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Random sequential adsorption of cubes.

Michał Cieśla1, Piotr Kubala1

  • 1M. Smoluchowski Institute of Physics, Department of Statistical Physics, Jagiellonian University, Łojasiewicza 11, 30-348 Kraków, Poland.

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This study numerically explores random cube packings using sequential adsorption. It identifies efficient algorithms for cube orientation and intersection, analyzing packing fraction and microstructure.

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Area of Science:

  • Computational physics
  • Materials science
  • Statistical mechanics

Background:

  • Understanding the packing of geometric objects is crucial in materials science and physics.
  • Random sequential adsorption (RSA) is a common method for simulating packing processes.

Purpose of the Study:

  • To numerically investigate random packings of cubes using RSA.
  • To compare different cube orientation sampling models and cube-cube intersection algorithms.
  • To analyze the mean saturated packing fraction, packing growth kinetics, and microstructural properties.

Main Methods:

  • Numerical simulation using a random sequential adsorption (RSA) algorithm.
  • Implementation and testing of three distinct cube orientation sampling models.
  • Evaluation of three different cube-cube intersection algorithms for efficiency.
  • Analysis of microstructural properties via the density autocorrelation function.

Main Results:

  • Identification of efficient algorithms for cube orientation and intersection in packing simulations.
  • Characterization of the mean saturated packing fraction for cube packings.
  • Analysis of the kinetics of packing growth.
  • Insights into the microstructural properties of the generated packings.

Conclusions:

  • The study provides a numerical framework for studying random cube packings.
  • Efficient computational methods for cube packing simulations were identified.
  • The research contributes to understanding the statistical properties of granular materials and disordered systems.