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Python-Based Custom Tools for Automotive CAE Teams

30 Chapters
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01
Course overview
scope, concepts, and technical context.
technical
02
Python environment setup for CAE teams
virtual environments, package management, and dependency isolation.
environmentCAE
03
Core Python for CAE automation
file I/O, path handling, and structured data parsing.
CAEautomation
04
Interfacing with CAE solvers
batch execution, input deck generation, and output parsing.
InterfacingCAE
05
Automating pre-processing tasks
mesh generation, boundary condition assignment, and model assembly.
Automatingpre-processing
06
Post-processing automation
extracting results, generating plots, and creating reports.
Post-processingautomation
07
Building custom CLI tools for CAE workflows
argument parsing, logging, and error handling.
BuildingCLI
08
Data management for CAE
handling large datasets, HDF5, and efficient storage formats.
managementCAE
09
Integrating Python with CAE APIs
ANSYS, Abaqus, LS-DYNA, and open-source solvers.
IntegratingCAE
10
Workflow orchestration
scheduling, job submission, and dependency management.
Workfloworchestration
11
Parameter studies and design of experiments
automating variations and collecting results.
Parameterexperiments
12
Optimization loops with Python
coupling CAE solvers with optimization libraries.
Optimizationcoupling
13
GUI development for CAE tools
PyQt, Tkinter, and web-based interfaces.
GUIdevelopment
14
Packaging and distribution
creating installable tools for team use.
Packagingdistribution
15
Testing and validation of CAE automation scripts
unit tests, regression tests, and CI/CD.
validationCAE
16
Performance optimization
profiling, parallelization, and memory management.
Performanceoptimization
17
Case study
automating a full vehicle crash simulation workflow.
automatingsimulation
18
Case study
building a custom tool for NVH analysis and reporting.
buildingNVH
19
Case study
CFD post-processing automation for aerodynamics teams.
CFDpost-processing
20
Case study
thermal management simulation automation for battery packs.
managementsimulation
21
Case study
fatigue and durability analysis automation.
durabilityanalysis
22
Case study
multi-body dynamics simulation and result extraction.
multi-bodydynamics
23
Case study
lightweighting and topology optimization workflow.
lightweightingtopology
24
Case study
automating material model calibration from test data.
automatingmaterial
25
Case study
integration with PLM and data management systems.
integrationPLM
26
Best practices for maintainable CAE automation code
modularity, documentation, and version control.
practicesmaintainable
27
Collaboration and sharing
internal libraries, documentation, and knowledge transfer.
Collaborationinternal
28
Deploying CAE tools in HPC environments
containers, job schedulers, and remote execution.
DeployingCAE
29
Emerging trends
AI/ML integration, cloud-based CAE, and real-time simulation.
EmergingAI/ML
30
Capstone project
designing and implementing a custom Python tool for a real CAE team challenge.
Capstonedesigning