基于模型和数据驱动的高速道岔不平顺与车辆响应映射关系
Model-based and Data-driven Mapping of High Speed Railway Turnout Irregularity and Vehicle Response
汤雪扬 1蔡小培 1杨飞 2侯博文1
作者信息
- 1. 北京交通大学,北京 100044
- 2. 中国铁道科学研究院集团有限公司 基础设施检测研究所,北京 100081
- 折叠
摘要
轨道不平顺是影响高速铁路车辆响应的主要因素.本文建立了车辆-道岔-下部基础刚柔耦合动力学模型,基于贝叶斯优化的双向长短时神经网络模型(BO-BiLSTM),分别从机理建模和数据驱动两个方面,揭示了高速道岔不平顺与车辆响应的映射关系.研究结果表明:转辙器区和辙叉区短波长(3～5 m)的高低不平顺和水平不平顺会显著影响列车过岔时轮轨的垂向作用,造成轮轨垂向力和轮重减载率的激增,列车有较大的脱轨安全风险.利用BO-BiLSTM模型,对于高低和水平不平顺,可实现对波长2 m以上不平顺的准确估计;对于轨向和轨距不平顺,可实现波长3 m以上不平顺的准确估计;对于三角坑不平顺,可实现波长1.5 m以上不平顺的准确估计.
Abstract
Track irregularity is the main factor affecting the response of high speed railway vehicles.In this paper,a vehicle-turnout-lower base rigid-flexible coupling dynamics model and a bidirectional short-time and long-time neural network model based on Bayesian optimisation(BO-BiLSTM)was established to reveal the mapping relationship between high speed railway turnout irregularity and vehicle response from the aspects of mechanistic modelling and data-driven,respectively.The results show that the short-wavelength(3～5 m)longitudinal levels and cross level in the turnout can significantly affect the wheel-rail vertical action when the train crosses the turnout,resulting in the surge of wheel-rail vertical force and wheel-weight reduction rate,and the train has a greater risk of derailment safety.The BO-BiLSTM model can achieve accurate estimation of irregularities above 2 m in wavelength for longitudinal levels,above 3 m in wavelength for alignments and gauge irregularities,and above 1.5 m in wavelength for twist.
关键词
道岔不平顺/车辆响应/机理建模/数据驱动/贝叶斯优化Key words
turnout irregularity/vehicle response/mechanism modelling/data-driven/bayesian optimisation引用本文复制引用
基金项目
中国国家铁路集团有限公司科技研发计划(N2023G083)
出版年
2024
NETL
NSTL
万方数据