A framework to predict the airborne noise inside railway vehicles with application to rolling noise

[EN] A framework is described for predicting the airborne noise inside railway vehicles which is applied to rolling noise sources. Statistical energy analysis (SEA) is used to predict the interior noise by subdividing the train cabin into several subsystems. The dissipation loss factors are obtained...

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Autores: Li, Hui, Thompson, David, Squicciarini, Giacomo, Liu, Xiaowan, Rissmann, Martin, Bouvet, Pascal, Martin Jarillo, Julian, Moreno Garcia-Loygorri, Juan, F. D. Denia|||0000-0003-4536-8610, Baeza González, Luis Miguel|||0000-0002-3815-8706
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/176434
Acceso en línea:https://riunet.upv.es/handle/10251/176434
Access Level:acceso abierto
Palabra clave:Railway vehicle
Interior noise
Statistical energy analysis
2.5D boundary element method
Rolling noise
INGENIERIA MECANICA
09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación
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network_acronym_str ES
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repository_id_str
spelling A framework to predict the airborne noise inside railway vehicles with application to rolling noiseLi, HuiThompson, DavidSquicciarini, GiacomoLiu, XiaowanRissmann, MartinBouvet, PascalMartin Jarillo, JulianMoreno Garcia-Loygorri, JuanF. D. Denia|||0000-0003-4536-8610Baeza González, Luis Miguel|||0000-0002-3815-8706Railway vehicleInterior noiseStatistical energy analysis2.5D boundary element methodRolling noiseINGENIERIA MECANICA09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación[EN] A framework is described for predicting the airborne noise inside railway vehicles which is applied to rolling noise sources. Statistical energy analysis (SEA) is used to predict the interior noise by subdividing the train cabin into several subsystems. The dissipation loss factors are obtained from the measured reverberation time in the train cabin. The power input to the interior SEA model is obtained from the external noise sources by multiplying the incident sound power on the external surfaces with measured transmission coefficients of the train floor and sidewalls. The sound power incident on the train floor is calculated by using an equivalent source model for the wheels and track together with an SEA model of the region below the floor. The incident sound power on the sides is obtained by using a waveguide boundary element (2.5D BE) method. The procedure is applied to a Spanish metro train vehicle running in the open field for which rolling noise is the main external noise source. The procedure is verified by field measurements of sound pressure beneath the carriage, on the sidewalls and inside the vehicle. The sensitivity of the results to changes in interior absorption is also studied, including the effect of passengers.This work has been funded by the China Scholarship Council and the RUN2Rail H2020/Shift2Rail project (Grant agreement No: 777564). The contents of this publication only reflect the authors' views and the Shift2Rail Joint Undertaking is not responsible for any use that may be made of the information contained in the papeElsevierDepartamento de Ingeniería Mecánica y de MaterialesCentro de Investigación en Ingeniería MecánicaInstituto Universitario de Investigación Concertado de Ingeniería Mecánica y BiomecánicaEscuela Técnica Superior de Ingeniería IndustrialShift2Rail Joint UndertakingRepositorio Institucional de la Universitat Politècnica de València Riunet20212021-08-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://riunet.upv.es/handle/10251/176434reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengEuropean Commission https://doi.org/10.13039/501100000780 H2020 777564 Innovative RUNning gear soluTiOns for new dependable, sustainable, intelligent and comfortable RAIL vehiclesopen accesshttp://purl.org/coar/access_right/c_abf2Reserva de todos los derechoshttp://rightsstatements.org/vocab/InC/1.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/1764342026-06-13T07:49:27Z
dc.title.none.fl_str_mv A framework to predict the airborne noise inside railway vehicles with application to rolling noise
title A framework to predict the airborne noise inside railway vehicles with application to rolling noise
spellingShingle A framework to predict the airborne noise inside railway vehicles with application to rolling noise
Li, Hui
Railway vehicle
Interior noise
Statistical energy analysis
2.5D boundary element method
Rolling noise
INGENIERIA MECANICA
09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación
title_short A framework to predict the airborne noise inside railway vehicles with application to rolling noise
title_full A framework to predict the airborne noise inside railway vehicles with application to rolling noise
title_fullStr A framework to predict the airborne noise inside railway vehicles with application to rolling noise
title_full_unstemmed A framework to predict the airborne noise inside railway vehicles with application to rolling noise
title_sort A framework to predict the airborne noise inside railway vehicles with application to rolling noise
dc.creator.none.fl_str_mv Li, Hui
Thompson, David
Squicciarini, Giacomo
Liu, Xiaowan
Rissmann, Martin
Bouvet, Pascal
Martin Jarillo, Julian
Moreno Garcia-Loygorri, Juan
F. D. Denia|||0000-0003-4536-8610
Baeza González, Luis Miguel|||0000-0002-3815-8706
author Li, Hui
author_facet Li, Hui
Thompson, David
Squicciarini, Giacomo
Liu, Xiaowan
Rissmann, Martin
Bouvet, Pascal
Martin Jarillo, Julian
Moreno Garcia-Loygorri, Juan
F. D. Denia|||0000-0003-4536-8610
Baeza González, Luis Miguel|||0000-0002-3815-8706
author_role author
author2 Thompson, David
Squicciarini, Giacomo
Liu, Xiaowan
Rissmann, Martin
Bouvet, Pascal
Martin Jarillo, Julian
Moreno Garcia-Loygorri, Juan
F. D. Denia|||0000-0003-4536-8610
Baeza González, Luis Miguel|||0000-0002-3815-8706
author2_role author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Departamento de Ingeniería Mecánica y de Materiales
Centro de Investigación en Ingeniería Mecánica
Instituto Universitario de Investigación Concertado de Ingeniería Mecánica y Biomecánica
Escuela Técnica Superior de Ingeniería Industrial
Shift2Rail Joint Undertaking
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Railway vehicle
Interior noise
Statistical energy analysis
2.5D boundary element method
Rolling noise
INGENIERIA MECANICA
09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación
topic Railway vehicle
Interior noise
Statistical energy analysis
2.5D boundary element method
Rolling noise
INGENIERIA MECANICA
09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación
description [EN] A framework is described for predicting the airborne noise inside railway vehicles which is applied to rolling noise sources. Statistical energy analysis (SEA) is used to predict the interior noise by subdividing the train cabin into several subsystems. The dissipation loss factors are obtained from the measured reverberation time in the train cabin. The power input to the interior SEA model is obtained from the external noise sources by multiplying the incident sound power on the external surfaces with measured transmission coefficients of the train floor and sidewalls. The sound power incident on the train floor is calculated by using an equivalent source model for the wheels and track together with an SEA model of the region below the floor. The incident sound power on the sides is obtained by using a waveguide boundary element (2.5D BE) method. The procedure is applied to a Spanish metro train vehicle running in the open field for which rolling noise is the main external noise source. The procedure is verified by field measurements of sound pressure beneath the carriage, on the sidewalls and inside the vehicle. The sensitivity of the results to changes in interior absorption is also studied, including the effect of passengers.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-08-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/176434
url https://riunet.upv.es/handle/10251/176434
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv European Commission https://doi.org/10.13039/501100000780 H2020 777564 Innovative RUNning gear soluTiOns for new dependable, sustainable, intelligent and comfortable RAIL vehicles
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reserva de todos los derechos
http://rightsstatements.org/vocab/InC/1.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reserva de todos los derechos
http://rightsstatements.org/vocab/InC/1.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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