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Vehicle System Dynamics
International Journal of Vehicle Mechanics and Mobility
Volume 56, 2018 - Issue 3
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Articles

A data-driven dynamics simulation framework for railway vehicles

Yinyu NieState Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu, People’s Republic of China;National Centre for Computer Animation, Bournemouth University, Poole, UKView further author information
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Zhao TangState Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu, People’s Republic of ChinaCorrespondence[email protected]
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Fengjia LiuState Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu, People’s Republic of ChinaView further author information
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Jian ChangNational Centre for Computer Animation, Bournemouth University, Poole, UKView further author information
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Jianjun ZhangState Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu, People’s Republic of China;National Centre for Computer Animation, Bournemouth University, Poole, UKView further author information
Pages 406-427 | Received 24 Feb 2017, Accepted 10 Sep 2017, Published online: 04 Oct 2017

Citations (12)

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Yunguang Ye, Dachuan Shi, Philipp Krause & Markus Hecht. (2020) A data-driven method for estimating wheel flat length. Vehicle System Dynamics 58:9, pages 1329-1347.
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Articles from other publishers (11)

Zhao Tang, Yuwei Hu, Shuangbu Wang, Liang Ling, Jianjun Zhang & Kaiyun Wang. (2023) Train post-derailment behaviours and containment methods: a review. Railway Engineering Science 32:1, pages 59-80.
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Sheng Zhang, Pu Huang & Wenyi Yan. (2024) A data-driven approach for railway in-train forces monitoring. Advanced Engineering Informatics 59, pages 102258.
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Zhao Tang, Shaodi Dong, Xiaosong Yang & Jianjun Zhang. (2023) Application of a parallel physics-informed neural network to solve the multi-body dynamic equations for full-scale train collisions. Applied Soft Computing 142, pages 110328.
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Tuo Xu, Ping Xu, Hui Zhao, Chengxing Yang & Yong Peng. (2023) Vehicle running attitude prediction model based on Artificial Neural Network-Parallel Connected (ANN-PL) in the single-vehicle collision. Advances in Engineering Software 175, pages 103356.
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Shaodi Dong, Zhao Tang, Michelle Wu & Jianjun Zhang. (2022) Stochastic dynamic simulation of railway vehicles collision using data-driven modelling approach. Railway Engineering Science 30:4, pages 512-531.
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Kai Wu, Faping Zhang, Yun He Zhang, Yan Yan & Shahid Ikramullah Butt. (2021) Surrogate-adjoint refine based global optimization method combining with multi-stage fuzzy clustering space reduction strategy for expensive problems. Applied Soft Computing 113, pages 107883.
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Zhixiang Li, Wen Ma, Shuguang Yao, Ping Xu, Lin Hou & Gongxun Deng. (2021) A machine learning based optimization method towards removing undesired deformation of energy-absorbing structures. Structural and Multidisciplinary Optimization 64:2, pages 919-934.
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Yu-Ru Li, Tao Zhu, Shou-Ne Xiao, Bing Yang, Guang-Wu Yang, Xiao-Lin Yuan & Zhao Tang. (2020) Application of the collision mathematical model based on a BP neural network in railway vehicles. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 235:6, pages 713-725.
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Yunguang Ye, Yu Sun, Shiping Dongfang, Dachuan Shi & Markus Hecht. (2020) Optimizing wheel profiles and suspensions for railway vehicles operating on specific lines to reduce wheel wear: a case study. Multibody System Dynamics 51:1, pages 91-122.
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Yu-Ru Li, Tao Zhu, Zhao Tang, Shou-Ne Xiao, Jun-Ke Xie, Zhong-Bin Liu & Shi-De Xiao. (2020) Inversion prediction of back propagation neural network in collision analysis of anti-climbing device. Advances in Mechanical Engineering 12:5, pages 168781402092205.
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Junguo Wang, Daoping Gong, Rui Sun & Yongxiang Zhao. (2019) Dynamic Performance of High-Speed Electric Multiple Unit within Multi-Body System Simulation. Recent Patents on Mechanical Engineering 12:4, pages 339-349.
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