Characterizing granular networks using topological metrics

Joshua A. Dijksman, Lenka Kovalcinova, Jie Ren, Robert P. Behringer, Miroslav Kramar, Konstantin Mischaikow, Lou Kondic

Research output: Contribution to journalArticleAcademicpeer-review

4 Citations (Scopus)

Abstract

We carry out a direct comparison of experimental and numerical realizations of the exact same granular system as it undergoes shear jamming. We adjust the numerical methods used to optimally represent the experimental settings and outcomes up to microscopic contact force dynamics. Measures presented here range from microscopic through mesoscopic to systemwide characteristics of the system. Topological properties of the mesoscopic force networks provide a key link between microscales and macroscales. We report two main findings: (1) The number of particles in the packing that have at least two contacts is a good predictor for the mechanical state of the system, regardless of strain history and packing density. All measures explored in both experiments and numerics, including stress-tensor-derived measures and contact numbers depend in a universal manner on the fraction of nonrattler particles, fNR. (2) The force network topology also tends to show this universality, yet the shape of the master curve depends much more on the details of the numerical simulations. In particular we show that adding force noise to the numerical data set can significantly alter the topological features in the data. We conclude that both fNR and topological metrics are useful measures to consider when quantifying the state of a granular system.
Original languageEnglish
Article number042903
JournalPhysical Review E
Volume97
Issue number4
DOIs
Publication statusPublished - 18 Apr 2018

Fingerprint Dive into the research topics of 'Characterizing granular networks using topological metrics'. Together they form a unique fingerprint.

Cite this