Research on the method of aerodynamic drag and noise reduction in the bogie area of higher-speed EMUs

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ID: 316004
2026
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Abstract
Abstract The operating speed of China’s next-generation high-speed trains is projected to reach 400 km·h‒1. This substantial increase in speed results in a notable surge in aerodynamic drag, thereby making aerodynamic noise the predominant noise source. Although techniques such as streamlined optimization of the train nose, pantograph smoothing, and windshield position optimization have approached their limits in terms of aerodynamic drag and noise reduction, the intricate structure of the bogie area generates turbulent flow fields that significantly contribute to both aerodynamic drag and noise. Therefore, this paper innovatively proposes an enclosure method for the bogie area to mitigate aerodynamic drag and noise. A systematic investigation is conducted into the impact of various bogie enclosure configurations on the aerodynamic drag and noise of high-speed trains through a combination of wind tunnel tests, field tests, and numerical simulations. The results demonstrate that covering the bogie area with side skirts and bottom covers can effectively reduce the train’s aerodynamic drag and far-field noise. The optimal enclosure configuration involves the use of full side skirts and full bottom covers in a coupled manner for both the front and rear train bogie areas. This configuration enables a 12% reduction in aerodynamic drag and a 1 dB(A) reduction in exterior far-field noise. The research findings provide experimental and simulation data support for aerodynamic drag and noise reduction in next-generation high-speed trains, facilitating advancements in high-speed rail technology.
Reference Key
openalex_W7163641767 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Yizheng Yu, Mingzhi Yang, Lei Zhang, Shengong Pan, Dongrun Liu, Yajun Cheng, Jian Sun
Journal Transportation Safety and Environment
Year 2026
DOI
10.1093/tse/tdag001
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