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Journal of Porous Media
Factor de Impacto: 1.49 Factor de Impacto de 5 años: 1.159 SJR: 0.43 SNIP: 0.671 CiteScore™: 1.58

ISSN Imprimir: 1091-028X
ISSN En Línea: 1934-0508

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Journal of Porous Media

DOI: 10.1615/JPorMedia.v17.i3.40
pages 225-237

AN ADAPTIVE MONTE CARLO MARKOV CHAIN METHOD APPLIED TO THE FLOW INVOLVING SELF-SIMILAR PROCESSES IN POROUS MEDIA

Juarez S. Azevedo
Centro de Ciencias Exatas e Tecnologicas da UFRB, Centro, 44380-000, Cruz das Almas-BA, Brazil
Gildeberto S. Cardoso
Centro de Ciencias Exatas e Tecnologicas da UFRB, Centro, 44380-000, Cruz das Almas-BA, Brazil
Leizer Schnitman
Programa de Pos-Graduacao em Mecatronica da UFBA, Federacao 40210-630 - Salvador-BA, Brazil

SINOPSIS

This article compares adaptive Markov Chain Monte Carlo methods, known as delayed rejection adaptive metropolis (DRAM), with a widely recommended method called Metropolis Hasting (MH), in self-similar media represented by power-law covariance. To carry out this study, we have considered a parameterization of the permeability field by Karhunen−Loeve expansion on porous media. As the numerical solution is approximated on a finite-dimensional space, this article analyzes the convergence of mean and variance obtained from DRAM and MH, refining the finite dimension of the Karhunen−Loeve expansion. It has been noted that the conditioning of data based on the union of the Karhunen−Loeve expansion of DRAM method performs better than the combination of this expansion with MH. Numerical examples related to transient and steady flow on porous media are given to illustrate the efficiency of the proposed method.


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