Large-scale frictionless jamming with power-law particle size distributions
Joseph M. Monti, Joel T. Clemmer, Ishan Srivastava, Leonardo E., Silbert, Gary S. Grest, and Jeremy B. Lechman

TL;DR
This study uses advanced algorithms to simulate large, size-disperse frictionless sphere packings, revealing how particle size distributions influence packing structure and stability, with implications for understanding granular materials.
Contribution
We developed a neighbor binning algorithm enabling large-scale simulations of size-disperse sphere packings with power-law distributions, analyzing their structural and mechanical properties.
Findings
Densest packings occur at balanced size distributions.
Mean coordination number of load-bearing particles is always six.
Particles above a critical size dominate mechanical stability.
Abstract
Due to significant computational expense, discrete element method simulations of jammed packings of size-dispersed spheres with size ratios greater than 1:10 have remained elusive, limiting the correspondence between simulations and real-world granular materials with large size dispersity. Invoking a recently developed neighbor binning algorithm, we generate mechanically-stable jammed packings of frictionless spheres with power-law size distributions containing up to nearly four million particles with size ratios up to 1:100. By systematically varying the width and exponent of the underlying power laws, we analyze the role of particle size distributions on the structure of jammed packings. The densest packings are obtained for size distributions that balance the relative abundance of large-large/intermediate and small-small particle contacts. Although the proportion of rattler particles…
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Taxonomy
TopicsMaterial Dynamics and Properties · Protein Structure and Dynamics · Pickering emulsions and particle stabilization
