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Application advances of artificial intelligence algorithms in dynamics simulation of railway vehicle.

Tang, Z, Dong, S., Luo, R, Jiang, T, Deng, R and Zhang, J., 2021. Application advances of artificial intelligence algorithms in dynamics simulation of railway vehicle. Jiaotong Yunshu Gongcheng Xuebao/Journal of Traffic and Transportation Engineering, 21 (1), 250 - 266.

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DOI: 10.19818/j.cnki.1671-1637.2021.01.012

Abstract

The application examples and domestic and foreign literatures using artificial intelligence algorithm for railway vehicle system dynamics simulation were reviewed. The machine learning and deep learning algorithms commonly used in railway vehicle dynamics simulation were summarized, and the application classifications of the 2 algorithms in railway vehicle system dynamics modelling and simulation were concluded and interpreted. According to railway vehicle system dynamics modelling, dynamics performance prediction and dynamics performance optimization, the advantages and limitations of applying artificial intelligence algorithms in force-elements modelling and simulation, track irregularity prediction, running stability prediction, noise prediction, crosswind safety prediction, running safety prediction, suspension optimization, wheel-rail matching optimization, structure optimization, and active and semi-active control were discussed in detail. The problems of applications of artificial intelligence algorithms in railway dynamics simulation were lack of training samples, generalization ability and interpretability. The development directions and key research contents of the interdisciplinary research between artificial intelligence and vehicle system dynamics were given. Research result shows that the hybrid modelling theory combining classical mechanics and artificial intelligence algorithms can be as a key research direction in the future. There is great potential to use the artificial intelligence algorithms to solve the random uncertainty in stochastic dynamics and improve the performance of stochastic dynamics. The artificial intelligence algorithms combinated with optimization algorithms can exploit their advantages in the dynamics performance optimization..

Item Type:Article
ISSN:1671-1637
Uncontrolled Keywords:railway vehicle; artificial intelligence algorithm; performance prediction; performance optimization; dynamics modeling and simulation; machine learning; deep learning
Group:UNSPECIFIED
ID Code:35806
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:20 Jul 2021 12:15
Last Modified:20 Jul 2021 12:15

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