Characterizing granular networks using topological metrics
Year: 2018
Type: article
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, f_{NR}. (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 f_{NR} and topological metrics are useful measures to consider when quantifying the state of a granular system.
Source: Physical review. E
Language: English
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Awards: INSPIRE: Nonlinear Data Reduction applied to Dense Granular Media, CDI-Type II: Collaborative Research: Computational Homology, Jamming, and Force Chains in Dense Granular Flows, G2316591953, Collaborative proposal: Computing Dynamics of Multiparameter Systems, G5222733069, G5270150132, G5418073398, CDI-TYPE II--COLLABORATIVE RESEARCH: Using Algebraic Topology to Connect Models with Measurements in Complex Nonequilibrium Systems, G668241585, Collaborative Research:Computational and Data-Enabled Science and Engineering: Characterizing Dynamics of Particle-based Systems, KEY SCIENTIFIC GOALS INCLUDE: 1) UNDERSTANDING THE RELATION BETWEEN CLASSICAL IDEAS, INCLUDING RANDOM-CLOSE AND RANDOM-LOOSE DENSITIES, AND RECENT AD, THE STUDY OF DENSE GRANULAR MATTER IS AN IMPORTANT TOPIC BOTH OF UNDAMENTAL SCIENCE (DISSIPATIVE NON-EQUILIBRIUM SYSTEMS) AND IN APPLICATIONS (HANDLI, CDI-Type II: Collaborative Research: Computational Homology, Jamming, and Force Chains in Dense Granular Flows
OpenAlex ID: W2608255245