Prediction and Analysis of Structural Noise from a U-beam Using the FE-SEA Hybrid Method

  • Wenjun Luo East China Jiaotong University
  • Long Cheng East China Jiaotong University
  • Lihong Tong East China Jiaotong University
  • Wennian Yu Queen's University http://orcid.org/0000-0002-0077-0836
  • Chris Mechefske Queen's University
DOI: https://doi.org/10.7307/ptt.v30i3.2721
Keywords: U-beam, local vibration, structural noise, sound pressure contribution, Finite Element-Statistical Energy Analysis hybrid method

Abstract

With urban rail transit noise becoming an increasingly serious issue, accurate and quick analysis of the low to medium frequency spectral characteristics of this noise has become important. Based on the FE-SEA (Finite Element - Statistical Energy Analysis) hybrid method, a vibration prediction model of a U-beam was established using a frequency-dividing strategy. The frequency domain and spatial characteristics of the vibration and structural noise of the U-beam within the 1.25-500 Hz frequency range, when subjected to vertical wheel-rail interaction forces, were analyzed. Compared with other methods described in the literature, the proposed FE-SEA hybrid method improves the calculation efficiency while ensuring better accuracy for a wide frequency range of structural noise and vibration. It was found that the excitation frequencies of the wheel-rail force dominate the spectra of the vibration and structural noise of the U-beam. Therefore, the frequency band containing the excitation frequencies should be the target for noise and vibration reduction when implementing strategies. The results show that the bottom plate contributes the most to the sound pressure level at all prediction points, and therefore should be the focus for noise and vibration reduction.

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Wenjun Luo, East China Jiaotong University
Wenjun Luo is the Vice dean of the school of civil engineering and architecture at East China Jiaotong University in Nanchang, Jiangxi, China. Her main research interest is the vibration and noise control of the railway bridge system. She is a memeber of the the excellent youth talent supporting program of Jiangxi Province.
Long Cheng, East China Jiaotong University
Long Cheng is currently a master student of the school of civil engineering and architecture at East China Jiaotong University in Nanchang, Jiangxi, China. His research interest is the vibration and noise control of the railway bridge system.
Lihong Tong, East China Jiaotong University
Lihong Tong is currently a Lecturer at East China Jiaotong University in Nanchang, Jiangxi, China. His main research interest is the saturated soil dynamics.
Wennian Yu, Queen's University
Wennia Yu is currently a PostDoctoral Fellow at the department of Mechanical and Materials Engineering in Kingston, Ontario, Canada. His research focus is on the dynamic modelling of gear transmission system, and condition monitoring of gear system for diagnostics, prognostics and health management.
Chris Mechefske, Queen's University

Chris Mechefske is a full Professor in the Department of Mechanical and Materials Engineering at Queen’s University in Kingston, Ontario, Canada. His research interests include vibration based machine condition monitoring and fault diagnostics, maintenance and reliability, machine dynamic analysis, and vibration and noise reduction. He is a member of the editorial board of the Journal of Condition Monitoring and Diagnostic Engineering Management; American Society of Mechanical Engineers; Canadian Machinery Vibration Association (past president 2003-2005); the International Institute of Acoustics and Vibration (Director 2007-2009); and a Fellow of the Canadian Society of Mechanical Engineers.

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Published
2018-06-28
How to Cite
1.
Luo W, Cheng L, Tong L, Yu W, Mechefske C. Prediction and Analysis of Structural Noise from a U-beam Using the FE-SEA Hybrid Method. Promet [Internet]. 2018Jun.28 [cited 2024Nov.21];30(3):333-42. Available from: https://traffic.fpz.hr/index.php/PROMTT/article/view/2721
Issue
Vol 30 No 3 (2018)
Section
Articles

Copyright (c) 2018 Wenjun Luo, Long Cheng, Wennian Yu, Chris Mechefske

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