Periodic and heterogeneous solid and velocity data used to train and validate CNN models

Data-driven deep learning models are emerging as a promising method for characterizing pore-scale flow through complex porous media while requiring minimal computational power. However, previous models often require extensive computation to simulate flow through synthetic porous media for use as training data. We propose a convolutional neural network trained solely on periodic unit cells to predict pore-scale velocity fields of complex heterogeneous porous media from binary images without the need for further image processing. Our model is trained using a range of simple and complex unit cells that can be obtained analytically or numerically at a low computational cost. Our results show that the model accurately predicts the permeability and pore-scale flow characteristics of synthetic porous media and real reticulated foams. We significantly improve the convergence of numerical simulations by using the predictions from our model as initial guesses. Our approach addresses the limitatio..., ,

数据驱动的深度学习模型正成为表征复杂多孔介质（porous media）内孔隙尺度流动（pore-scale flow）的极具前景的方法，且仅需极低的计算算力。然而，过往模型通常需要开展大量计算以模拟合成多孔介质内的流动，将其作为训练数据集。我们提出一种仅基于周期性单胞（periodic unit cells）训练的卷积神经网络（Convolutional Neural Network），可直接从二值图像（binary images）中预测复杂非均质多孔介质的孔隙尺度速度场，无需额外的图像处理步骤。本模型使用一系列简单与复杂的单胞进行训练，这些单胞可通过解析法或数值法获取，且计算成本极低。实验结果表明，本模型可准确预测合成多孔介质与真实开孔泡沫（reticulated foams）的渗透率（permeability）及孔隙尺度流动特性。通过将本模型的预测结果作为初始猜测值，我们显著提升了数值模拟的收敛性。本研究方法有效解决了……的局限