Microstructure characterization of anisotropic media using pore-network models

Characterization of porous media microstructures and flow performance predictions are fundamental for a wide range of applications. However, these tasks become increasingly challenging as smaller particulates are targeted and finer media pursued. Pore network models (PNM) have been widely applied for different media morphologies and a variety of physical and chemical processes. In this study, the application of PNM is investigated for characterizing anisotropic media computationally and differentiating their flow performance. The media samples’ SEM images are analyzed, and representative elementary volume (REV) is quantified, both of which form the basis for PNM construction. Different PNM construction methods are compared, including layered lattice-based networks and random networks using tessellation (Ref. 1). Their modeling efficiency and accuracy in simulating media macroscopic properties are evaluated. Using pathfinding algorithms (Ref. 2), geometric tortuosity is calculated from the networks. Additionally, microscopic parameters such as pore spacing and connectivity are analyzed to gain insights into media performance. The findings inform our design of next-generation media materials.

Ref. 1: Gostick et al. Comput Sci Eng. 2016 Jul; 18(4) 60-74

Ref. 2: Al-Raoush, Madhoun. Powder Technol. 2017 Oct; 320 99-107