Parameter identification of sound absorption model of porous materials based on modified particle swarm optimization algorithm.

Porous materials have been widely used in the field of noise control. The non-acoustical parameters involved in the sound absorption model have an important effect on the sound absorption performance of porous materials. How to identify these non-acoustical parameters efficiently and accurately is a...

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Autores principales:	Xiaomei Xu, Ping Lin
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d79fa0995f604dc5b2681ae947138672
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spelling	oai:doaj.org-article:d79fa0995f604dc5b2681ae9471386722021-11-25T06:19:23ZParameter identification of sound absorption model of porous materials based on modified particle swarm optimization algorithm.1932-620310.1371/journal.pone.0250950https://doaj.org/article/d79fa0995f604dc5b2681ae9471386722021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0250950https://doaj.org/toc/1932-6203Porous materials have been widely used in the field of noise control. The non-acoustical parameters involved in the sound absorption model have an important effect on the sound absorption performance of porous materials. How to identify these non-acoustical parameters efficiently and accurately is an active research area and many researchers have devoted contributions on it. In this study, a modified particle swarm optimization algorithm is adopted to identify the non-acoustical parameters of the jute fiber felt. Firstly, the sound absorption model used to predict the sound absorption coefficient of the porous materials is introduced. Secondly, the model of non-acoustical parameter identification of porous materials is established. Then the modified particle swarm optimization algorithm is introduced and the feasibility of the algorithm applied to the parameter identification of porous materials is investigated. Finally, based on the sound absorption coefficient measured by the impedance tube the modified particle swarm optimization algorithm is adopted to identify the non-acoustical parameters involved in the sound absorption model of the jute fiber felt, and the identification performance and the computational performance of the algorithm are discussed. Research results show that compared with other identification methods the modified particle swarm optimization algorithm has higher identification accuracy and is more suitable for the identification of non-acoustical parameters of the porous materials. The sound absorption coefficient curve predicted by the modified particle swarm optimization algorithm has good consistency with the experimental curve. In the aspect of computer running time, compared with the standard particle swarm optimization algorithm, the modified particle swarm optimization algorithm takes shorter running time. When the population size is larger, modified particle swarm optimization algorithm has more advantages in the running speed. In addition, this study demonstrates that the jute fiber felt is a good acoustical green fibrous material which has excellent sound absorbing performance in a wide frequency range and the peak value of its sound absorption coefficient can reach 0.8.Xiaomei XuPing LinPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 5, p e0250950 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Xiaomei Xu Ping Lin Parameter identification of sound absorption model of porous materials based on modified particle swarm optimization algorithm.
description	Porous materials have been widely used in the field of noise control. The non-acoustical parameters involved in the sound absorption model have an important effect on the sound absorption performance of porous materials. How to identify these non-acoustical parameters efficiently and accurately is an active research area and many researchers have devoted contributions on it. In this study, a modified particle swarm optimization algorithm is adopted to identify the non-acoustical parameters of the jute fiber felt. Firstly, the sound absorption model used to predict the sound absorption coefficient of the porous materials is introduced. Secondly, the model of non-acoustical parameter identification of porous materials is established. Then the modified particle swarm optimization algorithm is introduced and the feasibility of the algorithm applied to the parameter identification of porous materials is investigated. Finally, based on the sound absorption coefficient measured by the impedance tube the modified particle swarm optimization algorithm is adopted to identify the non-acoustical parameters involved in the sound absorption model of the jute fiber felt, and the identification performance and the computational performance of the algorithm are discussed. Research results show that compared with other identification methods the modified particle swarm optimization algorithm has higher identification accuracy and is more suitable for the identification of non-acoustical parameters of the porous materials. The sound absorption coefficient curve predicted by the modified particle swarm optimization algorithm has good consistency with the experimental curve. In the aspect of computer running time, compared with the standard particle swarm optimization algorithm, the modified particle swarm optimization algorithm takes shorter running time. When the population size is larger, modified particle swarm optimization algorithm has more advantages in the running speed. In addition, this study demonstrates that the jute fiber felt is a good acoustical green fibrous material which has excellent sound absorbing performance in a wide frequency range and the peak value of its sound absorption coefficient can reach 0.8.
format	article
author	Xiaomei Xu Ping Lin
author_facet	Xiaomei Xu Ping Lin
author_sort	Xiaomei Xu
title	Parameter identification of sound absorption model of porous materials based on modified particle swarm optimization algorithm.
title_short	Parameter identification of sound absorption model of porous materials based on modified particle swarm optimization algorithm.
title_full	Parameter identification of sound absorption model of porous materials based on modified particle swarm optimization algorithm.
title_fullStr	Parameter identification of sound absorption model of porous materials based on modified particle swarm optimization algorithm.
title_full_unstemmed	Parameter identification of sound absorption model of porous materials based on modified particle swarm optimization algorithm.
title_sort	parameter identification of sound absorption model of porous materials based on modified particle swarm optimization algorithm.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/d79fa0995f604dc5b2681ae947138672
work_keys_str_mv	AT xiaomeixu parameteridentificationofsoundabsorptionmodelofporousmaterialsbasedonmodifiedparticleswarmoptimizationalgorithm AT pinglin parameteridentificationofsoundabsorptionmodelofporousmaterialsbasedonmodifiedparticleswarmoptimizationalgorithm
_version_	1718413895116259328

Parameter identification of sound absorption model of porous materials based on modified particle swarm optimization algorithm.

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