Note
Click here to download the full example code
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)
print(model)
Out:
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]
stress_tensor.inputs.time_scoping.connect(time_scope)
stress_tensor.inputs.requested_location.connect("Nodal")
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()
p_inv.inputs.field.connect(stress_tensor_combi)
Print S1 - Maximum Principal stress
print(p_inv.outputs.field_eig_1().data)
Out:
[ 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
2.39727916e+03]
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.
plot.show_figure(show_axes=True)
Total running time of the script: ( 0 minutes 1.476 seconds)