Flows involving polydisperse granular media are widespread in nature and industry. Knowledge
of the momentum coupling between the fluid and solid phases comes mostly from empirical
and numerical correlations based on studies involving monodisperse materials and treatment of
polydispersity has had little attention. Accurate predictions of the flow induced forces acting on
grains are required to understand problems of relevance to geotechnical engineering involving
sand particle migration such as seepage induced instabilities and filtration processes. The Discrete Element Method (DEM) is widely used in the geomechanics community to study particle
scale behaviour of sands and soils, and is often combined with Computational Fluid Dynamics
(CFD) to simulate saturated materials. The popular coarse grid DEM-CFD approach, in which
fluid cells contain multiple particles, relies on empirical drag force relations to predict the interaction between phases based on the flow Reynolds number and the fluid cell porosity.
Highly resolved simulations of interstitial fluid flow at low Reynolds numbers in dense polydisperse sphere packings were conducted using the Immersed Boundary Method (IBM). Fluidparticle interaction forces from IBM simulations were used to investigate the role of grain size
polydispersity on drag and to critically assess the suitability of popular drag correlations for use
in geomechanics research. Polydispersity was systematically controlled by considering linearly
graded particle size distributions (PSDs) with uniformity coefficients between Cu = 1.01 –
2.50, and bimodal PSDs with diameter ratios 2 and 4. Flow fields obtained from the IBM were
used to validate Stokes flow simulations with a Pore Network Model (PNM) and to investigate
the role of constrictions in pore scale head loss.
The Ergun and Di Felice correlations are shown to not adequately predict the particle drag forces
in polydisperse systems. The polydispersity correction of van der Hoef et al. is shown to predict
the meso-scale drag adequately but fail to capture the correct partitioning of the drag between
different sized particles. An approach to correcting the predictions of monodisperse drag models
using local porosities for each particle calculated from the radical Voronoi tessellation is shown
to provide good predictions at the particle and meso scales. The PNM studied is shown to
accurately predict flow paths through porous media and provide accurate predictions of fluidparticle interaction forces using the method of Chareyre et al.