基于人工神经网络的共振吸声超材料声学性能快速预测及结构优化设计

高兆瑞; 李铮; 姜永烽; 沈承; 孟晗

doi:10.21656/1000-0887.450170

基于人工神经网络的共振吸声超材料声学性能快速预测及结构优化设计

doi: 10.21656/1000-0887.450170

高兆瑞^1,2,
李铮^1,2,
姜永烽^1,2,
沈承^1,2,
孟晗^1,2, ,

1. 南京航空航天大学航空航天结构力学及控制全国重点实验室，南京 210016;
2. 南京航空航天大学多功能轻量化材料与结构工信部重点实验室，南京 210016

基金项目:

国家自然科学基金（12202183；12202188；52361165626）

国家重点研发计划（2023YFB4604800）

详细信息

作者简介:
高兆瑞（2000—），男，硕士生(E-mail: gaozr123@nuaa.edu.cn);沈承（1986—），男，副教授，博士，硕士生导师(通讯作者. E-mail: cshen@nuaa.edu.cn);孟晗（1989—），女，教授，博士，博士生导师(通讯作者. E-mail: menghan@nuaa.edu.cn).

通讯作者:
孟晗（1989—），女，教授，博士，博士生导师(通讯作者. E-mail: menghan@nuaa.edu.cn).

中图分类号: TB535|TP183
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出版历程
- 收稿日期: 2024-06-11
- 修回日期: 2024-06-11
- 网络出版日期: 2024-09-06

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

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)

摘要

摘要: 针对共振吸声超材料声学性能快速预测及结构优化设计需求，提出了一种基于人工神经网络的共振吸声超材料性能预测方法.首先,建立了由微穿孔板和Helmholtz共振腔组成的多层穿孔型共振吸声超材料的理论模型，并通过仿真与实验验证其正确性；随后，通过理论模型生成数据集，并以此为基础，采用BP(back propagation)神经网络原理，搭建了结构特征参量与声学性能的人工神经网络模型；之后，将训练后的人工神经网络模型与遗传算法相结合，对共振吸声超材料进行声学性能最优化设计.结果表明：训练后的人工神经网络模型可以对目标结构的吸声性能进行准确预测，并且预测效率相较理论模型提高50%以上；人工神经网络模型与优化算法的结合不仅能提高优化效率，优化后的结构也具有良好的低频宽带吸声性能.人工神经网络为大规模结构性能预测计算提供了便利，在超材料等结构设计及优化领域具有广阔的应用前景.
- 共振吸声超材料 /
- 人工神经网络 /
- 吸声系数 /
- BP神经网络 /
- 遗传算法
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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参考文献(27)

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基于人工神经网络的共振吸声超材料声学性能快速预测及结构优化设计

doi: 10.21656/1000-0887.450170

作者简介:
高兆瑞（2000—），男，硕士生(E-mail: gaozr123@nuaa.edu.cn);沈承（1986—），男，副教授，博士，硕士生导师(通讯作者. E-mail: cshen@nuaa.edu.cn);孟晗（1989—），女，教授，博士，博士生导师(通讯作者. E-mail: menghan@nuaa.edu.cn).

通讯作者:
孟晗（1989—），女，教授，博士，博士生导师(通讯作者. E-mail: menghan@nuaa.edu.cn).

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

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留言板

基于人工神经网络的共振吸声超材料声学性能快速预测及结构优化设计

doi: 10.21656/1000-0887.450170

作者简介: 高兆瑞（2000—），男，硕士生(E-mail: gaozr123@nuaa.edu.cn);沈承（1986—），男，副教授，博士，硕士生导师(通讯作者. E-mail: cshen@nuaa.edu.cn);孟晗（1989—），女，教授，博士，博士生导师(通讯作者. E-mail: menghan@nuaa.edu.cn).

通讯作者: 孟晗（1989—），女，教授，博士，博士生导师(通讯作者. E-mail: menghan@nuaa.edu.cn).

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出版历程

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

计量

出版历程

目录

作者简介:
高兆瑞（2000—），男，硕士生(E-mail: gaozr123@nuaa.edu.cn);沈承（1986—），男，副教授，博士，硕士生导师(通讯作者. E-mail: cshen@nuaa.edu.cn);孟晗（1989—），女，教授，博士，博士生导师(通讯作者. E-mail: menghan@nuaa.edu.cn).

通讯作者:
孟晗（1989—），女，教授，博士，博士生导师(通讯作者. E-mail: menghan@nuaa.edu.cn).