Literature DB >> 14995308

Force network ensemble: a new approach to static granular matter.

Jacco H Snoeijer1, Thijs J H Vlugt, Martin van Hecke, Wim van Saarloos.   

Abstract

An ensemble approach for force distributions in static granular packings is developed. This framework is based on the separation of packing and force scales, together with an a priori flat measure in the force phase space under the constraints that the contact forces are repulsive and balance on every particle. We show how the formalism yields realistic results, both for disordered and regular triangular "snooker ball" configurations, and obtain a shear-induced unjamming transition of the type proposed recently for athermal media.

Entities:  

Year:  2004        PMID: 14995308     DOI: 10.1103/PhysRevLett.92.054302

Source DB:  PubMed          Journal:  Phys Rev Lett        ISSN: 0031-9007            Impact factor:   9.161


  7 in total

1.  Protocol dependence of mechanical properties in granular systems.

Authors:  S Inagaki; M Otsuki; S Sasa
Journal:  Eur Phys J E Soft Matter       Date:  2011-11-24       Impact factor: 1.890

2.  Impact of the timestep in some molecular dynamics simulations on compression of granular systems.

Authors:  Ignacio G Tejada; Rafael Jimenez
Journal:  Eur Phys J E Soft Matter       Date:  2014-03-18       Impact factor: 1.890

3.  Ensemble theory for slightly deformable granular matter.

Authors:  Ignacio G Tejada
Journal:  Eur Phys J E Soft Matter       Date:  2014-09-25       Impact factor: 1.890

4.  Quasilocalized states of self stress in packing-derived networks.

Authors:  Edan Lerner
Journal:  Eur Phys J E Soft Matter       Date:  2018-08-21       Impact factor: 1.890

5.  Cellular organization in lab-evolved and extant multicellular species obeys a maximum entropy law.

Authors:  Thomas C Day; Stephanie S Höhn; Seyed A Zamani-Dahaj; David Yanni; Anthony Burnetti; Jennifer Pentz; Aurelia R Honerkamp-Smith; Hugo Wioland; Hannah R Sleath; William C Ratcliff; Raymond E Goldstein; Peter J Yunker
Journal:  Elife       Date:  2022-02-21       Impact factor: 8.140

6.  Robust prediction of force chains in jammed solids using graph neural networks.

Authors:  Rituparno Mandal; Corneel Casert; Peter Sollich
Journal:  Nat Commun       Date:  2022-07-30       Impact factor: 17.694

7.  Similarities between protein folding and granular jamming.

Authors:  Prasanth P Jose; Ioan Andricioaei
Journal:  Nat Commun       Date:  2012       Impact factor: 14.919

  7 in total

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