Statistical approach of random packing of polydisperse similar hyperspheres: with application to binomial and lognormal size distributions
Abstract
This paper addresses the packing fraction of polydisperse similar hyperspheres with a discrete binomial and continuous lognormal particle size distribution. First, a closed form expression is derived of a mix consisting of multiple discrete particles in D-dimensional Euclidean space R^D with small size differences. A recently developed analytical model for binary packings with small size ratio u is used, here applying it to the statistically probable particle pairs in the multi-sized mix. The new model is subsequently applied to discretely sized particles with a binomial number distribution and a constant size ratio u between each subsequent particle size. By taking the limit of u -> 1 and an infinite number of particles sizes, the binomial particle distribution is transformed into a continuous lognormal distribution. The resulting expression for the packing fraction can be solved in closed form, resulting in an original expression for the packing fraction of mixtures of hyperparticles with a lognormal distribution. This explicit expression is governed by the standard deviation, the Euclidean dimension D, and the monosized packing fraction of the considered particle shape. For a number of particle shapes and their packing modes (from looser to denser) the scaled expression is thoroughly compared with data reported in the literature. They concern packed particles with small standard deviations and in Euclidean dimensions D = 2 and 3, and good agreement is found. For large standard deviations the scaled packing fraction approaches a limiting value, which appears not to depend on the Euclidean dimension. Based on the insight, a closed form expression is put forward for the packing fraction that matches the small standard deviation solution, and the large standard deviation asymptote, so covering arbitrary standard deviations, Euclidean dimensions, particle types, and their mode of packing.
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BibTeX
@misc{brouwers2026statistical,
title = {{Statistical approach of random packing of polydisperse similar hyperspheres: with application to binomial and lognormal size distributions}},
author = {H. J. H. Brouwers},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.04703},
eprint = {2611.04703},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.04703},
}
RIS
TY - UNPB
TI - Statistical approach of random packing of polydisperse similar hyperspheres: with application to binomial and lognormal size distributions
AU - H. J. H. Brouwers
PY - 2026
DA - 2026/11/03
PB - arXiv
DO - 10.48550/arxiv.2611.04703
UR - https://arxiv.org/abs/2611.04703
AB - This paper addresses the packing fraction of polydisperse similar hyperspheres with a discrete binomial and continuous lognormal particle size distribution. First, a closed form expression is derived of a mix consisting of multiple discrete particles in D-dimensional Euclidean space R^D with small size differences. A recently developed analytical model for binary packings with small size ratio u is used, here applying it to the statistically probable particle pairs in the multi-sized mix. The new model is subsequently applied to discretely sized particles with a binomial number distribution and a constant size ratio u between each subsequent particle size. By taking the limit of u -> 1 and an infinite number of particles sizes, the binomial particle distribution is transformed into a continuous lognormal distribution. The resulting expression for the packing fraction can be solved in closed form, resulting in an original expression for the packing fraction of mixtures of hyperparticles with a lognormal distribution. This explicit expression is governed by the standard deviation, the Euclidean dimension D, and the monosized packing fraction of the considered particle shape. For a number of particle shapes and their packing modes (from looser to denser) the scaled expression is thoroughly compared with data reported in the literature. They concern packed particles with small standard deviations and in Euclidean dimensions D = 2 and 3, and good agreement is found. For large standard deviations the scaled packing fraction approaches a limiting value, which appears not to depend on the Euclidean dimension. Based on the insight, a closed form expression is put forward for the packing fraction that matches the small standard deviation solution, and the large standard deviation asymptote, so covering arbitrary standard deviations, Euclidean dimensions, particle types, and their mode of packing.
ER -