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Pore network modelling of molecular diffusion in a single‐block model during lean gas injection, a comparative study on calculation approaches
Author(s) -
Mashayekhizadeh Vahid,
Rasaei Mohammad Reza
Publication year - 2019
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.23222
Subject(s) - mass transfer , diffusion , block (permutation group theory) , mass transfer coefficient , porosity , matrix (chemical analysis) , range (aeronautics) , chemistry , component (thermodynamics) , algorithm , flow (mathematics) , mechanics , thermodynamics , materials science , chromatography , computer science , mathematics , physics , geometry , organic chemistry , composite material
In this paper, we have proposed a new algorithm for pore network modelling of molecular diffusion in a single matrix block model. The block contains a volatile component adjacent to a gaseous lean component to mimic high capillarity‐induced flow rates caused by either high liquid to gas mass transfer rates and/or small throat size of the porous medium. Various types of boundary conditions for liquid pressure calculation are formulated. Advantages and the range of applicability of this algorithm are compared against an existing algorithm. A threshold value for surface mass transfer coefficient is obtained above which the algorithms that are based on the rule that only one bond is invaded at each step of invasion are no longer applicable. Simultaneous multiple throat invasion in a single time step, unnecessity of liquid cluster identification, and possibility of throat refilling are the major advantages of the new pressure‐based algorithm. While the phase distribution in the old approach is insensitive to surface mass transfer coefficient the proposed approach is severely dependent on the magnitude of this coefficient. The incident in which the liquid body is detached from the top surface in the proposed algorithm is also a strong function of surface mass transfer coefficient. The advantages of the new algorithm, however, come with a higher computational cost, especially for large network sizes.