高速列车动力学性能参数优化迭代设计

姜杰; 丁国富; 邹益胜; 张海柱; 黄海于; 黎荣; 张剑

doi:10.3969/j.issn.0258-2724.20220071

高速列车动力学性能参数优化迭代设计

doi: 10.3969/j.issn.0258-2724.20220071

西南交通大学机械工程学院，四川成都 610031

基金项目: 国家重点研发计划（2017YFB1201201）

详细信息

作者简介:
姜杰（1988—），男，博士，研究方向为复杂产品概念设计与多学科优化，E-mail：jj13688158635@126.com

通讯作者:
丁国富（1972—），男，教授，博士，研究方向为数字化设计与制造，E-mail：dingguofu@swjtu.edu.cn

中图分类号: U238
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- 文章访问数: 193
- HTML全文浏览量: 66
- PDF下载量: 46
- 被引次数: 0
出版历程
- 收稿日期: 2022-01-22
- 修回日期: 2022-05-17
- 网络出版日期: 2025-04-11
- 刊出日期: 2022-10-14

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

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

摘要

摘要:
针对高速列车动力学性能参数优化求解问题，首先，搭建高速列车动力学性能优化迭代设计框架，提取动力学属性设计参数并建立动力学性能分析模型；其次，基于设计参数重要度分析和自组织映射缩减设计空间维度，通过多学科领域耦合仿真计算生成性能参数试验样本集；最后，构建多工况下的多目标优化模型，并在此基础上建立基于贝叶斯优化随机森林的高速列车多工况代理模型，通过改进NSGA-Ⅱ算法找出满意的设计参数集. 以某工况为例，实验结果表明：优化后的横向平稳性、垂向平稳性、轮轨垂向力、轮轴横向力、脱轨系数、轮重减载率和倾覆系数性能分别提升1.14%、3.19%、2.86%、2.30%、8.33%、2.77%、8.11%，验证了所提迭代设计方法有效可行，对复杂装备正向创新设计具有一定参考价值.
- 高速列车 /
- 动力学性能参数 /
- 多领域耦合仿真 /
- 优化迭代
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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图 1 高速列车动力学性能参数闭环设计方法框架

Figure 1. Framework of closed-loop design method for dynamic performance parameters of high-speed train

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图 2 高速列车三维设计模型与分析模型的映射与参数提取

Figure 2. Mapping and parameter extraction of 3D design model and analysis model of high-speed train

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图 3 SOM神经网络结构

Figure 3. SOM neural network structure

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图 4 高速列车多工况下的多目标优化迭代思路

Figure 4. Iterative strategy to multi-objective optimization under multiple working conditions for high-speed train

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表 1 高速列车典型服役工况设计列表

Table 1. Typical service conditions designed for high-speed train

工况	运行速度/ （km•h⁻¹）	曲线半径/ m	超高/ mm	轨道不平顺
工况 1	300	∞		京津谱
工况 2	250	5000	80	秦沈谱
工况 3	300	7000	100	京津谱
工况 4	350	9000	140	武广谱

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表 2 高速列车设计空间缩减结果表

Table 2. Results of design space reduction of high-speed train

序号	参数符号	参数名称	初始取值范围	缩减后的范围
1	$ {x_1} $	车轮直径/mm	790～920	795～870
2	$ {x_2} $	轮对质量/kg	1200～2200	1800～2160
3	$ {x_3} $	轮对侧滚转动惯量/（kg•m²）	500～750	535～712
4	$ {x_4} $	轮对摇头转动惯量/（kg•m²）	500～800	522～764
5	$ {x_5} $	一系弹簧纵向刚度/（kN•m⁻¹）	800～1150	860～1080
6	$ {x_6} $	一系弹簧垂向刚度/（kN•m⁻¹）	900～1300	950～1230
7	$ {x_7} $	一系垂向阻尼/（kN•s•m⁻¹）	10～30	11.3～25.5
8	$ {x_8} $	轴箱转臂节点纵向刚度/（MN•m⁻¹）	5～15	6.5～14.2
9	$ {x_9} $	轴箱转臂节点横向刚度/（MN•m⁻¹）	4～10	4.2～9.5
10	$ {x_{10}} $	抗蛇行减振器横向跨距/mm	2400～2800	2450～2750
11	$ {x_{11}} $	空气弹簧垂向刚度/（kN•m⁻¹）	120～450	150～350
12	$ {x_{12}} $	空气弹簧横向刚度/（kN•m⁻¹）	150～400	160～350
13	$ {x_{13}} $	二系横向减振器节点刚度/（MN•m⁻¹）	10～50	12.0～45.6
14	$ {x_{14}} $	二系横向阻尼/（kN•s•m⁻¹）	30～65	34～60
15	$ {x_{15}} $	抗蛇行减振器节点刚度/（MN•m⁻¹）	5～13	7.2～12.5
16	$ {x_{16}} $	抗蛇行减振器阻尼（取非线性曲线的三点平均值）/（kN•s•m⁻¹）	50～3500	75～3200

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表 3 优化后的随机森林模型的超参数值

Table 3. Hyperparametric values of optimized random forest model

工况	树的棵数/棵	拆分内部节点最小样本数/个	叶子节点最小样本数/个	最大深度
1	127	2	11	9
2	132	3	15	9
3	326	2	10	9
4	271	4	7	12

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表 4 各工况贝叶斯优化超参数的随机森林代理模型综合平均值

Table 4. Comprehensive average values of random surrogate forest model hyperparameters optimized by Bayesian method under each working condition

参数	MAE	MSE	R²
工况 1	0.2324	0.3527	0.9981
工况 2	0.2864	0.4292	0.9968
工况 3	0.3533	0.3976	0.9945
工况 4	0.3924	0.4935	0.9883

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表 5 4种工况性能优化对比

Table 5. Comparison of performance optimization under four working conditions

工况	优化情形	横向平稳性	垂向平稳性	轮轨垂向力/kN	轮轴横向力/kN	脱轨系数	轮重减载率	倾覆系数
1	优化前	1.76	1.88	77.56	6.52	0.12	0.36	0.37
1	优化后	1.74	1.82	75.34	6.37	0.11	0.35	0.34
2	优化前	1.95	1.98	83.46	12.65	0.23	0.41	0.40
2	优化后	1.93	1.95	81.82	11.45	0.21	0.4	0.38
3	优化前	1.83	1.86	79.67	10.53	0.18	0.38	0.38
3	优化后	1.80	1.82	77.72	9.83	0.17	0.37	0.36
4	优化前	1.79	1.92	78.82	7.75	0.15	0.37	0.38
4	优化后	1.75	1.90	77.94	7.23	0.14	0.36	0.36

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