Load case combination for principal stress#

This example shows how to get a principal stress load case combination using DPF And highlight min/max values in the plot.

Import the ansys.dpf.core module, included examples file, and the DpfPlotter module.

from ansys.dpf import core as dpf
from ansys.dpf.core import examples
from ansys.dpf.core.plotter import DpfPlotter

Open an example and print the Model object. The Model class helps to organize access methods for the result by keeping track of the operators and data sources used by the result file.

Printing the model displays this metadata:

  • Analysis type

  • Available results

  • Size of the mesh

  • Number of results

model = dpf.Model(examples.find_msup_transient())
DPF Model
Transient analysis
Unit system: MKS: m, kg, N, s, V, A, degC
Physics Type: Mechanical
Available results:
     -  displacement: Nodal Displacement
     -  velocity: Nodal Velocity
     -  acceleration: Nodal Acceleration
     -  reaction_force: Nodal Force
     -  stress: ElementalNodal Stress
     -  elemental_volume: Elemental Volume
     -  stiffness_matrix_energy: Elemental Energy-stiffness matrix
     -  artificial_hourglass_energy: Elemental Hourglass Energy
     -  thermal_dissipation_energy: Elemental thermal dissipation energy
     -  kinetic_energy: Elemental Kinetic Energy
     -  co_energy: Elemental co-energy
     -  incremental_energy: Elemental incremental energy
     -  elastic_strain: ElementalNodal Strain
DPF  Meshed Region:
  393 nodes
  40 elements
  Unit: m
  With solid (3D) elements
DPF  Time/Freq Support:
  Number of sets: 20
Cumulative     Time (s)       LoadStep       Substep
1              0.010000       1              1
2              0.020000       1              2
3              0.030000       1              3
4              0.040000       1              4
5              0.050000       1              5
6              0.060000       1              6
7              0.070000       1              7
8              0.080000       1              8
9              0.090000       1              9
10             0.100000       1              10
11             0.110000       1              11
12             0.120000       1              12
13             0.130000       1              13
14             0.140000       1              14
15             0.150000       1              15
16             0.160000       1              16
17             0.170000       1              17
18             0.180000       1              18
19             0.190000       1              19
20             0.200000       1              20

Get the stress tensor and connect time scoping. Make sure that you define dpf.locations.nodal as the scoping location because labels are supported only for nodal results.

stress_tensor = model.results.stress()
time_scope = dpf.Scoping()
time_scope.ids = [1, 2]

This code performs solution combination on two load cases, LC1 and LC2. You can access individual load cases as the fields of a fields container for the stress tensor.

field_lc1 = stress_tensor.outputs.fields_container()[0]
field_lc2 = stress_tensor.outputs.fields_container()[1]

# Scale LC2 to -1.
stress_tensor_lc2_sc = dpf.operators.math.scale(field=field_lc2, ponderation=-1.0)

Add load cases.

stress_tensor_combi = dpf.operators.math.add(fieldA=field_lc1, fieldB=stress_tensor_lc2_sc)

Principal stresses are the Eigenvalues of the stress tensor. Use principal invariants to get S1, S2, and S3.

p_inv = dpf.operators.invariant.principal_invariants()

Print S1 (maximum principal stress).

