Auto-Correlation of an Occupant Restraint System Model Using a Bayesian Validation Metric 2009-01-1402
Computer Aided Engineering (CAE) has become a vital tool for product development in automotive industry. Various computer models for occupant restraint systems are developed. The models simulate the vehicle interior, restraint system, and occupants in different crash scenarios. In order to improve the efficiency during the product development process, the model quality and its predictive capabilities must be ensured. In this research, an objective model validation metric is developed to evaluate the model validity and its predictive capabilities when multiple occupant injury responses are simultaneously compared with test curves. This validation metric is based on the probabilistic principal component analysis method and Bayesian statistics approach for multivariate model assessment. It first quantifies the uncertainties in both test and simulation results, extracts key features, and then evaluates the model quality. This paper presents a newly developed auto-correlation method using both the objective model validation metric and a genetic algorithm based advanced optimization approach to automatically select the best values of the model parameters. A successful application of the auto-correlation method is demonstrated by a case study.
Citation: Fu, Y., Jiang, X., and Yang, R., "Auto-Correlation of an Occupant Restraint System Model Using a Bayesian Validation Metric," SAE Technical Paper 2009-01-1402, 2009, https://doi.org/10.4271/2009-01-1402. Download Citation
Author(s):
Yan Fu, Xiaomo Jiang, Ren-Jye Yang
Affiliated:
Ford Motor Company
Pages: 9
Event:
SAE World Congress & Exhibition
ISSN:
0148-7191
e-ISSN:
2688-3627
Also in:
Reliability and Robust Design in Automotive Engineering, 2009-SP-2232
Related Topics:
CAD, CAM, and CAE
Computer simulation
Product development
Restraint systems
Mathematical models
Optimization
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