Data-Driven Sound Quality Optimization of Acoustic Devices

XU Lei; ZHANG Weisheng; ZHU Bao; GUO Xu

doi:10.21656/1000-0887.440339

Volume 45 Issue 3

Mar. 2024

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Article Navigation > Applied Mathematics and Mechanics > 2024 > 45(3): 253-260

XU Lei, ZHANG Weisheng, ZHU Bao, GUO Xu. Data-Driven Sound Quality Optimization of Acoustic Devices[J]. Applied Mathematics and Mechanics, 2024, 45(3): 253-260. doi: 10.21656/1000-0887.440339

Citation:

XU Lei, ZHANG Weisheng, ZHU Bao, GUO Xu. Data-Driven Sound Quality Optimization of Acoustic Devices[J]. Applied Mathematics and Mechanics, 2024, 45(3): 253-260. doi: 10.21656/1000-0887.440339

Citation:

XU Lei, ZHANG Weisheng, ZHU Bao, GUO Xu. Data-Driven Sound Quality Optimization of Acoustic Devices[J]. Applied Mathematics and Mechanics, 2024, 45(3): 253-260. doi: 10.21656/1000-0887.440339

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Data-Driven Sound Quality Optimization of Acoustic Devices

doi: 10.21656/1000-0887.440339

Department of Engineering Mechanics, Dalian University of Technology, Dalian, Liaoning 116024, P.R.China

Received Date: 2023-11-23
Rev Recd Date: 2023-12-24
Publish Date: 2024-03-01

Abstract

Abstract

Sound quality is an important measure of the sound performance of acoustic devices. However, the process of optimizing the sound quality requires a collaborative optimization of the responses at multiple frequency points, resulting in poor solvability of the optimization problem. A data-driven acoustic channel topology optimization design method was proposed to enable fast prediction of the acoustic frequency responses in the acoustic-structural system and then optimize the sound quality of acoustic devices with explicit topology optimization techniques. The non-linear relationship between structural geometry parameters, excitation frequencies and acoustic frequency responses was modelled with artificial neural networks. An artificial neural network model for acoustic frequency responses was developed by training a multilayer feedforward network with the structural geometrical parameters in the moving morphable components method and the excitation frequencies as input variables, and the acoustic pressure frequency responses as output variables. The obtained results can effectively reduce the range difference of the sound pressure level (SPL) in the target frequency band from 44.89 dB to 6.49 dB. Compared with the traditional optimization method, the solution speed is about 16.3 times as before, which shows that the current method is effective for the rapid solution of sound quality optimization problems.
- topology optimization,
- acoustic-structural system,
- artificial neural network,
- moving morphable components method,
- sound quality

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References(17)

References

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