Estimating relative diffusion from 3D micro-CT images using CNNs
This work addresses the challenge of reduced accuracy in CNN-based predictions for partially saturated porous media, which is important for researchers in porous media and materials science, though it is incremental as it extends existing CNN methods to a more complex case.
The paper tackled the problem of predicting relative diffusion in partially saturated porous media from 3D micro-CT images using CNNs, achieving robust and accurate predictions directly from full pore-space geometries.
In the past several years, convolutional neural networks (CNNs) have proven their capability to predict characteristic quantities in porous media research directly from pore-space geometries. Due to the frequently observed significant reduction in computation time in comparison to classical computational methods, bulk parameter prediction via CNNs is especially compelling, e.g. for effective diffusion. While the current literature is mainly focused on fully saturated porous media, the partially saturated case is also of high interest. Due to the qualitatively different and more complex geometries of the domain available for diffusive transport present in this case, standard CNNs tend to lose robustness and accuracy with lower saturation rates. In this paper, we demonstrate the ability of CNNs to perform predictions of relative diffusion directly from full pore-space geometries. As such, our CNN conveniently fuses diffusion prediction and a well-established morphological model which describes phase distributions in partially saturated porous media.