高速列车车轮型面多目标优化研究

祁亚运; 戴焕云; 干锋

doi:10.3901/JME.2022.24.188

高速列车车轮型面多目标优化研究

doi: 10.3901/JME.2022.24.188

祁亚运^1,,
戴焕云^2,*, ,,
干锋²

1.
重庆交通大学机电与车辆工程学院重庆 400074
2.
西南交通大学牵引动力国家重点实验室成都 610031

基金项目:

国家自然科学基金 11202128

国家自然科学基金 U1934202

国家自然科学基金 51975485

重庆市基础研究与前沿探索 cstc2021jcyj-msxmX0621

重庆市教育委员会科学技术研究 KJQN202100711

详细信息

作者简介:
祁亚运, 男, 1990年出生, 博士, 讲师。主要研究方向为轨道车辆动力学与轮轨接触关系。E-mail: yayun_qi@163.com

通讯作者:
戴焕云(通信作者), 男, 1966年出生, 博士, 研究员, 博士研究生导师。主要研究方向为轨道车辆动力学。E-mail: daihuanyun@163.com

中图分类号: U270
计量
- 文章访问数: 26
- HTML全文浏览量: 12
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- 被引次数: 0
出版历程
- 收稿日期: 2022-07-05
- 修回日期: 2022-11-05
- 网络出版日期: 2024-03-07
- 刊出日期: 2022-12-20

Optimization of Wheel Profiles for High-speed Trains

QI Yayun^1
,,
DAI Huanyun^{2,*
, ,},
GAN Feng²

1.
School of Mmechanotronics and Vehicle Engineering, Chongqing Jiaotong University, Chongqing 400074
2.
State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031

摘要

摘要: 对于动车组车轮磨耗引起的动力学性能降低问题，车轮型面优化是一个很好的解决方案。采用旋转压缩微调法（Rotary-scaling fine-tuning method，RSFT）进行型面生成；建立某型动车组车辆动力学模型，采用该模型计算相应的优化目标和约束条件；利用径向基神经网络-粒子群（Radial-based neural network-particle swarm optimization，RBF-PSO）算法优化出最优廓形。通过对比优化前后车轮型面的动力学性能和磨耗性能，可以发现：优化后车轮型面临界速度为424.6 km/h，增大10.2%；横向平稳性和垂向平稳性指标整体减小，同时提高了曲线通过时的安全性指标，脱轨系数、倾覆系数和轮轴横向力都进一步减小。优化后车轮型面接触点分布相对更加均匀，等效锥度减小。同时优化后车轮型面有效减小车轮磨耗深度，并减小了轮缘根部磨耗，车轮最大磨耗深度减小9.8%。

对于动车组车轮磨耗引起的动力学性能降低问题，车轮型面优化是一个很好的解决方案。采用旋转压缩微调法(Rotary-scaling fine-tuning method，RSFT)进行型面生成；建立某型动车组车辆动力学模型，采用该模型计算相应的优化目标和约束条件；利用径向基神经网络-粒子群(Radial-based neural network-particle swarm optimization，RBF-PSO)算法优化出最优廓形。通过对比优化前后车轮型面的动力学性能和磨耗性能，可以发现：优化后车轮型面临界速度为424.6 km/h，增大10.2%；横向平稳性和垂向平稳性指标整体减小，同时提高了曲线通过时的安全性指标，脱轨系数、倾覆系数和轮轴横向力都进一步减小。优化后车轮型面接触点分布相对更加均匀，等效锥度减小。同时优化后车轮型面有效减小车轮磨耗深度，并减小了轮缘根部磨耗，车轮最大磨耗深度减小9.8%。
- 高速列车 /
- 车轮型面优化 /
- 多目标 /
- 车轮磨耗 /
- 轮轨接触
Abstract: Wheel profile optimization is a good solution to the problem of reduced dynamic performance caused by wheel wear of high-speed trains. A rotary-scaling fine-tuning method(RSFT) is used to generate the new wheel profile; a vehicle dynamics model for the certain high-speed trains is established, and the corresponding optimization objectives and constraints are calculated by the model; the optimal profile is optimized using a radial-based neural network-particle swarm optimization(RBF-PSO) algorithm. By comparing the dynamics and wear performance of the wheel profile before and after the optimization, it can be found that: the wheel profile critical speed after optimization is 424.6 km/h, an increase of 10.2%; the lateral and vertical ride indexes are reduced overall, while the safety indexes during curve passing are improved, and the derailment coefficient, overturning coefficient and lateral axle force are further reduced. The optimized wheel profile has a more evenly distribution of contact points and a reduced equivalent conicity. At the same time, the optimized wheel profile effectively reduces the depth of wheel wear and reduces the root wear of the wheel rim, reducing the maximum depth of wheel wear by 9.8%.
- high-speed trains /
- wheel profile optimization /
- multi-objective /
- wheel wear /
- wheel-rail contact

HTML全文

图 1 实测车体和构架横向加速度

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图 2 车辆动力学模型建立

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图 3 高速线路不平顺

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图 4 采用RSFT方法进行车轮型面生成(${\alpha _1}$=0.94，${\alpha _2}$=1.02)

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图 5 RBF神经网络示意图

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图 6 粒子群算法流程

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图 7 车轮型面优化过程

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图 8 RBF-PSO算法优化

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图 9 优化前后车轮型面(XP55和XP55opt)

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图 10 车轮型面优化前后的临界速度

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图 11 车轮型面优化前后的平稳性指标

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图 12 车轮型面优化前后的安全性指标

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图 13 车轮型面优化前后的轮轨接触点分布

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图 14 车轮型面优化前后的等效锥度

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图 15 USFD磨耗率

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图 16 车轮型面优化前后的磨耗深度

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表 1 平稳性频率修正系数

垂向振动		横向振动
0.5~5.9 Hz	$F\left( f \right) = 0.325{f^2}$	0.5~5.4 Hz	$ F(f) = 0.8{f^2} $
5.9~20 Hz	$F\left( f \right) = 400/{f^2}$	5.4~26Hz	$F\left( f \right) = 650/{f^2}$
> 20 Hz	$F\left( f \right) = 1$	> 26 Hz	$F\left( f \right) = 1$

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表 2 高速线路典型计算工况

曲线半径/m	超高/mm	缓和曲线长/m	圆曲线长/m	运行速度/ (km·h⁻¹)	所占比例(%)
直线	—	—	—	300	60
12 000	80	220	480	300	1
9 000	100	300	320	300	7
8 000	120	340	250	300	8
7 000	145	360	210	300	10
5 500	165	360	210	300	7
5 000	120	360	210	300	7

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