Model-Based and Data-Driven Global Optimization of Rainbow-Trapping Mufflers

This article belongs to the Section Environmental Technology.

Detalles Bibliográficos
Autores: Maury, Cédric, Bravo, Teresa, Mazzoni, Daniel, Amielh, Muriel, Reinoso, Antonio J.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/399008
Acceso en línea:http://hdl.handle.net/10261/399008
Access Level:acceso abierto
Palabra clave:Data-driven training
Model-based optimization
Cost-efficient muffler optimization
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spelling Model-Based and Data-Driven Global Optimization of Rainbow-Trapping MufflersMaury, CédricBravo, TeresaMazzoni, DanielAmielh, MurielReinoso, Antonio J.Data-driven trainingModel-based optimizationCost-efficient muffler optimizationThis article belongs to the Section Environmental Technology.Compared to rigidly-backed absorbers, the selection of appropriate optimization techniques for the optimal design of broadband acoustic mufflers remains under-investigated. This study determines the most effective optimization strategy for maximizing the total dissipation of rainbow-trapping silencers (RTSs), composed of graded side-branch cavities that enable broadband dissipation of sound through visco-thermal effects. Model-based and data-driven optimization strategies are compared, particularly in high-dimensional design spaces with flat cost function landscapes where gradient-based approaches are inadequate. It is found that model-based particle swarm optimization (PSO) outperforms simulated annealing, genetic algorithm, and surrogate method in maximizing RTS total dissipation, especially in high-dimensional designs. PSO uniquely handles flat or valleyed cost landscapes through efficient exploration–exploitation trade-offs. Data-driven approaches using Bayesian regularization neural networks (BRNNs) drastically reduce computational cost in high-dimensional spaces, though they require large datasets to avoid over-smoothing. In low dimensions, direct optimization on BRNN outputs suffices, making global search unnecessary. Both model-based and BRNN methods show robustness to input errors, but data-driven approaches handle output noise better. These findings, validated using transfer matrix models, offer strategic guidance for selecting optimization methods, especially when using computationally expensive visco-thermal finite element simulations.This work is part of the project PID2022-139414OB-I00 funded by MCIN/AEI/10.13039/501100011033/ and by “ERDF A way of making Europe”. It has also received support from the French government under the France 2030 investment plan, as part of the Initiative d’Excellence d’Aix-Marseille Université—A*MIDEX (AMX-22-RE-AB-157).Peer reviewedMultidisciplinary Digital Publishing InstituteAgencia Estatal de Investigación (España)Ministerio de Ciencia, Innovación y Universidades (España)European CommissionAix-Marseille UniversitéGouvernement de la République françaiseBravo, Teresa [0000-0003-2207-6383]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]2025202520252025info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/399008reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-139414OB-I00The underlying dataset has been published as supplementary material of the article in the publisher platform at DOI https://doi.org/10.3390/technologies13080356https://doi.org/10.3390/technologies13080356Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3990082026-05-22T06:33:51Z
dc.title.none.fl_str_mv Model-Based and Data-Driven Global Optimization of Rainbow-Trapping Mufflers
title Model-Based and Data-Driven Global Optimization of Rainbow-Trapping Mufflers
spellingShingle Model-Based and Data-Driven Global Optimization of Rainbow-Trapping Mufflers
Maury, Cédric
Data-driven training
Model-based optimization
Cost-efficient muffler optimization
title_short Model-Based and Data-Driven Global Optimization of Rainbow-Trapping Mufflers
title_full Model-Based and Data-Driven Global Optimization of Rainbow-Trapping Mufflers
title_fullStr Model-Based and Data-Driven Global Optimization of Rainbow-Trapping Mufflers
title_full_unstemmed Model-Based and Data-Driven Global Optimization of Rainbow-Trapping Mufflers
title_sort Model-Based and Data-Driven Global Optimization of Rainbow-Trapping Mufflers
dc.creator.none.fl_str_mv Maury, Cédric
Bravo, Teresa
Mazzoni, Daniel
Amielh, Muriel
Reinoso, Antonio J.
author Maury, Cédric
author_facet Maury, Cédric
Bravo, Teresa
Mazzoni, Daniel
Amielh, Muriel
Reinoso, Antonio J.
author_role author
author2 Bravo, Teresa
Mazzoni, Daniel
Amielh, Muriel
Reinoso, Antonio J.
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Agencia Estatal de Investigación (España)
Ministerio de Ciencia, Innovación y Universidades (España)
European Commission
Aix-Marseille Université
Gouvernement de la République française
Bravo, Teresa [0000-0003-2207-6383]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Data-driven training
Model-based optimization
Cost-efficient muffler optimization
topic Data-driven training
Model-based optimization
Cost-efficient muffler optimization
description This article belongs to the Section Environmental Technology.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/399008
url http://hdl.handle.net/10261/399008
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-139414OB-I00
The underlying dataset has been published as supplementary material of the article in the publisher platform at DOI https://doi.org/10.3390/technologies13080356
https://doi.org/10.3390/technologies13080356
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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