Load Case Combination for Principal Stress#

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

First, import the DPF-Core module as dpf_core and import the included examples file and DpfPlotter

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

Next, open an example and print out the model object. The :class:`Model <ansys.dpf.core.model.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:

  • Analysis type

  • Available results

  • Size of the mesh

  • Number of results

model = dpf.Model(examples.msup_transient)


DPF Model
Transient analysis
Unit system: MKS: m, kg, N, s, V, A, degC
Physics Type: Mecanic
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 to define "Nodal" as the requested location, as the 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 - LC2 You can access individual loadcases as the fields of a fields_container for stress_tensor

LC1: stress_tensor.outputs.fields_container.get_data()[0] LC2: stress_tensor.outputs.fields_container.get_data()[1]

Scale LC2 to -1

field_lc2 = stress_tensor.outputs.fields_container.get_data()[1]
stress_tensor_lc2_sc = dpf.operators.math.scale(field=field_lc2, ponderation=-1.0)

Add load cases

field_lc1 = stress_tensor.outputs.fields_container.get_data()[0]
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. label_text_size and label_point_size control 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 `cpos` represent camera position-
# focal point, and view up respectively.
02 solution combination

Total running time of the script: ( 0 minutes 1.476 seconds)

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