PyDPF throws an error when the rst file is large
Hi,
I am trying to compute strain energy for a cyclic symmetry model using cyclic_strain_energy() operator. However, the rst file that I have has appx. 250 modes. I tried to compute the strain energy using the following code:
cyc_strain_energy_op = dpf.operators.result.cyclic_strain_energy()
cyc_strain_energy_op.inputs.time_scoping.connect (range(1,200) )
cyc_strain_energy_op.inputs.data_sources.connect(data_source)
cyc_strain_energy_op.inputs.read_cyclic.connect(2)
cyc_strain_energy = cyc_strain_energy_op.outputs.fields_container()
It returns the following error:
Traceback (most recent call last):
File "d:\bitBucket\FCDSLib\fcdslib_cyclicsymmetry_utilities\cyclic_symmetry_utilities\calc_strain_energy.py", line 184, in
strain_energy = calc_strain_energy(f_rst, list_comp=['PART1','PART2','PART3'], list_modes=None, cyclic_expansion=True)
File "d:\bitBucket\FCDSLib\fcdslib_cyclicsymmetry_utilities\cyclic_symmetry_utilities\calc_strain_energy.py", line 54, in calc_strain_energy
cyc_strain_energy = cyc_strain_energy_op.outputs.fields_container()
File "D:\PythonVenvs\pydpf_13\lib\site-packages\ansys\dpf\core\outputs.py", line 75, in call
return self.get_data()
File "D:\PythonVenvs\pydpf_13\lib\site-packages\ansys\dpf\core\outputs.py", line 72, in get_data
return self._operator.get_output(self._pin, type_output)
File "D:\PythonVenvs\pydpf_13\lib\site-packages\ansys\dpf\core\dpf_operator.py", line 543, in get_output
internal_obj = type_tuple[1](self, pin)
File "D:\PythonVenvs\pydpf_13\lib\site-packages\ansys\dpf\gate\generated\operator_capi.py", line 422, in operator_getoutput_fields_container
raise errors.DPFServerException(sError.value)
ansys.dpf.gate.errors.DPFServerException: bad allocation
However, it works fine if I use fewer modes (approximately. less than 100). I presume this is because of the memory allocation issue. Is there any way to handle this issue?
Thanks,
Samukham
Comments
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Can someone suggest a solution for this?
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@AKD-Scripting-Team Could someone help here? Many thanks
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@Samukham, which DPF server are you using? There have been some improvements in the RAM consumption by large models in 24R2. If not already using it, please install and test with 24R2. If you can't install 24R2, you can try with the latest Standalone DPF server, hope this helps.
https://dpf.docs.pyansys.com/version/stable/getting_started/index.html#install-dpf-server
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