Continuum modeling of size-segregation and flow in dense, bidisperse granular media: Accounting for segregation driven by both pressure gradients and shear-strain-rate gradients

Abstract

Dense mixtures of particles of varying size tend to segregate based on size during flow. Granular size-segregation plays an important role in many industrial and geophysical processes, but the development of coupled, continuum models capable of predicting the evolution of segregation dynamics and flow fields in dense granular media across different geometries has remained a longstanding challenge. One reason is because size-segregation stems from two driving forces: (1) pressure gradients and (2) shear-strain-rate gradients. Another reason is due to the challenge of integrating segregation models with rheological constitutive equations for dense granular flow. In this paper, we build upon our prior work, which combined a model for shear-strain-rate-gradient-driven segregation with a nonlocal continuum model for dense granular flow rheology, and append a model for pressure-gradient-driven segregation. We perform discrete element method (DEM) simulations of dense flow of bidisperse granular systems in two flow geometries, in which both segregation driving forces are present: namely, inclined plane flow and planar shear flow with gravity. Steady-state DEM data from inclined plane flow is used to determine the dimensionless material parameters in the pressure-gradient-driven segregation model for both spheres and disks. Then, predictions of the coupled, continuum model accounting for both driving forces are tested against DEM simulation results across different cases of both inclined plane flow and planar shear flow with gravity, while varying parameters such as the size of the flow geometry, the driving conditions of flow, and the initial conditions. Overall, we find that it is crucial to account for both driving forces to capture segregation dynamics in dense, bidisperse granular media across both flow geometries with a single set of parameters.