Multiphase flow in porous media has been thoroughly studied over the years and its importance is encountered in several areas related to geo-materials. One of the most important parameters that control multiphase flow in any complex geometry is wettability, which is an affinity of a given fluid toward a surface. In this paper, we have quantified the effects of wettability on deformation in porous media, along with other parameters that are involved in this phenomenon. To this end, we conducted numerical simulations on a porous medium by coupling the exchanged forces between the fluid and solid. To include the effect of wettability in the medium, we used the Fictitious Domain methodology and coupled it with volume of fluid through which one can model more than one fluid in the system. To observe the effect of wettability on dynamic processes in the designated porous medium, such as deformation, particle–particle contact stresses, particle velocity, and injection pressure, a series of systematic computations were conducted where wettability is varied through five different contact angles. We found that wettability not only controls the fluid propagation patterns but also affects drag forces exerted on the particles during injection such that larger deformations are induced for particles with lower wettability. Our results are also verified against experimental tests.
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.
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