Iterative Optimization Design for Dynamic Performance Parameters of High-Speed Trains

JIANG Jie; DING Guofu; ZOU Yisheng; ZHANG Haizhu; HUANG Haiyu; LI Rong; ZHANG Jian

doi:10.3969/j.issn.0258-2724.20220071

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Journal of Southwest Jiaotong University > 2025 > Accepted Manuscript

JIANG Jie, DING Guofu, ZOU Yisheng, ZHANG Haizhu, HUANG Haiyu, LI Rong, ZHANG Jian. Iterative Optimization Design for Dynamic Performance Parameters of High-Speed Trains[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20220071

Citation:

JIANG Jie, DING Guofu, ZOU Yisheng, ZHANG Haizhu, HUANG Haiyu, LI Rong, ZHANG Jian. Iterative Optimization Design for Dynamic Performance Parameters of High-Speed Trains[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20220071

Citation:

JIANG Jie, DING Guofu, ZOU Yisheng, ZHANG Haizhu, HUANG Haiyu, LI Rong, ZHANG Jian. Iterative Optimization Design for Dynamic Performance Parameters of High-Speed Trains[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20220071

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Iterative Optimization Design for Dynamic Performance Parameters of High-Speed Trains

doi: 10.3969/j.issn.0258-2724.20220071

School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China

Received Date: 22 Jan 2022
Rev Recd Date: 17 May 2022

Available Online: 11 Apr 2025

Abstract

Abstract

To optimize the dynamic performance parameters of high-speed trains, an iterative design framework for dynamic performance optimization of high-speed trains was established. First, dynamic design parameters were extracted, and a dynamic performance analysis model was constructed. Next, the design space was reduced through importance analysis of the design parameters combined with self-organizing mapping, and an experimental dataset of performance parameters was generated through multidisciplinary coupled simulation. Finally, a multi-objective optimization model was established under multiple working conditions. Based on Bayesian optimization and random forest, a surrogate model for multiple working conditions was developed. An improved non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ) was employed to identify a set of optimal design parameters. Following optimization, the experiment conducted under a representative working condition demonstrates performance improvements of 1.14%, 3.19%, 2.86%, 2.30%, 8.33%, 2.77%, and 8.11% in lateral ride comfort, vertical ride comfort, vertical wheel-rail force, lateral wheelset force, derailment coefficient, wheel load reduction ratio, and overturning coefficient, respectively. These findings validate the feasibility and effectiveness of the proposed iterative design method, providing references for the forward innovative design of complex equipment.
- high-speed trains,
- dynamic performance parameters,
- multi-domain coupled simulation,
- iterative optimization

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References(26)

References

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