Pore-scale simulation and statistical investigation of velocity and drag force distribution of flow through randomly-packed porous media under low and intermediate Reynolds numbers

Fluid flow through packed porous media and fluid-particle interactions are of importance in various industrial and natural processes. However, the lack of knowledge about the velocity field in the pore space and distribution of drag force on individual particles has been a source of uncertainties in modeling these processes. Therefore, an improved understanding of the velocity field and fluid-particle interactions is a fundamental step for better understanding of these systems. In this article, the pore-scale velocity field and fluid-solid interaction from a single particle to randomly-packed mono-sized porous media are investigated using a 3D GPU-based parallel Lattice Boltzmann model. The packed porous media are generated by means of discrete element method and have a wide range of porosity values. The developed model is first validated by experimental results of fluid flow around single and two interactive particles; the validated model is then used to conduct statistical analysis of velocity in the pore space and drag force on individual particles. The results suggest that the velocity field in porous media can be divided into four zones, namely: zero-velocity zone, low-velocity zone, high-velocity zone, and recirculation zone. Moreover, the probability density distribution of velocity is highly dependent on Reynolds number and porosity and can be bi-modal, depending on a combination of Reynolds number and porosity. The probability density distribution of the drag force always shows a single peak at the mean value with a skewness to the right. Finally, a simpler and more accurate correlation for the mean drag force over a wide range of Reynolds number and porosity is proposed based on the numerical results. The accuracy and reliability of several empirical equations, including the one proposed in this study, for the mean drag force are compared through a statistical analysis.