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CAE Sensitivity Analysis and Response Surface Modeling

30 Chapters
View Course Samples Online
01
CAE-driven design under uncertainty
role of sensitivity analysis and response surface modeling in simulation-based engineering workflows.
CAE-drivenuncertainty
02
Design of experiments for CAE
screening factors, space-filling designs, and sample generation for sensitivity and metamodeling studies.
experimentsCAE
03
Local sensitivity methods in CAE
finite difference, complex-step, and adjoint formulations for gradient computation.
sensitivityCAE
04
Global sensitivity analysis fundamentals
variance-based decomposition, Sobol indices, and total-effect measures for CAE outputs.
sensitivityanalysis
05
Metamodeling foundations
polynomial response surfaces, least-squares fitting, and model adequacy checking for CAE approximations.
Metamodelingfoundations
06
Sensitivity analysis of a structural FEA model
identifying influential geometric and material parameters on stress and displacement responses.
Sensitivityanalysis
07
Response surface design for crashworthiness
constructing quadratic models for energy absorption and peak force using LS-DYNA simulations.
Responsecrashworthiness
08
Adjoint-based sensitivity in CFD
shape derivatives and their application to drag minimization of an airfoil.
Adjoint-basedsensitivity
09
Kriging and Gaussian process metamodels
correlation functions, trend modeling, and prediction uncertainty for CAE response surfaces.
Gaussianmetamodels
10
Radial basis function and support vector regression surrogates
comparative assessment for nonlinear CAE responses.
functionregression
11
Design space exploration with response surfaces
contour plots, trade-off studies, and feasible region identification for multidisciplinary CAE problems.
explorationresponse
12
Sensitivity analysis for thermal management
parameter ranking in conjugate heat transfer simulations of electronics cooling.
Sensitivityanalysis
13
Metamodel-based optimization
sequential quadratic programming and genetic algorithms coupled with response surface approximations.
Metamodel-basedoptimization
14
Efficient global optimization using Kriging
expected improvement criterion and infill sampling for CAE design optimization.
Efficientoptimization
15
Sensitivity analysis of modal parameters
eigenvalue derivatives and their use in frequency response prediction.
Sensitivityanalysis
16
Response surface modeling for NVH
building surrogates for sound pressure level and seat track vibration using DOE and FEA.
Responsemodeling
17
Uncertainty quantification with polynomial chaos expansion
non-intrusive spectral projection for CAE outputs.
Uncertaintyquantification
18
Sensitivity analysis in multi-physics CAE
coupled thermal-structural problems and cross-disciplinary parameter interactions.
Sensitivityanalysis
19
Validation and verification of response surface models
cross-validation, bootstrapping, and prediction error metrics.
Validationverification
20
Sensitivity-driven model calibration
inverse parameter estimation using CAE simulations and response surface surrogates.
Sensitivity-drivencalibration
21
Response surface modeling for fatigue life prediction
surrogate construction from transient dynamic FEA results.
Responsemodeling
22
Global sensitivity analysis of a vehicle crash model
Sobol indices for occupant injury criteria under material and thickness variability.
sensitivityanalysis
23
Metamodeling for real-time CAE applications
reduced-order surrogate models for interactive design review.
Metamodelingreal-time
24
Sensitivity analysis of composite structures
ply orientation and thickness effects on stiffness and failure indices.
Sensitivityanalysis
25
Response surface methods for aerodynamic shape optimization
surrogate-based drag and lift predictions from CFD samples.
Responseaerodynamic
26
Robust design using response surfaces
incorporating noise factors and minimizing performance variance in CAE studies.
responsesurfaces
27
Sensitivity analysis for manufacturing process simulation
welding distortion and residual stress parameter screening.
Sensitivityanalysis
28
Multi-fidelity metamodeling
combining coarse and fine CAE simulations for efficient response surface construction.
Multi-fidelitymetamodeling
29
Sensitivity analysis and response surface modeling for battery thermal runaway
parameter influence on temperature propagation.
Sensitivityanalysis
30
Capstone project
end-to-end sensitivity study and response surface development for a selected CAE application, from DOE to validated surrogate and design recommendation.
Capstoneend-to-end