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Crash Simulation Credibility Plan and Acceptance Thresholds

30 Chapters
View Course Samples Online
01
Introduction to crash simulation credibility
definitions, regulatory context, and the role of credibility plans in CAE-driven vehicle safety development.
simulationcredibility
02
Key standards and guidelines
ASME V&V 10, ASME V&V 20, ISO 26262, and NHTSA/IIHS protocols relevant to crash simulation acceptance.
standardsguidelines
03
Decomposition of the credibility plan
scope definition, use-case specification, and identification of critical simulation responses.
Decompositioncredibility
04
Verification activities
code verification, solution verification, and mesh convergence studies for explicit crash solvers.
Verificationactivities
05
Validation hierarchy
component, subsystem, and full-vehicle tests; selecting validation experiments and defining test matrices.
Validationhierarchy
06
Metrics for validation
correlation metrics (e.g., CORA, ISO/TS 18571), objective rating methods, and their limitations.
validationcorrelation
07
Acceptance thresholds
deriving thresholds from physical variability, test repeatability, and simulation uncertainty.
Acceptancethresholds
08
Uncertainty quantification
sources (material, geometry, boundary conditions) and propagation methods (Monte Carlo, polynomial chaos).
Uncertaintyquantification
09
Sensitivity analysis
identifying dominant parameters affecting crash responses and prioritizing validation efforts.
Sensitivityanalysis
10
Material model calibration and validation
tensile, shear, and component tests; calibration for steel, aluminum, and composites.
Materialcalibration
11
Boundary condition and loading validation
sled tests, barrier impacts, and fixture design for subsystem validation.
Boundarycondition
12
Occupant safety simulation credibility
dummy model validation, restraint system validation, and injury criteria correlation.
Occupantsimulation
13
Pedestrian protection simulation credibility
head impact, leg impact, and correlation with physical tests.
Pedestrianprotection
14
Structural crashworthiness validation
frontal, side, and rear impact; component-level validation of crush initiators and joints.
Structuralcrashworthiness
15
Battery electric vehicle crash simulation credibility
battery pack integrity, thermal runaway, and electrical isolation validation.
electricsimulation
16
Lightweight material simulation credibility
aluminum, magnesium, composites, and hybrid structures; failure modeling and validation.
Lightweightmaterial
17
Strain rate and temperature effects
material characterization and validation across crash-relevant strain rates.
temperaturematerial
18
Joining and fastening validation
spot welds, adhesives, rivets, and their failure modeling in crash simulations.
fasteningvalidation
19
Model updating and calibration
techniques (manual, optimization-based) and avoiding overfitting.
updatingcalibration
20
Validation experiment design
test repeatability, instrumentation, and data acquisition for correlation.
Validationexperiment
21
Correlation and acceptance reporting
documenting validation results, metrics, and threshold compliance.
Correlationacceptance
22
Case study
frontal impact full-vehicle validation – from test data to accepted simulation model.
full-vehiclevalidation
23
Case study
side impact pole test – subsystem validation and threshold setting for occupant protection.
subsystemvalidation
24
Case study
pedestrian head impact – component validation and correlation challenges.
pedestriancomponent
25
Case study
battery pack crush – validation of mechanical integrity and electrical safety.
validationmechanical
26
Integration of credibility plan into product development
milestones, reviews, and decision gates.
Integrationcredibility
27
Regulatory acceptance and certification
using simulation for homologation and safety ratings.
Regulatoryacceptance
28
Common pitfalls and best practices
avoiding false confidence, managing model complexity, and ensuring traceability.
pitfallspractices
29
Tools and automation
scripting for correlation, automated reporting, and data management.
automationscripting
30
Future trends
machine learning for surrogate modeling, digital twins, and evolving credibility standards.
surrogatemodeling