Random particle packing with large particle size variations using reduced-dimension algorithms

TL;DR

This paper introduces a reduced-dimension Monte Carlo algorithm for simulating particle packing in systems with broad size distributions, providing new microstructural insights beyond existing methods.

Contribution

It extends a previous approach to a quasi-3D method, enabling detailed analysis of microstructures in systems with large particle size variations.

Findings

Calculated packing fraction slightly below the maximally random jammed state

Provides detailed pair distribution functions for composite structures

Offers microstructural information not accessible with previous methods

Abstract

We present a reduced-dimension, ballistic deposition, Monte Carlo particle packing algorithm and discuss its application to the analysis of the microstructure of hard-sphere systems with broad particle size distributions. We extend our earlier approach (the ``central string'' algorithm) to a reduced-dimension, quasi-3D approach. Our results for monomodal hard-sphere packs exhibit a calculated packing fraction that is slightly less than the generally accepted value for a maximally random jammed state. The pair distribution functions obtained from simulations of composite structures with large particle size differences demonstrate that the algorithm provides information heretofore not attainable with existing simulation methods, and yields detailed understanding of the microstructure of these composite systems.