[ 9.89969387e+05  9.86979842e+05  6.46045019e+05  6.48932208e+05
  1.56976611e+04  2.38335566e+03  2.41021560e+03  1.55569949e+04
  6.46045018e+05  9.86979841e+05  2.41021536e+03  2.38335517e+03
  1.40298687e+06  1.40006022e+06  1.51284404e+04  2.32609985e+03
  1.40006022e+06  2.32609969e+03  1.88584658e+06  1.88308883e+06
  1.40245029e+04  2.28989834e+03  1.88308883e+06  2.28989838e+03
  2.43323154e+06  2.43097276e+06  1.13710605e+04  1.92191439e+03
  2.43097276e+06  1.92191428e+03  3.03740836e+06  3.03544790e+06
  8.36913086e+03  5.11051169e+03  3.03544790e+06  5.11051165e+03
  3.68414662e+06  3.68923438e+06 -4.49507141e+03 -3.86970389e+03
  3.68923438e+06 -3.86970407e+03  4.37493535e+06  4.36801081e+06
  4.62750323e+04  6.45366758e+04  4.36801080e+06  6.45366739e+04
  5.00024912e+06  5.15363818e+06 -1.72558410e+05 -1.68506344e+05
  5.15363818e+06 -1.68506347e+05  5.34040385e+06  6.25295625e+06
  9.15169741e+05  5.86694135e+05  6.25295625e+06  5.86694135e+05
  8.19942478e+06  5.11230070e+06 -3.42443463e+06 -1.72869110e+06
  5.11230070e+06 -1.72869110e+06  3.07108105e+03  4.85742708e+02
  2.08121801e+03  4.51329102e+03  1.15257484e+04  1.08729694e+04
  1.48084273e+02  6.58067584e+01  2.08121700e+03  4.85743242e+02
  1.48084276e+02  1.08729663e+04  3.99734802e+02  2.84198603e+02
  3.98308935e+04  3.99596263e+04  2.84199027e+02  3.99596267e+04
  8.05592976e+00  7.63702585e+01  7.02630967e+04  7.11528217e+04
  7.63704604e+01  7.11528219e+04  4.08416639e+00  8.37761486e+01
  8.97598110e+04  9.14268559e+04  8.37761220e+01  9.14268560e+04
  5.48381675e+00  1.42362400e+02  8.58241342e+04  8.84204337e+04
  1.42362254e+02  8.84204335e+04  1.36280987e+01  1.13419159e+03
  4.68127641e+04  5.12948274e+04  1.13419050e+03  5.12948265e+04
  3.78823067e+04  3.90599658e+04  1.20048086e+04  5.25881723e+03
  3.90599642e+04  5.25881571e+03  1.77336944e+05  1.75477888e+05
  1.36475040e+04  2.76759779e+03  1.75477887e+05  2.76759705e+03
  3.79160579e+05  3.76590414e+05  1.48577178e+04  2.47204930e+03
  3.76590413e+05  2.47204876e+03  9.87954234e+05  8.16487441e+05
  6.46963602e+05  8.19450620e+05  8.32083289e+03  2.37183132e+03
  8.24629107e+03  1.56273280e+04  4.22633967e+03  2.09163879e+04
  1.69460011e+04  3.38679027e+03  6.46963601e+05  8.16487441e+05
  9.87954234e+05  8.24629092e+03  2.37183096e+03  8.32083271e+03
  1.69460004e+04  2.09163879e+04  1.40100768e+06  1.19350278e+06
  1.19647800e+06  8.06338005e+03  2.33617878e+03  1.54130508e+04
  5.06746339e+03  2.48394848e+04  1.19350278e+06  1.40100768e+06
  2.33617846e+03  8.06337997e+03  2.48394841e+04  1.88395758e+06
  1.64156177e+06  1.64441663e+06  7.53335722e+03  2.29511028e+03
  1.45764717e+04  5.88761468e+03  2.85398928e+04  1.64156177e+06
  1.88395758e+06  2.29511022e+03  7.53335719e+03  2.85398937e+04
  2.43161333e+06  2.15701918e+06  2.15953899e+06  6.20773016e+03
  2.02784724e+03  1.26977817e+04  6.67395032e+03  3.18340564e+04
  2.15701918e+06  2.43161333e+06  2.02784721e+03  6.20773009e+03
  3.18340564e+04  3.03594055e+06  2.73320351e+06  2.73531989e+06
  5.60676164e+03  2.72710830e+03  9.87009570e+03  7.46575696e+03
  3.45048447e+04  2.73320351e+06  3.03594055e+06  2.72710810e+03
  5.60676149e+03  3.45048427e+04  3.68631143e+06  3.36231959e+06
  3.36077747e+06 -4.64521947e+03 -4.02685627e+00  1.93702972e+03
  8.47413202e+03  3.63891780e+04  3.36231959e+06  3.68631143e+06
 -4.02705347e+00 -4.64521954e+03  3.63891782e+04  4.37097945e+06
  4.02861843e+06  4.02954094e+06  5.48856888e+04  2.66267135e+04
