数据驱动下的声学器件音质优化

许磊; 张维声; 朱宝; 郭旭

doi:10.21656/1000-0887.440339

数据驱动下的声学器件音质优化

doi: 10.21656/1000-0887.440339

大连理工大学工程力学系，辽宁大连 116024

基金项目:

国家自然科学基金 12272075

详细信息

作者简介:
许磊(1995—)，男，博士生(E-mail: xulei1234@mail.dlut.edu.cn)

通讯作者:
张维声(1982—)，男，教授，博士，博士生导师(通讯作者. E-mail: weishengzhang@dlut.edu.cn)

中图分类号: O232
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- 被引次数: 0
出版历程
- 收稿日期: 2023-11-23
- 修回日期: 2023-12-24
- 刊出日期: 2024-03-01

Data-Driven Sound Quality Optimization of Acoustic Devices

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

摘要

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.
- 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
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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图 1 声学优化问题示意图

Figure 1. Schematic diagram of the acoustic optimization problem

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图 2 一个二维结构组件

Figure 2. A 2D structural component

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图 3 利用BP神经网络进行音质优化流程

Figure 3. The flow chart for the sound quality optimization with the BP neural network

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图 4 一个简化的二维声-结构耦合模型

Figure 4. A simple 2D acoustic-structural coupled model

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图 5 组件初始布置和最终优化设计

Figure 5. The initial design of components and the final optimized design

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图 6 目标函数I₁的迭代历史

Figure 6. The iteration history of objective function I₁

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图 7 f=7 000 Hz下二维结构的声压级分布

Figure 7. The distribution of SPL (dB) of the 2D structure at f=7 000 Hz

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图 8 纯空气设计和最优设计的声压级曲线([6 000 Hz, 10 000 Hz])

注为了解释图中的颜色，读者可以参考本文的电子网页版本，后同.

Figure 8. SPL curves of pure air design and the optimized design ([6 000 Hz, 10 000 Hz])

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图 9 最终优化构型

Figure 9. The final optimized design

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图 10 目标函数和约束函数的迭代历史

Figure 10. The iterative histories of the objective function and the constraint function

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图 11 纯空气设计和最优设计的声压级曲线([3 000 Hz, 4 000 Hz])

Figure 11. SPL curves of the pure air design and the optimized design ([3 000 Hz, 4 000 Hz])

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表 1 3种材料的参数

Table 1. The 3 materials' parameters

material parameter	Young’s modulus E/GPa	Poisson’s ratio ν	density ρ/(kg/m³)	bulk modulus K/GPa
acrylic plastic	3.2	0.35	1 190	-
air	-	-	1.2	1.41×10^-4
aluminum	-	-	2 650	68.9

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参考文献(17)

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