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Uncertainty Quantification for Occupant Injury Prediction

30 Chapters
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01
Course overview
role of uncertainty quantification in occupant injury prediction, key challenges, and workflow integration.
uncertaintyquantification
02
Injury prediction landscape
injury metrics (HIC, BrIC, chest deflection), regulatory tests, and CAE model chain for occupants.
predictionlandscape
03
Sources of uncertainty in occupant injury prediction
material properties, geometry variability, boundary conditions, and model form uncertainty.
uncertaintyoccupant
04
Probability and statistics foundations for UQ
random variables, distributions, expectation, variance, and common injury response distributions.
Probabilitystatistics
05
Sensitivity analysis fundamentals
local vs global methods, screening designs, and variance-based sensitivity indices for injury outputs.
Sensitivityanalysis
06
Monte Carlo simulation for occupant injury prediction
sampling strategies, convergence assessment, and computational cost management.
simulationoccupant
07
Design of experiments for UQ
factorial, Latin hypercube, and space-filling designs tailored to crashworthiness simulations.
experimentsfactorial
08
Surrogate modeling for UQ
Gaussian processes, polynomial chaos expansion, and neural network surrogates for injury metrics.
Surrogatemodeling
09
Polynomial chaos expansion
construction, coefficient estimation, and application to occupant response variability.
Polynomialexpansion
10
Gaussian process regression
kernel selection, hyperparameter tuning, and uncertainty-aware prediction of injury risk.
Gaussianregression
11
Model calibration under uncertainty
Bayesian calibration of material and restraint parameters using test data.
calibrationuncertainty
12
Bayesian inference for injury prediction
priors, likelihoods, MCMC sampling, and posterior predictive checks.
Bayesianinference
13
Propagation of uncertainty through multibody and finite element occupant models
forward UQ workflows and case studies.
Propagationuncertainty
14
Uncertainty quantification in restraint system design
airbag, seatbelt, and pretensioner parameter variability effects on injury.
Uncertaintyquantification
15
Reliability analysis for injury criteria
probability of exceeding thresholds, reliability index, and first/second-order methods.
Reliabilityanalysis
16
Global sensitivity analysis in practice
Sobol indices estimation, interpretation, and ranking of influential parameters.
sensitivityanalysis
17
Surrogate-based optimization under uncertainty
robust design and reliability-based design optimization for occupant safety.
Surrogate-basedoptimization
18
Treatment of model form uncertainty
model discrepancy, bias correction, and multi-model ensemble approaches.
Treatmentuncertainty
19
Validation and verification for UQ in injury prediction
metrics, validation experiments, and confidence in predictions.
Validationverification
20
Data-driven UQ
incorporating real-world crash data and machine learning for injury risk quantification.
Data-drivenincorporating
21
Uncertainty quantification for human body models
material, anatomical, and positioning variability in injury prediction.
Uncertaintyquantification
22
Case study
UQ for frontal impact occupant injury prediction using a full vehicle FE model.
occupantprediction
23
Case study
side impact injury prediction with uncertainty in door trim and padding parameters.
predictionuncertainty
24
Case study
rear impact whiplash injury prediction and sensitivity to seat design parameters.
whiplashprediction
25
Computational strategies for large-scale UQ
parallelization, adaptive sampling, and multi-fidelity methods.
Computationalstrategies
26
Software tools and frameworks for UQ in occupant safety
Dakota, UQLab, PyMC, and integration with LS-DYNA.
Softwareframeworks
27
Reporting and communication of uncertainty
visualization, probabilistic injury risk curves, and decision-making.
Reportingcommunication
28
Regulatory and industrial perspectives
incorporating UQ into safety regulations and design certification.
Regulatoryindustrial
29
Emerging trends
machine learning surrogates, digital twins, and real-time UQ for occupant protection.
Emergingsurrogates
30
Capstone project
end-to-end UQ study for a selected occupant injury prediction problem, from problem definition to reporting.
Capstoneend-to-end