  1.70544403e+04  9.30881824e+03  3.76058067e+04  4.02861843e+06
  4.37097945e+06  2.66267128e+04  5.48856879e+04  3.76058038e+04
  5.07685002e+06  4.75984186e+06  4.68757240e+06 -1.70825434e+05
 -5.92324427e+04 -6.58878990e+04  3.01483062e+04  2.40997043e+04
  4.75984186e+06  5.07685002e+06 -5.92324444e+04 -1.70825436e+05
  2.40996713e+04  5.79541432e+06  5.70261123e+06  5.17030113e+06
  7.41483955e+05  1.80559706e+05  3.68553935e+05  1.21181294e+04
  5.77464735e+04  5.70261123e+06  5.79541432e+06  1.80559705e+05
  7.41483955e+05  5.77464732e+04  6.76007838e+06  6.55138588e+06
  5.60950676e+06 -1.27232574e+06 -2.58721655e+06 -6.39421513e+05
  4.13265031e+05  4.45501864e+05  5.60950676e+06  6.55138588e+06
 -6.39421513e+05 -2.58721655e+06  4.45501864e+05  1.61943216e+03
  3.10965572e+02  2.25636060e+03  3.79218604e+03  1.10854389e+04
  4.53912469e+03  9.07955770e+01  4.93947446e+03  3.68349703e+02
  1.18342832e+03  3.42938262e+01  4.05442820e+01  2.25636052e+03
  3.10965881e+02  1.61943240e+03  9.07956839e+01  4.53912372e+03
  1.10854373e+04  3.42939134e+01  1.18342850e+03  2.65656153e+02
  3.16995504e+02  1.73540793e+03  3.98371654e+04  2.53482084e+04
  2.56765725e+04  4.87884515e+02  1.64634854e+03  3.16995859e+02
  2.65656367e+02  2.53482069e+04  3.98371655e+04  1.64634882e+03
  1.20868138e+01  1.53332553e+02  8.34477875e+00  7.06858860e+04
  5.55323220e+04  5.50465228e+04  4.32272090e+02  1.40242459e+03
  1.53332839e+02  1.20867966e+01  5.55323222e+04  7.06858860e+04
  1.40242520e+03  6.52978300e+00  6.11437148e+01  5.82177773e+00
  9.05926824e+04  8.12697662e+04  8.00112056e+04  2.33592980e+02
  4.52921390e+02  6.11437305e+01  6.52982809e+00  8.12697663e+04
  9.05926824e+04  4.52922199e+02  1.01781946e+01  1.06177194e+02
  4.46460174e+00  8.70987991e+04  8.98867421e+04  8.77916532e+04
  1.04133938e+02  1.18550598e+03  1.06177120e+02  1.01781534e+01
  8.98867421e+04  8.70987990e+04  1.18550523e+03  8.92616048e+01
  3.61429147e+02  7.74169288e+00  4.88073415e+04  6.96187475e+04
  6.63166348e+04  5.69214730e+02  3.42490749e+03  3.61428542e+02
  8.92614996e+01  6.96187470e+04  4.88073411e+04  3.42490700e+03
  3.71947352e+04  7.66384058e+03  1.66212838e+02  7.00641342e+03
  1.58464803e+04  1.09997182e+04  1.14790002e+03  6.21377392e+03
  7.66383803e+03  3.71947343e+04  1.58464779e+04  7.00641255e+03
  6.21377387e+03  1.75780678e+05  1.06374478e+05  1.07603370e+05
  7.35047360e+03  3.11936342e+03  1.28261563e+04  1.82375472e+03
  9.46675704e+03  1.06374477e+05  1.75780677e+05  3.11936250e+03
  7.35047326e+03  9.46675639e+03  3.77331700e+05  2.75920917e+05
  2.78248027e+05  7.90102104e+03  2.50682872e+03  1.42526109e+04
  2.57757172e+03  1.30834902e+04  2.75920916e+05  3.77331700e+05
  2.50682813e+03  7.90102076e+03  1.30834897e+04  5.11273760e+05
  5.14046095e+05  2.39727952e+03  1.52073563e+04  5.11273759e+05

Get the meshed region.

mesh_set = model.metadata.meshed_region

Plot the results on the mesh. The label_text_size and label_point_size arguments control the font size of the label.

plot = DpfPlotter()
plot.add_field(p_inv.outputs.field_eig_1(), meshed_region=mesh_set)

# You can set the camera positions using the ``cpos`` argument.
# The three tuples in the list for the ``cpos`` argument represent the camera
# position, focal point, and view respectively.
02 solution combination

Total running time of the script: (0 minutes 0.688 seconds)

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