Multi-Objective Aerodynamic Optimization of a High-Speed Train Head Shape Based on an Optimal Kriging Model

Document Type : Regular Article

Authors

1 State Key Laboratory of Heavy Duty AC Drive Electric Locomotive Systems Integration

2 CRRC Zhuzhou Locomotive Co.,Ltd. Zhuzhou 412001,China

3 Key Laboratory of Traffic Safety on Track, Ministry of Education, School of Traffic & Transportation Engineering, Central South University, Changsha, 410075, China

4 Joint International Research Laboratory of Key Technology for Rail Traffic Safety, Central South University, Changsha, 410075, China

5 National & Local Joint Engineering Research Center of Safety Technology for Rail Vehicle, Changsha, 410075, China

Abstract

An optimal Kriging surrogate model based on a 5-fold cross-validation method and improved artificial fish swarm optimization is developed for improving the aerodynamic optimization efficiency of a high-speed train running in the open air. The developed optimal Kriging model is compared with the original Kriging model in two test sample points, and the prediction errors are all reduced to within 5%. Thus, the optimal Kriging model is selected for use in each iteration to approximate the CFD simulation model of a high-speed train in subsequent optimization. After that, the strong Pareto evolutionary algorithm II (SPEA2) is adopted to obtain a series of Pareto-optimal solutions. Based on the above work, a multi-objective aerodynamic optimization design for the head shape of a high-speed train is performed using a free-form deformation (FFD) parameterization approach. After optimization, the aerodynamic drag coefficient of the head car and the aerodynamic lift coefficient of the tail car are reduced by 5.2% and 32.6%, respectively. The results demonstrate that the optimization framework developed in this paper can effectively improve optimization efficiency.

Keywords

References

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Journal of Applied Fluid Mechanics
Volume 15, Issue 3 - Serial Number 64
May and June 2022
Pages 803-813

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History

  • Received: 09 July 2021
  • Revised: 17 December 2021
  • Accepted: 19 December 2021
  • Available online: 18 March 2022

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