Simulation of the flow past random arrays of spherical particles: Microstructure-based tensor quantities as a tool to predict fluid-particle forces

Abstract

A recent challenge in the modelling of particle flows is to build microstructure-informed drag models to overcome the average description of the fluid–particle force in the drag force correlations currently used in Euler–Lagrange and Euler–Euler models. To that end, we study through particle-resolved direct numerical simulations (PR-DNS) the flow past random assemblies of mono-dispersed spherical particles at three particle Reynolds numbers (10, 50, 100) and four solid volume fractions (0.10, 0.20, 0.30, 0.40). The present methodology is validated against theoretical and numerical results for the mean drag force in Stokes flows and finite Reynolds numbers flows. PR-DNS results are then used to characterize in details the statistics of the force distribution over the particle array, highlighting the substantial dispersion of the fluid force along the streamwise and transverse directions. The microstructure formed by the solid phase is described by means of a limited number of tensor quantities inspired from the fabric tensor used in granular media. Significant correlations are identified between the force experienced by a given particle immersed in a random array and a few key quantities that describe the anisotropy of its neighbourhood. A microstructure-based multi-linear model is proposed and validated against independent test cases. The model appears to perform best in the viscous and dense regimes. The addition of a stochastic contribution to the model allows to recover the correct level of force fluctuations at the cost of a lower correlation between the model and the data.