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Python Automated CAE Optimization and Solver Integration

30 Chapters
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01
Course overview
scope, concepts, and technical context of Python-driven CAE automation, optimization, and solver integration.
technicalPython-driven
02
Python ecosystem for CAE
NumPy, SciPy, pandas, matplotlib, and file I/O for engineering data workflows.
ecosystemCAE
03
CAE solver landscape
commercial and open-source solvers, input formats, execution modes, and integration surfaces.
CAElandscape
04
Parametric modeling fundamentals
geometry, mesh, boundary conditions, and material parameters as automation variables.
Parametricmodeling
05
Scripting solver input decks
template generation, parameter substitution, and batch case preparation.
Scriptingtemplate
06
Subprocess orchestration
launching solvers, monitoring execution, capturing logs, and handling exit codes.
Subprocessorchestration
07
Parsing solver outputs
extracting scalar results, field data, and convergence histories into structured Python objects.
extractingconvergence
08
Automated post-processing
contour plots, XY charts, and report-ready artifacts from solver result files.
Automatedpost-processing
09
Design of experiments
full factorial, Latin hypercube, and response surface sampling for CAE studies.
experimentsfactorial
10
Surrogate modeling
polynomial regression, kriging, and radial basis functions for CAE response approximation.
Surrogatemodeling
11
Optimization algorithms
gradient-based, derivative-free, and global methods applied to CAE design problems.
Optimizationalgorithms
12
Constraint handling
geometric, manufacturing, and performance constraints in automated CAE optimization.
Constrainthandling
13
Multi-objective optimization
Pareto fronts, trade-off analysis, and decision-making for engineering designs.
Multi-objectiveoptimization
14
Sensitivity analysis
finite difference, adjoint, and variance-based methods for CAE parameter screening.
Sensitivityanalysis
15
Robust design and uncertainty quantification
Monte Carlo, interval analysis, and reliability-based optimization.
uncertaintyquantification
16
Workflow automation frameworks
job scheduling, parallel execution, and resource management for CAE runs.
Workflowautomation
17
Solver coupling strategies
one-way, two-way, and co-simulation approaches with Python as the orchestrator.
couplingstrategies
18
API-based solver integration
Python bindings, REST interfaces, and in-process solver libraries.
API-basedintegration
19
Database and data management
storing CAE inputs, outputs, and metadata for traceability and reuse.
Databasemanagement
20
Optimization loop implementation
convergence criteria, restart logic, and adaptive sampling strategies.
Optimizationimplementation
21
Case study
automated shape optimization of a structural component using a commercial FEA solver.
automatedoptimization
22
Case study
thermal-fluid system optimization with open-source CFD and Python-driven parametric studies.
thermal-fluidoptimization
23
Case study
multi-physics optimization of an electric motor using coupled electromagnetic and thermal solvers.
multi-physicsoptimization
24
Case study
crashworthiness design exploration with LS-DYNA and Python-based DOE and surrogate modeling.
crashworthinessexploration
25
Case study
additive manufacturing process parameter optimization via thermal simulation and Python automation.
additivemanufacturing
26
Performance tuning
reducing solver calls, caching results, and parallelizing CAE evaluations.
Performancereducing
27
Error handling and robustness
detecting solver failures, mesh issues, and non-convergence in automated loops.
handlingrobustness
28
Verification and validation
checking automation correctness, regression testing, and benchmark comparisons.
Verificationvalidation
29
Deployment and reproducibility
containerization, version control, and CI/CD for CAE automation pipelines.
Deploymentreproducibility
30
Capstone project
end-to-end Python framework for automated CAE optimization and solver integration on a real engineering problem.
Capstoneend-to-end