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Volume 59 Issue 1
Jan.  2024
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Journal of Southwest Jiaotong University  > 2024  >  59(1): 46-53.
DAI Zhiyuan, LI Tian, ZHANG Weihua, ZHANG Jiye. Multi-objective Aerodynamic Optimization on Head Shape of High-Speed Train Using Kriging Surrogate Model with Hybrid Infill Criterion[J]. Journal of Southwest Jiaotong University, 2024, 59(1): 46-53. doi: 10.3969/j.issn.0258-2724.20220218
Citation: DAI Zhiyuan, LI Tian, ZHANG Weihua, ZHANG Jiye. Multi-objective Aerodynamic Optimization on Head Shape of High-Speed Train Using Kriging Surrogate Model with Hybrid Infill Criterion[J]. Journal of Southwest Jiaotong University, 2024, 59(1): 46-53. doi: 10.3969/j.issn.0258-2724.20220218
shu

Multi-objective Aerodynamic Optimization on Head Shape of High-Speed Train Using Kriging Surrogate Model with Hybrid Infill Criterion

doi: 10.3969/j.issn.0258-2724.20220218

  • State Key Laboratory of Rail Transit Vehicle System, Southwest Jiaotong University, Chengdu 610031, China

  • Received Date: 23 Mar 2022
  • Rev Recd Date: 19 Sep 2022
    • Available Online: 18 Nov 2023
  • Publish Date: 19 Sep 2022
    Abstract

  • In the multi-objective aerodynamic optimization design of high-speed trains, the optimization efficiency of the surrogate model established using the traditional infill criterion is low when the initial sample points are few. To this end, a hybrid infill criterion (HIC) was proposed by combining the improved expectation infill criterion (EIC) and the Pareto solution infill criterion (PIC). Meanwhile, a Kriging surrogate model was established using the HIC method, and multi-objective aerodynamic optimization on the head shape of the high-speed train was conducted, with the minimum aerodynamic drag force of the leading car, the minimum aerodynamic drag and lift force of the rear car as the objectives. The single-objective Branin test function and the multi-objective Poloni test function were taken as examples, and the convergence speed of EIC, PIC, and HIC surrogate models was compared. The results show that the optimization efficiency of the HIC surrogate model is improved by 50.0% compared with the EIC and PIC surrogate models in the single-objective optimization. For the multi-objective test function, the efficiency of the HIC surrogate model is improved by 62.5% compared with the PIC surrogate model. Moreover, the HIC surrogate model is used to carry out the multi-objective aerodynamic optimization of the head shape of the high-speed train, and the optimal solution model obtained reduces the above three objectives respectively by 1.6%, 1.7%, and 3.0% compared with the original model. The heights of the nose, the coupler area, and the cab window of the optimal solution are all reduced. Meanwhile, the two lateral contour lines are retracted.

     

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