不同运行条件下 BEML 地铁客车脱轨和车轮磨损情况调查

IF 1.8 4区 工程技术 Q3 ENGINEERING, MECHANICAL
Sudhir Kumar Singh, Amit Kumar Das, Sanjay R. Singh, Vikranth Racherla
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引用次数: 0

摘要

铁路中的脱轨和车轮磨损是令人关注的主要问题,其中涉及复杂的运行参数和相互冲突的动力学参数。地铁列车要经历急转弯、陡坡、频繁的高加减速以及高峰时段的超载,这些都增加了问题的多元性。在这项工作中,我们试图研究 BEML(巴拉特运土有限公司)地铁列车在不同运行情况下所有八个车轮的脱轨系数和车轮磨损情况。通过响应面方法(RSM),改变车速、车轴载荷和轨道与车轮接触处的摩擦力,生成各种运行条件。为此,使用商业软件 Simpack 建立了一个复制 BEML 地铁客车的多体车辆动力学模型。在印度加尔各答进行的现场试验中,通过沿轨道匹配车辆运动和乘坐舒适度指数,对所开发的多体动力学模型进行了验证。验证后的多体动力学模型根据中央复合设计(CCD)方案用于模拟不同的运行场景。多体动力学模型在不同运行场景下生成的数据被用作深度神经网络(DNN)模型的输入和输出目标数据。RSM 方法的结果表明,轨道与车轮接触处的摩擦力越小,磨损指数越低,脱轨系数越小。在考虑的速度范围内,运行速度对磨损指数和脱轨系数的影响很小。所开发 DNN 模型的结果表明,在培训和测试中,所有八个车轮的平均绝对百分比误差 (MAPE) 值均低于 4%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Investigation of derailment and wheel wear in a BEML metro coach under different operating conditions

Investigation of derailment and wheel wear in a BEML metro coach under different operating conditions

Derailment and wheel wear in railways are of major concern which involve complex operating and conflicting dynamics parameters. Metro trains undergoes through sharp turns, steep gradients, frequent high acceleration and decelerations, and overloading during peak hours which heighten the multivariate ate aspect of the problems. In this work, an attempt has been made to investigate the derailment coefficient and wheel wear of all the eight wheels of a BEML (Bharat Earth Movers Limited) metro coach under different operating scenarios. Various running conditions are generated through response surface methodology (RSM) approach by varying vehicle speed, axle load and friction at the rail-wheel contact. For this, a multibody vehicle dynamics model replicating BEML metro coach is built in commercial software Simpack. The developed multibody dynamics model is validated from the field trials conducted in Kolkata, India, by matching vehicle motion and ride comfort indices along the track. Validated multibody dynamics model is then used for simulating different running scenarios according to the central composite design (CCD) scheme. Data generated from the multibody dynamics model under different operating scenarios are taken as inputs and outputs target data for a deep neural network (DNN) model. Results of the RSM approach indicate that lower friction at the rail-wheel contact is desirable for lower wear indices and smaller derailment coefficients. Operating speed, in the speed range considered, has little influence on wear index and derailment coefficient. Results of the developed DNN model demonstrate that the mean absolute percentage error (MAPE) value is lower than 4% for all the eight wheels in both training and test.

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来源期刊
CiteScore
3.60
自引率
13.60%
发文量
536
审稿时长
4.8 months
期刊介绍: The Journal of the Brazilian Society of Mechanical Sciences and Engineering publishes manuscripts on research, development and design related to science and technology in Mechanical Engineering. It is an interdisciplinary journal with interfaces to other branches of Engineering, as well as with Physics and Applied Mathematics. The Journal accepts manuscripts in four different formats: Full Length Articles, Review Articles, Book Reviews and Letters to the Editor. Interfaces with other branches of engineering, along with physics, applied mathematics and more Presents manuscripts on research, development and design related to science and technology in mechanical engineering.
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