Robust pore-resolved CFD through porous monoliths reconstructed by micro-computed tomography: From digitization to flow prediction

Abstract

Porous media are ubiquitous in energy storage and conversion, catalysis, biomechanics, hydrogeology, as well as many other fields. These materials possess high surface-to-volume ratios and their complex channels can restrict and guide the flow. However, optimizing design parameters for specific applications remains challenging due to the intricate structure of porous media. Pore-resolved CFD reveals the effects of their structure on flow characteristics, but is limited by the performance of mesh generation algorithms for such complex geometries. To alleviate this issue, we use a sharp immersed boundary method which enables usage of Cartesian, non-conformal grids, within a massively parallel finite element framework. This method preserves the order convergence of the scheme and allows for adaptive mesh refinement (AMR). We introduce a radial basis function-based representation of solids that allows to solve the flow through complex geometries with precision. We verify the method using the method of manufactured solutions. We validate it using pressure drop measurements through porous silicone monoliths digitized by X-ray computed microtomography, for pore Reynolds numbers up to 30. Simulations are conducted using grids of 200 M cells distributed over 8 k cores, which would require 16 times more cells without AMR. Results reveal that pore network structure is the principal factor describing pressure evolution and that preferential channels are dominant at this scale. In this work, we demonstrate a robust and efficient workflow for pore-resolved simulations of porous monoliths. This work bridges the gap between sub-millimetric flow and macroscopic properties, which will open the door to design and optimize processes through the usage of physics-based digital twins of complex porous media.