Versatile and efficient pore network extraction method using marker-based watershed segmentation

Abstract

Obtaining structural information from tomographic images of porous materials is a critical component of porous media research. Extracting pore networks is particularly valuable since it enables pore network modeling simulations which can be useful for a host of tasks from predicting transport properties to simulating performance of entire devices. This work reports an efficient algorithm for extracting networks using only standard image analysis techniques. The algorithm was applied to several standard porous materials ranging from sandstone to fibrous mats, and in all cases agreed very well with established or known values for pore and throat sizes, capillary pressure curves, and permeability. In the case of sandstone, the present algorithm was compared to the network obtained using the current state-of-the-art algorithm, and very good agreement was achieved. Most importantly, the network extracted from an image of fibrous media correctly predicted the anisotropic permeability tensor, demonstrating the critical ability to detect key structural features. The highly efficient algorithm allows extraction on fairly large images of 500 3 voxels in just over 200 s. The ability for one algorithm to match materials as varied as sandstone with 20% porosity and fibrous media with 75% porosity is a significant advancement. The source code for this algorithm is provided.

Insights into the Effect of Structural Heterogeneity in Carbonized Electrospun Fibrous Mats for Flow Battery Electrodes by X‐Ray Tomography

“…To simplify the results only the centers of the pores were considered. Centers were found using the recently reported SNOW algorithm . Despite the significant scatter, the result is a linear relationship, which was unexpected given that flow through a porous material is often approximated as flow through a set of cylindrical pipes where velocity scales with the squared‐distance from the solid walls.…”

“…Centers were found using the recently reported SNOW algorithm. [44] Despite the significant scatter, the result is a linear relationship, which was unexpected given that flow through a porous material is often approximated as flow through a set of cylindrical pipes where velocity scales with the squared-distance from the solid walls. This indicates that in this very high porosity fibrous media, that there is very limited interaction and drag between the fluid and solid.…”

Comparison of Electrospun Carbon−Carbon Composite and Commercial Felt for Their Activity and Electrolyte Utilization in Vanadium Redox Flow Batteries

“…Pore network modeling was used to calculate the transport properties of the electrode. A pore network extraction algorithm developed by Gostick was used to identify the pores and the throats connecting the pores within the binarized image obtained from the tomography as described above. The transport properties are calculated using an open source pore network modeling package – OpenPNM .…”

“…Pore network modelling involves the creation of an equivalent network of pores and throats which capture the morphology and transport properties of a porous medium. Image‐based pore network models have been developed, where the pore network is extracted from the image of the pore structure, allowing better representation of the morphology of the porous medium . The pore network determines the morphology as a network of pores and throats where the pores act as reservoirs and the connecting throats define the transport pathways.…”

Virtual Liquid Water Intrusion in Fuel Cell Gas Diffusion Media

“…In the PNM approach, the physical domain is idealized as a computational domain comprised of a network of pores and throats with certain radii. The computational domain is generated either from microstructure images 34,44,48,49 or random networks 3,36-39 calibrated against experimental data such as porosity, mercury intrusion porosimetry or saturation-pressure profiles. Due to the low computation cost of PNMs, it has recently been integrated into a full MEA model to predict the local saturation and effective properties of the GDL.…”