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Add example cycles_to_failure from McMunich (#591)
* Functioning cyclic_to_failure examples * Update style * Add test_examples::test_download_cycles_to_failure * Comments from Jenna Signed-off-by: paul.profizi <paul.profizi@ansys.com> * Update title of the example * Apply suggestions from code review * Remove section separation for a visually better example * Rework formatting of the explanation paragraph * Update examples/02-modal-harmonic/06-cycles_to_failure.py * Fix formatting of the description * Fix formatting of the description * Revert "Remove section separation for a visually better example" This reverts commit 52d1b1d. * Reformat sections * Reformat * Reformat * Reformat * Reformat * Reformat * Apply suggestions from code review Signed-off-by: paul.profizi <paul.profizi@ansys.com>
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| Original file line number | Diff line number | Diff line change |
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| """ | ||
| .. _ref_cycles_to_failure: | ||
| Calculate the number of cycles to fatigue failure | ||
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ | ||
| This example shows how to generate and use a result file to calculate the | ||
| cycles to failure result for a simple model. | ||
| Material data is manually imported, Structural Steel from Ansys Mechanical: | ||
| - Youngs Modulus (youngsSteel) | ||
| - Poisson's Ratio (prxySteel) | ||
| - SN curve (sn_data) | ||
| The first step is to generate a simple model with high stress and save the | ||
| results .rst file locally to myDir (default is "C:\\\\temp"). | ||
| For this, we provide a short pyMAPDL script. | ||
| .. line-block:: | ||
| The second step uses PyDPF-Core to generate the cycles to failure result: | ||
| The locally saved .rst file is imported and plotted. | ||
| Then the von Mises stress is generated and plotted with DPF operators. | ||
| The NumPy python package is then used to interpolate the cycles to failure values. | ||
| The nodal von Mises equivalent stress value is used in the interpolation. | ||
| (Note that the cycles to failure data must be manipulated to use NumPy interpolation) | ||
| An empty field is then created and filled with the resulting cycles to failure values. | ||
| The cycles to failure result is finally plotted. | ||
| The cycles to failure result is the (interpolated) negative of the stress result. | ||
| The higher the stress result, the lower the number of cycles to failure. | ||
| """ | ||
|
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| from ansys.dpf import core as dpf | ||
| from ansys.dpf.core import examples | ||
| import numpy as np | ||
|
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| ############################################################################### | ||
| # The first step is to generate a simple model with high stress | ||
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| # # Material parameters from Ansys Mechanical Structural Steel | ||
| youngsSteel = 200e9 | ||
| prxySteel = 0.3 | ||
| sn_data = np.empty((11, 2)) # initialize empty np matrix | ||
| sn_data[:, 0] = [10, 20, 50, 100, 200, 2000, 10000, 20000, 1e5, 2e5, 1e6] | ||
| sn_data[:, 1] = [3.999e9, 2.8327e9, 1.896e9, 1.413e9, 1.069e9, 4.41e8, 2.62e8, 2.14e8, 1.38e8, | ||
| 1.14e8, 8.62e7] | ||
|
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| ############################################################################### | ||
| # The .rst file used is already available, but can be obtained using the short pyMAPDL code below: | ||
|
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| # # ### Launch pymapdl to generate rst file in myDir | ||
| # from ansys.mapdl.core import launch_mapdl | ||
| # import os | ||
| # | ||
| # | ||
| # mapdl = launch_mapdl() | ||
| # mapdl.prep7() | ||
| # # Model | ||
| # mapdl.cylind(0.5, 0, 10, 0) | ||
| # mapdl.mp("EX", 1, youngsSteel) | ||
| # mapdl.mp("PRXY", 1, prxySteel) | ||
| # mapdl.mshape(key=1, dimension='3d') | ||
| # mapdl.et(1, "SOLID186") | ||
| # mapdl.esize(0.3) | ||
| # mapdl.vmesh('ALL') | ||
| # | ||
| # # #### Boundary Conditions: fixed constraint | ||
| # mapdl.nsel(type_='S', item='LOC', comp='Z', vmin=0) | ||
| # mapdl.d("all", "all") | ||
| # mapdl.nsel(type_='S', item='LOC', comp='Z', vmin=10) | ||
| # nnodes = mapdl.get("NumNodes", "NODE", 0, "COUNT") | ||
| # mapdl.f(node="ALL", lab="fy", value=-13e6 / nnodes) | ||
| # mapdl.allsel() | ||
| # | ||
| # # #### Solve | ||
| # mapdl.run("/SOLU") | ||
| # sol_output = mapdl.solve() | ||
| # rst = os.path.join(mapdl.directory, 'file.rst') | ||
| # mapdl.exit() | ||
| # print('apdl model solved.') | ||
|
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| ############################################################################### | ||
| # PyDPF-Core is then used to post-process the .rst file to estimate the cycles to failure. | ||
|
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| # Comment the following line if solving the MAPDL problem first. | ||
| rst = examples.download_cycles_to_failure() | ||
|
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| # Import the result as a DPF Model object. | ||
| model = dpf.Model(rst) | ||
| print(model) | ||
|
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| ############################################################################### | ||
| # Get the von mises equivalent stress, requires an operator. | ||
| s_eqv_op = dpf.operators.result.stress_von_mises(data_sources=model) | ||
| vm_stress_fc = s_eqv_op.eval() | ||
| vm_stress_field = vm_stress_fc[0] | ||
| vm_stress_field.plot(text="VM stress field") | ||
|
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| ############################################################################### | ||
| # Use NumPy to interpolate the results. | ||
|
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| # Inverse the sn_data | ||
| x_values = sn_data[:, 1][::-1] # the x values are the stress ranges in ascending order | ||
| y_values = sn_data[:, 0][::-1] # y values are inverted cycles to failure | ||
|
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| # Interpolate cycles to failure for the VM values | ||
| cycles_to_failure = np.interp(vm_stress_field.data, x_values, y_values) | ||
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| ############################################################################### | ||
| # Generate a cycles_to_failure DPF Field | ||
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| # Create an empty field | ||
| cycles_to_failure_field = dpf.Field(nentities=len(vm_stress_field.scoping), | ||
| nature=dpf.natures.scalar, | ||
| location=dpf.locations.nodal) | ||
| # Populate the field | ||
| cycles_to_failure_field.scoping = vm_stress_field.scoping | ||
| cycles_to_failure_field.meshed_region = vm_stress_field.meshed_region | ||
| cycles_to_failure_field.data = cycles_to_failure | ||
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| # Plot the field | ||
| sargs = dict(title="cycles", fmt="%.2e") | ||
| cycles_to_failure_field.plot(text="Cycles to failure", scalar_bar_args=sargs) |
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