Jump to main content
Jump to site search

Issue 14, 2015
Previous Article Next Article

Extraction of force-chain network architecture in granular materials using community detection

Author affiliations

Abstract

Force chains form heterogeneous physical structures that can constrain the mechanical stability and acoustic transmission of granular media. However, despite their relevance for predicting bulk properties of materials, there is no agreement on a quantitative description of force chains. Consequently, it is difficult to compare the force-chain structures in different materials or experimental conditions. To address this challenge, we treat granular materials as spatially-embedded networks in which the nodes (particles) are connected by weighted edges that represent contact forces. We use techniques from community detection, which is a type of clustering, to find sets of closely connected particles. By using a geographical null model that is constrained by the particles' contact network, we extract chain-like structures that are reminiscent of force chains. We propose three diagnostics to measure these chain-like structures, and we demonstrate the utility of these diagnostics for identifying and characterizing classes of force-chain network architectures in various materials. To illustrate our methods, we describe how force-chain architecture depends on pressure for two very different types of packings: (1) ones derived from laboratory experiments and (2) ones derived from idealized, numerically-generated frictionless packings. By resolving individual force chains, we quantify statistical properties of force-chain shape and strength, which are potentially crucial diagnostics of bulk properties (including material stability). These methods facilitate quantitative comparisons between different particulate systems, regardless of whether they are measured experimentally or numerically.

Graphical abstract: Extraction of force-chain network architecture in granular materials using community detection

Back to tab navigation

Publication details

The article was received on 17 Aug 2014, accepted on 06 Feb 2015 and first published on 23 Feb 2015


Article type: Paper
DOI: 10.1039/C4SM01821D
Citation: Soft Matter, 2015,11, 2731-2744
  •   Request permissions

    Extraction of force-chain network architecture in granular materials using community detection

    D. S. Bassett, E. T. Owens, M. A. Porter, M. L. Manning and K. E. Daniels, Soft Matter, 2015, 11, 2731
    DOI: 10.1039/C4SM01821D

Search articles by author

Spotlight

Advertisements