Cluster growth at the percolation threshold with a finite lifetime of growth sites
Anke Ordemann,
H.Eduardo Roman and
Armin Bunde
Physica A: Statistical Mechanics and its Applications, 1999, vol. 266, issue 1, 92-95
Abstract:
We revisit, by means of Monte Carlo simulations and scaling arguments, the growth model of Bunde et al. (J. Stat. Phys. 47(1987)1) where growth sites have a lifetime τ and are available with a probability p. For finite τ, the clusters are characterized by a crossover mass s×(τ)∝τφ. For masses s⪡s×, the grown clusters are percolation clusters, being compact for p>pc. For s⪢s×, the generated structures belong to the universality class of self-avoiding walk with a fractal dimension df=43 for p=1 and df≅1.28 for p=pc in d=2. We find that the number of clusters of mass s scales as N(s)=N(0)exp[−s/s×(τ)], indicating that in contrary to earlier assumptions, the asymptotic behavior of the structure is determined by rare events which get more unlikely as τ increases.
Keywords: Cluster growth; Incipient percolation cluster; Self-avoiding walk (search for similar items in EconPapers)
Date: 1999
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437198005809
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:266:y:1999:i:1:p:92-95
DOI: 10.1016/S0378-4371(98)00580-9
Access Statistics for this article
Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis
More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().