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""" | ||
.. _fatigue_plate_example: | ||
Evaluate fatigue for a composite plate | ||
-------------------------------------- | ||
This example shows how to evaluate fatigue for a flat plate. | ||
It shows how PyPDF Composites can be used to select specific layers and define a custom | ||
combination method. For this example, the custom combination method is stress in fibre | ||
direction. | ||
A random load time series is created, and taking into account that the load is assumed | ||
proportional, rainflow counting is applied to load time series. | ||
Load ranges are then applied on the stress combination method and damage is evaluated | ||
by using a dummy S-N curve. | ||
Be aware that the fatpack package is not developed by Ansys, so it is the responsibility | ||
of the user to verify that it works as expected. For further details: | ||
https://pypi.org/project/fatpack/ | ||
""" | ||
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# %% | ||
# Set up analysis | ||
# ~~~~~~~~~~~~~~~ | ||
# Setting up the analysis consists of loading the required modules, connecting to the | ||
# DPF server, and retrieving the example files. | ||
# | ||
# Load Ansys libraries and numpy, matplotlib and fatpack | ||
import ansys.dpf.core as dpf | ||
import fatpack | ||
import matplotlib.pyplot as plt | ||
import numpy as np | ||
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from ansys.dpf.composites.composite_model import CompositeModel | ||
from ansys.dpf.composites.constants import Sym3x3TensorComponent | ||
from ansys.dpf.composites.example_helper import get_continuous_fiber_example_files | ||
from ansys.dpf.composites.layup_info import AnalysisPlyInfoProvider | ||
from ansys.dpf.composites.select_indices import get_selected_indices_by_analysis_ply | ||
from ansys.dpf.composites.server_helpers import connect_to_or_start_server | ||
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# %% | ||
# Start a DPF server and copy the example files into the current working directory. | ||
server = connect_to_or_start_server() | ||
composite_files_on_server = get_continuous_fiber_example_files(server, "fatigue") | ||
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# %% | ||
# Create a composite model | ||
composite_model = CompositeModel(composite_files_on_server, server) | ||
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# %% | ||
# Read stresses and define a specific layer and a component of stress tensor | ||
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ | ||
# | ||
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# %% | ||
# Read stresses | ||
stress_operator = composite_model.core_model.results.stress() | ||
stress_operator.inputs.bool_rotate_to_global(False) | ||
stress_fc = stress_operator.get_output(pin=0, output_type=dpf.types.fields_container) | ||
stress_field = stress_fc.get_field_by_time_id(1) | ||
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# %% | ||
# Select layer P1L1__ModelingPly.2 | ||
analysis_ply_info_provider = AnalysisPlyInfoProvider( | ||
mesh=composite_model.get_mesh(), name="P1L1__ModelingPly.2" | ||
) | ||
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# %% | ||
# Select Sigma11 as the combination method | ||
component = Sym3x3TensorComponent.TENSOR11 | ||
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# %% | ||
# Load time series and apply rainflow counting | ||
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ | ||
# A random time series is created. Load is assumed proportional, so rainflow counting | ||
# can be directly done on load time series, to get load ranges. | ||
# No mean stress correction is applied. | ||
# | ||
load_factor_time_series = np.random.normal(-1, 2.5, size=100) | ||
x = np.linspace(1, 100, 100) | ||
plt.xlabel("Load Index") | ||
plt.ylabel("Load Factor") | ||
plt.plot(x, load_factor_time_series, color="red") | ||
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# %% | ||
# Fatpack package is used for doing the rainflow counting | ||
load_range_factors = fatpack.find_rainflow_ranges(load_factor_time_series) | ||
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# %% | ||
# S-N curve | ||
# ~~~~~~~~~ | ||
# A dummy S-N curve is created. Please be aware that this is not based on any | ||
# experimental data. We choose Sc to be the orthotropic stress limit in fiber direction | ||
# and Nc to be 1. | ||
# | ||
Sc = 1979 | ||
Nc = 1 | ||
s_n_curve = fatpack.LinearEnduranceCurve(Sc) | ||
# Value for UD materials | ||
s_n_curve.m = 14 | ||
s_n_curve.Nc = Nc | ||
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N = np.logspace(0, 9, 1000) | ||
S = s_n_curve.get_stress(N) | ||
line = plt.loglog(N, S) | ||
plt.grid(which="both") | ||
plt.title("Dummy Linear S-N curve") | ||
plt.xlabel("Cycles to failure") | ||
plt.ylabel("Stress range (MPa)") | ||
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# %% | ||
# Damage evaluation | ||
# ~~~~~~~~~~~~~~~~~ | ||
# Stress S11 at time 1 and layer P1L1__ModelingPly.2 are read | ||
# for each load range and its damage is evaluated, using the dummy S-N curve | ||
# | ||
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damage_result_field = dpf.field.Field(location=dpf.locations.elemental, nature=dpf.natures.scalar) | ||
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with damage_result_field.as_local_field() as local_result_field: | ||
element_ids = analysis_ply_info_provider.property_field.scoping.ids | ||
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for element_id in element_ids: | ||
stress_data = stress_field.get_entity_data_by_id(element_id) | ||
element_info = composite_model.get_element_info(element_id) | ||
assert element_info is not None | ||
selected_indices = get_selected_indices_by_analysis_ply( | ||
analysis_ply_info_provider, element_info | ||
) | ||
# Load Range scaled by S11 | ||
s_11 = max(stress_data[selected_indices][:, component]) | ||
stress_ranges = load_range_factors * s_11 | ||
fatigue_damage = s_n_curve.find_miner_sum(stress_ranges) | ||
local_result_field.append([fatigue_damage], element_id) | ||
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# %% | ||
# Plot damage | ||
composite_model.get_mesh().plot(damage_result_field, text="Fatigue Damage") | ||
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# %% | ||
# Identify the element with the maximum damage | ||
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ | ||
# | ||
maximum_element_scoping = damage_result_field.max().scoping | ||
max_element_id = maximum_element_scoping[0] | ||
print(f"The element with highest damage is {max_element_id}.") | ||
print(f"The highest damage value is {damage_result_field.max().data[0]}.") |
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