数据驱动下的声学器件音质优化  

Data⁃Driven Sound Quality Optimization of Acoustic Devices

作　　者：许磊 张维声 朱宝 郭旭[1] XU Lei;ZHANG Weisheng;ZHU Bao;GUO Xu(Department of Engineering Mechanics,Dalian University of Technology,Dalian,Liaoning 116024,P.R.China)

机构地区：[1]大连理工大学工程力学系,辽宁大连116024

出　　处：《应用数学和力学》2024年第3期253-260,共8页Applied Mathematics and Mechanics

基　　金：国家自然科学基金(12272075)。

摘　　要：音质是声学器件声音表现的重要衡量标准.但音质的优化过程需要对大量频点的响应进行协同优化,造成优化问题的可求解性较差.该文提出了一种数据驱动下的声学通道拓扑优化设计方法,可实现声-结构系统中的声频响快速预测,进而借助显式拓扑优化技术实现声学器件的音质优化.通过人工神经网络对结构几何参数、激励频率与声频响之间的非线性关系进行建模,以可移动变形组件(moving morphable components,MMC)法中的结构几何参数、激励频率为输入变量,以声压频响作为输出变量,通过训练多层前馈网络建立了声频响的人工神经网络模型.所得结果可以有效地将目标频带内的声压级范围差从44.89 dB缩小至6.49 dB,相较于传统优化方法,求解速度约为之前的16.3倍,表明了当前方法对音质优化问题的快速求解具有明显效果.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 

分 类 号：O232[理学—运筹学与控制论]