Replies: 6 comments
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Hello, Yes, from what I understand the coalescence process results in removing super-droplets. It was not considered in this example because it concerned only condensation. The code works when I change the for drop_id in range(self.particulator.n_sd):
attr[drop_id].append(attr_data[drop_id]) I wrote for drop_id in range(self.particulator.n_sd):
if drop_id < len(attr_data):
attr[drop_id].append(attr_data[drop_id])
else:
attr[drop_id].append(np.nan) Then the length of attributes remains the same, but attributes of the "missing" super-droplets are |
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Thank you @AgnieszkaMakulska ! Sorting by one of the attributes before plotting could be a way to identify where, in a given attribute dimension, super-droplet are removed from the system? |
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@huangynj, please confirm if that helps in your case, so we can add it to the |
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@AgnieszkaMakulska @slayoo yes, filling in with |
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Stale issue message |
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Let me close this one as addressed. Thanks @huangynj for reporting, thanks @AgnieszkaMakulska for help! |
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Hello,
I tested the example
Grabowski_and_Pawlowska_2023
, but encountered the following issue.In the
setting.py
, I changedisplacement: float = 1000 * si.m
todisplacement: float = 2000 * si.m
In the
simulation.py
, I addbuilder.add_dynamic(Coalescence(collision_kernel=Golovin(b=1.5e3 / si.second), adaptive=False))
Then run the initial part of
figure_1.ipynb
:However, I encountered the following error.
Does it mean the coalescence process results in removing super-droplets? So that the total size of super-droplets reduces from 200 to 199? If yes, how to know which super-droplet was removed? Is there unique drop_id for each super-droplet?
Thanks,
Yongjie
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