Effective response in nonlinear random composites
P.M. Hui,
P. Cheung and
Y.R. Kwong
Physica A: Statistical Mechanics and its Applications, 1997, vol. 241, issue 1, 301-309
Abstract:
A self-consistent mean field theory is presented for the effective response in random mixtures consisting of components with power law J-E relations of the form J = χ|E|βE, where J is the current density and E is the electric field. The nonlinear conductors are treated as conductors with a field-dependent conductivity. The mean field theory assumes a uniform local field in each component. The local field is then determined self-consistently. Results are compared with numerical data obtained by simulations on nonlinear random resistor networks. Results for random mixtures consisting of linear and strongly nonlinear components, and of two strongly nonlinear components are presented. The theory also gives a generalization of the Maxwell-Garnett approximation to the case of dilute strongly nonlinear random composites.
Keywords: Nonlinear composites; Macroscopic inhomogeneous media (search for similar items in EconPapers)
Date: 1997
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S037843719700099X
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:241:y:1997:i:1:p:301-309
DOI: 10.1016/S0378-4371(97)00099-X
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 ().