多孔吸声材料的吸声性能预测及吸声模型研究进展

doi:10.11896/cldb.21030180

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Abstract Porous materials have been used as sound-absorbing materials to reduce the impact of noise pollution on people's lives, work and studies, which raises a major research concern about how to design porous sound-absorbing materials with better sound absorption performance. Simulation technology can theoretically determine the structural parameters of porous sound-absorbing materials with optimal performance, effectively providing a theoretical foundation for designing porous sound-absorbing materials with excellent performance while reducing unnecessary experimental processes.
Due to the variety of simulation techniques available, it is crucial to choose the appropriate one for predicting the sound absorption coefficient of porous acoustic materials. Recently, various simulation techniques have been used for predicting the sound absorption coefficient of porous acoustic materials, but no systematic evaluation of the benefits and drawbacks of each simulation technique's prediction effect exists.
In this review, four nonlinear neural networks to predict the sound absorption coefficient of porous acoustic materials are discussed. The radial basis function neural network uses fewer samples and offers a higher prediction accuracy in terms of the prediction process and results. Further, an optimization algorithm can theoretically solve the problem of other neural networks requiring several samples; the optimization algorithm is used to process a small set of samples to obtain the best training and test samples before building the neural network model and then substituting them into the neural network model for simulation prediction. What's more, three classical theoretical models of empirical formulations for estimating the absorption coefficient of porous acoustic materials are discussed, among which the Johnson-Champoux-Allard model exhibits little error. Although the Johnson-Champoux-Allard model is a five-parameter model empirical formula, several factors (more than five) influence the absorption coefficient of porous sound-absorbing materials. Dimensional analysis, one of the important methods for establishing mathematical models, can transform complex mathematical physical problems into accurate mathematical model formulas using quantiles. Therefore, an equation relating all the influencing factors and the absorption coefficient of porous sound-absorbing materials can be established using the method of dimensional analysis, which reduces the absorption coefficient error calculated from the model equation. Two- and three-dimensional equivalent models are proposed to simulate the properties of porous acoustic materials. Although the modeling process is complicated, the three-dimensional model can simulate the properties of the material more intuitively and accurately than the two-dimensional model, and it can provide the basis for subsequent production and preparation with the three-dimensional modeling data of the material.
In this review, the classification of sound-absorbing materials and their principles, as well as nine simulation techniques in three methods, are briefly introduced. The technique with the best performance in each method is presented to provide a reference for simulation-based absorption coefficient prediction of porous sound-absorbing materials.

Key words： porous sound-absorbing material nonlinear neural network equivalent model empirical formula sound absorption property

Published: Online: 2022-12-09

CLC number:	TB34
	TB535

Fund:Key Laboratory Project of Shaanxi Provincial Department of Education(Z20200151), Youth Project of National Natural Science Foundation of China(51404187).

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	Articles by authors
	LIANG Lisi
	GUO Wenlong
	MA Hongyue
	MI Han
	ZHANG Yu
	MI Jiayu
	LI Linbo

Cite this article:

LIANG Lisi,GUO Wenlong,MA Hongyue, et al. Research Progress of Sound Absorption Performance Prediction and Sound Absorption Model of Porous Sound-absorbing Materials[J]. Materials Reports, 2022, 36(23): 21030180-8.

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http://www.mater-rep.com/EN/10.11896/cldb.21030180 OR http://www.mater-rep.com/EN/Y2022/V36/I23/21030180

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