Evolution of network architecture in a granular material under compression

TL;DR

This study uses multilayer network analysis to track how mesoscale force structures in compressed granular materials evolve, revealing differences between normal and tangential force networks and distinguishing particle friction properties.

Contribution

It introduces a multilayer network framework with community detection to quantify mesoscale structural evolution in granular systems under compression, advancing network-based characterization methods.

Findings

Normal and tangential force networks evolve differently during compression.

The method can distinguish low-friction particle subsystems within higher-friction environments.

Network analysis provides a new way to compare granular systems under various conditions.

Abstract

As a granular material is compressed, the particles and forces within the system arrange to form complex heterogeneous structures. Force chains are a prime example and are thought to constrain bulk properties such as mechanical stability and acoustic transmission. However, characterizing the dynamic nature of mesoscale architectures in granular systems can be challenging. A growing body of work has shown that graph theoretic approaches may provide a useful foundation for tackling these problems. Here, we extend current approaches by utilizing multilayer networks as a framework for directly quantifying the evolution of mesoscale architecture in a compressed granular system. We examine a quasi-two-dimensional aggregate of photoelastic disks, subject to biaxial compression through a series of small, quasistatic steps. Treating particles as network nodes and inter-particle forces as network edges, we construct a multilayer network by linking together the series of static force networks that exist at each strain step. We then extract the inherent mesoscale structure from the system by using a generalization of community detection methods, and we define quantitative measures to characterize the reconfiguration and evolution of this structure throughout compression. By separately considering the network of normal and tangential forces, we find that they display different structural evolution. To test the sensitivity of the network model to particle properties, we examine whether the method can distinguish a subsystem of low-friction particles within a bath of higher-friction particles. We find that this can be done by considering the network of tangential forces. The results discussed throughout this study suggest that these novel network science techniques may provide a direct way to compare and classify data from systems under different external conditions or with different physical makeup.