Abstract

In this work, we explore the influence of the grain size distribution (GSD) on density, connectivity and internal forces distributions, for both 2D and 3D granular packings built mechanically. For power law GSDs, we show that there is an exponent for which density and connectivity are optimized, and this exponent is close to those that characterize other well known GSDs such as the Fuller and Thompson distribution and the Appollonian packing. In addition, we studied the distributions of normal forces, finding that these can be well described by a power-law tail, specially for the GSDs with large size span. These results highlight the role of the GSD on internal structure and suggest important consequences on macroscopic properties.

Highlights

  • Increasing ηPacking materials optimally is an ancient problem with roots on the simple storage of agricultural and manufac- probability density functions of normal forces (PDF)[−]turing products, and still has implications even on seemingly unrelated topics such as secure coding through the Kepler problem [1]

  • In this work we explored the influence of the grain size distribution (GSD), in both 2D and 3D systems, on some packing descriptors such as density, connectivity, and internal forces distributions

  • By varying systematically the GSD described by eq (2) and using large size spans, we were able to investigate the influence of the GSD on the internal packing structure

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Summary

Introduction

Turing products, and still has implications even on seemingly unrelated topics such as secure coding through the Kepler problem [1]. Ρ is the cumulative volume fraction, d is the diameter, these samples, the number of particles must vary wildly in dmax is the maximal diameter, and the size span (ratio of the largest diameter to the smallest one) is very large. This order to maintain a maximum difference of 5% between the numerical data and eq (2). We find that for some systems κ can reach values as high as 95% This means that only a small subset of particles carries the force network.

Force distributions
Findings
Conclusions
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