Multiobjective optimization design for vehicle occupant restraint system under frontal impact

Authors:
Xianguang Gu;Guangyong Sun;Guangyao Li;Xiaodong Huang;Yongchi Li;Qing Li
Affiliations:
Department of Modern Mechanics, The University of Science and Technology of China, Hefei, China 230027 and Chery Automobile Corporation, Wuhu, China 241000;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 Civil, Environment and Chemical Engineering, RMIT University, Melbourne, Australia 3001;Department of Modern Mechanics, The University of Science and Technology of China, Hefei, China 230027;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

Occupant Restraint System (ORS) can effectively protect passengers from severe injury in vehicle collision, thus its design signifies a key issue in automobile engineering. To ensure a high safety rating, e.g. five or at least four stars in the European New Car Assessment Program (Euro-NCAP) rating system, which has been widely used to rate the different vehicles from different manufacturers, design optimization becomes essential. Nevertheless, the effectiveness of conventional mathematical programming methods directly integrated with numerical simulation and sensitivity analysis for optimization is of limited practical value, due to high complexity of structures, nonlinearity of materials and deformation involved. To address the issue, this paper combines a Kriging (KRG) model with Non-dominated Sorting Genetic Algorithm II (NSGA-II) for vehicle ORS design. The ORS design of a 40% Offset Deformable Barrier (ODB) frontal impact test with the collision speed of 64 km/h is exemplified for the presented method. The results show that the KRG model can well predict the ORS responses for the design. Finally, the optimum result is verified by using sled physical tests. It is found that the ORS performance can be substantially improved for meeting product development requirements through the proposed approach.