Evolution of Force Networks in Dense Particulate Media

TL;DR

This paper introduces a new method using persistent homology to analyze the evolution of force networks in dense particulate media, revealing detailed geometric changes during jamming transitions.

Contribution

The study presents a novel quantitative approach to characterize force network dynamics in particulate systems, incorporating both normal and tangential forces and considering friction effects.

Findings

Localized force network changes in unjammed states

Global network changes during jamming transition

Friction significantly influences network evolution

Abstract

We introduce novel sets of measures with the goal of describing dynamical properties of force networks in dense particulate systems. The presented approach is based on persistent homology and allows for extracting precise, quantitative measures that describe the evolution of geometric features of the interparticle forces, without necessarily considering the details related to individual contacts between particles. The networks considered emerge from discrete element simulations of two dimensional particulate systems consisting of compressible frictional circular disks. We quantify the evolution of the networks for slowly compressed systems undergoing jamming transition. The main findings include uncovering significant but localized changes of force networks for unjammed systems, global (system-wide) changes as the systems evolve through jamming, to be followed by significantly less dramatic evolution for the jammed states. We consider both connected components, related in loose sense to force chains, and loops, and find that both measures provide a significant insight into the evolution of force networks. In addition to normal, we consider also tangential forces between the particles and find that they evolve in the consistent manner. Consideration of both frictional and frictionless systems leads us to the conclusion that friction plays a significant role in determining the dynamical properties of the considered networks. We find that the proposed approach describes the considered networks in a precise yet tractable manner, allowing to identify novel features which could be difficult or impossible to describe using other approaches.