Open AccessA Genetic Algorithm for Predicting Pore Geometry Based on Air Permeability Measurements
Author(s) -
Unsal E.,
Dane J. H.,
Dozier G. V.
Publication year - 2005
Publication title -
vadose zone journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.036
H-Index - 81
ISSN - 1539-1663
DOI - 10.2136/vzj2004.0116
Subject(s) - permeability (electromagnetism) , porous medium , porosity , air permeability specific surface , capillary action , materials science , relative permeability , geometry , mechanics , geology , biological system , mineralogy , algorithm , geotechnical engineering , computer science , mathematics , chemistry , composite material , physics , membrane , biochemistry , layer (electronics) , biology
Pore size distributions of porous media are of interest to soil scientists, geologists, and engineers with a variety of backgrounds. If known, pore size distributions can be used to determine fluid retention and permeability relationships. In this study, we propose a methodology to predict pore size distributions from air permeability measurements combined with a numerical model representing a porous medium. The model is an extension of the capillary model, which was modified so that the capillaries are composed of sections with different diameters. An optimization scheme that makes use of the measured air permeability values was developed to predict the best possible pore size distribution and pore arrangement. A genetic algorithm, a popular evolutionary computational methodology, was chosen for the optimization process. During our numerical study, we observed that it is not only the pore size distribution that is important, but also how the pores are distributed, in other words, the pore geometry.