Literature DB >> 23679407

Persistence of force networks in compressed granular media.

M Kramar1, A Goullet, L Kondic, K Mischaikow.   

Abstract

We utilize the tools of persistent homology to analyze features of force networks in dense granular matter, modeled as a collection of circular, inelastic frictional particles. The proposed approach describes these networks in a precise and tractable manner, allowing us to identify features that are difficult or impossible to characterize by other means. In contrast to other techniques that consider each force threshold level separately, persistent homology allows us to consider all threshold levels at once to describe the force network in a complete and insightful manner. We consider continuously compressed system of particles characterized by varied polydispersity and friction in two spatial dimensions. We find significant differences between the force networks in these systems, suggesting that their mechanical response may differ considerably as well.

Year:  2013        PMID: 23679407     DOI: 10.1103/PhysRevE.87.042207

Source DB:  PubMed          Journal:  Phys Rev E Stat Nonlin Soft Matter Phys        ISSN: 1539-3755


  4 in total

1.  A roadmap for the computation of persistent homology.

Authors:  Nina Otter; Mason A Porter; Ulrike Tillmann; Peter Grindrod; Heather A Harrington
Journal:  EPJ Data Sci       Date:  2017-08-09       Impact factor: 3.184

2.  Noise robustness of persistent homology on greyscale images, across filtrations and signatures.

Authors:  Renata Turkeš; Jannes Nys; Tim Verdonck; Steven Latré
Journal:  PLoS One       Date:  2021-09-24       Impact factor: 3.240

Review 3.  Controlling disorder in self-assembled colloidal monolayers via evaporative processes.

Authors:  Lucien Roach; Adrian Hereu; Philippe Lalanne; Etienne Duguet; Mona Tréguer-Delapierre; Kevin Vynck; Glenna L Drisko
Journal:  Nanoscale       Date:  2022-03-07       Impact factor: 7.790

4.  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

  4 in total

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