Porous fibrous materials have been widely used as acoustic treatments for noise attenuation. Their acoustic properties are typically characterized by Johnson-Champoux-Allard (JCA) model, which includes five dominant parameters, i.e., open porosity, flow resistivity, tortuosity, viscous characteristic length, and thermal characteristic length. The JCA parameters depend on the microstructure configuration of the material, which can be attained by experimental measurements or numerically analyzing the flow field inside the microstructure, but significant efforts to predict the parameters are typically required. This study proposes a machine learning approach based on an artificial neural network (ANN) for predicting the JCA parameters of a fibrous material. Two geometric parameters that can characterize the fibrous material, i.e., the radius of the fiber and the equivalent throat size between neighbouring fibers, are set as inputs for the prediction model, while the five JCA parameters are set as outputs. The datasets for the network are prepared from finite element simulations. Results confirm that the trained model can predict the JCA parameters accurately and reliably based on the micro-structural geometric parameters. Finally, the model is further validated by the measured acoustic characteristics of a metal-based fibrous material in an impedance tube. The machine learning model opens up possibilities to facilitate the design of advanced porous materials.

中文翻译：

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用于预测纤维多孔材料的 Johnson-Champoux-Allard 参数的机器学习方法

多孔纤维材料已广泛用作降噪的声学处理。其声学特性通常采用Johnson-Champoux-Allard (JCA)模型来表征，该模型包括五个主要参数，即开孔率、流阻率、弯曲度、粘性特征长度和热特征长度。 JCA 参数取决于材料的微观结构配置，这可以通过实验测量或对微观结构内部的流场进行数值分析来获得，但通常需要付出大量努力来预测参数。本研究提出了一种基于人工神经网络 (ANN) 的机器学习方法，用于预测纤维材料的 JCA 参数。两个可以表征纤维材料的几何参数，即纤维的半径和相邻纤维之间的等效喉部尺寸，被设置为预测模型的输入，而五个JCA参数被设置为输出。网络数据集是通过有限元模拟准备的。结果证实，训练后的模型能够根据微观结构几何参数准确可靠地预测 JCA 参数。最后，通过测量阻抗管中金属基纤维材料的声学特性进一步验证该模型。机器学习模型为促进先进多孔材料的设计提供了可能性。