Prediction of Rail and Bridge Noise in Near- and Far-Field: A Combined 2.5-Dimensional and Two-Dimensional Method

Author:

Song X. D.1,Li Q.2,Wu D. J.3

Affiliation:

1. Department of Bridge Engineering, Southeast University, Nanjing 210096, China e-mail:

2. Associate Professor Department of Bridge Engineering, Tongji University, Shanghai 200092, China

3. Professor Department of Bridge Engineering, Tongji University, Shanghai 200092, China

Abstract

Bridge noise and rail noise induced by passing trains should be included while estimating low- and medium-frequency (20–1000 Hz) noise in railway viaducts. However, the prediction of bridge noise and rail noise using a three-dimensional (3D) acoustic model is not efficient, especially for far-field points. In this study, a combined 2.5-dimensional (2.5D) and two-dimensional (2D) method is proposed to predict bridge noise and rail noise in both the near- and far-field. First, the near-field noise is obtained by combining the 2.5D acoustic model and a 3D vehicle–track–bridge interaction analysis. Then, the 2D method is used to estimate the attenuation of bridge noise and rail noise in the far-field, and the accuracy is validated through comparison with the 2.5D method. Third, the near-field points are treated as reference sources, and the noise at far-field points is predicted by combining the 2.5D and 2D methods. Finally, the proposed method is used to predict the bridge noise and rail noise for a box girder and a U-shaped girder. The spatial distribution of the bridge noise and rail noise is investigated. Generally, the rail noise is dominant above the bridge, and the bridge noise has a larger contribution to the total noise beneath the bridge. The rail noise from the U-shaped girder is much smaller than that from the box girder due to the shielding effect of the webs.

Publisher

ASME International

Subject

General Engineering

Cited by 8 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3