Multi-point objective-oriented sequential sampling strategy for constrained robust design

Abstract

Metamodelling techniques are widely used to approximate system responses of expensive simulation models. In association with the use of metamodels, objective-oriented sequential sampling methods have been demonstrated to be effective in balancing the need for searching an optimal solution versus reducing the metamodelling uncertainty. However, existing infilling criteria are developed for deterministic problems and restricted to one sampling point in one iteration. To exploit the use of multiple samples and identify the true robust solution in fewer iterations, a multi-point objective-oriented sequential sampling strategy is proposed for constrained robust design problems. In this article, earlier development of objective-oriented sequential sampling strategy for unconstrained robust design is first extended to constrained problems. Next, a double-loop multi-point sequential sampling strategy is developed. The proposed methods are validated using two mathematical examples followed by a highly nonlinear automotive crashworthiness design example. The results show that the proposed method can mitigate the effect of both metamodelling uncertainty and design uncertainty, and identify the robust design solution more efficiently than the single-point sequential sampling approach.

Keywords:

• constrained robust design
• objective-oriented sequential sampling method
• multiple-point sampling
• crashworthiness design

Funding

The grant support from the National Natural Science Foundation of China [grant no. 50875164] and the US National Science Foundation [CMMI-1233403] is greatly acknowledged. This collaborative work was also made possible by the Chang Jiang Scholar Funds provided to Professor Wei Chen by the China Education Ministry through Shanghai Jiao Tong University.