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中文核心期刊

人工神经网络辅助的斐波那契式微穿孔板宽频吸声体结构优化

Structural optimization of Fibonacci micro-perforated plate broadband absorber by artificial neural networks

  • 摘要: 针对微穿孔板的低频宽带吸声问题, 提出了一种耦合微穿孔板和斐波那契螺旋型腔体的结构, 推导得到了该结构表面声阻抗的表达式, 并通过有限元仿真验证了模型的可靠性。在结构设计中, 开发了一种预测结构参数与吸声系数之间映射关系的神经网络模型, 其均方误差低于1.5 × 10−4。此外, 还提出了一种基于串联神经网络的吸声体逆向设计方法, 该方法的优化时间达到毫秒级, 显著提升了设计效率。结果表明, 结构厚度为49.65 mm的宽频吸声体在500~1500 Hz内达到了0.95的平均吸声系数。最后通过实验验证了该方法的可行性。

     

    Abstract: In response to the low-frequency broadband sound absorption problem of micro-perforated plates, a structure coupling a micro-perforated plate with a Fibonacci spiral cavity is proposed. The expression for the surface acoustic impedance of this structure is derived, and the reliability of the model is verified through finite element simulation. In structural design, a neural network model has been developed to predict the mapping relationship between structural parameters and sound absorption coefficients, with a mean squared error of less than 1.5 × 10−4. Additionally, an inverse design method for sound absorbers based on a tandem neural network has been proposed. The optimization time of this method reaches the millisecond level, significantly improving the design efficiency. The results indicate that the broadband sound absorber with structural thickness of 49.65 mm achieves an average sound absorption coefficient of 0.95 within the frequency range of 500−1500 Hz. Finally, the feasibility of this method has been verified through experiment.

     

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