Effective Diffusion in Fibrous Porous Media: A Comparison Study between Lattice Boltzmann and Pore Network Modeling Methods

Abstract

The understanding of the correlation between a pore-scale structure and its coupled diffusion transport property is crucial in the virtual design and performance optimization of porous fibrous material for various energy applications. Two most common and widely employed pore-scale modeling techniques are the lattice Boltzmann method (LBM) and the pore network modeling (PNM). However, little attention has been paid to the direct comparison between these two methods. To this end, stochastic porous fibrous structures are reconstructed reflecting the structural properties of the fibrous porous material on a statistical level with structural properties obtained from X-ray computed microtomography. Diffusion simulation through the porous phase was subsequently conducted using LBM of D3Q7 lattice and topological equivalent PNM derived from the watershed method, respectively. It is detected that the effective diffusion coefficients between these two methods are in good agreement when the throat radius in the pore network is estimated using the cross-section area equivalent radius. Like most literature, the diffusivity in the in-plane (IP) direction is larger than in the through-plane (TP) direction due to the laid fiber arrangement, but the values are very close. Besides, tortuosity was evaluated from both geometry and transport measurements. Tortuosity values deduced from both methods are in line with the anisotropy of the diffusion coefficients.

The Application of REV-LBM Double Mesh Local Refinement Algorithm in Porous Media Flow Simulation

“…Among them, the nodes that need spatial interpolation on the fine block boundary adopt the serendipity element as shown in Figure 5 for interpolation calculation, that is 6 . Since the interpolation element only depends on known nodes, which means different interpolation nodes are independent of each other.…”

“…Among them, the mesoscopic kinetic simulation method based on the mesoscopic kinetic model not only has free constraints in continuity assumptions as the microscopic molecular dynamics simulation does but also maintains the simplicity of the continuous media simulation method which ignores the description of individual molecular motion. Therefore, it is suitable for simulating flow patterns in porous media with micro-nano pore throat structure (e.g., tight sandstone [2,3], shale [4,5], fibrous porous media [6], and porous electrode [7]). This simulation method mainly has lattice Boltzmann method (LBM) [8], direct simulation Monte-Carlo (DSMC) [9], dissipative particle dynamics (DPD) [10], etc.…”

The Impacts of Surface Microchannels on the Transport Properties of Porous Fibrous Media Using Stochastic Pore Network Modeling

“…The material we discussed is a kind of fibrous porous material composed of curved copper fibers of a mean diameter of 100 µm, and these fibers are compressed in the mold and sintered to form a connected fibrous porous structure with a mean pore diameter of about 300 µm [11]; therefore, the diffusion phenomena occur at the meso, and macroscale and are driven by the concentration gradient (molecular diffusion) rather than the collision of gas molecules with pore walls (Knudsen diffusion). In our former works, we applied the watershed segmentation method to obtain the network structure from the 3D images of porous fibrous materials and effectively predicted transport properties (invasion percolation and diffusion) by characterizing the throat radius using the area-equivalent radius [15,16]. To investigate the impact of porosity on the pore network, 3D images of fibrous porous material of different porosities (48%, 61%, 68%, 80%, and 90%) are generated using our earlier methods [23].…”

“…Pore network modeling [14] is a kind of pore-scale model that accounts for the true geometrical heterogeneity but simplified geometrical details of the porous material at the pore scale. Despite the simplification, its validation is testified through a comparison with the Lattice Boltzmann method in our earlier efforts [15,16]. The pore network can be extracted from 3D images [17,18] using imaging processing, but the extraction process is nontrivial, let alone hierarchical materials with multi-scale structures.…”

A Tractable, Transferable, and Empirically Consistent Fibrous Biomaterial Model

“…The drive to obtain more detailed insights and to understand the structure–property relationships of FMs naturally inspires researchers to develop more realistic FM models. Existing approaches to modeling FM structures include physics-driven approaches [ 5 , 6 , 7 ], random sequential adsorption [ 7 , 8 , 9 , 10 , 11 , 12 ], image reconstruction [ 13 , 14 , 15 , 16 ], tessellation [ 1 , 17 , 18 , 19 , 20 ], and stochastic modeling [ 4 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ]. Among these methods, stochastic modeling has attracted attention for its utility in mimicking the real structure of FMs de novo.…”