J Shanghai Jiaotong Univ Sci

Journal of Shanghai Jiaotong University(Science) >

2020 , Vol. 25 >Issue 6: 727 - 738

DOI: https://doi.org/10.1007/s12204-020-2223-y

Multi-Objective Optimization for Structure Crashworthiness Based on Kriging Surrogate Model and Simulated Annealing Algorithm

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  • (1. School of Engineering, Dali University, Dali 671003, Yunnan, China; 2. Audi Sales Division, FAW-VW Automotive Co.,
    Ltd., Changchun 130011, China; 3. State Key Laboratory of Automotive Simulation and Control, Jilin University,
    Changchun 130025, China; 4. Logistics Group, Jilin University, Changchun 130025, China)

Online published: 2020-11-26

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Abstract

Multi-objective optimization of crashworthiness in automobile front-end structure was performed, and finite element model (FEM) was validated by experimental results to ensure that FEM can predict the response value with sufficient accuracy. Seven design variables and four crashworthiness indicators were defined. Through orthogonal design method, 18 FEMs were established, and the response values of crashworthiness indicators were extracted. By using the variable-response specimen matrix, Kriging surrogate model (KSM) was constructed to replace FEM to reflect the function correlation between variables and responses. The accuracy of KSM was also validated. Finally, the simulated annealing optimization algorithm was implemented in KSM to seek optimal and reliable solutions. Based on the optimal results and comparison analysis, the 9096-th iteration point was the optimal solution. Although the intrusion of firewall and the mass of optimal structure increased slightly, the vehicle acceleration of the optimal solution decreased by 6.9%, which effectively reduced the risk of occupant injury.

Key words： crashworthiness; multi-objective optimization; Kriging surrogate model (KSM); simulated annealing
algorithm

Cite this article

SUN Xilong, WANG Dengfeng, LI Ruheng, ZHANG Bin . Multi-Objective Optimization for Structure Crashworthiness Based on Kriging Surrogate Model and Simulated Annealing Algorithm[J]. Journal of Shanghai Jiaotong University(Science), 2020 , 25(6) : 727 -738 . DOI: 10.1007/s12204-020-2223-y

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