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

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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.

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Authors and Affiliations

  1. School of Engineering, Dali University, Dali, Yunnan, 671003, China

    Xilong Sun  (孙喜龙) & Ruheng Li  (李汝恒)

  2. Audi Sales Division, FAW-VW Automotive Co., Ltd., Changchun, 130011, China

    Xilong Sun  (孙喜龙)

  3. State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, 130025, China

    Dengfeng Wang  (王登峰)

  4. Logistics Group, Jilin University, Changchun, 130025, China

    Bin Zhang  (张斌)

Authors
  1. Xilong Sun  (孙喜龙)
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  2. Dengfeng Wang  (王登峰)
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  3. Ruheng Li  (李汝恒)
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  4. Bin Zhang  (张斌)
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Corresponding author

Correspondence to Xilong Sun  (孙喜龙).

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Sun, X., Wang, D., Li, R. et al. Multi-Objective Optimization for Structure Crashworthiness Based on Kriging Surrogate Model and Simulated Annealing Algorithm. J. Shanghai Jiaotong Univ. (Sci.) 25, 727–738 (2020). https://doi.org/10.1007/s12204-020-2223-y

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

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