期刊文献+

基于机器学习的长编重联动车组碰撞能量管理方案优化

Optimization of collision energy management for long series EMU based on machine learning
下载PDF
导出
摘要 为有效缓解交通运输压力,采用动车组重联运行可以成倍提高载客量,然而一旦发生碰撞事故,巨大的碰撞能量将造成严重的乘员损伤和财产损失,长编重联动车组碰撞能量管理已成为重点研究对象。本文提出均匀耗散和集中耗散2种碰撞能量管理模式,以头车和中间车车端的吸能装置平台力和压缩行程为设计参数,基于KNN、MLS、RBF和RF的4种机器学习算法,开展长编重联动车组碰撞能量管理方案优化设计。研究结果表明:预测头车吸能量和中间车自身耗散能量方差的最优机器学习模型分别是MLS和RBF,相对误差均在4%以内;头车和中间车的吸能元件平台力是影响头车界面吸能量的主要参数,中间车吸能装置参数是影响中间车界面碰撞能量分布是否均匀的主要参数;集中耗散模式下头车和重联界面吸收了碰撞能量48.24%,中间车界面吸收了碰撞能量51.76%,该能量分配模式要求头车前端吸能装置具有更高的吸能量;均匀耗散模式下头车和重联界面吸收了22.75%的碰撞能量,中间车界面吸收了77.25%的碰撞能量,该能量分配模式会增大车间距导致列车长度增加;优化获得的2种碰撞能量管理方案都能在时速36 km/h对撞工况下保证长编重联动车组车体结构完整,且车体120 ms最大平均加速度分别为2.64g和2.36g。 In order to effectively relieve the pressure of railway transportation,the passenger capacity can be increased doubly by adopting the reconnection operation of EMUs.However,once a collision accident occurs,the huge collision energy will cause serious occupant injury and property loss.Therefore,the research on collision energy management of long series EMU has become a focus object.In this paper,two modes of collision energy dissipation,namely concentrated dissipation and uniform dissipation,were proposed.With the platform force and compression stroke of the energy absorption device at the head and middle car ends as design parameters,the optimal design of collision energy management of long series EMU was carried out based on KNN,MLS,RBF and RF machine learning algorithms.The results show that MLS and RBF are the best machine learning models for predicting the energy absorption of the head car and the variance of energy absorption of the middle car,respectively,with relative errors within 4%.The platform force of the energy absorption element of the head car and the middle car is the main parameter affecting the energy absorption of the head car,and the parameters of the energy absorption device of the middle car are the main parameters that affect whether the energy distribution of middle vehicle is uniform.In the concentrated dissipation mode,48.24%of the collision energy is absorbed by the front car and the reconnection interfaces,and 51.76%of the collision energy is absorbed by the middle car,and this energy distribution mode requires higher energy absorption at the front end of the front car.In the uniform dissipation mode,only 22.75%of the collision energy is absorbed by the head and reconnection interfaces,while 77.25%is absorbed by the middle interfaces.This energy distribution mode will increase the distance between cars and lead to the increase of train length.This two optimized collision energy management modes can ensure the integrity of the car body structure of long series EMU under the collision condition of 36 km/h,and the maximum of 120 ms average acceleration of the car body is 2.64g and 2.36g respectively.
作者 姚曙光 谢旻翰 李治祥 张鹏 董云辉 YAO Shuguang;XIE Minhan;LI Zhixiang;ZHANG Peng;DONG Yunhui(Key Laboratory of Traffic Safety on Track of Ministry of Education,Central South University,Changsha 410075,China;Joint International Research Laboratory of Key Technology for Rail Traffic Safety,Central South University,Changsha 410075,China;National&Local Joint Engineering Research Center of Safety Technology for Rail Vehicle,Central South University,Changsha 410075,China)
出处 《中南大学学报(自然科学版)》 EI CAS CSCD 北大核心 2024年第3期1218-1230,共13页 Journal of Central South University:Science and Technology
基金 湖南省自然科学基金资助项目(2021JJ30853) 国家重点研发计划项目(2021YFB3703801-04)。
关键词 长编重联动车组 碰撞能量管理 多目标优化 NSGA-Ⅱ 机器学习 long series EMU crash energy management multi-objective optimization NSGA-II machine learning
  • 相关文献

参考文献10

二级参考文献80

共引文献277

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部