Improved Algorithms for Stochastic Pore Network Generation for Porous Materials

Pore network modeling is widely applied to investigate transport phenomena in porous media, as this approach allows for efficient and accurate pore-scale simulation. However, the direct extraction of the pore network (PN) from three-dimensional pore structure images can often not be achieved, due to...

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Bibliographic Details
Published in:Transport in porous media Vol. 152; no. 5; p. 33
Main Authors: Shi, Chengnan, Janssen, Hans
Format: Journal Article
Language:English
Published: Dordrecht Springer Nature B.V 01.05.2025
Subjects:
Algorithms
Complexity
Geometry
Imaging techniques
Moisture
Pore size
Porous materials
Porous media
Random sampling
Topology
Transport phenomena
Transport properties
ISSN:0169-3913, 1573-1634
Online Access:Get full text
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Summary:Pore network modeling is widely applied to investigate transport phenomena in porous media, as this approach allows for efficient and accurate pore-scale simulation. However, the direct extraction of the pore network (PN) from three-dimensional pore structure images can often not be achieved, due to the conflict between the wide pore size range of many porous materials and the limited image size inherent to many imaging techniques. This obstacle is typically overcome by stochastic PN generation, and this paper proposes and assesses improved stochastic algorithms to generate such statistically similar PNs. Four algorithms for geometry generation as well as two algorithms for topology generation are investigated, both qualitatively and quantitatively, for four porous materials with different degrees of complexity. Particularly, with each algorithm, the materials’ unsaturated moisture storage and transport properties are simulated and compared. The results demonstrate that, as the pore structure’s complexity increases, the basic stochastic algorithms available in the literature do not suffice for an accurate and dependable PN generation. The improved geometry and topology generation algorithms put forward in this paper, on the other hand, highly enhance the reliability of the generated PNs, by reducing the deviations for specific moisture contents and permeabilities by 67–98% on average. The improved stochastic algorithms also set the stage for generating PNs of porous materials with (very) wide pore size ranges, and future research can build on these algorithms to generate full-scale PNs using multiple 3D image sets with different resolutions.Article HighlightsBasic stochastic PN generation algorithms in the literature struggle with complex PNs.Improved algorithm boosts both geometry and topology generation accuracy.Stratified random sampling is employed for optimal PN geometry generation.Search range for pore body connections is pivotal in PN topology generation.Lays foundation for generating PNs across a very wide pore size spectrum.
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ISSN:0169-3913
1573-1634
DOI:10.1007/s11242-025-02174-4