Physics- and image-based prediction of fluid flow and transport in complex porous membranes and materials by deep learning

Abstract Although the morphology of porous membranes is the key factor in determining their flow, transport and separation properties, a general relation between the morphology and the physical properties has been difficult to identify. One promising approach to develop such a relation is through the application of a machine-learning (ML) algorithm to the problem. Over the last decade significant developments in the development of the ML approaches have led to many breakthroughs in various fields of science and engineering, but their application to porous media has been very limited. In this paper, we develop a deep network for predicting flow properties of porous membranes based on their morphology. The predicted properties include the spatial distributions of the fluid pressure and velocity throughout the entire membranes, provided that the deep network is properly trained by using high-resolution images of the membranes and the pressure and velocity distributions in their pore space at certain points in time. The network includes a residual U-net for developing a mapping between the input and output images, as well as a recurrent network for identifying physical correlations between the output data at various times. The results demonstrate that the deep network provides highly accurate predictions for the properties of interest. Thus, such a network may be used for predicting flow and transport properties of many other types of porous materials, as well as designing membranes for a specific application.