实现铁路动力学的大规模模拟:列车-轨道-离散元素法耦合模型

Zhongyi Liu, Travis Shoemaker, E. Tutumluer, Y. Hashash
{"title":"实现铁路动力学的大规模模拟:列车-轨道-离散元素法耦合模型","authors":"Zhongyi Liu, Travis Shoemaker, E. Tutumluer, Y. Hashash","doi":"10.1177/03611981241260688","DOIUrl":null,"url":null,"abstract":"The development of a large-scale high-fidelity model of train, rail, crosstie, and ballast offers a virtual laboratory for studying train–track dynamics. Currently, Train–Track (TT) models integrate the whole train and track system together, but lack explicit representation of ballast particles and simplify them as one-degree-of-freedom mass blocks only moving vertically, whereas models based on Discrete Element Method (DEM) for detailed ballast granular mechanics rarely include detailed representations of the rail and train because these multi-body systems are difficult to model within a DEM framework. To overcome these shortcomings, a large-scale TT-DEM coupled model with more than 480,000 polyhedron ballast particles was established to simulate track dynamic responses. To make this size model feasible with available computing resources, the TT and DEM models were coupled with a proportional–integral–derivative (PID) algorithm to eliminate the need for iteration within each time step. Additionally, the DEM time step was increased, cross-software communication was streamlined, and DEM data extraction was improved. Collectively, these improvements resulted in a model speed-up of about 200 times. The proposed TT-DEM model was validated by comparing predicted and field measured crosstie displacements. These comparisons showed that the TT-DEM model more closely represents the nonlinear system behavior than the conventional TT model and offers the advantage of studying the ballast at the particle level. A study of the thirty-crosstie TT-DEM ballast particle response to train track loading identified significant horizontal ballast forces that are not included in the TT model or single-crosstie TT-DEM models.","PeriodicalId":309251,"journal":{"name":"Transportation Research Record: Journal of the Transportation Research Board","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Toward Large-Scale Simulation of Railroad Dynamics: Coupled Train–Track–Discrete Element Method Model\",\"authors\":\"Zhongyi Liu, Travis Shoemaker, E. Tutumluer, Y. Hashash\",\"doi\":\"10.1177/03611981241260688\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The development of a large-scale high-fidelity model of train, rail, crosstie, and ballast offers a virtual laboratory for studying train–track dynamics. Currently, Train–Track (TT) models integrate the whole train and track system together, but lack explicit representation of ballast particles and simplify them as one-degree-of-freedom mass blocks only moving vertically, whereas models based on Discrete Element Method (DEM) for detailed ballast granular mechanics rarely include detailed representations of the rail and train because these multi-body systems are difficult to model within a DEM framework. To overcome these shortcomings, a large-scale TT-DEM coupled model with more than 480,000 polyhedron ballast particles was established to simulate track dynamic responses. To make this size model feasible with available computing resources, the TT and DEM models were coupled with a proportional–integral–derivative (PID) algorithm to eliminate the need for iteration within each time step. Additionally, the DEM time step was increased, cross-software communication was streamlined, and DEM data extraction was improved. Collectively, these improvements resulted in a model speed-up of about 200 times. The proposed TT-DEM model was validated by comparing predicted and field measured crosstie displacements. These comparisons showed that the TT-DEM model more closely represents the nonlinear system behavior than the conventional TT model and offers the advantage of studying the ballast at the particle level. A study of the thirty-crosstie TT-DEM ballast particle response to train track loading identified significant horizontal ballast forces that are not included in the TT model or single-crosstie TT-DEM models.\",\"PeriodicalId\":309251,\"journal\":{\"name\":\"Transportation Research Record: Journal of the Transportation Research Board\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Transportation Research Record: Journal of the Transportation Research Board\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1177/03611981241260688\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportation Research Record: Journal of the Transportation Research Board","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/03611981241260688","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

大规模高保真列车、轨道、横梁和道碴模型的开发为研究列车-轨道动力学提供了一个虚拟实验室。目前,列车-轨道(TT)模型将整个列车和轨道系统集成在一起,但缺乏对道碴颗粒的明确表示,并将其简化为仅垂直运动的单自由度质量块,而基于离散元素法(DEM)的详细道碴颗粒力学模型很少包含轨道和列车的详细表示,因为这些多体系统很难在 DEM 框架内建模。为了克服这些缺点,我们建立了一个包含超过 480,000 个多面体道碴颗粒的大型 TT-DEM 耦合模型,用于模拟轨道动态响应。为使这一规模的模型在现有计算资源条件下可行,TT 和 DEM 模型采用了比例-积分-派生(PID)算法进行耦合,以消除每个时间步长内的迭代需要。此外,还增加了 DEM 时间步长,简化了跨软件通信,并改进了 DEM 数据提取。总之,这些改进使模型速度提高了约 200 倍。通过比较预测和现场测量的横梁位移,对所提出的 TT-DEM 模型进行了验证。比较结果表明,TT-DEM 模型比传统的 TT 模型更贴近地反映了非线性系统行为,并具有从颗粒层面研究道碴的优势。通过研究 30 根横梁的 TT-DEM 有砟轨道颗粒对列车轨道荷载的响应,发现了 TT 模型或单横梁 TT-DEM 模型中未包含的显著水平有砟力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Toward Large-Scale Simulation of Railroad Dynamics: Coupled Train–Track–Discrete Element Method Model
The development of a large-scale high-fidelity model of train, rail, crosstie, and ballast offers a virtual laboratory for studying train–track dynamics. Currently, Train–Track (TT) models integrate the whole train and track system together, but lack explicit representation of ballast particles and simplify them as one-degree-of-freedom mass blocks only moving vertically, whereas models based on Discrete Element Method (DEM) for detailed ballast granular mechanics rarely include detailed representations of the rail and train because these multi-body systems are difficult to model within a DEM framework. To overcome these shortcomings, a large-scale TT-DEM coupled model with more than 480,000 polyhedron ballast particles was established to simulate track dynamic responses. To make this size model feasible with available computing resources, the TT and DEM models were coupled with a proportional–integral–derivative (PID) algorithm to eliminate the need for iteration within each time step. Additionally, the DEM time step was increased, cross-software communication was streamlined, and DEM data extraction was improved. Collectively, these improvements resulted in a model speed-up of about 200 times. The proposed TT-DEM model was validated by comparing predicted and field measured crosstie displacements. These comparisons showed that the TT-DEM model more closely represents the nonlinear system behavior than the conventional TT model and offers the advantage of studying the ballast at the particle level. A study of the thirty-crosstie TT-DEM ballast particle response to train track loading identified significant horizontal ballast forces that are not included in the TT model or single-crosstie TT-DEM models.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信