Shape-dependent effective diffusivity in packings of hard cubes and cuboids compared with spheres and ellipsoids

Review of modern models of porous media for numerical simulation of fluid flows

The current models of porous media for numerical simulation of multiphase fluid flows are reviewed and classified herein. Methods for building these models and problems of creating the same are examined, and examples where the models were used are given. This review has been made as part of hydrocarbon deposit hydrodynamic simulation problems arising in the oil-and-gas producing industry. The approaches and possibilities for creating a digital twin of actual core have been evaluated. It is noted that the porous medium models used cannot presently meet the industry demands to the full and the reasons behind that have been analyzed. A conclusion was made that an in-depth fundamental study on processes and phenomena affecting the multiphase flow structure at the micro-scale is required to take them into account properly in the description of the flow at a deposit scale.

Machine learning approaches for the optimization of packing densities in granular matter

The fundamental question of how densely granular matter can pack and how this density depends on the shape of the constituent particles has been a longstanding scientific problem. Previous work has mainly focused on empirical approaches based on simulations or mean-field theory to investigate the effect of shape variation on the resulting packing densities, focusing on a small set of pre-defined shapes like dimers, ellipsoids, and spherocylinders. Here we discuss how machine learning methods can support the search for optimally dense packing shapes in a high-dimensional shape space. We apply dimensional reduction and regression techniques based on random forests and neural networks to find novel dense packing shapes by numerical optimization. Moreover, an investigation of the regression function in the dimensionally reduced shape representation allows us to identify directions in the packing density landscape that lead to a strongly non-monotonic variation of the packing density. The predictions obtained by machine learning are compared with packing simulations. Our approach can be more widely applied to optimize the properties of granular matter by varying the shape of its constituent particles.