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Scale invariance and universality of force networks in static granular matter

Author

Listed:
  • Srdjan Ostojic

    (Universiteit van Amsterdam)

  • Ellák Somfai

    (Universiteit Leiden, Instituut-Lorentz
    University of Oxford)

  • Bernard Nienhuis

    (Universiteit van Amsterdam)

Abstract

Force networks form the skeleton of static granular matter1,2. They are the key factor that determines mechanical properties such as stability3, elasticity4,5 and sound transmission6,7, which are important for civil engineering and industrial processing. Previous studies have focused on investigations of the global structure of external forces8,9,10,11 (the boundary condition) and on the probability distribution of individual contact forces4,12. So far, however, precise knowledge of the disordered spatial structure of the force network has remained elusive. Here we report that molecular dynamics simulations of realistic granular packings reveal scale invariance of clusters of particles interacting by means of relatively strong forces. Despite visual variation, force networks for various values of the confining pressure and other parameters have identical scaling exponents and scaling function, thereby determining a universality class. Unexpectedly, the flat ensemble of force configurations13,14,15 (a simple generalization of equilibrium statistical mechanics) belongs to this universality class, whereas some widely studied simplified models16,17,18 do not. This implies that the elasticity of the grains and their geometrical disorder do not affect the universal mechanical properties.

Suggested Citation

  • Srdjan Ostojic & Ellák Somfai & Bernard Nienhuis, 2006. "Scale invariance and universality of force networks in static granular matter," Nature, Nature, vol. 439(7078), pages 828-830, February.
  • Handle: RePEc:nat:nature:v:439:y:2006:i:7078:d:10.1038_nature04549
    DOI: 10.1038/nature04549
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    Cited by:

    1. Rituparno Mandal & Corneel Casert & Peter Sollich, 2022. "Robust prediction of force chains in jammed solids using graph neural networks," Nature Communications, Nature, vol. 13(1), pages 1-7, December.

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