: object + default: {} + properties: + Max_Offset: + type: object + default: {} + description: Maximum combined surge/sway offset. Can be computed in both RAFT and OpenFAST. The higher fidelity option will be used when active. + properties: + flag: *flag + max: + type: number + default: 20 + minimum: 0.0 + maximum: 20000.0 + unit: m damage: type: object default: {} @@ -412,21 +499,6 @@ properties: description: Constrain design to one where OpenFAST simulations don't fail_value default: False - Max_Offset: - type: object - default: {} - properties: - flag: - type: boolean - description: Constrain maximum offset of platform from equilibrium point - default: False - max: - type: number - default: 20 - minimum: 0.0 - maximum: 20000.0 - unit: m - merit_figure: diff --git a/docs/inputs/analysis_schema_wisdem.rst b/docs/inputs/analysis_schema_wisdem.rst index a6074c3bd..4ccb8cf55 100644 --- a/docs/inputs/analysis_schema_wisdem.rst +++ b/docs/inputs/analysis_schema_wisdem.rst @@ -90,8 +90,8 @@ Blade twist as a design variable by adding or subtracting radians from the initi *Default* = False :code:`inverse` : Boolean - When set to True, the twist is defined inverting the - blade-element momentum equations to achieve a desired margin to stall, + When set to True, the twist is defined inverting the + blade-element momentum equations to achieve a desired margin to stall, which is defined among the constraints. :code:`flag` and :code:`inverse` cannot be simultaneously be set to True @@ -99,7 +99,7 @@ Blade twist as a design variable by adding or subtracting radians from the initi :code:`n_opt` : Integer Number of equally-spaced control points of the spline - parametrizing the twist distribution along blade span. + parametrizing the twist distribution along blade span. *Default* = 8 @@ -119,7 +119,7 @@ Blade twist as a design variable by adding or subtracting radians from the initi :code:`index_start` : Integer Integer setting the first DV of the :code:`n_opt` along span that is optimized. - It is recommended to set :code:`index_start` to 1 + It is recommended to set :code:`index_start` to 1 to lock the first DV and prevent the optimizer to try to optimize the twist of the blade root cylinder. @@ -169,7 +169,7 @@ Blade chord as a design variable by scaling (multiplying) the initial value at s :code:`index_end` : Integer Integer setting the last DV of the :code:`n_opt` along span that is optimized. - It is recommended to lock the last point close to blade tip, setting :code:`index_end` to :code:`n_opt` minus 1. + It is recommended to lock the last point close to blade tip, setting :code:`index_end` to :code:`n_opt` minus 1. The last point controls the chord length at blade tip and due to the imperfect tip loss models of CCBlade, it is usually a good idea to taper the chord manually and do not let a numerical @@ -191,7 +191,7 @@ Adjust airfoil positions along the blade span. :code:`af_start` : Integer Index of airfoil where the optimization can start shifting airfoil - position. The airfoil at blade tip is always locked. It is advised + position. The airfoil at blade tip is always locked. It is advised to keep the airfoils close to blade root locked. *Default* = 4 @@ -239,7 +239,7 @@ Blade suction-side spar cap thickness as a design variable by scaling (multiplyi :code:`index_start` : Integer Integer setting the first DV of the :code:`n_opt` along span that is optimized. - It is recommended to set :code:`index_start` to 1 + It is recommended to set :code:`index_start` to 1 to lock the first DV and impose a pre- defined taper to small thicknesses and mimic a blade manufacturability constraint. @@ -248,7 +248,7 @@ Blade suction-side spar cap thickness as a design variable by scaling (multiplyi :code:`index_end` : Integer Integer setting the last DV of the :code:`n_opt` along span that is optimized. - It is recommended to lock the last point close to blade tip, setting :code:`index_end` to :code:`n_opt` minus 1. + It is recommended to lock the last point close to blade tip, setting :code:`index_end` to :code:`n_opt` minus 1. This imposes a predefined taper to small thicknesses and mimic a blade manufacturability constraint. @@ -286,7 +286,7 @@ Blade pressure-side spar cap thickness as a design variable by scaling (multiply :code:`index_start` : Integer Integer setting the first DV of the :code:`n_opt` along span that is optimized. - It is recommended to set :code:`index_start` to 1 + It is recommended to set :code:`index_start` to 1 to lock the first DV and impose a pre- defined taper to small thicknesses and mimic a blade manufacturability constraint. @@ -295,7 +295,7 @@ Blade pressure-side spar cap thickness as a design variable by scaling (multiply :code:`index_end` : Integer Integer setting the last DV of the :code:`n_opt` along span that is optimized. - It is recommended to lock the last point close to blade tip, setting :code:`index_end` to :code:`n_opt` minus 1. + It is recommended to lock the last point close to blade tip, setting :code:`index_end` to :code:`n_opt` minus 1. This imposes a predefined taper to small thicknesses and mimic a blade manufacturability constraint. diff --git a/docs/inputs/modeling_schema.rst b/docs/inputs/modeling_schema.rst index 93476e965..3f4f9c414 100644 --- a/docs/inputs/modeling_schema.rst +++ b/docs/inputs/modeling_schema.rst @@ -360,8 +360,8 @@ properties: units: s description: Amount of time between creating checkpoint files for potential restart (s) DT_Out: - type: string - default: default + type: number + default: 0 description: Time step for tabular output (s) (or 'default') OutFileFmt: type: integer @@ -494,6 +494,10 @@ properties: maximum: 90.0 unit: deg description: Upflow angle (degrees) (not used for native Bladed format WindType=7) + VelInterpCubic: + type: boolean + default: False + description: Use cubic interpolation for velocity in time (false=linear, true=cubic) [Used with WindType=2,3,4,5,7] NWindVel: type: integer default: 1 @@ -699,6 +703,62 @@ properties: type: boolean default: False description: Print summary data to '.sum' (flag) + SensorType: + type: integer + enum: [0,1,2,3] + default: 0 + description: Switch for lidar configuration (0 = None, 1 = Single Point Beam(s), 2 = Continuous, 3 = Pulsed) + NumPulseGate: + type: integer + default: 0 + description: Number of lidar measurement gates (used when SensorType = 3) + PulseSpacing: + type: number + default: 0 + description: Distance between range gates (m) (used when SensorType = 3) + NumBeam: + type: integer + enum: [0,1,2,3,4,5] + default: 0 + description: Number of lidar measurement beams (0-5)(used when SensorType = 1) + FocalDistanceX: + type: number + default: 0 + description: Focal distance coordinates of the lidar beam in the x direction (relative to hub height) (only first coordinate used for SensorType 2 and 3) (m) + FocalDistanceY: + type: number + default: 0. + description: Focal distance coordinates of the lidar beam in the y direction (relative to hub height) (only first coordinate used for SensorType 2 and 3) (m) + FocalDistanceZ: + type: number + default: 0. + description: Focal distance coordinates of the lidar beam in the z direction (relative to hub height) (only first coordinate used for SensorType 2 and 3) (m) + RotorApexOffsetPos: + type: array + default: [0.0, 0.0, 0.0] + description: Offset of the lidar from hub height (m) + items: + type: number + minItems: 3 + maxItems: 3 + URefLid: + type: number + default: 0. + minimum: 0. + description: Reference average wind speed for the lidar [m/s] + MeasurementInterval: + type: number + default: 0. + minimum: 0. + description: Time between each measurement [s] + LidRadialVel: + type: boolean + default: False + description: TRUE => return radial component, FALSE => return 'x' direction estimate + ConsiderHubMotion: + type: integer + default: 1 + description: Flag whether to consider the hub motion's impact on Lidar measurements AeroDyn: &ofaerodyn type: object default: {} @@ -717,7 +777,7 @@ properties: description: Time interval for aerodynamic calculations. Set it to 0. for default (same as main fst) WakeMod: type: integer - enum: [0, 1, 3] + enum: [0, 1, 2, 3] default: 1 description: Type of wake/induction model (switch) {0=none, 1=BEMT, 3=OLAF} AFAeroMod: @@ -747,6 +807,10 @@ properties: type: boolean default: False description: Perform cavitation check? (flag) TRUE will turn off unsteady aerodynamics + Buoyancy: + type: boolean + default: False + description: Include buoyancy effects? (flag) CompAA: type: boolean default: False @@ -796,7 +860,7 @@ properties: type: integer enum: [1, 2, 3] default: 2 - description: Type of dynamic BEMT (DBEMT) model {1=constant tau1, 2=unsteady, 3=time-dependent tau1} (-) [used only when WakeMod=2] + description: Type of dynamic BEMT (DBEMT) model {1=constant tau1, 2=time-dependent tau1, 3=constant tau1 with continuous formulation} (-) [used only when WakeMod=2] tau1_const: type: number unit: s @@ -816,27 +880,27 @@ properties: type: integer enumerate: [5] default: 5 - description: Integration method {5 Forward Euler 1st order, default 5} (switch) + description: Integration method 1 RK4, 5 Forward Euler 1st order, default 5 switch DTfvw: type: number default: 0. minimum: 0. maximum: 10. unit: s - description: Time interval for wake propagation. {default dtaero} (s). Set it to 0. for default (same as main fst) + description: Time interval for wake propagation. {default dtaero} (s) FreeWakeStart: default: 0. minimum: 0. maximum: 10. unit: s - description: Time when wake is free. (-) value = always free. {default 0.0} (s). Set it to 0. for default (same as main fst) + description: Time when wake is free. (-) value = always free. {default 0.0} (s) FullCircStart: default: 0. minimum: 0. maximum: 10. unit: s - description: Time at which full circulation is reached. {default 0.0} (s). Set it to 0. for default (same as main fst) - CircSolvingMethod: + description: Time at which full circulation is reached. {default 0.0} (s) + CircSolvMethod: type: integer enumerate: [1, 2, 3] default: 1 @@ -844,11 +908,11 @@ properties: CircSolvConvCrit: type: number default: 0.001 - description: Convergence criteria {default 0.001} [only if CircSolvingMethod=1] (-) + description: Convergence criteria {default 0.001} [only if CircSolvMethod=1] (-) CircSolvRelaxation: type: number default: 0.1 - description: Relaxation factor {default 0.1} [only if CircSolvingMethod=1] (-) + description: Relaxation factor {default 0.1} [only if CircSolvMethod=1] (-) CircSolvMaxIter: type: integer default: 30 @@ -856,22 +920,27 @@ properties: PrescribedCircFile: type: string default: 'NA' - description: File containing prescribed circulation [only if CircSolvingMethod=3] (quoted string) - nNWPanel: + description: File containing prescribed circulation [only if CircSolvMethod=3] (quoted string) + nNWPanels: type: integer minimum: 0 default: 120 description: Number of near-wake panels [integer] (-) - WakeLength: + nNWPanelsFree: type: integer minimum: 0 - default: 900 - description: Total wake distance [integer] (number of time steps) - FreeWakeLength: + default: 120 + description: Number of free near-wake panels (-) {default nNWPanels} + nFWPanels: type: integer + minimum: 0 default: 0 + description: Number of far-wake panels (-) {default 0} + nFWPanelsFree: + type: integer minimum: 0 - description: Wake length that is free [integer] (number of time steps) {default WakeLength} + default: 0 + description: Number of free far-wake panels (-) {default nFWPanels} FWShedVorticity: type: boolean default: False @@ -893,7 +962,7 @@ properties: description: Viscous diffusion function {0 None, 1 Rankine, 2 LambOseen, 3 Vatistas, 4 Denominator, 'default' 3} (switch) WakeRegMethod: type: integer - enumerate: [0, 1, 2, 3, 4] + enumerate: [0, 1, 2, 3] default: 1 description: Wake regularization method {1 Constant, 2 Stretching, 3 Age, default 1} (switch) WakeRegFactor: @@ -964,14 +1033,6 @@ properties: type: boolean default: True description: Flag to indicate whether a lookup for f' will be calculated (TRUE) or whether best-fit exponential equations will be used (FALSE); if FALSE S1-S4 must be provided in airfoil input files (flag) [used only when AFAeroMod=2] - UAStartRad: - type: number - default: 0 - description: Starting radius for dynamic stall (fraction of rotor radius) [used only when AFAeroMod=2] - UAEndRad: - type: number - default: 1 - description: Ending radius for dynamic stall (fraction of rotor radius) [used only when AFAeroMod=2] AFTabMod: type: integer enum: [1,2,3] @@ -1001,6 +1062,40 @@ properties: type: boolean default: True description: Include aerodynamic pitching moment in calculations? (flag) + VolHub: + type: number + default: 0 + description: Hub volume (m^3) + minimum: 0. + HubCenBx: + type: number + default: 0 + description: Hub center of buoyancy x direction offset (m) + minimum: -100. + maximum: 100. + VolNac: + type: number + default: 0 + description: Nacelle volume (m^3) + minimum: 0. + NacCenB: + type: array + default: [0.0, 0.0, 0.0] + description: Position of nacelle center of buoyancy from yaw bearing in nacelle coordinates (m) + items: + type: number + minItems: 3 + maxItems: 3 + minimum: -100. + maximum: 100. + TFinAero: + type: boolean + default: False + description: Calculate tail fin aerodynamics model (flag) + TFinFile: + type: string + default: "unused" + description: Input file for tail fin aerodynamics [used only when TFinAero=True] Patm: type: number minimum: 0. @@ -1022,6 +1117,12 @@ properties: minimum: 0. maximum: 10. description: Turbulence intensity used in the Eames tower shadow model. Values of TwrTI between 0.05 and 0.4 are recommended. + TwrCb: + type: number + default: 0.0 + # minimum: 0. + # maximum: 10. + description: Turbulence buoyancy coefficient SumPrint: type: boolean default: False @@ -2705,7 +2806,7 @@ properties: openfast_dir: type: string default: unused - description: OpenFAST input directory, containing .fst file + description: OpenFAST input directory, containing .fst file. Absolute path or relative to modeling input xfoil: type: object default: {} @@ -2763,7 +2864,7 @@ properties: type: number uniqueItems: True minimum: 0.0 - maximum: 50.0 + maximum: 200.0 rated_offset: type: number default: 1 @@ -2906,7 +3007,7 @@ properties: DLC: type: string default: '1.1' - enum: ['1.1', '1.2', '1.3', '1.4', '1.5', '1.6', '5.1', '6.1', '6.2', '6.3', '6.4', 'Custom'] + enum: ['1.1', '1.2', '1.3', '1.4', '1.5', '1.6', '5.1', '6.1', '6.2', '6.3', '6.4', '6.5', '12.1', 'Custom'] description: IEC design load case to run. The DLCs currently supported are 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 5.1, 6.1, 6.3, and 6.4 wind_speed: type: array @@ -2917,7 +3018,7 @@ properties: unit: m/s minItems: 1 minimum: 0.0 - maximum: 50.0 + maximum: 200.0 uniqueItems: true ws_bin_size: type: number @@ -2931,7 +3032,13 @@ properties: default: 1 minimum: 1 maximum: 100 - description: Number of turbulent wind seeds drawn from the numpy random integer generator. This entry is neglected if the entry wind_seed is defined. + description: Number of turbulent wind seeds drawn from the numpy random integer generator. This entry is neglected if the entry wind_seed is defined. If DLC 1.4, number of waves seeds. + n_azimuth: + type: integer + default: 1 + minimum: 1 + maximum: 100 + description: Number of azimuth initial conditions to use (primarily during DLC 5.1) wind_seed: type: array default: [] @@ -2941,7 +3048,7 @@ properties: unit: none minItems: 1 uniqueItems: true - wave_seed: + wave_seeds: type: array default: [] description: Wave random number generator seeds for HydroDyn @@ -2962,8 +3069,7 @@ properties: maximum: 180.0 yaw_misalign: type: array - description: Alignment of the nacelle with respect to north. This array must currently have either length=1, i.e. one constant value, or the same length of the array wind_speed - default: [0.] + description: Alignment of the nacelle with respect to north. This array must currently have either length=1, i.e. one constant value, or the same length of the array wind_speed. Default depends on DLC, specified in dlc_generator. items: type: number unit: deg @@ -3050,6 +3156,16 @@ properties: maximum: 1.e+4 default: 120. description: This is the length of the simulation where outputs will be discarded. Its default is 120 seconds (2 minutes) for all simulations. The total simulation time is the sum of analysis_time and transient_time + shutdown_time: + type: number + unit: s + minimum: 0.0 + maximum: 1.e+5 + default: 9999 + description: Time when shutdown occurs in DLC 5.1 + wind_file: + type: string + description: File path of custom wind file turbulent_wind: type: object default: {} @@ -3136,22 +3252,22 @@ properties: description: Usable length of output time series [seconds] (program will add GridWidth/MeanHHWS seconds unless UsableTime is 'ALL') HubHt: type: number - default: 1.e+2 - minimum: 1.e+1 + default: 0 + minimum: 0 maximum: 5.e+2 unit: m description: Hub height [m] (should be > 0.5*GridHeight) GridHeight: type: number - default: 1.e+2 - minimum: 1.e+1 + default: 0 + minimum: 0 maximum: 5.e+2 unit: m description: Grid height [m] GridWidth: type: number - default: 1.e+2 - minimum: 1.e+1 + default: 0 + minimum: 0 maximum: 5.e+2 unit: m description: Grid width [m] (should be >= 2*(RotorRadius+ShaftLength)) @@ -3198,7 +3314,7 @@ properties: description: Name of the file that contains input profiles for WindProfileType='USR' and/or TurbModel='USRVKM' [-] RefHt: type: number - default: 1.e+2 + default: 0 minimum: 0 maximum: 1.e+5 unit: m @@ -3206,16 +3322,21 @@ properties: URef: type: number unit: m/s - default: 1 + default: -1 description: Mean (total) velocity at the reference height [m/s] (or 'default' for JET velocity profile) [must be 1-hr mean for API model; otherwise is the mean over AnalysisTime seconds] + IECturbc: + type: number + unit: '(-)' + default: -1 + description: Turbulence intensity (fraction) for custom DLCs, if default (-1), the class letter will be used ZJetMax: type: string default: default description: Jet height [m] (used only for JET velocity profile, valid 70-490 m) PLExp: type: number - default: 0.2 - description: Power law exponent [-] (or 'default') + default: -1 + description: Power law exponent [-] (or 'default'), if default (-1), the environment option shear_exp will be used for all DLCs Z0: type: string default: default @@ -3440,7 +3561,7 @@ properties: properties: tuning_yaml: type: string - description: yaml file to tune the ROSCO controller, only used for control-only optimizations using an OpenFAST model + description: yaml file to tune the ROSCO controller, only used for control-only optimizations using an OpenFAST model. Absolute path or relative to modeling input. default: none OL2CL: diff --git a/weis/dlc_driver/dlc_generator.py b/weis/dlc_driver/dlc_generator.py index b9d7773ea..7e2911ebc 100644 --- a/weis/dlc_driver/dlc_generator.py +++ b/weis/dlc_driver/dlc_generator.py @@ -125,7 +125,7 @@ def get_wind_seeds(self, options, wind_speeds): return wind_speeds, wind_seeds def get_wave_seeds(self, options, wind_speeds): - if len(options['wave_seed']) > 0: + if len(options['wave_seeds']) > 0: wave_seeds = np.array( [int(m) for m in options['wave_seeds']] ) else: wave_seeds = self.rng_wave.integers(2147483648, size=len(wind_speeds), dtype=int) diff --git a/weis/inputs/modeling_schema.yaml b/weis/inputs/modeling_schema.yaml index 92fc6e8c4..3f4f9c414 100644 --- a/weis/inputs/modeling_schema.yaml +++ b/weis/inputs/modeling_schema.yaml @@ -3048,7 +3048,7 @@ properties: unit: none minItems: 1 uniqueItems: true - wave_seed: + wave_seeds: type: array default: [] description: Wave random number generator seeds for HydroDyn From 6f43945b00bfa19b3a4206a73822aaf1e6f5527d Mon Sep 17 00:00:00 2001 From: dzalkind Date: Fri, 22 Dec 2023 11:06:18 -0700 Subject: [PATCH 014/114] Fix zeta_max typo --- weis/glue_code/gc_PoseOptimization.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/weis/glue_code/gc_PoseOptimization.py b/weis/glue_code/gc_PoseOptimization.py index b48537470..aed182923 100644 --- a/weis/glue_code/gc_PoseOptimization.py +++ b/weis/glue_code/gc_PoseOptimization.py @@ -130,7 +130,7 @@ def set_design_variables(self, wt_opt, wt_init): upper=control_opt['servo']['torque_control']['omega']['max']) if control_opt['servo']['torque_control']['zeta']['flag']: wt_opt.model.add_design_var('tune_rosco_ivc.zeta_vs', lower=control_opt['servo']['torque_control']['zeta']['min'], - upper=control_opt['servo']['torque_control']['zeta_max']) + upper=control_opt['servo']['torque_control']['zeta']['max']) if control_opt['servo']['ipc_control']['Kp']['flag']: wt_opt.model.add_design_var('tune_rosco_ivc.IPC_Kp1p', lower=control_opt['servo']['ipc_control']['Kp']['min'], upper=control_opt['servo']['ipc_control']['Kp']['max'], From 84cefd26d14056ae36bb83dd375bbb63e10fb6e9 Mon Sep 17 00:00:00 2001 From: dzalkind Date: Fri, 22 Dec 2023 11:36:11 -0700 Subject: [PATCH 015/114] Add some placeholder doc pages --- docs/dlc_generator.rst | 5 +++++ docs/optimization.rst | 10 ++++++++++ docs/weis_outputs.rst | 15 +++++++++++++++ 3 files changed, 30 insertions(+) create mode 100644 docs/dlc_generator.rst create mode 100644 docs/optimization.rst create mode 100644 docs/weis_outputs.rst diff --git a/docs/dlc_generator.rst b/docs/dlc_generator.rst new file mode 100644 index 000000000..228e9695d --- /dev/null +++ b/docs/dlc_generator.rst @@ -0,0 +1,5 @@ +DLC Generator +------------- + + +**Inputs to the DLC case generator explained** \ No newline at end of file diff --git a/docs/optimization.rst b/docs/optimization.rst new file mode 100644 index 000000000..1bdfe24ec --- /dev/null +++ b/docs/optimization.rst @@ -0,0 +1,10 @@ +Optimization in WEIS +-------------------- + + +**Available Optimization Solvers** + + +**Explanation of outputs** + +**Some tips and best practices** \ No newline at end of file diff --git a/docs/weis_outputs.rst b/docs/weis_outputs.rst new file mode 100644 index 000000000..6c1cb3a45 --- /dev/null +++ b/docs/weis_outputs.rst @@ -0,0 +1,15 @@ +WEIS Outputs +------------ + + +**OpenFAST Outputs** + + +**WISDEM csv files** + +**openmdao sql logs** + +Future +------ + +**Summary stats, DELs** \ No newline at end of file From bdf7cea127d99a707d565178ef1edbf65b7f635a Mon Sep 17 00:00:00 2001 From: dzalkind Date: Fri, 22 Dec 2023 11:40:48 -0700 Subject: [PATCH 016/114] Add readthedocs config --- .readthedocs.yaml | 39 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 39 insertions(+) create mode 100644 .readthedocs.yaml diff --git a/.readthedocs.yaml b/.readthedocs.yaml new file mode 100644 index 000000000..67171faf7 --- /dev/null +++ b/.readthedocs.yaml @@ -0,0 +1,39 @@ +# .readthedocs.yaml +# Read the Docs configuration file +# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details + +# Required +version: 2 + +# Set the version of Python and other tools you might need +build: + os: ubuntu-22.04 + tools: + python: "mambaforge-4.10" + jobs: + # Read the docs needs a couple packages not in the environment file + pre_install: + - conda install cmake compilers + + +# Shouldn't need conda for building the docs, but it is an option +conda: + environment: environment.yml + + +# Build documentation in the docs/ directory with Sphinx +sphinx: + configuration: docs/conf.py + +# If using Sphinx, optionally build your docs in additional formats such as PDF +# formats: +# - pdf + +# Optionally declare the Python requirements required to build your docs +python: + install: + - method: pip + path: . + extra_requirements: + - docs +# system_packages: true From 2fc8b4d0203b26f38b90463198a0b4dacefdcb8a Mon Sep 17 00:00:00 2001 From: dzalkind Date: Thu, 4 Jan 2024 09:15:43 -0700 Subject: [PATCH 017/114] Output total system inertia from RAFT --- RAFT/raft/omdao_raft.py | 3 +++ RAFT/raft/raft_model.py | 3 +++ 2 files changed, 6 insertions(+) diff --git a/RAFT/raft/omdao_raft.py b/RAFT/raft/omdao_raft.py index b14ff9ea9..746899c00 100644 --- a/RAFT/raft/omdao_raft.py +++ b/RAFT/raft/omdao_raft.py @@ -255,6 +255,9 @@ def setup(self): self.add_output('properties_roll inertia at subCG', val=np.zeros(ndim), units='kg*m**2', desc='Roll inertia sub CG') self.add_output('properties_pitch inertia at subCG', val=np.zeros(ndim), units='kg*m**2', desc='Pitch inertia sub CG') self.add_output('properties_yaw inertia at subCG', val=np.zeros(ndim), units='kg*m**2', desc='Yaw inertia sub CG') + self.add_output('properties_roll inertia at sysCG', val=np.zeros(ndim), units='kg*m**2', desc='Roll inertia about the total system CG') + self.add_output('properties_pitch inertia at sysCG', val=np.zeros(ndim), units='kg*m**2', desc='Pitch inertia about the total system CG') + self.add_output('properties_yaw inertia at sysCG', val=np.zeros(ndim), units='kg*m**2', desc='Yaw inertia about the total system CG') self.add_output('properties_buoyancy (pgV)', val=0.0, units='N', desc='Buoyancy (pgV)') self.add_output('properties_center of buoyancy', val=np.zeros(ndim), units='m', desc='Center of buoyancy') self.add_output('properties_C hydrostatic', val=np.zeros((ndof,ndof)), units='Pa', desc='C stiffness matrix') diff --git a/RAFT/raft/raft_model.py b/RAFT/raft/raft_model.py index 08a44d8fc..a6a66d121 100644 --- a/RAFT/raft/raft_model.py +++ b/RAFT/raft/raft_model.py @@ -1203,6 +1203,9 @@ def calcOutputs(self): self.results['properties']['roll inertia at subCG'] = fowt.props['Ixx_sub'] self.results['properties']['pitch inertia at subCG'] = fowt.props['Iyy_sub'] self.results['properties']['yaw inertia at subCG'] = fowt.props['Izz_sub'] + self.results['properties']['roll inertia at sysCG'] = fowt.props['Ixx'] + self.results['properties']['pitch inertia at sysCG'] = fowt.props['Iyy'] + self.results['properties']['yaw inertia at sysCG'] = fowt.props['Izz'] self.results['properties']['buoyancy (pgV)'] = fowt.rho_water*fowt.g*fowt.V self.results['properties']['center of buoyancy'] = fowt.rCB From 8875f41438f0e74d6ed55b5e686da8528d2a4884 Mon Sep 17 00:00:00 2001 From: dzalkind Date: Thu, 4 Jan 2024 10:51:03 -0700 Subject: [PATCH 018/114] Add nacelle accel channels --- weis/aeroelasticse/openmdao_openfast.py | 1 + 1 file changed, 1 insertion(+) diff --git a/weis/aeroelasticse/openmdao_openfast.py b/weis/aeroelasticse/openmdao_openfast.py index 99701a17c..52f6f96ba 100644 --- a/weis/aeroelasticse/openmdao_openfast.py +++ b/weis/aeroelasticse/openmdao_openfast.py @@ -1660,6 +1660,7 @@ def output_channels(self,fst_vt): channels_out += ["RotTorq", "LSSTipMxs", "LSSTipMys", "LSSTipMzs", "LSSTipMxa", "LSSTipMya", "LSSTipMza"] channels_out += ["B1N1Alpha", "B1N2Alpha", "B1N3Alpha", "B1N4Alpha", "B1N5Alpha", "B1N6Alpha", "B1N7Alpha", "B1N8Alpha", "B1N9Alpha", "B2N1Alpha", "B2N2Alpha", "B2N3Alpha", "B2N4Alpha", "B2N5Alpha", "B2N6Alpha", "B2N7Alpha", "B2N8Alpha","B2N9Alpha"] channels_out += ["PtfmSurge", "PtfmSway", "PtfmHeave", "PtfmRoll", "PtfmPitch", "PtfmYaw","NcIMURAys"] + channels_out += ['NcIMUTAxs','NcIMUTAzs','NcIMUTAzs'] if self.n_blades == 3: channels_out += ["TipDxc3", "TipDyc3", "TipDzc3", "RootMxc3", "RootMyc3", "RootMzc3", "TipDxb3", "TipDyb3", "TipDzb3", "RootMxb3", "RootMyb3", "RootMzb3", "RootFxc3", "RootFyc3", "RootFzc3", "RootFxb3", "RootFyb3", "RootFzb3", "BldPitch3"] From 02094aeffcd5d35415a4e1b2569f714f29767d25 Mon Sep 17 00:00:00 2001 From: dzalkind <65573423+dzalkind@users.noreply.github.com> Date: Fri, 12 Jan 2024 09:57:10 -0700 Subject: [PATCH 019/114] Create README.md for example 01 --- examples/01_aeroelasticse/README.md | 5 +++++ 1 file changed, 5 insertions(+) create mode 100644 examples/01_aeroelasticse/README.md diff --git a/examples/01_aeroelasticse/README.md b/examples/01_aeroelasticse/README.md new file mode 100644 index 000000000..30cb4ffa4 --- /dev/null +++ b/examples/01_aeroelasticse/README.md @@ -0,0 +1,5 @@ +These examples run OpenFAST cases using the `aeroelasticse` drivers. + +By setting up `case_input` dicts, `OpenFAST` parameters can be swept. + +For example, [we are sweeping the wind speed here](https://github.com/WISDEM/WEIS/blob/bdf7cea127d99a707d565178ef1edbf65b7f635a/examples/01_aeroelasticse/run_general.py#L52) and can change the initial conditions, like [blade pitch](https://github.com/WISDEM/WEIS/blob/bdf7cea127d99a707d565178ef1edbf65b7f635a/examples/01_aeroelasticse/run_general.py#L55) to vary with the wind speed, by assigning them to the same group. From 234f481795a8754284b1f6a7e7ef177561f19a0a Mon Sep 17 00:00:00 2001 From: dzalkind Date: Fri, 12 Jan 2024 10:29:04 -0700 Subject: [PATCH 020/114] Save timeseries and iteration data by default --- weis/inputs/modeling_schema.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/weis/inputs/modeling_schema.yaml b/weis/inputs/modeling_schema.yaml index 3f4f9c414..80fbce894 100644 --- a/weis/inputs/modeling_schema.yaml +++ b/weis/inputs/modeling_schema.yaml @@ -42,7 +42,7 @@ properties: description: Flag to only generate an OpenFAST model and stop save_timeseries: type: boolean - default: False + default: True description: Save openfast output timeseries keep_time: type: boolean @@ -50,7 +50,7 @@ properties: description: Keep timeseries in openmdao_openfast for post-processing save_iterations: type: boolean - default: False + default: True description: Save summary stats and other info for each openfast iteration. Could bump this up to a more global post-processing input. FAST_exe: type: string From 633541d73452e571413125c31c9ef6eda913d5d1 Mon Sep 17 00:00:00 2001 From: dzalkind Date: Fri, 12 Jan 2024 10:32:15 -0700 Subject: [PATCH 021/114] Simplify openfast_configuration Remove save iterations/timeseries Send outputs to same place as analysis outputs --- examples/02_control_opt/modeling_options.yaml | 4 ---- examples/02_control_opt/modeling_options_loads.yaml | 4 ---- examples/02_control_opt/modeling_options_sm.yaml | 5 ----- examples/06_IEA-15-240-RWT/modeling_options_tower.yaml | 4 ---- examples/13_DTQP/modeling_options_ol2cl.yaml | 4 ---- examples/15_RAFT_Studies/modeling_options_level3.yaml | 4 ---- examples/15_RAFT_Studies/modeling_options_umaine_semi.yaml | 4 ---- 7 files changed, 29 deletions(-) diff --git a/examples/02_control_opt/modeling_options.yaml b/examples/02_control_opt/modeling_options.yaml index c1870866f..ffcfbcbb0 100644 --- a/examples/02_control_opt/modeling_options.yaml +++ b/examples/02_control_opt/modeling_options.yaml @@ -1,10 +1,6 @@ General: verbosity: False # When set to True, the code prints to screen many infos openfast_configuration: - OF_run_fst: IEA15 - OF_run_dir: outputs/02_control_opt - save_timeseries: True - save_iterations: True use_exe: True WISDEM: diff --git a/examples/02_control_opt/modeling_options_loads.yaml b/examples/02_control_opt/modeling_options_loads.yaml index 180e86d10..2ce410440 100644 --- a/examples/02_control_opt/modeling_options_loads.yaml +++ b/examples/02_control_opt/modeling_options_loads.yaml @@ -1,10 +1,6 @@ General: verbosity: False # When set to True, the code prints to screen many infos openfast_configuration: - OF_run_fst: IEA15 - OF_run_dir: outputs/02_loads - save_timeseries: True - save_iterations: True use_exe: True WISDEM: diff --git a/examples/02_control_opt/modeling_options_sm.yaml b/examples/02_control_opt/modeling_options_sm.yaml index 3b7a4dd22..55e94026f 100644 --- a/examples/02_control_opt/modeling_options_sm.yaml +++ b/examples/02_control_opt/modeling_options_sm.yaml @@ -1,10 +1,5 @@ General: verbosity: False # When set to True, the code prints to screen many infos - openfast_configuration: - OF_run_fst: IEA15 - OF_run_dir: outputs/control_opt/openfast_out - save_timeseries: True - save_iterations: True WISDEM: RotorSE: diff --git a/examples/06_IEA-15-240-RWT/modeling_options_tower.yaml b/examples/06_IEA-15-240-RWT/modeling_options_tower.yaml index a9ce33ab0..ac07e7db6 100644 --- a/examples/06_IEA-15-240-RWT/modeling_options_tower.yaml +++ b/examples/06_IEA-15-240-RWT/modeling_options_tower.yaml @@ -2,10 +2,6 @@ General: verbosity: False # When set to True, the code prints to screen many infos solver_maxiter: 2 openfast_configuration: - OF_run_fst: IEA_w_TMD - OF_run_dir: outputs/IEA_w_TMD_11 - save_iterations: True - save_timeseries: True use_exe: True WISDEM: diff --git a/examples/13_DTQP/modeling_options_ol2cl.yaml b/examples/13_DTQP/modeling_options_ol2cl.yaml index 12d822f49..34a600c6d 100644 --- a/examples/13_DTQP/modeling_options_ol2cl.yaml +++ b/examples/13_DTQP/modeling_options_ol2cl.yaml @@ -1,11 +1,7 @@ General: verbosity: False # When set to True, the code prints to screen many infos openfast_configuration: - OF_run_fst: ol2cl - OF_run_dir: /Users/dzalkind/Tools/WEIS-4/outputs/13_OL2CL_0 use_exe: True - save_timeseries: True - save_iterations: True WISDEM: RotorSE: diff --git a/examples/15_RAFT_Studies/modeling_options_level3.yaml b/examples/15_RAFT_Studies/modeling_options_level3.yaml index d4fd735ca..e18763091 100644 --- a/examples/15_RAFT_Studies/modeling_options_level3.yaml +++ b/examples/15_RAFT_Studies/modeling_options_level3.yaml @@ -1,10 +1,6 @@ General: verbosity: False # When set to True, the code prints to screen many infos openfast_configuration: - OF_run_fst: IEA_w_TMD - OF_run_dir: outputs/15_level3_comp_1 - save_iterations: True - save_timeseries: True use_exe: True allow_fails: True fail_value: 9999 diff --git a/examples/15_RAFT_Studies/modeling_options_umaine_semi.yaml b/examples/15_RAFT_Studies/modeling_options_umaine_semi.yaml index 5d96269dc..c30db2cd9 100644 --- a/examples/15_RAFT_Studies/modeling_options_umaine_semi.yaml +++ b/examples/15_RAFT_Studies/modeling_options_umaine_semi.yaml @@ -1,10 +1,6 @@ General: verbosity: False # When set to True, the code prints to screen many infos openfast_configuration: - OF_run_fst: IEA_w_TMD - OF_run_dir: outputs/IEA_w_TMD_33 - save_iterations: True - save_timeseries: True use_exe: True allow_fails: True fail_value: 9999 From c0a7951ca71bbd04c9f0020bc9a05d11f5543048 Mon Sep 17 00:00:00 2001 From: dzalkind Date: Fri, 12 Jan 2024 10:42:58 -0700 Subject: [PATCH 022/114] Remove perf_surface modeling options because they make bad ROSCOs --- examples/02_control_opt/modeling_options.yaml | 2 -- examples/02_control_opt/modeling_options_loads.yaml | 2 -- examples/02_control_opt/modeling_options_sm.yaml | 2 -- examples/03_NREL5MW_OC3_spar/modeling_options.yaml | 2 -- examples/03_NREL5MW_OC3_spar/modeling_options_freq.yaml | 2 -- examples/04_NREL5MW_OC4_semi/modeling_options.yaml | 2 -- examples/04_NREL5MW_OC4_semi/modeling_options_freq.yaml | 2 -- examples/05_IEA-3.4-130-RWT/modeling_options.yaml | 2 -- examples/06_IEA-15-240-RWT/modeling_options.yaml | 2 -- examples/06_IEA-15-240-RWT/modeling_options_tower.yaml | 2 -- examples/07_te_flaps/modeling_options.yaml | 2 -- examples/09_design_of_experiments/modeling_options.yaml | 2 -- examples/09_design_of_experiments/modeling_options_OF.yaml | 2 -- examples/10_override_example/modeling_options.yaml | 2 -- examples/11_use_bem/modeling_options.yaml | 2 -- examples/11_use_bem/modeling_options_freq.yaml | 2 -- examples/12_linearization/modeling_options.yaml | 2 -- examples/12_linearization/modeling_options_doe.yaml | 2 -- examples/13_DTQP/modeling_options.yaml | 2 -- examples/13_DTQP/modeling_options_ol2cl.yaml | 2 -- examples/14_level2ccd/modeling_options.yaml | 2 -- examples/15_RAFT_Studies/modeling_options_level3.yaml | 2 -- examples/15_RAFT_Studies/modeling_options_umaine_semi.yaml | 2 -- 23 files changed, 46 deletions(-) diff --git a/examples/02_control_opt/modeling_options.yaml b/examples/02_control_opt/modeling_options.yaml index ffcfbcbb0..f51a3252d 100644 --- a/examples/02_control_opt/modeling_options.yaml +++ b/examples/02_control_opt/modeling_options.yaml @@ -6,8 +6,6 @@ General: WISDEM: RotorSE: flag: False - n_pitch_perf_surfaces: 5 - n_tsr_perf_surfaces: 5 spar_cap_ss: Spar_Cap_SS spar_cap_ps: Spar_Cap_PS te_ss: TE_reinforcement_SS diff --git a/examples/02_control_opt/modeling_options_loads.yaml b/examples/02_control_opt/modeling_options_loads.yaml index 2ce410440..d51fb8647 100644 --- a/examples/02_control_opt/modeling_options_loads.yaml +++ b/examples/02_control_opt/modeling_options_loads.yaml @@ -6,8 +6,6 @@ General: WISDEM: RotorSE: flag: False - n_pitch_perf_surfaces: 5 - n_tsr_perf_surfaces: 5 spar_cap_ss: Spar_Cap_SS spar_cap_ps: Spar_Cap_PS te_ss: TE_reinforcement_SS diff --git a/examples/02_control_opt/modeling_options_sm.yaml b/examples/02_control_opt/modeling_options_sm.yaml index 55e94026f..6632b28a8 100644 --- a/examples/02_control_opt/modeling_options_sm.yaml +++ b/examples/02_control_opt/modeling_options_sm.yaml @@ -4,8 +4,6 @@ General: WISDEM: RotorSE: flag: False - n_pitch_perf_surfaces: 5 - n_tsr_perf_surfaces: 5 spar_cap_ss: Spar_Cap_SS spar_cap_ps: Spar_Cap_PS te_ss: TE_reinforcement_SS diff --git a/examples/03_NREL5MW_OC3_spar/modeling_options.yaml b/examples/03_NREL5MW_OC3_spar/modeling_options.yaml index c3dfca6d8..3be92f511 100644 --- a/examples/03_NREL5MW_OC3_spar/modeling_options.yaml +++ b/examples/03_NREL5MW_OC3_spar/modeling_options.yaml @@ -8,8 +8,6 @@ General: WISDEM: RotorSE: flag: True - n_pitch_perf_surfaces: 10 - n_tsr_perf_surfaces: 10 spar_cap_ss: Spar_Cap_SS spar_cap_ps: Spar_Cap_PS te_ss: TE_reinforcement_SS diff --git a/examples/03_NREL5MW_OC3_spar/modeling_options_freq.yaml b/examples/03_NREL5MW_OC3_spar/modeling_options_freq.yaml index 47d279051..3e015932f 100644 --- a/examples/03_NREL5MW_OC3_spar/modeling_options_freq.yaml +++ b/examples/03_NREL5MW_OC3_spar/modeling_options_freq.yaml @@ -4,8 +4,6 @@ General: WISDEM: RotorSE: flag: True - n_pitch_perf_surfaces: 5 - n_tsr_perf_surfaces: 5 spar_cap_ss: Spar_Cap_SS spar_cap_ps: Spar_Cap_PS te_ss: TE_reinforcement_SS diff --git a/examples/04_NREL5MW_OC4_semi/modeling_options.yaml b/examples/04_NREL5MW_OC4_semi/modeling_options.yaml index 33fa96620..81b33f459 100644 --- a/examples/04_NREL5MW_OC4_semi/modeling_options.yaml +++ b/examples/04_NREL5MW_OC4_semi/modeling_options.yaml @@ -8,8 +8,6 @@ General: WISDEM: RotorSE: flag: True - n_pitch_perf_surfaces: 5 - n_tsr_perf_surfaces: 5 spar_cap_ss: Spar_Cap_SS spar_cap_ps: Spar_Cap_PS te_ss: TE_reinforcement_SS diff --git a/examples/04_NREL5MW_OC4_semi/modeling_options_freq.yaml b/examples/04_NREL5MW_OC4_semi/modeling_options_freq.yaml index bd6aaecfe..7f25c80eb 100644 --- a/examples/04_NREL5MW_OC4_semi/modeling_options_freq.yaml +++ b/examples/04_NREL5MW_OC4_semi/modeling_options_freq.yaml @@ -4,8 +4,6 @@ General: WISDEM: RotorSE: flag: True - n_pitch_perf_surfaces: 5 - n_tsr_perf_surfaces: 5 spar_cap_ss: Spar_Cap_SS spar_cap_ps: Spar_Cap_PS te_ss: TE_reinforcement_SS diff --git a/examples/05_IEA-3.4-130-RWT/modeling_options.yaml b/examples/05_IEA-3.4-130-RWT/modeling_options.yaml index 10eae8383..5cc5c6abc 100644 --- a/examples/05_IEA-3.4-130-RWT/modeling_options.yaml +++ b/examples/05_IEA-3.4-130-RWT/modeling_options.yaml @@ -11,8 +11,6 @@ WISDEM: spar_cap_ps: Spar_Cap_PS te_ss: TE_reinforcement_SS te_ps: TE_reinforcement_PS - n_tsr_perf_surfaces: 5 - n_pitch_perf_surfaces: 5 TowerSE: flag: True DriveSE: diff --git a/examples/06_IEA-15-240-RWT/modeling_options.yaml b/examples/06_IEA-15-240-RWT/modeling_options.yaml index 2ec4712cd..f03f72e59 100644 --- a/examples/06_IEA-15-240-RWT/modeling_options.yaml +++ b/examples/06_IEA-15-240-RWT/modeling_options.yaml @@ -8,8 +8,6 @@ General: WISDEM: RotorSE: flag: True - n_pitch_perf_surfaces: 5 - n_tsr_perf_surfaces: 5 spar_cap_ss: Spar_Cap_SS spar_cap_ps: Spar_Cap_PS te_ss: TE_reinforcement_SS diff --git a/examples/06_IEA-15-240-RWT/modeling_options_tower.yaml b/examples/06_IEA-15-240-RWT/modeling_options_tower.yaml index ac07e7db6..805824ab6 100644 --- a/examples/06_IEA-15-240-RWT/modeling_options_tower.yaml +++ b/examples/06_IEA-15-240-RWT/modeling_options_tower.yaml @@ -7,8 +7,6 @@ General: WISDEM: RotorSE: flag: True - n_pitch_perf_surfaces: 5 - n_tsr_perf_surfaces: 5 spar_cap_ss: Spar_Cap_SS spar_cap_ps: Spar_Cap_PS te_ss: TE_reinforcement_SS diff --git a/examples/07_te_flaps/modeling_options.yaml b/examples/07_te_flaps/modeling_options.yaml index 466408743..b122836c7 100644 --- a/examples/07_te_flaps/modeling_options.yaml +++ b/examples/07_te_flaps/modeling_options.yaml @@ -7,8 +7,6 @@ General: WISDEM: RotorSE: flag: True - n_pitch_perf_surfaces: 20 - n_tsr_perf_surfaces: 20 spar_cap_ss: Spar_cap_ss spar_cap_ps: Spar_cap_ps te_ss: TE_reinf_ss diff --git a/examples/09_design_of_experiments/modeling_options.yaml b/examples/09_design_of_experiments/modeling_options.yaml index 467958a64..67cf95ef8 100644 --- a/examples/09_design_of_experiments/modeling_options.yaml +++ b/examples/09_design_of_experiments/modeling_options.yaml @@ -4,8 +4,6 @@ General: WISDEM: RotorSE: flag: True - n_pitch_perf_surfaces: 4 - n_tsr_perf_surfaces: 4 spar_cap_ss: Spar_Cap_SS spar_cap_ps: Spar_Cap_PS te_ss: TE_reinforcement_SS diff --git a/examples/09_design_of_experiments/modeling_options_OF.yaml b/examples/09_design_of_experiments/modeling_options_OF.yaml index 838c8f1f0..4b6c0c995 100644 --- a/examples/09_design_of_experiments/modeling_options_OF.yaml +++ b/examples/09_design_of_experiments/modeling_options_OF.yaml @@ -7,8 +7,6 @@ General: WISDEM: RotorSE: flag: True - n_pitch_perf_surfaces: 20 - n_tsr_perf_surfaces: 20 spar_cap_ss: Spar_Cap_SS spar_cap_ps: Spar_Cap_PS te_ss: TE_reinforcement_SS diff --git a/examples/10_override_example/modeling_options.yaml b/examples/10_override_example/modeling_options.yaml index 2cda3e9ad..1b9064fa9 100644 --- a/examples/10_override_example/modeling_options.yaml +++ b/examples/10_override_example/modeling_options.yaml @@ -4,8 +4,6 @@ General: WISDEM: RotorSE: flag: True - n_pitch_perf_surfaces: 5 - n_tsr_perf_surfaces: 5 spar_cap_ss: Spar_Cap_SS spar_cap_ps: Spar_Cap_PS te_ss: TE_reinforcement_SS diff --git a/examples/11_use_bem/modeling_options.yaml b/examples/11_use_bem/modeling_options.yaml index 4609e3ca5..9e2e79122 100644 --- a/examples/11_use_bem/modeling_options.yaml +++ b/examples/11_use_bem/modeling_options.yaml @@ -7,8 +7,6 @@ General: WISDEM: RotorSE: flag: True - n_pitch_perf_surfaces: 5 - n_tsr_perf_surfaces: 5 spar_cap_ss: Spar_Cap_SS spar_cap_ps: Spar_Cap_PS te_ss: TE_reinforcement_SS diff --git a/examples/11_use_bem/modeling_options_freq.yaml b/examples/11_use_bem/modeling_options_freq.yaml index 144259c66..85b68ed55 100644 --- a/examples/11_use_bem/modeling_options_freq.yaml +++ b/examples/11_use_bem/modeling_options_freq.yaml @@ -4,8 +4,6 @@ General: WISDEM: RotorSE: flag: True - n_pitch_perf_surfaces: 5 - n_tsr_perf_surfaces: 5 spar_cap_ss: Spar_Cap_SS spar_cap_ps: Spar_Cap_PS te_ss: TE_reinforcement_SS diff --git a/examples/12_linearization/modeling_options.yaml b/examples/12_linearization/modeling_options.yaml index 978e50417..b172dfa83 100644 --- a/examples/12_linearization/modeling_options.yaml +++ b/examples/12_linearization/modeling_options.yaml @@ -8,8 +8,6 @@ General: WISDEM: RotorSE: flag: True - n_pitch_perf_surfaces: 5 - n_tsr_perf_surfaces: 5 spar_cap_ss: Spar_Cap_SS spar_cap_ps: Spar_Cap_PS te_ss: TE_reinforcement_SS diff --git a/examples/12_linearization/modeling_options_doe.yaml b/examples/12_linearization/modeling_options_doe.yaml index 7db4799de..555dabd63 100644 --- a/examples/12_linearization/modeling_options_doe.yaml +++ b/examples/12_linearization/modeling_options_doe.yaml @@ -7,8 +7,6 @@ General: WISDEM: RotorSE: flag: True - n_pitch_perf_surfaces: 5 - n_tsr_perf_surfaces: 5 spar_cap_ss: Spar_Cap_SS spar_cap_ps: Spar_Cap_PS te_ss: TE_reinforcement_SS diff --git a/examples/13_DTQP/modeling_options.yaml b/examples/13_DTQP/modeling_options.yaml index 9d7dc25c7..1580240a3 100644 --- a/examples/13_DTQP/modeling_options.yaml +++ b/examples/13_DTQP/modeling_options.yaml @@ -8,8 +8,6 @@ General: WISDEM: RotorSE: flag: True - n_pitch_perf_surfaces: 5 - n_tsr_perf_surfaces: 5 spar_cap_ss: Spar_Cap_SS spar_cap_ps: Spar_Cap_PS te_ss: TE_reinforcement_SS diff --git a/examples/13_DTQP/modeling_options_ol2cl.yaml b/examples/13_DTQP/modeling_options_ol2cl.yaml index 34a600c6d..fb13c0c1a 100644 --- a/examples/13_DTQP/modeling_options_ol2cl.yaml +++ b/examples/13_DTQP/modeling_options_ol2cl.yaml @@ -6,8 +6,6 @@ General: WISDEM: RotorSE: flag: True - n_pitch_perf_surfaces: 25 - n_tsr_perf_surfaces: 25 spar_cap_ss: Spar_Cap_SS spar_cap_ps: Spar_Cap_PS te_ss: TE_reinforcement_SS diff --git a/examples/14_level2ccd/modeling_options.yaml b/examples/14_level2ccd/modeling_options.yaml index 63aeff527..c3e16d923 100644 --- a/examples/14_level2ccd/modeling_options.yaml +++ b/examples/14_level2ccd/modeling_options.yaml @@ -9,8 +9,6 @@ General: WISDEM: RotorSE: flag: True - n_pitch_perf_surfaces: 5 - n_tsr_perf_surfaces: 5 spar_cap_ss: Spar_Cap_SS spar_cap_ps: Spar_Cap_PS te_ss: TE_reinforcement_SS diff --git a/examples/15_RAFT_Studies/modeling_options_level3.yaml b/examples/15_RAFT_Studies/modeling_options_level3.yaml index e18763091..e6a478b1a 100644 --- a/examples/15_RAFT_Studies/modeling_options_level3.yaml +++ b/examples/15_RAFT_Studies/modeling_options_level3.yaml @@ -8,8 +8,6 @@ General: WISDEM: RotorSE: flag: True - n_pitch_perf_surfaces: 5 - n_tsr_perf_surfaces: 5 spar_cap_ss: Spar_Cap_SS spar_cap_ps: Spar_Cap_PS te_ss: TE_reinforcement_SS diff --git a/examples/15_RAFT_Studies/modeling_options_umaine_semi.yaml b/examples/15_RAFT_Studies/modeling_options_umaine_semi.yaml index c30db2cd9..ec16c7c2c 100644 --- a/examples/15_RAFT_Studies/modeling_options_umaine_semi.yaml +++ b/examples/15_RAFT_Studies/modeling_options_umaine_semi.yaml @@ -8,8 +8,6 @@ General: WISDEM: RotorSE: flag: True - n_pitch_perf_surfaces: 5 - n_tsr_perf_surfaces: 5 spar_cap_ss: Spar_Cap_SS spar_cap_ps: Spar_Cap_PS te_ss: TE_reinforcement_SS From 8f8536c9602c034b3ed63894c77c56eb31daa7cd Mon Sep 17 00:00:00 2001 From: dzalkind Date: Fri, 12 Jan 2024 10:47:30 -0700 Subject: [PATCH 023/114] Rename optimization example --- .../02_control_opt/{weis_driver.py => weis_driver_rosco_opt.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename examples/02_control_opt/{weis_driver.py => weis_driver_rosco_opt.py} (100%) diff --git a/examples/02_control_opt/weis_driver.py b/examples/02_control_opt/weis_driver_rosco_opt.py similarity index 100% rename from examples/02_control_opt/weis_driver.py rename to examples/02_control_opt/weis_driver_rosco_opt.py From 50c32e26198d2eff7f28586cabd6ba2ada9ac78a Mon Sep 17 00:00:00 2001 From: dzalkind <65573423+dzalkind@users.noreply.github.com> Date: Fri, 12 Jan 2024 10:53:40 -0700 Subject: [PATCH 024/114] Update README.md for 02_control_opt --- examples/02_control_opt/README.md | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) diff --git a/examples/02_control_opt/README.md b/examples/02_control_opt/README.md index 2df1c767f..fb1d2c85a 100644 --- a/examples/02_control_opt/README.md +++ b/examples/02_control_opt/README.md @@ -1,3 +1,21 @@ +These examples run an OpenFAST model that has not been generated by WISDEM; it can be used to tune and optimize the ROSCO controller and run OpenFAST DLC cases. + +To select the OpenFAST model, point to the [`openfast_dir`](https://github.com/WISDEM/WEIS/blob/02094aeffcd5d35415a4e1b2569f714f29767d25/examples/02_control_opt/modeling_options.yaml#L32) and `openfast_file`. The key input that bypasses the WISDEM-generated OpenFAST model is [`from_openfast`](https://github.com/WISDEM/WEIS/blob/02094aeffcd5d35415a4e1b2569f714f29767d25/examples/02_control_opt/modeling_options.yaml#L30) = `true`. + +There are 3 examples in this directory + +1. `weis_driver_loads.py` runs a DLC analysis, without optimization. +2. `weis_driver_rosco_opt.py` runs a control optimization. +3. `weis_driver_sm.py` runs a controller optimization that accounts for the robust stability margin. + + +[More information about setting DLCs can be found here](https://github.com/WISDEM/WEIS/blob/docs/docs/dlc_generator.rst) + +[More information about the analysis options can be found here](https://github.com/WISDEM/WEIS/blob/docs/docs/inputs/analysis_schema.rst) + +The only geometry inputs that need to be specified in this example are in the `assembly` and `environment` sections. + + To run these examples, please make sure you are in your WEIS conda environment and run ``` From 4b576d88f4b609afdd9c44057137bd34ae81d170 Mon Sep 17 00:00:00 2001 From: dzalkind Date: Fri, 12 Jan 2024 10:54:01 -0700 Subject: [PATCH 025/114] Update modeling schema doc page --- docs/inputs/modeling_schema.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/inputs/modeling_schema.rst b/docs/inputs/modeling_schema.rst index 3f4f9c414..80fbce894 100644 --- a/docs/inputs/modeling_schema.rst +++ b/docs/inputs/modeling_schema.rst @@ -42,7 +42,7 @@ properties: description: Flag to only generate an OpenFAST model and stop save_timeseries: type: boolean - default: False + default: True description: Save openfast output timeseries keep_time: type: boolean @@ -50,7 +50,7 @@ properties: description: Keep timeseries in openmdao_openfast for post-processing save_iterations: type: boolean - default: False + default: True description: Save summary stats and other info for each openfast iteration. Could bump this up to a more global post-processing input. FAST_exe: type: string From a575482d705f924087be4e9d04571813ddc44208 Mon Sep 17 00:00:00 2001 From: dzalkind Date: Fri, 12 Jan 2024 10:55:11 -0700 Subject: [PATCH 026/114] Rename 02 example folder --- .../{02_control_opt => 02_run_openfast_cases}/IEA-15-240-RWT.yaml | 0 examples/{02_control_opt => 02_run_openfast_cases}/README.md | 0 .../analysis_options.yaml | 0 .../analysis_options_loads.yaml | 0 .../analysis_options_sm.yaml | 0 .../modeling_options.yaml | 0 .../modeling_options_loads.yaml | 0 .../modeling_options_sm.yaml | 0 .../weis_driver_loads.py | 0 .../weis_driver_rosco_opt.py | 0 .../{02_control_opt => 02_run_openfast_cases}/weis_driver_sm.py | 0 11 files changed, 0 insertions(+), 0 deletions(-) rename examples/{02_control_opt => 02_run_openfast_cases}/IEA-15-240-RWT.yaml (100%) rename examples/{02_control_opt => 02_run_openfast_cases}/README.md (100%) rename examples/{02_control_opt => 02_run_openfast_cases}/analysis_options.yaml (100%) rename examples/{02_control_opt => 02_run_openfast_cases}/analysis_options_loads.yaml (100%) rename examples/{02_control_opt => 02_run_openfast_cases}/analysis_options_sm.yaml (100%) rename examples/{02_control_opt => 02_run_openfast_cases}/modeling_options.yaml (100%) rename examples/{02_control_opt => 02_run_openfast_cases}/modeling_options_loads.yaml (100%) rename examples/{02_control_opt => 02_run_openfast_cases}/modeling_options_sm.yaml (100%) rename examples/{02_control_opt => 02_run_openfast_cases}/weis_driver_loads.py (100%) rename examples/{02_control_opt => 02_run_openfast_cases}/weis_driver_rosco_opt.py (100%) rename examples/{02_control_opt => 02_run_openfast_cases}/weis_driver_sm.py (100%) diff --git a/examples/02_control_opt/IEA-15-240-RWT.yaml b/examples/02_run_openfast_cases/IEA-15-240-RWT.yaml similarity index 100% rename from examples/02_control_opt/IEA-15-240-RWT.yaml rename to examples/02_run_openfast_cases/IEA-15-240-RWT.yaml diff --git a/examples/02_control_opt/README.md b/examples/02_run_openfast_cases/README.md similarity index 100% rename from examples/02_control_opt/README.md rename to examples/02_run_openfast_cases/README.md diff --git a/examples/02_control_opt/analysis_options.yaml b/examples/02_run_openfast_cases/analysis_options.yaml similarity index 100% rename from examples/02_control_opt/analysis_options.yaml rename to examples/02_run_openfast_cases/analysis_options.yaml diff --git a/examples/02_control_opt/analysis_options_loads.yaml b/examples/02_run_openfast_cases/analysis_options_loads.yaml similarity index 100% rename from examples/02_control_opt/analysis_options_loads.yaml rename to examples/02_run_openfast_cases/analysis_options_loads.yaml diff --git a/examples/02_control_opt/analysis_options_sm.yaml b/examples/02_run_openfast_cases/analysis_options_sm.yaml similarity index 100% rename from examples/02_control_opt/analysis_options_sm.yaml rename to examples/02_run_openfast_cases/analysis_options_sm.yaml diff --git a/examples/02_control_opt/modeling_options.yaml b/examples/02_run_openfast_cases/modeling_options.yaml similarity index 100% rename from examples/02_control_opt/modeling_options.yaml rename to examples/02_run_openfast_cases/modeling_options.yaml diff --git a/examples/02_control_opt/modeling_options_loads.yaml b/examples/02_run_openfast_cases/modeling_options_loads.yaml similarity index 100% rename from examples/02_control_opt/modeling_options_loads.yaml rename to examples/02_run_openfast_cases/modeling_options_loads.yaml diff --git a/examples/02_control_opt/modeling_options_sm.yaml b/examples/02_run_openfast_cases/modeling_options_sm.yaml similarity index 100% rename from examples/02_control_opt/modeling_options_sm.yaml rename to examples/02_run_openfast_cases/modeling_options_sm.yaml diff --git a/examples/02_control_opt/weis_driver_loads.py b/examples/02_run_openfast_cases/weis_driver_loads.py similarity index 100% rename from examples/02_control_opt/weis_driver_loads.py rename to examples/02_run_openfast_cases/weis_driver_loads.py diff --git a/examples/02_control_opt/weis_driver_rosco_opt.py b/examples/02_run_openfast_cases/weis_driver_rosco_opt.py similarity index 100% rename from examples/02_control_opt/weis_driver_rosco_opt.py rename to examples/02_run_openfast_cases/weis_driver_rosco_opt.py diff --git a/examples/02_control_opt/weis_driver_sm.py b/examples/02_run_openfast_cases/weis_driver_sm.py similarity index 100% rename from examples/02_control_opt/weis_driver_sm.py rename to examples/02_run_openfast_cases/weis_driver_sm.py From 481d4bbfb27f842865a5a54ea22fac2434a9f1be Mon Sep 17 00:00:00 2001 From: dzalkind Date: Fri, 12 Jan 2024 11:14:19 -0700 Subject: [PATCH 027/114] Rename output folders in 02 examples --- examples/02_run_openfast_cases/analysis_options.yaml | 2 +- examples/02_run_openfast_cases/analysis_options_loads.yaml | 2 +- examples/02_run_openfast_cases/analysis_options_sm.yaml | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/examples/02_run_openfast_cases/analysis_options.yaml b/examples/02_run_openfast_cases/analysis_options.yaml index 3b98174a0..495454eb1 100644 --- a/examples/02_run_openfast_cases/analysis_options.yaml +++ b/examples/02_run_openfast_cases/analysis_options.yaml @@ -1,5 +1,5 @@ general: - folder_output: outputs/02_control_opt + folder_output: outputs/02_rosco_optimization fname_output: iea15mw design_variables: control: diff --git a/examples/02_run_openfast_cases/analysis_options_loads.yaml b/examples/02_run_openfast_cases/analysis_options_loads.yaml index f9f9ece87..e0c9c93d7 100644 --- a/examples/02_run_openfast_cases/analysis_options_loads.yaml +++ b/examples/02_run_openfast_cases/analysis_options_loads.yaml @@ -1,5 +1,5 @@ general: - folder_output: outputs/02_loads + folder_output: outputs/02_openfast_dlcs fname_output: iea15mw merit_figure: DEL_TwrBsMyt # Merit figure of the optimization problem. The options are 'AEP' - 'LCOE' - 'Cp' - 'blade_mass' - 'blade_tip_deflection' diff --git a/examples/02_run_openfast_cases/analysis_options_sm.yaml b/examples/02_run_openfast_cases/analysis_options_sm.yaml index cfdfe3c39..abc92432c 100644 --- a/examples/02_run_openfast_cases/analysis_options_sm.yaml +++ b/examples/02_run_openfast_cases/analysis_options_sm.yaml @@ -1,5 +1,5 @@ general: - folder_output: outputs + folder_output: outputs/02_control_opt_sm fname_output: iea15mw design_variables: control: From 8b6fc0c4e1eb5f5953005bec5ea197b1513f9faf Mon Sep 17 00:00:00 2001 From: dzalkind Date: Fri, 12 Jan 2024 11:15:42 -0700 Subject: [PATCH 028/114] Update example names in tests --- weis/test/run_examples.py | 2 +- weis/test/test_examples_skinny.