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

Wael A Altabey^1,2, Mohammad Noori^3,4, Zhishen Wu¹

¹International Institute for Urban Systems Engineering (IIUSE), Southeast University, Nanjing 210096, China.

Materials (Basel, Switzerland)

|November 26, 2022

Summary

This study introduces a deep learning system using recurrent neural networks (RNN-LSTM) and convolutional neural networks (CNN) to predict the acoustic performance of basalt fiber reinforced polymer (BFRP) mufflers, significantly reducing design time.

Keywords:

BFRP acoustic characteristics deep learning dual-chamber muffler laminated composite

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Area of Science:

Acoustics
Materials Science
Computational Engineering

Background:

Investigating muffler materials' acoustic behavior requires extensive, time-consuming calculations, especially for advanced composites.
Traditional methods pose challenges for optimizing muffler design with novel materials like basalt fiber reinforced polymer (BFRP).

Purpose of the Study:

To develop a deep learning-based monitoring system for predicting the acoustic behavior of BFRP composite mufflers.
To reduce the time and effort required for muffler material selection and design optimization.

Main Methods:

Developed two deep neural network (DNN) architectures: recurrent neural network with long short-term memory (RNN-LSTM) and convolutional neural network (CNN) in Python.
Created a dual-chamber laminated composite muffler (DCLCM) model in MATLAB to generate acoustic datasets (e.g., transmission loss, power transmission coefficient).
Optimized model training parameters using Bayesian genetic algorithms (BGA) and validated results against experimental data.

Main Results:

Both RNN-LSTM and CNN models achieved over 90% accuracy on test and validation datasets.
The proposed deep learning approach significantly reduced the time and effort for muffler material selection and design.
Validated the accuracy and reliability of the predictive technique by comparing with existing experimental results.

Conclusions:

The developed indirect dual-chamber muffler concept using deep learning is an efficient method for acoustic performance prediction.
This approach accelerates the selection of muffler materials and optimal designs, benefiting industrial applications.
Future muffler designs may integrate deep learning algorithms for enhanced performance and efficiency.