1998
DOI: 10.1142/s0129183198001345
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Lattice-Gas Simulations of Ternary Amphiphilic Fluid Flow in Porous Media

Abstract: We develop our existing two-dimensional lattice gas model to simulate the flow of single phase, binary immiscible and ternary amphiphilic fluids. This involves the inclusion of fixed obstacles on the lattice, together with the inclusion of "no-slip" boundary conditions. Here we report on preliminary applications of this model to the flow of such fluids within model porous media. We also construct fluid invasion boundary conditions, and the effects of invading aqueous solutions of surfactant on oil-saturated ro… Show more

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Cited by 27 publications
(27 citation statements)
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References 15 publications
(29 reference statements)
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“…Similar algorithms have been found in soil researches, named Markov chain Monte Carlo methods, which also created two-dimensional structures with satisfactory agreements with various scanned real soil structure images [43,44]. Borrowing the spirit of cluster growing theory [45,46], Wang et al have recently developed a random generation-growth method to generate random microstructures of various multiphase micro porous media including granular porous media [47,48] and fibrous porous media [49]. The generated structures have been used to predict effective thermal properties of porous materials and good agreements have been obtained with the existing experimental data [47,48].…”
Section: Introductionmentioning
confidence: 94%
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“…Similar algorithms have been found in soil researches, named Markov chain Monte Carlo methods, which also created two-dimensional structures with satisfactory agreements with various scanned real soil structure images [43,44]. Borrowing the spirit of cluster growing theory [45,46], Wang et al have recently developed a random generation-growth method to generate random microstructures of various multiphase micro porous media including granular porous media [47,48] and fibrous porous media [49]. The generated structures have been used to predict effective thermal properties of porous materials and good agreements have been obtained with the existing experimental data [47,48].…”
Section: Introductionmentioning
confidence: 94%
“…The generated microstructure may be different from a real one in detail, but they have same structure characteristics in statistics. Several methods have been proposed to generate random porous structures in the past few years [36][37][38][39][40][41][42][43][44][45][46][47][48][49]. Here we follow the random generation-growth model for reproducing multiphase granular porous microstructures [47,48] and develop the algorithm into three dimensional cases.…”
Section: Generation Of Random Porous Structuresmentioning
confidence: 99%
“…The flow of oil-water mixtures in porous media has been successfully modelled using this technique [20].…”
Section: Mesoscale Modelling and Simulation Methodsmentioning
confidence: 99%
“…We perform sequential collisions on each of these three subsets, each augmented by the two rest particles to allow them to "communicate." It therefore becomes necessary to construct a lookup table for ten velocities; since there are two bits per velocity, the table is indexed by 20 bits, so the number of possible states is 2 20 , or about IM. Since the state outcomes are encoded into 4 byte words, the lookup table requires 4 Mbytes of local memory, which is easily within the capability of modern multiprocessors.…”
Section: Motivation For the Present Workmentioning
confidence: 99%
“…Finally, we have applied these methods to problems of porous flow and pollution remediation [20]. Fig.…”
Section: Motivation For the Present Workmentioning
confidence: 99%