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Prediction of Vibrations and Reradiated Noise Due to Railway Traffic: A Comprehensive Hybrid Model Based on a Finite Element Method and Method of Fundamental Solutions Approach

Title
Prediction of Vibrations and Reradiated Noise Due to Railway Traffic: A Comprehensive Hybrid Model Based on a Finite Element Method and Method of Fundamental Solutions Approach
Type
Article in International Scientific Journal
Year
2017
Authors
Pedro Alves Costa
(Author)
FEUP
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Aires Colaço
(Author)
FEUP
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Luís Godinho
(Author)
Other
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Paulo Amado Mendes
(Author)
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Journal
Vol. 139
Pages: 061009-1-061009-10
ISSN: 1048-9002
Publisher: ASME
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Other information
Authenticus ID: P-00N-4GN
Abstract (EN): The growing of railway infrastructures in urban environments demands accurate methods to predict and mitigate potential annoyance of the inhabitants of the surrounding buildings. The present paper aims to contribute to the goal by proposing a numerical model to predict vibrations and reradiated noise due to railway traffic. The model is based on a substructuring approach, where the whole propagation media are considered, from the vibration source (the vehicle-track interaction) to the receiver (the building and its interior acoustic environment). The system track-ground-building is simulated by a 2.5D finite element method-perfectly matched layers (FEM-PML) model, formulated in the frequency-wavenumber domain. The reradiated noise assessment is based on a 2.5D FEM-method of fundamental solutions (MFS) model, where the FEM is used to obtain the structural dynamic response. The structural displacements computed are used as the vibration input for the MFS model in order to assess the acoustic response inside the building's compartments. An application example is presented to assess vibrations and reradiated noise levels inside the building due to railway traffic. This is then followed by a discussion about the potential benefits of the introduction of floating-slab-track systems.
Language: English
Type (Professor's evaluation): Scientific
No. of pages: 10
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