Data-Driven Sound Quality Optimization of Acoustic Devices
-
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.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.
-
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
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
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
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])
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
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])
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 
下载: 导出CSV
-
[1] BENDSOE M P, SIGMUND O. Topology Optimization: Theory, Methods, and Applications[M]. Springer Berlin Heidelberg, 2003. [2] DILGEN C B, DILGEN S B, AAGE N, et al. Topology optimization of acoustic mechanical interaction problems: a comparative review[J]. Structural and Multidisciplinary Optimization, 2019, 60(2): 779-801. doi: 10.1007/s00158-019-02236-4 [3] DESAI J, FAURE A, MICHAILIDIS G, et al. Topology optimization in acoustics and elasto-acoustics via alevel-set method[J]. Journal of Sound and Vibration, 2018, 420: 73-103. doi: 10.1016/j.jsv.2018.01.032 [4] YOON G H, JENSEN J S, SIGMUND O. Topology optimization of acoustic-structure interaction problems using a mixed finite element formulation[J]. International Journal for Numerical Methods in Engineering, 2007, 70(9): 1049-1075. doi: 10.1002/nme.1900 [5] DU J B, OLHOFF N. Topological design of vibrating structures with respect to optimum sound pressure characteristics in a surrounding acoustic medium[J]. Structural and Multidisciplinary Optimization, 2010, 42: 43-54. doi: 10.1007/s00158-009-0477-y [6] LEE J, WANG S Y, DIKEC A. Topology optimization for the radiation and scattering of sound from thin-body using genetic algorithms[J]. Journal of Sound and Vibration, 2004, 276(3/5): 899-918. [7] HU J, YAO S, HUANG X D. Topology optimization of dynamic acoustic-mechanical structures using the ersatz material model[J]. Computer Methods in Applied Mechanics and Engineering, 2020, 372: 113387. doi: 10.1016/j.cma.2020.113387 [8] SHU L, WANG M Y, MA Z D. Level set based topology optimization of vibrating structures for coupled acoustic-structural dynamics[J]. Computers & Structures, 2014, 132: 34-42. [9] DVHRING M B, JENSEN J S, SIGMUND O. Acoustic design by topology optimization[J]. Journal of Sound and Vibration, 2008, 317(3/5): 557-575. [10] NIU B, OLHOFF N, LUND E, et al. Discrete material optimization of vibrating laminated composite plates for minimum sound radiation[J]. International Journal of Solids and Structures, 2010, 47(16): 2097-2114. doi: 10.1016/j.ijsolstr.2010.04.008 [11] GUO X, ZHANG W S, ZHONG W L. Doing topology optimization explicitly and geometrically: a new moving morphable components based framework[J]. Journal of Applied Mechanics, 2014, 81: 081009. doi: 10.1115/1.4027609 [12] XIA B Z, YU D J, LIU J. Hybrid uncertain analysis for structural-acoustic problem with random and interval parameters[J]. Journal of Sound and Vibration, 2013, 332(11): 2701-2720. doi: 10.1016/j.jsv.2012.12.028 [13] CHEN N, YU D J, XIA B Z, et al. Microstructural topology optimization of structural-acoustic coupled systems for minimizing sound pressure level[J]. Structural and Multidisciplinary Optimization, 2017, 56: 1259-1270. doi: 10.1007/s00158-017-1718-0 [14] ZHANG W S, YUAN J, ZHANG J, et al. A new topology optimization approach based on moving morphable components (MMC) and the ersatz material model[J]. Structural and Multidisciplinary Optimization, 2016, 53: 1243-1260. doi: 10.1007/s00158-015-1372-3 [15] 王沐晨, 李立州, 张珺, 等. 基于卷积神经网络气动力降阶模型的翼型优化方法[J]. 应用数学和力学, 2022, 43(1): 77-83. doi: 10.21656/1000-0887.420137WANG Muchen, LI Lizhou, ZHANG Jun, et al. An airfoil optimization method based on the convolutional neural network aerodynamic reduced order model[J]. Applied Mathematics and Mechanics, 2022, 43(1): 77-83. (in Chinese) doi: 10.21656/1000-0887.420137 [16] 姚明辉, 王兴志, 吴启亮, 等. 基于RBF神经网络的压气机叶片面压力场预测研究[J]. 应用数学和力学, 2023, 44(10): 1187-1199. doi: 10.21656/1000-0887.440054YAO Minghui, WANG Xingzhi, WU Qiliang, et al. RBF neural network based prediction on blade surface pressure fields in compressors[J]. Applied Mathematics and Mechanics, 2023, 44(10): 1187-1199. (in Chinese) doi: 10.21656/1000-0887.440054 [17] 王青山, 严波, 陈岩, 等. 基于降阶模型和数据驱动的动态结构数字孪生方法[J]. 应用数学和力学, 2023, 44(7): 757-768. doi: 10.21656/1000-0887.430384WANG Qingshan, YAN Bo, CHEN Yan, et al. Digital twin method for dynamic structures based on reduced order models and data driving[J]. Applied Mathematics and Mechanics, 2023, 44(7): 757-768. (in Chinese) doi: 10.21656/1000-0887.430384 -
点击查看大图
计量
- 文章访问数: 98
- HTML全文浏览量: 27
- PDF下载量: 49
- 被引次数: 0
