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Python CAE Design of Experiments and Sensitivity Analysis

30 Chapters
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01
CAE workflow automation with Python
integrating solvers, pre/post-processing, and parametric models for DOE.
CAEworkflow
02
Design of Experiments fundamentals
factors, levels, responses, randomization, blocking, and replication in CAE context.
Experimentsresponses
03
Full factorial and fractional factorial designs
construction, aliasing, resolution, and Python implementation with pyDOE2.
factorialfractional
04
Response Surface Methodology (RSM)
central composite and Box-Behnken designs, model fitting, and optimization in CAE.
ResponseMethodology
05
Latin Hypercube Sampling and space-filling designs
generation, optimization criteria, and use in CAE sensitivity studies.
HypercubeSampling
06
Sensitivity analysis basics
local vs global methods, one-at-a-time (OAT), and derivative-based approaches.
Sensitivityanalysis
07
Variance-based global sensitivity analysis
Sobol indices, Saltelli sampling, and estimation with SALib.
Variance-basedsensitivity
08
Morris method for screening
elementary effects, mu and sigma metrics, and Python implementation.
screeningelementary
09
Metamodeling for sensitivity analysis
polynomial regression, Gaussian processes, and polynomial chaos expansions.
Metamodelingsensitivity
10
Surrogate-based optimization and sensitivity
coupling DOE, metamodels, and optimization in Python.
Surrogate-basedoptimization
11
Handling multi-physics CAE models
parameterization, mesh morphing, and solver scripting for DOE.
Handlingmulti-physics
12
Uncertainty quantification in CAE
probabilistic inputs, Monte Carlo simulation, and confidence intervals.
Uncertaintyquantification
13
Case study
structural FEA sensitivity analysis using Python and a commercial solver API.
structuralFEA
14
Case study
CFD DOE and sensitivity analysis for aerodynamic shape optimization.
CFDDOE
15
Case study
thermal management system DOE and sensitivity using Python and open-source solvers.
managementDOE
16
Advanced DOE
optimal designs, D-optimal, I-optimal, and Bayesian experimental design.
AdvancedDOE
17
Sensitivity analysis for time-dependent CAE results
functional data analysis and dynamic systems.
Sensitivityanalysis
18
Multi-fidelity and multi-level DOE
combining low- and high-fidelity CAE simulations.
Multi-fidelitymulti-level
19
Robust design and Taguchi methods
orthogonal arrays, signal-to-noise ratios, and Python implementation.
orthogonalsignal-to-noise
20
Bayesian sensitivity analysis
Gaussian process emulators and probabilistic sensitivity measures.
Bayesiansensitivity
21
Global sensitivity analysis for high-dimensional problems
dimension reduction and sparse grids.
sensitivityanalysis
22
Workflow automation and reproducibility
Python scripting, job scheduling, and data management for DOE.
Workflowautomation
23
Visualization and reporting of DOE and sensitivity results
matplotlib, plotly, and dashboards.
Visualizationreporting
24
Verification and validation of DOE and sensitivity results
convergence, error estimation, and benchmarks.
Verificationvalidation
25
Integration with machine learning
feature importance, SHAP values, and surrogate-based sensitivity.
Integrationimportance
26
Case study
multidisciplinary DOE and sensitivity analysis for a vehicle crashworthiness model.
multidisciplinaryDOE
27
Case study
DOE and sensitivity analysis for additive manufacturing process parameters.
DOEsensitivity
28
Case study
sensitivity analysis of a coupled thermal-structural CAE model using Python.
sensitivityanalysis
29
Best practices and pitfalls in CAE DOE and sensitivity analysis
sampling, model bias, and interpretation.
practicespitfalls
30
Capstone project
end-to-end Python CAE DOE and sensitivity analysis for a real-world engineering problem.
Capstoneend-to-end