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数据驱动下的声学器件音质优化

许磊 张维声 朱宝 郭旭

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. doi: 10.21656/1000-0887.440339
引用本文: 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. doi: 10.21656/1000-0887.440339
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

数据驱动下的声学器件音质优化

doi: 10.21656/1000-0887.440339
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

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.
  • 图  1  声学优化问题示意图

    Figure  1.  Schematic diagram of the acoustic optimization problem

    图  2  一个二维结构组件

    Figure  2.  A 2D structural component

    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  4  一个简化的二维声-结构耦合模型

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

    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  6  目标函数I1的迭代历史

    Figure  6.  The iteration history of objective function I1

    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])

      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  9  最终优化构型

    Figure  9.  The final optimized design

    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
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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

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