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CAE Sensitivity Analysis and Response Surface Optimization

30 Chapters
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01
CAE-driven design optimization landscape
role of sensitivity analysis and response surface methods in product development cycles.
CAE-drivenoptimization
02
Design of experiments for CAE
screening designs, space-filling Latin hypercube, and optimal criteria for simulation-based sampling.
experimentsCAE
03
Parametric CAD and CAE model setup
defining design variables, constraints, and output responses for sensitivity studies.
ParametricCAD
04
Local sensitivity analysis
finite difference, complex step, and adjoint methods for gradient computation in CAE solvers.
sensitivityanalysis
05
Global sensitivity analysis
variance-based Sobol indices, Morris screening, and their implementation with CAE metamodels.
sensitivityanalysis
06
Sensitivity post-processing
ranking influential parameters, tornado charts, and interpreting sensitivity results for design decisions.
Sensitivitypost-processing
07
Response surface methodology fundamentals
polynomial regression, least squares fitting, and model adequacy checking.
Responsemethodology
08
Metamodeling techniques
radial basis functions, Kriging/Gaussian process, and support vector regression for CAE responses.
Metamodelingtechniques
09
Design space exploration and sampling
adaptive sampling, infill criteria, and balancing exploration vs. exploitation.
explorationsampling
10
Surrogate model validation
cross-validation, error metrics, and confidence assessment for CAE-based response surfaces.
Surrogatevalidation
11
Optimization algorithms on response surfaces
gradient-based, evolutionary, and hybrid strategies for constrained CAE problems.
Optimizationalgorithms
12
Multi-objective optimization with response surfaces
Pareto front generation, trade-off analysis, and decision making.
Multi-objectiveoptimization
13
Reliability-based design optimization
incorporating uncertainty quantification and probabilistic constraints using CAE surrogates.
Reliability-basedoptimization
14
Case study
sensitivity analysis and response surface optimization of a structural bracket under static loading.
sensitivityanalysis
15
Case study
aerodynamic shape optimization using CAE sensitivity and response surface methods.
aerodynamicoptimization
16
Case study
thermal management design optimization with CAE-based surrogate models.
managementoptimization
17
Case study
crashworthiness optimization using response surface and sensitivity analysis.
crashworthinessoptimization
18
Case study
multi-physics optimization of an electronic package with coupled thermal-structural CAE responses.
multi-physicsoptimization
19
Workflow automation
scripting sensitivity and optimization loops with Python and CAE solver APIs.
Workflowautomation
20
Integration with CAD/CAE platforms
parameter linking, design tables, and process capture for repeatable studies.
IntegrationCAD/CAE
21
High-dimensional sensitivity analysis
dimension reduction, active subspaces, and feature selection for CAE models.
High-dimensionalsensitivity
22
Handling noisy and discontinuous CAE responses
robust metamodeling and smoothing strategies.
Handlingdiscontinuous
23
Multi-fidelity optimization
combining coarse and fine CAE simulations for efficient response surface construction.
Multi-fidelityoptimization
24
Sensitivity analysis for transient and nonlinear CAE problems
time-dependent responses and path-dependent behavior.
Sensitivityanalysis
25
Response surface optimization under manufacturing variability
tolerance analysis and robust design.
Responseoptimization
26
Verification and validation of optimization results
confirming optimal designs with high-fidelity CAE runs.
Verificationvalidation
27
Industrial deployment
scaling sensitivity and response surface studies, data management, and collaboration.
Industrialdeployment
28
Emerging trends
machine learning surrogates, Bayesian optimization, and digital twin applications in CAE.
Emergingsurrogates
29
Practical project
end-to-end sensitivity analysis and response surface optimization for a chosen CAE component.
end-to-endsensitivity
30
Review and future directions
best practices, pitfalls, and advancing CAE-driven design optimization.
directionspractices