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Python Tools for CAE Postprocessing and Reporting

30 Chapters
View Course Samples Online
01
CAE postprocessing and reporting workflows
role of Python, common data formats, and toolchain overview.
CAEpostprocessing
02
Reading solver output files
parsing VTK, CGNS, HDF5, and CSV results with Python libraries.
VTKCGNS
03
Extracting field data from mesh-based results
nodes, elements, time steps, and solution arrays.
Extractingmesh-based
04
Data cleaning and validation
handling missing values, unit consistency, and mesh quality checks.
cleaningvalidation
05
Numerical operations on CAE results
gradients, integrals, averages, and derived quantities with NumPy.
Numericaloperations
06
Time-series analysis of transient simulations
filtering, FFT, and peak detection using SciPy.
Time-seriesanalysis
07
Spatial data interpolation and regridding
mapping results onto structured and unstructured meshes.
interpolationregridding
08
Automated extraction of key performance indicators from simulation batches.
Automated extraction of key performance indicators from simulation batches.
Automatedextraction
09
Visualizing 2D field results
contour plots, vector fields, and streamlines with Matplotlib.
Visualizingstreamlines
10
Visualizing 3D CAE results
isosurfaces, slices, and volume rendering with PyVista.
VisualizingCAE
11
Interactive exploration of large datasets
using Plotly and Dash for web-based postprocessing.
Interactiveexploration
12
Creating publication-quality figures
styling, annotations, and export for reports and papers.
Creatingpublication-quality
13
Animation and video generation from transient CAE results.
Animation and video generation from transient CAE results.
Animationgeneration
14
Building reusable postprocessing scripts and functions for common CAE tasks.
Building reusable postprocessing scripts and functions for common CAE tasks.
Buildingreusable
15
Automating report generation with Jinja2 templates and Python.
Automating report generation with Jinja2 templates and Python.
Automatinggeneration
16
Generating PDF and HTML reports with embedded plots and tables using ReportLab and WeasyPrint.
Generating PDF and HTML reports with embedded plots and tables using ReportLab and WeasyPrint.
GeneratingPDF
17
Integrating Excel and Word outputs for engineering documentation with openpyxl and python-docx.
Integrating Excel and Word outputs for engineering documentation with openpyxl and python-docx.
Integratingdocumentation
18
Batch processing of multiple simulation cases
directory traversal, parallelization, and logging.
processingmultiple
19
Parameter studies and design of experiments
automating data collection and result aggregation.
Parameterexperiments
20
Coupling Python postprocessing with CAE solvers
scripting interfaces and co-simulation basics.
Couplingpostprocessing
21
Case study
postprocessing structural FEA results for stress and displacement reporting.
postprocessingstructural
22
Case study
CFD postprocessing for pressure, velocity, and turbulence metrics.
CFDpostprocessing
23
Case study
thermal analysis results and heat flux visualization.
analysisvisualization
24
Case study
modal and harmonic response analysis for vibration reporting.
harmonicresponse
25
Case study
fatigue and durability postprocessing with cycle counting and damage metrics.
durabilitypostprocessing
26
Case study
multiphysics results correlation and comparison across solvers.
multiphysicscorrelation
27
Performance optimization
memory management, chunked reading, and GPU acceleration for large datasets.
Performanceoptimization
28
Reproducibility and version control for postprocessing scripts and reports.
Reproducibility and version control for postprocessing scripts and reports.
Reproducibilitypostprocessing
29
Deploying postprocessing tools
packaging, CLI interfaces, and integration with CI/CD pipelines.
Deployingpostprocessing
30
Emerging trends
machine learning for CAE result interpretation and automated report insights.
EmergingCAE