城市轨道交通列车轮对磨耗模型研究

吴健; 叶正君; 邢宗义

城市轨道交通列车轮对磨耗模型研究

1. 中车青岛四方机车车辆股份有限公司, 青岛 266111;
2. 南京理工大学自动化学院, 南京 210094

基金项目:

国家重点研发计划课题（2017YFB1201102）

详细信息

作者简介:
吴健,高级工程师;叶正君,在读硕士研究生。

中图分类号: U231;U260.331.1;TP39
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- 文章访问数: 58
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出版历程
- 收稿日期: 2019-11-09
- 刊出日期: 2020-08-03

Research on wheel set wear model of urban rail transit train

1. CRRC Qingdao Sifang Co. Ltd., Qingdao 266111, China;
2. School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China

摘要

摘要: 为实时掌握城市轨道交通列车轮对在列车运行过程中的磨耗情况,结合某地铁线网下实际轮/轨参数,建立机车及轨道多体动力学模型,以仿真实际的轮轨接触作用。将动力学模型输出的轮轨接触变量与接触斑分析算法相结合,实现接触斑内滑动矢量的计算及黏滑区的界定。利用Archard磨耗模型分析接触斑滑动区,以获得接触斑内垂直磨耗情况。最后以接触变量分析-磨耗计算-型面更新为主体结构,采用多次循环的轮对磨耗仿真流程模拟轮对的磨耗过程,完成轮对磨耗模型的建立。将模型输出的轮对型面与实测型面进行对比,验证了模型的准确性。
- 轮对磨耗 /
- 接触斑分析算法 /
- Archard磨耗模型
Abstract: In order to grasp the wheel set wear of urban rail transit during the train operation, this paper established a locomotive and track multi-body dynamic model based on the actual wheel/rail parameters under a subway line network, and simulated the contact relationship of actual wheel-rail. The wheel-rail contact variables output by the dynamic model were combined with the contact spot analysis algorithm to analyze the wheel-rail contact spots, and implement the calculation of the sliding vector and the definition of the stick-slip zone within the contact spots. The paper used an Archard abrasion model to analyze the sliding area in the contact spot, and obtain the vertical wear distribution in the contact spot. Finally, the paper used the contact variable analysis-abrasion calculation-profile updating as the main structure, used the wheel set wear simulation process to simulate the wheel-wear abrasion process multiple times, and complete the establishment of the wheel set wear model. The accuracy of the model was verified by comparing the model output profile of wheel set with the measured profile.
- wheel set wear /
- contact spot analysis algorithm /
- Archard wear model

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参考文献(15)

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施引文献(5)

期刊类型引用(3)

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2.	丁亚琦，吕晟，王子晗，张枝森，刘伟渭，沈钢. 基于实测数据的车辆轮对磨耗模型研究. 城市轨道交通研究. 2024(06): 64-67 . 百度学术
3.	高明亮，邵俊捷，常振臣，王连富，刘德权，牛振虎，陈之恒. 基于遗传算法-BP神经网络的动车组列车轮对磨耗模型. 城市轨道交通研究. 2022(06): 65-69 . 百度学术