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Hard spheres packings within cylinders: sequential linear programming

A python implementation.

References:

  • J. Norman, E. L. Sorrell, Y. Hu, V. Siripurapu, J. Garcia, J. Bagwell, P. Charbonneau, S. R. Lubkin, and M. Bagnat, Philosophical Transactions of the Royal Society B: Biological Sciences, 373, 1759, 20170320. doi:10.1098/rstb.2017.0320
  • L. Fu, W. Steinhardt, H. Zhao, J. E. Socolar and P. Charbonneau, Soft matter, 2016, 12, 2505-2514. doi:10.1039/C5SM02875B
  • S. Torquato and Y. Jiao, Physical Review E, 2010, 82, 061302. doi:10.1103/PhysRevE.82.061302
  • Torquato-Jiao Sequential Linear Programming Sphere Packing Algorithm https://github.com/sdatkinson/TJ

Summary

Finding the dense packing structure of hard spheres in cylindrical pores by sequantial linear programming (SLP) mthod. This python implementation applied scipy.optimize.linprog

File Description

Folders

  • CircularCylinder/: circular cylinder solver files
  • EllipticalCylinder/: elliptical cylinder solver files

File specification

  • batchrun.py : sample batch running script
  • config.json : configuration file
  • iomodule.py : module related to input and outputs
  • lp.py : sequantial linear programming solver
  • main.py : file to begin with the calculation
  • mcmove.py : conducting random moves
  • neilist.py : neighbor list module
  • randomconfig.py : generating random configurartion
  • tools.py : useful helper functions
  • visualize.py : visualizing the particle coordinates

Prerequisite:

  • Python (2 or 3)
  • numpy, scipy
  • mayavi (for visualize): mayavi may need wxpython

Usage

  • python main.py : conduct one simulation, where input parameters is from config.json
  • python main.py 10 : conduct 10 repeat simulations
  • python visualize.py resultConfig_0.dat : visualize the final configuration, read from resultConfig_0.dat