Crashworthiness optimization of foam-filled tapered thin-walled structure using multiple surrogate models

Authors:
Xueguan Song;Guangyong Sun;Guangyao Li;Weizhao Gao;Qing Li
Affiliations:
State Key Laboratory of Advanced Design and Manufacture for Vehicle Body, Hunan University, Changsha, China 410082 and Department of Mechanical Engineering, Dong-A University, Busan, South Korea 6 ...;State Key Laboratory of Advanced Design and Manufacture for Vehicle Body, Hunan University, Changsha, China 410082 and State Key Laboratory of Vehicle NVH and Safety Technology, China Automotive E ...;State Key Laboratory of Advanced Design and Manufacture for Vehicle Body, Hunan University, Changsha, China 410082;State Key Laboratory of Advanced Design and Manufacture for Vehicle Body, Hunan University, Changsha, China 410082;School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, Sydney, Australia 2006
Venue:
Structural and Multidisciplinary Optimization
Year:
2013

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Abstract

Despite the rapid growth of computing power and continuing advancements in numerical techniques, significant complexity exists when applying traditional sensitivity based optimization to such highly nonlinear problems as crashworthiness design. As a major alternative, surrogate modeling techniques have proven considerably effective. However the challenge remains how to determine the most suitable surrogate scheme for modeling nonlinear responses and conducting optimization. This paper presents a comparative study on the different surrogate models, such as polynomial response surface (PRS), Kriging (KRG), support vector regression (SVR) and radial basis function (RBF), which have been widely used for a variety of engineering problems, thereby gaining insights into their relative performance and features in computational modeling and design. In this study, a foam-filled tapered thin-walled structure is exemplified. Both the gradient and non-gradient algorithms, specifically sequential quadratic programming (SQP) and particle swarm optimization (PSO), are used for these abovementioned four surrogate models, respectively. The design results demonstrate that simultaneous use of different surrogate models can be essential for both gradient and non-gradient optimization algorithms because they may generate different outcomes in the crashworthiness design.