Maximally dense packings of two-dimensional convex and concave noncircular particles
Steven Atkinson, Yang Jiao, Salvatore Torquato

TL;DR
This paper extends a stochastic optimization algorithm to find maximally dense packings of various two-dimensional convex and concave particles, revealing diverse packing structures and principles based on particle shape.
Contribution
It introduces an extended algorithm for 2D noncircular particles and identifies densest packings for multiple complex shapes, including concave and curved particles.
Findings
Densest packings of convex and concave particles with central symmetry are lattice packings.
Non-symmetric particles tend to form non-lattice periodic packings.
Particle shape influences the diversity of achievable packing structures.
Abstract
Dense packings of hard particles have important applications in many fields, including condensed matter physics, discrete geometry and cell biology. In this paper, we employ a stochastic search implementation of the Torquato-Jiao Adaptive-Shrinking-Cell optimization scheme [Nature 460, 876 (2009)] to find maximally dense particle packings in d-dimensional Euclidean space . While the original implementation was designed to study spheres and convex polyhedra in , our implementation focuses on and extends the algorithm to include both concave polygons and certain convex or concave non-polygonal particle shapes. We verify the robustness of this packing protocol by successfully reproducing the known putative optimal packings of congruent copies of regular pentagons and octagons, then employ it to suggest dense packing arrangements of congruent copies of certain…
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