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Studying Acoustic Behavior of BFRP Laminated Composite in Dual-Chamber Muffler Application Using Deep Learning Algorithm

Over the last two decades, several experimental and numerical studies have been performed in order to investigate the acoustic behavior of different muffler materials. However, there is a problem in which it is necessary to perform large, important, time-consuming calculations particularly if the mu...

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Autores principales: Altabey, Wael A., Noori, Mohammad, Wu, Zhishen, Al-Moghazy, Mohamed A., Kouritem, Sallam A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9696476/
https://www.ncbi.nlm.nih.gov/pubmed/36431563
http://dx.doi.org/10.3390/ma15228071
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author Altabey, Wael A.
Noori, Mohammad
Wu, Zhishen
Al-Moghazy, Mohamed A.
Kouritem, Sallam A.
author_facet Altabey, Wael A.
Noori, Mohammad
Wu, Zhishen
Al-Moghazy, Mohamed A.
Kouritem, Sallam A.
author_sort Altabey, Wael A.
collection PubMed
description Over the last two decades, several experimental and numerical studies have been performed in order to investigate the acoustic behavior of different muffler materials. However, there is a problem in which it is necessary to perform large, important, time-consuming calculations particularly if the muffler was made from advanced materials such as composite materials. Therefore, this work focused on developing the concept of the indirect dual-chamber muffler made from a basalt fiber reinforced polymer (BFRP) laminated composite, which is a monitoring system that uses a deep learning algorithm to predict the acoustic behavior of the muffler material in order to save effort and time on muffler design optimization. Two types of deep neural networks (DNNs) architectures are developed in Python. The first DNN is called a recurrent neural network with long short-term memory blocks (RNN-LSTM), where the other is called a convolutional neural network (CNN). First, a dual-chamber laminated composite muffler (DCLCM) model is developed in MATLAB to provide the acoustic behavior datasets of mufflers such as acoustic transmission loss (TL) and the power transmission coefficient (PTC). The model training parameters are optimized by using Bayesian genetic algorithms (BGA) optimization. The acoustic results from the proposed method are compared with available experimental results in literature, thus validating the accuracy and reliability of the proposed technique. The results indicate that the present approach is efficient and significantly reduced the time and effort to select the muffler material and optimal design, where both models CNN and RNN-LSTM achieved accuracy above 90% on the test and validation dataset. This work will reinforce the mufflers’ industrials, and its design may one day be equipped with deep learning based algorithms.
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spelling pubmed-96964762022-11-26 Studying Acoustic Behavior of BFRP Laminated Composite in Dual-Chamber Muffler Application Using Deep Learning Algorithm Altabey, Wael A. Noori, Mohammad Wu, Zhishen Al-Moghazy, Mohamed A. Kouritem, Sallam A. Materials (Basel) Article Over the last two decades, several experimental and numerical studies have been performed in order to investigate the acoustic behavior of different muffler materials. However, there is a problem in which it is necessary to perform large, important, time-consuming calculations particularly if the muffler was made from advanced materials such as composite materials. Therefore, this work focused on developing the concept of the indirect dual-chamber muffler made from a basalt fiber reinforced polymer (BFRP) laminated composite, which is a monitoring system that uses a deep learning algorithm to predict the acoustic behavior of the muffler material in order to save effort and time on muffler design optimization. Two types of deep neural networks (DNNs) architectures are developed in Python. The first DNN is called a recurrent neural network with long short-term memory blocks (RNN-LSTM), where the other is called a convolutional neural network (CNN). First, a dual-chamber laminated composite muffler (DCLCM) model is developed in MATLAB to provide the acoustic behavior datasets of mufflers such as acoustic transmission loss (TL) and the power transmission coefficient (PTC). The model training parameters are optimized by using Bayesian genetic algorithms (BGA) optimization. The acoustic results from the proposed method are compared with available experimental results in literature, thus validating the accuracy and reliability of the proposed technique. The results indicate that the present approach is efficient and significantly reduced the time and effort to select the muffler material and optimal design, where both models CNN and RNN-LSTM achieved accuracy above 90% on the test and validation dataset. This work will reinforce the mufflers’ industrials, and its design may one day be equipped with deep learning based algorithms. MDPI 2022-11-15 /pmc/articles/PMC9696476/ /pubmed/36431563 http://dx.doi.org/10.3390/ma15228071 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Altabey, Wael A.
Noori, Mohammad
Wu, Zhishen
Al-Moghazy, Mohamed A.
Kouritem, Sallam A.
Studying Acoustic Behavior of BFRP Laminated Composite in Dual-Chamber Muffler Application Using Deep Learning Algorithm
title Studying Acoustic Behavior of BFRP Laminated Composite in Dual-Chamber Muffler Application Using Deep Learning Algorithm
title_full Studying Acoustic Behavior of BFRP Laminated Composite in Dual-Chamber Muffler Application Using Deep Learning Algorithm
title_fullStr Studying Acoustic Behavior of BFRP Laminated Composite in Dual-Chamber Muffler Application Using Deep Learning Algorithm
title_full_unstemmed Studying Acoustic Behavior of BFRP Laminated Composite in Dual-Chamber Muffler Application Using Deep Learning Algorithm
title_short Studying Acoustic Behavior of BFRP Laminated Composite in Dual-Chamber Muffler Application Using Deep Learning Algorithm
title_sort studying acoustic behavior of bfrp laminated composite in dual-chamber muffler application using deep learning algorithm
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9696476/
https://www.ncbi.nlm.nih.gov/pubmed/36431563
http://dx.doi.org/10.3390/ma15228071
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