In vehicle design, response surface model (RSM) is commonly used as a surrogate of the high fidelity Finite Element (FE) model to reduce the computational time and improve the efficiency of design process. However, RSM introduces additional sources of uncertainty, such as model bias, which largely affect the reliability and robustness of the prediction results. The bias of RSM need to be addressed before the model is ready for extrapolation and design optimization. This paper further investigates the Bayesian inference based model extrapolation method which is previously proposed by the authors, and provides a systematic and integrated stochastic bias corrected model extrapolation and robustness design process under uncertainty. A real world vehicle design example is used to demonstrate the validity of the proposed method.
A Stochastic Bias Corrected Response Surface Method and its Application to Reliability-Based Design Optimization
Sae Int. J. Mater. Manf
Sae International Journal of Materials and Manufacturing
SAE 2014 World Congress & Exhibition ; 2014
Sae International Journal of Materials and Manufacturing ; 7 , 2 ; 262-268
2014-04-01
7 pages
Conference paper
English
Reliability-Based Design Optimization with Model Bias and Data Uncertainty
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