Cluster identification and percolation analysis using a recursive algorithm

• A recursive algorithm for sampling properties of physical clusters such as size distribution andpercolation is presented. The approach can be applied to any system with periodic boundaryconditions, given a spatial definition of a cluster. We also introduce some modifications in thealgorithm that increases the efficiency considerably if one is only interested in percolationanalysis. The algorithm is implemented in Fortran 90 and is compared with a number ofiterative algorithms. The recursive cluster identification algorithm is somewhat slower than theiterative methods at low volume fraction but is at least as fast at high densities. The percolationanalysis, however, is considerably faster using recursion, for all systems studied. We also notethat the CPU time using recursion is independent on the static allocation of arrays, whereas theiterative method strongly depends on the size of the initially allocated arrays.

Subject headings

NATURVETENSKAP  -- Kemi -- Fysikalisk kemi (hsv//swe)

NATURAL SCIENCES  -- Chemical Sciences -- Physical Chemistry (hsv//eng)

Keyword

cluster analysis

percolation

recursion

Fortran 90

algorithm

in-oil microemulsions

transitions

mercury

model

Physical chemistry

Fysikalisk kemi

fysikalisk kemi

Physical Chemistry

Publication and Content Type

ref (subject category)

art (subject category)