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Modelling of fluid–particle interactions is a major area of research in many fields of science and engineering. There are several techniques that allow modelling of such interactions, among which the coupling of computational fluid dynamics (CFD) and the discrete element method (DEM) is one of the most convenient solutions due to the balance between accuracy and computational costs. However, the accuracy of this method is largely dependent upon mesh size, where obtaining realistic results always comes with the necessity of using a small mesh and thereby increasing computational intensity. To compensate for the inaccuracies of using a large mesh in such modelling, and still take advantage of rapid computations, we extended the classical modelling by combining it with a machine learning model. We have conducted seven simulations where the first one is a numerical model with a fine mesh (i.e. ground truth) with a very high computational time and accuracy, the next three models are constructed on coarse meshes with considerably less accuracy and computational burden and the last three models are assisted by machine learning, where we can obtain large improvements in terms of observing fine-scale features yet based on a coarse mesh. The results of this study show that there is a great opportunity in machine learning towards improving classical fluid–particle modelling approaches by producing highly accurate models for large-scale systems in a reasonable time. 

Numerical modelling of deformation in hydromechanical systems can be time-consuming using fully coupled classical numerical methods for large representative porous media samples. In this paper, we present a new two-way coupled partitioned fluid–solid system. The coupled system is applied for simulating geomechanical processes at the pore-scale. We track the deformation of the solid resulting from the drainage of resident fluids in the pores, as well as the evolution of fluid properties from dynamic loading. The finite element method is responsible for capturing the structural deformation in the coupled system while the dynamic pore network is used for modelling multiphase flow in the fluid subsystem. A fictitious fluid–solid interface is created at each pore network-finite element node junction via convex hulling, followed by data exchange using linear interpolation. The results show good agreement with a pre-existing coupled finite volume model and the computations are completed in much less time.