scholarly journals Generalized Lorentz-Lorenz homogenization formulas for binary lattice metamaterials

2015 ◽  
Vol 91 (20) ◽  
Author(s):  
Valentina Sozio ◽  
Andrea Vallecchi ◽  
Matteo Albani ◽  
Filippo Capolino
Keyword(s):  
1979 ◽  
Vol 20 (5) ◽  
pp. 2034-2060 ◽  
Author(s):  
J. Anthony Gualtieri ◽  
John C. Wheeler

Open Physics ◽  
2008 ◽  
Vol 6 (4) ◽  
Author(s):  
Lajos Szalmás

AbstractWe present a new method in order to obtain variable slip coefficient in binary lattice Boltzmann models to simulate gaseous flows. We present the Boundary layer theory. We study both the single-and multi-fluid BGK-type models as well. The boundary slip and the Knudsen layer are analyzed in detail. Benchmark simulations are carried out in order to compare the analytical derivation with the numerical results. Excellent agreement is found between the two analytical formalism and the numerical simulations.


1999 ◽  
Vol 43 (1) ◽  
pp. 75-85
Author(s):  
S.Y. Hwang ◽  
I.V. Basawa

1980 ◽  
Vol 12 (03) ◽  
pp. 655-671 ◽  
Author(s):  
David K. Pickard

Recently, there has been considerable interest in some specialized binary lattice processes. However, this exciting work has been rather fragmentary and heuristic. Rigorous proofs are provided for the existence of some classes of stationary unilateral Markov fields on infinite square lattices. (Neighbours are sites which are horizontally, vertically, or diagonally adjacent.) Many of the difficulties are avoided by characterizing stationary unilateral Markov fields on finite lattices first. Detailed analyses are given for both binary and Gaussian variables.


1980 ◽  
Vol 17 (03) ◽  
pp. 674-685 ◽  
Author(s):  
Richard J. Kryscio ◽  
Roy Saunders ◽  
Gerald M. Funk

Consider an array of binary random variables distributed over an m 1(n) by m 2(n) rectangular lattice and let Y 1(n) denote the number of pairs of variables d, units apart and both equal to 1. We show that if the binary variables are independent and identically distributed, then under certain conditions Y(n) = (Y 1(n), · ··, Yr (n)) is asymptotically multivariate normal for n large and r finite. This result is extended to versions of a model which provide clustering (repulsion) alternatives to randomness and have clustering (repulsion) parameter values nearly equal to 0. Statistical applications of these results are discussed.


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