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 limitations
of previous models and improves computational efficiency, enabling the
rigorous characterization of large batches of complex heterogeneous porous
media for a variety of engineering applications.

以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）的极具潜力的方案，且仅需极低的计算算力。然而，过往模型通常需要耗费大量计算资源，才能生成用作训练数据的合成多孔介质流动仿真结果。我们提出了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）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。本模型使用一系列可通过解析法或数值法以低计算成本获取的简单与复杂单胞完成训练。实验结果表明，该模型可精准预测合成多孔介质与真实网状泡沫（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，我们大幅提升了数值模拟（numerical simulations）的收敛效率。本研究方案解决了过往模型的局限性并提升了计算效率，可为各类工程应用中大规模批量处理复杂非均质多孔介质的精准表征提供支撑。