Improved algorithm for estimating pore size distribution from pore space images of porous media

Song, Shuai-Bing; Ding, Qi-Le; Wei, Jingna

doi:10.1103/physreve.100.053314

Cited by 14 publications

(8 citation statements)

References 31 publications

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“…This is typically quantified by the pore-size distribution. There has been recent interest in the characteristics of the pore sizes and pore-size distributions [13][14][15][16], as well as their effect on the properties of the materials [17][18][19][20]. Many nanocellular solids such as biopolymer aerogels exhibit pore sizes ranging from a very few nanometers up to 100-150 nm.…”

Section: Introductionmentioning

confidence: 99%

Influence of pore-size distributions and pore-wall mechanics on the mechanical behavior of cellular solids like aerogels

2021

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The pore-size distributions play a critical role in the determination of the properties of nanoporous cellular materials like aerogels. In this paper, we propose a micromechanical model, and by further designing artificial normal pore-size distributions, we inspect their effect on the macroscopic stress-strain curves. We show that the location of the mean pore size as well as the broadness of the distribution strongly affects the overall macroscopic behavior. Moreover, we also show that by using different damage criteria within the proposed model, the elastic, inelastic, and brittle nature of the macroscopic material can be captured. The damage criteria are based on the different modes of deformation in the pore walls, namely, elastic buckling, irreversible bending and brittle collapse under compression, and combined bending and stretching under tension. The proposed model approach serves as a reverse engineering tool to develop cellular solids with desired mechanical properties.

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Section: Introductionmentioning

confidence: 99%

Influence of pore-size distributions and pore-wall mechanics on the mechanical behavior of cellular solids like aerogels

2021

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“…Pore size distribution is another key parameter to characterize the pore structure of porous media. Using the improved algorithm we proposed previously (Song, Liu, et al., 2019; Song, Ding, & Wei, 2019), the corresponding pore size distribution information can be directly extracted from the pore space images of carbonate rock, sandstone and coal with different scales listed in Table 1, the specific results of are shown in Figure 11.…”

Section: Resultsmentioning

confidence: 99%

An Improved Universal Fusion Algorithm for Constructing 3D Multiscale Porous Media

Song

et al. 2021

Water Resources Research

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Various types of porous media materials inherently contain pore structures of different scales, ranging from nanoscale to millimeter scale. Due to the limitations of the existing imaging technology, it is challenging and intractable for any single method to obtain and characterize the multiscale pore structure features of porous media accurately and comprehensively. To address the issue, according to the inherent logical correspondence and mutual conversion relationship between different‐scale image pixels, we propose an improved universal fusion algorithm for constructing three‐dimensional (3D) multiscale porous media. We successfully applied this algorithm to the construction of multiscale pore structure model of carbonate rock, sandstone and coal, and subsequently made quantitative extractions and characterizations of their pore structure characteristics. In addition, the finite element method (FEM) was used to calculate their absolute permeability. The results show that the improved fusion algorithm can effectively solve the problem of pore bias of the existing algorithm, which reduces the porosity of the multiscale model to a certain extent, while maintaining good pore interconnectivity. Besides, the multiscale model obtained by the improved fusion algorithm has a wider pore size distribution interval than that of the existing algorithm, and the absolute permeability of the former, computed using the FEM, is closer to the laboratory‐measured value than that of the latter.

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“…e optimization goal is to find a PSD for which the simulated saturation is close to the measured one. ere are other methods as well that do not require material injection into the porous sample; e.g., imaging techniques [47][48][49] are common.…”

Section: Application For Porosimetry Percolationmentioning

confidence: 99%

A Size‐Perimeter Discrete Growth Model for Percolation Clusters

Bak

Kalmár‐Nagy

2021

Complexity

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Cluster growth models are utilized for a wide range of scientific and engineering applications, including modeling epidemics and the dynamics of liquid propagation in porous media. Invasion percolation is a stochastic branching process in which a network of sites is getting occupied that leads to the formation of clusters (group of interconnected, occupied sites). The occupation of sites is governed by their resistance distribution; the invasion annexes the sites with the least resistance. An iterative cluster growth model is considered for computing the expected size and perimeter of the growing cluster. A necessary ingredient of the model is the description of the mean perimeter as the function of the cluster size. We propose such a relationship for the site square lattice. The proposed model exhibits (by design) the expected phase transition of percolation models, i.e., it diverges at the percolation threshold p c . We describe an application for the porosimetry percolation model. The calculations of the cluster growth model compare well with simulation results.

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Improved algorithm for estimating pore size distribution from pore space images of porous media

Cited by 14 publications

References 31 publications

Influence of pore-size distributions and pore-wall mechanics on the mechanical behavior of cellular solids like aerogels

Influence of pore-size distributions and pore-wall mechanics on the mechanical behavior of cellular solids like aerogels

An Improved Universal Fusion Algorithm for Constructing 3D Multiscale Porous Media

A Size‐Perimeter Discrete Growth Model for Percolation Clusters

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