4.7 Article

Parametric modeling and multiobjective crashworthiness design optimization of a new front longitudinal beam

期刊

STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
卷 59, 期 5, 页码 1789-1812

出版社

SPRINGER
DOI: 10.1007/s00158-018-2134-9

关键词

Parametric modeling; Multiobjective crashworthiness optimization; Front longitudinal beam (FLB); Variable rolled blank (VRB); Variable cross-sectional shape (VCS)

资金

  1. National Natural Science Foundation of China [51805221]
  2. China Postdoctoral Science Foundation [2018M640460]
  3. Jiangsu Planned Projects for Postdoctoral Research Fund [2018K018C]
  4. high-performance computing platform of Jiangsu University

向作者/读者索取更多资源

The front longitudinal beam (FLB) is the most important energy-absorbing and crashing force-transmitting structure of a vehicle under front-impact collision. For better weight reduction and crashworthiness of the FLB, a new structure, variable rolled blank-variable cross-sectional shape FLB (VRB-VCS FLB), is proposed. It has both the continuous variation of thickness and variable cross-sectional shape in space. As the thickness distribution and cross-sectional shape change continuously, the proposed structure evolves into three distinct forms, i.e., the uniform-thickness FLB, variable rolled blank FLB, and variable cross-sectional shape FLB. However, literature on parametric modeling and crashworthiness design optimization of the VRB-VCS FLB is very limited. This paper proposes a parametric modeling method of VRB-VCS FLB with manufacturing constraints. Multiobjective crashworthiness design optimization is performed to explore the lightweightness and crashworthiness performance of the VRB-VCS FLB. Firstly, thickness distribution and cross-sectional shape parameters are defined. Secondly, local parametric subsystem front-impact model is established to balance accuracy and efficiency. Thirdly, a multiobjective optimization model of VRB-VCS FLB is constructed. Finally, a fully automated design of experiment platform is established to improve the data collection efficiency, and epsilon-support vector regression technique and non-dominated sorting genetic algorithm II are utilized to search the Pareto optimal frontier. The numerical results show that the lightweightness and crashworthiness of the VRB-VCS FLB are significantly improved when compared with the uniform-thickness FLB.

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