Welcome to Journal of Automotive Safety and Energy,
汽车安全与节能学报

Journal Of Automotive Safety And Energy ›› 2020, Vol. 11 ›› Issue (1): 53-60.DOI: 10.3969/j.issn.1674-8484.2020.01.005

• Automotive Safety • Previous Articles     Next Articles

Multi-objective optimization design of auto body considering aerodynamic resistance and crosswind stability

QI Chang 1,2,3, HAN Yuanji 2,4, YANG Shu 2*，LU Zhenhua 3   

  1. （1. State Key Laboratory of Structural Analyst for Industrial Equipment, Dalian University of Technology, Dalian 116024, China; 
        2. School of Automotive Engineering, Dalian University of Technology, Dalian 116024, China; 
        3. State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China; 
        4. Nuclear Power Institute of China, Science and Technology on Reactor System Design Technology Laboratory, Chengdu 610005, China）
  • Received:2019-06-04 Online:2020-03-31 Published:2020-04-01

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Abstract: A multi-objective automatic optimization-design of the body shape parameters was carried out considering aerodynamic drag characteristics and cross-wind stability. An automayic optimization process was set up on an optimization platform modeFRONTIER, comprehensively using parametric modeling technology, computational fluid dynamics (CFD), design of experiment (DOE), response surface model (RSM) and intelligent optimization algorithm, integrating Pro / Engineer, ICEM, and Fluent. Using this process, an MIRA fast-back auto body geometry was modified and designed to obtain an optimal trade-off design-solution-set considering the aerodynamic drag characteristics and cross-wind stability based on the genetic algorithm (GA). The result  reduces the aerodynamic drag coefficient by 5.2% and reduces the lateral force coefficient by 5.8%. Therefore,  a multi-objective optimization of aerodynamic resistance and crosswind stability of the vehicle body has been achieved.

Key words: auto-body design ,  aerodynamic , computational fluid dynamics (CFD) , multi-objective optimization , response surface model ,  genetic algorithm (GA)