Rotor misalignment faults

[ggl0001]

Hi
May I ask why the time-domain response simulation results are distorted when I am simulating rotor misalignment faults based on fluid dynamic pressure bearing support? If you could provide assistance, I would be extremely grateful!

[jguarato]

Hi @ggl0001!

Could you share your script so we can take a closer look at the issue?

[ggl0001]

Hi!

Thank you for your reply. The following is the script I used for simulation.

import numpy as np
import ross as rs
from ross.faults import *
from ross.units import Q_
from ross.probe import Probe

Make sure the default renderer is set to 'notebook' for inline plots in Jupyter

import plotly.io as pio

pio.renderers.default = "notebook"

"""Create example rotor with given number of elements."""
steel2 = rs.Material(name="Steel", rho=7850, E=2.17e11, G_s=81.2e9)

Rotor with 6 DoFs, with internal damping, with 10 shaft elements, 2 disks and 2 bearings.

i_d = 0
o_d = 0.019
n = 33

fmt: off

L = np.array(
[0, 25, 64, 104, 124, 143, 175, 207, 239, 271,
303, 335, 345, 355, 380, 408, 436, 466, 496, 526,
556, 586, 614, 647, 657, 667, 702, 737, 772, 807,
842, 862, 881, 914]
)/ 1000

fmt: on

L = [L[i] - L[i - 1] for i in range(1, len(L))]

shaft_elem = [
rs.ShaftElement(
material=steel2,
L=l,
idl=i_d,
odl=o_d,
idr=i_d,
odr=o_d,
alpha=8.0501,
beta=1.0e-5,
rotary_inertia=True,
shear_effects=True,
)
for l in L
]

Id = 0.003844540885417
Ip = 0.007513248437500

disk0 = rs.DiskElement(n=12, m=2.6375, Id=Id, Ip=Ip)
disk1 = rs.DiskElement(n=24, m=2.6375, Id=Id, Ip=Ip)

bearing = rs.BearingFluidFlow(
n=4,
nz=30,
ntheta=20,
length=0.03,
omega=np.linspace(1, 5000, 5),
p_in=0,
p_out=0,
radius_rotor=0.0499,
radius_stator=0.05,
visc=0.1,
rho=860.0,
load=525,
)

copy bearing to decrease compute time and set node

bearing_copy = rs.BearingElement(
n=31,
frequency=bearing.frequency,
kxx=bearing.kxx,
kxy=bearing.kxy,
kyx=bearing.kyx,
kyy=bearing.kyy,
cxx=bearing.cxx,
cxy=bearing.cxy,
cyx=bearing.cyx,
cyy=bearing.cyy,
)

bearing_seal_elements = [bearing, bearing_copy]

rotor_shyd = rs.Rotor(shaft_elem, [disk0, disk1], bearing_seal_elements)

rotor_shyd.plot_rotor()

"""Inserting a mass and phase unbalance and defining the local response."""
massunbt = np.array([5e-4, 0])
phaseunbt = np.array([-np.pi / 2, 0])
probe1_shyd = Probe(7, 0)
probe2_shyd = Probe(7, Q_(90, "deg"))

"""Calculate the response of the rotor connected by a flexible mechanical coupling with parallel,
angular or combined misalignment."""
misalignment_shyd = rotor_shyd.run_misalignment(
coupling="flex",
dt=0.001,
tI=0,
tF=0.5,
kd=40 * 10 ** (3),
ks=38 * 10 ** (3),
eCOUPx=2 * 10 ** (-4),
eCOUPy=2 * 10 ** (-4),
misalignment_angle=5 * np.pi / 180,
TD=0,
TL=0,
n1=0,
speed=Q_(1200, "RPM"),
unbalance_magnitude=[0, 0],
unbalance_phase=[0, 0],
mis_type="parallel", # change here the type: mis_type="parallel"、mis_type="angular" or mis_type="combined"
print_progress=False,
)

results_shyd = misalignment_shyd.run_time_response()
data_pf = results_shyd.data_time_response(probe=[probe1_shyd, probe2_shyd])
results_shyd.plot_1d([probe1_shyd, probe2_shyd]).show()

[jguarato]

I believe the issue you're experiencing is related to the integration method used for calculating the temporal response.

We recently refactored the Fault module, and as part of that, its integration method was updated [1]. Previously, it used the fourth-order Runge-Kutta method, which had some convergence problems. Now, it uses the same methods available in run_time_response.

Downloading the development version of ROSS [2] might resolve this problem for you. If you do, please note that you'll need to modify how you call the run_misalignment function:

results_shyd = rotor_shyd.run_misalignment(
    coupling="flex",
    n=0,
    mis_type="parallel",
    radial_stiffness=40e3,
    bending_stiffness=38e3,
    mis_distance_x=2e-4,
    mis_distance_y=2e-4,
    mis_angle=5 * np.pi / 180,
    input_torque=0,
    load_torque=0,
    node=[disk0.n, disk1.n],
    unbalance_magnitude=[0, 0],
    unbalance_phase=[0, 0],
    speed=Q_(1200, "RPM"),
    t=np.arange(0, 0.5, 0.001),
)
results_shyd.plot_1d([probe1_shyd, probe2_shyd]).show()

[ggl0001]

@jguarato Thank you very much for your help and answer!