基于卷积神经网络的多孔介质内湍流微尺度流场预测
Turbulent Microscale Flow Field Prediction In Porous Media Using Convolutional Neural Networks
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中文总结 AI 辅助
该研究提出用卷积神经网络（CNN）预测多孔介质内的湍流微尺度流场，在孔隙率0.45-0.92、雷诺数300的条件下，CNN全局误差小于10%，速度较LES提升约10^6倍，实现了高精度快速预测。
中文摘要 AI 辅助
将高分辨率数值方法与现代数据驱动技术结合，可大幅改进多孔介质中的湍流建模。开发精确的宏观尺度模型（长度尺度大于孔隙尺寸），将实现多孔介质流动的实时系统模拟。本文研究均质多孔介质中的湍流流动，这类流动常见于工程多孔介质（如热交换器、超材料、燃烧室等）中。其底层微尺度流场是非均匀的，由多孔介质的几何结构决定。神经网络能够解析多孔介质湍流流动的几何依赖性与非线性。本文提出将宏观尺度模型拆分为多个模块，分别预测微尺度流动的不同方面，例如微尺度空间流动分布和涡旋动力学。本研究中，我们确定了使用卷积神经网络（CNN）预测雷诺平均微尺度流型的可行性。多孔介质由圆柱固体障碍物的正方形晶格排列表示，流动的孔隙尺度雷诺数为300，多孔介质的孔隙率在0.45至0.92之间，共60个取值步长。微尺度流场通过大涡模拟（LES）结合紧凑六阶有限差分法进行模拟。我们证明，使用CNN可实现微尺度流场的满意预测，全局误差小于10%。我们还改变训练样本数量，研究模型精度的下降情况。CNN模型相比LES实现了约10^6倍的加速，仅存在10%的精度损失。
英文摘要
Turbulence modeling in porous media can be greatly improved by combining high-resolution numerical methods with modern data-driven techniques. The development of accurate macroscale models (length scale greater than the pore size) will enable real-time systemic simulations of porous media flow. We consider the case of turbulent flow in homogeneous porous media, typically encountered in engineered porous media (heat exchangers, metamaterials, combustors, etc.). The underlying microscale flow field is inhomogeneous and determined by the geometry of the porous medium. Neural Networks are able to resolve the geometry-dependence and the non-linearity of porous media turbulent flow. We are proposing to separate the macroscale model into individual blocks that predict a unique aspect of the microscale flow, such as microscale spatial flow distribution and vortex dynamics. In the present work, we determine the feasibility of the prediction of the Reynolds-averaged microscale flow patterns by using Convolutional Neural Networks (CNN). The porous medium is represented by using a square lattice arrangement of circular cylinder solid obstacles. The pore-scale Reynolds number of the flow is 300. The porosity of the porous medium is varied from 0.45 to 0.92 with 60 steps. The microscale flow field is simulated by using Large Eddy Simulation (LES) with a compact sixth-order finite difference method. We demonstrate satisfactory prediction of the microscale flow field using the CNN with a global error less than 10%. We vary the number of training samples to study the deterioration of the model accuracy. The CNN model offers a O(106) speedup over LES with only 10% loss in accuracy.