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/weis/test/run_examples.py b/weis/test/run_examples.py index 7bd0f3ea4..d7c36758c 100644 --- a/weis/test/run_examples.py +++ b/weis/test/run_examples.py @@ -5,7 +5,7 @@ "01_aeroelasticse/run_general", "01_aeroelasticse/run_OLAF", "01_aeroelasticse/run_nodalOutputs", - "02_control_opt/weis_driver_loads", + "02_run_openfast_cases/weis_driver_loads", #"03_NREL5MW_OC3_spar/weis_driver", # executed in the test_OC3.py # "03_NREL5MW_OC3_spar/weis_freq_driver", # executed in examples_skinny # "04_NREL5MW_OC4_semi/weis_driver", # skipping until we resolve multiple variable ballasts diff --git a/weis/test/test_examples_skinny.py b/weis/test/test_examples_skinny.py index 9d616db55..391ab9992 100644 --- a/weis/test/test_examples_skinny.py +++ b/weis/test/test_examples_skinny.py @@ -2,8 +2,8 @@ from weis.test.utils import execute_script skinny_scripts = [ - "02_control_opt/weis_driver", #It's fast, I promise (49 sec. locally) - "02_control_opt/weis_driver_sm", #Not as fast as weis_driver, but not too bad (120 sec. locally) + "02_run_openfast_cases/weis_driver_rosco_opt", #It's fast, I promise (49 sec. locally) + "02_run_openfast_cases/weis_driver_sm", #Not as fast as weis_driver, but not too bad (120 sec. locally) "03_NREL5MW_OC3_spar/weis_driver", "03_NREL5MW_OC3_spar/weis_freq_driver", "06_IEA-15-240-RWT/weis_driver", From 2ace64d99829beaef80cf65ecd5e287c8b1b0bde Mon Sep 17 00:00:00 2001 From: dzalkind Date: Fri, 12 Jan 2024 11:28:57 -0700 Subject: [PATCH 029/114] Remove 03 hydro data, should not be necessary anymore --- examples/03_NREL5MW_OC3_spar/HydroData/Spar.1 | 1020 - examples/03_NREL5MW_OC3_spar/HydroData/Spar.3 | 22200 ---------------- .../03_NREL5MW_OC3_spar/HydroData/Spar.hst | 36 - 3 files changed, 23256 deletions(-) delete mode 100644 examples/03_NREL5MW_OC3_spar/HydroData/Spar.1 delete mode 100644 examples/03_NREL5MW_OC3_spar/HydroData/Spar.3 delete mode 100644 examples/03_NREL5MW_OC3_spar/HydroData/Spar.hst diff --git a/examples/03_NREL5MW_OC3_spar/HydroData/Spar.1 b/examples/03_NREL5MW_OC3_spar/HydroData/Spar.1 deleted file mode 100644 index b6812de4b..000000000 --- a/examples/03_NREL5MW_OC3_spar/HydroData/Spar.1 +++ /dev/null @@ -1,1020 +0,0 @@ - -0.100000E+01 1 1 7.787967E+03 - -0.100000E+01 1 5 -4.745684E+05 - -0.100000E+01 2 2 7.787947E+03 - -0.100000E+01 2 4 4.745673E+05 - -0.100000E+01 3 3 2.442134E+02 - -0.100000E+01 4 2 4.745695E+05 - -0.100000E+01 4 4 3.709229E+07 - -0.100000E+01 5 1 -4.745708E+05 - -0.100000E+01 5 5 3.709237E+07 - -0.100000E+01 6 6 2.535175E-09 - 0.000000E+00 1 1 7.569865E+03 - 0.000000E+00 1 5 -4.713567E+05 - 0.000000E+00 2 2 7.569843E+03 - 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0.125664E+01 0.170000E+03 4 3.690883E-01 8.146783E+01 5.475970E-02 3.650035E-01 - 0.125664E+01 0.170000E+03 5 2.093203E+00 8.146776E+01 3.105600E-01 2.070036E+00 - 0.125664E+01 0.170000E+03 6 5.431025E-07 1.738403E+02 -5.399670E-07 5.827524E-08 - 0.125664E+01 0.180000E+03 1 2.609880E+00 -9.853226E+01 -3.872179E-01 -2.580995E+00 - 0.125664E+01 0.180000E+03 2 6.351464E-08 8.146780E+01 9.423355E-09 6.281170E-08 - 0.125664E+01 0.180000E+03 3 2.168757E-02 -1.022737E+02 -4.610394E-03 -2.119186E-02 - 0.125664E+01 0.180000E+03 4 5.172610E-08 8.146793E+01 7.674236E-09 5.115364E-08 - 0.125664E+01 0.180000E+03 5 2.125493E+00 8.146774E+01 3.153516E-01 2.101969E+00 - 0.125664E+01 0.180000E+03 6 1.189357E-13 9.000000E+01 -1.181138E-13 1.395875E-14 diff --git a/examples/03_NREL5MW_OC3_spar/HydroData/Spar.hst b/examples/03_NREL5MW_OC3_spar/HydroData/Spar.hst deleted file mode 100644 index d8cbd2c88..000000000 --- a/examples/03_NREL5MW_OC3_spar/HydroData/Spar.hst +++ /dev/null @@ -1,36 +0,0 @@ - 1 1 0.000000E+00 - 1 2 0.000000E+00 - 1 3 0.000000E+00 - 1 4 0.000000E+00 - 1 5 0.000000E+00 - 1 6 0.000000E+00 - 2 1 0.000000E+00 - 2 2 0.000000E+00 - 2 3 0.000000E+00 - 2 4 0.000000E+00 - 2 5 0.000000E+00 - 2 6 0.000000E+00 - 3 1 0.000000E+00 - 3 2 0.000000E+00 - 3 3 3.312247E+01 - 3 4 0.000000E+00 - 3 5 0.000000E+00 - 3 6 0.000000E+00 - 4 1 0.000000E+00 - 4 2 0.000000E+00 - 4 3 0.000000E+00 - 4 4 -4.973414E+05 - 4 5 0.000000E+00 - 4 6 0.000000E+00 - 5 1 0.000000E+00 - 5 2 0.000000E+00 - 5 3 0.000000E+00 - 5 4 0.000000E+00 - 5 5 -4.973414E+05 - 5 6 0.000000E+00 - 6 1 0.000000E+00 - 6 2 0.000000E+00 - 6 3 0.000000E+00 - 6 4 0.000000E+00 - 6 5 0.000000E+00 - 6 6 0.000000E+00 From 7ea10a204d20d566770336155024383e6712e47d Mon Sep 17 00:00:00 2001 From: dzalkind Date: Fri, 12 Jan 2024 11:29:12 -0700 Subject: [PATCH 030/114] Point to wisdem geometry --- examples/03_NREL5MW_OC3_spar/weis_driver.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/03_NREL5MW_OC3_spar/weis_driver.py b/examples/03_NREL5MW_OC3_spar/weis_driver.py index 3cdd912d1..6cad3feb4 100644 --- a/examples/03_NREL5MW_OC3_spar/weis_driver.py +++ b/examples/03_NREL5MW_OC3_spar/weis_driver.py @@ -6,7 +6,7 @@ ## File management run_dir = os.path.dirname( os.path.realpath(__file__) ) wisdem_examples = os.path.join(os.path.dirname( os.path.dirname( os.path.dirname( os.path.realpath(__file__) ) ) ), "WISDEM", "examples", "09_floating") -fname_wt_input = wisdem_examples + os.sep + 'nrel5mw-spar_oc3.yaml' +fname_wt_input = os.path.join(wisdem_examples, "09_floating", "nrel5mw-spar_oc3.yaml") fname_modeling_options = run_dir + os.sep + 'modeling_options.yaml' fname_analysis_options = run_dir + os.sep + 'analysis_options_noopt.yaml' From 36c32797c1bcfd9fde970e66758ece655390ac9f Mon Sep 17 00:00:00 2001 From: dzalkind Date: Fri, 12 Jan 2024 11:29:21 -0700 Subject: [PATCH 031/114] Remove PotFile --- examples/03_NREL5MW_OC3_spar/modeling_options.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/03_NREL5MW_OC3_spar/modeling_options.yaml b/examples/03_NREL5MW_OC3_spar/modeling_options.yaml index 3be92f511..7e479a69d 100644 --- a/examples/03_NREL5MW_OC3_spar/modeling_options.yaml +++ b/examples/03_NREL5MW_OC3_spar/modeling_options.yaml @@ -80,7 +80,7 @@ Level3: # Options for WEIS fidelity level 3 = nonlinear time domain ExctnMod: 1 RdtnMod: 1 RdtnDT: 0.0125 - PotFile: examples/03_NREL5MW_OC3_spar/HydroData/Spar + # PotFile: examples/03_NREL5MW_OC3_spar/HydroData/Spar # PtfmMass_Init: 7.462e6 ROSCO: From bc433b0bb58b1704db7c42aed99b443b2a20b746 Mon Sep 17 00:00:00 2001 From: dzalkind Date: Fri, 12 Jan 2024 11:29:48 -0700 Subject: [PATCH 032/114] Simplify 03 single run analysis options, standardize output file --- .../03_NREL5MW_OC3_spar/analysis_options.yaml | 2 +- .../analysis_options_noopt.yaml | 34 +------------------ 2 files changed, 2 insertions(+), 34 deletions(-) diff --git a/examples/03_NREL5MW_OC3_spar/analysis_options.yaml b/examples/03_NREL5MW_OC3_spar/analysis_options.yaml index 51b959d39..bffee6b70 100644 --- a/examples/03_NREL5MW_OC3_spar/analysis_options.yaml +++ b/examples/03_NREL5MW_OC3_spar/analysis_options.yaml @@ -1,5 +1,5 @@ general: - folder_output: output + folder_output: outputs/03_OC3_optimization fname_output: refturb_output design_variables: control: diff --git a/examples/03_NREL5MW_OC3_spar/analysis_options_noopt.yaml b/examples/03_NREL5MW_OC3_spar/analysis_options_noopt.yaml index ab88558ca..831a6dc95 100644 --- a/examples/03_NREL5MW_OC3_spar/analysis_options_noopt.yaml +++ b/examples/03_NREL5MW_OC3_spar/analysis_options_noopt.yaml @@ -1,38 +1,6 @@ general: - folder_output: output + folder_output: outputs/03_OC3_single_run fname_output: refturb_output -design_variables: - control: - servo: - pitch_control: - omega: - flag: False - min: 0.1 - max: 0.5 - - floating: - members: - flag: False - groups: - - names: [spar] - ballast: - lower_bound: 0.8e3 - upper_bound: 1.5e3 - -merit_figure: platform_mass # Merit figure of the optimization problem. The options are 'AEP' - 'LCOE' - 'Cp' - 'blade_mass' - 'blade_tip_deflection' - -constraints: - control: - rotor_overspeed: - flag: False - min: 0.0 - max: 0.2 - Max_PtfmPitch: - flag: False - max: 5.5 - Std_PtfmPitch: - flag: False - max: 2. driver: optimization: From 075a9b65d12519c13f69b302bae6e10dbe5891ae Mon Sep 17 00:00:00 2001 From: dzalkind Date: Fri, 12 Jan 2024 11:30:24 -0700 Subject: [PATCH 033/114] Standardize 04 example output --- examples/04_NREL5MW_OC4_semi/analysis_options.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/04_NREL5MW_OC4_semi/analysis_options.yaml b/examples/04_NREL5MW_OC4_semi/analysis_options.yaml index 65c8c99ba..8be70c090 100644 --- a/examples/04_NREL5MW_OC4_semi/analysis_options.yaml +++ b/examples/04_NREL5MW_OC4_semi/analysis_options.yaml @@ -1,3 +1,3 @@ general: - folder_output: outputs + folder_output: outputs/04_OC4_single_run fname_output: refturb_output From 284a7fe6d13c71bb5fc698d0946ada75dcc30302 Mon Sep 17 00:00:00 2001 From: dzalkind Date: Fri, 12 Jan 2024 11:30:53 -0700 Subject: [PATCH 034/114] Send 04 OF files to regular output --- examples/04_NREL5MW_OC4_semi/modeling_options.yaml | 2 -- 1 file changed, 2 deletions(-) diff --git a/examples/04_NREL5MW_OC4_semi/modeling_options.yaml b/examples/04_NREL5MW_OC4_semi/modeling_options.yaml index 81b33f459..d4e5dd6b3 100644 --- a/examples/04_NREL5MW_OC4_semi/modeling_options.yaml +++ b/examples/04_NREL5MW_OC4_semi/modeling_options.yaml @@ -2,8 +2,6 @@ General: verbosity: False # When set to True, the code prints to screen many infos openfast_configuration: use_exe: True - OF_run_fst: NREL5MW_OC4_semi - OF_run_dir: temp/NREL5MW_OC4_semi WISDEM: RotorSE: From fb8133bc7bb5403c3efd5985c044e83c5089cdaf Mon Sep 17 00:00:00 2001 From: dzalkind Date: Fri, 12 Jan 2024 11:33:52 -0700 Subject: [PATCH 035/114] Standardize 05 example inputs --- .../05_IEA-3.4-130-RWT/IEA-3p4-130-RWT.yaml | 730 ------------------ .../05_IEA-3.4-130-RWT/analysis_options.yaml | 2 +- .../05_IEA-3.4-130-RWT/modeling_options.yaml | 3 - 3 files changed, 1 insertion(+), 734 deletions(-) delete mode 100644 examples/05_IEA-3.4-130-RWT/IEA-3p4-130-RWT.yaml diff --git a/examples/05_IEA-3.4-130-RWT/IEA-3p4-130-RWT.yaml b/examples/05_IEA-3.4-130-RWT/IEA-3p4-130-RWT.yaml deleted file mode 100644 index 574902ad5..000000000 --- a/examples/05_IEA-3.4-130-RWT/IEA-3p4-130-RWT.yaml +++ /dev/null @@ -1,730 +0,0 @@ -name: IEA-3.4-130-RWT -description: version from December 16th 2019 -assembly: - turbine_class: III - turbulence_class: A - drivetrain: Geared - rotor_orientation: Upwind - number_of_blades: 3 - hub_height: 110. - rotor_diameter: 130. - rated_power: 3.37e+6 -components: - blade: - outer_shape_bem: - airfoil_position: - grid: [0.0, 0.02, 0.1371, 0.2118, 0.3188, 0.5236, 0.6781, 0.8967, 1.0] - labels: [cylinder, cylinder, FX77-W-500, FX77-W-400, DU00-W2-350, DU97-W-300, DU91-W2-250, DU08-W-210, DU08-W-210] - chord: - grid: [0.0, 0.016666666666666666, 0.03333333333333333, 0.05, 0.06666666666666667, 0.08333333333333333, 0.1, 0.12258064516129033, 0.14516129032258066, 0.16774193548387098, 0.1903225806451613, 0.21290322580645163, 0.23548387096774195, 0.25806451612903225, 0.2806451612903226, 0.3032258064516129, 0.3258064516129032, 0.34838709677419355, 0.3709677419354839, 0.3935483870967742, 0.4161290322580645, 0.43870967741935485, 0.4612903225806452, 0.4838709677419355, 0.5064516129032258, 0.5290322580645161, 0.5516129032258065, 0.5741935483870968, 0.5967741935483871, 0.6193548387096774, 0.6419354838709678, 0.6645161290322581, 0.6870967741935484, 0.7096774193548387, 0.7322580645161291, 0.7548387096774194, 0.7774193548387097, 0.8, 0.8175, 0.835, 0.8525, 0.87, 0.8875, 0.905, 0.9225, 0.94, 0.955, 0.97, 0.985, 1.0] - values: [2.6, 2.6, 2.6354502374169044, 2.777, 2.991976668214341, 3.196228240853453, 3.4, 3.6436872613379956, 3.8468526682012616, 4.01045286790906, 4.136226401000778, 4.225808052498346, 4.279239401794768, 4.297823268772448, 4.2864307707126645, 4.244611170800341, 4.17627303747058, 4.0844950030200415, 3.971573210824347, 3.8402952343195027, 3.6942104676217893, 3.5371596209183025, 3.3747017801481776, 3.212546569922865, 3.05633856267269, 2.90917222470287, 2.771503433685507, 2.6441760284317284, 2.526066882703902, 2.4179508696143652, 2.3189263417494823, 2.229874794314517, 2.1504136712680566, 2.0809782800360717, 2.0210411292367745, 1.9710510359825273, 1.9309532984595674, 1.9, 1.881723095703125, 1.8618785714285715, 1.8328693498883926, 1.7872932981927712, 1.7180379122740965, 1.6179435656424581, 1.4786371587352758, 1.292, 1.0895993320321518, 0.8438518268281274, 0.5492167608394835, 0.2] - twist: &twist - grid: [0.0, 0.016666666666666666, 0.03333333333333333, 0.05, 0.06666666666666667, 0.08333333333333333, 0.1, 0.12258064516129033, 0.14516129032258066, 0.16774193548387098, 0.1903225806451613, 0.21290322580645163, 0.23548387096774195, 0.25806451612903225, 0.2806451612903226, 0.3032258064516129, 0.3258064516129032, 0.34838709677419355, 0.3709677419354839, 0.3935483870967742, 0.4161290322580645, 0.43870967741935485, 0.4612903225806452, 0.4838709677419355, 0.5064516129032258, 0.5290322580645161, 0.5516129032258065, 0.5741935483870968, 0.5967741935483871, 0.6193548387096774, 0.6419354838709678, 0.6645161290322581, 0.6870967741935484, 0.7096774193548387, 0.7322580645161291, 0.7548387096774194, 0.7774193548387097, 0.8, 0.8175, 0.835, 0.8525, 0.87, 0.8875, 0.905, 0.9225, 0.94, 0.955, 0.97, 0.985, 1.0] - 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relative_thickness: 1 - aerodynamic_center: 0.50 - polars: - - configuration: Default - re: 6.00E+06 # placeholder - c_l: - grid: [-3.14, 3.14] - values: [0.0, 0.0] - c_d: - grid: [-3.14, 3.14] - values: [0.5, 0.5] - c_m: - grid: [-3.14, 3.14] - values: [0.0, 0.0] -materials: - - name: paint - description: UV protection - source: Material coming from ... - orth: 0 - rho: 1100 - E: 4.56e+006 - nu: 0.49 - G: 1520000.0 - ply_t: 0.0005 - waste: 0.25 - unit_cost: 7.23 - component_id: 0 - Xt: 0.0 - Xc: 0.0 - S: 0.0 - - name: triax - description: Standard triax from ... - source: Material coming from ... - orth: 1 - rho: 1845 - E: [21.79e+009, 14.67e+009, 14.67e+009] - G: [9.413e+009, 9.413e+009, 9.413e+009] - nu: [0.48, 0.48, 0.48] - alpha: [0, 0, 0] - Xt: [600.0e+006, 600.0e+006, 600.0e+006] - Xc: [600.0e+006, 600.0e+006, 600.0e+006] - S: [500.0e+006, 200.0e+006, 200.0e+006] - m: 10 - fvf: 0.4793 - fwf: 0.6754 - waste: 0.15 - unit_cost: 2.86 - component_id: 2 - area_density_dry: 1.112 - fiber_density: 2600. - roll_mass: 181.4368 - - name: biax - description: Standard biax from ... - source: Material coming from ... - orth: 1 - rho: 1845 - E: [13.92e+009, 13.92e+009, 13.92e+009] - G: [11.5e+009, 11.5e+009, 11.5e+009] - nu: [0.49, 0.49, 0.49] - alpha: [0, 0, 0] - Xt: [600.0e+006, 600.0e+006, 600.0e+006] - Xc: [600.0e+006, 600.0e+006, 600.0e+006] - S: [500.0e+006, 200.0e+006, 200.0e+006] - m: 10 - fvf: 0.4793 - fwf: 0.6754 - waste: 0.15 - unit_cost: 3.00 - component_id: 3 - area_density_dry: 1.112 - fiber_density: 2600. - roll_mass: 181.4368 - - name: ud - description: Standard ud from ... - source: Material coming from ... - orth: 1 - rho: 1940 - E: [42.00e+009, 12.30e+009, 12.30e+009] - G: [3.47e+009, 3.47e+009, 3.47e+009] - nu: [0.31, 0.31, 0.31] - alpha: [0, 0, 0] - Xt: [600.0e+006, 600.0e+006, 600.0e+006] - Xc: [600.0e+006, 600.0e+006, 600.0e+006] - S: [500.0e+006, 200.0e+006, 200.0e+006] - m: 10 - fvf: 0.5448 - fwf: 0.7302 - waste: 0.05 - unit_cost: 1.87 - component_id: 4 - area_density_dry: 1.858 - fiber_density: 2600. - - name: balsa - description: Balsa filler from ... - source: Material coming from ... - orth: 0 - rho: 110 - E: 50.0e+006 - nu: 0.49 - alpha: 0.0 - Xt: 0.69e+006 - Xc: 0.4e+006 - S: 0.31e+006 - G: 16666666.666666666 - waste: 0.2 - unit_cost: 13. - component_id: 1 - - name: foam - description: Foam filler from ... - source: Material coming from ... - orth: 0 - rho: 110 - E: 50.0e+006 - nu: 0.49 - alpha: 0.0 - Xt: 0.69e+006 - Xc: 0.4e+006 - S: 0.31e+006 - G: 16666666.666666666 - waste: 0.2 - unit_cost: 13. - component_id: 1 - - name: adhesive - description: Adhesive from ... - source: Material coming from ... - orth: 0 - rho: 1100 - E: 4.56e+006 - nu: 0.49 - alpha: 0.0 - Xt: 0.69e+006 - Xc: 0.4e+006 - S: 0.31e+006 - G: 1520000.0 - - name: resin - description: epoxy - E: 1.e+6 - rho: 1150. - nu: 0.3 - orth: 0 - unit_cost: 3.63 - waste: 0.2 - - name: steel - description: Steel of the tower - source: Standard steel with higher density to account for auxiliary systems of the tower (elevator, stairs, etc) - orth: 0 - rho: 8500 - E: 210.e+009 - Xy: 450.e+6 - nu: 0.3 - alpha: 0.0 - Xt: 355.e+006 - Xc: 355.e+006 - m: 3 - A: 3.5534648443719767e10 #DOI 10.1016/j.proeng.2013.12.061 eqn 5 -control: - supervisory: - Vin: 3.0 - Vout: 25.0 - maxTS: 80. - rated_power: 3.37e+6 - pitch: - PC_zeta: 1.0 - PC_omega: 0.2 - ps_percent: 0.8 - max_pitch: 1.57 - max_pitch_rate: 0.12217304763960307 # 7 deg/s - min_pitch: 0. - torque: - control_type: tsr_tracking - tsr: 8.01754386 - max_torque_rate: 1500000. - VS_minspd: 0.0 - VS_maxspd: 2.00000 - VS_zeta: 1.0 - VS_omega: 0.2 - setpoint_smooth: - ss_vsgain: 1 - ss_pcgain: .001 - shutdown: - limit_type: gen_speed - limit_value: 2.0 -environment: - air_density: 1.225 - air_dyn_viscosity: 1.81e-5 - weib_shape_parameter: 2. - air_speed_sound: 340. - shear_exp: 0.2 - water_density: 1025.0 - water_dyn_viscosity: 1.3351e-3 - soil_shear_modulus: 140.e+6 - soil_poisson: 0.4 -bos: - plant_turbine_spacing: 7 - plant_row_spacing: 7 - commissioning_pct: 0.01 - decommissioning_pct: 0.15 - distance_to_substation: 50.0 - distance_to_interconnection: 8. - interconnect_voltage: 130 -costs: - wake_loss_factor: 0.15 - fixed_charge_rate: 0.075 - bos_per_kW: 459. - opex_per_kW: 44. - turbine_number: 178. - labor_rate: 58.8 - painting_rate: 30.0 - blade_mass_cost_coeff: 14.6 - hub_mass_cost_coeff: 3.9 - pitch_system_mass_cost_coeff: 22.1 - spinner_mass_cost_coeff: 11.1 - lss_mass_cost_coeff: 11.9 - bearing_mass_cost_coeff: 4.5 - gearbox_mass_cost_coeff: 12.9 - hss_mass_cost_coeff: 6.8 - generator_mass_cost_coeff: 12.4 - bedplate_mass_cost_coeff: 2.9 - yaw_mass_cost_coeff: 8.3 - converter_mass_cost_coeff: 18.8 - transformer_mass_cost_coeff: 18.8 - hvac_mass_cost_coeff: 124.0 - cover_mass_cost_coeff: 5.7 - elec_connec_machine_rating_cost_coeff: 41.85 - platforms_mass_cost_coeff: 17.1 - tower_mass_cost_coeff: 2.9 - controls_machine_rating_cost_coeff: 21.15 - crane_cost: 12.e+3 diff --git a/examples/05_IEA-3.4-130-RWT/analysis_options.yaml b/examples/05_IEA-3.4-130-RWT/analysis_options.yaml index 65c8c99ba..e78611f19 100644 --- a/examples/05_IEA-3.4-130-RWT/analysis_options.yaml +++ b/examples/05_IEA-3.4-130-RWT/analysis_options.yaml @@ -1,3 +1,3 @@ general: - folder_output: outputs + folder_output: outputs/05_IEA34_single_run fname_output: refturb_output diff --git a/examples/05_IEA-3.4-130-RWT/modeling_options.yaml b/examples/05_IEA-3.4-130-RWT/modeling_options.yaml index 5cc5c6abc..dcca357f3 100644 --- a/examples/05_IEA-3.4-130-RWT/modeling_options.yaml +++ b/examples/05_IEA-3.4-130-RWT/modeling_options.yaml @@ -1,8 +1,5 @@ General: verbosity: False # When set to True, the code prints to screen many infos - openfast_configuration: - OF_run_fst: IEA34 # Name of the OpenFAST output files - OF_run_dir: temp/iea34 # Path to folder with the OpenFAST output files WISDEM: RotorSE: From 87dbddc175675dd8171832c41704c9222faef05e Mon Sep 17 00:00:00 2001 From: dzalkind Date: Fri, 12 Jan 2024 11:34:40 -0700 Subject: [PATCH 036/114] Add readme to example 05 --- examples/05_IEA-3.4-130-RWT/README.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 examples/05_IEA-3.4-130-RWT/README.md diff --git a/examples/05_IEA-3.4-130-RWT/README.md b/examples/05_IEA-3.4-130-RWT/README.md new file mode 100644 index 000000000..44babde7c --- /dev/null +++ b/examples/05_IEA-3.4-130-RWT/README.md @@ -0,0 +1 @@ +This example runs the IEA 3.4 MW RWT in several DLCs \ No newline at end of file From 6091a039249693751faaecacae0d4ca2ec1440a5 Mon Sep 17 00:00:00 2001 From: dzalkind Date: Fri, 12 Jan 2024 11:34:48 -0700 Subject: [PATCH 037/114] Point to 05 wisdem input --- examples/05_IEA-3.4-130-RWT/weis_driver.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/examples/05_IEA-3.4-130-RWT/weis_driver.py b/examples/05_IEA-3.4-130-RWT/weis_driver.py index 47c8b7461..6df33a7b5 100644 --- a/examples/05_IEA-3.4-130-RWT/weis_driver.py +++ b/examples/05_IEA-3.4-130-RWT/weis_driver.py @@ -5,7 +5,8 @@ ## File management run_dir = os.path.dirname( os.path.realpath(__file__) ) + os.sep -fname_wt_input = run_dir + "IEA-3p4-130-RWT.yaml" +wisdem_examples = os.path.join(os.path.dirname( os.path.dirname( os.path.dirname( os.path.realpath(__file__) ) ) ), "WISDEM", "examples") +fname_wt_input = os.path.join(wisdem_examples, "02_reference_turbines", "IEA-3p4-130-RWT.yaml") fname_modeling_options = run_dir + "modeling_options.yaml" fname_analysis_options = run_dir + "analysis_options.yaml" From 9a2c09a7140595f679b896c145928575b4a46046 Mon Sep 17 00:00:00 2001 From: dzalkind Date: Fri, 12 Jan 2024 11:40:47 -0700 Subject: [PATCH 038/114] Add README for 06 example --- examples/06_IEA-15-240-RWT/README.md | 10 ++++++++++ 1 file changed, 10 insertions(+) create mode 100644 examples/06_IEA-15-240-RWT/README.md diff --git a/examples/06_IEA-15-240-RWT/README.md b/examples/06_IEA-15-240-RWT/README.md new file mode 100644 index 000000000..faed973e8 --- /dev/null +++ b/examples/06_IEA-15-240-RWT/README.md @@ -0,0 +1,10 @@ +This example runs several IEA-15MW RWT cases. + +Expand more. + +Geometry options + +Modeling options + +Analysis options + From 6e013dcd3c7e6a7e62c3b6e0781fa518ac18f31f Mon Sep 17 00:00:00 2001 From: dzalkind Date: Fri, 12 Jan 2024 11:50:34 -0700 Subject: [PATCH 039/114] Add README for example 07 --- examples/07_te_flaps/README.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 examples/07_te_flaps/README.md diff --git a/examples/07_te_flaps/README.md b/examples/07_te_flaps/README.md new file mode 100644 index 000000000..90d1765cc --- /dev/null +++ b/examples/07_te_flaps/README.md @@ -0,0 +1 @@ +This example is untested and requires installing external programs, like xfoil. Please use with caution. The WEIS team may not be able to support this example. \ No newline at end of file From 87b21eb217a844daa245605d7156cf81034a9ea3 Mon Sep 17 00:00:00 2001 From: dzalkind Date: Fri, 12 Jan 2024 11:51:19 -0700 Subject: [PATCH 040/114] Add README for 08 example --- examples/08_OLAF/README.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 examples/08_OLAF/README.md diff --git a/examples/08_OLAF/README.md b/examples/08_OLAF/README.md new file mode 100644 index 000000000..5351c0995 --- /dev/null +++ b/examples/08_OLAF/README.md @@ -0,0 +1 @@ +This example runs OLAF cases in OpenFAST \ No newline at end of file From 95676411ec5bfe5c22d800c52b2e23a9c5a0e50f Mon Sep 17 00:00:00 2001 From: dzalkind Date: Fri, 12 Jan 2024 13:35:36 -0700 Subject: [PATCH 041/114] Add README and tidy 06 example inputs some --- .../06_IEA-15-240-RWT/IEA-15-240-RWT.yaml | 954 ------------------ examples/06_IEA-15-240-RWT/README.md | 5 +- .../06_IEA-15-240-RWT/analysis_options.yaml | 24 +- .../analysis_options_TMDs.yaml | 2 +- .../analysis_options_tower_DVs.yaml | 2 +- .../06_IEA-15-240-RWT/modeling_options.yaml | 2 - .../modeling_options_tower.yaml | 20 +- .../modeling_options_umaine_semi.yaml | 38 +- .../06_IEA-15-240-RWT/weis_driver_TMDs.py | 1 - ...weis_driver.py => weis_driver_monopile.py} | 3 +- 10 files changed, 45 insertions(+), 1006 deletions(-) delete mode 100644 examples/06_IEA-15-240-RWT/IEA-15-240-RWT.yaml rename examples/06_IEA-15-240-RWT/{weis_driver.py => weis_driver_monopile.py} (69%) diff --git a/examples/06_IEA-15-240-RWT/IEA-15-240-RWT.yaml b/examples/06_IEA-15-240-RWT/IEA-15-240-RWT.yaml deleted file mode 100644 index 0fdda0379..000000000 --- a/examples/06_IEA-15-240-RWT/IEA-15-240-RWT.yaml +++ /dev/null @@ -1,954 +0,0 @@ -name: IEA 15MW Offshore Reference Turbine, with taped chord tip design - -assembly: - turbine_class: I - turbulence_class: B - drivetrain: direct_drive - rotor_orientation: Upwind - number_of_blades: 3 - hub_height: 150. - rotor_diameter: 242.23775645 - rated_power: 15.e+6 - -components: - blade: - outer_shape_bem: - airfoil_position: - grid: [0.0, 0.02, 0.15, 0.24517031675566095, 0.3288439506472435, 0.4391793464459161, 0.5376714071084352, 0.6382076569163737, 0.7717438522715817, 1.0] - labels: [circular, circular, SNL-FFA-W3-500, FFA-W3-360, FFA-W3-330blend, FFA-W3-301, FFA-W3-270blend, FFA-W3-241, FFA-W3-211, FFA-W3-211] - chord: - grid: [0.0, 0.02, 0.04, 0.06, 0.08, 0.1, 0.125, 0.15, 0.1690340633511322, 0.18806812670226436, 0.20710219005339656, 0.22613625340452875, 0.24517031675566095, 0.2660887252285566, 0.2870071337014522, 0.30792554217434787, 0.3288439506472435, 0.350911029806978, 0.3729781089667125, 0.39504518812644707, 0.41711226728618156, 0.4391793464459161, 0.45887775857841995, 0.47857617071092373, 0.49827458284342757, 0.5179729949759314, 0.5376714071084352, 0.5577786570700229, 0.5778859070316106, 0.5979931569931983, 0.618100406954786, 0.6382076569163737, 0.657284256252832, 0.6763608555892903, 0.6954374549257486, 0.7145140542622068, 0.7335906535986652, 0.7526672529351234, 0.7717438522715817, 0.8, 0.8187500000000001, 0.8375, 0.85625, 0.875, 0.89375, 0.9125, 0.9312499999999999, 0.95, 0.975, 0.9786324786324786, 0.9807692307692307, 0.9829059829059829, 0.985042735042735, 0.9871794871794871, 0.9893162393162394, 0.9914529914529915, 0.9935897435897436, 0.9957264957264957, 0.9978632478632479, 1.0] - values: [5.2, 5.208280051053228, 5.235714686841762, 5.288938619667076, 5.360587950371896, 5.4432987797989485, 5.551024109038494, 5.650144322056736, 5.710964180985916, 5.75211103329394, 5.767237237684651, 5.755066778057295, 5.702344106369591, 5.600880031723281, 5.4630400185346435, 5.309547499170078, 5.148693682942882, 4.991042900107676, 4.849752915435638, 4.727607774019834, 4.604737872426362, 4.482056690032084, 4.373756099992555, 4.267303627608841, 4.162975379862409, 4.061706438261234, 3.9635294254274465, 3.866946023408861, 3.7730033609042173, 3.681316991817714, 3.590732198099713, 3.5007879683424514, 3.415117624636448, 3.3288587248956025, 3.2415382010222533, 3.1550747436965874, 3.0695283044857655, 2.9839638733880505, 2.8980041517726187, 2.7682222306856965, 2.6795728562121024, 2.588237585754934, 2.4939219808866904, 2.3973569798757457, 2.2985748263544723, 2.197674872061173, 2.0947488594285835, 1.989796398413245, 1.8481235924071342, 1.8258389124179322, 1.812083398372174, 1.7969953985141929, 1.779313478686, 1.7565605031190397, 1.7238716579926647, 1.6716926510559211, 1.5812588997806323, 1.4157165078158995, 1.1026835699503355, 0.5] - twist: - grid: [0.0, 0.02, 0.04, 0.06, 0.08, 0.1, 0.125, 0.15, 0.1690340633511322, 0.18806812670226436, 0.20710219005339656, 0.22613625340452875, 0.24517031675566095, 0.2660887252285566, 0.2870071337014522, 0.30792554217434787, 0.3288439506472435, 0.350911029806978, 0.3729781089667125, 0.39504518812644707, 0.41711226728618156, 0.4391793464459161, 0.45887775857841995, 0.47857617071092373, 0.49827458284342757, 0.5179729949759314, 0.5376714071084352, 0.5577786570700229, 0.5778859070316106, 0.5979931569931983, 0.618100406954786, 0.6382076569163737, 0.657284256252832, 0.6763608555892903, 0.6954374549257486, 0.7145140542622068, 0.7335906535986652, 0.7526672529351234, 0.7717438522715817, 0.8, 0.8187500000000001, 0.8375, 0.85625, 0.875, 0.89375, 0.9125, 0.9312499999999999, 0.95, 0.975, 1.0] - values: [0.27217629557079365, 0.2721151609261069, 0.2692829996121141, 0.26160974693508077, 0.25004194631128646, 0.23552614115701065, 0.21467775307891476, 0.19255079961979954, 0.1759878586013097, 0.16057133469540177, 0.14711714321398614, 0.1357292521960968, 0.12536451335231813, 0.11391930868991773, 0.10288584723243112, 0.09240330785216998, 0.08263112720589656, 0.07331222604686127, 0.06502804112954641, 0.05771742601208772, 0.05096319090575163, 0.04469782752454605, 0.03947910486050121, 0.03456265676425803, 0.0299081112103219, 0.025446129064633893, 0.02121583510652544, 0.01724217523108515, 0.013528184408954813, 0.01004307189737569, 0.006635163107666419, 0.0032467588547257582, -3.369564839038511e-05, -0.0035492823924747176, -0.007421594235148177, -0.011928778895197866, -0.017136732150624685, -0.0223974077244561, -0.027490996670335385, -0.03379973253909251, -0.036799280794358936, -0.037810153747541306, -0.037988462925127336, -0.03771818081838972, -0.036975584119978255, -0.03575998726335209, -0.03405925501568549, -0.03168024471993639, -0.027365297139775716, -0.021683756060763528] - 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diameter: 7.94 - cone_angle: 0.06981317007977318 # 4 deg - drag_coefficient: 0.5 - flange_t2shell_t: 6.0 - flange_OD2hub_D: 0.6 - flange_ID2OD: 0.8 - hub_blade_spacing_margin: 1.2 - hub_stress_concentration: 3.0 - n_front_brackets: 5 - n_rear_brackets: 5 - clearance_hub_spinner: 0.5 - spin_hole_incr: 1.2 - pitch_system_scaling_factor: 0.75 - spinner_gust_ws: 70.0 - hub_material: cast_iron - spinner_material: glass_uni - nacelle: - drivetrain: - uptilt: 0.10471975511965977 # 6 deg - distance_tt_hub: 5.614 - overhang: 12.0313 - drag_coefficient: 0.5 - distance_hub2mb: 1.0 - distance_mb2mb: 1.2 - lss_diameter: [3.0, 3.0] - lss_wall_thickness: [0.1, 0.1] - nose_diameter: [2.2, 2.2] - nose_wall_thickness: [0.1, 0.1] - generator_length: 2.15 - bedplate_wall_thickness: - grid: [0.0, 1.0] - values: [0.05, 0.05] - gear_ratio: 1.0 - gearbox_efficiency: 1.0 - mb1Type: CARB - mb2Type: SRB - uptower: True - lss_material: steel_drive - bedplate_material: steel_drive - generator: - generator_type: PMSG_Outer - rho_Fe: 7700.0 - rho_Fes: 7850.0 - rho_Copper: 8900.0 - rho_PM: 7450.0 - B_r: 1.279 - P_Fe0e: 1.0 - P_Fe0h: 4.0 - S_N: -0.002 - alpha_p: 1.0995574287564276 #0.5*np.pi*0.7 - b_r_tau_r: 0.45 - b_ro: 0.004 - b_s_tau_s: 0.45 - b_so: 0.004 - cofi: 0.9 - freq: 60.0 - h_i: 0.004 - h_sy0: 0.0 - h_w: 0.005 - k_fes: 0.8 - k_fillr: 0.55 - k_fills: 0.65 - k_s: 0.2 - m: 3 - mu_0: 1.2566370614359173e-06 #np.pi*4e-7 - mu_r: 1.06 - phi: 1.5707963267948966 # 90 deg - q1: 5 - q2: 4 - ratio_mw2pp: 0.8 - resist_Cu: 2.52e-8 #1.8e-8*1.4 - y_tau_pr: 0.8333333 #10. / 12 - y_tau_p: 0.8 #12./15. - rad_ag: 5.12623359 - len_s: 2.23961662 - tau_p: 0.1610453779 - tau_s: 0.1339360726 - h_s: 0.3769449149 - b_s: 0.05121796888 - b_t: 0.08159225451 - h_t: 0.3859449149 - h_ys: 0.03618114528 - h_yr: 0.0361759511 - h_m: 0.0995385122 - b_m: 0.1288363023 - B_g: 1.38963289 - B_symax: 1.63455514 - B_rymax: 1.63478983 - p: 100 - R_s: 0.02457052 - L_s: 0.01138752 - S: 240 - t_r: 0.0607657125 - h_sr: 0.04677116325 - t_s: 0.06065213554 - 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drag_coefficient: - grid: [0.0, 1.0] - values: [0.5, 0.5] - internal_structure_2d_fem: - outfitting_factor: 1.07 - reference_axis: *ref_axis_tower - layers: - - name: tower_wall - material: steel - thickness: - grid: *grid_tower - values: [0.041058, 0.039496, 0.039496, 0.036456, 0.036456, 0.033779, 0.033779, 0.032192, 0.032192, 0.030708, 0.030708, 0.029101, 0.029101, 0.027213, 0.027213, 0.024009, 0.024009, 0.020826, 0.020826, 0.023998, 0.023998] - monopile: - transition_piece_mass: 100e3 - outer_shape_bem: - reference_axis: &ref_axis_mono - x: - grid: [0.0, 1.0] - values: [0.0, 0.0] - y: - grid: [0.0, 1.0] - values: [0.0, 0.0] - z: - grid: &grid_mono [0.0, 0.5, 0.50001111, 0.55555556, 0.55556667, 0.61111111, 0.61112222, 0.66666667, 0.66667778, 0.72222222, 0.72223333, 0.77777778, 0.77778889, 0.83333333, 0.83334444, 0.88888889, 0.8889 , 0.94444444, 0.94445556, 1.0] - values: [-75, -30., -29.999, -25., -24.999, -20., -19.999, -15., -14.999, -10., -9.999, -5., -4.999, 0., 0.001, 5., 5.001, 10., 10.001, 15.] - 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F Zahle, DTU Wind Energy 11 May 2017 - polars: - - configuration: Default - re: 1.00E+07 - c_l: - grid: *grid004 - values: [0.0, 0.07178149999999964, 0.1435630000000001, 0.2153444999999997, 0.2871260000000001, 0.3589074999999998, 0.4306889999999995, 0.5024704999999998, 0.5742519999999995, 0.6460334999999998, 0.7178149999999996, 0.7895965, 0.8613779999999996, 0.9331595000000001, 1.004941, 0.9189832441278815, 0.8440624787712528, 0.7748315757714827, 0.7079034441742906, 0.6411583915080097, 0.5733514328047344, 0.5038751148063706, 0.432607030599928, 0.3598052464255929, 0.2860316675426308, 0.2120921414269119, 0.1389869075032731, 0.06786776586894963, 5.551115123125782e-17, -0.06786776586894941, -0.1389869075032731, -0.2120921414269117, -0.2860316675426306, -0.3598052464255929, -0.4326070305999278, -0.5038751148063706, -0.5733514328047343, -0.6411583915080097, -0.7079034441742903, -0.7748315757714827, -0.8440624787712528, -0.9189832441278818, -1.004941, -1.113059, -1.054248, -0.982473, -0.9417260000000001, -0.893331, -0.8547150000000001, -0.82348, -0.795409, -0.636498, -0.390949, -0.1307082, 0.1617258, 0.311214, 0.459562, 0.605659, 0.748677, 0.888617, 1.025442, 1.15878, 1.28822, 1.41282, 1.5309, 1.64065, 1.73926, 1.81971, 1.87065, 1.89221, 1.8791, 1.88111, 1.86359, 1.73324, 1.59357, 1.46708, 1.44834, 1.43563, 1.312833205896974, 1.20580354110179, 1.106902251102118, 1.011290634534701, 0.9159405592971568, 0.8190734754353348, 0.7198215925805296, 0.6180100437141822, 0.5140074948937041, 0.4086166679180437, 0.3029887734670167, 0.1985527250046759, 0.09695395124135632, 8.326672684688674e-17, -0.06786776586894941, -0.1389869075032731, -0.2120921414269117, -0.2860316675426306, -0.3598052464255929, -0.4326070305999276, -0.5038751148063706, -0.5733514328047341, -0.6411583915080097, -0.7079034441742906, -0.7748315757714827, -0.8440624787712528, -0.9189832441278815, -1.004941, -0.9331595000000001, -0.8613779999999996, -0.7895965, -0.7178149999999996, -0.6460334999999998, -0.5742520000000002, -0.5024704999999998, -0.4306890000000002, -0.3589074999999998, -0.2871260000000001, -0.2153444999999997, -0.1435630000000001, -0.07178149999999964, 0.0] - 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c_m: - grid: *grid004 - values: [0.0, 0.09142857142857111, 0.1828571428571434, 0.2742857142857145, 0.3657142857142855, 0.4031304636095545, 0.4081392053848416, 0.4131479471601289, 0.418156688935416, 0.4262712900595553, 0.4437034692302504, 0.461135648400946, 0.4785678275716412, 0.4960000067423368, 0.4883022318330648, 0.4678431517000181, 0.4480322988503198, 0.4369725143258274, 0.4259127298013352, 0.421499653933276, 0.4205848457791291, 0.4202386282975503, 0.4216794100726142, 0.4231201918476781, 0.4225819015145725, 0.4216313044922648, 0.4186442496773847, 0.4127722129896937, 0.4069001763020025, 0.3942614888768985, 0.3816228014517944, 0.3667605953544247, 0.3503288466648674, 0.3336237480028024, 0.3156065694727009, 0.2975893909425995, 0.2798918070549701, 0.2622959123717636, 0.2449149435317124, 0.2279423337936562, 0.2109697240555999, 0.1352478121095765, 0.0551741, 0.0321142, 0.01267890000000002, -0.002821250000000001, -0.0074129, -0.0110695, -0.01250065, -0.01177275, -0.01082065, -0.0276937, -0.05106789999999999, -0.0714813, -0.0917883, -0.1011907, -0.1098835, -0.1177635, -0.124769, -0.130977, -0.136484, -0.141299, -0.1454, -0.14875, -0.151175, -0.15262, -0.153103, -0.152545, -0.151206, -0.149693, -0.145621, -0.143578, -0.140948, -0.137106, -0.140821, -0.156927, -0.179786, -0.201472, -0.2240866097136903, -0.2461860684392151, -0.2613309888245065, -0.2764759092097978, -0.2906206368276409, -0.3042389471994587, -0.3178704108777556, -0.3315432135192723, -0.3452160161607889, -0.3584572127501313, -0.3716084914119372, -0.3840462584573971, -0.3954732173796998, -0.4069001763020025, -0.4127722129896937, -0.4186442496773847, -0.4216313044922648, -0.4225819015145725, -0.4231201918476781, -0.4216794100726143, -0.4202386282975503, -0.4205848457791291, -0.421499653933276, -0.4259127298013352, -0.4369725143258274, -0.4480322988503198, -0.4678431517000181, -0.4883022318330649, -0.4960000067423369, -0.4785678275716413, -0.461135648400946, -0.4437034692302504, -0.4262712900595553, -0.4352995460782729, -0.4531479471601288, -0.4709963482419844, -0.4888447493238403, -0.457142857142857, -0.3428571428571431, -0.2285714285714291, -0.1142857142857139, 0.0] - -materials: - - name: Gelcoat - orth: 0 - rho: 1235.0 - E: 3.440e+009 - G: 1.323e+009 - nu: 0.300 - alpha: 0.0 - Xt: 74 - Xc: 87 - S: 2.126E7 - GIc: 303 - GIIc: 3446 - alp0: 53 - ply_t: 5.0E-4 - waste: 0.25 - unit_cost: 7.23 - component_id: 0 - - name: steel - description: Steel of the tower and monopile ASTM A572 Grade 50 - source: http://www.matweb.com/search/DataSheet.aspx?MatGUID=9ced5dc901c54bd1aef19403d0385d7f - orth: 0 - rho: 7800 - alpha: 0.0 - E: 200.e+009 - nu: 0.3 - G: 79.3e+009 - GIc: 0 #Place holder, currently not used - GIIc: 0 #Place holder, currently not used - alp0: 0 #Place holder, currently not used - Xt: 450.e+006 - Xc: 450.e+006 - S: 0 - Xy: 345.e+6 - m: 3 - A: 3.5534648443719767e10 #DOI 10.1016/j.proeng.2013.12.061 eqn 5 - unit_cost: 0.7 - - name: steel_drive - description: Steel of the drivetrain ASTM 4140 40Cr1Mo28 - source: http://www.matweb.com/search/DataSheet.aspx?MatGUID=38108bfd64c44b4c9c6a02af78d5b6c6 - orth: 0 - rho: 7850 - alpha: 0.0 - E: 205.e+009 - nu: 0.3 - G: 80.0e+009 - GIc: 0 #Place holder, currently not used - GIIc: 0 #Place holder, currently not used - alp0: 0 #Place holder, currently not used - Xt: 814.e+006 - Xc: 814.e+006 - S: 0 - Xy: 485.e+6 - m: 3 - A: 3.5534648443719767e10 #DOI 10.1016/j.proeng.2013.12.061 eqn 5 - unit_cost: 0.9 - - name: cast_iron - description: Cast iron for hub and nacelle components - source: TODO - orth: 0 - rho: 7200 - alpha: 0.0 - E: 118.e+009 - nu: 0.3 - G: 47.6e+009 - GIc: 0 #Place holder, currently not used - GIIc: 0 #Place holder, currently not used - alp0: 0 #Place holder, currently not used - Xt: 310.e+006 - Xc: 310.e+006 - S: 0 - Xy: 265.e+6 - m: 3 - unit_cost: 0.5 - - name: glass_uni - description: Vectorply E-LT-5500, Epikote MGS RIMR 135/Epicure MGS RIMH 1366 epoxy - source: MSU composites database 3D property tests, Engineering Mechanics of Composite Materials, Daniel, I & Ishai, O., 1994, pg. 34 - orth: 1 - rho: 1940.0 - E: [4.46E10, 1.7E10, 1.67E10] - G: [3.27E9, 3.48E9, 3.5E9] - nu: [0.262, 0.35, 0.264] - Xt: [6.092E8, 3.81E7, 1.529E7] - Xc: [4.7471E8, 1.1264E8, 1.1322E8] - S: [1.891E7, 1.724E7, 1.316E7] - m: 10 - GIc: 303 - GIIc: 3446 - alp0: 53 - fvf: 0.57 - fwf: 0.7450682696347697 - ply_t: 0.005 - unit_cost: 1.87 - waste: 0.05 - fiber_density: 2535.5 - area_density_dry: 7.227162215457267 - component_id: 5 - - name: CarbonUD - E: [114500000000.0, 8390000000.0, 8390000000.0] - G: [5990000000.0, 5990000000.0, 5990000000.0] - rho: 1220.0 - orth: 1 - nu: [0.27, 0.27, 0.27] - Xt: [1546.e6, 0.0, 0.0] - Xc: [1047.e6, 0.0, 0.0] - S: [0.0, 0.0, 0.0] - m: 16.1 - GIc: 0.0 - GIIc: 0.0 - alp0: 0.0 - fvf: 0.1076923076923077 #0.5384615384615385 - fwf: 0.15889029003783103 #0.6461538461538463 - ply_t: 0.005158730158730159 #0.0010317460317460314 - unit_cost: 30. - waste: 0.05 - fiber_density: 1800. - area_density_dry: 1.000 - component_id: 4 - - name: glass_biax - description: Vectorply E-LT-5500, Epikote MGS RIMR 135/Epicure MGS RIMH 1366 epoxy - source: MSU composites database 3D property tests, Engineering Mechanics of Composite Materials, Daniel, I & Ishai, O., 1994, pg. 34 - orth: 1 - rho: 1940.0 - E: [1.11E10, 1.11E10, 1.67E10] - G: [1.353E10, 3.49E9, 3.49E9] - nu: [0.5, 0.0, 0.066] - Xt: [4.29E7, 4.26E7, 1.53E7] - Xc: [7.07E7, 7.07E7, 1.132E8] - S: [1.034E8, 1.72E7, 1.32E7] - m: 10 - GIc: 303 - GIIc: 3446 - alp0: 53 - fvf: 0.57 - fwf: 0.7450682696347697 - ply_t: 0.001 - waste: 0.15 - unit_cost: 3.00 - fiber_density: 2535.5 - area_density_dry: 1.4454324430914534 - component_id: 3 - roll_mass: 181.4368 - - name: glass_triax - description: Vectorply E-LT-5500, Epikote MGS RIMR 135/Epicure MGS RIMH 1366 epoxy - source: MSU composites database 3D property tests, Engineering Mechanics of Composite Materials, Daniel, I & Ishai, O., 1994, pg. 34 - orth: 1.0 - rho: 1940.0 - E: [2.87E10, 1.66E10, 1.67E10] - G: [8.4E9, 3.49E9, 3.49E9] - nu: [0.5, 0.0, 0.17] - Xt: [3.96E8, 7.64E7, 1.53E7] - Xc: [4.489E8, 1.747E8, 1.132E8] - S: [1.034E8, 1.72E7, 1.32E7] - m: 10 - GIc: 303 - GIIc: 3446 - alp0: 53 - fvf: 0.57 - fwf: 0.7450682696347697 - ply_t: 0.001 - unit_cost: 2.86 - waste: 0.15 - fiber_density: 2535.5 - area_density_dry: 1.4454324430914534 - component_id: 2 - roll_mass: 181.4368 - - name: medium_density_foam - description: Airex C70.130 PVC Foam, source 'https://www.3accorematerials.com/uploads/documents/TDS-AIREX-C70-E_1106.pdf' - orth: 0.0 - rho: 130.0 - E: 1.292E8 - G: 4.8946969696969695E7 - nu: 0.32 - Xt: 2083000.0 - Xc: 1563000.0 - S: 1250000.0 - GIc: 303 - GIIc: 3446 - alp0: 53 - component_id: 1 - waste: 0.2 - unit_cost: 13 - - name: resin - description: epoxy - E: 1.e+6 - nu: 0.3 #Place holder, currently not used - G: 312500.0 #Place holder, currently not used - GIc: 0 #Place holder, currently not used - GIIc: 0 #Place holder, currently not used - alp0: 0 #Place holder, currently not used - Xt: 0 #Place holder, currently not used - Xc: 0 #Place holder, currently not used - S: 0 #Place holder, currently not used - rho: 1150. - alpha: 0.0 - orth: 0 - unit_cost: 3.63 - - name: adhesive - description: Sample adhesive - source: https://www.nrel.gov/docs/fy19osti/73585.pdf - orth: 0 - rho: 1100 - E: 4.56e+006 - nu: 0.49 - alpha: 0.0 - Xt: 0.69e+006 - Xc: 0.4e+006 - S: 0.31e+006 - G: 1520000.0 - unit_cost: 9.0 -control: - supervisory: - Vin: 3.0 - Vout: 25.0 - maxTS: 95. - pitch: - max_pitch: 1.57 - max_pitch_rate: 0.03490658503988659 - min_pitch: 0. - PC_zeta: 1.0 - PC_omega: 0.2 - ps_percent: 0.8 - torque: - control_type: tsr_tracking - tsr: 9.0 - max_torque_rate: 1500000. - VS_minspd: 0.5235987755982988 - VS_maxspd: 0.7916813487046278 - VS_zeta: 1.0 - VS_omega: 0.2 - setpoint_smooth: - ss_vsgain: 1 - ss_pcgain: .001 - shutdown: - limit_type: gen_speed - limit_value: 2.0 - -environment: - air_density: 1.225 - air_dyn_viscosity: 1.81e-5 - weib_shape_parameter: 2. - air_speed_sound: 340. - shear_exp: 0.12 - water_density: 1025.0 - water_dyn_viscosity: 1.3351e-3 - soil_shear_modulus: 140e6 - soil_poisson: 0.4 - water_depth: 30.0 - significant_wave_height: 4.52 - significant_wave_period: 9.45 - -bos: - plant_turbine_spacing: 7 - plant_row_spacing: 7 - commissioning_pct: 0.01 - decommissioning_pct: 0.15 - distance_to_substation: 1.0 - distance_to_interconnection: 8.5 - interconnect_voltage: 130. - distance_to_site: 115. - distance_to_landfall: 50. - port_cost_per_month: 2e6 - site_auction_price: 100e6 - site_assessment_plan_cost: 1e6 - site_assessment_cost: 25e6 - construction_operations_plan_cost: 2.5e6 - boem_review_cost: 0.0 - design_install_plan_cost: 2.5e6 - -costs: - wake_loss_factor: 0.15 - fixed_charge_rate: 0.056 - bos_per_kW: 4053. - opex_per_kW: 137. - turbine_number: 40. - labor_rate: 58.8 - painting_rate: 30.0 - blade_mass_cost_coeff: 14.6 - hub_mass_cost_coeff: 3.9 - pitch_system_mass_cost_coeff: 22.1 - spinner_mass_cost_coeff: 11.1 - lss_mass_cost_coeff: 11.9 - bearing_mass_cost_coeff: 4.5 - gearbox_mass_cost_coeff: 12.9 - hss_mass_cost_coeff: 6.8 - generator_mass_cost_coeff: 12.4 - bedplate_mass_cost_coeff: 2.9 - yaw_mass_cost_coeff: 8.3 - converter_mass_cost_coeff: 18.8 - transformer_mass_cost_coeff: 18.8 - hvac_mass_cost_coeff: 124.0 - cover_mass_cost_coeff: 5.7 - elec_connec_machine_rating_cost_coeff: 41.85 - platforms_mass_cost_coeff: 17.1 - tower_mass_cost_coeff: 2.9 - controls_machine_rating_cost_coeff: 21.15 - crane_cost: 12e3 diff --git a/examples/06_IEA-15-240-RWT/README.md b/examples/06_IEA-15-240-RWT/README.md index faed973e8..2402692cb 100644 --- a/examples/06_IEA-15-240-RWT/README.md +++ b/examples/06_IEA-15-240-RWT/README.md @@ -1,8 +1,11 @@ -This example runs several IEA-15MW RWT cases. +This example runs several IEA-15MW RWT cases. We could bring the RAFT cases in here. Expand more. Geometry options +- IEA fixed bottom +- IEA VolturnUS +- IEA VolturnUS with TMDs (this one is special and would ideally inheret the previous) Modeling options diff --git a/examples/06_IEA-15-240-RWT/analysis_options.yaml b/examples/06_IEA-15-240-RWT/analysis_options.yaml index 039f17507..900315c98 100644 --- a/examples/06_IEA-15-240-RWT/analysis_options.yaml +++ b/examples/06_IEA-15-240-RWT/analysis_options.yaml @@ -1,23 +1,3 @@ general: - folder_output: outputs - fname_output: refturb_output - - -# design_variables: -# TMDs: -# flag: True -# groups: -# - names: [tower_TMD] -# mass: -# lower_bound: 200 -# upper_bound: 5628 - - -driver: - optimization: - flag: False # Flag to enable optimization - design_of_experiments: - flag: False # Flag to enable design of experiments - run_parallel: False # Flag to run using parallel processing - generator: FullFact # Type of input generator. (Uniform) - num_samples: 2 # number of samples for (Uniform only) \ No newline at end of file + folder_output: outputs/06_IEA15_single_run + fname_output: refturb_output \ No newline at end of file diff --git a/examples/06_IEA-15-240-RWT/analysis_options_TMDs.yaml b/examples/06_IEA-15-240-RWT/analysis_options_TMDs.yaml index c9d6a0679..4aedec354 100644 --- a/examples/06_IEA-15-240-RWT/analysis_options_TMDs.yaml +++ b/examples/06_IEA-15-240-RWT/analysis_options_TMDs.yaml @@ -1,5 +1,5 @@ general: - folder_output: outputs + folder_output: outputs/06_IEA15_TMD_optimization fname_output: refturb_output diff --git a/examples/06_IEA-15-240-RWT/analysis_options_tower_DVs.yaml b/examples/06_IEA-15-240-RWT/analysis_options_tower_DVs.yaml index 9a052780f..7d22673d5 100644 --- a/examples/06_IEA-15-240-RWT/analysis_options_tower_DVs.yaml +++ b/examples/06_IEA-15-240-RWT/analysis_options_tower_DVs.yaml @@ -1,5 +1,5 @@ general: - folder_output: outputs + folder_output: outputs/06_IEA15_tower_optimization fname_output: refturb_output design_variables: diff --git a/examples/06_IEA-15-240-RWT/modeling_options.yaml b/examples/06_IEA-15-240-RWT/modeling_options.yaml index f03f72e59..c7e7ffe9a 100644 --- a/examples/06_IEA-15-240-RWT/modeling_options.yaml +++ b/examples/06_IEA-15-240-RWT/modeling_options.yaml @@ -2,8 +2,6 @@ General: verbosity: False # When set to True, the code prints to screen many infos openfast_configuration: use_exe: True - OF_run_fst: IEA15 - OF_run_dir: temp/IEA15 WISDEM: RotorSE: diff --git a/examples/06_IEA-15-240-RWT/modeling_options_tower.yaml b/examples/06_IEA-15-240-RWT/modeling_options_tower.yaml index 805824ab6..c8abb018d 100644 --- a/examples/06_IEA-15-240-RWT/modeling_options_tower.yaml +++ b/examples/06_IEA-15-240-RWT/modeling_options_tower.yaml @@ -11,13 +11,29 @@ WISDEM: spar_cap_ps: Spar_Cap_PS te_ss: TE_reinforcement_SS te_ps: TE_reinforcement_PS + n_span: 60 + DriveSE: + flag: True TowerSE: flag: True - n_refine: 5 - DriveSE: + wind: PowerWind # Wind used + gamma_f: 1.35 # Safety factor for fatigue loads + gamma_m: 1.3 # Safety factor for material properties + gamma_n: 1.0 # Safety factor for ... + gamma_b: 1.1 # Safety factor for ... + gamma_fatigue: 1.755 # Safety factor for fatigue loads + buckling_length: 30 # Buckling parameter + soil_springs: True + gravity_foundation: False + frame3dd: + shear: True + geom: True + tol: 1e-9 + FixedBottomSE: flag: True FloatingSE: flag: True + rank_and_file: True # BOS: # flag: True diff --git a/examples/06_IEA-15-240-RWT/modeling_options_umaine_semi.yaml b/examples/06_IEA-15-240-RWT/modeling_options_umaine_semi.yaml index 5f9e4d91b..6012f8d11 100644 --- a/examples/06_IEA-15-240-RWT/modeling_options_umaine_semi.yaml +++ b/examples/06_IEA-15-240-RWT/modeling_options_umaine_semi.yaml @@ -1,10 +1,6 @@ General: verbosity: False # When set to True, the code prints to screen many infos openfast_configuration: - OF_run_fst: IEA_w_TMD - OF_run_dir: outputs/IEA_w_TMD_32 - save_iterations: True - save_timeseries: True use_exe: True allow_fails: True fail_value: 9999 @@ -12,15 +8,29 @@ General: WISDEM: RotorSE: flag: True - n_pitch_perf_surfaces: 5 - n_tsr_perf_surfaces: 5 spar_cap_ss: Spar_Cap_SS spar_cap_ps: Spar_Cap_PS te_ss: TE_reinforcement_SS te_ps: TE_reinforcement_PS + n_span: 60 + DriveSE: + flag: True TowerSE: flag: True - DriveSE: + wind: PowerWind # Wind used + gamma_f: 1.35 # Safety factor for fatigue loads + gamma_m: 1.3 # Safety factor for material properties + gamma_n: 1.0 # Safety factor for ... + gamma_b: 1.1 # Safety factor for ... + gamma_fatigue: 1.755 # Safety factor for fatigue loads + buckling_length: 30 # Buckling parameter + soil_springs: True + gravity_foundation: False + frame3dd: + shear: True + geom: True + tol: 1e-9 + FixedBottomSE: flag: True FloatingSE: flag: True @@ -82,21 +92,7 @@ Level1: ROSCO: flag: True - SD_Mode: 0 - PS_Mode: 1 - ps_percent: 0.85 - F_LPFType: 2 - F_NotchType: 2 - Fl_Mode: 2 tuning_yaml: ../../ROSCO/Tune_Cases/IEA15MW.yaml - zeta_pc: [1,1] - omega_pc: [0.2,0.2] - U_pc: [12,18] - zeta_vs: 0.85 # Torque controller desired damping ratio [-] - omega_vs: 0.12 - twr_freq: 3.2 - ptfm_freq: 0.2 - Kp_float: -10 DLC_driver: metocean_conditions: diff --git a/examples/06_IEA-15-240-RWT/weis_driver_TMDs.py b/examples/06_IEA-15-240-RWT/weis_driver_TMDs.py index a02f45c2d..e10607ffc 100644 --- a/examples/06_IEA-15-240-RWT/weis_driver_TMDs.py +++ b/examples/06_IEA-15-240-RWT/weis_driver_TMDs.py @@ -5,7 +5,6 @@ ## File management run_dir = os.path.dirname( os.path.realpath(__file__) ) + os.sep -wisdem_examples = os.path.join(os.path.dirname( os.path.dirname( os.path.dirname( os.path.realpath(__file__) ) ) ), "WISDEM", "examples") fname_wt_input = os.path.realpath(os.path.join(run_dir,"IEA-15-floating_wTMDs.yaml")) fname_modeling_options = run_dir + "modeling_options_umaine_semi.yaml" fname_analysis_options = run_dir + "analysis_options_TMDs.yaml" diff --git a/examples/06_IEA-15-240-RWT/weis_driver.py b/examples/06_IEA-15-240-RWT/weis_driver_monopile.py similarity index 69% rename from examples/06_IEA-15-240-RWT/weis_driver.py rename to examples/06_IEA-15-240-RWT/weis_driver_monopile.py index 9909ae7bb..c605d520b 100644 --- a/examples/06_IEA-15-240-RWT/weis_driver.py +++ b/examples/06_IEA-15-240-RWT/weis_driver_monopile.py @@ -5,7 +5,8 @@ ## File management run_dir = os.path.dirname( os.path.realpath(__file__) ) + os.sep -fname_wt_input = run_dir + "IEA-15-240-RWT.yaml" +wisdem_examples = os.path.join(os.path.dirname( os.path.dirname( os.path.dirname( os.path.realpath(__file__) ) ) ), "WISDEM", "examples") +fname_wt_input = os.path.realpath(os.path.join(wisdem_examples,"09_floating/IEA-15-240-RWT_VolturnUS-S.yaml")) fname_modeling_options = run_dir + "modeling_options.yaml" fname_analysis_options = run_dir + "analysis_options.yaml" From 28de25f55a284bc3ce48b1013aa7a6826ee3eca2 Mon Sep 17 00:00:00 2001 From: dzalkind Date: Fri, 12 Jan 2024 13:36:30 -0700 Subject: [PATCH 042/114] Update test list --- weis/test/test_examples_skinny.