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Random dynamic analysis for simplified vehicle model based on explicit time-domain method

  • Huan Huang (College of Water Conservancy and Civil Engineering, South China Agricultural University) ;
  • Yuyu Li (College of Water Conservancy and Civil Engineering, South China Agricultural University) ;
  • Wenxiong Li (College of Water Conservancy and Civil Engineering, South China Agricultural University) ;
  • Guihe Tang (College of Water Conservancy and Civil Engineering, South China Agricultural University)
  • Received : 2021.06.05
  • Accepted : 2022.06.21
  • Published : 2023.02.25

Abstract

On the basis of the explicit time-domain method, an investigation is performed on the influence of the rotational stiffness and rotational damping of the vehicle body and front-rear bogies on the dynamic responses of the vehicle-bridge coupled systems. The equation of motion for the vehicle subsystem is derived employing rigid dynamical theories without considering the rotational stiffness and rotational damping of the vehicle body, as well as the front-rear bogies. The explicit expressions for the dynamic responses of the vehicle and bridge subsystems to contact forces are generated utilizing the explicit time-domain method. Due to the compact wheel-rail model, which reflects the compatibility requirement of the two subsystems, the explicit expression of the evolutionary statistical moment for the contact forces may be performed with relative ease. Then, the evolutionary statistical moments for the respective responses of the two subsystems can be determined. The numerical results indicate that the simplification of vehicle model has little effect on the responses of the bridge subsystem and the vehicle body, except for the responses of the rotational degrees of freedom for the vehicle subsystem, regardless of whether deterministic or random analyses are performed.

Keywords

Acknowledgement

The research described in this paper was financially supported by Guangdong Basic and Applied Basic Research Foundation, China (Grant No. 2020A1515010611, Grant No. 2021A1515012280).

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