A 2.5D BEM-based approach in the Bézier–Bernstein space for railway noise prediction and acoustic barrier assessment

Abstract

Noise pollution from railway traffic, primarily caused by rolling noise resulting from the vibrations of the track and wheels, is a major public health concern. While traditional acoustic barriers are effective, they are often visually intrusive, particularly in urban settings. This has led to growing interest in more integrated solutions, such as low, close barriers, which require accurate noise prediction tools. This paper presents a two-and-a-half-dimensional BEM for predicting and mitigating railway noise. The method uses Bézier–Bernstein space to accurately model complex geometries, enhancing noise prediction across different rail profiles. Several rail configurations are compared to evaluate their impact on noise emissions and to support the design of more effective and adaptable barrier solutions. The method is then applied to evaluate the performance of a specific low-height barrier configuration, considering the presence of the vehicle to assess its impact on noise reduction. Numerical predictions are validated through comparison with experimental data and other numerical approaches. Results highlight the importance of accurate source modelling for barrier design and demonstrate the potential of the proposed method as a flexible tool for developing noise mitigation solutions that utilize the barrier’s geometry to improve acoustic performance and support visual integration in urban environments.

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Identifiers

ORCID for Christopher Knuth: orcid.org/0000-0003-4995-2179

ORCID for Giacomo Squicciarini: orcid.org/0000-0003-2437-6398

ORCID for David Thompson: orcid.org/0000-0002-7964-5906

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