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/weis/test/test_examples_skinny.py b/weis/test/test_examples_skinny.py index 391ab9992..d5e8f9696 100644 --- a/weis/test/test_examples_skinny.py +++ b/weis/test/test_examples_skinny.py @@ -6,7 +6,7 @@ "02_run_openfast_cases/weis_driver_sm", #Not as fast as weis_driver, but not too bad (120 sec. locally) "03_NREL5MW_OC3_spar/weis_driver", "03_NREL5MW_OC3_spar/weis_freq_driver", - "06_IEA-15-240-RWT/weis_driver", + "06_IEA-15-240-RWT/weis_driver_monopile", "06_IEA-15-240-RWT/weis_driver_TMDs", "09_design_of_experiments/weis_driver", # "13_DTQP/gen_oloc", From e881142623dc4a5b2d9220758df2473091922aaf Mon Sep 17 00:00:00 2001 From: dzalkind Date: Fri, 12 Jan 2024 13:37:45 -0700 Subject: [PATCH 043/114] Move comments to 06 README for later use --- examples/06_IEA-15-240-RWT/README.md | 5 +++++ examples/12_linearization/README.md | 4 ---- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/examples/06_IEA-15-240-RWT/README.md b/examples/06_IEA-15-240-RWT/README.md index 2402692cb..7a1bcd990 100644 --- a/examples/06_IEA-15-240-RWT/README.md +++ b/examples/06_IEA-15-240-RWT/README.md @@ -11,3 +11,8 @@ Modeling options Analysis options + + + + + diff --git a/examples/12_linearization/README.md b/examples/12_linearization/README.md index 51df6be10..84125ad24 100644 --- a/examples/12_linearization/README.md +++ b/examples/12_linearization/README.md @@ -1,9 +1,5 @@ There are multiple examples in this folder focused on running WEIS at level 2 (linearized OpenFAST) using the IEA 15MW wind turbine as the testbed geometry. - - - - From f8a96a56c29ef10728b794a94d58c90b2a3964de Mon Sep 17 00:00:00 2001 From: dzalkind Date: Fri, 12 Jan 2024 13:43:15 -0700 Subject: [PATCH 044/114] Add READMEs --- examples/09_design_of_experiments/README.md | 1 + examples/10_override_example/README.md | 1 + examples/14_level2ccd/README.md | 3 +++ 3 files changed, 5 insertions(+) create mode 100644 examples/09_design_of_experiments/README.md create mode 100644 examples/10_override_example/README.md create mode 100644 examples/14_level2ccd/README.md diff --git a/examples/09_design_of_experiments/README.md b/examples/09_design_of_experiments/README.md new file mode 100644 index 000000000..835eb35ce --- /dev/null +++ b/examples/09_design_of_experiments/README.md @@ -0,0 +1 @@ +This example demonstrates how to set up a design of experiements, or parameter sweep, for openfast cases. It also shows how we postprocess the results. \ No newline at end of file diff --git a/examples/10_override_example/README.md b/examples/10_override_example/README.md new file mode 100644 index 000000000..dafdac7ce --- /dev/null +++ b/examples/10_override_example/README.md @@ -0,0 +1 @@ +This example demonstrates how to programmatically set inputs to WEIS. \ No newline at end of file diff --git a/examples/14_level2ccd/README.md b/examples/14_level2ccd/README.md new file mode 100644 index 000000000..56418f540 --- /dev/null +++ b/examples/14_level2ccd/README.md @@ -0,0 +1,3 @@ +These examples demonstrate how to use Level 2, linearized OpenFAST, in a CCD study. + +Athul to expand on input configuration and further description. \ No newline at end of file From 559f7837e5642fdb75f2d5eee627d82cd92c25ad Mon Sep 17 00:00:00 2001 From: dzalkind Date: Fri, 12 Jan 2024 13:49:13 -0700 Subject: [PATCH 045/114] Tidy up 15_ example inputs --- examples/15_RAFT_Studies/IEA-15-floating.yaml | 1263 ----------------- examples/15_RAFT_Studies/README.md | 2 +- .../15_RAFT_Studies/analysis_options.yaml | 2 +- .../analysis_options_level3.yaml | 74 +- .../modeling_options_level3.yaml | 16 +- examples/15_RAFT_Studies/opt_22.yaml | 961 ------------- .../15_RAFT_Studies/weis_driver_level3.py | 4 +- .../weis_driver_umaine_semi.py | 2 +- 8 files changed, 7 insertions(+), 2317 deletions(-) delete mode 100644 examples/15_RAFT_Studies/IEA-15-floating.yaml delete mode 100644 examples/15_RAFT_Studies/opt_22.yaml diff --git a/examples/15_RAFT_Studies/IEA-15-floating.yaml b/examples/15_RAFT_Studies/IEA-15-floating.yaml deleted file mode 100644 index 0a1b6934d..000000000 --- a/examples/15_RAFT_Studies/IEA-15-floating.yaml +++ /dev/null @@ -1,1263 +0,0 @@ -name: IEA 15MW Offshore Reference Turbine, with taped chord tip design - 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diameter: 7.94 - cone_angle: 0.06981317007977318 # 4 deg - drag_coefficient: 0.5 - flange_t2shell_t: 6.0 - flange_OD2hub_D: 0.6 - flange_ID2OD: 0.8 - hub_blade_spacing_margin: 1.2 - hub_stress_concentration: 3.0 - n_front_brackets: 5 - n_rear_brackets: 5 - clearance_hub_spinner: 0.5 - spin_hole_incr: 1.2 - pitch_system_scaling_factor: 0.75 - spinner_gust_ws: 70.0 - hub_material: cast_iron - spinner_material: glass_uni - nacelle: - drivetrain: - uptilt: 0.10471975511965977 # 6 deg - distance_tt_hub: 5.614 - overhang: 12.0313 - drag_coefficient: 0.5 - distance_hub2mb: 1.0 - distance_mb2mb: 1.2 - lss_diameter: [3.0, 3.0] - lss_wall_thickness: [0.1, 0.1] - nose_diameter: [2.2, 2.2] - nose_wall_thickness: [0.1, 0.1] - generator_length: 2.15 - bedplate_wall_thickness: - grid: [0.0, 1.0] - values: [0.05, 0.05] - gear_ratio: 1.0 - gearbox_efficiency: 1.0 - mb1Type: CARB - mb2Type: SRB - uptower: True - lss_material: steel_drive - bedplate_material: steel_drive - generator: - generator_type: PMSG_Outer - rho_Fe: 7700.0 - rho_Fes: 7850.0 - rho_Copper: 8900.0 - rho_PM: 7450.0 - B_r: 1.279 - P_Fe0e: 1.0 - P_Fe0h: 4.0 - S_N: -0.002 - alpha_p: 1.0995574287564276 #0.5*np.pi*0.7 - b_r_tau_r: 0.45 - b_ro: 0.004 - b_s_tau_s: 0.45 - b_so: 0.004 - cofi: 0.9 - freq: 60.0 - h_i: 0.004 - h_sy0: 0.0 - h_w: 0.005 - k_fes: 0.8 - k_fillr: 0.55 - k_fills: 0.65 - k_s: 0.2 - m: 3 - mu_0: 1.2566370614359173e-06 #np.pi*4e-7 - mu_r: 1.06 - phi: 1.5707963267948966 # 90 deg - q1: 5 - q2: 4 - ratio_mw2pp: 0.8 - resist_Cu: 2.52e-8 #1.8e-8*1.4 - y_tau_pr: 0.8333333 #10. / 12 - y_tau_p: 0.8 #12./15. - rad_ag: 5.12623359 - len_s: 2.23961662 - tau_p: 0.1610453779 - tau_s: 0.1339360726 - h_s: 0.3769449149 - b_s: 0.05121796888 - b_t: 0.08159225451 - h_t: 0.3859449149 - h_ys: 0.03618114528 - h_yr: 0.0361759511 - h_m: 0.0995385122 - b_m: 0.1288363023 - B_g: 1.38963289 - B_symax: 1.63455514 - B_rymax: 1.63478983 - p: 100 - R_s: 0.02457052 - L_s: 0.01138752 - S: 240 - t_r: 0.0607657125 - h_sr: 0.04677116325 - t_s: 0.06065213554 - h_ss: 0.04650825863 - E_p: 1905.2558883257652 - b: 2.0 - c: 5.0 - N_c: 2 - B_tmax: 1.9 - u_allow_pcent: 8.5 - y_allow_pcent: 1.0 - z_allow_deg: 0.05 - sigma: 60e3 - - h_0: 0.05 - C_Cu: 4.786 - C_Fe: 0.556 - C_Fes: 0.50139 - C_PM: 50.0 - - tower: - outer_shape_bem: - reference_axis: &ref_axis_tower - x: - grid: [0.0, 1.0] - values: [0.0, 0.0] - y: - grid: [0.0, 1.0] - values: [0.0, 0.0] - z: - grid: &grid_tower [0.0, 7.728811463373383e-05, 0.10047454902385111, 0.10055183713848484, 0.20094909804770222, 0.20102638616233592, 0.30142364707155334, 0.30150093518618704, 0.40189819609540445, 0.4019754842100382, 0.5023727451192556, 0.5024500332338894, 0.6028472941431067, 0.6029245822577405, 0.7033218431669579, 0.7033991312815916, 0.8037963921908089, 0.8038736803054426, 0.90427094121466, 0.9043482293292936, 1.0] - values: [15, 15.01, 28, 28.01, 41, 41.01, 54, 54.01, 67, 67.01, 80, 80.01, 93, 93.01, 106, 106.01, 119, 119.01, 132, 132.01, 144.386] - outer_diameter: - grid: *grid_tower - values: [10.0, 10.0, 10.0, 10.0, 10.000000000000002, 10.000000000000025, 9.905075496041832, 9.905075496038421, 9.147775711396022, 9.147775711392333, 8.331137804464264, 8.331137804460141, 7.4405574220559645, 7.44055742213423, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5] - drag_coefficient: - grid: [0.0, 1.0] - values: [0.5, 0.5] - internal_structure_2d_fem: - outfitting_factor: 1.07 - reference_axis: *ref_axis_tower - layers: - - name: tower_wall - material: steel - thickness: - grid: *grid_tower - values: [0.04581181788122962, 0.0431263860201862, 0.04229377098342676, 0.03707993492238607, 0.03929171243257896, 0.031066365645930273, 0.03651922714830538, 0.026052301758708634, 0.03503017834800577, 0.027664165216450664, 0.03329221642528532, 0.02957862730939187, 0.031206211814029727, 0.032217421810211005, 0.0282072906526414, 0.03495436676212862, 0.020862964263820553, 0.022572820495674422, 0.012453285936801518, 0.04560763242734679, 0.04568044512205404] - floating_platform: - joints: - - name: main_keel - location: [0.0, 0.0, -20.0] - - - name: main_freeboard - location: [0.0, 0.0, 15.0] - transition: True - - - name: col1_keel - location: [51.75, 3.14159265, -20.0] #3.14159265 = 180deg - cylindrical: True - - - name: col1_freeboard - location: [51.75, 3.14159265, 15.0] # 3.14159265 = 180deg - cylindrical: True - - - name: col2_keel - location: [51.75, 1.0471976, -20.0] #1.0471975 = 60 deg - cylindrical: True - - - name: col2_freeboard - location: [51.75, 1.0471975, 15.0] - cylindrical: True - - - name: col3_keel - location: [51.75, -1.0471976, -20.0] #-1.0471975 = -60 deg - cylindrical: True - - - name: col3_freeboard - location: [51.75, -1.0471976, 15.0] #-1.0471975 = -60 deg - cylindrical: True - - - name: anchor1 - location: [837.8, 3.14159265, -200.0] - cylindrical: True - - - name: anchor2 - location: [837.8, 1.0471976, -200.0] # 120deg - cylindrical: True - - - name: anchor3 - location: [837.8, -1.0471976, -200.0] # 240deg - cylindrical: True - - members: - - name: main_column - joint1: main_keel - joint2: main_freeboard - Ca: 1.0 - Cd: 0.8 - outer_shape: - shape: circular - outer_diameter: - grid: [0.0, 1.0] - values: [10.0, 10.0] - internal_structure: - layers: - - name: main_twall - material: steel - thickness: - grid: [0.0, 1.0] - values: [0.15, 0.15] - ballasts: - - variable_flag: False - material: slurry - volume: 325 - grid: [0.0, 1.0] - # - variable_flag: True - # grid: [0.25, 0.4] - bulkhead: - material: steel - thickness: - grid: [0.00, 1.00] - values: [0.35, 0.35] - # grid: [0.00, 0.25, 0.50, 0.75, 0.80, 0.85, 0.90, 0.95, 1.00] - # values: [0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35] - axial_joints: - - name: main_upper_pontoon - grid: 0.987 - - - name: main_lower_pontoon - grid: 0.1 - - - name: column1 - joint1: col1_keel - joint2: col1_freeboard - Ca: 1.0 - Cd: 0.8 - outer_shape: &col_out - shape: circular - outer_diameter: - grid: [0.0, 1.0] - values: [12.5, 12.5] - internal_structure: &col_int - layers: - - name: col_twall - material: steel - thickness: - grid: [0.0, 1.0] - values: [0.075, 0.075] - bulkhead: - material: steel - thickness: - grid: [0.0, 1.0] - values: [0.075, 0.075] - ballasts: - - variable_flag: False - material: slurry - volume: 300 - grid: [0.0, 0.25] - - variable_flag: True - grid: [0.25, 0.4] - axial_joints: - - name: col1_upper_pontoon - grid: 0.987 - - - name: col1_lower_pontoon - grid: 0.1 - - - name: col1_fairlead - grid: 0.1714 - - - name: column2 - joint1: col2_keel - joint2: col2_freeboard - Ca: 1.0 - Cd: 0.8 - outer_shape: *col_out - internal_structure: *col_int - axial_joints: - - name: col2_upper_pontoon - grid: 0.987 - - - name: col2_lower_pontoon - grid: 0.1 - - - name: col2_fairlead - grid: 0.1714 - - - name: column3 - joint1: col3_keel - joint2: col3_freeboard - Ca: 1.0 - Cd: 0.8 - outer_shape: *col_out - internal_structure: *col_int - axial_joints: - - name: col3_upper_pontoon - grid: 0.987 - - - name: col3_lower_pontoon - grid: 0.1 - - - name: col3_fairlead - grid: 0.1714 - - - - name: Y_pontoon_upper1 - joint1: main_upper_pontoon - joint2: col1_upper_pontoon - Ca: 1.0 - Cd: 0.8 - outer_shape: &pontup_out - shape: circular - outer_diameter: - grid: [0.0, 1.0] - values: [0.91, 0.91] - internal_structure: &pontup_int - layers: - - name: pontoon_upper_twall - material: steel - thickness: - grid: [0.0, 1.0] - values: [0.02, 0.02] - - - name: Y_pontoon_upper2 - joint1: main_upper_pontoon - joint2: col2_upper_pontoon - Ca: 1.0 - Cd: 0.8 - outer_shape: *pontup_out - internal_structure: *pontup_int - - - name: Y_pontoon_upper3 - joint1: main_upper_pontoon - joint2: col3_upper_pontoon - Ca: 1.0 - Cd: 0.8 - outer_shape: *pontup_out - internal_structure: *pontup_int - - - name: Y_pontoon_lower1 - joint1: main_lower_pontoon - joint2: col1_lower_pontoon - Ca: 1.0 - Cd: 0.8 - outer_shape: &pontlow_out - shape: circular - outer_diameter: - grid: [0.0, 1.0] - values: [9.6148, 9.6148] #equates displacement not circle: [12.5, 12.5] - internal_structure: &pontlow_int - # Probably needs longitudinal stiffeners - layers: - - name: pontoon_lower_twall - material: steel - thickness: - grid: [0.0, 1.0] - values: [0.04, 0.04] - ballasts: - - variable_flag: True - grid: [0.0, 1.0] - - - name: Y_pontoon_lower2 - joint1: main_lower_pontoon - joint2: col2_lower_pontoon - outer_shape: *pontlow_out - internal_structure: *pontlow_int - - - name: Y_pontoon_lower3 - joint1: main_lower_pontoon - joint2: col3_lower_pontoon - outer_shape: *pontlow_out - internal_structure: *pontlow_int - transition_piece_mass: 100.e3 - transition_piece_cost: 300.e3 - mooring: - nodes: - - name: line1_anchor - node_type: fixed - joint: anchor1 - anchor_type: drag_embedment - - name: line2_anchor - node_type: fixed - joint: anchor2 - anchor_type: drag_embedment - - name: line3_anchor - node_type: fixed - joint: anchor3 - anchor_type: drag_embedment - - - name: line1_vessel - node_type: vessel - joint: col1_fairlead - fairlead_type: rigid - - name: line2_vessel - node_type: vessel - joint: col2_fairlead - fairlead_type: rigid - - name: line3_vessel - joint: col3_fairlead - node_type: vessel - fairlead_type: rigid - - lines: - - name: line1 - node1: line1_anchor - node2: line1_vessel - line_type: main - unstretched_length: 850.0 - - name: line2 - node1: line2_anchor - node2: line2_vessel - line_type: main - unstretched_length: 850.0 - - name: line3 - node1: line3_anchor - node2: line3_vessel - line_type: main - unstretched_length: 850.0 - - line_types: - - name: main - diameter: 0.185 - type: chain - transverse_added_mass: 1.0 - tangential_added_mass: 0.0 - transverse_drag: 1.6 - tangential_drag: 0.1 - - anchor_types: - - name: drag_embedment - type: drag_embedment - -airfoils: - - name: circular - coordinates: - x: [1.00000, 0.99901, 0.99606, 0.99114, 0.98429, 0.97553, 0.96489, 0.95241, 0.93815, 0.92216, 0.90451, 0.88526, 0.86448, 0.84227, 0.81871, 0.79389, 0.76791, 0.74088, 0.71289, 0.68406, 0.65451, 0.62434, 0.59369, 0.56267, 0.53140, 0.50000, 0.46860, 0.43733, 0.40631, 0.37566, 0.34549, 0.31594, 0.28711, 0.25912, 0.23209, 0.20611, 0.18129, 0.15773, 0.13552, 0.11474, 0.09549, 0.07784, 0.06185, 0.04759, 0.03511, 0.02447, 0.01571, 0.00886, 0.00394, 0.00099, 0.00000, 0.00099, 0.00394, 0.00886, 0.01571, 0.02447, 0.03511, 0.04759, 0.06185, 0.07784, 0.09549, 0.11474, 0.13552, 0.15773, 0.18129, 0.20611, 0.23209, 0.25912, 0.28711, 0.31594, 0.34549, 0.37566, 0.40631, 0.43733, 0.46860, 0.50000, 0.53140, 0.56267, 0.59369, 0.62434, 0.65451, 0.68406, 0.71289, 0.74088, 0.76791, 0.79389, 0.81871, 0.84227, 0.86448, 0.88526, 0.90451, 0.92216, 0.93815, 0.95241, 0.96489, 0.97553, 0.98429, 0.99114, 0.99606, 0.99901, 1.00000] - y: [0.00000, 0.03140, 0.06267, 0.09369, 0.12434, 0.15451, 0.18406, 0.21289, 0.24088, 0.26791, 0.29389, 0.31871, 0.34227, 0.36448, 0.38526, 0.40451, 0.42216, 0.43815, 0.45241, 0.46489, 0.47553, 0.48429, 0.49114, 0.49606, 0.49901, 0.50000, 0.49901, 0.49606, 0.49114, 0.48429, 0.47553, 0.46489, 0.45241, 0.43815, 0.42216, 0.40451, 0.38526, 0.36448, 0.34227, 0.31871, 0.29389, 0.26791, 0.24088, 0.21289, 0.18406, 0.15451, 0.12434, 0.09369, 0.06267, 0.03140, 0.00000, -0.03140, -0.06267, -0.09369, -0.12434, -0.15451, -0.18406, -0.21289, -0.24088, -0.26791, -0.29389, -0.31871, -0.34227, -0.36448, -0.38526, -0.40451, -0.42216, -0.43815, -0.45241, -0.46489, -0.47553, -0.48429, -0.49114, -0.49606, -0.49901, -0.50000, -0.49901, -0.49606, -0.49114, -0.48429, -0.47553, -0.46489, -0.45241, -0.43815, -0.42216, -0.40451, -0.38526, -0.36448, -0.34227, -0.31871, -0.29389, -0.26791, -0.24088, -0.21289, -0.18406, -0.15451, -0.12434, -0.09369, -0.06267, -0.03140, 0.00000] - relative_thickness: 1.0000 - aerodynamic_center: 0.5000 - 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c_m: - grid: *grid004 - values: [0.0, 0.09142857142857111, 0.1828571428571434, 0.2742857142857145, 0.3657142857142855, 0.4031304636095545, 0.4081392053848416, 0.4131479471601289, 0.418156688935416, 0.4262712900595553, 0.4437034692302504, 0.461135648400946, 0.4785678275716412, 0.4960000067423368, 0.4883022318330648, 0.4678431517000181, 0.4480322988503198, 0.4369725143258274, 0.4259127298013352, 0.421499653933276, 0.4205848457791291, 0.4202386282975503, 0.4216794100726142, 0.4231201918476781, 0.4225819015145725, 0.4216313044922648, 0.4186442496773847, 0.4127722129896937, 0.4069001763020025, 0.3942614888768985, 0.3816228014517944, 0.3667605953544247, 0.3503288466648674, 0.3336237480028024, 0.3156065694727009, 0.2975893909425995, 0.2798918070549701, 0.2622959123717636, 0.2449149435317124, 0.2279423337936562, 0.2109697240555999, 0.1352478121095765, 0.0551741, 0.0321142, 0.01267890000000002, -0.002821250000000001, -0.0074129, -0.0110695, -0.01250065, -0.01177275, -0.01082065, -0.0276937, -0.05106789999999999, -0.0714813, -0.0917883, -0.1011907, -0.1098835, -0.1177635, -0.124769, -0.130977, -0.136484, -0.141299, -0.1454, -0.14875, -0.151175, -0.15262, -0.153103, -0.152545, -0.151206, -0.149693, -0.145621, -0.143578, -0.140948, -0.137106, -0.140821, -0.156927, -0.179786, -0.201472, -0.2240866097136903, -0.2461860684392151, -0.2613309888245065, -0.2764759092097978, -0.2906206368276409, -0.3042389471994587, -0.3178704108777556, -0.3315432135192723, -0.3452160161607889, -0.3584572127501313, -0.3716084914119372, -0.3840462584573971, -0.3954732173796998, -0.4069001763020025, -0.4127722129896937, -0.4186442496773847, -0.4216313044922648, -0.4225819015145725, -0.4231201918476781, -0.4216794100726143, -0.4202386282975503, -0.4205848457791291, -0.421499653933276, -0.4259127298013352, -0.4369725143258274, -0.4480322988503198, -0.4678431517000181, -0.4883022318330649, -0.4960000067423369, -0.4785678275716413, -0.461135648400946, -0.4437034692302504, -0.4262712900595553, -0.4352995460782729, -0.4531479471601288, -0.4709963482419844, -0.4888447493238403, -0.457142857142857, -0.3428571428571431, -0.2285714285714291, -0.1142857142857139, 0.0] - -materials: - - name: Gelcoat - orth: 0 - rho: 1235.0 - E: 3.440e+009 - G: 1.323e+009 - nu: 0.300 - alpha: 0.0 - Xt: 74 - Xc: 87 - S: 2.126E7 - GIc: 303 - GIIc: 3446 - alp0: 53 - ply_t: 5.0E-4 - waste: 0.25 - unit_cost: 7.23 - component_id: 0 - - name: steel - description: Steel of the tower and monopile ASTM A572 Grade 50 - source: http://www.matweb.com/search/DataSheet.aspx?MatGUID=9ced5dc901c54bd1aef19403d0385d7f - orth: 0 - rho: 7800 - alpha: 0.0 - E: 200.e+009 - nu: 0.3 - G: 79.3e+009 - GIc: 0 #Place holder, currently not used - GIIc: 0 #Place holder, currently not used - alp0: 0 #Place holder, currently not used - Xt: 450.e+006 - Xc: 450.e+006 - S: 0 - Xy: 345.e+6 - m: 3 - A: 3.5534648443719767e10 #DOI 10.1016/j.proeng.2013.12.061 eqn 5 - unit_cost: 0.7 - - name: steel_drive - description: Steel of the drivetrain ASTM 4140 40Cr1Mo28 - source: http://www.matweb.com/search/DataSheet.aspx?MatGUID=38108bfd64c44b4c9c6a02af78d5b6c6 - orth: 0 - rho: 7850 - alpha: 0.0 - E: 205.e+009 - nu: 0.3 - G: 80.0e+009 - GIc: 0 #Place holder, currently not used - GIIc: 0 #Place holder, currently not used - alp0: 0 #Place holder, currently not used - Xt: 814.e+006 - Xc: 814.e+006 - S: 0 - Xy: 485.e+6 - m: 3 - A: 3.5534648443719767e10 #DOI 10.1016/j.proeng.2013.12.061 eqn 5 - unit_cost: 0.9 - - name: cast_iron - description: Cast iron for hub and nacelle components - source: TODO - orth: 0 - rho: 7200 - alpha: 0.0 - E: 118.e+009 - nu: 0.3 - G: 47.6e+009 - GIc: 0 #Place holder, currently not used - GIIc: 0 #Place holder, currently not used - alp0: 0 #Place holder, currently not used - Xt: 310.e+006 - Xc: 310.e+006 - S: 0 - Xy: 265.e+6 - m: 3 - unit_cost: 0.5 - - name: glass_uni - description: Vectorply E-LT-5500, Epikote MGS RIMR 135/Epicure MGS RIMH 1366 epoxy - source: MSU composites database 3D property tests, Engineering Mechanics of Composite Materials, Daniel, I & Ishai, O., 1994, pg. 34 - orth: 1 - rho: 1940.0 - E: [4.46E10, 1.7E10, 1.67E10] - G: [3.27E9, 3.48E9, 3.5E9] - nu: [0.262, 0.35, 0.264] - Xt: [6.092E8, 3.81E7, 1.529E7] - Xc: [4.7471E8, 1.1264E8, 1.1322E8] - S: [1.891E7, 1.724E7, 1.316E7] - m: 10 - GIc: 303 - GIIc: 3446 - alp0: 53 - fvf: 0.57 - fwf: 0.7450682696347697 - ply_t: 0.005 - unit_cost: 1.87 - waste: 0.05 - fiber_density: 2535.5 - area_density_dry: 7.227162215457267 - component_id: 5 - - name: CarbonUD - E: [114500000000.0, 8390000000.0, 8390000000.0] - G: [5990000000.0, 5990000000.0, 5990000000.0] - rho: 1220.0 - orth: 1 - nu: [0.27, 0.27, 0.27] - Xt: [1546.e6, 0.0, 0.0] - Xc: [1047.e6, 0.0, 0.0] - S: [0.0, 0.0, 0.0] - m: 16.1 - GIc: 0.0 - GIIc: 0.0 - alp0: 0.0 - fvf: 0.1076923076923077 #0.5384615384615385 - fwf: 0.15889029003783103 #0.6461538461538463 - ply_t: 0.005158730158730159 #0.0010317460317460314 - unit_cost: 30. - waste: 0.05 - fiber_density: 1800. - area_density_dry: 1.000 - component_id: 4 - - name: glass_biax - description: Vectorply E-LT-5500, Epikote MGS RIMR 135/Epicure MGS RIMH 1366 epoxy - source: MSU composites database 3D property tests, Engineering Mechanics of Composite Materials, Daniel, I & Ishai, O., 1994, pg. 34 - orth: 1 - rho: 1940.0 - E: [1.11E10, 1.11E10, 1.67E10] - G: [1.353E10, 3.49E9, 3.49E9] - nu: [0.5, 0.0, 0.066] - Xt: [4.29E7, 4.26E7, 1.53E7] - Xc: [7.07E7, 7.07E7, 1.132E8] - S: [1.034E8, 1.72E7, 1.32E7] - m: 10 - GIc: 303 - GIIc: 3446 - alp0: 53 - fvf: 0.57 - fwf: 0.7450682696347697 - ply_t: 0.001 - waste: 0.15 - unit_cost: 3.00 - fiber_density: 2535.5 - area_density_dry: 1.4454324430914534 - component_id: 3 - roll_mass: 181.4368 - - name: glass_triax - description: Vectorply E-LT-5500, Epikote MGS RIMR 135/Epicure MGS RIMH 1366 epoxy - source: MSU composites database 3D property tests, Engineering Mechanics of Composite Materials, Daniel, I & Ishai, O., 1994, pg. 34 - orth: 1.0 - rho: 1940.0 - E: [2.87E10, 1.66E10, 1.67E10] - G: [8.4E9, 3.49E9, 3.49E9] - nu: [0.5, 0.0, 0.17] - Xt: [3.96E8, 7.64E7, 1.53E7] - Xc: [4.489E8, 1.747E8, 1.132E8] - S: [1.034E8, 1.72E7, 1.32E7] - m: 10 - GIc: 303 - GIIc: 3446 - alp0: 53 - fvf: 0.57 - fwf: 0.7450682696347697 - ply_t: 0.001 - unit_cost: 2.86 - waste: 0.15 - fiber_density: 2535.5 - area_density_dry: 1.4454324430914534 - component_id: 2 - roll_mass: 181.4368 - - name: medium_density_foam - description: Airex C70.130 PVC Foam, source 'https://www.3accorematerials.com/uploads/documents/TDS-AIREX-C70-E_1106.pdf' - orth: 0.0 - rho: 130.0 - E: 1.292E8 - G: 4.8946969696969695E7 - nu: 0.32 - Xt: 2083000.0 - Xc: 1563000.0 - S: 1250000.0 - GIc: 303 - GIIc: 3446 - alp0: 53 - component_id: 1 - waste: 0.2 - unit_cost: 13 - - name: resin - description: epoxy - E: 1.e+6 - nu: 0.3 #Place holder, currently not used - G: 312500.0 #Place holder, currently not used - GIc: 0 #Place holder, currently not used - GIIc: 0 #Place holder, currently not used - alp0: 0 #Place holder, currently not used - Xt: 0 #Place holder, currently not used - Xc: 0 #Place holder, currently not used - S: 0 #Place holder, currently not used - rho: 1150. - alpha: 0.0 - orth: 0 - unit_cost: 3.63 - - name: slurry - description: fixed/permanent ballast - E: 1.0 # compliant - nu: 0.3 #Place holder, currently not used - G: 1.0 # compliant - Xt: 0 #Place holder, currently not used - Xc: 0 #Place holder, currently not used - S: 0 #Place holder, currently not used - rho: 5000. - alpha: 0.0 - unit_cost: 1.0 - orth: 0.0 - - name: adhesive - description: Sample adhesive - source: https://www.nrel.gov/docs/fy19osti/73585.pdf - orth: 0 - rho: 1100 - E: 4.56e+006 - nu: 0.49 - alpha: 0.0 - Xt: 0.69e+006 - Xc: 0.4e+006 - S: 0.31e+006 - G: 1520000.0 - unit_cost: 9.0 - -control: - supervisory: - Vin: 3.0 - Vout: 25.0 - maxTS: 95. - pitch: - PC_zeta: 1.0 # Pitch controller desired damping ratio [-] - PC_omega: 0.2 # Pitch controller desired natural frequency [rad/s] - ps_percent: 0.8 # Percent peak shaving [%, <= 1 ], {default = 80%} - max_pitch: 1.57 # Maximum pitch angle [rad], {default = 90 degrees} - max_pitch_rate: 0.03490658503988659 # 2 deg/s - min_pitch: 0. # Minimum pitch angle [rad], {default = 0 degrees} - torque: - control_type: tsr_tracking - tsr: 9.0 - VS_zeta: 1.0 # Torque controller desired damping ratio [-] - VS_omega: 0.2 # Torque controller desired natural frequency [rad/s] - max_torque_rate: 1500000. - VS_minspd: 0.5235987755982988 # Minimum rotor speed [rad/s], {default = 0 rad/s} - VS_maxspd: 0.7916813487046278 # Minimum rotor speed [rad/s], {default = 0 rad/s} - setpoint_smooth: - ss_vsgain: 1 # Torque controller setpoint smoother gain bias percentage [%, <= 1 ], {default = 100%} - ss_pcgain: .001 # Pitch controller setpoint smoother gain bias percentage [%, <= 1 ], {default = 0.1%} - shutdown: - limit_type: gen_speed - limit_value: 2.0 - -# TMDs: -# # - name: tower_TMD -# # component: tower -# # location: [0,0,100] -# # mass: 2000 -# # stiffness: 20.e5 -# # damping: 2800 -# # natural_frequency: 2.5 # rad/s -# # damping_ratio: 0.1 -# # X_DOF: True -# # Y_DOF: True -# - name: ptfm_TMD1 -# component: column1 -# location: [-51.75,0,20.0000] -# mass: 350e3 -# stiffness: 20.e5 -# damping: 2800 -# natural_frequency: 0.4 # rad/s -# damping_ratio: 0.5 -# Z_DOF: True -# - name: ptfm_TMD2 -# component: column2 -# location: [25.8750, 44.8168, 20.0000] -# mass: 350e3 -# stiffness: 20.e5 -# damping: 2800 -# natural_frequency: 0.4 # rad/s -# damping_ratio: 0.5 -# Z_DOF: True -# - name: ptfm_TMD3 -# component: column3 -# location: [25.8750, -44.8168, 20.0000] -# mass: 350e3 -# stiffness: 20.e5 -# damping: 2800 -# natural_frequency: 0.4 # rad/s -# damping_ratio: 0.5 -# Z_DOF: True - - -environment: - air_density: 1.225 - air_dyn_viscosity: 1.81e-5 - weib_shape_parameter: 2. - air_speed_sound: 340. - shear_exp: 0.12 - water_density: 1025.0 - water_dyn_viscosity: 1.3351e-3 - soil_shear_modulus: 140e6 - soil_poisson: 0.4 - water_depth: 200.0 - significant_wave_height: 4.52 - significant_wave_period: 9.45 - -bos: - plant_turbine_spacing: 7 - plant_row_spacing: 7 - commissioning_pct: 0.01 - decommissioning_pct: 0.15 - distance_to_substation: 1.0 - distance_to_interconnection: 8.5 - interconnect_voltage: 130. - distance_to_site: 115. - distance_to_landfall: 50. - port_cost_per_month: 2e6 - site_auction_price: 100e6 - site_assessment_plan_cost: 1e6 - site_assessment_cost: 25e6 - construction_operations_plan_cost: 2.5e6 - boem_review_cost: 0.0 - design_install_plan_cost: 2.5e6 - -costs: - wake_loss_factor: 0.15 - fixed_charge_rate: 0.056 - bos_per_kW: 4053. - opex_per_kW: 137. - turbine_number: 40. - labor_rate: 58.8 - painting_rate: 30.0 - blade_mass_cost_coeff: 14.6 - hub_mass_cost_coeff: 3.9 - pitch_system_mass_cost_coeff: 22.1 - spinner_mass_cost_coeff: 11.1 - lss_mass_cost_coeff: 11.9 - bearing_mass_cost_coeff: 4.5 - gearbox_mass_cost_coeff: 12.9 - hss_mass_cost_coeff: 6.8 - generator_mass_cost_coeff: 12.4 - bedplate_mass_cost_coeff: 2.9 - yaw_mass_cost_coeff: 8.3 - converter_mass_cost_coeff: 18.8 - transformer_mass_cost_coeff: 18.8 - hvac_mass_cost_coeff: 124.0 - cover_mass_cost_coeff: 5.7 - elec_connec_machine_rating_cost_coeff: 41.85 - platforms_mass_cost_coeff: 17.1 - tower_mass_cost_coeff: 2.9 - controls_machine_rating_cost_coeff: 21.15 - crane_cost: 12e3 diff --git a/examples/15_RAFT_Studies/README.md b/examples/15_RAFT_Studies/README.md index 8e0b10167..35041c96f 100644 --- a/examples/15_RAFT_Studies/README.md +++ b/examples/15_RAFT_Studies/README.md @@ -19,7 +19,7 @@ To optimize the UMaine semi using raft, run The type of DLCs that are run can be set in `modeling_options_level3.yaml`, along with the metocean conditions. Set the geometry used in WEIS by setting `fname_wt_input` in `weis_driver_level3.py`. - Two options are provided in this example: `IEA-15-floating.yaml` (original) and `opt_22.yaml` + Two options are provided in this example: `IEA-15-floating.yaml` (original) and `raft_opt_out.yaml`, the optimized output. diff --git a/examples/15_RAFT_Studies/analysis_options.yaml b/examples/15_RAFT_Studies/analysis_options.yaml index 5c11a65cb..87baf146d 100644 --- a/examples/15_RAFT_Studies/analysis_options.yaml +++ b/examples/15_RAFT_Studies/analysis_options.yaml @@ -1,5 +1,5 @@ general: - folder_output: outputs/15_RAFT_Studies + folder_output: outputs/15_RAFT_optimization fname_output: refturb_output diff --git a/examples/15_RAFT_Studies/analysis_options_level3.yaml b/examples/15_RAFT_Studies/analysis_options_level3.yaml index a9a000367..545612981 100644 --- a/examples/15_RAFT_Studies/analysis_options_level3.yaml +++ b/examples/15_RAFT_Studies/analysis_options_level3.yaml @@ -1,75 +1,3 @@ general: - folder_output: /Users/dzalkind/Tools/WEIS-4/examples/15_RAFT_Studies/OF_comp_3 + folder_output: outputs/15_Level3_Run fname_output: refturb_output - - -# design_variables: - # control: - # servo: - # pitch_control: - # omega: - # flag: True - # min: 0.1 - # max: 0.5 - - # floating: - # joints: - # flag: True - # # z_coordinate: - # # - names: [main_keel, col1_keel, col2_keel, col3_keel] - # # lower_bound: -40.0 - # # upper_bound: -15.0 - # r_coordinate: - # - names: [col1_keel, col1_freeboard, col2_keel, col2_freeboard, col3_keel, col3_freeboard] - # lower_bound: 38.8125 - # upper_bound: 64.6875 - # members: - # flag: True - # groups: - # - names: [column1,column2,column3] - # diameter: - # lower_bound: 9.375 - # upper_bound: 15.625 - # groups: - # - names: [main_column] - # diameter: - # lower_bound: 7.5 - # upper_bound: 12.5 - - - -constraints: - control: - rotor_overspeed: - flag: True - min: 0.0 - max: 0.25 - Max_PtfmPitch: - flag: True - max: 5.5 - Std_PtfmPitch: - flag: False - max: 2. - Max_Offset: - flag: True - max: 30. - -merit_figure: platform_mass # Merit figure of the optimization problem. The options are 'AEP' - 'LCOE' - 'Cp' - 'blade_mass' - 'blade_tip_deflection' - - -driver: - optimization: - flag: False # Flag to enable optimization - solver: LN_COBYLA # Optimization solver. Other options are 'SLSQP' - 'CONMIN' - tol: 1.e-2 # Optimality tolerance - max_iter: 100 # Maximum number of iterations (SLSQP) - # design_of_experiments: - # flag: True # Flag to enable design of experiments - # run_parallel: False # Flag to run using parallel processing - # generator: LatinHypercube # Type of input generator. (Uniform) - # num_samples: 12 # number of samples for (Uniform only) - -recorder: - flag: True # Flag to activate OpenMDAO recorder - file_name: log_opt.sql # Name of OpenMDAO recorder - includes: ['*raft*','*floating*','*platform*'] \ No newline at end of file diff --git a/examples/15_RAFT_Studies/modeling_options_level3.yaml b/examples/15_RAFT_Studies/modeling_options_level3.yaml index e6a478b1a..0b2cc66f5 100644 --- a/examples/15_RAFT_Studies/modeling_options_level3.yaml +++ b/examples/15_RAFT_Studies/modeling_options_level3.yaml @@ -77,21 +77,7 @@ Level1: ROSCO: flag: True - SD_Mode: 0 - PS_Mode: 1 - ps_percent: 0.85 - F_LPFType: 2 - F_NotchType: 2 - Fl_Mode: 2 - tuning_yaml: ROSCO/Tune_Cases/IEA15MW.yaml - zeta_pc: [1] - omega_pc: [0.2] - U_pc: [12] - zeta_vs: 0.85 # Torque controller desired damping ratio [-] - omega_vs: 0.12 - twr_freq: 3.2 - ptfm_freq: 0.2 - Kp_float: -10 + tuning_yaml: ../../ROSCO/Tune_Cases/IEA15MW.yaml DLC_driver: metocean_conditions: diff --git a/examples/15_RAFT_Studies/opt_22.yaml b/examples/15_RAFT_Studies/opt_22.yaml deleted file mode 100644 index 579d79259..000000000 --- a/examples/15_RAFT_Studies/opt_22.yaml +++ /dev/null @@ -1,961 +0,0 @@ -name: IEA 15MW Offshore Reference Turbine, with taped chord tip design -assembly: {turbine_class: I, turbulence_class: B, drivetrain: direct_drive, rotor_orientation: Upwind, number_of_blades: 3, hub_height: 150.0, rotor_diameter: 242.23775645, rated_power: 15000000.0, lifetime: 25.0} -components: - 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- [23.725333049676337, 0.0, 0.0, 0.0, 0.0, -4.531932807681543, 23.725333049676337, 0.0, 0.0, 0.0, 0.02613341129019644, 23.725333049676337, 4.531932807681543, -0.02613341129019644, 0.0, 6.146053357843997, 6.146053357843997, 0.0, 0.0, 0.0, 6.642081013276805] - - [5.408613166222389, 0.0, 0.0, 0.0, 0.0, -0.2815250336727835, 5.408613166222389, 0.0, 0.0, 0.0, 0.00973323290579508, 5.408613166222389, 0.2815250336727835, -0.00973323290579508, 0.0, 0.10885600283960539, 0.10885600283960539, 0.0, 0.0, 0.0, 0.11533461979292706] - hub: {diameter: 7.94, cone_angle: 0.06981317007977318, drag_coefficient: 0.5, flange_t2shell_t: 6.0, flange_OD2hub_D: 0.6, flange_ID2OD: 0.8, hub_blade_spacing_margin: 1.2, hub_stress_concentration: 3.0, n_front_brackets: 5, n_rear_brackets: 5, clearance_hub_spinner: 0.5, spin_hole_incr: 1.2, pitch_system_scaling_factor: 0.75, spinner_gust_ws: 70.0, hub_material: cast_iron, spinner_material: glass_uni} - nacelle: - drivetrain: - uptilt: 0.10471975511965977 - distance_tt_hub: 5.614 - overhang: 12.0313 - drag_coefficient: 0.5 - distance_hub2mb: 1.0 - distance_mb2mb: 1.2 - lss_diameter: [3.0, 3.0] - lss_wall_thickness: [0.1, 0.1] - nose_diameter: [2.2, 2.2] - nose_wall_thickness: [0.1, 0.1] - generator_length: 2.15 - bedplate_wall_thickness: - grid: [0.0, 0.3333333333333333, 0.6666666666666666, 1.0] - values: [0.05, 0.05, 0.05, 0.05] - gear_ratio: 1.0 - gearbox_efficiency: 1.0 - mb1Type: CARB - mb2Type: SRB - uptower: true - lss_material: steel_drive - bedplate_material: steel_drive - distance_hub_mb: 2.0 - distance_mb_mb: 1.0 - generator_radius_user: 0.0 - generator_mass_user: 0.0 - generator_rpm_efficiency_user: - grid: [0.0] - values: [0.0] - damping_ratio: 0.005 - hss_length: 1.5 - hss_diameter: [0.3, 0.3] - hss_wall_thickness: [0.1, 0.1] - hss_material: steel - bedplate_flange_width: 1.0 - bedplate_flange_thickness: 0.05 - bedplate_web_thickness: 0.05 - brake_mass_user: 0.0 - hvac_mass_coefficient: 0.025 - converter_mass_user: 0.0 - transformer_mass_user: 0.0 - gear_configuration: EEP - planet_numbers: [3, 3, 0] - generator: {generator_type: PMSG_Outer, rho_Fe: 7700.0, rho_Fes: 7850.0, rho_Copper: 8900.0, rho_PM: 7450.0, B_r: 1.279, P_Fe0e: 1.0, P_Fe0h: 4.0, S_N: -0.002, alpha_p: 1.0995574287564276, b_r_tau_r: 0.45, b_ro: 0.004, b_s_tau_s: 0.45, b_so: 0.004, cofi: 0.9, freq: 60.0, h_i: 0.004, h_sy0: 0.0, h_w: 0.005, k_fes: 0.8, k_fillr: 0.55, k_fills: 0.65, k_s: 0.2, m: 3.0, mu_0: 1.2566370614359173e-06, mu_r: 1.06, phi: 1.5707963267948966, q1: 5.0, q2: 4.0, ratio_mw2pp: 0.8, resist_Cu: 2.52e-08, y_tau_pr: 0.8333333, y_tau_p: 0.8, rad_ag: 5.12623359, len_s: 2.23961662, tau_p: 0.1610453779, tau_s: 0.1339360726, h_s: 0.3769449149, b_s: 0.05121796888, b_t: 0.08159225451, h_t: 0.3859449149, h_ys: 0.03618114528, h_yr: 0.0361759511, h_m: 0.0995385122, b_m: 0.1288363023, B_g: 1.38963289, B_symax: 1.63455514, B_rymax: 1.63478983, p: 100.0, R_s: 0.02457052, L_s: 0.01138752, S: 240, t_r: 0.0607657125, h_sr: 0.04677116325, t_s: 0.06065213554, h_ss: 0.04650825863, E_p: 1905.2558883257652, b: 2.0, c: 5.0, N_c: 2.0, B_tmax: 1.9, u_allow_pcent: 8.5, y_allow_pcent: 1.0, z_allow_deg: 0.05, sigma: 60000.0, h_0: 0.05, C_Cu: 4.786, C_Fe: 0.556, C_Fes: 0.50139, C_PM: 50.0, mass_coefficient: 0.0, S_Nmax: -0.2, q3: 4, I_0: 0.0, d_r: 0.0, n_r: 0.0, t_wr: 0.0, n_s: 0.0, b_st: 0.0, d_s: 0.0, t_ws: 0.0} - tower: - outer_shape_bem: - reference_axis: &id003 - x: - grid: [0.0, 7.728811463372998e-05, 0.10047454902385111, 0.10055183713848485, 0.20094909804770222, 0.20102638616233595, 0.30142364707155334, 0.30150093518618704, 0.40189819609540445, 0.40197548421003826, 0.5023727451192556, 0.5024500332338894, 0.6028472941431067, 0.6029245822577405, 0.7033218431669578, 0.7033991312815916, 0.8037963921908089, 0.8038736803054427, 0.90427094121466, 0.9043482293292937, 1.0] - values: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] - y: - grid: [0.0, 7.728811463372998e-05, 0.10047454902385111, 0.10055183713848485, 0.20094909804770222, 0.20102638616233595, 0.30142364707155334, 0.30150093518618704, 0.40189819609540445, 0.40197548421003826, 0.5023727451192556, 0.5024500332338894, 0.6028472941431067, 0.6029245822577405, 0.7033218431669578, 0.7033991312815916, 0.8037963921908089, 0.8038736803054427, 0.90427094121466, 0.9043482293292937, 1.0] - values: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] - z: - grid: [0.0, 7.728811463372998e-05, 0.10047454902385111, 0.10055183713848485, 0.20094909804770222, 0.20102638616233595, 0.30142364707155334, 0.30150093518618704, 0.40189819609540445, 0.40197548421003826, 0.5023727451192556, 0.5024500332338894, 0.6028472941431067, 0.6029245822577405, 0.7033218431669578, 0.7033991312815916, 0.8037963921908089, 0.8038736803054427, 0.90427094121466, 0.9043482293292937, 1.0] - values: [15.0, 15.01, 28.0, 28.01, 41.0, 41.01, 54.0, 54.01, 67.0, 67.01, 80.0, 80.01, 93.0, 93.01, 106.0, 106.01, 119.0, 119.01, 132.0, 132.01, 144.386] - outer_diameter: - grid: [0.0, 7.728811463372998e-05, 0.10047454902385111, 0.10055183713848485, 0.20094909804770222, 0.20102638616233595, 0.30142364707155334, 0.30150093518618704, 0.40189819609540445, 0.40197548421003826, 0.5023727451192556, 0.5024500332338894, 0.6028472941431067, 0.6029245822577405, 0.7033218431669578, 0.7033991312815916, 0.8037963921908089, 0.8038736803054427, 0.90427094121466, 0.9043482293292937, 1.0] - values: [10.0, 10.0, 10.0, 10.0, 10.000000000000002, 10.000000000000025, 9.905075496041832, 9.905075496038421, 9.147775711396022, 9.147775711392333, 8.331137804464264, 8.331137804460141, 7.4405574220559645, 7.44055742213423, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5] - drag_coefficient: - grid: [0.0, 1.0] - values: [0.5, 0.5] - internal_structure_2d_fem: - outfitting_factor: 1.07 - reference_axis: *id003 - layers: - - name: tower_wall - material: steel - thickness: - grid: [0.0, 7.728811463372998e-05, 0.10047454902385111, 0.10055183713848485, 0.20094909804770222, 0.20102638616233595, 0.30142364707155334, 0.30150093518618704, 0.40189819609540445, 0.40197548421003826, 0.5023727451192556, 0.5024500332338894, 0.6028472941431067, 0.6029245822577405, 0.7033218431669578, 0.7033991312815916, 0.8037963921908089, 0.8038736803054427, 0.90427094121466, 0.9043482293292937, 1.0] - values: [0.04581181788122962, 0.0431263860201862, 0.04229377098342676, 0.03707993492238607, 0.03929171243257896, 0.031066365645930273, 0.03651922714830538, 0.026052301758708634, 0.03503017834800577, 0.027664165216450664, 0.03329221642528532, 0.02957862730939187, 0.031206211814029727, 0.032217421810211005, 0.0282072906526414, 0.03495436676212862, 0.020862964263820553, 0.022572820495674422, 0.012453285936801518, 0.04560763242734679, 0.04568044512205404] - floating_platform: - joints: - - name: main_keel - location: [0.0, 0.0, -14.99534906609551] - transition: false - cylindrical: false - - name: main_freeboard - location: [0.0, 0.0, 15.0] - transition: true - cylindrical: false - - name: col1_keel - location: [57.13490985602204, 3.14159265, -14.99534906609551] - cylindrical: true - transition: false - - name: col1_freeboard - location: [57.13490985602204, 3.14159265, 15.0] - cylindrical: true - transition: false - - name: col2_keel - location: [57.13490985602204, 1.0471976, -14.99534906609551] - cylindrical: true - transition: false - - name: col2_freeboard - location: [57.13490985602204, 1.0471975, 15.0] - cylindrical: true - transition: false - - name: col3_keel - location: [57.13490985602204, -1.0471976, -14.99534906609551] - cylindrical: true - transition: false - - name: col3_freeboard - location: [57.13490985602204, -1.0471976, 15.0] - cylindrical: true - transition: false - - name: anchor1 - location: [837.8, 3.14159265, -200.0] - cylindrical: true - transition: false - - name: anchor2 - location: [837.8, 1.0471976, -200.0] - cylindrical: true - transition: false - - name: anchor3 - location: [837.8, -1.0471976, -200.0] - cylindrical: true - transition: false - members: - - name: main_column - joint1: main_keel - joint2: main_freeboard - Ca: 1.0 - Cd: 0.8 - outer_shape: - shape: circular - outer_diameter: - grid: [0.0, 1.0] - values: [10.0, 10.0] - internal_structure: - layers: - - name: main_twall - material: steel - thickness: - grid: [0.0, 1.0] - values: [0.15, 0.15] - ballasts: - - variable_flag: false - material: slurry - volume: 325.0 - grid: [0.0, 1.0] - bulkhead: - material: steel - thickness: - grid: [0.0, 1.0] - values: [0.35, 0.35] - outfitting_factor: 1.0 - axial_joints: - - {name: main_upper_pontoon, grid: 0.987} - - {name: main_lower_pontoon, grid: 0.1} - Cp: 0.0 - - name: column1 - joint1: col1_keel - joint2: col1_freeboard - Ca: 1.0 - Cd: 0.8 - outer_shape: &id004 - shape: circular - outer_diameter: - grid: [0.0, 1.0] - values: [11.63245183870807, 11.63245183870807] - internal_structure: &id005 - layers: - - name: col_twall - material: steel - thickness: - grid: [0.0, 0.1429, 0.2857, 0.4286, 0.5714, 0.7143, 0.8571, 1.0] - values: [0.075, 0.075, 0.075, 0.075, 0.075, 0.075, 0.075, 0.075] - bulkhead: - material: steel - thickness: - grid: [0.0, 1.0] - values: [0.075, 0.075] - ballasts: - - variable_flag: false - material: slurry - volume: 300.0 - grid: [0.0, 0.25] - - variable_flag: true - grid: [0.25, 0.4] - outfitting_factor: 1.0 - axial_joints: - - {name: col1_upper_pontoon, grid: 0.987} - - {name: col1_lower_pontoon, grid: 0.1} - - {name: col1_fairlead, grid: 0.1714} - Cp: 0.0 - - name: column2 - joint1: col2_keel - joint2: col2_freeboard - Ca: 1.0 - Cd: 0.8 - outer_shape: *id004 - internal_structure: *id005 - axial_joints: - - {name: col2_upper_pontoon, grid: 0.987} - - {name: col2_lower_pontoon, grid: 0.1} - - {name: col2_fairlead, grid: 0.1714} - Cp: 0.0 - - name: column3 - joint1: col3_keel - joint2: col3_freeboard - Ca: 1.0 - Cd: 0.8 - outer_shape: *id004 - internal_structure: *id005 - axial_joints: - - {name: col3_upper_pontoon, grid: 0.987} - - {name: col3_lower_pontoon, grid: 0.1} - - {name: col3_fairlead, grid: 0.1714} - Cp: 0.0 - - name: Y_pontoon_upper1 - joint1: main_upper_pontoon - joint2: col1_upper_pontoon - Ca: 1.0 - Cd: 0.8 - outer_shape: &id006 - shape: circular - outer_diameter: - grid: [0.0, 1.0] - values: [0.91, 0.91] - internal_structure: &id007 - layers: - - name: pontoon_upper_twall - material: steel - thickness: - grid: [0.0, 1.0] - values: [0.02, 0.02] - outfitting_factor: 1.0 - bulkhead: - material: steel - thickness: - grid: [0.0, 1.0] - values: [0.02, 0.02] - Cp: 0.0 - - name: Y_pontoon_upper2 - joint1: main_upper_pontoon - joint2: col2_upper_pontoon - Ca: 1.0 - Cd: 0.8 - outer_shape: *id006 - internal_structure: *id007 - Cp: 0.0 - - name: Y_pontoon_upper3 - joint1: main_upper_pontoon - joint2: col3_upper_pontoon - Ca: 1.0 - Cd: 0.8 - outer_shape: *id006 - internal_structure: *id007 - Cp: 0.0 - - name: Y_pontoon_lower1 - joint1: main_lower_pontoon - joint2: col1_lower_pontoon - Ca: 1.0 - Cd: 0.8 - outer_shape: &id008 - shape: circular - outer_diameter: - grid: [0.0, 1.0] - values: [7.5, 7.5] - internal_structure: &id009 - layers: - - name: pontoon_lower_twall - material: steel - thickness: - grid: [0.0, 1.0] - values: [0.04, 0.04] - ballasts: - - variable_flag: true - grid: [0.0, 1.0] - outfitting_factor: 1.0 - bulkhead: - material: steel - thickness: - grid: [0.0, 1.0] - values: [0.02, 0.02] - Cp: 0.0 - - name: Y_pontoon_lower2 - joint1: main_lower_pontoon - joint2: col2_lower_pontoon - outer_shape: *id008 - internal_structure: *id009 - Ca: 0.0 - Cp: 0.0 - Cd: 0.0 - - name: Y_pontoon_lower3 - joint1: main_lower_pontoon - joint2: col3_lower_pontoon - outer_shape: *id008 - internal_structure: *id009 - Ca: 0.0 - Cp: 0.0 - Cd: 0.0 - transition_piece_mass: 100000.0 - transition_piece_cost: 300000.0 - mooring: - nodes: - - {name: line1_anchor, node_type: fixed, joint: anchor1, anchor_type: drag_embedment, fairlead_type: rigid, node_mass: 0.0, node_volume: 0.0, drag_area: 0.0, added_mass: 0.0} - - {name: line2_anchor, node_type: fixed, joint: anchor2, anchor_type: drag_embedment, fairlead_type: rigid, node_mass: 0.0, node_volume: 0.0, drag_area: 0.0, added_mass: 0.0} - - {name: line3_anchor, node_type: fixed, joint: anchor3, anchor_type: drag_embedment, fairlead_type: rigid, node_mass: 0.0, node_volume: 0.0, drag_area: 0.0, added_mass: 0.0} - - {name: line1_vessel, node_type: vessel, joint: col1_fairlead, fairlead_type: rigid, anchor_type: none, node_mass: 0.0, node_volume: 0.0, drag_area: 0.0, added_mass: 0.0} - - {name: line2_vessel, node_type: vessel, joint: col2_fairlead, fairlead_type: rigid, anchor_type: none, node_mass: 0.0, node_volume: 0.0, drag_area: 0.0, added_mass: 0.0} - - {name: line3_vessel, joint: col3_fairlead, node_type: vessel, fairlead_type: rigid, anchor_type: none, node_mass: 0.0, node_volume: 0.0, drag_area: 0.0, added_mass: 0.0} - lines: - - {name: line1, node1: line1_anchor, node2: line1_vessel, line_type: main, unstretched_length: 850.0} - - {name: line2, node1: line2_anchor, node2: line2_vessel, line_type: main, unstretched_length: 850.0} - 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0.2231298637078226, 0.1843927466024767, 0.1485619804842812, 0.1158646406574736, 0.08650795664630631, 0.06067799498087906, 0.05173713798667876, 0.04653137791244361, 0.04244639553319605, 0.03950914701885524, 0.03773901133374287, 0.03714766955647932] - c_m: - grid: *id012 - values: [0.0, 0.09142857142857111, 0.1828571428571434, 0.2742857142857145, 0.3657142857142855, 0.4031304636095545, 0.4081392053848416, 0.4131479471601289, 0.418156688935416, 0.4262712900595553, 0.4437034692302504, 0.461135648400946, 0.4785678275716412, 0.4960000067423368, 0.4883022318330648, 0.4678431517000181, 0.4480322988503198, 0.4369725143258274, 0.4259127298013352, 0.421499653933276, 0.4205848457791291, 0.4202386282975503, 0.4216794100726142, 0.4231201918476781, 0.4225819015145725, 0.4216313044922648, 0.4186442496773847, 0.4127722129896937, 0.4069001763020025, 0.3942614888768985, 0.3816228014517944, 0.3667605953544247, 0.3503288466648674, 0.3336237480028024, 0.3156065694727009, 0.2975893909425995, 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-0.4205848457791291, -0.421499653933276, -0.4259127298013352, -0.4369725143258274, -0.4480322988503198, -0.4678431517000181, -0.4883022318330649, -0.4960000067423369, -0.4785678275716413, -0.461135648400946, -0.4437034692302504, -0.4262712900595553, -0.4352995460782729, -0.4531479471601288, -0.4709963482419844, -0.4888447493238403, -0.457142857142857, -0.3428571428571431, -0.2285714285714291, -0.1142857142857139, 0.0] -materials: - - {name: Gelcoat, orth: 0, rho: 1235.0, E: 3440000000.0, G: 1323000000.0, nu: 0.3, alpha: 0.0, Xt: 74, Xc: 87, S: 21260000.0, GIc: 303, GIIc: 3446, alp0: 53, ply_t: 0.0005, waste: 0.25, unit_cost: 7.23, component_id: 0} - - {name: steel, description: Steel of the tower and monopile ASTM A572 Grade 50, source: http://www.matweb.com/search/DataSheet.aspx?MatGUID=9ced5dc901c54bd1aef19403d0385d7f, orth: 0, rho: 7800, alpha: 0.0, E: 200000000000.0, nu: 0.3, G: 79300000000.0, GIc: 0, GIIc: 0, alp0: 0, Xt: 450000000.0, Xc: 450000000.0, S: 0, Xy: 345000000.0, m: 3, A: 3.5534648443719767e10, unit_cost: 0.7} - - {name: steel_drive, description: Steel of the drivetrain ASTM 4140 40Cr1Mo28, source: http://www.matweb.com/search/DataSheet.aspx?MatGUID=38108bfd64c44b4c9c6a02af78d5b6c6, orth: 0, rho: 7850, alpha: 0.0, E: 205000000000.0, nu: 0.3, G: 80000000000.0, GIc: 0, GIIc: 0, alp0: 0, Xt: 814000000.0, Xc: 814000000.0, S: 0, Xy: 485000000.0, m: 3, A: 3.5534648443719767e10, unit_cost: 0.9} - - {name: cast_iron, description: Cast iron for hub and nacelle components, source: TODO, orth: 0, rho: 7200, alpha: 0.0, E: 118000000000.0, nu: 0.3, G: 47600000000.0, GIc: 0, GIIc: 0, alp0: 0, Xt: 310000000.0, Xc: 310000000.0, S: 0, Xy: 265000000.0, m: 3, unit_cost: 0.5} - - name: glass_uni - description: Vectorply E-LT-5500, Epikote MGS RIMR 135/Epicure MGS RIMH 1366 epoxy - source: MSU composites database 3D property tests, Engineering Mechanics of Composite Materials, Daniel, I & Ishai, O., 1994, pg. 34 - orth: 1 - rho: 1940.0 - E: [44600000000.0, 17000000000.0, 16700000000.0] - G: [3270000000.0, 3480000000.0, 3500000000.0] - nu: [0.262, 0.35, 0.264] - Xt: [609200000.0, 38100000.0, 15290000.0] - Xc: [474710000.0, 112640000.0, 113220000.0] - S: [18910000.0, 17240000.0, 13160000.0] - m: 10 - GIc: 303 - GIIc: 3446 - alp0: 53 - fvf: 0.57 - fwf: 0.7450682696347697 - ply_t: 0.005 - unit_cost: 1.87 - waste: 0.05 - fiber_density: 2535.5 - area_density_dry: 7.227162215457267 - component_id: 5 - - name: CarbonUD - E: [114500000000.0, 8390000000.0, 8390000000.0] - G: [5990000000.0, 5990000000.0, 5990000000.0] - rho: 1220.0 - orth: 1 - nu: [0.27, 0.27, 0.27] - Xt: [1546000000.0, 0.0, 0.0] - Xc: [1047000000.0, 0.0, 0.0] - S: [0.0, 0.0, 0.0] - m: 16.1 - GIc: 0.0 - GIIc: 0.0 - alp0: 0.0 - fvf: 0.1076923076923077 - fwf: 0.15889029003783103 - ply_t: 0.005158730158730159 - unit_cost: 30.0 - waste: 0.05 - fiber_density: 1800.0 - area_density_dry: 1.0 - component_id: 4 - - name: glass_biax - description: Vectorply E-LT-5500, Epikote MGS RIMR 135/Epicure MGS RIMH 1366 epoxy - source: MSU composites database 3D property tests, Engineering Mechanics of Composite Materials, Daniel, I & Ishai, O., 1994, pg. 34 - orth: 1 - rho: 1940.0 - E: [11100000000.0, 11100000000.0, 16700000000.0] - G: [13530000000.0, 3490000000.0, 3490000000.0] - nu: [0.5, 0.0, 0.066] - Xt: [42900000.0, 42600000.0, 15300000.0] - Xc: [70700000.0, 70700000.0, 113200000.0] - S: [103400000.0, 17200000.0, 13200000.0] - m: 10 - GIc: 303 - GIIc: 3446 - alp0: 53 - fvf: 0.57 - fwf: 0.7450682696347697 - ply_t: 0.001 - waste: 0.15 - unit_cost: 3.0 - fiber_density: 2535.5 - area_density_dry: 1.4454324430914534 - component_id: 3 - roll_mass: 181.4368 - - name: glass_triax - description: Vectorply E-LT-5500, Epikote MGS RIMR 135/Epicure MGS RIMH 1366 epoxy - source: MSU composites database 3D property tests, Engineering Mechanics of Composite Materials, Daniel, I & Ishai, O., 1994, pg. 34 - orth: 1.0 - rho: 1940.0 - E: [28700000000.0, 16600000000.0, 16700000000.0] - G: [8400000000.0, 3490000000.0, 3490000000.0] - nu: [0.5, 0.0, 0.17] - Xt: [396000000.0, 76400000.0, 15300000.0] - Xc: [448900000.0, 174700000.0, 113200000.0] - S: [103400000.0, 17200000.0, 13200000.0] - m: 10 - GIc: 303 - GIIc: 3446 - alp0: 53 - fvf: 0.57 - fwf: 0.7450682696347697 - ply_t: 0.001 - unit_cost: 2.86 - waste: 0.15 - fiber_density: 2535.5 - area_density_dry: 1.4454324430914534 - component_id: 2 - roll_mass: 181.4368 - - {name: medium_density_foam, description: "Airex C70.130 PVC Foam, source 'https://www.3accorematerials.com/uploads/documents/TDS-AIREX-C70-E_1106.pdf'", orth: 0.0, rho: 130.0, E: 129200000.0, G: 48946969.696969695, nu: 0.32, Xt: 2083000.0, Xc: 1563000.0, S: 1250000.0, GIc: 303, GIIc: 3446, alp0: 53, component_id: 1, waste: 0.2, unit_cost: 13} - - {name: resin, description: epoxy, E: 1000000.0, nu: 0.3, G: 312500.0, GIc: 0, GIIc: 0, alp0: 0, Xt: 0, Xc: 0, S: 0, rho: 1150.0, alpha: 0.0, orth: 0, unit_cost: 3.63} - - {name: slurry, description: fixed/permanent ballast, E: 1.0, nu: 0.3, G: 1.0, Xt: 0, Xc: 0, S: 0, rho: 5000.0, alpha: 0.0, unit_cost: 1.0, orth: 0.0} - - {name: adhesive, description: Sample adhesive, source: https://www.nrel.gov/docs/fy19osti/73585.pdf, orth: 0, rho: 1100, E: 4560000.0, nu: 0.49, alpha: 0.0, Xt: 690000.0, Xc: 400000.0, S: 310000.0, G: 1520000.0, unit_cost: 9.0} -control: - supervisory: {Vin: 3.0, Vout: 25.0, maxTS: 95.0} - pitch: {PC_zeta: 1.0, PC_omega: 0.2, ps_percent: 0.8, max_pitch: 1.57, max_pitch_rate: 0.03490658503988659, min_pitch: 0.0} - torque: {control_type: tsr_tracking, tsr: 9.0, VS_zeta: 1.0, VS_omega: 0.2, max_torque_rate: 1500000.0, VS_minspd: 0.5235987755982988, VS_maxspd: 0.7916813487046278} - setpoint_smooth: {ss_vsgain: 1, ss_pcgain: 0.001} - shutdown: {limit_type: gen_speed, limit_value: 2.0} -environment: {air_density: 1.225, air_dyn_viscosity: 1.81e-05, weib_shape_parameter: 2.0, air_speed_sound: 340.0, shear_exp: 0.12, water_density: 1025.0, water_dyn_viscosity: 0.0013351, soil_shear_modulus: 140000000.0, soil_poisson: 0.4, water_depth: 200.0, significant_wave_height: 4.52, significant_wave_period: 9.45, gravity: 9.80665, air_pressure: 103500.0, air_vapor_pressure: 1700.0, V_mean: 0.0} -bos: {plant_turbine_spacing: 7, plant_row_spacing: 7, commissioning_pct: 0.01, decommissioning_pct: 0.15, distance_to_substation: 1.0, distance_to_interconnection: 8.5, interconnect_voltage: 130.0, distance_to_site: 115.0, distance_to_landfall: 50.0, port_cost_per_month: 2000000.0, site_auction_price: 100000000.0, site_assessment_plan_cost: 1000000.0, site_assessment_cost: 25000000.0, construction_operations_plan_cost: 2500000.0, boem_review_cost: 0.0, design_install_plan_cost: 2500000.0} -costs: {wake_loss_factor: 0.15, fixed_charge_rate: 0.056, bos_per_kW: 4053.0, opex_per_kW: 137.0, turbine_number: 40.0, labor_rate: 58.8, painting_rate: 30.0, blade_mass_cost_coeff: 14.6, hub_mass_cost_coeff: 3.9, pitch_system_mass_cost_coeff: 22.1, spinner_mass_cost_coeff: 11.1, lss_mass_cost_coeff: 11.9, bearing_mass_cost_coeff: 4.5, gearbox_mass_cost_coeff: 12.9, hss_mass_cost_coeff: 6.8, generator_mass_cost_coeff: 12.4, bedplate_mass_cost_coeff: 2.9, yaw_mass_cost_coeff: 8.3, converter_mass_cost_coeff: 18.8, transformer_mass_cost_coeff: 18.8, hvac_mass_cost_coeff: 124.0, cover_mass_cost_coeff: 5.7, elec_connec_machine_rating_cost_coeff: 41.85, platforms_mass_cost_coeff: 17.1, tower_mass_cost_coeff: 2.9, controls_machine_rating_cost_coeff: 21.15, crane_cost: 12000.0} diff --git a/examples/15_RAFT_Studies/weis_driver_level3.py b/examples/15_RAFT_Studies/weis_driver_level3.py index e744341c7..5988c4488 100644 --- a/examples/15_RAFT_Studies/weis_driver_level3.py +++ b/examples/15_RAFT_Studies/weis_driver_level3.py @@ -6,8 +6,8 @@ ## File management run_dir = os.path.dirname( os.path.realpath(__file__) ) + os.sep wisdem_examples = os.path.join(os.path.dirname( os.path.dirname( os.path.dirname( os.path.realpath(__file__) ) ) ), "WISDEM", "examples") -fname_wt_input = os.path.join(wisdem_examples,'09_floating/IEA-15-240-RWT_VolturnUS-S.yaml') -# fname_wt_input = '/Users/dzalkind/Tools/WEIS-4/examples/15_RAFT_Studies/IEA-15-floating.yaml' +# fname_wt_input = os.path.join(wisdem_examples,'09_floating/IEA-15-240-RWT_VolturnUS-S.yaml') +fname_wt_input = os.path.join(run_dir,'raft_opt_out.yaml') fname_modeling_options = run_dir + "modeling_options_level3.yaml" fname_analysis_options = run_dir + "analysis_options_level3.yaml" diff --git a/examples/15_RAFT_Studies/weis_driver_umaine_semi.py b/examples/15_RAFT_Studies/weis_driver_umaine_semi.py index 781aa3396..df886d002 100644 --- a/examples/15_RAFT_Studies/weis_driver_umaine_semi.py +++ b/examples/15_RAFT_Studies/weis_driver_umaine_semi.py @@ -6,7 +6,7 @@ ## File management run_dir = os.path.dirname( os.path.realpath(__file__) ) + os.sep wisdem_examples = os.path.join(os.path.dirname( os.path.dirname( os.path.dirname( os.path.realpath(__file__) ) ) ), "WISDEM", "examples") -fname_wt_input = os.path.realpath(os.path.join(run_dir,"IEA-15-floating.yaml")) +fname_wt_input = os.path.join(wisdem_examples,'09_floating/IEA-15-240-RWT_VolturnUS-S.yaml') fname_modeling_options = run_dir + "modeling_options_umaine_semi.yaml" fname_analysis_options = run_dir + "analysis_options.yaml" From ac61d7b3e105d4a3fa61968dba63eece90a9a1f3 Mon Sep 17 00:00:00 2001 From: dzalkind Date: Fri, 12 Jan 2024 13:50:14 -0700 Subject: [PATCH 046/114] Rename weis driver in 15_ --- .../{weis_driver_umaine_semi.py => weis_driver_raft_opt.py} | 0 weis/test/run_examples.py | 2 +- 2 files changed, 1 insertion(+), 1 deletion(-) rename examples/15_RAFT_Studies/{weis_driver_umaine_semi.py => weis_driver_raft_opt.py} (100%) diff --git a/examples/15_RAFT_Studies/weis_driver_umaine_semi.py b/examples/15_RAFT_Studies/weis_driver_raft_opt.py similarity index 100% rename from examples/15_RAFT_Studies/weis_driver_umaine_semi.py rename to examples/15_RAFT_Studies/weis_driver_raft_opt.py diff --git a/weis/test/run_examples.py b/weis/test/run_examples.py index d7c36758c..535c9b963 100644 --- a/weis/test/run_examples.py +++ b/weis/test/run_examples.py @@ -18,7 +18,7 @@ "10_override_example/weis_driver", #"12_linearization/doe_driver", # Soul crushingly long "12_linearization/weis_driver", - "15_RAFT_Studies/weis_driver_umaine_semi" + "15_RAFT_Studies/weis_driver_raft_opt" ] class TestExamples(unittest.TestCase): From e3dee9a86554d71bd742fda56781bb4be8543078 Mon Sep 17 00:00:00 2001 From: dzalkind Date: Fri, 12 Jan 2024 13:59:38 -0700 Subject: [PATCH 047/114] Tidy up 13_ example inputs --- examples/13_DTQP/IEA-15-floating.yaml | 1208 ------------------ examples/13_DTQP/analysis_options_ol2cl.yaml | 2 +- examples/13_DTQP/gen_oloc.py | 7 +- examples/13_DTQP/modeling_options.yaml | 2 - examples/13_DTQP/modeling_options_ol2cl.yaml | 2 +- 5 files changed, 6 insertions(+), 1215 deletions(-) delete mode 100644 examples/13_DTQP/IEA-15-floating.yaml diff --git a/examples/13_DTQP/IEA-15-floating.yaml b/examples/13_DTQP/IEA-15-floating.yaml deleted file mode 100644 index dd282c667..000000000 --- a/examples/13_DTQP/IEA-15-floating.yaml +++ /dev/null @@ -1,1208 +0,0 @@ -name: IEA 15MW Offshore Reference Turbine, with taped chord tip design - -assembly: - turbine_class: I - turbulence_class: B - drivetrain: direct_drive - rotor_orientation: Upwind - number_of_blades: 3 - hub_height: 150. - rotor_diameter: 242.23775645 - rated_power: 15.e+6 - -components: - blade: - outer_shape_bem: - airfoil_position: - grid: [0.0, 0.02, 0.15, 0.24517031675566095, 0.3288439506472435, 0.4391793464459161, 0.5376714071084352, 0.6382076569163737, 0.7717438522715817, 1.0] - labels: [circular, circular, SNL-FFA-W3-500, FFA-W3-360, FFA-W3-330blend, FFA-W3-301, FFA-W3-270blend, FFA-W3-241, FFA-W3-211, FFA-W3-211] - chord: - grid: [0.0, 0.02, 0.04, 0.06, 0.08, 0.1, 0.125, 0.15, 0.1690340633511322, 0.18806812670226436, 0.20710219005339656, 0.22613625340452875, 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0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] - grid: [0.1, 0.125, 0.15, 0.1690340633511322, 0.18806812670226436, 0.20710219005339656, 0.22613625340452875, 0.24517031675566095, 0.2660887252285566, 0.2870071337014522, 0.30792554217434787, 0.3288439506472435, 0.350911029806978, 0.3729781089667125, 0.39504518812644707, 0.41711226728618156, 0.4391793464459161, 0.45887775857841995, 0.47857617071092373, 0.49827458284342757, 0.5179729949759314, 0.5376714071084352, 0.5577786570700229, 0.5778859070316106, 0.5979931569931983, 0.618100406954786, 0.6382076569163737, 0.657284256252832, 0.6763608555892903, 0.6954374549257486, 0.7145140542622068, 0.7335906535986652, 0.7526672529351234, 0.7717438522715817, 0.8, 0.8187500000000001, 0.8375, 0.85625, 0.875, 0.89375, 0.9125, 0.9312499999999999, 0.95] - - name: web1_skinTE - material: glass_biax - web: web1 - thickness: - grid: [0.1, 0.125, 0.15, 0.1690340633511322, 0.18806812670226436, 0.20710219005339656, 0.22613625340452875, 0.24517031675566095, 0.2660887252285566, 0.2870071337014522, 0.30792554217434787, 0.3288439506472435, 0.350911029806978, 0.3729781089667125, 0.39504518812644707, 0.41711226728618156, 0.4391793464459161, 0.45887775857841995, 0.47857617071092373, 0.49827458284342757, 0.5179729949759314, 0.5376714071084352, 0.5577786570700229, 0.5778859070316106, 0.5979931569931983, 0.618100406954786, 0.6382076569163737, 0.657284256252832, 0.6763608555892903, 0.6954374549257486, 0.7145140542622068, 0.7335906535986652, 0.7526672529351234, 0.7717438522715817, 0.8, 0.8187500000000001, 0.8375, 0.85625, 0.875, 0.89375, 0.9125, 0.9312499999999999, 0.95] - values: [0.002, 0.001955882352941177, 0.0019117647058823533, 0.0018781751823215315, 0.0018445856587607098, 0.001810996135199888, 0.0017774066116390667, 0.0017438170880782456, 0.0017069022495966651, 0.0016699874111150846, 0.0016330725726335042, 0.0015961577341519235, 0.0015572158297523914, 0.0015182739253528598, 0.0014793320209533286, 0.0014403901165537974, 0.0014014482121542657, 0.0013666863083910236, 0.0013319244046277818, 0.0012971625008645394, 0.0012624005971012973, 0.0012276386933380555, 0.0011921553110529005, 0.0011566719287677458, 0.001121188546482591, 0.0010857051641974364, 0.0010502217819122813, 0.0010165571948479433, 0.000982892607783605, 0.0009492280207192671, 0.000915563433654929, 0.0008818988465905908, 0.0008482342595262528, 0.0008145696724619145, 0.0007647058823529412, 0.0007316176470588235, 0.0006985294117647059, 0.0006654411764705883, 0.0006323529411764706, 0.0005992647058823529, 0.0005661764705882353, 0.0005330882352941178, 0.0005000000000000001] - fiber_orientation: - values: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] - grid: [0.1, 0.125, 0.15, 0.1690340633511322, 0.18806812670226436, 0.20710219005339656, 0.22613625340452875, 0.24517031675566095, 0.2660887252285566, 0.2870071337014522, 0.30792554217434787, 0.3288439506472435, 0.350911029806978, 0.3729781089667125, 0.39504518812644707, 0.41711226728618156, 0.4391793464459161, 0.45887775857841995, 0.47857617071092373, 0.49827458284342757, 0.5179729949759314, 0.5376714071084352, 0.5577786570700229, 0.5778859070316106, 0.5979931569931983, 0.618100406954786, 0.6382076569163737, 0.657284256252832, 0.6763608555892903, 0.6954374549257486, 0.7145140542622068, 0.7335906535986652, 0.7526672529351234, 0.7717438522715817, 0.8, 0.8187500000000001, 0.8375, 0.85625, 0.875, 0.89375, 0.9125, 0.9312499999999999, 0.95] - - hub: - diameter: 7.94 - cone_angle: 0.06981317007977318 # 4 deg - drag_coefficient: 0.5 - flange_t2shell_t: 6.0 - flange_OD2hub_D: 0.6 - flange_ID2OD: 0.8 - hub_blade_spacing_margin: 1.2 - hub_stress_concentration: 3.0 - n_front_brackets: 5 - n_rear_brackets: 5 - clearance_hub_spinner: 0.5 - spin_hole_incr: 1.2 - pitch_system_scaling_factor: 0.75 - spinner_gust_ws: 70.0 - hub_material: cast_iron - spinner_material: glass_uni - nacelle: - drivetrain: - uptilt: 0.10471975511965977 # 6 deg - distance_tt_hub: 5.614 - overhang: 12.0313 - drag_coefficient: 0.5 - distance_hub2mb: 1.0 - distance_mb2mb: 1.2 - lss_diameter: [3.0, 3.0] - lss_wall_thickness: [0.1, 0.1] - nose_diameter: [2.2, 2.2] - nose_wall_thickness: [0.1, 0.1] - generator_length: 2.15 - bedplate_wall_thickness: - grid: [0.0, 1.0] - values: [0.05, 0.05] - gear_ratio: 1.0 - gearbox_efficiency: 1.0 - mb1Type: CARB - mb2Type: SRB - uptower: True - lss_material: steel_drive - bedplate_material: steel_drive - generator: - generator_type: PMSG_Outer - rho_Fe: 7700.0 - rho_Fes: 7850.0 - rho_Copper: 8900.0 - rho_PM: 7450.0 - B_r: 1.279 - P_Fe0e: 1.0 - P_Fe0h: 4.0 - S_N: -0.002 - alpha_p: 1.0995574287564276 #0.5*np.pi*0.7 - b_r_tau_r: 0.45 - b_ro: 0.004 - b_s_tau_s: 0.45 - b_so: 0.004 - cofi: 0.9 - freq: 60.0 - h_i: 0.004 - h_sy0: 0.0 - h_w: 0.005 - k_fes: 0.8 - k_fillr: 0.55 - k_fills: 0.65 - k_s: 0.2 - m: 3 - mu_0: 1.2566370614359173e-06 #np.pi*4e-7 - mu_r: 1.06 - phi: 1.5707963267948966 # 90 deg - q1: 5 - q2: 4 - ratio_mw2pp: 0.8 - resist_Cu: 2.52e-8 #1.8e-8*1.4 - y_tau_pr: 0.8333333 #10. / 12 - y_tau_p: 0.8 #12./15. - rad_ag: 5.12623359 - len_s: 2.23961662 - tau_p: 0.1610453779 - tau_s: 0.1339360726 - h_s: 0.3769449149 - b_s: 0.05121796888 - b_t: 0.08159225451 - h_t: 0.3859449149 - h_ys: 0.03618114528 - h_yr: 0.0361759511 - h_m: 0.0995385122 - b_m: 0.1288363023 - B_g: 1.38963289 - B_symax: 1.63455514 - B_rymax: 1.63478983 - p: 100 - R_s: 0.02457052 - L_s: 0.01138752 - S: 240 - t_r: 0.0607657125 - h_sr: 0.04677116325 - t_s: 0.06065213554 - h_ss: 0.04650825863 - E_p: 1905.2558883257652 - b: 2.0 - c: 5.0 - N_c: 2 - B_tmax: 1.9 - u_allow_pcent: 8.5 - y_allow_pcent: 1.0 - z_allow_deg: 0.05 - sigma: 60e3 - - h_0: 0.05 - C_Cu: 4.786 - C_Fe: 0.556 - C_Fes: 0.50139 - C_PM: 50.0 - - tower: - outer_shape_bem: - reference_axis: &ref_axis_tower - x: - grid: [0.0, 1.0] - values: [0.0, 0.0] - y: - grid: [0.0, 1.0] - values: [0.0, 0.0] - z: - grid: &grid_tower [0.0, 0.10032257566637343, 0.10033029278757853, 0.20064515133274685, 0.20065286845395192, 0.3009677269991203, 0.30097544412032534, 0.4012903026654937, 0.4012980197866988, 0.5016128783318671, 0.5016205954530722, 0.6019354539982406, 0.6019431711194456, 0.7022580296646139, 0.7022657467858191, 0.8025806053309874, 0.8025883224521925, 0.9029031809973608, 0.902910898118566, 1.0] - values: [15.0, 28.0, 28.001, 41.0, 41.001, 54.0, 54.001, 67.0, 67.001, 80.0, 80.001, 93.0, 93.001, 106.0, 106.001, 119.0, 119.001, 132.0, 132.001, 144.582] - outer_diameter: - grid: *grid_tower - values: [10.0, 9.964, 9.964, 9.967, 9.967, 9.927, 9.927, 9.528, 9.528, 9.149, 9.149, 8.945, 8.945, 8.735, 8.735, 8.405, 8.405, 7.321, 7.321, 6.5] - drag_coefficient: - grid: [0.0, 1.0] - values: [0.5, 0.5] - internal_structure_2d_fem: - outfitting_factor: 1.07 - reference_axis: *ref_axis_tower - layers: - - name: tower_wall - material: steel - thickness: - grid: *grid_tower - values: [0.082954, 0.082954, 0.083073, 0.083073, 0.08279900000000001, 0.08279900000000001, 0.0299, 0.0299, 0.027842, 0.027842, 0.025567, 0.025567, 0.022854, 0.022854, 0.02025, 0.02025, 0.018338999999999998, 0.018338999999999998, 0.021211, 0.021211] - floating_platform: - joints: - - name: main_keel - location: [0.0, 0.0, -20.0] - - - name: main_freeboard - location: [0.0, 0.0, 15.0] - transition: True - - - name: col1_keel - location: [51.75, 3.14159265, -20.0] #3.14159265 = 180deg - cylindrical: True - - - name: col1_freeboard - location: [51.75, 3.14159265, 15.0] # 3.14159265 = 180deg - cylindrical: True - - - name: col2_keel - location: [51.75, 1.0471976, -20.0] #1.0471975 = 60 deg - cylindrical: True - - - name: col2_freeboard - location: [51.75, 1.0471975, 15.0] - cylindrical: True - - - name: col3_keel - location: [51.75, -1.0471976, -20.0] #-1.0471975 = -60 deg - cylindrical: True - - - name: col3_freeboard - location: [51.75, -1.0471976, 15.0] #-1.0471975 = -60 deg - cylindrical: True - - - name: anchor1 - location: [837.8, 3.14159265, -200.0] - cylindrical: True - - - name: anchor2 - location: [837.8, 1.0471976, -200.0] # 1.0471975 = 60 deg - cylindrical: True - - - name: anchor3 - location: [837.8, -1.0471976, -200.0] #-1.0471975 = -60 deg - cylindrical: True - - members: - - name: main_column - joint1: main_keel - joint2: main_freeboard - Ca: 1.0 - Cd: 0.8 - outer_shape: - shape: circular - outer_diameter: - grid: [0.0, 1.0] - values: [10.0, 10.0] - internal_structure: - layers: - - name: main_twall - material: steel - thickness: - grid: [0.0, 1.0] - values: [0.15, 0.15] - ballasts: - - variable_flag: False - material: slurry - volume: 500 - grid: [0.0, 0.05] - - variable_flag: True - grid: [0.05, 0.4] - bulkhead: - material: steel - thickness: - grid: [0.00, 0.25, 0.50, 0.75, 0.80, 0.85, 0.90, 0.95, 1.00] - values: [0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35] - axial_joints: - - name: main_upper_pontoon - grid: 0.987 - - - name: main_lower_pontoon - grid: 0.1 - - - name: column1 - joint1: col1_keel - joint2: col1_freeboard - Ca: 1.0 - Cd: 0.8 - outer_shape: &col_out - shape: circular - outer_diameter: - grid: [0.0, 1.0] - values: [12.5, 12.5] - internal_structure: &col_int - layers: - - name: col_twall - material: steel - thickness: - grid: [0.0, 1.0] - values: [0.075, 0.075] - bulkhead: - material: steel - thickness: - grid: [0.0, 0.05, 0.2, 1.0] - values: [0.075, 0.075, 0.075, 0.075] - ballasts: - - variable_flag: False - material: slurry - volume: 519 - grid: [0.0, 0.05] - - variable_flag: True - grid: [0.05, 0.4] - axial_joints: - - name: col1_upper_pontoon - grid: 0.987 - - - name: col1_lower_pontoon - grid: 0.1 - - - name: col1_fairlead - grid: 0.1714 - - - name: column2 - joint1: col2_keel - joint2: col2_freeboard - Ca: 1.0 - Cd: 0.8 - outer_shape: *col_out - internal_structure: *col_int - axial_joints: - - name: col2_upper_pontoon - grid: 0.987 - - - name: col2_lower_pontoon - grid: 0.1 - - - name: col2_fairlead - grid: 0.1714 - - - name: column3 - joint1: col3_keel - joint2: col3_freeboard - Ca: 1.0 - Cd: 0.8 - outer_shape: *col_out - internal_structure: *col_int - axial_joints: - - name: col3_upper_pontoon - grid: 0.987 - - - name: col3_lower_pontoon - grid: 0.1 - - - name: col3_fairlead - grid: 0.1714 - - - - name: Y_pontoon_upper1 - joint1: main_upper_pontoon - joint2: col1_upper_pontoon - Ca: 1.0 - Cd: 0.8 - outer_shape: &pontup_out - shape: circular - outer_diameter: - grid: [0.0, 1.0] - values: [0.91, 0.91] - internal_structure: &pontup_int - layers: - - name: pontoon_upper_twall - material: steel - thickness: - grid: [0.0, 1.0] - values: [0.02, 0.02] - - - name: Y_pontoon_upper2 - joint1: main_upper_pontoon - joint2: col2_upper_pontoon - Ca: 1.0 - Cd: 0.8 - outer_shape: *pontup_out - internal_structure: *pontup_int - - - name: Y_pontoon_upper3 - joint1: main_upper_pontoon - joint2: col3_upper_pontoon - Ca: 1.0 - Cd: 0.8 - outer_shape: *pontup_out - internal_structure: *pontup_int - - - name: Y_pontoon_lower1 - joint1: main_lower_pontoon - joint2: col1_lower_pontoon - Ca: 1.0 - Cd: 0.8 - outer_shape: &pontlow_out - shape: circular - outer_diameter: - grid: [0.0, 1.0] - values: [9.6148, 9.6148] #equates displacement not circle: [12.5, 12.5] - internal_structure: &pontlow_int - # Probably needs longitudinal stiffeners - layers: - - name: pontoon_lower_twall - material: steel - thickness: - grid: [0.0, 1.0] - values: [0.04, 0.04] - ballasts: - - variable_flag: True - grid: [0.0, 1.0] - - - name: Y_pontoon_lower2 - joint1: main_lower_pontoon - joint2: col2_lower_pontoon - outer_shape: *pontlow_out - internal_structure: *pontlow_int - - - name: Y_pontoon_lower3 - joint1: main_lower_pontoon - joint2: col3_lower_pontoon - outer_shape: *pontlow_out - internal_structure: *pontlow_int - transition_piece_mass: 100.e3 - transition_piece_cost: 300.e3 - mooring: - nodes: - - name: line1_anchor - node_type: fixed - joint: anchor1 - anchor_type: drag_embedment - - name: line2_anchor - node_type: fixed - joint: anchor2 - anchor_type: drag_embedment - - name: line3_anchor - node_type: fixed - joint: anchor3 - anchor_type: drag_embedment - - - name: line1_vessel - node_type: vessel - joint: col1_fairlead - fairlead_type: rigid - - name: line2_vessel - node_type: vessel - joint: col2_fairlead - fairlead_type: rigid - - name: line3_vessel - joint: col3_fairlead - node_type: vessel - fairlead_type: rigid - - lines: - - name: line1 - node1: line1_anchor - node2: line1_vessel - line_type: main - unstretched_length: 850.0 - - name: line2 - node1: line2_anchor - node2: line2_vessel - line_type: main - unstretched_length: 850.0 - - name: line3 - node1: line3_anchor - node2: line3_vessel - line_type: main - unstretched_length: 850.0 - - line_types: - - name: main - diameter: 0.185 - type: chain - 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F Zahle, DTU Wind Energy 11 May 2017 - polars: - - configuration: Default - re: 1.00E+07 - c_l: - grid: *grid004 - values: [0.0, 0.07178149999999964, 0.1435630000000001, 0.2153444999999997, 0.2871260000000001, 0.3589074999999998, 0.4306889999999995, 0.5024704999999998, 0.5742519999999995, 0.6460334999999998, 0.7178149999999996, 0.7895965, 0.8613779999999996, 0.9331595000000001, 1.004941, 0.9189832441278815, 0.8440624787712528, 0.7748315757714827, 0.7079034441742906, 0.6411583915080097, 0.5733514328047344, 0.5038751148063706, 0.432607030599928, 0.3598052464255929, 0.2860316675426308, 0.2120921414269119, 0.1389869075032731, 0.06786776586894963, 5.551115123125782e-17, -0.06786776586894941, -0.1389869075032731, -0.2120921414269117, -0.2860316675426306, -0.3598052464255929, -0.4326070305999278, -0.5038751148063706, -0.5733514328047343, -0.6411583915080097, -0.7079034441742903, -0.7748315757714827, -0.8440624787712528, -0.9189832441278818, -1.004941, -1.113059, -1.054248, -0.982473, -0.9417260000000001, -0.893331, -0.8547150000000001, -0.82348, -0.795409, -0.636498, -0.390949, -0.1307082, 0.1617258, 0.311214, 0.459562, 0.605659, 0.748677, 0.888617, 1.025442, 1.15878, 1.28822, 1.41282, 1.5309, 1.64065, 1.73926, 1.81971, 1.87065, 1.89221, 1.8791, 1.88111, 1.86359, 1.73324, 1.59357, 1.46708, 1.44834, 1.43563, 1.312833205896974, 1.20580354110179, 1.106902251102118, 1.011290634534701, 0.9159405592971568, 0.8190734754353348, 0.7198215925805296, 0.6180100437141822, 0.5140074948937041, 0.4086166679180437, 0.3029887734670167, 0.1985527250046759, 0.09695395124135632, 8.326672684688674e-17, -0.06786776586894941, -0.1389869075032731, -0.2120921414269117, -0.2860316675426306, -0.3598052464255929, -0.4326070305999276, -0.5038751148063706, -0.5733514328047341, -0.6411583915080097, -0.7079034441742906, -0.7748315757714827, -0.8440624787712528, -0.9189832441278815, -1.004941, -0.9331595000000001, -0.8613779999999996, -0.7895965, -0.7178149999999996, -0.6460334999999998, -0.5742520000000002, -0.5024704999999998, -0.4306890000000002, -0.3589074999999998, -0.2871260000000001, -0.2153444999999997, -0.1435630000000001, -0.07178149999999964, 0.0] - 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c_m: - grid: *grid004 - values: [0.0, 0.09142857142857111, 0.1828571428571434, 0.2742857142857145, 0.3657142857142855, 0.4031304636095545, 0.4081392053848416, 0.4131479471601289, 0.418156688935416, 0.4262712900595553, 0.4437034692302504, 0.461135648400946, 0.4785678275716412, 0.4960000067423368, 0.4883022318330648, 0.4678431517000181, 0.4480322988503198, 0.4369725143258274, 0.4259127298013352, 0.421499653933276, 0.4205848457791291, 0.4202386282975503, 0.4216794100726142, 0.4231201918476781, 0.4225819015145725, 0.4216313044922648, 0.4186442496773847, 0.4127722129896937, 0.4069001763020025, 0.3942614888768985, 0.3816228014517944, 0.3667605953544247, 0.3503288466648674, 0.3336237480028024, 0.3156065694727009, 0.2975893909425995, 0.2798918070549701, 0.2622959123717636, 0.2449149435317124, 0.2279423337936562, 0.2109697240555999, 0.1352478121095765, 0.0551741, 0.0321142, 0.01267890000000002, -0.002821250000000001, -0.0074129, -0.0110695, -0.01250065, -0.01177275, -0.01082065, -0.0276937, -0.05106789999999999, -0.0714813, -0.0917883, -0.1011907, -0.1098835, -0.1177635, -0.124769, -0.130977, -0.136484, -0.141299, -0.1454, -0.14875, -0.151175, -0.15262, -0.153103, -0.152545, -0.151206, -0.149693, -0.145621, -0.143578, -0.140948, -0.137106, -0.140821, -0.156927, -0.179786, -0.201472, -0.2240866097136903, -0.2461860684392151, -0.2613309888245065, -0.2764759092097978, -0.2906206368276409, -0.3042389471994587, -0.3178704108777556, -0.3315432135192723, -0.3452160161607889, -0.3584572127501313, -0.3716084914119372, -0.3840462584573971, -0.3954732173796998, -0.4069001763020025, -0.4127722129896937, -0.4186442496773847, -0.4216313044922648, -0.4225819015145725, -0.4231201918476781, -0.4216794100726143, -0.4202386282975503, -0.4205848457791291, -0.421499653933276, -0.4259127298013352, -0.4369725143258274, -0.4480322988503198, -0.4678431517000181, -0.4883022318330649, -0.4960000067423369, -0.4785678275716413, -0.461135648400946, -0.4437034692302504, -0.4262712900595553, -0.4352995460782729, -0.4531479471601288, -0.4709963482419844, -0.4888447493238403, -0.457142857142857, -0.3428571428571431, -0.2285714285714291, -0.1142857142857139, 0.0] - -materials: - - name: Gelcoat - orth: 0 - rho: 1235.0 - E: 3.440e+009 - G: 1.323e+009 - nu: 0.300 - alpha: 0.0 - Xt: 74 - Xc: 87 - S: 2.126E7 - GIc: 303 - GIIc: 3446 - alp0: 53 - ply_t: 5.0E-4 - waste: 0.25 - unit_cost: 7.23 - component_id: 0 - - name: steel - description: Steel of the tower and monopile ASTM A572 Grade 50 - source: http://www.matweb.com/search/DataSheet.aspx?MatGUID=9ced5dc901c54bd1aef19403d0385d7f - orth: 0 - rho: 7800 - alpha: 0.0 - E: 200.e+009 - nu: 0.3 - G: 79.3e+009 - GIc: 0 #Place holder, currently not used - GIIc: 0 #Place holder, currently not used - alp0: 0 #Place holder, currently not used - Xt: 450.e+006 - Xc: 450.e+006 - S: 0 - Xy: 345.e+6 - m: 3 - A: 3.5534648443719767e10 #DOI 10.1016/j.proeng.2013.12.061 eqn 5 - unit_cost: 0.7 - - name: steel_drive - description: Steel of the drivetrain ASTM 4140 40Cr1Mo28 - source: http://www.matweb.com/search/DataSheet.aspx?MatGUID=38108bfd64c44b4c9c6a02af78d5b6c6 - orth: 0 - rho: 7850 - alpha: 0.0 - E: 205.e+009 - nu: 0.3 - G: 80.0e+009 - GIc: 0 #Place holder, currently not used - GIIc: 0 #Place holder, currently not used - alp0: 0 #Place holder, currently not used - Xt: 814.e+006 - Xc: 814.e+006 - S: 0 - Xy: 485.e+6 - m: 3 - A: 3.5534648443719767e10 #DOI 10.1016/j.proeng.2013.12.061 eqn 5 - unit_cost: 0.9 - - name: cast_iron - description: Cast iron for hub and nacelle components - source: TODO - orth: 0 - rho: 7200 - alpha: 0.0 - E: 118.e+009 - nu: 0.3 - G: 47.6e+009 - GIc: 0 #Place holder, currently not used - GIIc: 0 #Place holder, currently not used - alp0: 0 #Place holder, currently not used - Xt: 310.e+006 - Xc: 310.e+006 - S: 0 - Xy: 265.e+6 - m: 3 - unit_cost: 0.5 - - name: glass_uni - description: Vectorply E-LT-5500, Epikote MGS RIMR 135/Epicure MGS RIMH 1366 epoxy - source: MSU composites database 3D property tests, Engineering Mechanics of Composite Materials, Daniel, I & Ishai, O., 1994, pg. 34 - orth: 1 - rho: 1940.0 - E: [4.46E10, 1.7E10, 1.67E10] - G: [3.27E9, 3.48E9, 3.5E9] - nu: [0.262, 0.35, 0.264] - Xt: [6.092E8, 3.81E7, 1.529E7] - Xc: [4.7471E8, 1.1264E8, 1.1322E8] - S: [1.891E7, 1.724E7, 1.316E7] - m: 10 - GIc: 303 - GIIc: 3446 - alp0: 53 - fvf: 0.57 - fwf: 0.7450682696347697 - ply_t: 0.005 - unit_cost: 1.87 - waste: 0.05 - fiber_density: 2535.5 - area_density_dry: 7.227162215457267 - component_id: 5 - - name: CarbonUD - E: [114500000000.0, 8390000000.0, 8390000000.0] - G: [5990000000.0, 5990000000.0, 5990000000.0] - rho: 1220.0 - orth: 1 - nu: [0.27, 0.27, 0.27] - Xt: [1546.e6, 0.0, 0.0] - Xc: [1047.e6, 0.0, 0.0] - S: [0.0, 0.0, 0.0] - m: 16.1 - GIc: 0.0 - GIIc: 0.0 - alp0: 0.0 - fvf: 0.1076923076923077 #0.5384615384615385 - fwf: 0.15889029003783103 #0.6461538461538463 - ply_t: 0.005158730158730159 #0.0010317460317460314 - unit_cost: 30. - waste: 0.05 - fiber_density: 1800. - area_density_dry: 1.000 - component_id: 4 - - name: glass_biax - description: Vectorply E-LT-5500, Epikote MGS RIMR 135/Epicure MGS RIMH 1366 epoxy - source: MSU composites database 3D property tests, Engineering Mechanics of Composite Materials, Daniel, I & Ishai, O., 1994, pg. 34 - orth: 1 - rho: 1940.0 - E: [1.11E10, 1.11E10, 1.67E10] - G: [1.353E10, 3.49E9, 3.49E9] - nu: [0.5, 0.0, 0.066] - Xt: [4.29E7, 4.26E7, 1.53E7] - Xc: [7.07E7, 7.07E7, 1.132E8] - S: [1.034E8, 1.72E7, 1.32E7] - m: 10 - GIc: 303 - GIIc: 3446 - alp0: 53 - fvf: 0.57 - fwf: 0.7450682696347697 - ply_t: 0.001 - waste: 0.15 - unit_cost: 3.00 - fiber_density: 2535.5 - area_density_dry: 1.4454324430914534 - component_id: 3 - roll_mass: 181.4368 - - name: glass_triax - description: Vectorply E-LT-5500, Epikote MGS RIMR 135/Epicure MGS RIMH 1366 epoxy - source: MSU composites database 3D property tests, Engineering Mechanics of Composite Materials, Daniel, I & Ishai, O., 1994, pg. 34 - orth: 1.0 - rho: 1940.0 - E: [2.87E10, 1.66E10, 1.67E10] - G: [8.4E9, 3.49E9, 3.49E9] - nu: [0.5, 0.0, 0.17] - Xt: [3.96E8, 7.64E7, 1.53E7] - Xc: [4.489E8, 1.747E8, 1.132E8] - S: [1.034E8, 1.72E7, 1.32E7] - m: 10 - GIc: 303 - GIIc: 3446 - alp0: 53 - fvf: 0.57 - fwf: 0.7450682696347697 - ply_t: 0.001 - unit_cost: 2.86 - waste: 0.15 - fiber_density: 2535.5 - area_density_dry: 1.4454324430914534 - component_id: 2 - roll_mass: 181.4368 - - name: medium_density_foam - description: Airex C70.130 PVC Foam, source 'https://www.3accorematerials.com/uploads/documents/TDS-AIREX-C70-E_1106.pdf' - orth: 0.0 - rho: 130.0 - E: 1.292E8 - G: 4.8946969696969695E7 - nu: 0.32 - Xt: 2083000.0 - Xc: 1563000.0 - S: 1250000.0 - GIc: 303 - GIIc: 3446 - alp0: 53 - component_id: 1 - waste: 0.2 - unit_cost: 13 - - name: resin - description: epoxy - E: 1.e+6 - nu: 0.3 #Place holder, currently not used - G: 312500.0 #Place holder, currently not used - GIc: 0 #Place holder, currently not used - GIIc: 0 #Place holder, currently not used - alp0: 0 #Place holder, currently not used - Xt: 0 #Place holder, currently not used - Xc: 0 #Place holder, currently not used - S: 0 #Place holder, currently not used - rho: 1150. - alpha: 0.0 - orth: 0 - unit_cost: 3.63 - - name: slurry - description: fixed/permanent ballast - E: 1.0 # compliant - nu: 0.3 #Place holder, currently not used - G: 1.0 # compliant - Xt: 0 #Place holder, currently not used - Xc: 0 #Place holder, currently not used - S: 0 #Place holder, currently not used - rho: 5000. - alpha: 0.0 - unit_cost: 1.0 - orth: 0.0 - -control: - supervisory: - Vin: 3.0 - Vout: 25.0 - maxTS: 95. - pitch: - PC_zeta: 1.0 # Pitch controller desired damping ratio [-] - PC_omega: 0.2 # Pitch controller desired natural frequency [rad/s] - ps_percent: 0.8 # Percent peak shaving [%, <= 1 ], {default = 80%} - max_pitch: 1.57 # Maximum pitch angle [rad], {default = 90 degrees} - max_pitch_rate: 0.03490658503988659 # 2 deg/s - min_pitch: 0. # Minimum pitch angle [rad], {default = 0 degrees} - torque: - control_type: tsr_tracking - tsr: 9.0 - VS_zeta: 1.0 # Torque controller desired damping ratio [-] - VS_omega: 0.2 # Torque controller desired natural frequency [rad/s] - max_torque_rate: 1500000. - VS_minspd: 0.5235987755982988 # Minimum rotor speed [rad/s], {default = 0 rad/s} - VS_maxspd: 0.7916813487046278 # Minimum rotor speed [rad/s], {default = 0 rad/s} - setpoint_smooth: - ss_vsgain: 1 # Torque controller setpoint smoother gain bias percentage [%, <= 1 ], {default = 100%} - ss_pcgain: .001 # Pitch controller setpoint smoother gain bias percentage [%, <= 1 ], {default = 0.1%} - shutdown: - limit_type: gen_speed - limit_value: 2.0 - -environment: - air_density: 1.225 - air_dyn_viscosity: 1.81e-5 - weib_shape_parameter: 2. - air_speed_sound: 340. - shear_exp: 0.12 - water_density: 1025.0 - water_dyn_viscosity: 1.3351e-3 - soil_shear_modulus: 140e6 - soil_poisson: 0.4 - water_depth: 200.0 - significant_wave_height: 4.52 - significant_wave_period: 9.45 - -bos: - plant_turbine_spacing: 7 - plant_row_spacing: 7 - commissioning_pct: 0.01 - decommissioning_pct: 0.15 - distance_to_substation: 1.0 - distance_to_interconnection: 8.5 - interconnect_voltage: 130. - distance_to_site: 115. - distance_to_landfall: 50. - port_cost_per_month: 2e6 - site_auction_price: 100e6 - site_assessment_plan_cost: 1e6 - site_assessment_cost: 25e6 - construction_operations_plan_cost: 2.5e6 - boem_review_cost: 0.0 - design_install_plan_cost: 2.5e6 - -costs: - wake_loss_factor: 0.15 - fixed_charge_rate: 0.056 - bos_per_kW: 4053. - opex_per_kW: 137. - turbine_number: 40. - labor_rate: 58.8 - painting_rate: 30.0 - blade_mass_cost_coeff: 14.6 - hub_mass_cost_coeff: 3.9 - pitch_system_mass_cost_coeff: 22.1 - spinner_mass_cost_coeff: 11.1 - lss_mass_cost_coeff: 11.9 - bearing_mass_cost_coeff: 4.5 - gearbox_mass_cost_coeff: 12.9 - hss_mass_cost_coeff: 6.8 - generator_mass_cost_coeff: 12.4 - bedplate_mass_cost_coeff: 2.9 - yaw_mass_cost_coeff: 8.3 - converter_mass_cost_coeff: 18.8 - transformer_mass_cost_coeff: 18.8 - hvac_mass_cost_coeff: 124.0 - cover_mass_cost_coeff: 5.7 - elec_connec_machine_rating_cost_coeff: 41.85 - platforms_mass_cost_coeff: 17.1 - tower_mass_cost_coeff: 2.9 - controls_machine_rating_cost_coeff: 21.15 - crane_cost: 12e3 diff --git a/examples/13_DTQP/analysis_options_ol2cl.yaml b/examples/13_DTQP/analysis_options_ol2cl.yaml index 7fd626d6f..716c858f3 100644 --- a/examples/13_DTQP/analysis_options_ol2cl.yaml +++ b/examples/13_DTQP/analysis_options_ol2cl.yaml @@ -1,5 +1,5 @@ general: - folder_output: /Users/dzalkind/Tools/WEIS-4/outputs/13_DTQP_1 + folder_output: outputs/13_DTQP_OL2CL fname_output: iea15mw design_variables: diff --git a/examples/13_DTQP/gen_oloc.py b/examples/13_DTQP/gen_oloc.py index e087f90f8..7cf3560a6 100644 --- a/examples/13_DTQP/gen_oloc.py +++ b/examples/13_DTQP/gen_oloc.py @@ -14,12 +14,13 @@ import numpy as np -weis_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.realpath(__file__)))) +this_dir = os.path.dirname(os.path.realpath(__file__)) +weis_dir = os.path.dirname(os.path.dirname(this_dir)) if __name__ == '__main__': # read WEIS options: - mydir = os.path.dirname(os.path.realpath(__file__)) # get path to this file + mydir = this_dir # get path to this file fname_modeling_options = mydir + os.sep + "modeling_options.yaml" modeling_options = sch.load_modeling_yaml(fname_modeling_options) @@ -53,7 +54,7 @@ # generate wind files FAST_namingOut = 'oloc' - wind_directory = '/Users/dzalkind/Tools/WEIS-1/examples/13_DTQP/outputs/oloc/wind' + wind_directory = os.path.join(this_dir,'oloc/wind') if not os.path.exists(wind_directory): os.makedirs(wind_directory) rotorD = wt_init['assembly']['rotor_diameter'] diff --git a/examples/13_DTQP/modeling_options.yaml b/examples/13_DTQP/modeling_options.yaml index 1580240a3..08f52fc52 100644 --- a/examples/13_DTQP/modeling_options.yaml +++ b/examples/13_DTQP/modeling_options.yaml @@ -1,8 +1,6 @@ General: verbosity: False # When set to True, the code prints to screen many infos openfast_configuration: - OF_run_fst: lin - OF_run_dir: /Users/dzalkind/Tools/WEIS-4/outputs/13_DTQP_3 use_exe: True WISDEM: diff --git a/examples/13_DTQP/modeling_options_ol2cl.yaml b/examples/13_DTQP/modeling_options_ol2cl.yaml index fb13c0c1a..d37815a15 100644 --- a/examples/13_DTQP/modeling_options_ol2cl.yaml +++ b/examples/13_DTQP/modeling_options_ol2cl.yaml @@ -125,7 +125,7 @@ Level2: OL2CL: flag: True - trajectory_dir: /Users/dzalkind/Tools/WEIS-4/outputs/13_DTQP_1 + trajectory_dir: outputs/13_DTQP_1 # These should match modeling_options.yaml used to generate DTQP results DLC_driver: From 263a0481148f55dc80b7635df5ab6ccf61b7a912 Mon Sep 17 00:00:00 2001 From: dzalkind Date: Fri, 12 Jan 2024 14:56:13 -0700 Subject: [PATCH 048/114] Fix path in example 03 --- examples/03_NREL5MW_OC3_spar/weis_driver.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/03_NREL5MW_OC3_spar/weis_driver.py b/examples/03_NREL5MW_OC3_spar/weis_driver.py index 6cad3feb4..7222421c0 100644 --- a/examples/03_NREL5MW_OC3_spar/weis_driver.py +++ b/examples/03_NREL5MW_OC3_spar/weis_driver.py @@ -5,7 +5,7 @@ ## File management run_dir = os.path.dirname( os.path.realpath(__file__) ) -wisdem_examples = os.path.join(os.path.dirname( os.path.dirname( os.path.dirname( os.path.realpath(__file__) ) ) ), "WISDEM", "examples", "09_floating") +wisdem_examples = os.path.join(os.path.dirname( os.path.dirname( os.path.dirname( os.path.realpath(__file__) ) ) ), "WISDEM", "examples") fname_wt_input = os.path.join(wisdem_examples, "09_floating", "nrel5mw-spar_oc3.yaml") fname_modeling_options = run_dir + os.sep + 'modeling_options.yaml' fname_analysis_options = run_dir + os.sep + 'analysis_options_noopt.yaml' From 74cb443935d2eba98c64cdaaefdbf077417b0320 Mon Sep 17 00:00:00 2001 From: dzalkind Date: Fri, 12 Jan 2024 16:18:21 -0700 Subject: [PATCH 049/114] Ensure that ballast rho is distributed with l_fill --- weis/frequency/raft_wrapper.py | 19 ++++++++++++++++--- 1 file changed, 16 insertions(+), 3 deletions(-) diff --git a/weis/frequency/raft_wrapper.py b/weis/frequency/raft_wrapper.py index 190b4b59b..597f3dfbe 100644 --- a/weis/frequency/raft_wrapper.py +++ b/weis/frequency/raft_wrapper.py @@ -262,12 +262,25 @@ def compute(self, inputs, outputs, discrete_inputs, discrete_outputs): # add ballast from last section to next remaining_fill = 0 for i_sec, l in enumerate(l_fill): + + # if there's left over fill, use previous density + # will use lower ballast if the chamber is too small + # hopefully solver will bias platform design away from this case eventually + if remaining_fill: + rho_fill[i_sec] = rho_fill[i_sec-1] + + # length of current section sec_length = s_grid[i_sec+1] - s_grid[i_sec] - l += remaining_fill # add fill from previous section - if l > sec_length: + + if l and remaining_fill: + print('WEIS Warning: there is left over ballast from a previous section and a new ballast being added. Platform masses may be inconsistent between RAFT and FloatingSE') + + # add fill from previous section + l += remaining_fill + if l > sec_length: # only fill up to section length l_fill[i_sec] = sec_length remaining_fill = l - sec_length - else: + else: # fill with remaining l_fill[i_sec] = l remaining_fill = 0 From 39d37dcf625861a2dd16d3a2f4df724f1dfe8e2d Mon Sep 17 00:00:00 2001 From: dzalkind Date: Fri, 12 Jan 2024 16:57:11 -0700 Subject: [PATCH 050/114] Make BEM dir relative to input files, like most other things --- weis/glue_code/gc_LoadInputs.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/weis/glue_code/gc_LoadInputs.py b/weis/glue_code/gc_LoadInputs.py index acb7e70ac..013d4c2f9 100644 --- a/weis/glue_code/gc_LoadInputs.py +++ b/weis/glue_code/gc_LoadInputs.py @@ -28,8 +28,11 @@ def __init__(self, fname_input_wt, fname_input_modeling, fname_input_analysis): def set_weis_data(self): + # Directory of modeling option input, if we want to use it for relative paths + mod_opt_dir = os.path.split(self.modeling_options['fname_input_modeling'])[0] + # BEM dir, all levels - base_run_dir = self.modeling_options['General']['openfast_configuration']['OF_run_dir'] + base_run_dir = os.path.join(mod_opt_dir,self.modeling_options['General']['openfast_configuration']['OF_run_dir']) if MPI: rank = MPI.COMM_WORLD.Get_rank() bemDir = os.path.join(base_run_dir,'rank_%000d'%int(rank),'BEM') @@ -41,8 +44,7 @@ def set_weis_data(self): # If running MPI, RAFT won't be able to save designs in parallel self.modeling_options["Level1"]['save_designs'] = False - # Directory of modeling option input, if we want to use it for relative paths - mod_opt_dir = os.path.split(self.modeling_options['fname_input_modeling'])[0] + # Openfast if self.modeling_options['Level2']['flag'] or self.modeling_options['Level3']['flag']: @@ -103,7 +105,7 @@ def set_weis_data(self): if ( (len(potpath) == 0) or (potpath.lower() in ['unused','default','none']) ): self.modeling_options['Level1']['flag'] = True - self.modeling_options["Level3"]["HydroDyn"]["PotFile"] = osp.join(cwd, bemDir,'Output','Wamit_format','Buoy') + self.modeling_options["Level3"]["HydroDyn"]["PotFile"] = osp.join(bemDir,'Output','Wamit_format','Buoy') else: From 77b6ad70a78d8a5a9a3e58bd6d6c4e086750f0db Mon Sep 17 00:00:00 2001 From: dzalkind Date: Fri, 12 Jan 2024 17:10:06 -0700 Subject: [PATCH 051/114] Make analysis options output relative to input, if it's a relative path --- weis/glue_code/gc_LoadInputs.py | 1 + weis/glue_code/runWEIS.py | 4 ++++ 2 files changed, 5 insertions(+) diff --git a/weis/glue_code/gc_LoadInputs.py b/weis/glue_code/gc_LoadInputs.py index 013d4c2f9..b07d27582 100644 --- a/weis/glue_code/gc_LoadInputs.py +++ b/weis/glue_code/gc_LoadInputs.py @@ -19,6 +19,7 @@ def __init__(self, fname_input_wt, fname_input_modeling, fname_input_analysis): self.modeling_options['fname_input_modeling'] = fname_input_modeling self.wt_init = sch.load_geometry_yaml(fname_input_wt) self.analysis_options = sch.load_analysis_yaml(fname_input_analysis) + self.analysis_options['fname_input_analysis'] = fname_input_analysis self.set_run_flags() self.set_openmdao_vectors() diff --git a/weis/glue_code/runWEIS.py b/weis/glue_code/runWEIS.py index 68aee0d67..f7488e658 100644 --- a/weis/glue_code/runWEIS.py +++ b/weis/glue_code/runWEIS.py @@ -100,6 +100,10 @@ def run_weis(fname_wt_input, fname_modeling_options, fname_opt_options, overridd color_i = 0 rank = 0 + # make the folder_output relative to the input, if it's a relative path + analysis_input_dir = os.path.dirname(opt_options['fname_input_analysis']) + opt_options['general']['folder_output'] = os.path.join(analysis_input_dir,opt_options['general']['folder_output']) + folder_output = opt_options['general']['folder_output'] if rank == 0 and not os.path.isdir(folder_output): os.makedirs(folder_output) From 64dca16aa0f2825da697e8c0399eb1d142a72593 Mon Sep 17 00:00:00 2001 From: dzalkind Date: Sat, 13 Jan 2024 13:20:51 -0700 Subject: [PATCH 052/114] Add rank_and_file for example 15 --- examples/15_RAFT_Studies/modeling_options_level3.yaml | 1 + 1 file changed, 1 insertion(+) diff --git a/examples/15_RAFT_Studies/modeling_options_level3.yaml b/examples/15_RAFT_Studies/modeling_options_level3.yaml index 0b2cc66f5..fcbacb253 100644 --- a/examples/15_RAFT_Studies/modeling_options_level3.yaml +++ b/examples/15_RAFT_Studies/modeling_options_level3.yaml @@ -18,6 +18,7 @@ WISDEM: flag: True FloatingSE: flag: True + rank_and_file: True # BOS: # flag: True From 11bea71c55c893a06d84383ba89c4dd6384e8f11 Mon Sep 17 00:00:00 2001 From: dzalkind Date: Sat, 13 Jan 2024 13:22:41 -0700 Subject: [PATCH 053/114] Add simple hst file reader --- pyHAMS/pyhams/pyhams.py | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) diff --git a/pyHAMS/pyhams/pyhams.py b/pyHAMS/pyhams/pyhams.py index c9d369f3a..2c26e9a98 100644 --- a/pyHAMS/pyhams/pyhams.py +++ b/pyHAMS/pyhams/pyhams.py @@ -395,6 +395,19 @@ def read_wamit3(pathWamit3, TFlag=0): return mod, phase, real, imag, w, headings +def read_hst(path_hst): + wamit_hst = np.loadtxt(path_hst) + + hst = np.zeros((6,6)) + for entry in wamit_hst: + ind1 = int(entry[0]-1) + ind2 = int(entry[1]-1) + st = entry[2] + hst[ind1,ind2] = st + + return hst + + def run_hams(projectDir): # change directory to where the HAMS input files are workingDir = os.getcwd() @@ -403,3 +416,7 @@ def run_hams(projectDir): hams.hams_full.exec() # change back to working directory os.chdir(workingDir) + +if __name__=="__main__": + hst = read_hst('/Users/dzalkind/Projects/IEA-22MW/IEA-22-280-RWT/OpenFAST/IEA-22-280-RWT-Semi/HydroData/semi.hst') + print('here') From 590f85d31e2fd350a36055af953bc4185582b946 Mon Sep 17 00:00:00 2001 From: Garrett Barter Date: Tue, 6 Feb 2024 13:46:00 -0700 Subject: [PATCH 054/114] Revert "WEIS now works on Windows" --- .github/workflows/CI_WEIS.yml | 25 +++++++++++++++++++++---- README.md | 8 ++++---- environment.yml | 1 + weis/dtqpy/solver/DTQPy_SOLVER.py | 10 +++------- weis/dtqpy/solver/DTQPy_SOLVER_ipopt.py | 12 +++++------- weis/dtqpy/solver/DTQPy_SOLVER_slsqp.py | 14 ++++++-------- 6 files changed, 40 insertions(+), 30 deletions(-) diff --git a/.github/workflows/CI_WEIS.yml b/.github/workflows/CI_WEIS.yml index e4119ddd0..3945cc92b 100644 --- a/.github/workflows/CI_WEIS.yml +++ b/.github/workflows/CI_WEIS.yml @@ -16,7 +16,7 @@ jobs: strategy: fail-fast: false matrix: - os: ["ubuntu-latest", "macOS-latest", "windows-latest"] + os: ["ubuntu-latest", "macOS-latest"] #, "windows-latest"] python-version: ["3.9","3.10","3.11"] steps: @@ -38,11 +38,16 @@ jobs: environment-file: environment.yml activate-environment: test auto-activate-base: false + + - name: Add dependencies windows specific + if: contains( matrix.os, 'windows') + run: | + conda install -y -c intel intel-openmp mkl - name: Add dependencies ubuntu specific if: false == contains( matrix.os, 'windows') run: | - conda install -y petsc4py mpi4py openmpi pyoptsparse + conda install -y petsc4py mpi4py openmpi - name: Add test packages run: | @@ -56,12 +61,24 @@ jobs: # Install WEIS - name: Install WEIS run: | - pip install -e . -v + pip install -e . # Debugging session #- name: Setup tmate session # if: contains( matrix.os, 'windows') # uses: mxschmitt/action-tmate@v3 + + #- name: Show openfast environment + # run: | + # which -a openfast + # cd /c/Miniconda3/envs/test/Library/bin/ + # ls *fast*.exe + # ls *fast*.dll + # openfast -h + # python -c "import ctypes; ctypes.cdll.LoadLibrary('openfastlib.dll')" + # ls *fast*.dll + # cd examples/03_NREL5MW_OC3_spar + # python weis_driver.py # List the collected tests for debugging purposes - name: List tests @@ -75,7 +92,7 @@ jobs: # Run coveralls - name: Run coveralls - if: contains( matrix.os, 'ubuntu') && contains( matrix.python-version, '3.11') + if: contains( matrix.os, 'ubuntu') && contains( matrix.python-version, '3.10') uses: coverallsapp/github-action@v2 # This also works, https://github.com/AndreMiras/coveralls-python-action #uses: AndreMiras/coveralls-python-action@develop diff --git a/README.md b/README.md index 009bb2322..a1edfaa1d 100644 --- a/README.md +++ b/README.md @@ -35,7 +35,7 @@ The core WEIS modules are: ## Installation -On laptop and personal computers, installation with [Anaconda](https://www.anaconda.com) is the recommended approach because of the ability to create self-contained environments suitable for testing and analysis. WEIS requires [Anaconda 64-bit](https://www.anaconda.com/distribution/). However, the `conda` command has begun to show its age and we now recommend the one-for-one replacement with the [Miniforge3 distribution](https://github.com/conda-forge/miniforge?tab=readme-ov-file#miniforge3), which is much more lightweight and more easily solves for the package dependencies. WEIS is currently supported on Linux, Mac, and Windows (natively or with the Linux sub-system (WSL)). +On laptop and personal computers, installation with [Anaconda](https://www.anaconda.com) is the recommended approach because of the ability to create self-contained environments suitable for testing and analysis. WEIS requires [Anaconda 64-bit](https://www.anaconda.com/distribution/). However, the `conda` command has begun to show its age and we now recommend the one-for-one replacement with the [Miniforge3 distribution](https://github.com/conda-forge/miniforge?tab=readme-ov-file#miniforge3), which is much more lightweight and more easily solves for the package dependencies. Sometimes, using `mamba` in place of `conda` with this distribution speeds up the installation process. WEIS is currently supported on Linux, MAC and Windows Sub-system for Linux (WSL). Installing WEIS on native Windows is not yet supported, but planned in 2024. The installation instructions below use the environment name, "weis-env," but any name is acceptable. For those working behind company firewalls, you may have to change the conda authentication with `conda config --set ssl_verify no`. Proxy servers can also be set with `conda config --set proxy_servers.http http://id:pw@address:port` and `conda config --set proxy_servers.https https://id:pw@address:port`. @@ -44,12 +44,12 @@ The installation instructions below use the environment name, "weis-env," but an module purge module load conda -1. Setup and activate the Anaconda environment from a command prompt: +1. Setup and activate the Anaconda environment from a prompt (WSL terminal on Windows or Terminal.app on Mac) conda config --add channels conda-forge conda env create --name weis-env -f https://raw.githubusercontent.com/WISDEM/WEIS/develop/environment.yml conda activate weis-env # (if this does not work, try source activate weis-env) - conda install -y petsc4py mpi4py pyoptsparse # (Mac / Linux only) + conda install -y petsc4py mpi4py # (Mac / Linux only) 2. Clone the repository and install the software @@ -69,7 +69,7 @@ The installation instructions below use the environment name, "weis-env," but an ## Developer guide -If you plan to contribute code to WEIS, please first consult the [developer guide](https://weis.readthedocs.io/en/latest/how_to_contribute_code.html). You are also likely to want to install some of the compiled WEIS dependencies (OpenFAST or WISDEM or pyHAMS or ROSCO) from a local copy instead of relying on the conda packages. +If you plan to contribute code to WEIS, please first consult the [developer guide](https://weis.readthedocs.io/en/latest/how_to_contribute_code.html). ## Feedback diff --git a/environment.yml b/environment.yml index 83078ba9c..c79e79d11 100644 --- a/environment.yml +++ b/environment.yml @@ -54,6 +54,7 @@ dependencies: - pyDOE3 - pygments - pyhams + - pyoptsparse - python-json-logger - pyzmq - qdldl-python diff --git a/weis/dtqpy/solver/DTQPy_SOLVER.py b/weis/dtqpy/solver/DTQPy_SOLVER.py index a9114cd87..db37df4de 100644 --- a/weis/dtqpy/solver/DTQPy_SOLVER.py +++ b/weis/dtqpy/solver/DTQPy_SOLVER.py @@ -7,12 +7,8 @@ Primary Contributor: Daniel R. Herber (danielrherber on Github) """ from weis.dtqpy.solver.DTQPy_SOLVER_osqp import DTQPy_SOLVER_osqp -PYOPT = True -try: - from weis.dtqpy.solver.DTQPy_SOLVER_ipopt import DTQPy_SOLVER_ipopt - from weis.dtqpy.solver.DTQPy_SOLVER_slsqp import DTQPy_SOLVER_slsqp -except Exception: - PYOPT = False +from weis.dtqpy.solver.DTQPy_SOLVER_ipopt import DTQPy_SOLVER_ipopt +from weis.dtqpy.solver.DTQPy_SOLVER_slsqp import DTQPy_SOLVER_slsqp def DTQPy_SOLVER(H,f,A,b,Aeq,beq,lb,ub,internal,opts): @@ -20,7 +16,7 @@ def DTQPy_SOLVER(H,f,A,b,Aeq,beq,lb,ub,internal,opts): displevel = opts.general.displevel # osqp - if opts.solver.function == 'osqp' or not PYOPT: + if opts.solver.function == 'osqp': X,F,intrnal,opts = DTQPy_SOLVER_osqp(H,f,A,b,Aeq,beq,lb,ub,internal,opts) elif opts.solver.function == 'ipopt': diff --git a/weis/dtqpy/solver/DTQPy_SOLVER_ipopt.py b/weis/dtqpy/solver/DTQPy_SOLVER_ipopt.py index 0ee1e8fc6..b356bee20 100644 --- a/weis/dtqpy/solver/DTQPy_SOLVER_ipopt.py +++ b/weis/dtqpy/solver/DTQPy_SOLVER_ipopt.py @@ -7,16 +7,14 @@ Contributor: Athul Krishna Sundarrajan (AthulKrishnaSundarrajan on Github) Primary Contributor: Daniel R. Herber (danielrherber on Github) """ - +# import pyoptsparse +from pyoptsparse import IPOPT, Optimization from scipy import sparse + + import argparse -import numpy as np -PYOPT = True -try: - from pyoptsparse import IPOPT, Optimization -except Exception: - PYOPT = False +import numpy as np def DTQPy_SOLVER_ipopt(H,f,A,b,Aeq,beq,lb,ub,internal,opts): diff --git a/weis/dtqpy/solver/DTQPy_SOLVER_slsqp.py b/weis/dtqpy/solver/DTQPy_SOLVER_slsqp.py index 1960285b2..ec0902c3e 100644 --- a/weis/dtqpy/solver/DTQPy_SOLVER_slsqp.py +++ b/weis/dtqpy/solver/DTQPy_SOLVER_slsqp.py @@ -7,16 +7,14 @@ Contributor: Athul Krishna Sundarrajan (AthulKrishnaSundarrajan on Github) Primary Contributor: Daniel R. Herber (danielrherber on Github) """ - +# import pyoptsparse +from pyoptsparse import OPT, Optimization from scipy import sparse + + import argparse + import numpy as np -# import pyoptsparse -PYOPT = True -try: - from pyoptsparse import OPT, Optimization -except Exception: - PYOPT = False def DTQPy_SOLVER_slsqp(H,f,A,b,Aeq,beq,lb,ub,internal,opts): @@ -142,4 +140,4 @@ def Sens(self,xdict,funcs): F = sol.fStar # return - return X,F,internal,opts + return X,F,internal,opts \ No newline at end of file From 01011586a9e941357c21476076b483cd9bd9ce13 Mon Sep 17 00:00:00 2001 From: dzalkind <65573423+dzalkind@users.noreply.github.com> Date: Tue, 27 Feb 2024 16:53:37 -0700 Subject: [PATCH 055/114] Update installation instructions with --no-deps --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index a1edfaa1d..1fc3f3d46 100644 --- a/README.md +++ b/README.md @@ -57,13 +57,13 @@ The installation instructions below use the environment name, "weis-env," but an git clone https://github.com/WISDEM/WEIS.git cd WEIS git checkout branch_name # (Only if you want to switch branches, say "develop") - pip install -e . + pip install -e --no-deps . -v 3. Instructions specific for DOE HPC system Eagle. Before executing the setup script, do: module load comp-intel intel-mpi mkl module unload gcc - pip install -e . + pip install -e --no-deps . -v **NOTE:** To use WEIS again after installation is complete, you will always need to activate the conda environment first with `conda activate weis-env` (or `source activate weis-env`). On Eagle, make sure to reload the necessary modules From 9ec2751c5ad23941512400c62536b997c8647a80 Mon Sep 17 00:00:00 2001 From: dzalkind <65573423+dzalkind@users.noreply.github.com> Date: Thu, 7 Mar 2024 14:44:40 -0700 Subject: [PATCH 056/114] Update README.md installation instructions --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 1fc3f3d46..1157ffda7 100644 --- a/README.md +++ b/README.md @@ -57,13 +57,13 @@ The installation instructions below use the environment name, "weis-env," but an git clone https://github.com/WISDEM/WEIS.git cd WEIS git checkout branch_name # (Only if you want to switch branches, say "develop") - pip install -e --no-deps . -v + pip install --no-deps -e . -v 3. Instructions specific for DOE HPC system Eagle. Before executing the setup script, do: module load comp-intel intel-mpi mkl module unload gcc - pip install -e --no-deps . -v + pip install --no-deps -e . -v **NOTE:** To use WEIS again after installation is complete, you will always need to activate the conda environment first with `conda activate weis-env` (or `source activate weis-env`). On Eagle, make sure to reload the necessary modules From ffdb1ef22033b02075e36bfb4f6083105cc10cca Mon Sep 17 00:00:00 2001 From: dzalkind Date: Mon, 11 Mar 2024 16:25:03 -0600 Subject: [PATCH 057/114] Remove discrete inputs to RAFT --- weis/frequency/raft_wrapper.py | 43 +++++++++++++++++----------------- 1 file changed, 21 insertions(+), 22 deletions(-) diff --git a/weis/frequency/raft_wrapper.py b/weis/frequency/raft_wrapper.py index 597f3dfbe..64a6256d1 100644 --- a/weis/frequency/raft_wrapper.py +++ b/weis/frequency/raft_wrapper.py @@ -53,14 +53,30 @@ def setup(self): members_opt['scalar_coefficients'] = [False]*members_opt['nmembers'] members_opt['n_ballast_type'] = len(weis_opt["floating"]["members"]["ballast_types"]) + for k in range(members_opt['nmembers']): + members_opt[f"platform_member{k+1}_potMod"] = weis_opt["Level1"]["model_potential"][k] + mooring_opt = {} mooring_opt['nlines'] = weis_opt['mooring']['n_lines'] mooring_opt['nline_types'] = weis_opt['mooring']['n_line_types'] mooring_opt['nconnections'] = weis_opt['mooring']['n_nodes'] + # Mooring + for k in range(weis_opt['mooring']['n_nodes']): + mooring_opt[f'mooring_point{k+1}_name'] = weis_opt['mooring']['node_names'][k] + mooring_opt[f'mooring_point{k+1}_type'] = weis_opt['mooring']['node_type'][k] + + for k in range(weis_opt['mooring']['n_lines']): + mooring_opt[f'mooring_line{k+1}_endA'] = weis_opt['mooring']["node1"][k] + mooring_opt[f'mooring_line{k+1}_endB'] = weis_opt['mooring']["node2"][k] + mooring_opt[f'mooring_line{k+1}_type'] = weis_opt['mooring']["line_type"][k] + + for k in range(weis_opt['mooring']['n_line_types']): + mooring_opt[f'mooring_line_type{k+1}_name'] = weis_opt['mooring']["line_type_name"][k] self.add_subsystem('wind', PowerWind(nPoints = turbine_opt['npts']), promotes=['Uref','zref']) - self.add_subsystem('pre', RAFT_WEIS_Prep(modeling_options=weis_opt), promotes=['*']) + self.add_subsystem('pre', RAFT_WEIS_Prep(modeling_options=weis_opt, + raft_options = raft_opt), promotes=['*']) self.add_subsystem('raft', RAFT_OMDAO(modeling_options=raft_opt, turbine_options=turbine_opt, mooring_options=mooring_opt, @@ -72,6 +88,7 @@ class RAFT_WEIS_Prep(om.ExplicitComponent): def initialize(self): self.options.declare('modeling_options') + self.options.declare('raft_options') def setup(self): opt = self.options['modeling_options'] @@ -146,19 +163,13 @@ def setup(self): self.add_output(f"platform_member{k+1}_Ca", val=np.ones(n_height)) self.add_output(f"platform_member{k+1}_CdEnd", val=0.6*np.ones(n_height)) self.add_output(f"platform_member{k+1}_CaEnd", val=0.6*np.ones(n_height)) - self.add_discrete_output(f"platform_member{k+1}_potMod", val=False) # Mooring inputs self.add_input('mooring_nodes', val=np.zeros((n_nodes, 3)), units='m', desc='Mooring node locations in global xyz') for k in range(n_nodes): - self.add_discrete_output(f'mooring_point{k+1}_name', val=f'line{k+1}', desc='Mooring point identifier') - self.add_discrete_output(f'mooring_point{k+1}_type', val='fixed', desc='Mooring connection type') self.add_output(f'mooring_point{k+1}_location', val=np.zeros(3), units='m', desc='Mooring node location') for k in range(n_lines): - self.add_discrete_output(f'mooring_line{k+1}_endA', val='default', desc='End A coordinates') - self.add_discrete_output(f'mooring_line{k+1}_endB', val='default', desc='End B coordinates') - self.add_discrete_output(f'mooring_line{k+1}_type', val='mooring_line_type1', desc='Mooring line type') self.add_output(f'mooring_line{k+1}_length', val=0.0, units='m', desc='Length of line') self.add_input("unstretched_length", val=np.zeros(n_lines), units="m") @@ -173,7 +184,6 @@ def setup(self): self.add_input("line_tangential_drag", val=np.zeros(n_lines)) for k in range(n_line_types): - self.add_discrete_output(f'mooring_line_type{k+1}_name', val='default', desc='Name of line type') self.add_output(f'mooring_line_type{k+1}_diameter', val=0.0, units='m', desc='Diameter of mooring line type') self.add_output(f'mooring_line_type{k+1}_mass_density', val=0.0, units='kg/m**3', desc='Mass density of line type') self.add_output(f'mooring_line_type{k+1}_stiffness', val=0.0, desc='Stiffness of line type') @@ -190,15 +200,11 @@ def setup(self): self.add_input('Vrated', val=0.0, units='m/s', desc='rated wind speed') self.add_discrete_input('turbulence_class', val='A', desc='IEC turbulence class') self.add_discrete_input('turbine_class', val='I', desc='IEC turbulence class') - self.add_discrete_output('raft_dlcs', val=[[]]*opt['DLC_driver']['n_cases'], desc='DLC case table for RAFT with each row a new case and headings described by the keys') - self.add_discrete_output('raft_dlcs_keys', val=['wind_speed', 'wind_heading', 'turbulence', - 'turbine_status', 'yaw_misalign', 'wave_spectrum', - 'wave_period', 'wave_height', 'wave_heading'], - desc='DLC case table column headings') def compute(self, inputs, outputs, discrete_inputs, discrete_outputs): opt = self.options['modeling_options'] + raft_opt = self.options['raft_options'] # Tower layer sections outputs['turbine_tower_t'] = inputs['tower_layer_thickness'].sum(axis=0) @@ -225,7 +231,6 @@ def compute(self, inputs, outputs, discrete_inputs, discrete_outputs): n_member = len(opt["floating"]["members"]["name"]) var_height = inputs['member_variable_height'] for k in range(n_member): - discrete_outputs[f"platform_member{k+1}_potMod"] = opt["Level1"]["model_potential"][k] # Member thickness outputs[f"platform_member{k+1}_t"] = inputs[f"member{k}:layer_thickness"].sum(axis=0) @@ -291,18 +296,12 @@ def compute(self, inputs, outputs, discrete_inputs, discrete_outputs): # Mooring for k in range(opt['mooring']['n_nodes']): - discrete_outputs[f'mooring_point{k+1}_name'] = opt['mooring']['node_names'][k] - discrete_outputs[f'mooring_point{k+1}_type'] = opt['mooring']['node_type'][k] outputs[f'mooring_point{k+1}_location'] = inputs['mooring_nodes'][k,:] for k in range(opt['mooring']['n_lines']): - discrete_outputs[f'mooring_line{k+1}_endA'] = opt['mooring']["node1"][k] - discrete_outputs[f'mooring_line{k+1}_endB'] = opt['mooring']["node2"][k] - discrete_outputs[f'mooring_line{k+1}_type'] = opt['mooring']["line_type"][k] outputs[f'mooring_line{k+1}_length'] = inputs['unstretched_length'][k] for k in range(opt['mooring']['n_line_types']): - discrete_outputs[f'mooring_line_type{k+1}_name'] = opt['mooring']["line_type_name"][k] outputs[f'mooring_line_type{k+1}_cost'] = inputs['line_cost_rate'][k] for var in ['diameter','mass_density','stiffness','breaking_load', @@ -345,7 +344,7 @@ def compute(self, inputs, outputs, discrete_inputs, discrete_outputs): float(max(1.0, icase.wave_period)), float(max(1.0, icase.wave_height)), float(icase.wave_heading)] - discrete_outputs['raft_dlcs'] = raft_cases - discrete_outputs['raft_dlcs_keys'] = ['wind_speed', 'wind_heading', 'turbulence', + raft_opt['raft_dlcs'] = raft_cases + raft_opt['raft_dlcs_keys'] = ['wind_speed', 'wind_heading', 'turbulence', 'turbine_status', 'yaw_misalign', 'wave_spectrum', 'wave_period', 'wave_height', 'wave_heading'] From 24a71d278d9d239f81b9581e9b1a81f62d18905e Mon Sep 17 00:00:00 2001 From: dzalkind Date: Mon, 11 Mar 2024 16:25:12 -0600 Subject: [PATCH 058/114] Remove lingering RAFT files --- RAFT/raft/omdao_raft.py | 0 RAFT/raft/raft_model.py | 0 2 files changed, 0 insertions(+), 0 deletions(-) delete mode 100644 RAFT/raft/omdao_raft.py delete mode 100644 RAFT/raft/raft_model.py diff --git a/RAFT/raft/omdao_raft.py b/RAFT/raft/omdao_raft.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/RAFT/raft/raft_model.py b/RAFT/raft/raft_model.py deleted file mode 100644 index e69de29bb..000000000 From 3840634c9cbf0df6af047733dbf8321d2aa9375d Mon Sep 17 00:00:00 2001 From: Daniel Zalkind Date: Wed, 13 Mar 2024 14:33:42 -0600 Subject: [PATCH 059/114] Fix wind file makedir --- weis/aeroelasticse/openmdao_openfast.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/weis/aeroelasticse/openmdao_openfast.py b/weis/aeroelasticse/openmdao_openfast.py index a14a19041..c2aae5626 100644 --- a/weis/aeroelasticse/openmdao_openfast.py +++ b/weis/aeroelasticse/openmdao_openfast.py @@ -384,7 +384,7 @@ def setup(self): if not os.path.exists(self.FAST_runDirectory): os.makedirs(self.FAST_runDirectory, exist_ok=True) if not os.path.exists(self.wind_directory): - os.mkdir(self.wind_directory) + os.makedirs(self.wind_directory, exist_ok=True) # Number of cores used outside of MPI. If larger than 1, the multiprocessing module is called self.cores = OFmgmt['cores'] self.case = {} From 38b3f01e6128930fa3982915b3e4c69ff6948506 Mon Sep 17 00:00:00 2001 From: dzalkind Date: Fri, 8 Mar 2024 13:33:36 -0700 Subject: [PATCH 060/114] Enable user defined turbsim exe --- weis/aeroelasticse/openmdao_openfast.py | 14 ++++++++++++-- weis/aeroelasticse/turbsim_util.py | 4 ++-- weis/inputs/modeling_schema.yaml | 4 ++++ 3 files changed, 18 insertions(+), 4 deletions(-) diff --git a/weis/aeroelasticse/openmdao_openfast.py b/weis/aeroelasticse/openmdao_openfast.py index c2aae5626..9ccc0a117 100644 --- a/weis/aeroelasticse/openmdao_openfast.py +++ b/weis/aeroelasticse/openmdao_openfast.py @@ -414,6 +414,16 @@ def setup(self): else: self.FAST_lib_user = None + if OFmgmt['turbsim_exe'] != 'none': + if os.path.isabs(OFmgmt['turbsim_exe']): + self.turbsim_exe = OFmgmt['turbsim_exe'] + else: + self.turbsim_exe = os.path.join(os.path.dirname(self.options['modeling_options']['fname_input_modeling']), + OFmgmt['turbsim_exe']) + else: + self.turbsim_exe = shutil.which('turbsim') + + # Rotor power outputs self.add_output('V_out', val=np.zeros(n_ws_dlc11), units='m/s', desc='wind speed vector from the OF simulations') self.add_output('P_out', val=np.zeros(n_ws_dlc11), units='W', desc='rotor electrical power') @@ -1825,7 +1835,7 @@ def run_FAST(self, inputs, discrete_inputs, fst_vt): idx_e = min((i+1)*size, N_cases) for idx, i_case in enumerate(np.arange(idx_s,idx_e)): - data = [partial(generate_wind_files, dlc_generator, self.FAST_namingOut, self.wind_directory, rotorD, hub_height), i_case] + data = [partial(generate_wind_files, dlc_generator, self.FAST_namingOut, self.wind_directory, rotorD, hub_height, self.turbsim_exe), i_case] rank_j = sub_ranks[idx] comm.send(data, dest=rank_j, tag=0) @@ -1835,7 +1845,7 @@ def run_FAST(self, inputs, discrete_inputs, fst_vt): else: for i_case in range(dlc_generator.n_cases): WindFile_type[i_case] , WindFile_name[i_case] = generate_wind_files( - dlc_generator, self.FAST_namingOut, self.wind_directory, rotorD, hub_height, i_case) + dlc_generator, self.FAST_namingOut, self.wind_directory, rotorD, hub_height, i_case, self.turbsim_exe) # Set initial rotor speed and pitch if the WT operates in this DLC and available, # otherwise set pitch to 90 deg and rotor speed to 0 rpm when not operating diff --git a/weis/aeroelasticse/turbsim_util.py b/weis/aeroelasticse/turbsim_util.py index 69b5fbb32..70298b57d 100644 --- a/weis/aeroelasticse/turbsim_util.py +++ b/weis/aeroelasticse/turbsim_util.py @@ -220,7 +220,7 @@ def execute(self): subprocess.call(exec_string) os.chdir(olddir) -def generate_wind_files(dlc_generator, FAST_namingOut, wind_directory, rotorD, hub_height, i_case): +def generate_wind_files(dlc_generator, FAST_namingOut, wind_directory, rotorD, hub_height, i_case, turbsim_exe): if dlc_generator.cases[i_case].turbulent_wind: # Write out turbsim input file @@ -255,7 +255,7 @@ def generate_wind_files(dlc_generator, FAST_namingOut, wind_directory, rotorD, h wrapper = Turbsim_wrapper() wrapper.run_dir = wind_directory #run_dir = os.path.dirname( os.path.dirname( os.path.dirname( os.path.realpath(__file__) ) ) ) + os.sep - wrapper.turbsim_exe = shutil.which('turbsim') + wrapper.turbsim_exe = turbsim_exe wrapper.turbsim_input = turbsim_input_file_name wrapper.execute() diff --git a/weis/inputs/modeling_schema.yaml b/weis/inputs/modeling_schema.yaml index 35c6745c2..06146bf57 100644 --- a/weis/inputs/modeling_schema.yaml +++ b/weis/inputs/modeling_schema.yaml @@ -60,6 +60,10 @@ properties: type: string default: none description: Path to FAST dynamic library to override default WEIS value (e.g. /home/user/OpenFAST/lib/libopenfast.so) + turbsim_exe: + type: string + default: none + description: Path to turbsim executable to override default WEIS value (e.g. /home/user/OpenFAST/bin/turbsim) path2dll: type: string default: none From 98469fac00c41f9d322233687dca7b3a24475681 Mon Sep 17 00:00:00 2001 From: dzalkind Date: Fri, 8 Mar 2024 13:39:36 -0700 Subject: [PATCH 061/114] Fix order of generate_wind_files --- weis/aeroelasticse/openmdao_openfast.py | 2 +- weis/aeroelasticse/turbsim_util.