基于机器学习的含圆柱形孔洞聚氨酯水声涂层的加速逆设计
A machine learning accelerated inverse design of underwater acoustic polyurethane coatings with cylindrical voids
摘要：
在这里，我们报告了一个详细的“材料信息学”框架的开发，通过将机器学习（ML）和统计优化算法与有限元模型（FEM）相结合，用于设计用于水下声衰减的声学涂层。建立了含圆柱形孔洞的聚氨酯弹性体声学涂层的有限元模型，模拟了其声学性能。有限元分析结果表明，聚氨酯基体的频率依赖性粘弹性行为对圆柱体在低频下的吸收峰的大小和频率有显著影响，而这在以往的研究中通常被忽略。从FEM生成的数据用于训练深度神经网络（DNN）以加速设计过程，随后与遗传算法（GA）集成以确定圆柱体的最佳几何参数，以实现最大化的宽带低频水上声衰减。通过优化配置圆柱形空隙层并使用衰减机制（包括空隙层的法布里-珀罗共振和布拉格散射）来实现显著的宽带低频衰减。将机器学习技术集成到优化算法中，进一步加速了对高维设计空间的探索，以实现目标性能。与传统的FEM相比，开发的DNN在预测吸收系数方面表现出显著增加的速度（通过$4.5\times 10^3$的因子）。因此，与传统的试错实践相比，材料信息学框架带来的加速为声学涂层的设计和开发带来了范式转变。
英文摘要：
Here, we report the development of a detailed "Materials Informatics" framework for the design of acoustic coatings for underwater sound attenuation through integrating Machine Learning (ML) and statistical optimization algorithms with a Finite Element Model (FEM). The finite element models were developed to simulate the realistic performance of the acoustic coatings based on polyurethane (PU) elastomers with embedded cylindrical voids. The FEM results revealed that the frequency-dependent viscoelastic behavior of the polyurethane matrix has a significant impact on the magnitude and frequency of the absorption peak associated with the cylinders at low frequencies, which has been commonly ignored in previous studies on similar systems. The data generated from the FEM was used to train a Deep Neural Network (DNN) to accelerate the design process, and subsequently, was integrated with a Genetic Algorithm (GA) to determine the optimal geometric parameters of the cylinders to achieve maximized, broadband, low-frequency waterborne sound attenuation. A significant, broadband, low-frequency attenuation is achieved by optimally configuring the layers of cylindrical voids and using attenuation mechanisms, including Fabry-Pérot resonance and Bragg scattering of the layers of voids. Integration of the machine learning technique into the optimization algorithm further accelerated the exploration of the high dimensional design space for the targeted performance. The developed DNN exhibited significantly increased speed (by a factor of $4.5\times 10^3$ ) in predicting the absorption coefficient compared to the conventional FEM(s). Therefore, the acceleration brought by the materials informatics framework brings a paradigm shift to the design and development of acoustic coatings compared to the conventional trial-and-error practices.
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