Simulating the rheology of dense suspensions using pairwise formulation of contact, lubrication and Brownian forces

TL;DR

This paper introduces a minimal particle-based simulation model for dense colloidal suspensions that efficiently captures rheological behavior across different shear rates by incorporating Brownian, contact, and lubrication forces, and is scalable for large systems.

Contribution

The authors develop a computationally efficient pairwise force model for dense colloidal suspensions that accurately reproduces key rheological features and is implemented in LAMMPS for large-scale simulations.

Findings

Reproduces shear-thinning and shear-thickening behaviors near the colloidal-to-granular transition.

Incorporates Brownian, contact, and lubrication forces in a minimal model.

Scales linearly with the number of particles, enabling large-scale simulations.

Abstract

Dense suspensions of solid particles in viscous liquid are ubiquitous in both industry and nature, and there is a clear need for efficient numerical routines to simulate their rheology and microstructure. Particles of micron size present a particular challenge: at low shear rates colloidal interactions control their dynamics while at high rates granular-like contacts dominate. While there are established particle-based simulation schemes for large-scale non-Brownian suspensions using only pairwise lubrication and contact forces, common schemes for colloidal suspensions generally are more computationally costly and thus restricted to relatively small system sizes. Here we present a minimal particle-based numerical model for dense colloidal suspensions which incorporates Brownian forces in pairwise form alongside contact and lubrication forces. We show that this scheme reproduces key features of dense suspension rheology near the collodial-to-granular transition, including both shear-thinning due to entropic forces at low rates and shear thickening at high rate due to contact formation. This scheme is implemented in LAMMPS, a widely-used open source code for parallelized particle-based simulations, with a runtime that scales linearly with the number of particles making it amenable for large-scale simulations.