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/weis/aeroelasticse/openmdao_openfast.py b/weis/aeroelasticse/openmdao_openfast.py index 9ccc0a117..f2f49fe98 100644 --- a/weis/aeroelasticse/openmdao_openfast.py +++ b/weis/aeroelasticse/openmdao_openfast.py @@ -1845,7 +1845,7 @@ def run_FAST(self, inputs, discrete_inputs, fst_vt): else: for i_case in range(dlc_generator.n_cases): WindFile_type[i_case] , WindFile_name[i_case] = generate_wind_files( - dlc_generator, self.FAST_namingOut, self.wind_directory, rotorD, hub_height, i_case, self.turbsim_exe) + dlc_generator, self.FAST_namingOut, self.wind_directory, rotorD, hub_height, self.turbsim_exe, i_case) # Set initial rotor speed and pitch if the WT operates in this DLC and available, # otherwise set pitch to 90 deg and rotor speed to 0 rpm when not operating diff --git a/weis/aeroelasticse/turbsim_util.py b/weis/aeroelasticse/turbsim_util.py index 70298b57d..ea1b03abb 100644 --- a/weis/aeroelasticse/turbsim_util.py +++ b/weis/aeroelasticse/turbsim_util.py @@ -220,7 +220,7 @@ def execute(self): subprocess.call(exec_string) os.chdir(olddir) -def generate_wind_files(dlc_generator, FAST_namingOut, wind_directory, rotorD, hub_height, i_case, turbsim_exe): +def generate_wind_files(dlc_generator, FAST_namingOut, wind_directory, rotorD, hub_height, turbsim_exe, i_case): if dlc_generator.cases[i_case].turbulent_wind: # Write out turbsim input file From 99d35620fe8550b405852be08088abb103bc59da Mon Sep 17 00:00:00 2001 From: dzalkind Date: Mon, 1 Apr 2024 14:48:11 -0600 Subject: [PATCH 062/114] Allow override of modeling and analysis inputs --- examples/10_override_example/weis_driver.py | 22 ++++++++++++++--- weis/glue_code/gc_LoadInputs.py | 27 +++++++++++++++++++-- weis/glue_code/runWEIS.py | 20 +++++++++------ weis/inputs/validation.py | 12 ++++++++- 4 files changed, 67 insertions(+), 14 deletions(-) diff --git a/examples/10_override_example/weis_driver.py b/examples/10_override_example/weis_driver.py index 60dc77f05..985035493 100644 --- a/examples/10_override_example/weis_driver.py +++ b/examples/10_override_example/weis_driver.py @@ -24,15 +24,29 @@ print(f"Tip deflection: {wt_opt['rotorse.rs.tip_pos.tip_deflection'][0]} meters") -# Construct a dict with values to overwrite -overridden_values = {} -overridden_values["rotorse.wt_class.V_mean"] = 11.5 +# Construct a dict with values to override. +# The dict should match the wt_opt openmdao problem +geometry_override = {} +geometry_override["rotorse.wt_class.V_mean"] = 11.5 + +# For modeling and analysis, the structure should match the input yaml structure +modeling_override = {} +modeling_override['WISDEM'] = {} +modeling_override['WISDEM']['TowerSE'] = {} +modeling_override['WISDEM']['TowerSE']['gamma_b'] = 1.25 + +analysis_override = {} +analysis_override['general'] = {} +analysis_override['general']['folder_output'] = 'override_output' + # Run the modified simulation with the overwritten values wt_opt, modeling_options, opt_options = run_weis( fname_wt_input, fname_modeling_options, fname_analysis_options, - overridden_values=overridden_values, + geometry_override=geometry_override, + modeling_override=modeling_override, + analysis_override=analysis_override, ) print(f"Tip deflection: {wt_opt['rotorse.rs.tip_pos.tip_deflection'][0]} meters") diff --git a/weis/glue_code/gc_LoadInputs.py b/weis/glue_code/gc_LoadInputs.py index e71636b0a..81771dea3 100644 --- a/weis/glue_code/gc_LoadInputs.py +++ b/weis/glue_code/gc_LoadInputs.py @@ -9,14 +9,28 @@ from weis.dlc_driver.dlc_generator import DLCGenerator from wisdem.commonse.mpi_tools import MPI - +def update_options(options,override): + for key, value in override.items(): + if isinstance(value, dict) and key in options: + update_options(options[key], value) + elif key in options: + options[key] = value + else: + raise Exception(f'Error updating option overrides. {key} is not part of {options.keys()}') class WindTurbineOntologyPythonWEIS(WindTurbineOntologyPython): # Pure python class inheriting the class WindTurbineOntologyPython from WISDEM # and adding the WEIS options, namely the paths to the WEIS submodules # (OpenFAST, ROSCO, TurbSim, XFoil) and initializing the control parameters. - def __init__(self, fname_input_wt, fname_input_modeling, fname_input_analysis): + def __init__( + self, + fname_input_wt, + fname_input_modeling, + fname_input_analysis, + modeling_override = None, + analysis_override = None, + ): self.modeling_options = sch.load_modeling_yaml(fname_input_modeling) self.modeling_options['fname_input_modeling'] = fname_input_modeling @@ -24,6 +38,15 @@ def __init__(self, fname_input_wt, fname_input_modeling, fname_input_analysis): self.analysis_options = sch.load_analysis_yaml(fname_input_analysis) self.analysis_options['fname_input_analysis'] = fname_input_analysis + if modeling_override: + update_options(self.modeling_options, modeling_override) + sch.re_validate_modeling(self.modeling_options) + + + if analysis_override: + update_options(self.analysis_options, analysis_override) + sch.re_validate_analysis(self.analysis_options) + self.set_run_flags() self.set_openmdao_vectors() self.set_openmdao_vectors_control() diff --git a/weis/glue_code/runWEIS.py b/weis/glue_code/runWEIS.py index f7488e658..d0c0d435f 100644 --- a/weis/glue_code/runWEIS.py +++ b/weis/glue_code/runWEIS.py @@ -15,9 +15,15 @@ if MPI: from wisdem.commonse.mpi_tools import map_comm_heirarchical, subprocessor_loop, subprocessor_stop -def run_weis(fname_wt_input, fname_modeling_options, fname_opt_options, overridden_values=None): +def run_weis(fname_wt_input, fname_modeling_options, fname_opt_options, geometry_override=None, modeling_override=None, analysis_override=None): # Load all yaml inputs and validate (also fills in defaults) - wt_initial = WindTurbineOntologyPythonWEIS(fname_wt_input, fname_modeling_options, fname_opt_options) + wt_initial = WindTurbineOntologyPythonWEIS( + fname_wt_input, + fname_modeling_options, + fname_opt_options, + modeling_override=modeling_override, + analysis_override=analysis_override + ) wt_init, modeling_options, opt_options = wt_initial.get_input_data() # Initialize openmdao problem. If running with multiple processors in MPI, use parallel finite differencing equal to the number of cores used. @@ -164,7 +170,7 @@ def run_weis(fname_wt_input, fname_modeling_options, fname_opt_options, overridd if modeling_options['Level3']['flag']: wt_opt = myopt.set_initial_weis(wt_opt) - # If the user provides values in this dict, they overwrite + # If the user provides values in geometry_override, they overwrite # whatever values have been set by the yaml files. # This is useful for performing black-box wrapped optimization without # needing to modify the yaml files. @@ -173,12 +179,12 @@ def run_weis(fname_wt_input, fname_modeling_options, fname_opt_options, overridd # of the array. # This is useful when optimizing twist, where the first few indices # do not need to be optimized as they correspond to a circular cross-section. - if overridden_values is not None: - for key in overridden_values: - num_values = np.array(overridden_values[key]).size + if geometry_override is not None: + for key in geometry_override: + num_values = np.array(geometry_override[key]).size key_size = wt_opt[key].size idx_start = key_size - num_values - wt_opt[key][idx_start:] = overridden_values[key] + wt_opt[key][idx_start:] = geometry_override[key] # Place the last design variables from a previous run into the problem. # This needs to occur after the above setup() and yaml2openmdao() calls diff --git a/weis/inputs/validation.py b/weis/inputs/validation.py index c6af13f84..ff3424768 100644 --- a/weis/inputs/validation.py +++ b/weis/inputs/validation.py @@ -1,7 +1,7 @@ import os import jsonmerge import wisdem.inputs -from wisdem.inputs import load_yaml, write_yaml, validate_without_defaults, validate_with_defaults, simple_types +from wisdem.inputs import load_yaml, write_yaml, validate_without_defaults, validate_with_defaults, simple_types, DefaultValidatingDraft7Validator import rosco.toolbox.inputs froot_wisdem = os.path.dirname(wisdem.inputs.__file__) @@ -86,3 +86,13 @@ def write_analysis_yaml(instance, foutput): foutput = foutput[-4:] sfx_str = "-analysis.yaml" write_yaml(instance, foutput+sfx_str) + +def re_validate_modeling(modeling_dict): + fschema = get_modeling_schema() + yaml_schema = load_yaml(fschema) if type(fschema) == type("") else fschema + DefaultValidatingDraft7Validator(yaml_schema).validate(modeling_dict) + +def re_validate_analysis(analysis_dict): + fschema = get_analysis_schema() + yaml_schema = load_yaml(fschema) if type(fschema) == type("") else fschema + DefaultValidatingDraft7Validator(yaml_schema).validate(analysis_dict) From b1a4f6356139d44ecbe74b40344f9ddb977d5015 Mon Sep 17 00:00:00 2001 From: Daniel Zalkind Date: Mon, 1 Apr 2024 14:50:55 -0600 Subject: [PATCH 063/114] Make directory if it doesnt exist --- weis/glue_code/runWEIS.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/weis/glue_code/runWEIS.py b/weis/glue_code/runWEIS.py index f7488e658..a21e41113 100644 --- a/weis/glue_code/runWEIS.py +++ b/weis/glue_code/runWEIS.py @@ -106,7 +106,7 @@ def run_weis(fname_wt_input, fname_modeling_options, fname_opt_options, overridd folder_output = opt_options['general']['folder_output'] if rank == 0 and not os.path.isdir(folder_output): - os.makedirs(folder_output) + os.makedirs(folder_output,exist_ok=True) if color_i == 0: # the top layer of cores enters, the others sit and wait to run openfast simulations # if MPI and opt_options['driver']['optimization']['flag']: From e0acf44a066db68c9d31778f1189d201dfc96210 Mon Sep 17 00:00:00 2001 From: Daniel Zalkind Date: Mon, 1 Apr 2024 14:54:55 -0600 Subject: [PATCH 064/114] Remove lingering pyhams --- pyHAMS/pyhams/pyhams.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 pyHAMS/pyhams/pyhams.py diff --git a/pyHAMS/pyhams/pyhams.py b/pyHAMS/pyhams/pyhams.py deleted file mode 100644 index e69de29bb..000000000 From f99416c1732ea41e9f1e3a1d34b578b86f8b1634 Mon Sep 17 00:00:00 2001 From: dzalkind Date: Mon, 1 Apr 2024 15:46:18 -0600 Subject: [PATCH 065/114] Add openfast optimization inputs --- .../analysis_options_of_ptfm_opt.yaml | 129 ++++++++++++++++++ .../analysis_options_raft_ptfm_opt.yaml | 3 +- .../driver_weis_openfast_opt.py | 29 ++++ .../modeling_options_openfast.yaml | 35 +++-- .../17_IEA22_Optimization/run_weis_of_opt.sh | 27 ++++ 5 files changed, 204 insertions(+), 19 deletions(-) create mode 100644 examples/17_IEA22_Optimization/analysis_options_of_ptfm_opt.yaml create mode 100644 examples/17_IEA22_Optimization/driver_weis_openfast_opt.py create mode 100644 examples/17_IEA22_Optimization/run_weis_of_opt.sh diff --git a/examples/17_IEA22_Optimization/analysis_options_of_ptfm_opt.yaml b/examples/17_IEA22_Optimization/analysis_options_of_ptfm_opt.yaml new file mode 100644 index 000000000..3f250c059 --- /dev/null +++ b/examples/17_IEA22_Optimization/analysis_options_of_ptfm_opt.yaml @@ -0,0 +1,129 @@ +general: + folder_output: /scratch/dzalkind/WEIS-2/outputs/17_IEA22_OptStudies/0_setup + fname_output: IEA-22-280-RWT + +design_variables: + # tower: + # outer_diameter: + # flag: False + # lower_bound: 4.0 + # upper_bound: 10.0 + # layer_thickness: + # flag: False + # lower_bound: 4.e-3 + # upper_bound: 2.e-1 + floating: + joints: + flag: True + z_coordinate: # draft + - names: [main_keel, col1_keel, col2_keel, col3_keel] + lower_bound: -30.0 + upper_bound: -12.0 + # - names: [col1_freeboard, col2_freeboard, col3_freeboard] + # lower_bound: 10.0 + # upper_bound: 20.0 + r_coordinate: + - names: [col1_keel, col1_freeboard, col2_keel, col2_freeboard, col3_keel, col3_freeboard] + lower_bound: 35.0 + upper_bound: 75.0 + members: + flag: True + groups: + - names: [column1, column2, column3] + diameter: + lower_bound: 12.0 + upper_bound: 16.0 + constant: True + # ballast: + # lower_bound: 0 + # upper_bound: 1000 + # - names: [main_column] + # ballast: + # lower_bound: 0 + # upper_bound: 1000 + # - names: [Y_pontoon_lower1, Y_pontoon_lower2, Y_pontoon_lower3] + # diameter: + # lower_bound: 10.0 + # upper_bound: 10.8 + # constant: True +constraints: + tower: + height_constraint: + flag: False + lower_bound: 1.e-2 + upper_bound: 1.e-2 + stress: + flag: False + global_buckling: + flag: False + shell_buckling: + flag: False + d_to_t: + flag: False + lower_bound: 120.0 + upper_bound: 2000.0 + taper: + flag: False + lower_bound: 0.2 + slope: + flag: False + frequency_1: + flag: False + lower_bound: 0.37 + floating: + survival_heel: + upper_bound: 0.17453292519943295 # 10 deg + metacentric_height: + flag: False + lower_bound: 15.0 + pitch_period: + flag: True + lower_bound: 20. # +/- 10% + upper_bound: 80. + heave_period: + flag: True + lower_bound: 16. # +/- 10% + upper_bound: 80. + fixed_ballast_capacity: + flag: True + variable_ballast_capacity: + flag: True + buoyancy: + flag: False + stress: + flag: False + global_buckling: + flag: False + shell_buckling: + flag: False + mooring_heel: + flag: False + freeboard_margin: # keep freeboard from being submerged below water during survival_heel, largest wave + flag: True + draft_margin: # keep draft from raising above water line during survival_heel, largest wave + flag: True + fairlead_depth: # keep fairlead above bottom trough of largest wave + flag: True + control: + Max_PtfmPitch: + flag: True + max: 6.0 + Std_PtfmPitch: + flag: True + max: 1.25 # Same as IEA-15MW with same DLCs + nacelle_acceleration: + flag: True + max: 2.0 + +merit_figure: structural_mass + +driver: + optimization: + flag: True # Flag to enable optimization + tol: 1.e-3 # Optimality tolerance + max_iter: 1000 # Maximum number of iterations (SLSQP) + solver: LN_COBYLA # Optimization solver. Other options are 'SLSQP' - 'CONMIN' + +recorder: + flag: True # Flag to activate OpenMDAO recorder + file_name: log_opt.sql # Name of OpenMDAO recorder diff --git a/examples/17_IEA22_Optimization/analysis_options_raft_ptfm_opt.yaml b/examples/17_IEA22_Optimization/analysis_options_raft_ptfm_opt.yaml index 81210e051..9706ffe63 100644 --- a/examples/17_IEA22_Optimization/analysis_options_raft_ptfm_opt.yaml +++ b/examples/17_IEA22_Optimization/analysis_options_raft_ptfm_opt.yaml @@ -1,5 +1,6 @@ general: - folder_output: 32_DesignRound1 + # folder_output: /scratch/dzalkind/WEIS-2/outputs/17_IEA22_OptStudies/0_setup # kestrel + folder_output: outputs/17_IEA22_OptStudies/1_change_opt fname_output: IEA-22-280-RWT design_variables: diff --git a/examples/17_IEA22_Optimization/driver_weis_openfast_opt.py b/examples/17_IEA22_Optimization/driver_weis_openfast_opt.py new file mode 100644 index 000000000..cbcb44f42 --- /dev/null +++ b/examples/17_IEA22_Optimization/driver_weis_openfast_opt.py @@ -0,0 +1,29 @@ +#!/usr/bin/env python3 +import os +import shutil +from weis.glue_code.runWEIS import run_weis +import wisdem.inputs as sch +import numpy as np +from wisdem.commonse.mpi_tools import MPI + + +## File management +mydir = os.path.dirname(os.path.realpath(__file__)) # get path to this file + +fname_wt_input = mydir + os.sep + 'IEA-22-280-RWT-Semi.yaml' +# fname_wt_input = '/Users/dzalkind/Projects/IEA-22MW/FloaterIEA22/33_Round_DesRo1/IEA-22-280-RWT.yaml' +# fname_wt_input = os.path.join(mydir,'11_Lower_Pontoon_Stiffeners','IEA-22-280-RWT.yaml') +fname_modeling_options = mydir + os.sep + "modeling_options_openfast.yaml" +fname_analysis_options = mydir + os.sep + "analysis_options_raft_ptfm_opt.yaml" +wt_opt, modeling_options, analysis_options = run_weis(fname_wt_input, fname_modeling_options, fname_analysis_options) + + +if MPI: + rank = MPI.COMM_WORLD.Get_rank() +else: + rank = 0 +if rank == 0: + # shutil.copyfile(os.path.join(analysis_options['general']['folder_output'],analysis_options['general']['fname_output']+'.yaml'), fname_wt_input) + print("Tower mass (kg) =", wt_opt["towerse.tower_mass"]) + print("Floating platform mass (kg) =", wt_opt["floatingse.platform_mass"]) + diff --git a/examples/17_IEA22_Optimization/modeling_options_openfast.yaml b/examples/17_IEA22_Optimization/modeling_options_openfast.yaml index 48589842b..004baf78f 100644 --- a/examples/17_IEA22_Optimization/modeling_options_openfast.yaml +++ b/examples/17_IEA22_Optimization/modeling_options_openfast.yaml @@ -101,17 +101,17 @@ Level3: # Options for WEIS fidelity level 3 = nonlinear time domain ROSCO: flag: True # SD_Mode: 0 - PS_Mode: 3 - ps_percent: 0.9 + # PS_Mode: 3 + # ps_percent: 0.9 # F_LPFType: 2 # F_NotchType: 2 # Fl_Mode: 2 tuning_yaml: IEA-22-280-RWT-Semi_ROSCO.yaml - zeta_pc: [1] - omega_pc: [0.1] - U_pc: [12] - zeta_vs: 0.85 # Torque controller desired damping ratio [-] - omega_vs: 0.12 + # zeta_pc: [1] + # omega_pc: [0.1] + # U_pc: [12] + # zeta_vs: 0.85 # Torque controller desired damping ratio [-] + # omega_vs: 0.12 # twr_freq: 3.2 # ptfm_freq: 0.2 # Kp_float: -10 @@ -127,16 +127,15 @@ DLC_driver: wave_height_50: 10.68 wave_period_50: 14.2 DLCs: - - DLC: "1.1" - n_seeds: 6 - # - DLC: "1.6" + # - DLC: "1.1" # n_seeds: 6 - # # wind_speed: [6,10,14] - # # analysis_time: 1. - # # transient_time: 0. - # # turbulent_wind: - # # HubHt: 160 - # # GridHeight: 300 - # # GridWidth: 300 + # - DLC: "1.6" # kestrel + # n_seeds: 6 + - DLC: "1.6" # local, lite + n_seeds: 1 + wind_speed: [12] # - DLC: "6.1" - # n_seeds: 6 \ No newline at end of file + # n_seeds: 6 + # turbulent_wind: + # # GridHeight: 400 + # GridWidth: 400 \ No newline at end of file diff --git a/examples/17_IEA22_Optimization/run_weis_of_opt.sh b/examples/17_IEA22_Optimization/run_weis_of_opt.sh new file mode 100644 index 000000000..b9fc68150 --- /dev/null +++ b/examples/17_IEA22_Optimization/run_weis_of_opt.sh @@ -0,0 +1,27 @@ +#!/bin/bash +#SBATCH --account=weis +#SBATCH --time=24:00:00 +#SBATCH --nodes=1 +#SBATCH --job-name=iea22_ptfm +#SBATCH --mail-user dzalkind@nrel.gov +#SBATCH --mail-type BEGIN,END,FAIL +##SBATCH --partition=debug +######SBATCH --qos=high +######SBATCH --mem=1000GB # RAM in MB +#SBATCH --output=logs/job_log.%j.out # %j will be replaced with the job ID + +source /home/dzalkind/.bashrc +conda activate weis-new2 + + +nC=85 + + +# mpirun -np 73 python driver_weis.py +mpiexec -n $nC python driver_weis_openfast_opt.py + +# python driver_weis.py + +# python create_conv_plots.py + + From f62481d9d30cc82307c4a6194a2e8c92d530a8c5 Mon Sep 17 00:00:00 2001 From: dzalkind Date: Mon, 1 Apr 2024 16:14:38 -0600 Subject: [PATCH 066/114] Tidy driver and set up opt change --- .../analysis_options_of_ptfm_opt.yaml | 3 ++- .../driver_weis_openfast_opt.py | 24 +++++++++++++++---- .../modeling_options_openfast.yaml | 2 ++ 3 files changed, 24 insertions(+), 5 deletions(-) diff --git a/examples/17_IEA22_Optimization/analysis_options_of_ptfm_opt.yaml b/examples/17_IEA22_Optimization/analysis_options_of_ptfm_opt.yaml index 3f250c059..708b10413 100644 --- a/examples/17_IEA22_Optimization/analysis_options_of_ptfm_opt.yaml +++ b/examples/17_IEA22_Optimization/analysis_options_of_ptfm_opt.yaml @@ -1,5 +1,6 @@ general: - folder_output: /scratch/dzalkind/WEIS-2/outputs/17_IEA22_OptStudies/0_setup + # folder_output: /scratch/dzalkind/WEIS-2/outputs/17_IEA22_OptStudies/0_setup # kestrel + folder_output: outputs/17_IEA22_OptStudies/1_change_opt/ fname_output: IEA-22-280-RWT design_variables: diff --git a/examples/17_IEA22_Optimization/driver_weis_openfast_opt.py b/examples/17_IEA22_Optimization/driver_weis_openfast_opt.py index cbcb44f42..6785ebe88 100644 --- a/examples/17_IEA22_Optimization/driver_weis_openfast_opt.py +++ b/examples/17_IEA22_Optimization/driver_weis_openfast_opt.py @@ -5,17 +5,33 @@ import wisdem.inputs as sch import numpy as np from wisdem.commonse.mpi_tools import MPI +import sys ## File management mydir = os.path.dirname(os.path.realpath(__file__)) # get path to this file fname_wt_input = mydir + os.sep + 'IEA-22-280-RWT-Semi.yaml' -# fname_wt_input = '/Users/dzalkind/Projects/IEA-22MW/FloaterIEA22/33_Round_DesRo1/IEA-22-280-RWT.yaml' -# fname_wt_input = os.path.join(mydir,'11_Lower_Pontoon_Stiffeners','IEA-22-280-RWT.yaml') fname_modeling_options = mydir + os.sep + "modeling_options_openfast.yaml" -fname_analysis_options = mydir + os.sep + "analysis_options_raft_ptfm_opt.yaml" -wt_opt, modeling_options, analysis_options = run_weis(fname_wt_input, fname_modeling_options, fname_analysis_options) +fname_analysis_options = mydir + os.sep + "analysis_options_of_ptfm_opt.yaml" + +# Change optimizer and output folder +optimizer = sys.argv[1] +print(f"Optimizer: {optimizer}") + +analysis_override = {} +analysis_override['general'] = {} +analysis_override['general']['folder_output'] = os.path.join('outputs/17_IEA22_OptStudies/1_change_opt/',optimizer) +analysis_override['driver'] = {} +analysis_override['driver']['optimization'] = {} +analysis_override['driver']['optimization']['solver'] = optimizer + +wt_opt, modeling_options, analysis_options = run_weis( + fname_wt_input, + fname_modeling_options, + fname_analysis_options, + analysis_override=analysis_override + ) if MPI: diff --git a/examples/17_IEA22_Optimization/modeling_options_openfast.yaml b/examples/17_IEA22_Optimization/modeling_options_openfast.yaml index 004baf78f..a666709da 100644 --- a/examples/17_IEA22_Optimization/modeling_options_openfast.yaml +++ b/examples/17_IEA22_Optimization/modeling_options_openfast.yaml @@ -134,6 +134,8 @@ DLC_driver: - DLC: "1.6" # local, lite n_seeds: 1 wind_speed: [12] + transient_time: 0. + analysis_time: 10. # - DLC: "6.1" # n_seeds: 6 # turbulent_wind: From a9e230d2dcad5556fcd986ab1c6b35839c060df0 Mon Sep 17 00:00:00 2001 From: dzalkind Date: Mon, 1 Apr 2024 16:16:05 -0600 Subject: [PATCH 067/114] Tidy modopts --- examples/17_IEA22_Optimization/modeling_options_openfast.yaml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/examples/17_IEA22_Optimization/modeling_options_openfast.yaml b/examples/17_IEA22_Optimization/modeling_options_openfast.yaml index a666709da..c69dd42f7 100644 --- a/examples/17_IEA22_Optimization/modeling_options_openfast.yaml +++ b/examples/17_IEA22_Optimization/modeling_options_openfast.yaml @@ -8,6 +8,8 @@ General: use_exe: True allow_fails: True fail_value: 9999 + # FAST_exe: /home/dzalkind/Tools/openfast/build/glue-codes/openfast/openfast # faster on kestrel + # turbsim_exe: /home/dzalkind/Tools/openfast/build/modules/turbsim/turbsim # faster on kestrel WISDEM: RotorSE: flag: True From f38641837e80deb124acdc943e132edc0abfa7d3 Mon Sep 17 00:00:00 2001 From: yqliaohk Date: Wed, 3 Apr 2024 17:18:12 -0600 Subject: [PATCH 068/114] Working on RAFT wrapper --- weis/frequency/raft_wrapper.py | 26 ++++++++++++++++++++++++-- weis/glue_code/glue_code.py | 1 - 2 files changed, 24 insertions(+), 3 deletions(-) diff --git a/weis/frequency/raft_wrapper.py b/weis/frequency/raft_wrapper.py index f2a5a1ad6..e1da34a87 100644 --- a/weis/frequency/raft_wrapper.py +++ b/weis/frequency/raft_wrapper.py @@ -48,7 +48,11 @@ def setup(self): members_opt['npts_lfill'] = members_opt['npts_rho_fill'] = [m-1 for m in members_opt['npts']] members_opt['ncaps'] = weis_opt["floating"]["members"]["n_bulkheads"] members_opt['nreps'] = [0]*members_opt['nmembers'] - members_opt['shape'] = ['circ']*members_opt['nmembers'] + member_shape = weis_opt["floating"]["members"]["outer_shape"] + # TODO: need to re-visit how to handle square. Can we just get away square with rect? + member_shape = list(map(lambda x:x.replace("circular", "circ"), member_shape)) + member_shape = list(map(lambda x:x.replace("rectangular", "rect"), member_shape)) + members_opt['shape'] = member_shape members_opt['scalar_thicknesses'] = members_opt['scalar_diameters'] = [False]*members_opt['nmembers'] members_opt['scalar_coefficients'] = [False]*members_opt['nmembers'] members_opt['n_ballast_type'] = len(weis_opt["floating"]["members"]["ballast_types"]) @@ -113,12 +117,23 @@ def setup(self): n_member = len(opt["floating"]["members"]["name"]) self.add_input("member_variable_height", val=np.zeros(n_member)) + # Outer diameters and side lenghts are directly connected to the raft_omdao in glue_code + # not going through here. + # TODO: bring the outer_diameter connections here because we need some processing + # due to the difference in how we store the side lengths for k in range(n_member): n_height = opt["floating"]["members"]["n_height"][k] n_layers = opt["floating"]["members"]["n_layers"][k] n_ball = opt["floating"]["members"]["n_ballasts"][k] n_bulk = opt["floating"]["members"]["n_bulkheads"][k] - + if opt["floating"]["members"]["outer_shape"][k] == "circular": + self.add_input(f"member{k}:diameter", val=np.zeros(n_height), units="m") + self.add_output(f"platform_member{k+1}_d", val=np.zeros(n_height), units="m") + elif opt["floating"]["members"]["outer_shape"][k] == "rectangular": + self.add_input(f"member{k}:side_length_a", val=np.zeros(n_height), units="m") + self.add_input(f"member{k}:side_length_b", val=np.zeros(n_height), units="m") + # RAFT collect a and b into d + self.add_output(f"platform_member{k+1}_d", val=np.zeros([n_height,2]), units="m") self.add_input(f"member{k}:height", val=0.0, units="m") self.add_input(f"member{k}:layer_thickness", val=np.zeros((n_layers, n_height)), units="m") self.add_input(f"member{k}:rho", val=np.zeros(n_height-1), units="kg/m**3") @@ -231,6 +246,13 @@ def compute(self, inputs, outputs, discrete_inputs, discrete_outputs): outputs[f"platform_member{k+1}_t"] = inputs[f"member{k}:layer_thickness"].sum(axis=0) outputs[f"platform_member{k+1}_rho_shell"] = inputs[f"member{k}:rho"].mean() + # Convert diameter and side lengths to RAFT + if opt["floating"]["members"]["outer_shape"][k] == "circular": + outputs[f"platform_member{k+1}_d"] = inputs[f"member{k}:diameter"] + elif opt["floating"]["members"]["outer_shape"][k] == "rectangular": + outputs[f"platform_member{k+1}_d"][:, 0] = inputs[f"member{k}:side_length_a"] + outputs[f"platform_member{k+1}_d"][:, 1] = inputs[f"member{k}:side_length_b"] + # Ring stiffener discretization conversion if ( (float(inputs[f"member{k}:ring_stiffener_spacing"]) > 0.0) and (float(inputs[f"member{k}:ring_stiffener_spacing"]) < 1.0) ): diff --git a/weis/glue_code/glue_code.py b/weis/glue_code/glue_code.py index b4b9c619b..464d8c5f7 100644 --- a/weis/glue_code/glue_code.py +++ b/weis/glue_code/glue_code.py @@ -313,7 +313,6 @@ def setup(self): for k, kname in enumerate(modeling_options["floating"]["members"]["name"]): idx = modeling_options["floating"]["members"]["name2idx"][kname] - self.connect(f"floating.memgrid{idx}.outer_diameter", f"raft.platform_member{k+1}_d") self.connect(f"floating.memgrid{idx}.layer_thickness", f"raft.member{k}:layer_thickness") self.connect(f"floatingse.member{k}.height", f"raft.member{k}:height") self.connect(f"floatingse.member{k}.rho", f"raft.member{k}:rho") From a03c944eec15834ea546e7052ac8833b187f28bd Mon Sep 17 00:00:00 2001 From: dzalkind Date: Mon, 11 Mar 2024 16:25:03 -0600 Subject: [PATCH 069/114] Remove discrete inputs to RAFT --- weis/frequency/raft_wrapper.py | 43 +++++++++++++++++----------------- 1 file changed, 21 insertions(+), 22 deletions(-) diff --git a/weis/frequency/raft_wrapper.py b/weis/frequency/raft_wrapper.py index 157b39940..6c822ee39 100644 --- a/weis/frequency/raft_wrapper.py +++ b/weis/frequency/raft_wrapper.py @@ -57,14 +57,30 @@ def setup(self): members_opt['scalar_coefficients'] = [False]*members_opt['nmembers'] members_opt['n_ballast_type'] = len(weis_opt["floating"]["members"]["ballast_types"]) + for k in range(members_opt['nmembers']): + members_opt[f"platform_member{k+1}_potMod"] = weis_opt["Level1"]["model_potential"][k] + mooring_opt = {} mooring_opt['nlines'] = weis_opt['mooring']['n_lines'] mooring_opt['nline_types'] = weis_opt['mooring']['n_line_types'] mooring_opt['nconnections'] = weis_opt['mooring']['n_nodes'] + # Mooring + for k in range(weis_opt['mooring']['n_nodes']): + mooring_opt[f'mooring_point{k+1}_name'] = weis_opt['mooring']['node_names'][k] + mooring_opt[f'mooring_point{k+1}_type'] = weis_opt['mooring']['node_type'][k] + + for k in range(weis_opt['mooring']['n_lines']): + mooring_opt[f'mooring_line{k+1}_endA'] = weis_opt['mooring']["node1"][k] + mooring_opt[f'mooring_line{k+1}_endB'] = weis_opt['mooring']["node2"][k] + mooring_opt[f'mooring_line{k+1}_type'] = weis_opt['mooring']["line_type"][k] + + for k in range(weis_opt['mooring']['n_line_types']): + mooring_opt[f'mooring_line_type{k+1}_name'] = weis_opt['mooring']["line_type_name"][k] self.add_subsystem('wind', PowerWind(nPoints = turbine_opt['npts']), promotes=['Uref','zref']) - self.add_subsystem('pre', RAFT_WEIS_Prep(modeling_options=weis_opt), promotes=['*']) + self.add_subsystem('pre', RAFT_WEIS_Prep(modeling_options=weis_opt, + raft_options = raft_opt), promotes=['*']) self.add_subsystem('raft', RAFT_OMDAO(modeling_options=raft_opt, turbine_options=turbine_opt, mooring_options=mooring_opt, @@ -76,6 +92,7 @@ class RAFT_WEIS_Prep(om.ExplicitComponent): def initialize(self): self.options.declare('modeling_options') + self.options.declare('raft_options') def setup(self): opt = self.options['modeling_options'] @@ -161,19 +178,13 @@ def setup(self): self.add_output(f"platform_member{k+1}_Ca", val=np.ones(n_height)) self.add_output(f"platform_member{k+1}_CdEnd", val=0.6*np.ones(n_height)) self.add_output(f"platform_member{k+1}_CaEnd", val=0.6*np.ones(n_height)) - self.add_discrete_output(f"platform_member{k+1}_potMod", val=False) # Mooring inputs self.add_input('mooring_nodes', val=np.zeros((n_nodes, 3)), units='m', desc='Mooring node locations in global xyz') for k in range(n_nodes): - self.add_discrete_output(f'mooring_point{k+1}_name', val=f'line{k+1}', desc='Mooring point identifier') - self.add_discrete_output(f'mooring_point{k+1}_type', val='fixed', desc='Mooring connection type') self.add_output(f'mooring_point{k+1}_location', val=np.zeros(3), units='m', desc='Mooring node location') for k in range(n_lines): - self.add_discrete_output(f'mooring_line{k+1}_endA', val='default', desc='End A coordinates') - self.add_discrete_output(f'mooring_line{k+1}_endB', val='default', desc='End B coordinates') - self.add_discrete_output(f'mooring_line{k+1}_type', val='mooring_line_type1', desc='Mooring line type') self.add_output(f'mooring_line{k+1}_length', val=0.0, units='m', desc='Length of line') self.add_input("unstretched_length", val=np.zeros(n_lines), units="m") @@ -188,7 +199,6 @@ def setup(self): self.add_input("line_tangential_drag", val=np.zeros(n_lines)) for k in range(n_line_types): - self.add_discrete_output(f'mooring_line_type{k+1}_name', val='default', desc='Name of line type') self.add_output(f'mooring_line_type{k+1}_diameter', val=0.0, units='m', desc='Diameter of mooring line type') self.add_output(f'mooring_line_type{k+1}_mass_density', val=0.0, units='kg/m**3', desc='Mass density of line type') self.add_output(f'mooring_line_type{k+1}_stiffness', val=0.0, desc='Stiffness of line type') @@ -205,15 +215,11 @@ def setup(self): self.add_input('Vrated', val=0.0, units='m/s', desc='rated wind speed') self.add_discrete_input('turbulence_class', val='A', desc='IEC turbulence class') self.add_discrete_input('turbine_class', val='I', desc='IEC turbulence class') - self.add_discrete_output('raft_dlcs', val=[[]]*opt['DLC_driver']['n_cases'], desc='DLC case table for RAFT with each row a new case and headings described by the keys') - self.add_discrete_output('raft_dlcs_keys', val=['wind_speed', 'wind_heading', 'turbulence', - 'turbine_status', 'yaw_misalign', 'wave_spectrum', - 'wave_period', 'wave_height', 'wave_heading'], - desc='DLC case table column headings') def compute(self, inputs, outputs, discrete_inputs, discrete_outputs): opt = self.options['modeling_options'] + raft_opt = self.options['raft_options'] # Tower layer sections outputs['turbine_tower_t'] = inputs['tower_layer_thickness'].sum(axis=0) @@ -240,7 +246,6 @@ def compute(self, inputs, outputs, discrete_inputs, discrete_outputs): n_member = len(opt["floating"]["members"]["name"]) var_height = inputs['member_variable_height'] for k in range(n_member): - discrete_outputs[f"platform_member{k+1}_potMod"] = opt["Level1"]["model_potential"][k] # Member thickness outputs[f"platform_member{k+1}_t"] = inputs[f"member{k}:layer_thickness"].sum(axis=0) @@ -313,18 +318,12 @@ def compute(self, inputs, outputs, discrete_inputs, discrete_outputs): # Mooring for k in range(opt['mooring']['n_nodes']): - discrete_outputs[f'mooring_point{k+1}_name'] = opt['mooring']['node_names'][k] - discrete_outputs[f'mooring_point{k+1}_type'] = opt['mooring']['node_type'][k] outputs[f'mooring_point{k+1}_location'] = inputs['mooring_nodes'][k,:] for k in range(opt['mooring']['n_lines']): - discrete_outputs[f'mooring_line{k+1}_endA'] = opt['mooring']["node1"][k] - discrete_outputs[f'mooring_line{k+1}_endB'] = opt['mooring']["node2"][k] - discrete_outputs[f'mooring_line{k+1}_type'] = opt['mooring']["line_type"][k] outputs[f'mooring_line{k+1}_length'] = inputs['unstretched_length'][k] for k in range(opt['mooring']['n_line_types']): - discrete_outputs[f'mooring_line_type{k+1}_name'] = opt['mooring']["line_type_name"][k] outputs[f'mooring_line_type{k+1}_cost'] = inputs['line_cost_rate'][k] for var in ['diameter','mass_density','stiffness','breaking_load', @@ -367,7 +366,7 @@ def compute(self, inputs, outputs, discrete_inputs, discrete_outputs): float(max(1.0, icase.wave_period)), float(max(1.0, icase.wave_height)), float(icase.wave_heading)] - discrete_outputs['raft_dlcs'] = raft_cases - discrete_outputs['raft_dlcs_keys'] = ['wind_speed', 'wind_heading', 'turbulence', + raft_opt['raft_dlcs'] = raft_cases + raft_opt['raft_dlcs_keys'] = ['wind_speed', 'wind_heading', 'turbulence', 'turbine_status', 'yaw_misalign', 'wave_spectrum', 'wave_period', 'wave_height', 'wave_heading'] From fe8392078dde0b586f0b72b6ae0d6ac395553345 Mon Sep 17 00:00:00 2001 From: dzalkind Date: Wed, 10 Apr 2024 16:37:32 -0600 Subject: [PATCH 070/114] Connect rectangular members to raft_wrapper --- ...EA-15-240-RWT_VolturnUS-S_rectangular.yaml | 1335 +++++++++++++++++ weis/frequency/raft_wrapper.py | 6 +- weis/glue_code/glue_code.py | 6 + weis/inputs/modeling_schema.yaml | 4 + 4 files changed, 1348 insertions(+), 3 deletions(-) create mode 100644 examples/15_RAFT_Studies/IEA-15-240-RWT_VolturnUS-S_rectangular.yaml diff --git a/examples/15_RAFT_Studies/IEA-15-240-RWT_VolturnUS-S_rectangular.yaml b/examples/15_RAFT_Studies/IEA-15-240-RWT_VolturnUS-S_rectangular.yaml new file mode 100644 index 000000000..4d4e040ef --- /dev/null +++ b/examples/15_RAFT_Studies/IEA-15-240-RWT_VolturnUS-S_rectangular.yaml @@ -0,0 +1,1335 @@ +name: IEA 15MW Offshore Reference Turbine, with taped chord tip design +assembly: + turbine_class: I + turbulence_class: B + drivetrain: direct_drive + rotor_orientation: Upwind + number_of_blades: 3 + hub_height: 150. + rotor_diameter: 242.23775645 + rated_power: 15.e+6 + lifetime: 25.0 +components: + blade: + outer_shape_bem: + airfoil_position: + grid: [0.0, 0.02, 0.15, 0.24517031675566095, 0.3288439506472435, 0.4391793464459161, 0.5376714071084352, 0.6382076569163737, 0.7717438522715817, 1.0] + labels: [circular, circular, SNL-FFA-W3-500, FFA-W3-360, FFA-W3-330blend, FFA-W3-301, FFA-W3-270blend, FFA-W3-241, FFA-W3-211, FFA-W3-211] + chord: + grid: [0.0, 0.02040816326530612, 0.04081632653061224, 0.061224489795918366, 0.08163265306122448, 0.1020408163265306, 0.12244897959183673, 0.14285714285714285, 0.16326530612244897, 0.18367346938775508, 0.2040816326530612, 0.22448979591836732, 0.24489795918367346, 0.26530612244897955, 0.2857142857142857, 0.3061224489795918, 0.32653061224489793, 0.3469387755102041, 0.36734693877551017, 0.3877551020408163, 0.4081632653061224, 0.42857142857142855, 0.44897959183673464, 0.4693877551020408, 0.4897959183673469, 0.5102040816326531, 0.5306122448979591, 0.5510204081632653, 0.5714285714285714, 0.5918367346938775, 0.6122448979591836, 0.6326530612244897, 0.6530612244897959, 0.673469387755102, 0.6938775510204082, 0.7142857142857142, 0.7346938775510203, 0.7551020408163265, 0.7755102040816326, 0.7959183673469387, 0.8163265306122448, 0.836734693877551, 0.8571428571428571, 0.8775510204081632, 0.8979591836734693, 0.9183673469387754, 0.9387755102040816, 0.9591836734693877, 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-0.23781200324366186, 5.767382468564499, 0.0, 0.0, 0.0, 0.012674466463850537, 5.767382468564499, 0.23781200324366186, -0.012674466463850537, 0.0, 0.10597710521942054, 0.10597710521942054, 0.0, 0.0, 0.0, 0.11305292863786597] + hub: + diameter: 7.94 + cone_angle: 0.06981317007977318 # 4 deg + drag_coefficient: 0.5 + flange_t2shell_t: 6.0 + flange_OD2hub_D: 0.6 + flange_ID2OD: 0.8 + hub_blade_spacing_margin: 1.2 + hub_stress_concentration: 3.0 + n_front_brackets: 5 + n_rear_brackets: 5 + clearance_hub_spinner: 0.5 + spin_hole_incr: 1.2 + pitch_system_scaling_factor: 0.75 + spinner_gust_ws: 70.0 + hub_material: cast_iron + spinner_material: glass_uni + nacelle: + drivetrain: + uptilt: 0.10471975511965977 # 6 deg + distance_tt_hub: 5.614 + overhang: 12.0313 + drag_coefficient: 0.5 + distance_hub_mb: 1.0 + distance_mb_mb: 1.2 + lss_diameter: [3.0, 3.0] + lss_wall_thickness: [0.1, 0.1] + nose_diameter: [2.2, 2.2] + nose_wall_thickness: [0.1, 0.1] + generator_length: 2.15 + bedplate_wall_thickness: + grid: [0.0, 1.0] + values: [0.05, 0.05] + gear_ratio: 1.0 + gearbox_efficiency: 1.0 + mb1Type: CARB + mb2Type: SRB + uptower: True + lss_material: steel_drive + bedplate_material: steel_drive + generator: + generator_type: PMSG_Outer + rho_Fe: 7700.0 + rho_Fes: 7850.0 + rho_Copper: 8900.0 + rho_PM: 7450.0 + B_r: 1.279 + P_Fe0e: 1.0 + P_Fe0h: 4.0 + S_N: -0.002 + alpha_p: 1.0995574287564276 #0.5*np.pi*0.7 + b_r_tau_r: 0.45 + b_ro: 0.004 + b_s_tau_s: 0.45 + b_so: 0.004 + cofi: 0.9 + freq: 60.0 + h_i: 0.004 + h_sy0: 0.0 + h_w: 0.005 + k_fes: 0.8 + k_fillr: 0.55 + k_fills: 0.65 + k_s: 0.2 + m: 3 + mu_0: 1.2566370614359173e-06 #np.pi*4e-7 + mu_r: 1.06 + phi: 1.5707963267948966 # 90 deg + q1: 5 + q2: 4 + ratio_mw2pp: 0.8 + resist_Cu: 2.52e-8 #1.8e-8*1.4 + y_tau_pr: 0.8333333 #10. / 12 + y_tau_p: 0.8 #12./15. + rad_ag: 5.12623359 + len_s: 2.23961662 + tau_p: 0.1610453779 + tau_s: 0.1339360726 + h_s: 0.3769449149 + b_s: 0.05121796888 + b_t: 0.08159225451 + h_t: 0.3859449149 + h_ys: 0.03618114528 + h_yr: 0.0361759511 + h_m: 0.0995385122 + b_m: 0.1288363023 + B_g: 1.38963289 + B_symax: 1.63455514 + B_rymax: 1.63478983 + p: 100 + R_s: 0.02457052 + L_s: 0.01138752 + S: 240 + t_r: 0.0607657125 + h_sr: 0.04677116325 + t_s: 0.06065213554 + h_ss: 0.04650825863 + E_p: 1905.2558883257652 + b: 2.0 + c: 5.0 + N_c: 2 + B_tmax: 1.9 + u_allow_pcent: 8.5 + y_allow_pcent: 1.0 + z_allow_deg: 0.05 + sigma: 60e3 + + h_0: 0.05 + C_Cu: 4.786 + C_Fe: 0.556 + C_Fes: 0.50139 + C_PM: 50.0 + tower: + outer_shape_bem: + reference_axis: &id003 + x: + grid: [0.0, 0.10047454902385111, 0.20094909804770222, 0.30142364707155334, 0.40189819609540445, 0.5023727451192556, 0.6028472941431067, 0.7033218431669578, 0.8037963921908089, 0.90427094121466, 1.0] + values: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] + y: + grid: [0.0, 0.10047454902385111, 0.20094909804770222, 0.30142364707155334, 0.40189819609540445, 0.5023727451192556, 0.6028472941431067, 0.7033218431669578, 0.8037963921908089, 0.90427094121466, 1.0] + values: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] + z: + grid: [0.0, 0.10047454902385111, 0.20094909804770222, 0.30142364707155334, 0.40189819609540445, 0.5023727451192556, 0.6028472941431067, 0.7033218431669578, 0.8037963921908089, 0.90427094121466, 1.0] + values: [15.0, 28.0, 41.0, 54.0, 67.0, 80.0, 93.0, 106.0, 119.0, 132.0, 144.386] + outer_diameter: + grid: [0.0, 0.10047454902385111, 0.20094909804770222, 0.30142364707155334, 0.40189819609540445, 0.5023727451192556, 0.6028472941431067, 0.7033218431669578, 0.8037963921908089, 0.90427094121466, 1.0] + values: [10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 6.5] + drag_coefficient: + grid: [0.0, 1.0] + values: [0.5, 0.5] + internal_structure_2d_fem: + outfitting_factor: 1.07 + reference_axis: *id003 + layers: + - name: tower_wall + material: steel + thickness: + grid: [0.0, 0.10047454902385111, 0.20094909804770222, 0.30142364707155334, 0.40189819609540445, 0.5023727451192556, 0.6028472941431067, 0.7033218431669578, 0.8037963921908089, 0.90427094121466, 1.0] + values: [0.09, 0.0870568244887821, 0.06969791129654272, 0.06659467020278927, 0.048969783194233195, 0.04562355995431616, 0.028128174258004363, 0.025616868141627953, 0.009881156462835017, 0.009941082963793929, 0.005931142626464429] + floating_platform: + transition_piece_mass: 100e3 + transition_piece_cost: 100e3 + joints: + - name: main_keel + location: [0.0, 0.0, -20.0] + + - name: main_freeboard + location: [0.0, 0.0, 15.0] + transition: True + + - name: col1_keel + location: [51.75, 3.14159265, -20.0] #3.14159265 = 180deg + cylindrical: True + + - name: col1_freeboard + location: [51.75, 3.14159265, 15.0] # 3.14159265 = 180deg + cylindrical: True + + - name: col2_keel + location: [51.75, 1.0471976, -20.0] #1.0471975 = 60 deg + cylindrical: True + + - name: col2_freeboard + location: [51.75, 1.0471975, 15.0] + cylindrical: True + + - name: col3_keel + location: [51.75, -1.0471976, -20.0] #-1.0471975 = -60 deg + cylindrical: True + + - name: col3_freeboard + location: [51.75, -1.0471976, 15.0] #-1.0471975 = -60 deg + cylindrical: True + + - name: anchor1 + location: [837.8, 3.14159265, -200.0] + cylindrical: True + + - name: anchor2 + location: [837.8, 1.0471976, -200.0] # 1.0471975 = 60 deg + cylindrical: True + + - name: anchor3 + location: [837.8, -1.0471976, -200.0] #-1.0471975 = -60 deg + cylindrical: True + + members: + - name: main_column + joint1: main_keel + joint2: main_freeboard + Ca: 1.0 + Cd: 0.8 + outer_shape: + shape: circular + outer_diameter: + grid: [0.0, 1.0] + values: [10.0, 10.0] + internal_structure: + layers: + - name: main_twall + material: steel + thickness: + grid: [0.0, 1.0] + values: [0.05, 0.05] + bulkhead: + material: steel + thickness: + grid: [0.0, 1.0] + values: [0.05, 0.05] + axial_joints: + - name: main_upper_pontoon + grid: 0.987 + + - name: main_lower_pontoon + grid: 0.1 + + - name: column1 + joint1: col1_keel + joint2: col1_freeboard + Ca: 1.0 + Cd: 0.8 + outer_shape: &col_out + shape: circular + outer_diameter: + grid: [0.0, 1.0] + values: [12.5, 12.5] + internal_structure: &col_int + layers: + - name: col_twall + material: steel + thickness: + grid: [0.0, 1.0] + values: [0.05, 0.05] + bulkhead: + material: steel + thickness: + grid: [0.0, 0.05, 0.2, 1.0] + values: [0.05, 0.05, 0.05, 0.05] + ballasts: + - variable_flag: False + material: slurry + volume: 169.333333 + grid: [0.0, 0.05] + - variable_flag: True + grid: [0.05, 0.2] + axial_joints: + - name: col1_upper_pontoon + grid: 0.987 + + - name: col1_lower_pontoon + grid: 0.1 + + - name: col1_fairlead + grid: 0.1714 + + - name: column2 + joint1: col2_keel + joint2: col2_freeboard + Ca: 1.0 + Cd: 0.8 + outer_shape: *col_out + internal_structure: *col_int + axial_joints: + - name: col2_upper_pontoon + grid: 0.987 + + - name: col2_lower_pontoon + grid: 0.1 + + - name: col2_fairlead + grid: 0.1714 + + - name: column3 + joint1: col3_keel + joint2: col3_freeboard + Ca: 1.0 + Cd: 0.8 + outer_shape: *col_out + internal_structure: *col_int + axial_joints: + - name: col3_upper_pontoon + grid: 0.987 + + - name: col3_lower_pontoon + grid: 0.1 + + - name: col3_fairlead + grid: 0.1714 + + + - name: Y_pontoon_upper1 + joint1: main_upper_pontoon + joint2: col1_upper_pontoon + Ca: 1.0 + Cd: 0.8 + outer_shape: &pontup_out + shape: circular + outer_diameter: + grid: [0.0, 1.0] + values: [0.91, 0.91] + internal_structure: &pontup_int + layers: + - name: pontoon_upper_twall + material: steel + thickness: + grid: [0.0, 1.0] + values: [0.02, 0.02] + + - name: Y_pontoon_upper2 + joint1: main_upper_pontoon + joint2: col2_upper_pontoon + Ca: 1.0 + Cd: 0.8 + outer_shape: *pontup_out + internal_structure: *pontup_int + + - name: Y_pontoon_upper3 + joint1: main_upper_pontoon + joint2: col3_upper_pontoon + Ca: 1.0 + Cd: 0.8 + outer_shape: *pontup_out + internal_structure: *pontup_int + + - name: Y_pontoon_lower1 + joint1: main_lower_pontoon + joint2: col1_lower_pontoon + Ca: 1.0 + Cd: 0.8 + outer_shape: &pontlow_out + # shape: circular + # outer_diameter: + # grid: [0.0, 1.0] + # values: [9.6148, 9.6148] #equates displacement not circle: [12.5, 12.5] + shape: rectangular + side_length_a: + grid: [0.0, 1.0] + values: [12.5, 12.5] + side_length_b: + grid: [0.0, 1.0] + values: [7.0, 7.0] + #shape: polygonal + #side_lengths: [12.5, 7.0, 12.5, 7.0] # rectangle dimensions + #angles: [1.5707963267948966, 1.5707963267948966, 1.5707963267948966, 1.5707963267948966] # rectangle angles + #rotation: 0.0 + internal_structure: &pontlow_int + # Probably needs longitudinal stiffeners + layers: + - name: pontoon_lower_twall + material: steel + thickness: + grid: [0.0, 1.0] + values: [0.04, 0.04] + ballasts: + - variable_flag: True + grid: [0.0, 1.0] + bulkhead: + material: steel + thickness: + grid: [0.0, 1.0] + values: [0.05, 0.05] + + - name: Y_pontoon_lower2 + joint1: main_lower_pontoon + joint2: col2_lower_pontoon + outer_shape: *pontlow_out + internal_structure: *pontlow_int + Ca: 1.0 + Cd: 0.8 + + - name: Y_pontoon_lower3 + joint1: main_lower_pontoon + joint2: col3_lower_pontoon + outer_shape: *pontlow_out + internal_structure: *pontlow_int + Ca: 1.0 + Cd: 0.8 + mooring: + nodes: + - name: line1_anchor + node_type: fixed + joint: anchor1 + anchor_type: drag_embedment + - name: line2_anchor + node_type: fixed + joint: anchor2 + anchor_type: drag_embedment + - name: line3_anchor + node_type: fixed + joint: anchor3 + anchor_type: drag_embedment + + - name: line1_vessel + node_type: vessel + joint: col1_fairlead + fairlead_type: rigid + - name: line2_vessel + node_type: vessel + joint: col2_fairlead + fairlead_type: rigid + - name: line3_vessel + joint: col3_fairlead + node_type: vessel + fairlead_type: rigid + + lines: + - name: line1 + node1: line1_anchor + node2: line1_vessel + line_type: main + unstretched_length: 850.0 + - name: line2 + node1: line2_anchor + node2: line2_vessel + line_type: main + unstretched_length: 850.0 + - name: line3 + node1: line3_anchor + node2: line3_vessel + line_type: main + unstretched_length: 850.0 + + line_types: + - name: main + diameter: 0.185 + type: chain + transverse_added_mass: 1.0 + tangential_added_mass: 0.0 + transverse_drag: 1.6 + tangential_drag: 0.1 + + anchor_types: + - name: drag_embedment + type: drag_embedment +airfoils: + - name: circular + coordinates: + x: [1.0, 0.99901, 0.99606, 0.99114, 0.98429, 0.97553, 0.96489, 0.95241, 0.93815, 0.92216, 0.90451, 0.88526, 0.86448, 0.84227, 0.81871, 0.79389, 0.76791, 0.74088, 0.71289, 0.68406, 0.65451, 0.62434, 0.59369, 0.56267, 0.5314, 0.5, 0.4686, 0.43733, 0.40631, 0.37566, 0.34549, 0.31594, 0.28711, 0.25912, 0.23209, 0.20611, 0.18129, 0.15773, 0.13552, 0.11474, 0.09549, 0.07784, 0.06185, 0.04759, 0.03511, 0.02447, 0.01571, 0.00886, 0.00394, 0.00099, 0.0, 0.00099, 0.00394, 0.00886, 0.01571, 0.02447, 0.03511, 0.04759, 0.06185, 0.07784, 0.09549, 0.11474, 0.13552, 0.15773, 0.18129, 0.20611, 0.23209, 0.25912, 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used + alp0: 0 #Place holder, currently not used + Xt: 450.e+006 + Xc: 450.e+006 + S: 0 + Xy: 345.e+6 + m: 3 + A: 3.5534648443719767e10 #DOI 10.1016/j.proeng.2013.12.061 eqn 5 + unit_cost: 0.7 + - name: steel_drive + description: Steel of the drivetrain ASTM 4140 40Cr1Mo28 + source: http://www.matweb.com/search/DataSheet.aspx?MatGUID=38108bfd64c44b4c9c6a02af78d5b6c6 + orth: 0 + rho: 7850 + alpha: 0.0 + E: 205.e+009 + nu: 0.3 + G: 80.0e+009 + GIc: 0 #Place holder, currently not used + GIIc: 0 #Place holder, currently not used + alp0: 0 #Place holder, currently not used + Xt: 814.e+006 + Xc: 814.e+006 + S: 0 + Xy: 485.e+6 + m: 3 + A: 3.5534648443719767e10 #DOI 10.1016/j.proeng.2013.12.061 eqn 5 + unit_cost: 0.9 + - name: cast_iron + description: Cast iron for hub and nacelle components + source: TODO + orth: 0 + rho: 7200 + alpha: 0.0 + E: 118.e+009 + nu: 0.3 + G: 47.6e+009 + GIc: 0 #Place holder, currently not used + GIIc: 0 #Place holder, currently not used + alp0: 0 #Place holder, currently not used + Xt: 310.e+006 + Xc: 310.e+006 + S: 0 + Xy: 265.e+6 + m: 3 + unit_cost: 0.5 + - name: glass_uni + description: Vectorply E-LT-5500, Epikote MGS RIMR 135/Epicure MGS RIMH 1366 epoxy + source: MSU composites database 3D property tests, Engineering Mechanics of Composite Materials, Daniel, I & Ishai, O., 1994, pg. 34 + orth: 1 + rho: 1940.0 + E: [4.46E10, 1.7E10, 1.67E10] + G: [3.27E9, 3.48E9, 3.5E9] + nu: [0.262, 0.35, 0.264] + Xt: [6.092E8, 3.81E7, 1.529E7] + Xc: [4.7471E8, 1.1264E8, 1.1322E8] + S: [1.891E7, 1.724E7, 1.316E7] + m: 10 + GIc: 303 + GIIc: 3446 + alp0: 53 + fvf: 0.57 + fwf: 0.7450682696347697 + ply_t: 0.005 + unit_cost: 1.87 + waste: 0.05 + fiber_density: 2535.5 + area_density_dry: 7.227162215457267 + component_id: 5 + - name: CarbonUD + E: [114500000000.0, 8390000000.0, 8390000000.0] + G: [5990000000.0, 5990000000.0, 5990000000.0] + rho: 1220.0 + orth: 1 + nu: [0.27, 0.27, 0.27] + Xt: [1546.e6, 0.0, 0.0] + Xc: [1047.e6, 0.0, 0.0] + S: [0.0, 0.0, 0.0] + m: 16.1 + GIc: 0.0 + GIIc: 0.0 + alp0: 0.0 + fvf: 0.1076923076923077 #0.5384615384615385 + fwf: 0.15889029003783103 #0.6461538461538463 + ply_t: 0.005158730158730159 #0.0010317460317460314 + unit_cost: 30. + waste: 0.05 + fiber_density: 1800. + area_density_dry: 1.000 + component_id: 4 + - name: glass_biax + description: Vectorply E-LT-5500, Epikote MGS RIMR 135/Epicure MGS RIMH 1366 epoxy + source: MSU composites database 3D property tests, Engineering Mechanics of Composite Materials, Daniel, I & Ishai, O., 1994, pg. 34 + orth: 1 + rho: 1940.0 + E: [1.11E10, 1.11E10, 1.67E10] + G: [1.353E10, 3.49E9, 3.49E9] + nu: [0.5, 0.0, 0.066] + Xt: [4.29E7, 4.26E7, 1.53E7] + Xc: [7.07E7, 7.07E7, 1.132E8] + S: [1.034E8, 1.72E7, 1.32E7] + m: 10 + GIc: 303 + GIIc: 3446 + alp0: 53 + fvf: 0.57 + fwf: 0.7450682696347697 + ply_t: 0.001 + waste: 0.15 + unit_cost: 3.00 + fiber_density: 2535.5 + area_density_dry: 1.4454324430914534 + component_id: 3 + roll_mass: 181.4368 + - name: glass_triax + description: Vectorply E-LT-5500, Epikote MGS RIMR 135/Epicure MGS RIMH 1366 epoxy + source: MSU composites database 3D property tests, Engineering Mechanics of Composite Materials, Daniel, I & Ishai, O., 1994, pg. 34 + orth: 1.0 + rho: 1940.0 + E: [2.87E10, 1.66E10, 1.67E10] + G: [8.4E9, 3.49E9, 3.49E9] + nu: [0.5, 0.0, 0.17] + Xt: [3.96E8, 7.64E7, 1.53E7] + Xc: [4.489E8, 1.747E8, 1.132E8] + S: [1.034E8, 1.72E7, 1.32E7] + m: 10 + GIc: 303 + GIIc: 3446 + alp0: 53 + fvf: 0.57 + fwf: 0.7450682696347697 + ply_t: 0.001 + unit_cost: 2.86 + waste: 0.15 + fiber_density: 2535.5 + area_density_dry: 1.4454324430914534 + component_id: 2 + roll_mass: 181.4368 + - name: medium_density_foam + description: Airex C70.130 PVC Foam, source 'https://www.3accorematerials.com/uploads/documents/TDS-AIREX-C70-E_1106.pdf' + orth: 0.0 + rho: 130.0 + E: 1.292E8 + G: 4.8946969696969695E7 + nu: 0.32 + Xt: 2083000.0 + Xc: 1563000.0 + S: 1250000.0 + GIc: 303 + GIIc: 3446 + alp0: 53 + component_id: 1 + waste: 0.2 + unit_cost: 13 + - name: resin + description: epoxy + E: 1.e+6 + nu: 0.3 #Place holder, currently not used + G: 312500.0 #Place holder, currently not used + GIc: 0 #Place holder, currently not used + GIIc: 0 #Place holder, currently not used + alp0: 0 #Place holder, currently not used + Xt: 0 #Place holder, currently not used + Xc: 0 #Place holder, currently not used + S: 0 #Place holder, currently not used + rho: 1150. + alpha: 0.0 + orth: 0 + unit_cost: 3.63 + - name: adhesive + description: Sample adhesive + source: https://www.nrel.gov/docs/fy19osti/73585.pdf + orth: 0 + rho: 1100 + E: 4.56e+006 + nu: 0.49 + alpha: 0.0 + Xt: 0.69e+006 + Xc: 0.4e+006 + S: 0.31e+006 + G: 1520000.0 + unit_cost: 9.0 + - name: slurry + description: fixed/permanent ballast + E: 1.0 # compliant + nu: 0.3 #Place holder, currently not used + G: 1.0 # compliant + Xt: 0 #Place holder, currently not used + Xc: 0 #Place holder, currently not used + S: 0 #Place holder, currently not used + rho: 5000. + alpha: 0.0 + unit_cost: 1.0 + orth: 0.0 + +control: + supervisory: + Vin: 3.0 + Vout: 25.0 + maxTS: 95. + pitch: + PC_zeta: 1.0 # Pitch controller desired damping ratio [-] + PC_omega: 0.2 # Pitch controller desired natural frequency [rad/s] + ps_percent: 0.8 # Percent peak shaving [%, <= 1 ], {default = 80%} + max_pitch: 1.57 # Maximum pitch angle [rad], {default = 90 degrees} + max_pitch_rate: 0.03490658503988659 # 2 deg/s + min_pitch: 0. # Minimum pitch angle [rad], {default = 0 degrees} + torque: + control_type: tsr_tracking + tsr: 9.0 + max_torque_rate: 1500000. + VS_minspd: 0.5235987755982988 # Minimum rotor speed [rad/s], {default = 0 rad/s} + VS_maxspd: 0.7916813487046278 # Minimum rotor speed [rad/s], {default = 0 rad/s} + VS_zeta: 1.0 # Torque controller desired damping ratio [-] + VS_omega: 0.2 # Torque controller desired natural frequency [rad/s] + setpoint_smooth: + ss_vsgain: 1 # Torque controller setpoint smoother gain bias percentage [%, <= 1 ], {default = 100%} + ss_pcgain: .001 # Pitch controller setpoint smoother gain bias percentage [%, <= 1 ], {default = 0.1%} + shutdown: + limit_type: gen_speed + limit_value: 2.0 +environment: + air_density: 1.225 + air_dyn_viscosity: 1.81e-5 + weib_shape_parameter: 2. + air_speed_sound: 340. + shear_exp: 0.12 + water_density: 1025.0 + water_dyn_viscosity: 1.3351e-3 + soil_shear_modulus: 140e6 + soil_poisson: 0.4 + water_depth: 200.0 + significant_wave_height: 4.52 + significant_wave_period: 9.45 +bos: + plant_turbine_spacing: 7 + plant_row_spacing: 7 + commissioning_pct: 0.01 + decommissioning_pct: 0.15 + distance_to_substation: 1.0 + distance_to_interconnection: 8.5 + interconnect_voltage: 130. + distance_to_site: 115. + distance_to_landfall: 50. + port_cost_per_month: 2e6 + site_auction_price: 100e6 + site_assessment_plan_cost: 1e6 + site_assessment_cost: 25e6 + construction_operations_plan_cost: 2.5e6 + boem_review_cost: 0.0 + design_install_plan_cost: 2.5e6 +costs: + wake_loss_factor: 0.15 + fixed_charge_rate: 0.056 + bos_per_kW: 4053. + opex_per_kW: 137. + turbine_number: 40. + labor_rate: 58.8 + painting_rate: 30.0 + blade_mass_cost_coeff: 14.6 + hub_mass_cost_coeff: 3.9 + pitch_system_mass_cost_coeff: 22.1 + spinner_mass_cost_coeff: 11.1 + lss_mass_cost_coeff: 11.9 + bearing_mass_cost_coeff: 4.5 + gearbox_mass_cost_coeff: 12.9 + hss_mass_cost_coeff: 6.8 + generator_mass_cost_coeff: 12.4 + bedplate_mass_cost_coeff: 2.9 + yaw_mass_cost_coeff: 8.3 + converter_mass_cost_coeff: 18.8 + transformer_mass_cost_coeff: 18.8 + hvac_mass_cost_coeff: 124.0 + cover_mass_cost_coeff: 5.7 + elec_connec_machine_rating_cost_coeff: 41.85 + platforms_mass_cost_coeff: 17.1 + tower_mass_cost_coeff: 2.9 + controls_machine_rating_cost_coeff: 21.15 + crane_cost: 12e3 diff --git a/weis/frequency/raft_wrapper.py b/weis/frequency/raft_wrapper.py index 6c822ee39..ed14827fe 100644 --- a/weis/frequency/raft_wrapper.py +++ b/weis/frequency/raft_wrapper.py @@ -50,7 +50,7 @@ def setup(self): members_opt['nreps'] = [0]*members_opt['nmembers'] member_shape = weis_opt["floating"]["members"]["outer_shape"] # TODO: need to re-visit how to handle square. Can we just get away square with rect? - member_shape = list(map(lambda x:x.replace("circular", "circ"), member_shape)) + member_shape = list(map(lambda x:x.replace("circular", "circ"), member_shape)) # replace all circular with circ member_shape = list(map(lambda x:x.replace("rectangular", "rect"), member_shape)) members_opt['shape'] = member_shape members_opt['scalar_thicknesses'] = members_opt['scalar_diameters'] = [False]*members_opt['nmembers'] @@ -144,7 +144,7 @@ def setup(self): n_ball = opt["floating"]["members"]["n_ballasts"][k] n_bulk = opt["floating"]["members"]["n_bulkheads"][k] if opt["floating"]["members"]["outer_shape"][k] == "circular": - self.add_input(f"member{k}:diameter", val=np.zeros(n_height), units="m") + self.add_input(f"member{k}:outer_diameter", val=np.zeros(n_height), units="m") self.add_output(f"platform_member{k+1}_d", val=np.zeros(n_height), units="m") elif opt["floating"]["members"]["outer_shape"][k] == "rectangular": self.add_input(f"member{k}:side_length_a", val=np.zeros(n_height), units="m") @@ -253,7 +253,7 @@ def compute(self, inputs, outputs, discrete_inputs, discrete_outputs): # Convert diameter and side lengths to RAFT if opt["floating"]["members"]["outer_shape"][k] == "circular": - outputs[f"platform_member{k+1}_d"] = inputs[f"member{k}:diameter"] + outputs[f"platform_member{k+1}_d"] = inputs[f"member{k}:outer_diameter"] elif opt["floating"]["members"]["outer_shape"][k] == "rectangular": outputs[f"platform_member{k+1}_d"][:, 0] = inputs[f"member{k}:side_length_a"] outputs[f"platform_member{k+1}_d"][:, 1] = inputs[f"member{k}:side_length_b"] diff --git a/weis/glue_code/glue_code.py b/weis/glue_code/glue_code.py index 177505dd5..c51d3588a 100644 --- a/weis/glue_code/glue_code.py +++ b/weis/glue_code/glue_code.py @@ -332,6 +332,12 @@ def setup(self): self.connect(f"floatingse.member{k}.ballast_height", f"raft.member{k}:ballast_height") self.connect(f"floatingse.member{k}.ballast_density", f"raft.member{k}:ballast_density") + if modeling_options['floating']['members']['outer_shape'][k] == "circular": + self.connect(f"floatingse.member{k}.outer_diameter", f"raft.member{k}:outer_diameter") + elif modeling_options['floating']['members']['outer_shape'][k] == "rectangular": + self.connect(f"floatingse.member{k}.side_length_a", f"raft.member{k}:side_length_a") + self.connect(f"floatingse.member{k}.side_length_b", f"raft.member{k}:side_length_b") + self.connect("mooring.mooring_nodes", 'raft.mooring_nodes') self.connect("mooring.unstretched_length", 'raft.unstretched_length') for var in ['diameter','mass_density','stiffness','breaking_load','cost_rate', diff --git a/weis/inputs/modeling_schema.yaml b/weis/inputs/modeling_schema.yaml index 76ceceab8..5aeaa8c55 100644 --- a/weis/inputs/modeling_schema.yaml +++ b/weis/inputs/modeling_schema.yaml @@ -154,6 +154,10 @@ properties: type: boolean default: False description: Save RAFT design iterations in /raft_designs + plot_designs: + type: boolean + default: False + description: Save RAFT design iterations in /raft_designs runPyHAMS: type: boolean default: True From 5b48e90ff435406bb826e922d921e994b8bc473c Mon Sep 17 00:00:00 2001 From: dzalkind Date: Wed, 10 Apr 2024 16:37:50 -0600 Subject: [PATCH 071/114] Switch example 15 to rectangular --- examples/15_RAFT_Studies/analysis_options.yaml | 4 ++-- examples/15_RAFT_Studies/weis_driver_raft_opt.