应用数学和力学2024，Vol.45Issue(3)：253-260,8.DOI:10.21656/1000-0887.440339
数据驱动下的声学器件音质优化
Data-Driven Sound Quality Optimization of Acoustic Devices
摘要
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 fre-quency 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 a-coustic-structural system and then optimize the sound quality of acoustic devices with explicit topology optimi-zation 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 ef-fectively 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 be-fore,which shows that the current method is effective for the rapid solution of sound quality optimization prob-lems.关键词
拓扑优化/声-结构系统/人工神经网络/可移动变形组件法/音质Key words
topology optimization/acoustic-structural system/artificial neural network/moving morphable components method/sound quality分类
数学引用本文复制引用
许磊,张维声,朱宝,郭旭..数据驱动下的声学器件音质优化[J].应用数学和力学,2024,45(3):253-260,8.基金项目
国家自然科学基金(12272075) （12272075）
