Acoustic Performance Rapid Prediction and Structural Optimization for Resonant SoundAbsorbing Metamaterials Based on Artificial Neural Networks

GAO Zhaorui; LI Zheng; JIANG Yongfeng; SHEN Cheng; MENG Han

doi:10.21656/1000-0887.450170

Volume 45 Issue 8

Aug. 2024

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Article Navigation > Applied Mathematics and Mechanics > 2024 > 45(8): 1058-1069

GAO Zhaorui, LI Zheng, JIANG Yongfeng, SHEN Cheng, MENG Han. Acoustic Performance Rapid Prediction and Structural Optimization for Resonant SoundAbsorbing Metamaterials Based on Artificial Neural Networks[J]. Applied Mathematics and Mechanics, 2024, 45(8): 1058-1069. doi: 10.21656/1000-0887.450170

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Acoustic Performance Rapid Prediction and Structural Optimization for Resonant SoundAbsorbing Metamaterials Based on Artificial Neural Networks

doi: 10.21656/1000-0887.450170

GAO Zhaorui^1,2,
LI Zheng^1,2,
JIANG Yongfeng^1,2,
SHEN Cheng^1,2,
MENG Han^{1,2
,
,}

1. State Key Laboratory of Mechanics and Control for Aerospace Structures, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, P.R.China;
2. MIIT Key Laboratory of Multifunctional Lightweight Materials and Structures, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, P.R.China

Funds:

The National Science Foundation of China(（12202183；12202188；52361165626)

Received Date: 2024-06-11
Rev Recd Date: 2024-06-11

Available Online: 2024-09-06

Abstract

Abstract

A sound performance prediction method based on the artificial neural network (ANN) was proposed to meet the requirements of rapid prediction and optimization design of resonant sound-absorbing metamaterials. Firstly, a theoretical model was established for multilayer perforated resonant sound-absorbing metamaterials (MPRSMs) composed of microperforated panels and Helmholtz resonators, which was then verified through simulation and experiments; subsequently, a dataset was generated with the theoretical model, and in turn an ANN model was constructed by means of the back propagation (BP) neural network to build the mapping relationship between structural parameters and acoustic performances; afterwards, the trained ANN model was combined with the genetic algorithm to optimize the acoustic performance of the MPRSMs. The results show that, the trained ANN model can accurately predict the sound absorption performance of the MPRSMs, and the prediction efficiency improves by more than 50% compared to the theoretical model; the combination of the ANN model and the optimization algorithm can not only improve the optimization efficiency, but bring good low-frequency broadband sound absorption performance of the optimized structure. The ANN provides convenience for large-scale structural performance prediction calculations and has broad application prospects in structural design and optimization of metamaterials.
- resonant sound-absorbing metamaterial,
- artificial neural network,
- sound absorption coefficient,
- BP neural network,
- genetic algorithm

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References(27)

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