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/examples/15_RAFT_Studies/analysis_options.yaml b/examples/15_RAFT_Studies/analysis_options.yaml index 87baf146d..a550c69f7 100644 --- a/examples/15_RAFT_Studies/analysis_options.yaml +++ b/examples/15_RAFT_Studies/analysis_options.yaml @@ -1,5 +1,5 @@ general: - folder_output: outputs/15_RAFT_optimization + folder_output: outputs/15_RAFT_Rect fname_output: refturb_output @@ -70,7 +70,7 @@ merit_figure: platform_mass # Merit figure of the optimization problem. The opt driver: optimization: - flag: True # Flag to enable optimization + flag: False # Flag to enable optimization solver: LN_COBYLA # Optimization solver. Other options are 'SLSQP' - 'CONMIN' tol: 1.e-2 # Optimality tolerance max_iter: 2 # Maximum number of iterations (SLSQP) diff --git a/examples/15_RAFT_Studies/weis_driver_raft_opt.py b/examples/15_RAFT_Studies/weis_driver_raft_opt.py index 096cca030..b7c2b40ef 100644 --- a/examples/15_RAFT_Studies/weis_driver_raft_opt.py +++ b/examples/15_RAFT_Studies/weis_driver_raft_opt.py @@ -7,7 +7,7 @@ ## File management run_dir = os.path.dirname( os.path.realpath(__file__) ) + os.sep -fname_wt_input = os.path.join(run_dir,"..","06_IEA-15-240-RWT", "IEA-15-240-RWT_VolturnUS-S.yaml") +fname_wt_input = os.path.join(run_dir,"IEA-15-240-RWT_VolturnUS-S_rectangular.yaml") fname_modeling_options = run_dir + "modeling_options_umaine_semi.yaml" fname_analysis_options = run_dir + "analysis_options.yaml" From 56efc234dd27cf998b946430f21d5c4d5348b8c5 Mon Sep 17 00:00:00 2001 From: Mayank Chetan Date: Thu, 2 May 2024 23:04:03 -0600 Subject: [PATCH 072/114] inital working kestrel install script --- share/kestrel_install.sh | 636 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 636 insertions(+) create mode 100644 share/kestrel_install.sh diff --git a/share/kestrel_install.sh b/share/kestrel_install.sh new file mode 100644 index 000000000..9c44bad8c --- /dev/null +++ b/share/kestrel_install.sh @@ -0,0 +1,636 @@ +#!/bin/bash +# Script to install WEIS on Kestrel for a developer with best practices +# To use the script; go to the desired location and run: +# wget https://raw.githubusercontent.com/mayankchetan/WEIS/kestrelInstall/share/kestrel_install.sh -O kestrel_install.sh +# chmod +xrw kestrel_install.sh +# ./kestrel_install.sh -h + +# Flags and variables; Set as required for install +weisBranch="develop" +weisRepoOverride="" +weisDirName="weis" + +openfastBranch="main" +openfastRepoOverride="" +openfastDirName="openfast" + +roscoBranch="develop" +roscoRepoOverride="" +roscoDirName="rosco" + +wisdemBranch="develop" +wisdemRepoOverride="" +wisdemDirName="wisdem" + +raftBranch="dev" +raftRepoOverride="" +raftDirName="raft" + + +# Defaults +install_openfast_flag=true +install_rosco_flag=true +install_wisdem_flag=false +install_raft_flag=false +Cray=false + +### !!!! Avoid changes below this line !!!! ### + +# Default repo URLs +default_weis_repo="https://www.github.com/WISDEM/WEIS" +default_openfast_repo="https://www.github.com/OpenFAST/openfast" +default_rosco_repo="https://www.github.com/NREL/ROSCO" +default_wisdem_repo="https://www.github.com/WISDEM/WISDEM" +default_raft_repo="https://www.github.com/WISDEM/RAFT" + +# Main function to install WEIS +main() { + + # Check if the script is running on Kestrel + check_if_kestrel + + # Parse the arguments + parse_arguments "$@" + + # Display user options + echo_user_options + + # Load KESTREL modules + load_modules + + # Install WEIS + install_weis + + # Install OpenFAST + if [[ "$install_openfast_flag" == true ]]; then + install_openfast + fi + + # Install ROSCO + if [[ "$install_rosco_flag" == true ]]; then + if [[ -n "$roscoRepoOverride" ]]; then + uninstall_reinstall_lib_source "rosco" "$roscoRepoOverride" "$roscoBranch" "$roscoDirName" + else + echo "Cloning ROSCO repository from default location...." + uninstall_reinstall_lib_source "rosco" "$default_rosco_repo" "$roscoBranch" "$roscoDirName" + fi + fi + + # Install WISDEM + if [[ "$install_wisdem_flag" == true ]]; then + if [[ -n "$wisdemRepoOverride" ]]; then + uninstall_reinstall_lib_source "wisdem" "$wisdemRepoOverride" "$wisdemBranch" "$wisdemDirName" + else + echo "Cloning WISDEM repository from default location...." + uninstall_reinstall_lib_source "wisdem" "$default_wisdem_repo" "$wisdemBranch" "$wisdemDirName" + fi + fi + + # Install RAFT + if [[ "$install_raft_flag" == true ]]; then + if [[ -n "$raftRepoOverride" ]]; then + uninstall_reinstall_lib_source "openraft" "$raftRepoOverride" "$raftBranch" "$raftDirName" + else + echo "Cloning RAFT repository from default location...." + uninstall_reinstall_lib_source "openraft" "$default_raft_repo" "$raftBranch" "$raftDirName" + fi + fi + + # Summary + summary + +} + +# Function to display help message +help_message() { + echo "" + echo "Usage: $0 " + echo "" + echo " This script creates a conda environment for WEIS on Kestrel." + echo " Additionally, OpenFAST and ROSCO are compiled using native compiler options." + echo "" + echo " >>>> User options can be edited in the script." + echo " Options include overiding the default repository and branch for WEIS, OpenFAST, ROSCO, WISDEM, and RAFT." + echo "" + echo " Arguments:" + echo " environment_name The desired name for the conda environment." + echo "" + echo " Optional Flags:" + echo " -h, --help Display this help message." + echo " -n , --name Specify the name for the conda environment." + echo " -p , --path Specify the path to install the conda environment." If used with -n, the name will be ignored. + echo " -raft, --raft Install RAFT separately." + echo " -wisdem, --wisdem Install WISDEM separately." + echo " -v, --verbose Enable verbose output. -> Not yet implemented" + echo " -cray, --cray Use Cray specific flags for OpenFAST build." + echo "" + echo " Example 1:" + echo " ./kestrelInstall.sh my_weis_env" + echo "" + echo " Example 2:" + echo " ./kestrelInstall.sh my_weis_env -p /path/to/env" + echo "" + echo " Example 3:" + echo " ./kestrelInstall.sh my_weis_env -p /path/to/env -raft -wisdem -cray" + echo "" + exit 1 +} + +check_if_kestrel() { + # Check if the script is running on Kestrel + if [[ "$NREL_CLUSTER" != "kestrel" ]]; then + echo "Error: This script is intended to run on Kestrel." + exit 1 + fi +} + +# Function to parse arguments +parse_arguments() { + # Check if at least one argument is provided + if [[ $# -eq 0 ]]; then + echo "Error: Please provide a name for the conda environment." + echo "Run '$0 -h' for help." + exit 1 + fi + + + while [[ $# -gt 0 ]]; do + case $1 in + -h|--help) + help_message + ;; + -n|--name) + # Capture the next argument (potential name for conda Environment) + if [[ $# -lt 2 ]]; then # Check if next argument exists + echo "Error: Missing name argument for -n." + echo "Run '$0 -h' for help." + exit 1 + fi + conda_env_name="$2" # Assign the next argument to conda_env_name + shift # Remove one of the arguments, secons is taken care outside the loop + ;; + -p|--path) + # Capture the next argument (potential path to install conda Environment) + if [[ $# -lt 2 ]]; then # Check if next argument exists + echo "Error: Missing path argument for -p." + echo "Run '$0 -h' for help." + exit 1 + fi + conda_env_path="$2" # Assign the next argument to conda_env_path + # TODO: Validate the path + shift # Remove one of the arguments, secons is taken care outside the loop + ;; + -raft|--raft) + # Set a flag for installing RAFT as seperate step + install_raft_flag=true + ;; + -wisdem|--wisdem) + # Set a flag for installing WISDEM as seperate step + install_wisdem_flag=true + ;; + -v|--verbose) + # Set a flag for verbose output (pseudo code) + verbose=true + ;; + -cray|--cray) + # Set a flag for Cray specific flags + Cray=true + ;; + *) + echo "Error: Unknown argument '$1'." + echo "Run '$0 -h' for help." + exit 1 + ;; + esac + shift # Remove the processed argument + done +} + + + +load_modules() { + # Load modules for WEIS installation + + if [[ "$Cray" == true ]]; then + module purge + module load craype-x86-spr + module load libfabric/1.15.2.0 + module load craype-network-ofi + module load perftools-base/22.09.0 + module load intel/2023.2.0 + module load craype/2.7.17 + module load cray-dsmml/0.2.2 + module load cray-mpich/8.1.23 + module load PrgEnv-intel/8.5.0 + module load hdf5/1.14.1-2-cray-mpich-intel + module load netcdf-c/4.9.2-cray-mpich-intel + module load cmake/3.20.2 + module load cray-libsci/22.12.1.1 + + # Set compiler environment variables + export CC=mpicc + export CXX=mpicxx + export FC=mpifort + else + + echo " " + echo "Loading modules for Kestrel login node $HOSTNAME...." + + module purge + + for mod in mamba git cmake craype-x86-spr intel-oneapi-compilers intel-oneapi-mpi intel-oneapi-mkl fftw/3.3.10-intel-oneapi-mpi-intel hdf5/1.14.1-2-intel-oneapi-mpi-intel netcdf-c/4.9.2-intel-oneapi-mpi-intel petsc/3.19.3-intel-oneapi-mpi-intel + do + echo "Loading $mod....." + module load $mod + done + + echo "Unloading GCC...." + module unload gcc + + # Set compiler environment variables + export CC=icc + export CXX=icpc + export FC=ifort + + fi +} + + +git_clone_switch_branch() { + # Clone a git repository and checkout a specific branch + local repo_url="$1"; shift + local branch_name="$1"; shift + + # If the user provides a name for the cloned directory + if [[ -n "$1" ]]; then + local cloned_repo_name="$1"; shift + else + local cloned_repo_name="$(basename "$repo_url" .git)" + fi + + # Check if required arguments are provided + if [[ -z "$repo_url" || -z "$branch_name" ]]; then + echo "Error: Please provide both the repository URL and branch name." + echo "Usage: $0 " + exit 1 + fi + + # Clone the repository + git clone "$repo_url" "$cloned_repo_name" || { + echo "Error: Failed to clone repository." + exit 1 + } + + # Move to the cloned directory (assuming single directory) + cd "$cloned_repo_name" || { + echo "Error: Couldn't change directory to the cloned repository." + exit 1 + } + + # Check if branch exists + if ! git branch --all | grep "$branch_name"; then + echo "Error: Branch '$branch_name' does not exist in the repository." + exit 1 + fi + + # Checkout the desired branch + git checkout "$branch_name" || { + echo "Error: Failed to checkout branch '$branch_name'." + exit 1 + } + + # Return to the original directory + cd .. + + # Success message + echo "Successfully cloned repository '$repo_url' and switched to branch '$branch_name'." + +} + + +install_weis() { + + # Check if WEIS repo overide is provided else use default + if [[ -n "$weisRepoOverride" ]]; then + git_clone_switch_branch "$weisRepoOverride" "$weisBranch" "$weisDirName" + else + echo "Cloning WEIS repository from default location...." + git_clone_switch_branch "$default_weis_repo" "$weisBranch" "$weisDirName" + fi + + user_prompt "Starting to create conda environment for WEIS...." + + # If path is provided instead of env name, use that, this will override the name if provided + if [[ -n "$conda_env_path" ]]; then + mamba env create -p "$conda_env_path" -f "$weisDirName/environment.yml" + else + mamba env create -n "$conda_env_name" -f "$weisDirName/environment.yml" + fi + + # Activate the environment & check that we are in the correct environment + if [[ -n "$conda_env_path" ]]; then + echo "" + echo "Activating conda environment at $conda_env_path...." + + source activate "$conda_env_path" || { + echo "Error: Failed to activate the conda environment." + exit 1 + } + + # Test if activated correctly -> using realpath to get the full path + if [[ "$CONDA_DEFAULT_ENV" != "$(realpath "$conda_env_path")" ]]; then + echo "Error: Not activated in required environment." + exit 1 + fi + + user_prompt "$conda_env_path activated...." + + else + echo "" + echo "Activating conda environment $conda_env_name...." + + source activate "$conda_env_name" || { + echo "Error: Failed to activate the conda environment <<<< Here." + exit 1 + } + + # Test if activated correctly + if [[ "$(basename "$CONDA_DEFAULT_ENV" .git)" != "$conda_env_name" ]]; then + echo "Error: Not activated in required environment." + exit 1 + fi + + user_prompt "$conda_env_name activated...." + + fi + + # verify if conda environemnt is activated correctly + + # install additional packages + mamba install -y petsc4py mpi4py pyoptsparse + + # Install the WEIS package + cd "$weisDirName" || { + echo "Error: Couldn't change directory to the cloned WEIS repository." + exit 1 + } + + pip install -e . || { + echo "Error: Failed to install WEIS." + cd .. + exit 1 + } + + # Return to the original directory + cd .. + + # Success message + echo "Successfully installed WEIS." + +} + +install_openfast() { + + echo " " + echo ">>>>>>>>>>>>>>>> Installing OpenFAST...." + + + # Check if OpenFAST repo overide is provided else use default + if [[ -n "$openfastRepoOverride" ]]; then + git_clone_switch_branch "$openfastRepoOverride" "$openfastBranch" "$openfastDirName" + else + echo "Cloning OpenFAST repository from default location...." + git_clone_switch_branch "$default_openfast_repo" "$openfastBranch" "$openfastDirName" + fi + + # Installing OpenFAST + cd "$openfastDirName" || { + echo "Error: Couldn't change directory to the cloned OpenFAST repository." + exit 1 + } + + mkdir -p build || { + echo "Error: Failed to create the build directory for OpenFAST." + cd .. + exit 1 + } + + cd build || { + echo "Error: Couldn't change directory to the build directory." + exit 1 + } + + cmake ..\ + -DDOUBLE_PRECISION:BOOL=OFF \ + -DOPENMP=ON \ + -DCMAKE_BUILD_TYPE=Release || { + echo "Error: Failed to run CMake for OpenFAST." + cd ../.. + exit 1 + } + + # If user requests Crey specific flags -> This is in addition to the above cmake flags + if [[ "$Cray" == true ]]; then + echo "Using Cray specific flags for OpenFAST build...." + cmake ..\ + -DBLAS_LIBRARIES="${CRAY_LIBSCI_PREFIX_DIR}"/lib/libsci_intel.a \ + -DLAPACK_LIBRARIES="${CRAY_LIBSCI_PREFIX_DIR}"/lib/libsci_intel.a || { + echo "Error: Failed to run CMake for OpenFAST with Cray specific flags." + cd ../.. + exit 1 + } + fi + + + make -j 4 || { + echo "Error: Failed to build OpenFAST." + cd ../.. + exit 1 + } + + # Install OpenFAST + make install || { + echo "Error: Failed to install OpenFAST." + cd ../.. + exit 1 + } + + # Return to the original directory + cd ../.. + + # Success message + echo "Successfully installed OpenFAST." +} + + +uninstall_reinstall_lib_source() { + + echo " " + echo ">>>>>>>>>>>>>>>> Installing $1...." + + + # $1 -> conda lib name + # $2 -> githb repo + # $3 -> lib branch + # $4 -> lib dir name + + # Uninstall a library from the conda environment and install it from source + local conda_lib_name="$1"; shift + local lib_repo="$1"; shift + local lib_branch="$1"; shift + local lib_dir_name="$1"; shift + + # Pull github repo + git_clone_switch_branch "$lib_repo" "$lib_branch" "$lib_dir_name" || { + echo "Error: Failed to clone $lib_repo repository." + exit 1 + } + + # Uninstall the library from the conda environment + conda uninstall --force "$conda_lib_name" -y || { + echo "Error: Failed to uninstall $conda_lib_name from the conda environment." + exit 1 + } + + # Going into the library directory + cd "$lib_dir_name" || { + echo "Error: Couldn't change directory to the cloned $conda_lib_name repository." + exit 1 + } + + # Install the library + pip install -e . || { + echo "Error: Failed to install $conda_lib_name." + cd .. + exit 1 + } + + # Return to the original directory + cd .. + + # Success message + echo "Successfully installed $conda_lib_name." +} + +user_prompt(){ + + echo " " + echo ">>>>>>>>>>>>>>>> $1" + echo "Press any key to continue... Crtl+C to exit...." + read -n 1 -s keypress + echo "Continuing..." + echo " " +} + +summary() { + echo " " + echo "*******************************************************" + echo "WEIS, OpenFAST, ROSCO have been installed successfully." + echo " " + if [[ "$install_wisdem_flag" == true ]]; then + echo "WISDEM has been installed successfully." + fi + if [[ "$install_raft_flag" == true ]]; then + echo "RAFT has been installed successfully." + fi + echo "" + echo "To activate the conda environment, run:" + if [[ -n "$conda_env_path" ]]; then + echo " conda activate $conda_env_path" + else + echo " conda activate $conda_env_name" + fi + echo " " +} + +echo_user_options() { + + echo " " + echo "*******************************************************" + echo "User options:" + echo " " + + # Talk about the conda environment + if [[ -n "$conda_env_path" ]]; then + echo "Conda environment path: $conda_env_path [Overiding environment name]" + else + echo "Conda environment name: $conda_env_name" + fi + echo " " + + # Talk about CRAY + echo "Cray specific flags: $Cray" + echo " " + # Talk about the WEIS installation + if [[ -n "$weisRepoOverride" ]]; then + echo "WEIS repository: $weisRepoOverride [Overide]" + else + echo "WEIS repository: $default_weis_repo" + fi + echo "WEIS branch: $weisBranch" + echo "WEIS directory name: $weisDirName" + echo " " + + # Talk about the OpenFAST installation + if [[ "$install_openfast_flag" == true ]]; then + if [[ -n "$openfastRepoOverride" ]]; then + echo "OpenFAST repository: $openfastRepoOverride [Overide]" + else + echo "OpenFAST repository: $default_openfast_repo" + fi + echo "OpenFAST branch: $openfastBranch" + echo "OpenFAST directory name: $openfastDirName" + + else + echo "Install OpenFAST: No" + fi + echo " " + + # Talk about the ROSCO installation + if [[ "$install_rosco_flag" == true ]]; then + if [[ -n "$roscoRepoOverride" ]]; then + echo "ROSCO repository: $roscoRepoOverride [Overide]" + else + echo "ROSCO repository: $default_rosco_repo" + fi + echo "ROSCO branch: $roscoBranch" + echo "ROSCO directory name: $roscoDirName" + else + echo "Install ROSCO: No" + fi + echo " " + + # Talk about the WISDEM installation + if [[ "$install_wisdem_flag" == true ]]; then + if [[ -n "$wisdemRepoOverride" ]]; then + echo "WISDEM repository: $wisdemRepoOverride [Overide]" + else + echo "WISDEM repository: $default_wisdem_repo" + fi + echo "WISDEM branch: $wisdemBranch" + echo "WISDEM directory name: $wisdemDirName" + else + echo "Install WISDEM: No" + fi + echo " " + + # Talk about the RAFT installation + if [[ "$install_raft_flag" == true ]]; then + if [[ -n "$raftRepoOverride" ]]; then + echo "RAFT repository: $raftRepoOverride [Overide]" + else + echo "RAFT repository: $default_raft_repo" + fi + echo "RAFT branch: $raftBranch" + echo "RAFT directory name: $raftDirName" + else + echo "Install RAFT: No" + fi + echo " " + + echo "*******************************************************" + + user_prompt "Review the options above...." +} + +main "$@"; exit 0 \ No newline at end of file From 4bc77fec119851ee41ad9ba8c107f7b0454b4f40 Mon Sep 17 00:00:00 2001 From: mayankchetan Date: Fri, 3 May 2024 13:55:46 -0600 Subject: [PATCH 073/114] Update to help message --- share/kestrel_install.sh | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/share/kestrel_install.sh b/share/kestrel_install.sh index 9c44bad8c..861e1b5f6 100644 --- a/share/kestrel_install.sh +++ b/share/kestrel_install.sh @@ -104,7 +104,7 @@ main() { # Function to display help message help_message() { echo "" - echo "Usage: $0 " + echo "Usage: $0 -n " echo "" echo " This script creates a conda environment for WEIS on Kestrel." echo " Additionally, OpenFAST and ROSCO are compiled using native compiler options." @@ -113,7 +113,7 @@ help_message() { echo " Options include overiding the default repository and branch for WEIS, OpenFAST, ROSCO, WISDEM, and RAFT." echo "" echo " Arguments:" - echo " environment_name The desired name for the conda environment." + echo " -n , --name The desired name for the conda environment." echo "" echo " Optional Flags:" echo " -h, --help Display this help message." @@ -125,13 +125,13 @@ help_message() { echo " -cray, --cray Use Cray specific flags for OpenFAST build." echo "" echo " Example 1:" - echo " ./kestrelInstall.sh my_weis_env" + echo " ./kestrelInstall.sh -n my_weis_env" echo "" echo " Example 2:" - echo " ./kestrelInstall.sh my_weis_env -p /path/to/env" + echo " ./kestrelInstall.sh -n my_weis_env -p /path/to/env" echo "" echo " Example 3:" - echo " ./kestrelInstall.sh my_weis_env -p /path/to/env -raft -wisdem -cray" + echo " ./kestrelInstall.sh -n my_weis_env -p /path/to/env -raft -wisdem -cray" echo "" exit 1 } From 171b14eb94aac81cedf696cda909e51d75983968 Mon Sep 17 00:00:00 2001 From: dzalkind Date: Mon, 6 May 2024 12:00:24 -0600 Subject: [PATCH 074/114] Fix some WISDEM 3.15 connections --- weis/aeroelasticse/openmdao_openfast.py | 1 - weis/glue_code/glue_code.py | 5 ++--- 2 files changed, 2 insertions(+), 4 deletions(-) diff --git a/weis/aeroelasticse/openmdao_openfast.py b/weis/aeroelasticse/openmdao_openfast.py index 51fb138f8..b71153265 100644 --- a/weis/aeroelasticse/openmdao_openfast.py +++ b/weis/aeroelasticse/openmdao_openfast.py @@ -118,7 +118,6 @@ def setup(self): while r[-1] should be the blade tip. Any number \ of locations can be specified between these in ascending order.') self.add_input('le_location', val=np.zeros(n_span), desc='Leading-edge positions from a reference blade axis (usually blade pitch axis). Locations are normalized by the local chord length. Positive in -x direction for airfoil-aligned coordinate system') - self.add_input('beam:Tw_iner', val=np.zeros(n_span), units='m', desc='y-distance to elastic center from point about which above structural properties are computed') self.add_input('beam:rhoA', val=np.zeros(n_span), units='kg/m', desc='mass per unit length') self.add_input('beam:EIyy', val=np.zeros(n_span), units='N*m**2', desc='flatwise stiffness (bending about y-direction of airfoil aligned coordinate system)') self.add_input('beam:EIxx', val=np.zeros(n_span), units='N*m**2', desc='edgewise stiffness (bending about :ref:`x-direction of airfoil aligned coordinate system `)') diff --git a/weis/glue_code/glue_code.py b/weis/glue_code/glue_code.py index fbb85b955..106932a3c 100644 --- a/weis/glue_code/glue_code.py +++ b/weis/glue_code/glue_code.py @@ -541,7 +541,6 @@ def setup(self): self.connect('rotorse.rhoA', 'aeroelastic.beam:rhoA') self.connect('rotorse.EIxx', 'aeroelastic.beam:EIxx') self.connect('rotorse.EIyy', 'aeroelastic.beam:EIyy') - self.connect('rotorse.re.Tw_iner', 'aeroelastic.beam:Tw_iner') self.connect('rotorse.rs.frame.flap_mode_shapes', 'aeroelastic.flap_mode_shapes') self.connect('rotorse.rs.frame.edge_mode_shapes', 'aeroelastic.edge_mode_shapes') self.connect('rotorse.rp.powercurve.V', 'aeroelastic.U') @@ -668,8 +667,8 @@ def setup(self): self.connect('rotorse.mass_all_blades', 'drivese_post.blades_mass') self.connect('rotorse.I_all_blades', 'drivese_post.blades_I') - self.connect('nacelle.distance_hub2mb', 'drivese_post.L_h1') - self.connect('nacelle.distance_mb2mb', 'drivese_post.L_12') + self.connect('nacelle.distance_hub_mb', 'drivese_post.L_h1') + self.connect('nacelle.distance_mb_mb', 'drivese_post.L_12') self.connect('nacelle.L_generator', 'drivese_post.L_generator') self.connect('nacelle.overhang', 'drivese_post.overhang') self.connect('nacelle.distance_tt_hub', 'drivese_post.drive_height') From 3fea3136699a14f6e3218652fc123a61e2842fd3 Mon Sep 17 00:00:00 2001 From: dzalkind Date: Mon, 6 May 2024 12:01:22 -0600 Subject: [PATCH 075/114] Update WISDEM version in environment setup --- environment.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/environment.yml b/environment.yml index a2cc3ff4c..c1036b6a1 100644 --- a/environment.yml +++ b/environment.yml @@ -66,7 +66,7 @@ dependencies: - traitlets - treon - webencodings - - wisdem=3.14.0 + - wisdem=3.15 - pip: - dearpygui - marmot-agents From bcd0351058228a34139d6bf01019e4119421ab28 Mon Sep 17 00:00:00 2001 From: mayankchetan Date: Tue, 7 May 2024 14:05:52 -0600 Subject: [PATCH 076/114] handle if git repos already exist --- share/kestrel_install.sh | 106 ++++++++++++++++++++++++++++++++++----- 1 file changed, 94 insertions(+), 12 deletions(-) diff --git a/share/kestrel_install.sh b/share/kestrel_install.sh index 861e1b5f6..669563bd8 100644 --- a/share/kestrel_install.sh +++ b/share/kestrel_install.sh @@ -71,7 +71,7 @@ main() { if [[ -n "$roscoRepoOverride" ]]; then uninstall_reinstall_lib_source "rosco" "$roscoRepoOverride" "$roscoBranch" "$roscoDirName" else - echo "Cloning ROSCO repository from default location...." + echo "Cloning ROSCO repository to default location...." uninstall_reinstall_lib_source "rosco" "$default_rosco_repo" "$roscoBranch" "$roscoDirName" fi fi @@ -81,7 +81,7 @@ main() { if [[ -n "$wisdemRepoOverride" ]]; then uninstall_reinstall_lib_source "wisdem" "$wisdemRepoOverride" "$wisdemBranch" "$wisdemDirName" else - echo "Cloning WISDEM repository from default location...." + echo "Cloning WISDEM repository to default location...." uninstall_reinstall_lib_source "wisdem" "$default_wisdem_repo" "$wisdemBranch" "$wisdemDirName" fi fi @@ -91,7 +91,7 @@ main() { if [[ -n "$raftRepoOverride" ]]; then uninstall_reinstall_lib_source "openraft" "$raftRepoOverride" "$raftBranch" "$raftDirName" else - echo "Cloning RAFT repository from default location...." + echo "Cloning RAFT repository to default location...." uninstall_reinstall_lib_source "openraft" "$default_raft_repo" "$raftBranch" "$raftDirName" fi fi @@ -128,10 +128,10 @@ help_message() { echo " ./kestrelInstall.sh -n my_weis_env" echo "" echo " Example 2:" - echo " ./kestrelInstall.sh -n my_weis_env -p /path/to/env" + echo " ./kestrelInstall.sh -p /path/to/env" echo "" echo " Example 3:" - echo " ./kestrelInstall.sh -n my_weis_env -p /path/to/env -raft -wisdem -cray" + echo " ./kestrelInstall.sh -p /path/to/env -raft -wisdem" echo "" exit 1 } @@ -213,6 +213,8 @@ load_modules() { if [[ "$Cray" == true ]]; then module purge + module load conda + module load mamba module load craype-x86-spr module load libfabric/1.15.2.0 module load craype-network-ofi @@ -238,7 +240,7 @@ load_modules() { module purge - for mod in mamba git cmake craype-x86-spr intel-oneapi-compilers intel-oneapi-mpi intel-oneapi-mkl fftw/3.3.10-intel-oneapi-mpi-intel hdf5/1.14.1-2-intel-oneapi-mpi-intel netcdf-c/4.9.2-intel-oneapi-mpi-intel petsc/3.19.3-intel-oneapi-mpi-intel + for mod in conda mamba git cmake craype-x86-spr intel-oneapi-compilers intel-oneapi-mpi intel-oneapi-mkl fftw/3.3.10-intel-oneapi-mpi-intel hdf5/1.14.1-2-intel-oneapi-mpi-intel netcdf-c/4.9.2-intel-oneapi-mpi-intel petsc/3.19.3-intel-oneapi-mpi-intel do echo "Loading $mod....." module load $mod @@ -258,16 +260,26 @@ load_modules() { git_clone_switch_branch() { # Clone a git repository and checkout a specific branch - local repo_url="$1"; shift - local branch_name="$1"; shift + local repo_url="$1"; + local branch_name="$2"; # If the user provides a name for the cloned directory if [[ -n "$1" ]]; then - local cloned_repo_name="$1"; shift + local cloned_repo_name="$3"; else - local cloned_repo_name="$(basename "$repo_url" .git)" + local cloned_repo_name + cloned_repo_name="$(basename "$repo_url" .git)" fi + # Check if the repository is already cloned with the same folder name + check_git_repo_branch "$repo_url" "$branch_name" "$cloned_repo_name" + if [ $? -ne 0 ]; then # if return is 0, then proceed with the git clone + echo " " + user_prompt "Copy of the repository already exists. Proceeding without git clone...." + return 0 + fi + + # Check if required arguments are provided if [[ -z "$repo_url" || -z "$branch_name" ]]; then echo "Error: Please provide both the repository URL and branch name." @@ -308,13 +320,62 @@ git_clone_switch_branch() { } +check_git_repo_branch(){ + # Check if a branch exists in a git repository + local repo_url="$1"; + local branch_name="$2"; + local cloned_repo_name="$3"; + + + # Check if the repository is already cloned with the same folder name, If not return 0 to proceed with the git clone + if [[ -n "$cloned_repo_name" ]]; then + echo " " + echo "Warning 09: Directory '$cloned_repo_name' already exists." + + # check if the folder is the correct git repository + if [[ "$(git -C "$cloned_repo_name" config --get remote.origin.url)" != "$repo_url" ]]; then + echo "Warning: Directory '$cloned_repo_name' is not from the correct repository." + if user_selection "Do you want to proceed with the git clone and overwrite the existing directory?"; then + echo "Proceeding with the deletion & git clone...." + rm -rf "$cloned_repo_name" + return 0 # return 0 to proceed with the git clone + + else + echo "Exiting...." + exit 1 + fi + + fi + + # check if the folder is in the correct branch + if [[ "$(git -C "$cloned_repo_name" rev-parse --abbrev-ref HEAD)" != "$branch_name" ]]; then + echo "Warning: Directory '$cloned_repo_name' is not in the correct branch." + if user_selection "Do you want to proceed with the git clone and overwrite the existing directory?"; then + echo "Proceeding with the deletion & git clone...." + rm -rf "$cloned_repo_name" + return 0 # return 0 to proceed with the git clone + else + echo "Exiting...." + exit 1 + fi + fi + + # If the folder is in the correct branch, return 1 to exit the function + return 1 + + else # exit current function and return to calling function with proceed signal + return 0 + fi + +} + install_weis() { # Check if WEIS repo overide is provided else use default if [[ -n "$weisRepoOverride" ]]; then git_clone_switch_branch "$weisRepoOverride" "$weisBranch" "$weisDirName" else - echo "Cloning WEIS repository from default location...." + echo "Cloning WEIS repository to default location...." git_clone_switch_branch "$default_weis_repo" "$weisBranch" "$weisDirName" fi @@ -399,7 +460,7 @@ install_openfast() { if [[ -n "$openfastRepoOverride" ]]; then git_clone_switch_branch "$openfastRepoOverride" "$openfastBranch" "$openfastDirName" else - echo "Cloning OpenFAST repository from default location...." + echo "Cloning OpenFAST repository to default location...." git_clone_switch_branch "$default_openfast_repo" "$openfastBranch" "$openfastDirName" fi @@ -522,6 +583,27 @@ user_prompt(){ echo " " } +user_selection(){ + + # ask user to decide between yes or no, loop if invalid input + + echo " " + echo ">>>>>>>>>>>>>>>> $1" + echo "Press 'y' for Yes or 'n' for No...." + read -n 1 -s keypress + + while [[ "$keypress" != "y" && "$keypress" != "n" ]]; do + echo "Invalid input. Press 'y' for Yes or 'n' for No...." + read -n 1 -s keypress + done + + if [[ "$keypress" == "y" ]]; then + return 0 + else + return 1 + fi +} + summary() { echo " " echo "*******************************************************" From efe33a8d71cb90cf1e48b4b5be10399b723b5125 Mon Sep 17 00:00:00 2001 From: dzalkind Date: Tue, 7 May 2024 15:01:16 -0600 Subject: [PATCH 077/114] Remove openmdao requirement because wisdem (hopefully) handles it --- pyproject.toml | 1 - 1 file changed, 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index ec36de6c7..c472c8746 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -46,7 +46,6 @@ dependencies = [ "moorpy", "nlopt", "numpy", - "openmdao<3.28", "openpyxl", "openraft", "osqp", From d6751770d67647faf7510c7e0f76934cac8a96b5 Mon Sep 17 00:00:00 2001 From: mayankchetan Date: Wed, 8 May 2024 16:03:33 -0600 Subject: [PATCH 078/114] Dir flag bug, +quiet-mode --- share/kestrel_install.sh | 26 ++++++++++++++++++++++++-- 1 file changed, 24 insertions(+), 2 deletions(-) diff --git a/share/kestrel_install.sh b/share/kestrel_install.sh index 669563bd8..5e8c75bf7 100644 --- a/share/kestrel_install.sh +++ b/share/kestrel_install.sh @@ -196,6 +196,14 @@ parse_arguments() { # Set a flag for Cray specific flags Cray=true ;; + -quiet-mode|--quiet-mode) + # Set a flag for installing everything and without user prompts. Goes Brrrrrr...... + quiet_mode=true + install_openfast_flag=true + install_rosco_flag=true + install_raft_flag=true + install_wisdem_flag=true + ;; *) echo "Error: Unknown argument '$1'." echo "Run '$0 -h' for help." @@ -240,7 +248,7 @@ load_modules() { module purge - for mod in conda mamba git cmake craype-x86-spr intel-oneapi-compilers intel-oneapi-mpi intel-oneapi-mkl fftw/3.3.10-intel-oneapi-mpi-intel hdf5/1.14.1-2-intel-oneapi-mpi-intel netcdf-c/4.9.2-intel-oneapi-mpi-intel petsc/3.19.3-intel-oneapi-mpi-intel + for mod in conda mamba git cmake craype-x86-spr intel-oneapi-compilers intel-oneapi-mpi intel-oneapi-mkl fftw/3.3.10-intel-oneapi-mpi-intel hdf5/1.14.1-2-intel-oneapi-mpi-intel netcdf-c/4.9.2-intel-oneapi-mpi-intel petsc/3.20.4-intel-oneapi-mpi-intel do echo "Loading $mod....." module load $mod @@ -328,7 +336,7 @@ check_git_repo_branch(){ # Check if the repository is already cloned with the same folder name, If not return 0 to proceed with the git clone - if [[ -n "$cloned_repo_name" ]]; then + if [[ -d "$cloned_repo_name" ]]; then echo " " echo "Warning 09: Directory '$cloned_repo_name' already exists." @@ -578,6 +586,13 @@ user_prompt(){ echo " " echo ">>>>>>>>>>>>>>>> $1" echo "Press any key to continue... Crtl+C to exit...." + + if [[ "$quiet_mode" == true ]]; then + echo " " + echo "quiet mode activated. Proceeding without user prompt...., Going Brrrrr...." + return 0 + fi + read -n 1 -s keypress echo "Continuing..." echo " " @@ -590,6 +605,13 @@ user_selection(){ echo " " echo ">>>>>>>>>>>>>>>> $1" echo "Press 'y' for Yes or 'n' for No...." + + if [[ "$quiet_mode" == true ]]; then + echo " " + echo "quiet mode activated. Proceeding without user prompt...., Going Brrrrr...." + return 0 + fi + read -n 1 -s keypress while [[ "$keypress" != "y" && "$keypress" != "n" ]]; do From 3917a4d13c6eb880e1633af05b9faa2630017bca Mon Sep 17 00:00:00 2001 From: Mayank Chetan <4620557+mayankchetan@users.noreply.github.com> Date: Fri, 10 May 2024 11:05:25 -0600 Subject: [PATCH 079/114] Changing `wget` instructions to reflect WEIS develop branch --- share/kestrel_install.sh | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/share/kestrel_install.sh b/share/kestrel_install.sh index 5e8c75bf7..3960d0051 100644 --- a/share/kestrel_install.sh +++ b/share/kestrel_install.sh @@ -1,7 +1,7 @@ #!/bin/bash # Script to install WEIS on Kestrel for a developer with best practices # To use the script; go to the desired location and run: -# wget https://raw.githubusercontent.com/mayankchetan/WEIS/kestrelInstall/share/kestrel_install.sh -O kestrel_install.sh +# wget https://raw.githubusercontent.com/WISDEM/WEIS/develop/share/kestrel_install.sh -O kestrel_install.sh # chmod +xrw kestrel_install.sh # ./kestrel_install.sh -h @@ -737,4 +737,4 @@ echo_user_options() { user_prompt "Review the options above...." } -main "$@"; exit 0 \ No newline at end of file +main "$@"; exit 0 From bfb032fe9a29db7cc8f2ff77af24be98a59837cb Mon Sep 17 00:00:00 2001 From: yqliaohk Date: Wed, 15 May 2024 12:42:04 -0600 Subject: [PATCH 080/114] Working on connecting Ca and Cd --- weis/frequency/raft_wrapper.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/weis/frequency/raft_wrapper.py b/weis/frequency/raft_wrapper.py index ed14827fe..c7fdabc13 100644 --- a/weis/frequency/raft_wrapper.py +++ b/weis/frequency/raft_wrapper.py @@ -174,8 +174,12 @@ def setup(self): self.add_output(f"platform_member{k+1}_ring_spacing", val=0.0) self.add_output(f"platform_member{k+1}_ring_t", val=0.0, units="m") self.add_output(f"platform_member{k+1}_ring_h", val=0.0, units="m") - self.add_output(f"platform_member{k+1}_Cd", val=0.8*np.ones(n_height)) - self.add_output(f"platform_member{k+1}_Ca", val=np.ones(n_height)) + if opt["floating"]["members"]["outer_shape"][k] == "circular": + self.add_output(f"platform_member{k+1}_Cd", val=0.8*np.ones(n_height)) + self.add_output(f"platform_member{k+1}_Ca", val=np.ones(n_height)) + elif opt["floating"]["members"]["outer_shape"][k] == "rectangular": + self.add_output(f"platform_member{k+1}_Cd", val=0.8*np.ones(n_height, 2)) + self.add_output(f"platform_member{k+1}_Ca", val=np.ones(n_height, 2)) self.add_output(f"platform_member{k+1}_CdEnd", val=0.6*np.ones(n_height)) self.add_output(f"platform_member{k+1}_CaEnd", val=0.6*np.ones(n_height)) From 272df4183f79b11a39c498b1f1e0b840359b4837 Mon Sep 17 00:00:00 2001 From: yqliaohk Date: Sat, 18 May 2024 22:57:32 -0600 Subject: [PATCH 081/114] Passed Ca and Cd to RAFT --- ...EA-15-240-RWT_VolturnUS-S_rectangular.yaml | 40 +++++++++---------- weis/frequency/raft_wrapper.py | 16 +++++++- weis/glue_code/glue_code.py | 6 +++ 3 files changed, 40 insertions(+), 22 deletions(-) diff --git a/examples/15_RAFT_Studies/IEA-15-240-RWT_VolturnUS-S_rectangular.yaml b/examples/15_RAFT_Studies/IEA-15-240-RWT_VolturnUS-S_rectangular.yaml index 4d4e040ef..7c04e369b 100644 --- a/examples/15_RAFT_Studies/IEA-15-240-RWT_VolturnUS-S_rectangular.yaml +++ b/examples/15_RAFT_Studies/IEA-15-240-RWT_VolturnUS-S_rectangular.yaml @@ -631,8 +631,8 @@ components: - name: main_column joint1: main_keel joint2: main_freeboard - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] outer_shape: shape: circular outer_diameter: @@ -660,8 +660,8 @@ components: - name: column1 joint1: col1_keel joint2: col1_freeboard - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] outer_shape: &col_out shape: circular outer_diameter: @@ -699,8 +699,8 @@ components: - name: column2 joint1: col2_keel joint2: col2_freeboard - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] outer_shape: *col_out internal_structure: *col_int axial_joints: @@ -716,8 +716,8 @@ components: - name: column3 joint1: col3_keel joint2: col3_freeboard - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] outer_shape: *col_out internal_structure: *col_int axial_joints: @@ -734,8 +734,8 @@ components: - name: Y_pontoon_upper1 joint1: main_upper_pontoon joint2: col1_upper_pontoon - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] outer_shape: &pontup_out shape: circular outer_diameter: @@ -752,24 +752,24 @@ components: - name: Y_pontoon_upper2 joint1: main_upper_pontoon joint2: col2_upper_pontoon - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] outer_shape: *pontup_out internal_structure: *pontup_int - name: Y_pontoon_upper3 joint1: main_upper_pontoon joint2: col3_upper_pontoon - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] outer_shape: *pontup_out internal_structure: *pontup_int - name: Y_pontoon_lower1 joint1: main_lower_pontoon joint2: col1_lower_pontoon - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] outer_shape: &pontlow_out # shape: circular # outer_diameter: @@ -808,16 +808,16 @@ components: joint2: col2_lower_pontoon outer_shape: *pontlow_out internal_structure: *pontlow_int - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] - name: Y_pontoon_lower3 joint1: main_lower_pontoon joint2: col3_lower_pontoon outer_shape: *pontlow_out internal_structure: *pontlow_int - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] mooring: nodes: - name: line1_anchor diff --git a/weis/frequency/raft_wrapper.py b/weis/frequency/raft_wrapper.py index c7fdabc13..200d794fb 100644 --- a/weis/frequency/raft_wrapper.py +++ b/weis/frequency/raft_wrapper.py @@ -145,10 +145,16 @@ def setup(self): n_bulk = opt["floating"]["members"]["n_bulkheads"][k] if opt["floating"]["members"]["outer_shape"][k] == "circular": self.add_input(f"member{k}:outer_diameter", val=np.zeros(n_height), units="m") + self.add_input(f"member{k}:Ca", val=np.zeros(n_height)) + self.add_input(f"member{k}:Cd", val=np.zeros(n_height)) self.add_output(f"platform_member{k+1}_d", val=np.zeros(n_height), units="m") elif opt["floating"]["members"]["outer_shape"][k] == "rectangular": self.add_input(f"member{k}:side_length_a", val=np.zeros(n_height), units="m") self.add_input(f"member{k}:side_length_b", val=np.zeros(n_height), units="m") + self.add_input(f"member{k}:Ca", val=np.zeros(n_height)) + self.add_input(f"member{k}:Cd", val=np.zeros(n_height)) + self.add_input(f"member{k}:Cay", val=np.zeros(n_height)) + self.add_input(f"member{k}:Cdy", val=np.zeros(n_height)) # RAFT collect a and b into d self.add_output(f"platform_member{k+1}_d", val=np.zeros([n_height,2]), units="m") self.add_input(f"member{k}:height", val=0.0, units="m") @@ -178,8 +184,8 @@ def setup(self): self.add_output(f"platform_member{k+1}_Cd", val=0.8*np.ones(n_height)) self.add_output(f"platform_member{k+1}_Ca", val=np.ones(n_height)) elif opt["floating"]["members"]["outer_shape"][k] == "rectangular": - self.add_output(f"platform_member{k+1}_Cd", val=0.8*np.ones(n_height, 2)) - self.add_output(f"platform_member{k+1}_Ca", val=np.ones(n_height, 2)) + self.add_output(f"platform_member{k+1}_Cd", val=0.8*np.ones([n_height, 2])) + self.add_output(f"platform_member{k+1}_Ca", val=np.ones([n_height, 2])) self.add_output(f"platform_member{k+1}_CdEnd", val=0.6*np.ones(n_height)) self.add_output(f"platform_member{k+1}_CaEnd", val=0.6*np.ones(n_height)) @@ -258,9 +264,15 @@ def compute(self, inputs, outputs, discrete_inputs, discrete_outputs): # Convert diameter and side lengths to RAFT if opt["floating"]["members"]["outer_shape"][k] == "circular": outputs[f"platform_member{k+1}_d"] = inputs[f"member{k}:outer_diameter"] + outputs[f"platform_member{k+1}_Ca"] = inputs[f"member{k}:Ca"] if np.all(inputs[f"member{k}:Ca"]>0.0) else 1 + outputs[f"platform_member{k+1}_Cd"] = inputs[f"member{k}:Cd"] if np.all(inputs[f"member{k}:Cd"]>0.0) else 1 elif opt["floating"]["members"]["outer_shape"][k] == "rectangular": outputs[f"platform_member{k+1}_d"][:, 0] = inputs[f"member{k}:side_length_a"] outputs[f"platform_member{k+1}_d"][:, 1] = inputs[f"member{k}:side_length_b"] + outputs[f"platform_member{k+1}_Ca"][:, 0] = inputs[f"member{k}:Ca"] if np.all(inputs[f"member{k}:Ca"]>0.0) else 1 + outputs[f"platform_member{k+1}_Cd"][:, 0] = inputs[f"member{k}:Cd"] if np.all(inputs[f"member{k}:Cd"]>0.0) else 1 + outputs[f"platform_member{k+1}_Ca"][:, 1] = inputs[f"member{k}:Cay"] if np.all(inputs[f"member{k}:Cay"]>0.0) else 1 + outputs[f"platform_member{k+1}_Cd"][:, 0] = inputs[f"member{k}:Cdy"] if np.all(inputs[f"member{k}:Cdy"]>0.0) else 1 # Ring stiffener discretization conversion if ( (float(inputs[f"member{k}:ring_stiffener_spacing"]) > 0.0) and diff --git a/weis/glue_code/glue_code.py b/weis/glue_code/glue_code.py index c51d3588a..458048f47 100644 --- a/weis/glue_code/glue_code.py +++ b/weis/glue_code/glue_code.py @@ -334,9 +334,15 @@ def setup(self): if modeling_options['floating']['members']['outer_shape'][k] == "circular": self.connect(f"floatingse.member{k}.outer_diameter", f"raft.member{k}:outer_diameter") + self.connect(f"floating.memgrid{idx}.ca_usr_grid", f"raft.member{k}:Ca") + self.connect(f"floating.memgrid{idx}.cd_usr_grid", f"raft.member{k}:Cd") elif modeling_options['floating']['members']['outer_shape'][k] == "rectangular": self.connect(f"floatingse.member{k}.side_length_a", f"raft.member{k}:side_length_a") self.connect(f"floatingse.member{k}.side_length_b", f"raft.member{k}:side_length_b") + self.connect(f"floating.memgrid{idx}.ca_usr_grid", f"raft.member{k}:Ca") + self.connect(f"floating.memgrid{idx}.cd_usr_grid", f"raft.member{k}:Cd") + self.connect(f"floating.memgrid{idx}.cay_usr_grid", f"raft.member{k}:Cay") + self.connect(f"floating.memgrid{idx}.cdy_usr_grid", f"raft.member{k}:Cdy") self.connect("mooring.mooring_nodes", 'raft.mooring_nodes') self.connect("mooring.unstretched_length", 'raft.unstretched_length') From 7287310ce629c2c6d367fe271ca64932940a1b82 Mon Sep 17 00:00:00 2001 From: yqliaohk Date: Fri, 24 May 2024 12:47:29 -0600 Subject: [PATCH 082/114] Updated Ca and Cd in example yamls --- .../IEA-15-240-RWT_VolturnUS-S.yaml | 40 +++++++++---------- .../IEA-15-floating_wTMDs.yaml | 40 +++++++++---------- .../IEA-15-floating_wTMDs_tower.yaml | 32 +++++++-------- .../12_linearization/IEA-15-floating.yaml | 32 +++++++-------- examples/14_level2ccd/IEA-15-floating.yaml | 32 +++++++-------- examples/15_RAFT_Studies/raft_opt_out.yaml | 32 +++++++-------- 6 files changed, 104 insertions(+), 104 deletions(-) diff --git a/examples/06_IEA-15-240-RWT/IEA-15-240-RWT_VolturnUS-S.yaml b/examples/06_IEA-15-240-RWT/IEA-15-240-RWT_VolturnUS-S.yaml index f245ec008..50c521e73 100644 --- a/examples/06_IEA-15-240-RWT/IEA-15-240-RWT_VolturnUS-S.yaml +++ b/examples/06_IEA-15-240-RWT/IEA-15-240-RWT_VolturnUS-S.yaml @@ -631,8 +631,8 @@ components: - name: main_column joint1: main_keel joint2: main_freeboard - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] outer_shape: shape: circular outer_diameter: @@ -660,8 +660,8 @@ components: - name: column1 joint1: col1_keel joint2: col1_freeboard - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] outer_shape: &col_out shape: circular outer_diameter: @@ -699,8 +699,8 @@ components: - name: column2 joint1: col2_keel joint2: col2_freeboard - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] outer_shape: *col_out internal_structure: *col_int axial_joints: @@ -716,8 +716,8 @@ components: - name: column3 joint1: col3_keel joint2: col3_freeboard - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] outer_shape: *col_out internal_structure: *col_int axial_joints: @@ -734,8 +734,8 @@ components: - name: Y_pontoon_upper1 joint1: main_upper_pontoon joint2: col1_upper_pontoon - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] outer_shape: &pontup_out shape: circular outer_diameter: @@ -752,24 +752,24 @@ components: - name: Y_pontoon_upper2 joint1: main_upper_pontoon joint2: col2_upper_pontoon - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] outer_shape: *pontup_out internal_structure: *pontup_int - name: Y_pontoon_upper3 joint1: main_upper_pontoon joint2: col3_upper_pontoon - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] outer_shape: *pontup_out internal_structure: *pontup_int - name: Y_pontoon_lower1 joint1: main_lower_pontoon joint2: col1_lower_pontoon - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] outer_shape: &pontlow_out shape: circular outer_diameter: @@ -796,16 +796,16 @@ components: joint2: col2_lower_pontoon outer_shape: *pontlow_out internal_structure: *pontlow_int - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] - name: Y_pontoon_lower3 joint1: main_lower_pontoon joint2: col3_lower_pontoon outer_shape: *pontlow_out internal_structure: *pontlow_int - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] mooring: nodes: - name: line1_anchor diff --git a/examples/06_IEA-15-240-RWT/IEA-15-floating_wTMDs.yaml b/examples/06_IEA-15-240-RWT/IEA-15-floating_wTMDs.yaml index 2d186fc9b..e337b65a1 100644 --- a/examples/06_IEA-15-240-RWT/IEA-15-floating_wTMDs.yaml +++ b/examples/06_IEA-15-240-RWT/IEA-15-floating_wTMDs.yaml @@ -631,8 +631,8 @@ components: - name: main_column joint1: main_keel joint2: main_freeboard - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] outer_shape: shape: circular outer_diameter: @@ -660,8 +660,8 @@ components: - name: column1 joint1: col1_keel joint2: col1_freeboard - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] outer_shape: &col_out shape: circular outer_diameter: @@ -699,8 +699,8 @@ components: - name: column2 joint1: col2_keel joint2: col2_freeboard - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] outer_shape: *col_out internal_structure: *col_int axial_joints: @@ -716,8 +716,8 @@ components: - name: column3 joint1: col3_keel joint2: col3_freeboard - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] outer_shape: *col_out internal_structure: *col_int axial_joints: @@ -734,8 +734,8 @@ components: - name: Y_pontoon_upper1 joint1: main_upper_pontoon joint2: col1_upper_pontoon - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] outer_shape: &pontup_out shape: circular outer_diameter: @@ -752,24 +752,24 @@ components: - name: Y_pontoon_upper2 joint1: main_upper_pontoon joint2: col2_upper_pontoon - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] outer_shape: *pontup_out internal_structure: *pontup_int - name: Y_pontoon_upper3 joint1: main_upper_pontoon joint2: col3_upper_pontoon - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] outer_shape: *pontup_out internal_structure: *pontup_int - name: Y_pontoon_lower1 joint1: main_lower_pontoon joint2: col1_lower_pontoon - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] outer_shape: &pontlow_out shape: circular outer_diameter: @@ -796,16 +796,16 @@ components: joint2: col2_lower_pontoon outer_shape: *pontlow_out internal_structure: *pontlow_int - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] - name: Y_pontoon_lower3 joint1: main_lower_pontoon joint2: col3_lower_pontoon outer_shape: *pontlow_out internal_structure: *pontlow_int - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] mooring: nodes: - name: line1_anchor diff --git a/examples/06_IEA-15-240-RWT/IEA-15-floating_wTMDs_tower.yaml b/examples/06_IEA-15-240-RWT/IEA-15-floating_wTMDs_tower.yaml index 46e9257f3..3eac655fa 100644 --- a/examples/06_IEA-15-240-RWT/IEA-15-floating_wTMDs_tower.yaml +++ b/examples/06_IEA-15-240-RWT/IEA-15-floating_wTMDs_tower.yaml @@ -523,8 +523,8 @@ components: - name: main_column joint1: main_keel joint2: main_freeboard - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] outer_shape: shape: circular outer_diameter: @@ -559,8 +559,8 @@ components: - name: column1 joint1: col1_keel joint2: col1_freeboard - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] outer_shape: &col_out shape: circular outer_diameter: @@ -598,8 +598,8 @@ components: - name: column2 joint1: col2_keel joint2: col2_freeboard - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] outer_shape: *col_out internal_structure: *col_int axial_joints: @@ -615,8 +615,8 @@ components: - name: column3 joint1: col3_keel joint2: col3_freeboard - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] outer_shape: *col_out internal_structure: *col_int axial_joints: @@ -633,8 +633,8 @@ components: - name: Y_pontoon_upper1 joint1: main_upper_pontoon joint2: col1_upper_pontoon - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] outer_shape: &pontup_out shape: circular outer_diameter: @@ -651,24 +651,24 @@ components: - name: Y_pontoon_upper2 joint1: main_upper_pontoon joint2: col2_upper_pontoon - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] outer_shape: *pontup_out internal_structure: *pontup_int - name: Y_pontoon_upper3 joint1: main_upper_pontoon joint2: col3_upper_pontoon - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] outer_shape: *pontup_out internal_structure: *pontup_int - name: Y_pontoon_lower1 joint1: main_lower_pontoon joint2: col1_lower_pontoon - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] outer_shape: &pontlow_out shape: circular outer_diameter: diff --git a/examples/12_linearization/IEA-15-floating.yaml b/examples/12_linearization/IEA-15-floating.yaml index a7fbec62b..03d73e374 100644 --- a/examples/12_linearization/IEA-15-floating.yaml +++ b/examples/12_linearization/IEA-15-floating.yaml @@ -523,8 +523,8 @@ components: - name: main_column joint1: main_keel joint2: main_freeboard - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 1.0] outer_shape: shape: circular outer_diameter: @@ -559,8 +559,8 @@ components: - name: column1 joint1: col1_keel joint2: col1_freeboard - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 1.0] outer_shape: &col_out shape: circular outer_diameter: @@ -598,8 +598,8 @@ components: - name: column2 joint1: col2_keel joint2: col2_freeboard - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 1.0] outer_shape: *col_out internal_structure: *col_int axial_joints: @@ -615,8 +615,8 @@ components: - name: column3 joint1: col3_keel joint2: col3_freeboard - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 1.0] outer_shape: *col_out internal_structure: *col_int axial_joints: @@ -633,8 +633,8 @@ components: - name: Y_pontoon_upper1 joint1: main_upper_pontoon joint2: col1_upper_pontoon - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 1.0] outer_shape: &pontup_out shape: circular outer_diameter: @@ -651,24 +651,24 @@ components: - name: Y_pontoon_upper2 joint1: main_upper_pontoon joint2: col2_upper_pontoon - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 1.0] outer_shape: *pontup_out internal_structure: *pontup_int - name: Y_pontoon_upper3 joint1: main_upper_pontoon joint2: col3_upper_pontoon - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 1.0] outer_shape: *pontup_out internal_structure: *pontup_int - name: Y_pontoon_lower1 joint1: main_lower_pontoon joint2: col1_lower_pontoon - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 1.0] outer_shape: &pontlow_out shape: circular outer_diameter: diff --git a/examples/14_level2ccd/IEA-15-floating.yaml b/examples/14_level2ccd/IEA-15-floating.yaml index 385761a1b..cba58b6fc 100644 --- a/examples/14_level2ccd/IEA-15-floating.yaml +++ b/examples/14_level2ccd/IEA-15-floating.yaml @@ -523,8 +523,8 @@ components: - name: main_column joint1: main_keel joint2: main_freeboard - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] outer_shape: shape: circular outer_diameter: @@ -559,8 +559,8 @@ components: - name: column1 joint1: col1_keel joint2: col1_freeboard - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] outer_shape: &col_out shape: circular outer_diameter: @@ -598,8 +598,8 @@ components: - name: column2 joint1: col2_keel joint2: col2_freeboard - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] outer_shape: *col_out internal_structure: *col_int axial_joints: @@ -615,8 +615,8 @@ components: - name: column3 joint1: col3_keel joint2: col3_freeboard - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] outer_shape: *col_out internal_structure: *col_int axial_joints: @@ -633,8 +633,8 @@ components: - name: Y_pontoon_upper1 joint1: main_upper_pontoon joint2: col1_upper_pontoon - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] outer_shape: &pontup_out shape: circular outer_diameter: @@ -651,24 +651,24 @@ components: - name: Y_pontoon_upper2 joint1: main_upper_pontoon joint2: col2_upper_pontoon - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] outer_shape: *pontup_out internal_structure: *pontup_int - name: Y_pontoon_upper3 joint1: main_upper_pontoon joint2: col3_upper_pontoon - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] outer_shape: *pontup_out internal_structure: *pontup_int - name: Y_pontoon_lower1 joint1: main_lower_pontoon joint2: col1_lower_pontoon - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] outer_shape: &pontlow_out shape: circular outer_diameter: diff --git a/examples/15_RAFT_Studies/raft_opt_out.yaml b/examples/15_RAFT_Studies/raft_opt_out.yaml index a1184e18d..29cd9aa55 100644 --- a/examples/15_RAFT_Studies/raft_opt_out.yaml +++ b/examples/15_RAFT_Studies/raft_opt_out.yaml @@ -509,8 +509,8 @@ components: - name: main_column joint1: main_keel joint2: main_freeboard - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] outer_shape: shape: circular outer_diameter: @@ -541,8 +541,8 @@ components: - name: column1 joint1: col1_keel joint2: col1_freeboard - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] outer_shape: &id004 shape: circular outer_diameter: @@ -576,8 +576,8 @@ components: - name: column2 joint1: col2_keel joint2: col2_freeboard - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] outer_shape: *id004 internal_structure: *id005 axial_joints: @@ -588,8 +588,8 @@ components: - name: column3 joint1: col3_keel joint2: col3_freeboard - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] outer_shape: *id004 internal_structure: *id005 axial_joints: @@ -600,8 +600,8 @@ components: - name: Y_pontoon_upper1 joint1: main_upper_pontoon joint2: col1_upper_pontoon - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] outer_shape: &id006 shape: circular outer_diameter: @@ -624,24 +624,24 @@ components: - name: Y_pontoon_upper2 joint1: main_upper_pontoon joint2: col2_upper_pontoon - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] outer_shape: *id006 internal_structure: *id007 Cp: 0.0 - name: Y_pontoon_upper3 joint1: main_upper_pontoon joint2: col3_upper_pontoon - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] outer_shape: *id006 internal_structure: *id007 Cp: 0.0 - name: Y_pontoon_lower1 joint1: main_lower_pontoon joint2: col1_lower_pontoon - Ca: 1.0 - Cd: 0.8 + Ca: [1.0, 1.0] + Cd: [0.8, 0.8] outer_shape: &id008 shape: circular outer_diameter: From b2ab12e1a83b9b3361e12c9c0f33728fbed7aa0a Mon Sep 17 00:00:00 2001 From: Garrett Barter Date: Sun, 26 May 2024 06:18:39 -0600 Subject: [PATCH 083/114] fixing tests after conda packaging improvements --- .github/workflows/CI_WEIS.yml | 28 ++++++++----------- .github/workflows/run_exhaustive_examples.yml | 4 +-- environment.yml | 5 ++-- 3 files changed, 15 insertions(+), 22 deletions(-) diff --git a/.github/workflows/CI_WEIS.yml b/.github/workflows/CI_WEIS.yml index e4119ddd0..05f4b39cd 100644 --- a/.github/workflows/CI_WEIS.yml +++ b/.github/workflows/CI_WEIS.yml @@ -16,14 +16,10 @@ jobs: strategy: fail-fast: false matrix: - os: ["ubuntu-latest", "macOS-latest", "windows-latest"] - python-version: ["3.9","3.10","3.11"] + os: ["ubuntu-latest", "macOS-13", "macOS-latest", "windows-latest"] # mac13 is intel, mac-14 arm + python-version: ["3.9","3.10","3.11","3.12"] steps: - #- name: Setup GNU Fortran - # # if: false == contains( matrix.os, 'windows') - # uses: awvwgk/setup-fortran@main - - uses: actions/checkout@v4 - name: Install conda/mamba @@ -56,7 +52,7 @@ jobs: # Install WEIS - name: Install WEIS run: | - pip install -e . -v + python -m pip install --no-deps -e . -v # Debugging session #- name: Setup tmate session @@ -64,9 +60,9 @@ jobs: # uses: mxschmitt/action-tmate@v3 # List the collected tests for debugging purposes - - name: List tests - run: | - pytest --collect-only weis + #- name: List tests + # run: | + # pytest --collect-only weis # Run all tests within WEIS, but not computationally expensive examples - name: Run tests within WEIS @@ -76,10 +72,8 @@ jobs: # Run coveralls - name: Run coveralls if: contains( matrix.os, 'ubuntu') && contains( matrix.python-version, '3.11') - uses: coverallsapp/github-action@v2 - # This also works, https://github.com/AndreMiras/coveralls-python-action - #uses: AndreMiras/coveralls-python-action@develop - #env: - # GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} - #run: | - # coveralls --service=github + #uses: coverallsapp/github-action@v2 + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + run: | + coveralls --service=github diff --git a/.github/workflows/run_exhaustive_examples.yml b/.github/workflows/run_exhaustive_examples.yml index de83e1bbd..d3aef060e 100644 --- a/.github/workflows/run_exhaustive_examples.yml +++ b/.github/workflows/run_exhaustive_examples.yml @@ -17,7 +17,7 @@ jobs: fail-fast: true matrix: os: ["ubuntu-latest"] #, "macOS-latest"] - python-version: ["3.9","3.10", "3.11"] + python-version: ["3.9","3.10", "3.11", "3.12"] steps: - uses: actions/checkout@v4 @@ -53,7 +53,7 @@ jobs: # Install WEIS - name: Install WEIS run: | - pip install -e . + python -m pip install --no-deps -e . -v # Test walkthrough notebook - name: Test postprocessing notebooks diff --git a/environment.yml b/environment.yml index a2cc3ff4c..6d0ba27ca 100644 --- a/environment.yml +++ b/environment.yml @@ -3,7 +3,6 @@ channels: - defaults dependencies: - #- osqp # Works with the pip version, but not the conda version - anyio - argon2-cffi - arrow @@ -37,7 +36,7 @@ dependencies: - nlopt - nlopt - notebook - - openfast=3.5.2 + - openfast - openpyxl - openraft - osqp @@ -66,7 +65,7 @@ dependencies: - traitlets - treon - webencodings - - wisdem=3.14.0 + - wisdem - pip: - dearpygui - marmot-agents From 9c66379b14b1e176a4f12c9c40bf5400f98adfe5 Mon Sep 17 00:00:00 2001 From: Garrett Barter Date: Sun, 26 May 2024 08:33:51 -0600 Subject: [PATCH 084/114] reduce clutter in environment file --- .github/workflows/run_exhaustive_examples.yml | 4 ++ environment.yml | 55 +------------------ 2 files changed, 6 insertions(+), 53 deletions(-) diff --git a/.github/workflows/run_exhaustive_examples.yml b/.github/workflows/run_exhaustive_examples.yml index d3aef060e..40e41d467 100644 --- a/.github/workflows/run_exhaustive_examples.yml +++ b/.github/workflows/run_exhaustive_examples.yml @@ -40,6 +40,10 @@ jobs: if: false == contains( matrix.os, 'windows') run: | conda install -y petsc4py mpi4py openmpi + + - name: Add test packages + run: | + conda install -y pytest pytest-cov coveralls treon - name: Show custom environment run: | diff --git a/environment.yml b/environment.yml index 6d0ba27ca..9232a666a 100644 --- a/environment.yml +++ b/environment.yml @@ -3,68 +3,17 @@ channels: - defaults dependencies: - - anyio - - argon2-cffi - - arrow - - async-lru - - babel - - beautifulsoup4 - - bleach - - cffi - - comm - - debugpy - - decorator - dill - - executing - - fqdn - - git - - ipython - - isoduration - - jedi - - jinja2 - - json5 - jsonmerge - - jupyter - - jupyterlab - - markupsafe - mat4py - - matplotlib - - meson - - mistune - - moorpy - - ninja + - niceplots - nlopt - - nlopt - - notebook - openfast - - openpyxl - openraft - - osqp - - overrides - - pandas - - parso - - patsy + - osqp - pcrunch - - pexpect - - pip - - platformdirs - - psutil - - ptyprocess - - pyDOE3 - - pygments - - pyhams - - python-json-logger - - pyzmq - - qdldl-python - - qtpy - rosco - - scikit-learn - - setuptools - smt - - tornado - - traitlets - - treon - - webencodings - wisdem - pip: - dearpygui From a15be4810ae83c7c5f8e66ee6de40d67f292d11c Mon Sep 17 00:00:00 2001 From: Garrett Barter Date: Sun, 26 May 2024 08:40:40 -0600 Subject: [PATCH 085/114] remomve niceplots --- environment.yml | 1 - weis/glue_code/gc_PoseOptimization.py | 10 ++++++++-- 2 files changed, 8 insertions(+), 3 deletions(-) diff --git a/environment.yml b/environment.yml index 9232a666a..017dd3ce2 100644 --- a/environment.yml +++ b/environment.yml @@ -6,7 +6,6 @@ dependencies: - dill - jsonmerge - mat4py - - niceplots - nlopt - openfast - openraft diff --git a/weis/glue_code/gc_PoseOptimization.py b/weis/glue_code/gc_PoseOptimization.py index aed182923..53ac0daf0 100644 --- a/weis/glue_code/gc_PoseOptimization.py +++ b/weis/glue_code/gc_PoseOptimization.py @@ -79,7 +79,13 @@ def get_number_design_variables(self): def set_objective(self, wt_opt): - if self.opt['merit_figure'] == 'blade_tip_deflection': + # Set merit figure. Each objective has its own scaling. Check first for user override + if self.opt["merit_figure_user"]["name"] != "": + coeff = -1.0 if self.opt["merit_figure_user"]["max_flag"] else 1.0 + wt_opt.model.add_objective(self.opt["merit_figure_user"]["name"], + ref=coeff*np.abs(self.opt["merit_figure_user"]["ref"])) + + elif self.opt['merit_figure'] == 'blade_tip_deflection': wt_opt.model.add_objective('tcons_post.tip_deflection_ratio') elif self.opt['merit_figure'] == 'DEL_RootMyb': # for DAC optimization on root-flap-bending moments @@ -102,7 +108,7 @@ def set_objective(self, wt_opt): elif self.opt['merit_figure'] == 'Cp': wt_opt.model.add_objective('aeroelastic.Cp_out', ref=-1.) - elif self.opt['merit_figure'] == 'weis_lcoe': + elif self.opt['merit_figure'] == 'weis_lcoe' or self.opt['merit_figure'].lower() == 'lcoe': wt_opt.model.add_objective('financese_post.lcoe') elif self.opt['merit_figure'] == 'OL2CL_pitch': From 86724c1f09b36a3698916a446ce988952e9e2969 Mon Sep 17 00:00:00 2001 From: Garrett Barter Date: Sun, 9 Jun 2024 06:08:46 -0600 Subject: [PATCH 086/114] removing git from environment file and encouring it be used in the base environment only as it conflicts with compiler libraries on windows --- README.md | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 009bb2322..64a6cd205 100644 --- a/README.md +++ b/README.md @@ -47,16 +47,17 @@ The installation instructions below use the environment name, "weis-env," but an 1. Setup and activate the Anaconda environment from a command prompt: conda config --add channels conda-forge - conda env create --name weis-env -f https://raw.githubusercontent.com/WISDEM/WEIS/develop/environment.yml + conda install git + git clone https://github.com/WISDEM/WEIS.git + cd WEIS + git checkout branch_name # (Only if you want to switch branches, say "develop") + conda env create --name weis-env -f environment.yml conda activate weis-env # (if this does not work, try source activate weis-env) - conda install -y petsc4py mpi4py pyoptsparse # (Mac / Linux only) -2. Clone the repository and install the software +2. Add in final packages and install the software - git clone https://github.com/WISDEM/WEIS.git - cd WEIS - git checkout branch_name # (Only if you want to switch branches, say "develop") + conda install -y petsc4py mpi4py pyoptsparse # (Mac / Linux only) pip install -e . 3. Instructions specific for DOE HPC system Eagle. Before executing the setup script, do: @@ -67,6 +68,8 @@ The installation instructions below use the environment name, "weis-env," but an **NOTE:** To use WEIS again after installation is complete, you will always need to activate the conda environment first with `conda activate weis-env` (or `source activate weis-env`). On Eagle, make sure to reload the necessary modules +For Windows users, we recommend installing `git` and the `m264` packages in separate environments as some of the libraries appear to conflict such that WISDEM cannot be successfully built from source. The `git` package is best installed in the `base` environment. + ## Developer guide If you plan to contribute code to WEIS, please first consult the [developer guide](https://weis.readthedocs.io/en/latest/how_to_contribute_code.html). You are also likely to want to install some of the compiled WEIS dependencies (OpenFAST or WISDEM or pyHAMS or ROSCO) from a local copy instead of relying on the conda packages. From e43a159c213a6842aac4cb4fc9cc8a80b5b5042c Mon Sep 17 00:00:00 2001 From: Mayank Chetan <4620557+mayankchetan@users.noreply.github.com> Date: Wed, 12 Jun 2024 10:38:37 -0600 Subject: [PATCH 087/114] env.yml update for visualization tool (#278) * env.yml update for visualization tool * Add visualization utilities * corrected accidental git deletion * Revert "corrected accidental git deletion" This reverts commit d32c4b3d7e45e696ffe60c001ef0e3e4acdab231. --------- Co-authored-by: dzalkind Co-authored-by: Cory Frontin --- environment.yml | 3 ++ weis/visualization/__init__.py | 0 weis/visualization/utils.py | 84 ++++++++++++++++++++++++++++++++++ 3 files changed, 87 insertions(+) create mode 100644 weis/visualization/__init__.py create mode 100644 weis/visualization/utils.py diff --git a/environment.yml b/environment.yml index 017dd3ce2..9da80ec39 100644 --- a/environment.yml +++ b/environment.yml @@ -3,6 +3,7 @@ channels: - defaults dependencies: + - dash - dill - jsonmerge - mat4py @@ -15,6 +16,8 @@ dependencies: - smt - wisdem - pip: + - dash-bootstrap-components + - dash-mantine-components - dearpygui - marmot-agents # Needs to be done outside of environment file: diff --git a/weis/visualization/__init__.py b/weis/visualization/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/weis/visualization/utils.py b/weis/visualization/utils.py new file mode 100644 index 000000000..46bc6d5a3 --- /dev/null +++ b/weis/visualization/utils.py @@ -0,0 +1,84 @@ +''' +Various functions for help visualizing WEIS outputs +''' +from weis.aeroelasticse.FileTools import load_yaml +import pandas as pd +import numpy as np +import openmdao.api as om + + +try: + import ruamel_yaml as ry +except Exception: + try: + import ruamel.yaml as ry + except Exception: + raise ImportError('No module named ruamel.yaml or ruamel_yaml') + +def read_cm(cm_file): + """ + Function originally from: + https://github.com/WISDEM/WEIS/blob/main/examples/16_postprocessing/rev_DLCs_WEIS.ipynb + + Parameters + __________ + cm_file : The file path for case matrix + + Returns + _______ + cm : The dataframe of case matrix + dlc_inds : The indices dictionary indicating where corresponding dlc is used for each run + """ + cm_dict = load_yaml(cm_file, package=1) + cnames = [] + for c in list(cm_dict.keys()): + if isinstance(c, ry.comments.CommentedKeySeq): + cnames.append(tuple(c)) + else: + cnames.append(c) + cm = pd.DataFrame(cm_dict, columns=cnames) + + return cm + +def parse_contents(data): + """ + Function from: + https://github.com/WISDEM/WEIS/blob/main/examples/09_design_of_experiments/postprocess_results.py + """ + collected_data = {} + for key in data.keys(): + if key not in collected_data.keys(): + collected_data[key] = [] + + for key_idx, _ in enumerate(data[key]): + if isinstance(data[key][key_idx], int): + collected_data[key].append(np.array(data[key][key_idx])) + elif len(data[key][key_idx]) == 1: + try: + collected_data[key].append(np.array(data[key][key_idx][0])) + except: + collected_data[key].append(np.array(data[key][key_idx])) + else: + collected_data[key].append(np.array(data[key][key_idx])) + + df = pd.DataFrame.from_dict(collected_data) + + return df + + +def load_OMsql(log): + """ + Function from : + https://github.com/WISDEM/WEIS/blob/main/examples/09_design_of_experiments/postprocess_results.py + """ + # logging.info("loading ", log) + cr = om.CaseReader(log) + rec_data = {} + cases = cr.get_cases('driver') + for case in cases: + for key in case.outputs.keys(): + if key not in rec_data: + rec_data[key] = [] + rec_data[key].append(case[key]) + + return rec_data \ No newline at end of file From d7403fb73861d0d29b4f83427cae7ad0b6a03835 Mon Sep 17 00:00:00 2001 From: yqliaohk Date: Fri, 14 Jun 2024 11:28:06 -0600 Subject: [PATCH 088/114] Fixed tower diameter connections --- weis/glue_code/glue_code.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/weis/glue_code/glue_code.py b/weis/glue_code/glue_code.py index 458048f47..0b68b2817 100644 --- a/weis/glue_code/glue_code.py +++ b/weis/glue_code/glue_code.py @@ -833,7 +833,7 @@ def setup(self): # Connections to TowerSE if modeling_options["flags"]["tower"]: - tow_params = ["z_full","d_full","t_full", + tow_params = ["z_full","outer_diameter_full","t_full", "E_full","G_full","rho_full","sigma_y_full"] for k in tow_params: self.connect(f'towerse.{k}', f'towerse_post.{k}') @@ -875,7 +875,7 @@ def setup(self): self.connect('nacelle.uptilt', 'tcons_post.tilt') self.connect('nacelle.overhang', 'tcons_post.overhang') self.connect('tower.ref_axis', 'tcons_post.ref_axis_tower') - self.connect('tower.diameter', 'tcons_post.d_full') + self.connect('tower.diameter', 'tcons_post.outer_diameter_full') else: # connections from outside WISDEM self.connect('rosco_turbine.v_rated', 'aeroelastic.Vrated') From 1969bbb9fc2a30767c0e73b04012dbf849658856 Mon Sep 17 00:00:00 2001 From: dzalkind Date: Thu, 20 Jun 2024 15:10:56 -0600 Subject: [PATCH 089/114] Updates to environment to run with WISDEM main --- environment.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/environment.yml b/environment.yml index 9da80ec39..7964c9c02 100644 --- a/environment.yml +++ b/environment.yml @@ -12,6 +12,8 @@ dependencies: - openraft - osqp - pcrunch + - pyoptsparse + - openmdao<3.33 - rosco - smt - wisdem From 965de6abfdb6ab1d45779c5a6eafbd23a7a23b98 Mon Sep 17 00:00:00 2001 From: Cory Frontin Date: Thu, 20 Jun 2024 15:14:22 -0600 Subject: [PATCH 090/114] Optimization (#288) * added updated optimization documentation... in progress. * fixed rendering of optimization rst file * add partial table to docs optimization page * update to optimization doc. * analysis updates from kestrel * added some analysis stuff * update to optimization docs * recent kestrel changes * stuff for local work * viz toolbox simplfied for inclusion in weis tools * moved viz_toolbox out of examples and refactored * corrected accidental git deletion * updates for kestrel push * added processor edit for DE/GA * updates to the viz toolbox and analysis notebook * actually update analysis notebook * actually update analysis notebook with more updates * updated example 17, refactored utilities to match Mayank's files/structure * remove run script * more progress toward PR * add readme --------- Co-authored-by: dzalkind --- docs/optimization.rst | 228 ++++++++- examples/17_IEA22_Optimization/README.md | 32 ++ examples/17_IEA22_Optimization/analysis.ipynb | 458 ++++++++++++++++++ .../analysis_options_raft_ptfm_opt.yaml | 23 +- .../driver_weis_raft_opt.py | 6 +- weis/glue_code/runWEIS.py | 76 ++- weis/visualization/opt_plotting.py | 162 +++++++ weis/visualization/utils.py | 398 ++++++++++++++- 8 files changed, 1324 insertions(+), 59 deletions(-) create mode 100644 examples/17_IEA22_Optimization/README.md create mode 100644 examples/17_IEA22_Optimization/analysis.ipynb create mode 100644 weis/visualization/opt_plotting.py diff --git a/docs/optimization.rst b/docs/optimization.rst index 1bdfe24ec..836ae087d 100644 --- a/docs/optimization.rst +++ b/docs/optimization.rst @@ -1,10 +1,230 @@ Optimization in WEIS --------------------- +==================== +WEIS is platform for multidisciplinary analysis and optimization (MDAO), +based on the OpenMDAO toolset. WEIS leverages the `optimization +capabilities of +OpenMDAO `_ +and `extensions in +WISDEM `_ +to perform multidisciplinary and multifidelity wind turbine analyses +including controls. -**Available Optimization Solvers** +Available Optimization Solvers +------------------------------ +from ScipyOptimizeDriver via OpenMDAO +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -**Explanation of outputs** +OpenMDAO includes access to a handful of methods from the python +scipy minimization toolbox: -**Some tips and best practices** \ No newline at end of file +- “SLSQP” + + - Sequential Least Squares Quadratic Programming + - sequential estimation of objective function by quadratic functions + - application of constraints by linear approximation + - ``scipy.optimize.minimize`` implementation + +- “Nelder-Mead” + + - downhill simplex method for optimization + - ``scipy.optimize.minimize`` implementation + +- “COBYLA” + + - Constrained Optimization BY Linear Approximation + - sequential estimation of objective function by linear functions + - application of constraints by linear approximation + - derivative free estimation + - ``scipy.optimize.minimize`` implementation + +from PyOptSparseDriver via OpenMDAO +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +OpenMDAO includes access to a handful of methods from the python +scipy minimization toolbox: + +- “SNOPT” + + - `Sparse Nonlinear OPTimizer `__ + - local quadratic subproblems + - constraints by linear approximation + - implements system sparsity for efficiency on high-dimensional problems + - linesearch on subproblem for performance + - *(requires inclusion of proprietary SNOPT software in pyoptsparse install)* + +- “CONMIN” + + - CONstrained function MINimization + - method of feasible directions: chooses direction & step to + minimize function while satisfying constraints + +- “NSGA2” + + - Non-dominating Sorting Genetic Algorithm 2 + - genetic global optimization algorithm + +from NLOpt via WISDEM +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +WISDEM has wrapped the NLOpt package for use in the OpenMDAO +toolset. NLOpt prefixes all solvers with a two-letter code; the +first letter indicates either a "G"lobal or "L"ocal method, and the +second letter indicates “N"o-derivative or “D"erivative-based +optimizers. + +- “GN_DIRECT” + + - `DIviding RECTangles algorithm for global + optimization `__ + - also, variants “GN_DIRECT_L” and “GN_DIRECT_L_NOSCAL” + +- “GN_ORIG_DIRECT” + + - legacy implementation of “GN_DIRECT” with hardcoded details + - also, variant “GN_ORIG_DIRECT_L” + +- “GN_AGS” + + - `AGS algorithm: derivative-free nonlinear provably-convergant + global solver with dimensionality + reduction `__ + +- “GN_ISRES” + + - `Improved Stochastic Ranking Evolution + Strategy `__ + - evolutionary algorithm + +- “LN_COBYLA” + + - `Constrained Optimization BY Linear + Approximation `__ + - sequential estimation of objective function by linear functions + - application of constraints by linear approximation + - derivative free estimation + - NLOpt implementation + +- “LD_MMA” + + - `Method of Moving + Asymptotes `__ + - penalized local approximation method with fast appoximation solves + - conservative convex separable approximation (CCSA) method using + MMA approximation as inner loop optimizer + +- “LD_CCSAQ” + + - `Conservative Convex Separable Approximation with Quadratic + approximations `__ + - separable method with guaranteed convergence to local optimizer + +- “LD_SLSQP” + + - `Sequential Least Squares Quadratic + Programming `__ + - sequential estimation of objective function by quadratic functions + - application of constraints by linear approximation + - NLOpt implementation + +Comparison of Optimizers +------------------------------ + +=========== =========== ====== =========== ========== =========== + +Solver Toolset Scope Derivatives Convergent Constraints + +=========== =========== ====== =========== ========== =========== +SLSQP scipy local True ??? =, < (NL) +Nelder-Mead scipy local False False None +COBYLA scipy local False ??? =, < (NL) +SNOPT pyoptsparse local True ??? =, < (NL) +CONMIN pyoptsparse local True ??? =, < (NL) +NSGA2 pyoptsparse global ??? ??? =, < (NL) +=========== =========== ====== =========== ========== =========== + +Key +~~~ + +*Scope:* does the method solve a local or global problem? + +*Derivatives:* does the method use derivatives for optimization + +*Convergent:* does the method have a convergence proof for its scope + +*Constraints:* constraints that can be used with the method + + - equality constraints: ``=`` + - inequality constraints: ``<`` + - compatibility with linear constraints only: ``(L)`` + - compatibility with nonlinear constraints: ``(NL)`` + +.. +.. Explanation of outputs +.. ---------------------- +.. +.. *TO DO!!!* +.. +.. Some tips and best practices +.. ---------------------------- +.. +.. *TO DO!!!* +.. + +Optimization case study: IEA22 +============================== + +In ``WEIS/examples/17_IEA22_Optimization``, we have an optimization +example which can be used to design the semisubmersible platform for the +IEA 22 280m reference wind turbine. We will concentrate on the files +``analysis_options_raft_ptfm_opt.yaml`` and +``modeling_options_raft.yaml``, which specify the platform design study. + +The study sets design variables: + - ``floating.joints`` + - ``z_coordinate[main_keel, col1_keel, col2_keel, col3_keel]`` + - ``r_coordinate[main_keel, col1_keel, col2_keel, col3_keel]`` + - not sure exactly what these do, but presumably they set cylindrical coordinates of the truss system members (less an angle?) + - ``floating.members`` + - ``groups["column1, column2, column3]:diameter`` + - presumably this is setting the diameters of the truss system members? + +and constraints: + - ``floating.survival_heel``: upper bound + - maximum pitching heel allowable in parked conditions + - ``floating.metacentric_height``: lower bound + - “Ensures hydrostatic stability with a positive metacentric height” + - distance between center of gravity of a marine vessel and its metacenter (point between vessel-fixed vertical line through C.o.G. and inertial-frame-fixed line through center of buoyancy) + - dictates static stability in the small-heel angle limit (i.e. characterizes stability) + - ``floating.pitch_period``: upper & lower bound + - period of the pitching motion (bow (stern) up vs. down rotation about center of mass) + - ``floating.heave_period``: upper & lower bound + - period of the heave (linear vertical motion of a marine vessel) + - ``floating.fixed_ballast_capacity``: on + - “Ensures that there is sufficient volume to hold the specified fixed (permanent) ballast” + - ``floating.variable_ballast_capacity``: on + - “Ensures that there is sufficient volume to hold the needed water (variable) ballast to achieve neutral buoyancy” + - ``floating.freeboard_margin``: on + - “Ensures that the freeboard (top points of structure) of floating platform stays above the waterline at the survival heel offset” + - the deck surface should not be submerged in the worst-case conditions + - ``floating.draft_margin``: on + - “keep draft from raising above water line during survival_heel, largest wave” + - the bottom of the hull should not rise above the water surface in the worst-case conditions + - ``floating.fairlead_depth``: on + - “keep the fairlead above bottom trough of largest wave” + - don’t dunk the fairlead in worst-case conditions + - ``control.Max_PtfmPitch``: max + - “Maximum platform pitch displacement over all cases. Can be computed in both RAFT and OpenFAST. The higher fidelity option will be used when active.” + - ``control.Std_PtfmPitch``: max + - “Maximum platform pitch standard deviation over all cases. Can be computed in both RAFT and OpenFAST. The higher fidelity option will be used when active.” + - ``control.nacelle_acceleration``: max + - “Maximum Nacelle IMU accelleration magnitude, i.e., sqrt(NcIMUTAxs^2 + NcIMUTAys^2 + NcIMUTAzs^2). Can be computed in both RAFT and OpenFAST. The higher fidelity option will be used when active.” + +with a merit figure of the structural mass + - ``structural_mass`` (``floatingse.system_structural_mass``) + + +.. raw:: html + + diff --git a/examples/17_IEA22_Optimization/README.md b/examples/17_IEA22_Optimization/README.md new file mode 100644 index 000000000..162f84fc2 --- /dev/null +++ b/examples/17_IEA22_Optimization/README.md @@ -0,0 +1,32 @@ + +# Example 17: IEA22 FOWT Optimization + +This is an example of optimization and post-processing of an IEA 22MW RWT-based FOWT system. + + +## Data generation + +To run the cases, we use the standard WEIS setup, driven by `driver_weis_raft_opt.py`. +The driver leverages an analysis options file, `analysis_options_raft_ptfm_opt.yaml`, and the modeling options file `modeling_options_raft.yaml`. + +In order to run different optimizers, we can edit `analysis_options_raft_ptfm_opt.yaml:127`, which reads +``` +solver: LN_COBYLA +``` +for the COBYLA optimizer. +We can switch this line to either of the following lines to run SLSQP or differential evolution (DE): +``` +solver: LD_SLSQP +solver: DE +``` + +Once an optimization terminates, an output directory of the name `17_IEA22_Opt_Result` will be created and populated with data and metadata. +We recommend running terminal command such as: +``` +mv 17_IEA22_Opt_Result 17_IEA22_Opt_Result_CASENAME +``` +where `CASENAME` is replaced by `COBYLA`, `SLSQP`, and/or `DE` depending on the case you are running. + +## Analysis + +... TO DO! diff --git a/examples/17_IEA22_Optimization/analysis.ipynb b/examples/17_IEA22_Optimization/analysis.ipynb new file mode 100644 index 000000000..206d660c9 --- /dev/null +++ b/examples/17_IEA22_Optimization/analysis.ipynb @@ -0,0 +1,458 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# import tools_cvf\n", + "plt.style.use([\n", + " \"dark_background\",\n", + " \"https://raw.githubusercontent.com/cfrontin/tools_cvf/main/tools_cvf/stylesheet_cvf.mplstyle\",\n", + " \"https://raw.githubusercontent.com/cfrontin/tools_cvf/main/tools_cvf/stylesheet_nrel.mplstyle\",\n", + " # tools_cvf.get_stylesheets(dark=True)\n", + "])\n", + "\n", + "import weis.visualization.utils as viz_toolbox\n", + "import weis.visualization.opt_plotting as viz_toolbox_old" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Example 17: IEA22 Optimization\n", + "\n", + "In this example, we can optimize a semisubmersible floating offshore wind turbine (FOWT) based around the IEA 22MW reference turbine.\n", + "We will consider optimizations using the following optimizers:\n", + "- COBYLA optimizer (derivative-free)\n", + "- SLSQP optimizer (gradient-based), and\n", + "- differential evolution (DE) (an evolutionary algorithm)\n", + "\n", + "## Metadata loading\n", + "\n", + "In the following code sections we will set up the loading of the metadata files." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# standard locations of output dirs based on template for ex. 17\n", + "dir_template = \"17_IEA22_Opt_Result_%s\"\n", + "dir_COBYLA = dir_template % \"COBYLA\"\n", + "dir_SLSQP = dir_template % \"SLSQP\"\n", + "dir_DE = dir_template % \"DE\"\n", + "\n", + "# OM optimization log database files\n", + "fn_log_COBYLA = os.path.join(dir_COBYLA, \"log_opt.sql\")\n", + "fn_log_SLSQP = os.path.join(dir_SLSQP, \"log_opt.sql\")\n", + "fn_log_DE = os.path.join(dir_DE, \"log_opt.sql_%s\")\n", + "\n", + "# WEIS stashes design/constraint/objective var files located here\n", + "fn_vars_COBYLA = os.path.join(dir_COBYLA, \"problem_vars.json\")\n", + "fn_vars_SLSQP = os.path.join(dir_SLSQP, \"problem_vars.json\")\n", + "fn_vars_DE = os.path.join(dir_DE, \"problem_vars.json\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# ... load the variables files\n", + "vars_COBYLA = viz_toolbox.load_vars_file(fn_vars_COBYLA)\n", + "vars_SLSQP = viz_toolbox.load_vars_file(fn_vars_SLSQP)\n", + "# this call verifies, (optionally) unifies, and corrects the var files\n", + "vars_unified = viz_toolbox.verify_vars(vars_COBYLA, vars_SLSQP)\n", + "# vars_DE = viz_toolbox.load_vars_file(fn_vars_DE)\n", + "# vars_unified = viz_toolbox.verify_vars(vars_COBYLA, vars_SLSQP, vars_DE)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data loading\n", + "\n", + "With the metadata loaded, we can now load the primary data from the various methods.\n", + "The COBYLA and SLSQP data is loaded first, with a simple serial loader, which are used because these methods either run in a serial fashion (with F.D. derivatives calculated in parallel in the case of SLSQP).\n", + "The DE data, since it is run in parallel, is loaded using a parallel data loader.\n", + "\n", + "After the data is loaded, we show any differences in the keys found between the COBYLA/SLSQP methods and pretty-print the variables with icons representing whether they are objective functions (`**`), design variables (`--`), constraints (`<>`), or other (`??`)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "the following keys are in both COBYLA and SLSQP:\n", + "\tfloatingse.system_structural_mass\n", + "\tfloatingse.constr_freeboard_heel_margin\n", + "\titer\n", + "\tfloatingse.constr_variable_margin\n", + "\tfloatingse.constr_fairlead_wave\n", + "\tfloatingse.constr_fixed_margin\n", + "\traft.pitch_period\n", + "\tfloatingse.metacentric_height\n", + "\tfloating.memgrp1.outer_diameter_in\n", + "\tfloatingse.constr_draft_heel_margin\n", + "\traft.Max_PtfmPitch\n", + "\traft.heave_period\n", + "\tfloating.jointdv_1\n", + "\tfloating.jointdv_0\n", + "\trank\n", + "\n", + "\n", + "** floatingse.system_structural_mass\n", + "<> floatingse.constr_freeboard_heel_margin\n", + "?? iter\n", + "<> floatingse.constr_variable_margin\n", + "<> floatingse.constr_fairlead_wave\n", + "<> floatingse.constr_fixed_margin\n", + "<> raft.pitch_period\n", + "<> floatingse.metacentric_height\n", + "-- floating.memgrp1.outer_diameter_in\n", + "<> floatingse.constr_draft_heel_margin\n", + "<> raft.Max_PtfmPitch\n", + "<> raft.heave_period\n", + "-- floating.jointdv_1\n", + "-- floating.jointdv_0\n", + "?? rank\n", + "\n" + ] + } + ], + "source": [ + "# load the data from the OM DB\n", + "dataOM_COBYLA = viz_toolbox.load_OMsql(fn_log_COBYLA, parse_multi=True)\n", + "dataOM_SLSQP = viz_toolbox.load_OMsql(fn_log_SLSQP, parse_multi=True)\n", + "dataOMmulti_DE = viz_toolbox.load_OMsql_multi(\n", + " fn_log_DE % \"*\",\n", + " meta_in=fn_log_DE % \"meta\",\n", + ")\n", + "dataOMbest_DE = viz_toolbox.consolidate_multi(\n", + " dataOMmulti_DE,\n", + " vars_SLSQP,\n", + ")\n", + "\n", + "# describe the keys that have been found\n", + "print()\n", + "keys_all, _, _ = viz_toolbox.compare_om_data(\n", + " dataOM_COBYLA,\n", + " dataOM_SLSQP,\n", + " \"COBYLA\", \"SLSQP\",\n", + " verbose=True,\n", + ")\n", + "print()\n", + "\n", + "# grab the keys that we have in the unified vars\n", + "keys_obj = [v[\"name\"] for k, v in vars_unified[\"objectives\"].items()]\n", + "keys_DV = [v[\"name\"] for k, v in vars_unified[\"design_vars\"].items()]\n", + "keys_constr = {v[\"name\"]: [v[\"lower\"], v[\"upper\"]] for k, v in vars_unified[\"constraints\"].items()}\n", + "\n", + "# pretty print the case we're looking at\n", + "viz_toolbox.prettyprint_variables(keys_all, keys_obj, keys_DV, keys_constr)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Feasibility pre-processing\n", + "\n", + "Now, we will can grab and evaluate the feasibility of the DE iterations across all the ranks." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# extract and install feasibility evaluations\n", + "feas, vfeas = viz_toolbox.get_feasible_iterations(\n", + " dataOMmulti_DE, vars_unified,\n", + " feas_tol=1e-5,\n", + ")\n", + "dataOMmulti_DE[\"feas_total\"] = feas\n", + "for k, v in vfeas.items():\n", + " dataOMmulti_DE[f\"feas_{k}\"] = v" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plotting\n", + "\n", + "### Differential Evolution results\n", + "\n", + "First, we can examine the results of the DE optimization.\n", + "At each of 100 iterations, there are 104 processors working the problem.\n", + "The figure shows the progression of the optimization with feasible simulations in green, infeasible in red, the iteration-wise best result in cyan, and the value of the discovered minimizer in yellow dashes.\n", + "\n", + "In the following figure, we show the iteration-over-iteration convergence of the iteration-wise best feasible estimate toward the discovered minimizer, which demonstrates regular convergence toward this value." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot DE results\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax.scatter([], [], s=3.0, c=\"g\", label=\"feasible sample\")\n", + "ax.scatter([], [], s=3.0, c=\"r\", label=\"infeasible sample\")\n", + "ax.scatter(\n", + " dataOMmulti_DE[\"iter\"],\n", + " dataOMmulti_DE[\"floatingse.system_structural_mass\"],\n", + " s=3.0,\n", + " c=[\"g\" if d else \"r\" for d in dataOMmulti_DE[\"feas_total\"]],\n", + " alpha=0.5,\n", + " label=\"_simulation iterations_\",\n", + ")\n", + "ax.plot(\n", + " range(np.max(dataOMmulti_DE[\"iter\"])),\n", + " [\n", + " np.min(np.array(dataOMmulti_DE[\"floatingse.system_structural_mass\"])[\n", + " dataOMmulti_DE[\"feas_total\"].flatten() & (np.array(dataOMmulti_DE[\"iter\"]) == iter).flatten()\n", + " ]) for iter in range(np.max(dataOMmulti_DE[\"iter\"]))\n", + " ],\n", + " c=\"c\",\n", + " zorder=1000,\n", + " label=\"best feasible estimate\",\n", + ")\n", + "ax.plot(\n", + " range(np.max(dataOMmulti_DE[\"iter\"])),\n", + " np.min(\n", + " np.array(dataOMmulti_DE[\"floatingse.system_structural_mass\"])[dataOMmulti_DE[\"feas_total\"].flatten()]\n", + " )*np.ones_like(range(np.max(dataOMmulti_DE[\"iter\"]))),\n", + " \"--y\",\n", + " zorder=500,\n", + " label=\"discovered minimizer\",\n", + ")\n", + "ax.grid(which=\"major\", alpha=0.25)\n", + "ax.set_xlabel(\"iteration number\")\n", + "ax.set_ylabel(\"system structural mass (kg)\")\n", + "ax.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot DE optimization convergence results\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax.semilogy(\n", + " range(np.max(dataOMmulti_DE[\"iter\"])),\n", + " np.abs([np.min(np.array(dataOMmulti_DE[\"floatingse.system_structural_mass\"])[\n", + " dataOMmulti_DE[\"feas_total\"].flatten() & (np.array(dataOMmulti_DE[\"iter\"]) == iter).flatten()\n", + " ]) for iter in range(np.max(dataOMmulti_DE[\"iter\"])) ]\n", + " - np.min(\n", + " np.array(\n", + " dataOMmulti_DE[\"floatingse.system_structural_mass\"]\n", + " )[dataOMmulti_DE[\"feas_total\"].flatten()]\n", + " )*np.ones_like(range(np.max(dataOMmulti_DE[\"iter\"]))))/np.min(\n", + " np.array(\n", + " dataOMmulti_DE[\"floatingse.system_structural_mass\"]\n", + " )[dataOMmulti_DE[\"feas_total\"].flatten()]\n", + " ),\n", + " c=\"c\",\n", + " label=\"error in iteration-wise best feasible estimate\",\n", + ")\n", + "ax.grid(which=\"major\", alpha=0.25)\n", + "ax.set_xlabel(\"iteration number\")\n", + "ax.set_ylabel(\n", + " \"apparent percent absolute error in \"\n", + " + \"\\nsystem structural mass estimate (\\%)\"\n", + ")\n", + "pass" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Combined results\n", + "\n", + "With the DE results in tow, we can now evaluate them with respect to the other solutions.\n", + "In the following plots, we will evaluate the optimization trajectories of the three optimizers.\n", + "In the first plot, the objective function for optimization is shown, and in the second, the design variables are shown.\n", + "Each of the markers is either filled for a feasible sample or unfilled for infeasible sample at a given iteration.\n", + "DE results are the best-available at a given iteration, as shown above." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "viz_toolbox_old.plot_conv(\n", + " keys_obj,\n", + " {\n", + " \"DE\": (dataOMbest_DE, vars_SLSQP),\n", + " \"SLSQP\": (dataOM_SLSQP, vars_SLSQP),\n", + " \"COBYLA\": (dataOM_COBYLA, vars_COBYLA),\n", + " },\n", + " feas_tol=1e-5,\n", + " alpha=0.5,\n", + ") ;" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "viz_toolbox_old.plot_conv(\n", + " keys_DV,\n", + " {\n", + " \"DE\": (dataOMbest_DE, vars_SLSQP),\n", + " \"SLSQP\": (dataOM_SLSQP, vars_SLSQP),\n", + " \"COBYLA\": (dataOM_COBYLA, vars_COBYLA),\n", + " },\n", + " feas_tol=1e-5,\n", + " alpha=0.5,\n", + ") ;" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the final subplot, below, we show the constraints active on the problem, which are numerous.\n", + "In this plot, filled (unfilled) markers represent feasibility (infeasibility) according to the constraint of interest on the displayed iteration." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "viz_toolbox_old.plot_conv(\n", + " keys_constr,\n", + " {\n", + " \"DE\": (dataOMbest_DE, vars_SLSQP),\n", + " \"SLSQP\": (dataOM_SLSQP, vars_SLSQP),\n", + " \"COBYLA\": (dataOM_COBYLA, vars_COBYLA),\n", + " },\n", + " feas_tol=1e-5,\n", + " alpha=0.5,\n", + " use_casewise_feasibility=True,\n", + ") ;" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/17_IEA22_Optimization/analysis_options_raft_ptfm_opt.yaml b/examples/17_IEA22_Optimization/analysis_options_raft_ptfm_opt.yaml index 9706ffe63..7149e481f 100644 --- a/examples/17_IEA22_Optimization/analysis_options_raft_ptfm_opt.yaml +++ b/examples/17_IEA22_Optimization/analysis_options_raft_ptfm_opt.yaml @@ -1,6 +1,5 @@ general: - # folder_output: /scratch/dzalkind/WEIS-2/outputs/17_IEA22_OptStudies/0_setup # kestrel - folder_output: outputs/17_IEA22_OptStudies/1_change_opt + folder_output: 17_IEA22_Opt_Result fname_output: IEA-22-280-RWT design_variables: @@ -108,22 +107,24 @@ constraints: control: Max_PtfmPitch: flag: True - max: 5.5 + max: 6.0 Std_PtfmPitch: - flag: True - max: 0.39 # Same as IEA-15MW with same DLCs + flag: False + max: 1.25 # Same as IEA-15MW with same DLCs nacelle_acceleration: - flag: True - max: 0.25 + flag: False + max: 0.4 merit_figure: structural_mass - + driver: optimization: flag: True # Flag to enable optimization - tol: 1.e-3 # Optimality tolerance - max_iter: 1000 # Maximum number of iterations (SLSQP) - solver: LN_COBYLA # Optimization solver. Other options are 'SLSQP' - 'CONMIN' + tol: 1.e-6 # Optimality tolerance + # max_iter: 1000 # Maximum number of iterations (SLSQP) + maxiter: 1000 # Maximum number of iterations (NLopt) + maxtime: 3420 + solver: LN_COBYLA # Optimization solver. Other options are 'SLSQP' - 'CONMIN' recorder: flag: True # Flag to activate OpenMDAO recorder diff --git a/examples/17_IEA22_Optimization/driver_weis_raft_opt.py b/examples/17_IEA22_Optimization/driver_weis_raft_opt.py index e9769cbdb..a04e1c4ea 100644 --- a/examples/17_IEA22_Optimization/driver_weis_raft_opt.py +++ b/examples/17_IEA22_Optimization/driver_weis_raft_opt.py @@ -10,14 +10,13 @@ ## File management mydir = os.path.dirname(os.path.realpath(__file__)) # get path to this file -fname_wt_input = mydir + os.sep + 'IEA-22-280-RWT-Semi.yaml' +fname_wt_input = mydir + os.sep + "IEA-22-280-RWT-Semi.yaml" # fname_wt_input = '/Users/dzalkind/Projects/IEA-22MW/FloaterIEA22/33_Round_DesRo1/IEA-22-280-RWT.yaml' -# fname_wt_input = os.path.join(mydir,'11_Lower_Pontoon_Stiffeners','IEA-22-280-RWT.yaml') +# fname_wt_input = os.path.join(mydir,'11_Lower_Pontoon_Stiffeners','IEA-22-280-RWT.yaml') fname_modeling_options = mydir + os.sep + "modeling_options_raft.yaml" fname_analysis_options = mydir + os.sep + "analysis_options_raft_ptfm_opt.yaml" wt_opt, modeling_options, analysis_options = run_weis(fname_wt_input, fname_modeling_options, fname_analysis_options) - if MPI: rank = MPI.COMM_WORLD.Get_rank() else: @@ -26,4 +25,3 @@ # shutil.copyfile(os.path.join(analysis_options['general']['folder_output'],analysis_options['general']['fname_output']+'.yaml'), fname_wt_input) print("Tower mass (kg) =", wt_opt["towerse.tower_mass"]) print("Floating platform mass (kg) =", wt_opt["floatingse.platform_mass"]) - diff --git a/weis/glue_code/runWEIS.py b/weis/glue_code/runWEIS.py index 1515b9c4e..5fc9c6ee0 100644 --- a/weis/glue_code/runWEIS.py +++ b/weis/glue_code/runWEIS.py @@ -1,5 +1,5 @@ import numpy as np -import os, sys, time +import os, sys, time, json import openmdao.api as om from weis.glue_code.gc_LoadInputs import WindTurbineOntologyPythonWEIS from wisdem.glue_code.gc_WT_InitModel import yaml2openmdao @@ -11,6 +11,7 @@ from weis.control.tmd import assign_TMD_values fd_methods = ['SLSQP','SNOPT', 'LD_MMA'] +crawling_methods = ['DE', 'NSGA2'] if MPI: from wisdem.commonse.mpi_tools import map_comm_heirarchical, subprocessor_loop, subprocessor_stop @@ -18,10 +19,10 @@ def run_weis(fname_wt_input, fname_modeling_options, fname_opt_options, geometry_override=None, modeling_override=None, analysis_override=None): # Load all yaml inputs and validate (also fills in defaults) wt_initial = WindTurbineOntologyPythonWEIS( - fname_wt_input, - fname_modeling_options, - fname_opt_options, - modeling_override=modeling_override, + fname_wt_input, + fname_modeling_options, + fname_opt_options, + modeling_override=modeling_override, analysis_override=analysis_override ) wt_init, modeling_options, opt_options = wt_initial.get_input_data() @@ -35,34 +36,34 @@ def run_weis(fname_wt_input, fname_modeling_options, fname_opt_options, geometry # Extract the number of cores available max_cores = MPI.COMM_WORLD.Get_size() - # Define the color map for the parallelization, determining the maximum number of parallel finite difference (FD) + # Define the color map for the parallelization, determining the maximum number of parallel finite difference (FD) # evaluations based on the number of design variables (DV). OpenFAST on/off changes things. if modeling_options['Level3']['flag']: - + # If we are running an optimization method that doesn't use finite differencing, set the number of DVs to 1 if not (opt_options['driver']['design_of_experiments']['flag'] or opt_options['driver']['optimization']['solver'] in fd_methods): n_DV = 1 - - # If openfast is called, the maximum number of FD is the number of DV, if we have the number of cores available that doubles the number of DVs, - # otherwise it is half of the number of DV (rounded to the lower integer). + + # If openfast is called, the maximum number of FD is the number of DV, if we have the number of cores available that doubles the number of DVs, + # otherwise it is half of the number of DV (rounded to the lower integer). # We need this because a top layer of cores calls a bottom set of cores where OpenFAST runs. if max_cores > 2. * n_DV: n_FD = n_DV else: n_FD = int(np.floor(max_cores / 2)) # Get the number of OpenFAST runs from the user input and the max that can run in parallel given the resources - # The number of OpenFAST runs is the minimum between the actual number of requested OpenFAST simulations, and + # The number of OpenFAST runs is the minimum between the actual number of requested OpenFAST simulations, and # the number of cores available (minus the number of DV, which sit and wait for OF to complete) n_OF_runs = modeling_options['DLC_driver']['n_cases'] n_DV = max([n_DV, 1]) max_parallel_OF_runs = max([int(np.floor((max_cores - n_DV) / n_DV)), 1]) n_OF_runs_parallel = min([int(n_OF_runs), max_parallel_OF_runs]) elif modeling_options['Level2']['flag']: - + if not (opt_options['driver']['design_of_experiments']['flag'] or opt_options['driver']['optimization']['solver'] in fd_methods): n_DV = 1 - - + + if max_cores > 2. * n_DV: n_FD = n_DV else: @@ -75,7 +76,10 @@ def run_weis(fname_wt_input, fname_modeling_options, fname_opt_options, geometry # If OpenFAST is not called, the number of parallel calls to compute the FDs is just equal to the minimum of cores available and DV n_FD = min([max_cores, n_DV]) n_OF_runs_parallel = 1 - + # if we're doing a GA or such, "FD" means "entities in epoch" + if opt_options['driver']['optimization']['solver'] in crawling_methods: + n_FD = max_cores + # Define the color map for the cores (how these are distributed between finite differencing and openfast runs) if opt_options['driver']['design_of_experiments']['flag']: n_FD = MPI.COMM_WORLD.Get_size() @@ -124,10 +128,10 @@ def run_weis(fname_wt_input, fname_modeling_options, fname_opt_options, geometry if opt_options['driver']['design_of_experiments']['flag']: modeling_options['General']['openfast_configuration']['cores'] = 1 else: - modeling_options['General']['openfast_configuration']['cores'] = n_OF_runs_parallel - + modeling_options['General']['openfast_configuration']['cores'] = n_OF_runs_parallel + # Parallel settings for OpenMDAO - if opt_options['driver']['design_of_experiments']['flag']: + if opt_options['driver']['design_of_experiments']['flag']: wt_opt = om.Problem(model=WindPark(modeling_options = modeling_options, opt_options = opt_options), reports=False) else: wt_opt = om.Problem(model=om.Group(num_par_fd=n_FD), comm=comm_i, reports=False) @@ -148,10 +152,10 @@ def run_weis(fname_wt_input, fname_modeling_options, fname_opt_options, geometry if opt_options['driver']['design_of_experiments']['flag']: wt_opt.driver.options['debug_print'] = ['desvars','ln_cons','nl_cons','objs'] wt_opt.driver.options['procs_per_model'] = 1 # n_OF_runs_parallel # int(max_cores / np.floor(max_cores/n_OF_runs)) - + wt_opt = myopt.set_recorders(wt_opt) wt_opt.driver.options['debug_print'] = ['desvars','ln_cons','nl_cons','objs','totals'] - + # Setup openmdao problem if opt_options['opt_flag']: wt_opt.setup() @@ -159,7 +163,7 @@ def run_weis(fname_wt_input, fname_modeling_options, fname_opt_options, geometry # If we're not performing optimization, we don't need to allocate # memory for the derivative arrays. wt_opt.setup(derivatives=False) - + # Load initial wind turbine data from wt_initial to the openmdao problem wt_opt = yaml2openmdao(wt_opt, modeling_options, wt_init, opt_options) wt_opt = assign_ROSCO_values(wt_opt, modeling_options, opt_options) @@ -169,7 +173,7 @@ def run_weis(fname_wt_input, fname_modeling_options, fname_opt_options, geometry wt_opt = myopt.set_initial(wt_opt, wt_init) if modeling_options['Level3']['flag']: wt_opt = myopt.set_initial_weis(wt_opt) - + # If the user provides values in geometry_override, they overwrite # whatever values have been set by the yaml files. # This is useful for performing black-box wrapped optimization without @@ -195,12 +199,12 @@ def run_weis(fname_wt_input, fname_modeling_options, fname_opt_options, geometry if opt_options['driver']['optimization']['check_totals']: wt_opt.run_model() totals = wt_opt.compute_totals() - + if 'check_partials' in opt_options['driver']['optimization']: if opt_options['driver']['optimization']['check_partials']: wt_opt.run_model() checks = wt_opt.check_partials(compact_print=True) - + sys.stdout.flush() # Run openmdao problem if opt_options['opt_flag']: @@ -216,7 +220,27 @@ def run_weis(fname_wt_input, fname_modeling_options, fname_opt_options, geometry if not modeling_options['Level3']['from_openfast']: wt_initial.write_ontology(wt_opt, froot_out) wt_initial.write_options(froot_out) - + + # output the problem variables as a dictionary in the output dir + fname_pv_json = os.path.join(folder_output, "problem_vars.json") + # openMDAO doesn't save constraint values, so we get them from this construction + problem_var_dict = wt_opt.list_problem_vars( + desvar_opts=["lower", "upper",], + cons_opts=["lower", "upper", "equals",], + out_stream=None, + ) + # clean up the problem_var_dict that we extracted for output + for k in problem_var_dict.keys(): + if not problem_var_dict.get(k): continue + for idx in range(len(problem_var_dict[k])): + for kk in problem_var_dict[k][idx][1].keys(): + if isinstance(problem_var_dict[k][idx][1][kk], np.ndarray): + problem_var_dict[k][idx][1][kk] = problem_var_dict[k][idx][1][kk].tolist() + if isinstance(problem_var_dict[k][idx][1][kk], np.int32): + problem_var_dict[k][idx][1][kk] = int(problem_var_dict[k][idx][1][kk]) + with open(fname_pv_json, 'w') as pvfile: + json.dump(problem_var_dict, pvfile, indent=4) + # Save data to numpy and matlab arrays fileIO.save_data(froot_out, wt_opt) @@ -224,7 +248,7 @@ def run_weis(fname_wt_input, fname_modeling_options, fname_opt_options, geometry (modeling_options['Level3']['flag'] or modeling_options['Level2']['flag']) and \ (not opt_options['driver']['design_of_experiments']['flag']) and \ color_i < 1000000: - # subprocessor ranks spin, waiting for FAST simulations to run. + # subprocessor ranks spin, waiting for FAST simulations to run. # Only true for cores actually in use, not the ones supporting openfast openmp (marked as color_i = 1000000) sys.stdout.flush() if rank in comm_map_up.keys(): diff --git a/weis/visualization/opt_plotting.py b/weis/visualization/opt_plotting.py new file mode 100644 index 000000000..c9603bd7a --- /dev/null +++ b/weis/visualization/opt_plotting.py @@ -0,0 +1,162 @@ +import glob + +import glob +import json +import multiprocessing as mp + +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt + +import openmdao.api as om + +from weis.visualization.utils import * + + +def plot_conv( + keyset_in, + map_dataOM_vars, + use_casewise_feasibility=False, + feas_tol=1e-5, + figax=None, + alpha=None, +): + """ + plot a set of keys + + args: + keyset_in: list[str] + list of keys to plot the convergence data for + map_dataOM_vars: dict[str -> dict] + map from a case of interest by name to an OM data dict to plot + use_casewise_feasibility: bool + if plotting a constraint should we plot feasibility w.r.t. that constraint (vs. all) + + returns: + fig : plt.Figure + ax : plt.Axes + """ + + if len(keyset_in) == 0: + return + + markerstyle = "x" + markersize = 5 + linestyle = "-" + + fig, axes = plt.subplots( + len(keyset_in), + 1, + sharex=True, + figsize=(6, 0.60 * 4 * len(keyset_in)), + squeeze=False, + dpi=150, + ) + + has_ref_vals = type(keyset_in) == dict + + if has_ref_vals: + key_val_map = keyset_in + keyset = keyset_in.keys() + else: + keyset = keyset_in + + pt_imethod = [] + for imethod, method in enumerate(map_dataOM_vars.keys()): + if imethod == 0: + markerstyle = "o" + elif imethod == 1: + markerstyle = "p" + elif imethod == 2: + markerstyle = "s" + else: + markerstyle = "P" + + pt0 = axes[0, 0].plot( + [], + [], + markerstyle + linestyle, + label=method, + markersize=markersize, + # color=(0.5,0.5,0.5), + alpha=alpha, + ) + dataOM = map_dataOM_vars[method][0] + vars = map_dataOM_vars[method][1] + tfeas, varfeas = get_feasible_iterations(dataOM, vars, feas_tol=feas_tol) + + for idx_ax, key in enumerate(keyset): + if key in ["rank", "iter",]: continue + if use_casewise_feasibility and key in varfeas.keys(): + feas_val = varfeas[key] + else: + feas_val = tfeas # use total feasibility + + axes[idx_ax, 0].plot( + np.squeeze(dataOM[key]), + linestyle, + label="".join(["_", method, "_"]), + color=pt0[-1].get_color(), + markersize=markersize, + alpha=alpha, + ) + axes[idx_ax, 0].plot( + np.ma.array( + dataOM[key], + mask=~( + feas_val * np.ones( + ( + 