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Response Surface Methods for Crash CAE Design Optimization

30 Chapters
View Course Samples Online
01
Crash CAE optimization landscape
role of response surface methods (RSM), design drivers, and workflow integration.
CAEoptimization
02
Crashworthiness responses and constraints
intrusion, acceleration, force, energy, and mass objectives in RSM studies.
Crashworthinessresponses
03
Design space definition for crash CAE
parameter selection, bounds, and screening using engineering judgment and sensitivity analysis.
definitionCAE
04
Design of experiments (DOE) for crash simulations
full factorial, fractional factorial, and Plackett-Burman designs.
experimentsDOE
05
Space-filling DOE
Latin hypercube and optimal Latin hypercube sampling for crash response surfaces.
Space-fillingDOE
06
DOE execution with crash CAE solvers
job setup, run management, and metadata tracking for large simulation campaigns.
DOEexecution
07
Response extraction and data hygiene
parsing LS-DYNA/PAM-CRASH outputs, filtering noise, and handling failed runs.
Responseextraction
08
Exploratory data analysis for crash DOE results
distributions, correlations, and outlier detection before model fitting.
Exploratoryanalysis
09
Polynomial response surface models
linear, quadratic, and interaction terms for crash responses.
Polynomialresponse
10
Model fitting and validation
least squares, cross-validation, and goodness-of-fit metrics for crash RSM.
validationcross-validation
11
Kriging and Gaussian process models
when and how to use them for nonlinear crash responses.
Gaussiannonlinear
12
Radial basis functions and support vector regression
comparative modeling for crash CAE data.
functionsregression
13
Model selection and error assessment
RMSE, R-squared, PRESS, and prediction uncertainty for crash responses.
selectionassessment
14
Adaptive sampling and sequential DOE
infill criteria to improve crash response surface accuracy.
Adaptivesampling
15
Sensitivity analysis on response surfaces
variance-based and regression-based methods for crash design parameters.
Sensitivityanalysis
16
Single-objective optimization using RSM
formulation, surrogate-based search, and constraint handling.
Single-objectiveoptimization
17
Multi-objective optimization with response surfaces
Pareto fronts, trade-offs, and crashworthiness vs. weight.
Multi-objectiveoptimization
18
Reliability-based design optimization
incorporating uncertainty in crash responses via RSM.
Reliability-basedoptimization
19
Robust design and tolerance analysis
minimizing performance variation in crash CAE using RSM.
toleranceanalysis
20
Case study
frontal impact energy absorber optimization using quadratic RSM.
absorberoptimization
21
Case study
side impact intrusion minimization with Kriging-based RSM.
intrusionminimization
22
Case study
pedestrian protection head impact response surface optimization.
pedestrianprotection
23
Case study
battery pack crash safety optimization using multi-response RSM.
optimizationmulti-response
24
Case study
full vehicle crashworthiness multi-objective optimization with adaptive sampling.
crashworthinessmulti-objective
25
Integration of RSM with crash CAE workflows
automation, scripting, and data management.
IntegrationRSM
26
Verification and validation of optimized designs
confirming surrogate predictions with high-fidelity crash simulations.
Verificationvalidation
27
Scaling and high-dimensional challenges
dimension reduction and variable screening for crash RSM.
high-dimensionalchallenges
28
Handling noisy and discontinuous crash responses
smoothing, robust fitting, and mixed models.
Handlingdiscontinuous
29
Machine learning surrogates vs. classical RSM
trade-offs for crash CAE optimization.
surrogatesclassical
30
Course project
end-to-end RSM-driven crash CAE optimization study from DOE to validated design.
end-to-endRSM-driven