To tackle engineering design problems engaging both aleatory and epistemic uncertainties, Reliability-Based Design Optimization (RBDO) has been integrated with Bayes Theorem, referred to as Bayesian RBDO. However, Bayesian RBDO becomes expensive when employing the First- or Second-Order Reliability Methods for reliability predictions. This paper proposes an Adaptive Response Surface Method (ARSM) for efficient Bayesian reliability analysis and design optimization. The ARSM integrates the iterative design optimization process with the local response surface methodology through an adaptive sampling scheme. Through this integration, the information for reliability analysis generated at early design stages can be used adaptively to construct local response surfaces for later design iterations. Thus, the computational efficiency of the Bayesian RBDO can be improved as substantially fewer experiments are required in the overall design process. The proposed methodology is demonstrated with a ground vehicle lower control arm design case study.

Paper presented to the 7th Annual Symposium on Graduate Research and Scholarly Projects (GRASP) held at the Marcus Welcome Center, Wichita State University, May 4, 2011.Research completed at the Department of Industrial and Manufacturing Engineering