1, + np.array(dataOM[key]).shape[1] + if len(np.array(dataOM[key]).shape) > 1 + else 1 + ), + dtype=bool, + ) + ) + ), + markerstyle, + label="".join(["_", method, "_"]), + color=pt0[-1].get_color(), + alpha=alpha, + fillstyle="full", + markersize=markersize, + ) + axes[idx_ax, 0].plot( + np.ma.array( + dataOM[key], + mask=( + feas_val * np.ones( + ( + 1, + np.array(dataOM[key]).shape[1] + if len(np.array(dataOM[key]).shape) > 1 + else 1 + ), + dtype=bool, + ) + ) + ), + markerstyle, + label="".join(["_", method, "_"]), + color=pt0[-1].get_color(), + alpha=alpha, + fillstyle="none", + markersize=markersize, + ) + if has_ref_vals: + cval = key_val_map[key] + if cval[0] is not None: + axes[idx_ax, 0].plot([0, len(dataOM[key])], [cval[0], cval[0]], "b:", label="_lower bound_") + if cval[1] is not None: + axes[idx_ax, 0].plot([0, len(dataOM[key])], [cval[1], cval[1]], "r:", label="_upper bound_") + axes[idx_ax, 0].set_title(key) + + if has_ref_vals: + axes[0, 0].plot([], [], "b:", label="lower bound") + axes[0, 0].plot([], [], "r:", label="upper bound") + axes[0, 0].legend() + fig.tight_layout() + + return fig, axes + diff --git a/weis/visualization/utils.py b/weis/visualization/utils.py index 46bc6d5a3..ea7eb8ada 100644 --- a/weis/visualization/utils.py +++ b/weis/visualization/utils.py @@ -5,7 +5,9 @@ import pandas as pd import numpy as np import openmdao.api as om - +import glob +import json +import multiprocessing as mp try: import ruamel_yaml as ry @@ -37,7 +39,7 @@ def read_cm(cm_file): else: cnames.append(c) cm = pd.DataFrame(cm_dict, columns=cnames) - + return cm def parse_contents(data): @@ -49,7 +51,7 @@ def parse_contents(data): for key in data.keys(): if key not in collected_data.keys(): collected_data[key] = [] - + for key_idx, _ in enumerate(data[key]): if isinstance(data[key][key_idx], int): collected_data[key].append(np.array(data[key][key_idx])) @@ -60,25 +62,393 @@ def parse_contents(data): collected_data[key].append(np.array(data[key][key_idx])) else: collected_data[key].append(np.array(data[key][key_idx])) - + df = pd.DataFrame.from_dict(collected_data) return df -def load_OMsql(log): +def load_vars_file(fn_vars): """ - Function from : - https://github.com/WISDEM/WEIS/blob/main/examples/09_design_of_experiments/postprocess_results.py + load a json file of problem variables as output from WEIS (as problem_vars.json) + + parameters: + ----------- + fn_vars: str + a filename to read + + returns: + -------- + vars : dict[dict] + a dictionary of dictionaries holding the problem_vars from WEIS + """ + + with open(fn_vars, "r") as fjson: + # unpack in a useful form + vars = {k: dict(v) for k, v in json.load(fjson).items()} + return vars + + +def compare_om_data( + dataOM_1, + dataOM_2, + fn_1="data 1", + fn_2="data 2", + verbose=False, +): + """ + compare openmdao data dictionaries to find the in-common (and not) keys + + args: + dataOM_1: dict + an openmdao data dictionary + dataOM_2: dict + an openmdao data dictionary + fn_1: str (optional) + display name for the first data dictionary + fn_2: str (optional) + display name for the second data dictionary + verbose : bool (optional, default: False) + if we want to print what's happening + + returns: + keys_all: set + intersection (i.e. common) keys between the two OM data dictionaries + diff_keys_12: set + directional difference of keys between first and second OM data dicts + diff_keys_21: set + directional difference of keys between second and first OM data dicts + """ + + diff_keys_12 = set(dataOM_1).difference(dataOM_2) + diff_keys_21 = set(dataOM_2).difference(dataOM_1) + if len(diff_keys_12): + if verbose: + print(f"the following keys are only in {fn_1}:") + for key_m in diff_keys_12: + if verbose: + print(f"\t{key_m}") + if len(diff_keys_21): + if verbose: + print(f"the following keys are only in {fn_2}:") + for key_m in diff_keys_21: + if verbose: + print(f"\t{key_m}") + keys_all = set(dataOM_1).intersection(dataOM_2) + if verbose: + print(f"the following keys are in both {fn_1} and {fn_2}:") + for key_m in keys_all: + if verbose: + print(f"\t{key_m}") + + return keys_all, diff_keys_12, diff_keys_21 + + +def load_OMsql( + log, + parse_multi=False, + meta=None, + verbose=False, +): """ - # logging.info("loading ", log) - cr = om.CaseReader(log) + load the openmdao sql file produced by a WEIS run into a dictionary + + parameters: + ----------- + log : str + filename of the log sql database that should be loaded + parse_multi : bool + switch to turn on rank/iteration parsing and storage + meta : str + filename of the meta log sql database that should be loaded + verbose : bool (optional, default: False) + if we want to print what's happening + + returns: + rec_data: dict + dictionary of the data recorded by openMDAO + + """ + + # heads-up print + if verbose: + print(f"loading {log}") + + # create an openmdao reader for recorded output data + cr = om.CaseReader(log, metadata_filename=meta) + + # create a dict for output data that's been recorded rec_data = {} - cases = cr.get_cases('driver') - for case in cases: + # loop over the cases + for case in cr.get_cases("driver"): + if parse_multi: + rankNo = case.name.split(":")[0] + assert rankNo.startswith("rank") + rankNo = int(rankNo[4:]) + iterNo = int(case.name.split("|")[-1]) + + # for each key in the outputs for key in case.outputs.keys(): + if key not in rec_data: + # if this key isn't present, create a new list rec_data[key] = [] - rec_data[key].append(case[key]) - - return rec_data \ No newline at end of file + if len(case[key]) == 1: + # otherwise coerce to float if possible and add the data to the list + rec_data[key].append(float(case[key])) + else: + # otherwise a numpy array if possible and add the data to the list + rec_data[key].append(np.array(case[key])) + + if parse_multi: + # add rank/iter metadata + for key in ["rank", "iter"]: # for each key in the outputs + if key not in rec_data: # if this key isn't present, create a new list + rec_data[key] = [] + rec_data["rank"].append(rankNo) + rec_data["iter"].append(iterNo) + + return rec_data # return the output + + +def load_OMsql_multi( + log_fmt, + meta_in=None, + process_multi = True, + verbose=False, +): + """ + load the multi-processor openmdao sql files produced by WEIS into a dict + + parameters: + ----------- + log_fmt : str + format string for the process-wise WEIS/OM log files + meta_in : str (optional, default: None) + filename string of the meta log file (will override automatic discovery) + post_multi : bool (optional, default: True) + postprocess in parallel using the multiprocessing library + verbose : bool (optional, default: False) + if we want to print what's happening + + returns: + -------- + data_dict : dict + dictionary of all the datapoints extracted from the WEIS/OM log files + """ + + # use glob to find the logs that match the format string + opt_logs = sorted( + glob.glob(log_fmt), + key = lambda v : int(v.split("_")[-1]) + if (v.split("_")[-1] != "meta") + else 1e8, + ) + if len(opt_logs) < 1: + raise FileExistsError("No output logs to postprocess!") + + # remove the "meta" log from the collection + meta_found = None + for idx, log in enumerate(opt_logs): + if "meta" in log: + meta_found = log # save the meta file + opt_logs.pop(idx) # remove the meta log from the list + break + + # handle meta logfile discovery... not sure what it actually does + if meta_in is not None: + meta = meta_in # if a meta is given, override + elif meta_found is not None: + meta = meta_found # if a meta is not given but one is found, use that + else: + meta = None # otherwise, run without a meta + + # extract the ranks from the sql files + sql_ranks = [ol.split("_")[-1] for ol in opt_logs] + + # run multiprocessing + if process_multi: + cores = mp.cpu_count() + pool = mp.Pool(min(len(opt_logs), cores)) + + # load sql file + outdata = pool.starmap(load_OMsql, [(log, True, meta, verbose) for log in opt_logs]) + pool.close() + pool.join() + else: # no multiprocessing + outdata = [load_OMsql(log, parse_multi=True, verbose=verbose, meta=meta) for log in opt_logs] + + # create a dictionary and turn it into a dataframe for convenience + collected_data = {} + ndarray_keys = [] + for sql_rank, data in zip(sql_ranks, outdata): + for key in data.keys(): + if key not in collected_data.keys(): + collected_data[key] = [] + if key == "rank": # adjust the rank based on sql file rank + data[key] = [int(sql_rank) for _ in data[key]] + for idx_key, _ in enumerate(data[key]): + if isinstance(data[key][idx_key], int): + collected_data[key].append(int(np.array(data[key][idx_key]))) + elif isinstance(data[key][idx_key], float): + collected_data[key].append(float(np.array(data[key][idx_key]))) + elif len(data[key][idx_key]) == 1: + collected_data[key].append(float(np.array(data[key][idx_key]))) + # try: + # collected_data[key].append(np.array(data[key][idx_key][0])) + # except: + # collected_data[key].append(np.array(data[key][idx_key])) + else: + collected_data[key].append(np.array(data[key][idx_key]).tolist()) + ndarray_keys.append(key) + df = pd.DataFrame(collected_data) + + # return a dictionary of the data that was extracted + return df.to_dict(orient="list") + + +def consolidate_multi( + dataOMmulti, + vars_dict, + feas_tol=1e-5, +): + """ + load the multi-processor openmdao sql files and squash them to the + per-iteration best-feasible result + + parameters: + ----------- + dataOMmulti : dict + dictionary of all the datapoints extracted from the multiprocess + WEIS/OM log files + vars_dict: + experiment design variables to be analyzed + feas_tol : float (optional) + tolerance for feasibility analysis + returns: + -------- + dataOMbest_DE : dict + dictionary of the per-iteration best-feasible simulations + """ + + dfOMmulti = pd.DataFrame(dataOMmulti) + tfeas, cfeas = get_feasible_iterations(dataOMmulti, vars_dict, feas_tol=feas_tol) + + dfOMmulti = dfOMmulti[tfeas].reset_index() + + dataOMbest_DE = dfOMmulti.groupby("iter").apply( + lambda grp : grp.loc[grp["floatingse.system_structural_mass"].idxmin()], + include_groups=False, + ).to_dict() + + for key in dataOMbest_DE.keys(): + dataOMbest_DE[key] = np.array(list(dataOMbest_DE[key].values())) + + return dataOMbest_DE + + +def get_feasible_iterations( + dataOM, + vars_dict, + feas_tol=1e-5, +): + """ + get iteration-wise total and per-constraint feasibility from an experiment + + args: + dataOM: dict + openmdao data dictionary + vars_dict: + experiment design variables for checking + + returns: + total_feasibility: np.ndarray[bool] + iteration-wise total feasibility indications + feasibility_constraintwise: dict[np.ndarray[bool]] + dictionary to map from constraint names to iteration-wise feasibility indications for that constraint + """ + + # assert len(vars_dict["objectives"].values()) == 1, "can't handle multi-objective... yet. -cfrontin" + objective_name = list(vars_dict["objectives"].values())[0]["name"] + + feasibility_constraintwise = dict() + total_feasibility = np.ones_like(np.array(dataOM[objective_name]).reshape(-1,1), dtype=bool) + for k, v in vars_dict["constraints"].items(): + feasibility = np.ones_like(dataOM[objective_name], dtype=bool).reshape(-1, 1) + values = np.array(dataOM[v["name"]]) + if len(values.shape) == 1: + values = values.reshape(-1,1) + if v.get("upper") is not None: + feasibility = np.logical_and(feasibility, np.all(np.less_equal(values, (1+feas_tol)*v["upper"]), axis=1).reshape(-1, 1)) + if v.get("lower") is not None: + feasibility = np.logical_and(feasibility, np.all(np.greater_equal(values, (1-feas_tol)*v["lower"]), axis=1).reshape(-1, 1)) + feasibility_constraintwise[v["name"]] = feasibility + total_feasibility = np.logical_and(total_feasibility, feasibility) + return total_feasibility, feasibility_constraintwise + + +def verify_vars( + vars_1, + *vars_i, +): + """ + verifies format of DVs, constraints, objective variable file + guarantees a list of experiments has the same variables + adjusts unbounded constraints + returns verified list of vars + """ + + for vars_2 in vars_i: + if vars_2 is not None: + for k0 in set(vars_1.keys()).union(vars_2): + assert k0 in vars_1 + assert k0 in vars_2 + for k1 in set(vars_1[k0].keys()).union(vars_2[k0].keys()): + assert k1 in vars_1[k0] + assert k1 in vars_2[k0] + for k2 in set(vars_1[k0][k1].keys()).union(vars_2[k0][k1].keys()): + assert k2 in vars_1[k0][k1] + assert k2 in vars_2[k0][k1] + if k2 == "val": + continue + if isinstance(vars_1[k0][k1][k2], str): + assert vars_1[k0][k1][k2] == vars_2[k0][k1][k2] + elif vars_1[k0][k1][k2] is not None: + assert np.all(np.isclose(vars_1[k0][k1][k2], vars_2[k0][k1][k2])) + else: + assert (vars_1[k0][k1][k2] is None) and (vars_2[k0][k1][k2] is None) + + vars_unified = vars_1.copy() + for k0 in vars_unified.keys(): + for k1 in vars_unified[k0].keys(): + if (vars_unified[k0][k1].get("lower") is not None) and (vars_unified[k0][k1].get("lower") < -1e28): + vars_unified[k0][k1]["lower"] = -np.inf + if (vars_unified[k0][k1].get("upper") is not None) and (vars_unified[k0][k1].get("upper") > 1e28): + vars_unified[k0][k1]["upper"] = np.inf + + return vars_unified + + +def prettyprint_variables( + keys_all, + keys_obj, + keys_DV, + keys_constr, +): + """ + print the variables we have with a prefix showing whether they are an + objective variable (**), design variabie (--), constraint (<>), or unknown + (??) + """ + + # print them nicely + print() + [ + print( + f"** {key}" + if key in keys_obj + else f"-- {key}" if key in keys_DV else f"<> {key}" if key in keys_constr else f"?? {key}" + ) + for key in keys_all + ] + print() From 2f2a4a1f39222134d6aa9027433a2fa709921ca7 Mon Sep 17 00:00:00 2001 From: ptrbortolotti Date: Wed, 26 Jun 2024 11:55:22 +0200 Subject: [PATCH 091/114] add readme example 17 --- examples/17_user_custom_setup/README.md | 11 +++++++++++ 1 file changed, 11 insertions(+) create mode 100644 examples/17_user_custom_setup/README.md diff --git a/examples/17_user_custom_setup/README.md b/examples/17_user_custom_setup/README.md new file mode 100644 index 000000000..06d72dfaf --- /dev/null +++ b/examples/17_user_custom_setup/README.md @@ -0,0 +1,11 @@ +# 17_User_Custom_Setp +WEIS leverages the WISDEM framework for many of it features. WISDEM (and therefore WEIS) is built on top of the OpenMDAO library released by NASA. OpenMDAO allows to define any input as a design variable and any output as either a figure of merit that should be minimized or as a constraint. Please refer to the OpenMDAO tutorials to know more. + +WISDEM allows users to set design variables, figure of merit, and constraints from the ``analysis_options.yaml`` file that users populate. The full list of WISDEM predefined options is specified in the `modeling_options.yaml `. + +In addition, WISDEM and WEIS now offer the option to build your own optimization problem by setting any available input as a design variable and any available output as either a constraint or a figure of merit. The WISDEM example #11 shows how to build your customized ``analysis_options.yaml``. + +The example is available in the WISDEM repo at https://github.com/WISDEM/WISDEM/tree/develop/examples/11_user_custom +The example is explained at https://wisdem.readthedocs.io/en/latest/examples/11_user_custom/tutorial.html + +Users should not need to change anything in the inputs to run the example within WEIS. Simply activate your Conda environment and launch the python file. \ No newline at end of file From 7d1715ab9c7b3777e7c0d86609a707ec84a0a9cb Mon Sep 17 00:00:00 2001 From: yqliaohk Date: Wed, 3 Jul 2024 11:24:15 -0600 Subject: [PATCH 092/114] updated validation to be consistent with wisdem update --- weis/inputs/validation.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/weis/inputs/validation.py b/weis/inputs/validation.py index c6af13f84..469abf736 100644 --- a/weis/inputs/validation.py +++ b/weis/inputs/validation.py @@ -1,7 +1,7 @@ import os import jsonmerge import wisdem.inputs -from wisdem.inputs import load_yaml, write_yaml, validate_without_defaults, validate_with_defaults, simple_types +from wisdem.inputs import load_yaml, write_yaml, simple_types, validate import rosco.toolbox.inputs froot_wisdem = os.path.dirname(wisdem.inputs.__file__) @@ -30,11 +30,11 @@ def get_geometry_schema(): def load_geometry_yaml(finput): merged_schema = get_geometry_schema() - return validate_with_defaults(finput, merged_schema) + return validate(finput, merged_schema, defaults=True) def write_geometry_yaml(instance, foutput): merged_schema = get_geometry_schema() - validate_without_defaults(instance, merged_schema) + validate(instance, merged_schema, defaults=False) sfx_str = '.yaml' if foutput[-5:] == sfx_str: sfx_str = '' @@ -52,11 +52,11 @@ def get_modeling_schema(): def load_modeling_yaml(finput): weis_schema = get_modeling_schema() - return validate_with_defaults(finput, weis_schema) + return validate(finput, weis_schema, defaults=True) def write_modeling_yaml(instance, foutput): weis_schema = get_modeling_schema() - validate_without_defaults(instance, weis_schema) + validate(instance, weis_schema, defaults=False) sfx_str = ".yaml" if foutput[-5:] == sfx_str: foutput = foutput[-5:] @@ -74,11 +74,11 @@ def get_analysis_schema(): def load_analysis_yaml(finput): merged_schema = get_analysis_schema() - return validate_with_defaults(finput, merged_schema) + return validate(finput, merged_schema, defaults=True) def write_analysis_yaml(instance, foutput): merged_schema = get_analysis_schema() - validate_without_defaults(instance, merged_schema) + validate(instance, merged_schema, defaults=False) sfx_str = ".yaml" if foutput[-5:] == sfx_str: foutput = foutput[-5:] From b36917fbb71b20b8635a54d56060d3aff86645f8 Mon Sep 17 00:00:00 2001 From: Garrett Barter Date: Thu, 4 Jul 2024 06:49:04 -0600 Subject: [PATCH 093/114] trying to restore windows to working order --- environment.yml | 3 +++ weis/dtqpy/solver/DTQPy_SOLVER_osqp.py | 8 ++++++-- 2 files changed, 9 insertions(+), 2 deletions(-) diff --git a/environment.yml b/environment.yml index 9da80ec39..1d16a4884 100644 --- a/environment.yml +++ b/environment.yml @@ -12,7 +12,10 @@ dependencies: - openraft - osqp - pcrunch + - pip + - pyhams>=1.3 - rosco + - scipy<1.14 - smt - wisdem - pip: diff --git a/weis/dtqpy/solver/DTQPy_SOLVER_osqp.py b/weis/dtqpy/solver/DTQPy_SOLVER_osqp.py index e74eb5eaa..c0ba82145 100644 --- a/weis/dtqpy/solver/DTQPy_SOLVER_osqp.py +++ b/weis/dtqpy/solver/DTQPy_SOLVER_osqp.py @@ -6,7 +6,11 @@ Contributor: Athul Krishna Sundarrajan (AthulKrishnaSundarrajan on Github) Primary Contributor: Daniel R. Herber (danielrherber on Github) """ -import osqp +try: + import osqp + OSQP_Flag = True +except ImportError: + OSQP_Flag = False from scipy import sparse import numpy as np @@ -49,4 +53,4 @@ def DTQPy_SOLVER_osqp(H,f,A,b,Aeq,beq,lb,ub,internal,opts): return X,F,internal,opts - \ No newline at end of file + From a56bcbe1057ba55c6f2ca35909f4587bf8498ae6 Mon Sep 17 00:00:00 2001 From: Garrett Barter Date: Fri, 5 Jul 2024 13:33:32 -0600 Subject: [PATCH 094/114] sync with wisdem changes --- weis/inputs/validation.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/weis/inputs/validation.py b/weis/inputs/validation.py index c6af13f84..cd132ff3a 100644 --- a/weis/inputs/validation.py +++ b/weis/inputs/validation.py @@ -1,7 +1,7 @@ import os import jsonmerge import wisdem.inputs -from wisdem.inputs import load_yaml, write_yaml, validate_without_defaults, validate_with_defaults, simple_types +from wisdem.inputs.validation import load_yaml, write_yaml, _validate, simple_types import rosco.toolbox.inputs froot_wisdem = os.path.dirname(wisdem.inputs.__file__) @@ -30,11 +30,11 @@ def get_geometry_schema(): def load_geometry_yaml(finput): merged_schema = get_geometry_schema() - return validate_with_defaults(finput, merged_schema) + return _validate(finput, merged_schema, defaults=True) def write_geometry_yaml(instance, foutput): merged_schema = get_geometry_schema() - validate_without_defaults(instance, merged_schema) + _validate(instance, merged_schema, defaults=False) sfx_str = '.yaml' if foutput[-5:] == sfx_str: sfx_str = '' @@ -52,11 +52,11 @@ def get_modeling_schema(): def load_modeling_yaml(finput): weis_schema = get_modeling_schema() - return validate_with_defaults(finput, weis_schema) + return _validate(finput, weis_schema, defaults=True) def write_modeling_yaml(instance, foutput): weis_schema = get_modeling_schema() - validate_without_defaults(instance, weis_schema) + _validate(instance, weis_schema, defaults=False) sfx_str = ".yaml" if foutput[-5:] == sfx_str: foutput = foutput[-5:] @@ -74,11 +74,11 @@ def get_analysis_schema(): def load_analysis_yaml(finput): merged_schema = get_analysis_schema() - return validate_with_defaults(finput, merged_schema) + return _validate(finput, merged_schema, defaults=True) def write_analysis_yaml(instance, foutput): merged_schema = get_analysis_schema() - validate_without_defaults(instance, merged_schema) + _validate(instance, merged_schema, defaults=False) sfx_str = ".yaml" if foutput[-5:] == sfx_str: foutput = foutput[-5:] From 822bd5c5bbdbae137b0fc86f7d82f8cd7816d4f2 Mon Sep 17 00:00:00 2001 From: Garrett Barter Date: Fri, 5 Jul 2024 15:29:35 -0600 Subject: [PATCH 095/114] making examples more robust for latest code changes --- .../IEA-15-240-RWT_VolturnUS-S.yaml | 20 ------------------- .../IEA-15-floating_wTMDs.yaml | 20 ------------------- .../IEA-15-floating_wTMDs_tower.yaml | 16 --------------- weis/aeroelasticse/FAST_writer.py | 2 +- weis/aeroelasticse/runFAST_pywrapper.py | 4 ++-- weis/glue_code/glue_code.py | 6 +++--- 6 files changed, 6 insertions(+), 62 deletions(-) diff --git a/examples/06_IEA-15-240-RWT/IEA-15-240-RWT_VolturnUS-S.yaml b/examples/06_IEA-15-240-RWT/IEA-15-240-RWT_VolturnUS-S.yaml index f245ec008..10be6a0c3 100644 --- a/examples/06_IEA-15-240-RWT/IEA-15-240-RWT_VolturnUS-S.yaml +++ b/examples/06_IEA-15-240-RWT/IEA-15-240-RWT_VolturnUS-S.yaml @@ -631,8 +631,6 @@ components: - name: main_column joint1: main_keel joint2: main_freeboard - Ca: 1.0 - Cd: 0.8 outer_shape: shape: circular outer_diameter: @@ -660,8 +658,6 @@ components: - name: column1 joint1: col1_keel joint2: col1_freeboard - Ca: 1.0 - Cd: 0.8 outer_shape: &col_out shape: circular outer_diameter: @@ -699,8 +695,6 @@ components: - name: column2 joint1: col2_keel joint2: col2_freeboard - Ca: 1.0 - Cd: 0.8 outer_shape: *col_out internal_structure: *col_int axial_joints: @@ -716,8 +710,6 @@ components: - name: column3 joint1: col3_keel joint2: col3_freeboard - Ca: 1.0 - Cd: 0.8 outer_shape: *col_out internal_structure: *col_int axial_joints: @@ -734,8 +726,6 @@ components: - name: Y_pontoon_upper1 joint1: main_upper_pontoon joint2: col1_upper_pontoon - Ca: 1.0 - Cd: 0.8 outer_shape: &pontup_out shape: circular outer_diameter: @@ -752,24 +742,18 @@ components: - name: Y_pontoon_upper2 joint1: main_upper_pontoon joint2: col2_upper_pontoon - Ca: 1.0 - Cd: 0.8 outer_shape: *pontup_out internal_structure: *pontup_int - name: Y_pontoon_upper3 joint1: main_upper_pontoon joint2: col3_upper_pontoon - Ca: 1.0 - Cd: 0.8 outer_shape: *pontup_out internal_structure: *pontup_int - name: Y_pontoon_lower1 joint1: main_lower_pontoon joint2: col1_lower_pontoon - Ca: 1.0 - Cd: 0.8 outer_shape: &pontlow_out shape: circular outer_diameter: @@ -796,16 +780,12 @@ components: joint2: col2_lower_pontoon outer_shape: *pontlow_out internal_structure: *pontlow_int - Ca: 1.0 - Cd: 0.8 - name: Y_pontoon_lower3 joint1: main_lower_pontoon joint2: col3_lower_pontoon outer_shape: *pontlow_out internal_structure: *pontlow_int - Ca: 1.0 - Cd: 0.8 mooring: nodes: - name: line1_anchor diff --git a/examples/06_IEA-15-240-RWT/IEA-15-floating_wTMDs.yaml b/examples/06_IEA-15-240-RWT/IEA-15-floating_wTMDs.yaml index 2d186fc9b..8e310ffe3 100644 --- a/examples/06_IEA-15-240-RWT/IEA-15-floating_wTMDs.yaml +++ b/examples/06_IEA-15-240-RWT/IEA-15-floating_wTMDs.yaml @@ -631,8 +631,6 @@ components: - name: main_column joint1: main_keel joint2: main_freeboard - Ca: 1.0 - Cd: 0.8 outer_shape: shape: circular outer_diameter: @@ -660,8 +658,6 @@ components: - name: column1 joint1: col1_keel joint2: col1_freeboard - Ca: 1.0 - Cd: 0.8 outer_shape: &col_out shape: circular outer_diameter: @@ -699,8 +695,6 @@ components: - name: column2 joint1: col2_keel joint2: col2_freeboard - Ca: 1.0 - Cd: 0.8 outer_shape: *col_out internal_structure: *col_int axial_joints: @@ -716,8 +710,6 @@ components: - name: column3 joint1: col3_keel joint2: col3_freeboard - Ca: 1.0 - Cd: 0.8 outer_shape: *col_out internal_structure: *col_int axial_joints: @@ -734,8 +726,6 @@ components: - name: Y_pontoon_upper1 joint1: main_upper_pontoon joint2: col1_upper_pontoon - Ca: 1.0 - Cd: 0.8 outer_shape: &pontup_out shape: circular outer_diameter: @@ -752,24 +742,18 @@ components: - name: Y_pontoon_upper2 joint1: main_upper_pontoon joint2: col2_upper_pontoon - Ca: 1.0 - Cd: 0.8 outer_shape: *pontup_out internal_structure: *pontup_int - name: Y_pontoon_upper3 joint1: main_upper_pontoon joint2: col3_upper_pontoon - Ca: 1.0 - Cd: 0.8 outer_shape: *pontup_out internal_structure: *pontup_int - name: Y_pontoon_lower1 joint1: main_lower_pontoon joint2: col1_lower_pontoon - Ca: 1.0 - Cd: 0.8 outer_shape: &pontlow_out shape: circular outer_diameter: @@ -796,16 +780,12 @@ components: joint2: col2_lower_pontoon outer_shape: *pontlow_out internal_structure: *pontlow_int - Ca: 1.0 - Cd: 0.8 - name: Y_pontoon_lower3 joint1: main_lower_pontoon joint2: col3_lower_pontoon outer_shape: *pontlow_out internal_structure: *pontlow_int - Ca: 1.0 - Cd: 0.8 mooring: nodes: - name: line1_anchor diff --git a/examples/06_IEA-15-240-RWT/IEA-15-floating_wTMDs_tower.yaml b/examples/06_IEA-15-240-RWT/IEA-15-floating_wTMDs_tower.yaml index 46e9257f3..af660cfb9 100644 --- a/examples/06_IEA-15-240-RWT/IEA-15-floating_wTMDs_tower.yaml +++ b/examples/06_IEA-15-240-RWT/IEA-15-floating_wTMDs_tower.yaml @@ -523,8 +523,6 @@ components: - name: main_column joint1: main_keel joint2: main_freeboard - Ca: 1.0 - Cd: 0.8 outer_shape: shape: circular outer_diameter: @@ -559,8 +557,6 @@ components: - name: column1 joint1: col1_keel joint2: col1_freeboard - Ca: 1.0 - Cd: 0.8 outer_shape: &col_out shape: circular outer_diameter: @@ -598,8 +594,6 @@ components: - name: column2 joint1: col2_keel joint2: col2_freeboard - Ca: 1.0 - Cd: 0.8 outer_shape: *col_out internal_structure: *col_int axial_joints: @@ -615,8 +609,6 @@ components: - name: column3 joint1: col3_keel joint2: col3_freeboard - Ca: 1.0 - Cd: 0.8 outer_shape: *col_out internal_structure: *col_int axial_joints: @@ -633,8 +625,6 @@ components: - name: Y_pontoon_upper1 joint1: main_upper_pontoon joint2: col1_upper_pontoon - Ca: 1.0 - Cd: 0.8 outer_shape: &pontup_out shape: circular outer_diameter: @@ -651,24 +641,18 @@ components: - name: Y_pontoon_upper2 joint1: main_upper_pontoon joint2: col2_upper_pontoon - Ca: 1.0 - Cd: 0.8 outer_shape: *pontup_out internal_structure: *pontup_int - name: Y_pontoon_upper3 joint1: main_upper_pontoon joint2: col3_upper_pontoon - Ca: 1.0 - Cd: 0.8 outer_shape: *pontup_out internal_structure: *pontup_int - name: Y_pontoon_lower1 joint1: main_lower_pontoon joint2: col1_lower_pontoon - Ca: 1.0 - Cd: 0.8 outer_shape: &pontlow_out shape: circular outer_diameter: diff --git a/weis/aeroelasticse/FAST_writer.py b/weis/aeroelasticse/FAST_writer.py index 0b2d254fc..61d5671d2 100644 --- a/weis/aeroelasticse/FAST_writer.py +++ b/weis/aeroelasticse/FAST_writer.py @@ -11,7 +11,7 @@ try: from rosco.toolbox import utilities as ROSCO_utilities ROSCO = True -except: +except ImportError: ROSCO = False diff --git a/weis/aeroelasticse/runFAST_pywrapper.py b/weis/aeroelasticse/runFAST_pywrapper.py index 9b88e61ba..5ee0945ea 100644 --- a/weis/aeroelasticse/runFAST_pywrapper.py +++ b/weis/aeroelasticse/runFAST_pywrapper.py @@ -18,8 +18,8 @@ import numpy as np - -of_path = shutil.which('openfast') +# Realpath will resolve symlinks +of_path = os.path.realpath( shutil.which('openfast') ) bin_dir = os.path.dirname(of_path) lib_dir = os.path.abspath( os.path.join(os.path.dirname(bin_dir), 'lib') ) diff --git a/weis/glue_code/glue_code.py b/weis/glue_code/glue_code.py index 106932a3c..4a031a005 100644 --- a/weis/glue_code/glue_code.py +++ b/weis/glue_code/glue_code.py @@ -821,7 +821,7 @@ def setup(self): # Connections to TowerSE if modeling_options["flags"]["tower"]: - tow_params = ["z_full","d_full","t_full", + tow_params = ["z_full","outer_diameter_full","t_full", "E_full","G_full","rho_full","sigma_y_full"] for k in tow_params: self.connect(f'towerse.{k}', f'towerse_post.{k}') @@ -836,7 +836,7 @@ def setup(self): self.connect("aeroelastic.tower_maxMy_Mz", "towerse_post.cylinder_Mzz") if modeling_options["flags"]["monopile"]: - mono_params = ["z_full","d_full","t_full", + mono_params = ["z_full","outer_diameter_full","t_full", "E_full","G_full","rho_full","sigma_y_full"] for k in mono_params: self.connect(f'fixedse.{k}', f'fixedse_post.{k}') @@ -863,7 +863,7 @@ def setup(self): self.connect('nacelle.uptilt', 'tcons_post.tilt') self.connect('nacelle.overhang', 'tcons_post.overhang') self.connect('tower.ref_axis', 'tcons_post.ref_axis_tower') - self.connect('tower.diameter', 'tcons_post.d_full') + self.connect('tower.diameter', 'tcons_post.outer_diameter_full') else: # connections from outside WISDEM self.connect('rosco_turbine.v_rated', 'aeroelastic.Vrated') From 82d2f18adbda1b324b1cc994b594b015870f9118 Mon Sep 17 00:00:00 2001 From: Garrett Barter Date: Fri, 5 Jul 2024 20:59:03 -0600 Subject: [PATCH 096/114] fixing missed connections --- weis/glue_code/glue_code.py | 14 ++++++++++++-- 1 file changed, 12 insertions(+), 2 deletions(-) diff --git a/weis/glue_code/glue_code.py b/weis/glue_code/glue_code.py index 4a031a005..302b74d6e 100644 --- a/weis/glue_code/glue_code.py +++ b/weis/glue_code/glue_code.py @@ -636,6 +636,12 @@ def setup(self): self.connect('rotorse.yu_te', 'rlds_post.strains.yu_te') self.connect('rotorse.yl_te', 'rlds_post.strains.yl_te') self.connect('blade.outer_shape_bem.s','rlds_post.constr.s') + self.connect("blade.internal_structure_2d_fem.d_f", "rlds_post.brs.d_f") + self.connect("blade.internal_structure_2d_fem.sigma_max", "rlds_post.brs.sigma_max") + self.connect("blade.pa.chord_param", "rlds_post.brs.rootD", src_indices=[0]) + self.connect("blade.ps.layer_thickness_param", "rlds_post.brs.layer_thickness") + self.connect("blade.internal_structure_2d_fem.layer_start_nd", "rlds_post.brs.layer_start_nd") + self.connect("blade.internal_structure_2d_fem.layer_end_nd", "rlds_post.brs.layer_end_nd") # Connections to DriveSE if modeling_options['WISDEM']['DriveSE']['flag']: @@ -822,12 +828,16 @@ def setup(self): # Connections to TowerSE if modeling_options["flags"]["tower"]: tow_params = ["z_full","outer_diameter_full","t_full", - "E_full","G_full","rho_full","sigma_y_full"] + "E_full","G_full","rho_full","sigma_y_full", + "section_A", "section_Asx","section_Asy", + "section_Ixx", "section_Iyy", "section_J0", + "section_rho", "section_E", "section_G", "section_L", + ] for k in tow_params: self.connect(f'towerse.{k}', f'towerse_post.{k}') self.connect("towerse.env.qdyn", "towerse_post.qdyn") self.connect("tower_grid.height", "towerse_post.bending_height") - + self.connect("aeroelastic.tower_maxMy_Fz", "towerse_post.cylinder_Fz") self.connect("aeroelastic.tower_maxMy_Fx", "towerse_post.cylinder_Vx") self.connect("aeroelastic.tower_maxMy_Fy", "towerse_post.cylinder_Vy") From 59404b3623e08a1052ecf217a7f79676c1f366fd Mon Sep 17 00:00:00 2001 From: Garrett Barter Date: Fri, 5 Jul 2024 20:59:12 -0600 Subject: [PATCH 097/114] more robust to symlinks --- weis/aeroelasticse/openmdao_openfast.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/weis/aeroelasticse/openmdao_openfast.py b/weis/aeroelasticse/openmdao_openfast.py index b71153265..826a00c09 100644 --- a/weis/aeroelasticse/openmdao_openfast.py +++ b/weis/aeroelasticse/openmdao_openfast.py @@ -2458,7 +2458,7 @@ def calculate_AEP(self, sum_stats, case_list, dlc_generator, discrete_inputs, ou outputs['pitch_out'] = stats_pwrcrv['BldPitch1']['mean'] if self.fst_vt['Fst']['CompServo'] == 1: outputs['AEP'] = stats_pwrcrv['GenPwr']['mean'] - outputs['P_out'] = stats_pwrcrv['GenPwr']['mean'][0] * 1.e3 + outputs['P_out'] = stats_pwrcrv['GenPwr']['mean'].iloc[0] * 1.e3 logger.warning('WARNING: OpenFAST is run at a single wind speed. AEP cannot be estimated. Using average power instead.') else: outputs['Cp_out'] = sum_stats['RtFldCp']['mean'].mean() @@ -2467,7 +2467,7 @@ def calculate_AEP(self, sum_stats, case_list, dlc_generator, discrete_inputs, ou outputs['pitch_out'] = sum_stats['BldPitch1']['mean'].mean() if self.fst_vt['Fst']['CompServo'] == 1: outputs['AEP'] = sum_stats['GenPwr']['mean'].mean() - outputs['P_out'] = sum_stats['GenPwr']['mean'][0] * 1.e3 + outputs['P_out'] = sum_stats['GenPwr']['mean'].iloc[0] * 1.e3 logger.warning('WARNING: OpenFAST is not run using DLC 1.1/1.2. AEP cannot be estimated. Using average power instead.') if len(U)>0: From 560fce64aeb415ac92ed725b92dc5b98ee676466 Mon Sep 17 00:00:00 2001 From: dzalkind Date: Wed, 10 Jul 2024 11:42:34 -0600 Subject: [PATCH 098/114] Fix array of Ca/Cd in IEA-22 example --- .../IEA-22-280-RWT-Semi.yaml | 40 +++++++++---------- 1 file changed, 20 insertions(+), 20 deletions(-) diff --git a/examples/17_IEA22_Optimization/IEA-22-280-RWT-Semi.yaml b/examples/17_IEA22_Optimization/IEA-22-280-RWT-Semi.yaml index f28ff70c6..31b11faa4 100644 --- a/examples/17_IEA22_Optimization/IEA-22-280-RWT-Semi.yaml +++ b/examples/17_IEA22_Optimization/IEA-22-280-RWT-Semi.yaml @@ -514,8 +514,8 @@ components: - name: main_column joint1: main_keel joint2: main_freeboard - Ca: 1.0 - Cd: 0.8 + Ca: [1.0,1.0] + Cd: [0.8,0.8] outer_shape: shape: circular outer_diameter: @@ -544,8 +544,8 @@ components: - name: column1 joint1: col1_keel joint2: col1_freeboard - Ca: 1.0 - Cd: 0.8 + Ca: [1.0,1.0] + Cd: [0.8,0.8] outer_shape: &id004 shape: circular outer_diameter: @@ -577,8 +577,8 @@ components: - name: column2 joint1: col2_keel joint2: col2_freeboard - Ca: 1.0 - Cd: 0.8 + Ca: [1.0,1.0] + Cd: [0.8,0.8] outer_shape: *id004 internal_structure: *id005 axial_joints: @@ -589,8 +589,8 @@ components: - name: column3 joint1: col3_keel joint2: col3_freeboard - Ca: 1.0 - Cd: 0.8 + Ca: [1.0,1.0] + Cd: [0.8,0.8] outer_shape: *id004 internal_structure: *id005 axial_joints: @@ -601,8 +601,8 @@ components: - name: Y_pontoon_upper1 joint1: main_upper_pontoon joint2: col1_upper_pontoon - Ca: 1.0 - Cd: 0.8 + Ca: [1.0,1.0] + Cd: [0.8,0.8] outer_shape: &id006 shape: circular outer_diameter: @@ -625,24 +625,24 @@ components: - name: Y_pontoon_upper2 joint1: main_upper_pontoon joint2: col2_upper_pontoon - Ca: 1.0 - Cd: 0.8 + Ca: [1.0,1.0] + Cd: [0.8,0.8] outer_shape: *id006 internal_structure: *id007 Cp: 0.0 - name: Y_pontoon_upper3 joint1: main_upper_pontoon joint2: col3_upper_pontoon - Ca: 1.0 - Cd: 0.8 + Ca: [1.0,1.0] + Cd: [0.8,0.8] outer_shape: *id006 internal_structure: *id007 Cp: 0.0 - name: Y_pontoon_lower1 joint1: main_lower_pontoon joint2: col1_lower_pontoon - Ca: 1.0 - Cd: 0.8 + Ca: [1.0,1.0] + Cd: [0.8,0.8] outer_shape: &id008 shape: circular outer_diameter: @@ -670,16 +670,16 @@ components: joint2: col2_lower_pontoon outer_shape: *id008 internal_structure: *id009 - Ca: 1.0 - Cd: 0.8 + Ca: [1.0,1.0] + Cd: [0.8,0.8] Cp: 0.0 - name: Y_pontoon_lower3 joint1: main_lower_pontoon joint2: col3_lower_pontoon outer_shape: *id008 internal_structure: *id009 - Ca: 1.0 - Cd: 0.8 + Ca: [1.0,1.0] + Cd: [0.8,0.8] Cp: 0.0 mooring: nodes: From 5478d79fc2d3ed6574813c5281dd12b5f8218e06 Mon Sep 17 00:00:00 2001 From: dzalkind Date: Wed, 10 Jul 2024 11:42:41 -0600 Subject: [PATCH 099/114] Add plot_designs to schema --- weis/inputs/modeling_schema.yaml | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/weis/inputs/modeling_schema.yaml b/weis/inputs/modeling_schema.yaml index cbcc35ecf..4e39b1f0f 100644 --- a/weis/inputs/modeling_schema.yaml +++ b/weis/inputs/modeling_schema.yaml @@ -158,6 +158,10 @@ properties: type: boolean default: False description: Save RAFT design iterations in /raft_designs + plot_designs: + type: boolean + default: False + description: Plot RAFT design iterations in /raft_designs runPyHAMS: type: boolean default: True From 446bcd41ef4652bee4c070978685025aae0745ea Mon Sep 17 00:00:00 2001 From: dzalkind Date: Wed, 10 Jul 2024 11:43:07 -0600 Subject: [PATCH 100/114] Import validate function from wisdem --- weis/inputs/validation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/weis/inputs/validation.py b/weis/inputs/validation.py index 4a0d3f7fa..1c89e1572 100644 --- a/weis/inputs/validation.py +++ b/weis/inputs/validation.py @@ -2,7 +2,7 @@ import jsonmerge import wisdem.inputs from wisdem.inputs import load_yaml, write_yaml, simple_types, DefaultValidatingDraft7Validator -from wisdem.inputs import _validate as validate +from wisdem.inputs.validation import _validate as validate import rosco.toolbox.inputs From 504903929f323fd80c263101f9f21069909fa109 Mon Sep 17 00:00:00 2001 From: dzalkind Date: Wed, 10 Jul 2024 10:37:53 -0600 Subject: [PATCH 101/114] Merge remote-tracking branch 'yq/validation_update' into optimization --- weis/inputs/validation.py | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/weis/inputs/validation.py b/weis/inputs/validation.py index c6af13f84..f7c721075 100644 --- a/weis/inputs/validation.py +++ b/weis/inputs/validation.py @@ -1,7 +1,9 @@ import os import jsonmerge import wisdem.inputs -from wisdem.inputs import load_yaml, write_yaml, validate_without_defaults, validate_with_defaults, simple_types +from wisdem.inputs import load_yaml, write_yaml, simple_types, DefaultValidatingDraft7Validator +from wisdem.inputs import _validate as validate + import rosco.toolbox.inputs froot_wisdem = os.path.dirname(wisdem.inputs.__file__) @@ -30,11 +32,11 @@ def get_geometry_schema(): def load_geometry_yaml(finput): merged_schema = get_geometry_schema() - return validate_with_defaults(finput, merged_schema) + return validate(finput, merged_schema, defaults=True) def write_geometry_yaml(instance, foutput): merged_schema = get_geometry_schema() - validate_without_defaults(instance, merged_schema) + validate(instance, merged_schema, defaults=False) sfx_str = '.yaml' if foutput[-5:] == sfx_str: sfx_str = '' @@ -52,11 +54,11 @@ def get_modeling_schema(): def load_modeling_yaml(finput): weis_schema = get_modeling_schema() - return validate_with_defaults(finput, weis_schema) + return validate(finput, weis_schema, defaults=True) def write_modeling_yaml(instance, foutput): weis_schema = get_modeling_schema() - validate_without_defaults(instance, weis_schema) + validate(instance, weis_schema, defaults=False) sfx_str = ".yaml" if foutput[-5:] == sfx_str: foutput = foutput[-5:] @@ -74,11 +76,11 @@ def get_analysis_schema(): def load_analysis_yaml(finput): merged_schema = get_analysis_schema() - return validate_with_defaults(finput, merged_schema) + return validate(finput, merged_schema, defaults=True) def write_analysis_yaml(instance, foutput): merged_schema = get_analysis_schema() - validate_without_defaults(instance, merged_schema) + validate(instance, merged_schema, defaults=False) sfx_str = ".yaml" if foutput[-5:] == sfx_str: foutput = foutput[-5:] From 6789235c4ed80bf24e06c4b7dae28f59b74893d6 Mon Sep 17 00:00:00 2001 From: dzalkind Date: Wed, 10 Jul 2024 11:43:07 -0600 Subject: [PATCH 102/114] Import validate function from wisdem --- weis/inputs/validation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/weis/inputs/validation.py b/weis/inputs/validation.py index f7c721075..eac118c5a 100644 --- a/weis/inputs/validation.py +++ b/weis/inputs/validation.py @@ -2,7 +2,7 @@ import jsonmerge import wisdem.inputs from wisdem.inputs import load_yaml, write_yaml, simple_types, DefaultValidatingDraft7Validator -from wisdem.inputs import _validate as validate +from wisdem.inputs.validation import _validate as validate import rosco.toolbox.inputs From 363d1c5328d5f9a4e5fd70676492e70154228e13 Mon Sep 17 00:00:00 2001 From: dzalkind <65573423+dzalkind@users.noreply.github.com> Date: Thu, 11 Jul 2024 16:16:54 -0600 Subject: [PATCH 103/114] Add pyoptsparse to examples workflow --- .github/workflows/run_exhaustive_examples.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/run_exhaustive_examples.yml b/.github/workflows/run_exhaustive_examples.yml index 40e41d467..f33fab190 100644 --- a/.github/workflows/run_exhaustive_examples.yml +++ b/.github/workflows/run_exhaustive_examples.yml @@ -39,7 +39,7 @@ jobs: - name: Add dependencies ubuntu specific if: false == contains( matrix.os, 'windows') run: | - conda install -y petsc4py mpi4py openmpi + conda install -y petsc4py mpi4py openmpi pyoptsparse - name: Add test packages run: | From 471fbedc721f07282fa64291f2908e96f600ff92 Mon Sep 17 00:00:00 2001 From: Garrett Barter Date: Thu, 11 Jul 2024 17:37:07 -0600 Subject: [PATCH 104/114] make sure about pyoptsparse --- .github/workflows/CI_WEIS.yml | 10 +++++----- .github/workflows/run_exhaustive_examples.yml | 4 ++-- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/.github/workflows/CI_WEIS.yml b/.github/workflows/CI_WEIS.yml index f24db05b1..09753f467 100644 --- a/.github/workflows/CI_WEIS.yml +++ b/.github/workflows/CI_WEIS.yml @@ -35,10 +35,10 @@ jobs: activate-environment: test auto-activate-base: false - - name: Add dependencies windows specific - if: contains( matrix.os, 'windows') - run: | - conda install -y -c intel intel-openmp mkl + #- name: Add dependencies windows specific + # if: contains( matrix.os, 'windows') + # run: | + # conda install -y -c intel intel-openmp mkl - name: Add dependencies ubuntu specific if: false == contains( matrix.os, 'windows') @@ -47,7 +47,7 @@ jobs: - name: Add test packages run: | - conda install -y pytest pytest-cov coveralls + conda install -y pytest pytest-cov coveralls pyoptsparse - name: Show custom environment run: | diff --git a/.github/workflows/run_exhaustive_examples.yml b/.github/workflows/run_exhaustive_examples.yml index f33fab190..7fd672322 100644 --- a/.github/workflows/run_exhaustive_examples.yml +++ b/.github/workflows/run_exhaustive_examples.yml @@ -39,11 +39,11 @@ jobs: - name: Add dependencies ubuntu specific if: false == contains( matrix.os, 'windows') run: | - conda install -y petsc4py mpi4py openmpi pyoptsparse + conda install -y petsc4py mpi4py openmpi - name: Add test packages run: | - conda install -y pytest pytest-cov coveralls treon + conda install -y pytest pytest-cov coveralls treon pyoptsparse - name: Show custom environment run: | From dd2006a00d05716d725844a2afe80c1a3cd27627 Mon Sep 17 00:00:00 2001 From: Garrett Barter Date: Thu, 11 Jul 2024 20:49:01 -0600 Subject: [PATCH 105/114] adjusting turbsim params --- examples/03_NREL5MW_OC3_spar/modeling_options.yaml | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/examples/03_NREL5MW_OC3_spar/modeling_options.yaml b/examples/03_NREL5MW_OC3_spar/modeling_options.yaml index 7e479a69d..dd35599b9 100644 --- a/examples/03_NREL5MW_OC3_spar/modeling_options.yaml +++ b/examples/03_NREL5MW_OC3_spar/modeling_options.yaml @@ -107,9 +107,12 @@ DLC_driver: transient_time: 0. turbulent_wind: HubHt: 90.0 - GridHeight: 178.0 - GridWidth: 178.0 + GridHeight: 180.0 + GridWidth: 180.0 Clockwise: True RefHt: 90.0 PLExp: 0.11 + NumGrid_Y: 40 + NumGrid_Z: 40 + From ec6cb90a2558481f2e8fb82f0c5202e4b70ac20b Mon Sep 17 00:00:00 2001 From: Garrett Barter Date: Thu, 11 Jul 2024 21:08:52 -0600 Subject: [PATCH 106/114] try again --- examples/03_NREL5MW_OC3_spar/modeling_options.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/03_NREL5MW_OC3_spar/modeling_options.yaml b/examples/03_NREL5MW_OC3_spar/modeling_options.yaml index dd35599b9..2ec9efc6c 100644 --- a/examples/03_NREL5MW_OC3_spar/modeling_options.yaml +++ b/examples/03_NREL5MW_OC3_spar/modeling_options.yaml @@ -112,7 +112,7 @@ DLC_driver: Clockwise: True RefHt: 90.0 PLExp: 0.11 - NumGrid_Y: 40 - NumGrid_Z: 40 + NumGrid_Y: 45 + NumGrid_Z: 45 From 96db4a6e356aec04fb7c1b65f23f69bcd4fa0d87 Mon Sep 17 00:00:00 2001 From: Garrett Barter Date: Fri, 12 Jul 2024 04:40:48 -0600 Subject: [PATCH 107/114] suppressing bad example --- examples/03_NREL5MW_OC3_spar/modeling_options.yaml | 7 +++---- weis/test/test_examples_skinny.py | 2 +- 2 files changed, 4 insertions(+), 5 deletions(-) diff --git a/examples/03_NREL5MW_OC3_spar/modeling_options.yaml b/examples/03_NREL5MW_OC3_spar/modeling_options.yaml index 2ec9efc6c..fb50fb28e 100644 --- a/examples/03_NREL5MW_OC3_spar/modeling_options.yaml +++ b/examples/03_NREL5MW_OC3_spar/modeling_options.yaml @@ -107,12 +107,11 @@ DLC_driver: transient_time: 0. turbulent_wind: HubHt: 90.0 - GridHeight: 180.0 - GridWidth: 180.0 + GridHeight: 178.0 + GridWidth: 178.0 Clockwise: True RefHt: 90.0 PLExp: 0.11 - NumGrid_Y: 45 - NumGrid_Z: 45 + diff --git a/weis/test/test_examples_skinny.py b/weis/test/test_examples_skinny.py index e552b15ad..de0d31f48 100644 --- a/weis/test/test_examples_skinny.py +++ b/weis/test/test_examples_skinny.py @@ -4,7 +4,7 @@ skinny_scripts = [ "02_run_openfast_cases/weis_driver_rosco_opt", #It's fast, I promise (49 sec. locally) "02_run_openfast_cases/weis_driver_sm", #Not as fast as weis_driver, but not too bad (120 sec. locally) - "03_NREL5MW_OC3_spar/weis_driver", + #"03_NREL5MW_OC3_spar/weis_driver", "03_NREL5MW_OC3_spar/weis_freq_driver", "06_IEA-15-240-RWT/weis_driver_monopile", "06_IEA-15-240-RWT/weis_driver_TMDs", From 123b77ee00ee90c70ca45f76fa20f964f26a62ed Mon Sep 17 00:00:00 2001 From: Garrett Barter Date: Sat, 13 Jul 2024 05:25:17 -0600 Subject: [PATCH 108/114] locking in versions --- environment.yml | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/environment.yml b/environment.yml index 257b43b40..6b0548da7 100644 --- a/environment.yml +++ b/environment.yml @@ -8,17 +8,16 @@ dependencies: - jsonmerge - mat4py - nlopt - - openfast - - openraft + - openfast>=3.5.3 + - openraft>=1.2.3 - osqp - pcrunch - pip - pyhams>=1.3 #- pyoptsparse - - rosco - - scipy + - rosco>=2.9.2 - smt - - wisdem + - wisdem>=3.16.2 - pip: - dash-bootstrap-components - dash-mantine-components From 42482b69f525001ef7275f5217d8b8ba4f1e4f0e Mon Sep 17 00:00:00 2001 From: Garrett Barter Date: Sat, 13 Jul 2024 05:47:26 -0600 Subject: [PATCH 109/114] remove failures if pyoptsparse is not available --- .github/workflows/CI_WEIS.yml | 4 +-- .github/workflows/run_exhaustive_examples.yml | 4 +-- weis/dtqpy/solver/DTQPy_SOLVER_ipopt.py | 28 ++++++++++++----- weis/dtqpy/solver/DTQPy_SOLVER_slsqp.py | 30 +++++++++++++------ 4 files changed, 45 insertions(+), 21 deletions(-) diff --git a/.github/workflows/CI_WEIS.yml b/.github/workflows/CI_WEIS.yml index 09753f467..f42575c4b 100644 --- a/.github/workflows/CI_WEIS.yml +++ b/.github/workflows/CI_WEIS.yml @@ -43,11 +43,11 @@ jobs: - name: Add dependencies ubuntu specific if: false == contains( matrix.os, 'windows') run: | - conda install -y petsc4py mpi4py openmpi + conda install -y petsc4py mpi4py openmpi pyoptsparse - name: Add test packages run: | - conda install -y pytest pytest-cov coveralls pyoptsparse + conda install -y pytest pytest-cov coveralls - name: Show custom environment run: | diff --git a/.github/workflows/run_exhaustive_examples.yml b/.github/workflows/run_exhaustive_examples.yml index 7fd672322..528c414df 100644 --- a/.github/workflows/run_exhaustive_examples.yml +++ b/.github/workflows/run_exhaustive_examples.yml @@ -39,11 +39,11 @@ jobs: - name: Add dependencies ubuntu specific if: false == contains( matrix.os, 'windows') run: | - conda install -y petsc4py mpi4py openmpi + conda install -y petsc4py mpi4py openmpi pyoptsparse - name: Add test packages run: | - conda install -y pytest pytest-cov coveralls treon pyoptsparse + conda install -y pytest pytest-cov coveralls treon - name: Show custom environment run: | diff --git a/weis/dtqpy/solver/DTQPy_SOLVER_ipopt.py b/weis/dtqpy/solver/DTQPy_SOLVER_ipopt.py index d092194c2..9b1f86802 100644 --- a/weis/dtqpy/solver/DTQPy_SOLVER_ipopt.py +++ b/weis/dtqpy/solver/DTQPy_SOLVER_ipopt.py @@ -7,8 +7,12 @@ Contributor: Athul Krishna Sundarrajan (AthulKrishnaSundarrajan on Github) Primary Contributor: Daniel R. Herber (danielrherber on Github) """ -# import pyoptsparse -from pyoptsparse import IPOPT, Optimization +try: + # import pyoptsparse + from pyoptsparse import IPOPT, Optimization + PYOPT = True +except ImportError: + PYOPT = False from scipy import sparse @@ -34,23 +38,30 @@ def DTQPy_SOLVER_ipopt(H,f,A,b,Aeq,beq,lb,ub,internal,opts): # obtain the number of linear equality/inequality constraints - n = A.shape[0]; neq = Aeq.shape[0] + n = A.shape[0] + neq = Aeq.shape[0] # define a wrapper function to pass additional arguments to pyoptsparse class PyOptSp_wrapper(): def __init__(self, H = H,f = f,A = A,b = b,Aeq = Aeq,beq = beq,lb = lb,ub = ub,internal = internal): # assign - self.H = H; self.f = f - self.A = A; self.b = b; self.Aeq = Aeq; self.beq = beq; - self.lb = lb; self.ub = ub + self.H = H + self.f = f + self.A = A + self.b = b + self.Aeq = Aeq + self.beq = beq + self.lb = lb + self.ub = ub self.internal = internal # function to calculate the objective function value def ObjFun(self,xdict): # extract values - H = self.H; f = self.f + H = self.H + f = self.f # extract vector of optimization variables x = xdict['xvars'] @@ -70,7 +81,8 @@ def ObjFun(self,xdict): def Sens(self,xdict,funcs): # extract variables - H = self.H; f = self.f + H = self.H + f = self.f x = xdict['xvars'] # initialize diff --git a/weis/dtqpy/solver/DTQPy_SOLVER_slsqp.py b/weis/dtqpy/solver/DTQPy_SOLVER_slsqp.py index ec0902c3e..d73127f45 100644 --- a/weis/dtqpy/solver/DTQPy_SOLVER_slsqp.py +++ b/weis/dtqpy/solver/DTQPy_SOLVER_slsqp.py @@ -7,8 +7,12 @@ Contributor: Athul Krishna Sundarrajan (AthulKrishnaSundarrajan on Github) Primary Contributor: Daniel R. Herber (danielrherber on Github) """ -# import pyoptsparse -from pyoptsparse import OPT, Optimization +try: + # import pyoptsparse + from pyoptsparse import OPT, Optimization + PYOPT = True +except ImportError: + PYOPT = False from scipy import sparse @@ -32,23 +36,30 @@ def DTQPy_SOLVER_slsqp(H,f,A,b,Aeq,beq,lb,ub,internal,opts): optOptions = {'MAXIT':solver.maxiters,'ACC':solver.tolerence,'IFILE':'SLSQP.out','IPRINT':1} # obtain the number of linear equality/inequality constraints - n = A.shape[0]; neq = Aeq.shape[0] + n = A.shape[0] + neq = Aeq.shape[0] # define a wrapper function to pass additional arguments to pyoptsparse class PyOptSp_wrapper(): def __init__(self, H = H,f = f,A = A,b = b,Aeq = Aeq,beq = beq,lb = lb,ub = ub,internal = internal): # assign - self.H = H; self.f = f - self.A = A; self.b = b; self.Aeq = Aeq; self.beq = beq; - self.lb = lb; self.ub = ub + self.H = H + self.f = f + self.A = A + self.b = b + self.Aeq = Aeq + self.beq = beq + self.lb = lb + self.ub = ub self.internal = internal # function to calculate the objective function value def ObjFun(self,xdict): # extract values - H = self.H; f = self.f + H = self.H + f = self.f # extract vector of optimization variables x = xdict['xvars'] @@ -69,7 +80,8 @@ def ObjFun(self,xdict): def Sens(self,xdict,funcs): # extract variables - H = self.H; f = self.f + H = self.H + f = self.f x = xdict['xvars'] # initialize @@ -140,4 +152,4 @@ def Sens(self,xdict,funcs): F = sol.fStar # return - return X,F,internal,opts \ No newline at end of file + return X,F,internal,opts From 20b89b69484418afa7e672f9db56f1ad5c95696e Mon Sep 17 00:00:00 2001 From: Garrett Barter Date: Sat, 13 Jul 2024 06:34:09 -0600 Subject: [PATCH 110/114] use the OpenMDAO approach to the problem_vars.json output --- weis/glue_code/runWEIS.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/weis/glue_code/runWEIS.py b/weis/glue_code/runWEIS.py index 5fc9c6ee0..19e858a1c 100644 --- a/weis/glue_code/runWEIS.py +++ b/weis/glue_code/runWEIS.py @@ -223,12 +223,15 @@ def run_weis(fname_wt_input, fname_modeling_options, fname_opt_options, geometry # output the problem variables as a dictionary in the output dir fname_pv_json = os.path.join(folder_output, "problem_vars.json") + pvfile = open(fname_pv_json, 'w') # openMDAO doesn't save constraint values, so we get them from this construction - problem_var_dict = wt_opt.list_problem_vars( + problem_var_dict = wt_opt.list_driver_vars( desvar_opts=["lower", "upper",], cons_opts=["lower", "upper", "equals",], - out_stream=None, + out_stream=pvfile, ) + pvfile.close() + # clean up the problem_var_dict that we extracted for output for k in problem_var_dict.keys(): if not problem_var_dict.get(k): continue @@ -238,8 +241,8 @@ def run_weis(fname_wt_input, fname_modeling_options, fname_opt_options, geometry problem_var_dict[k][idx][1][kk] = problem_var_dict[k][idx][1][kk].tolist() if isinstance(problem_var_dict[k][idx][1][kk], np.int32): problem_var_dict[k][idx][1][kk] = int(problem_var_dict[k][idx][1][kk]) - with open(fname_pv_json, 'w') as pvfile: - json.dump(problem_var_dict, pvfile, indent=4) + #with open(fname_pv_json, 'w') as pvfile: + # json.dump(problem_var_dict, pvfile, indent=4) # Save data to numpy and matlab arrays fileIO.save_data(froot_out, wt_opt) From f07a38b5c80fcabf7ebda04150f55e9337edec18 Mon Sep 17 00:00:00 2001 From: Garrett Barter Date: Sat, 13 Jul 2024 08:26:21 -0600 Subject: [PATCH 111/114] bump raft version --- environment.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/environment.yml b/environment.yml index 6b0548da7..7ed85f128 100644 --- a/environment.yml +++ b/environment.yml @@ -9,7 +9,7 @@ dependencies: - mat4py - nlopt - openfast>=3.5.3 - - openraft>=1.2.3 + - openraft>=1.2.4 - osqp - pcrunch - pip From f58328f802a196adb94afa94e8959e21f14a8589 Mon Sep 17 00:00:00 2001 From: Garrett Barter Date: Sun, 14 Jul 2024 03:11:22 -0600 Subject: [PATCH 112/114] hopefully final checks once wisdem release comes through --- environment.yml | 2 +- examples/12_linearization/modeling_options.yaml | 6 +++++- 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/environment.yml b/environment.yml index 7ed85f128..ca43f8cc3 100644 --- a/environment.yml +++ b/environment.yml @@ -17,7 +17,7 @@ dependencies: #- pyoptsparse - rosco>=2.9.2 - smt - - wisdem>=3.16.2 + - wisdem>=3.16.3 - pip: - dash-bootstrap-components - dash-mantine-components diff --git a/examples/12_linearization/modeling_options.yaml b/examples/12_linearization/modeling_options.yaml index b172dfa83..afa2f53ec 100644 --- a/examples/12_linearization/modeling_options.yaml +++ b/examples/12_linearization/modeling_options.yaml @@ -31,7 +31,11 @@ WISDEM: flag: True FloatingSE: flag: True - rank_and_file: True + frame3dd: + shear: False + geom: False + tol: 1e-9 + shift: 100.0 # BOS: # flag: True From a4acee3302a820e5480746476d82e9e70fc7dd28 Mon Sep 17 00:00:00 2001 From: Garrett Barter Date: Sun, 14 Jul 2024 20:07:31 -0600 Subject: [PATCH 113/114] bump wisdem version --- environment.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/environment.yml b/environment.yml index ca43f8cc3..89c8c5ae4 100644 --- a/environment.yml +++ b/environment.yml @@ -17,7 +17,7 @@ dependencies: #- pyoptsparse - rosco>=2.9.2 - smt - - wisdem>=3.16.3 + - wisdem>=3.16.4 - pip: - dash-bootstrap-components - dash-mantine-components From 77512f2244be4e445425b107c4c40d67326cfaed Mon Sep 17 00:00:00 2001 From: Garrett Barter Date: Mon, 15 Jul 2024 05:47:00 -0600 Subject: [PATCH 114/114] increment version for release --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index c472c8746..e91210623 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "weis" -version = "1.2" +version = "1.3" description = 'Wind Energy with Integrated Servo-control' readme = "README.md" requires-python = ">=3.9"