Characterisation of conduction phenomena in soils at the particle-scale: Finite element analyses in conjunction with synthetic 3D imaging

Narsilio, Guillermo A.; Kress, Jeremy; Yun, Tae Sup

doi:10.1016/j.compgeo.2010.07.002

Cited by 30 publications

(6 citation statements)

References 29 publications

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“…In this work, however, we construct a pore network that more closely represents the physical pore domains in a manner similar to those adopted in past network models of porous, granular media [23,[62][63][64]. Finally, at the macroscopic level, we compute the permeability of each of our samples by performing a finite element simulation of the fluid flow through the pore space, using a model that has been validated against physical experiments [11,[65][66][67] (step 2, Figure 1). …”

Section: A Proposed Frameworkmentioning

confidence: 99%

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Machine learning framework for analysis of transport through complex networks in porous, granular media: A focus on permeability

2016

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We present a data-driven framework to study the relationship between fluid flow at the macroscale and the internal pore structure, across the micro-and meso-scales, in porous, granular media. Sphere packings with varying particle size distribution and confining pressure are generated using the discrete element method. For each sample, a finite element analysis of the fluid flow is performed to compute the permeability. We construct a pore network and a particle contact network to quantify the connectivity of the pores and particles across the mesoscopic spatial scales. Machine learning techniques for feature selection are employed to identify sets of microstructural properties and multiscale complex network features that optimally characterize permeability. We find a linear correlation (in log-log scale) between permeability and the average closeness centrality of the weighted pore network. With the pore network links weighted by the local conductance, the average closeness centrality represents a multiscale measure of efficiency of flow through the pore network in terms of the mean geodesic distance (or shortest path) between all pore bodies in the pore network. Specifically, this study objectively quantifies a hypothesized link between high permeability and efficient shortest paths that thread through relatively large pore bodies connected to each other by high conductance pore throats, embodying connectivity and pore structure.

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Section: A Proposed Frameworkmentioning

confidence: 99%

“…We develop a model for Ottawa sand and sandstone, comprising rounded quartz particles [10], for which DEM can provide an reasonable approximation of real geomechanical behavior [20,67]. The simulation is implemented in Yade [77].…”

Section: B Discrete Element Modelingmentioning

confidence: 99%

Machine learning framework for analysis of transport through complex networks in porous, granular media: A focus on permeability

2016

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“…The latter group of features is less correlated with the sample compression and the resulting effect on the void ratio. The third group of features, 16-20, is both inter-correlated (0.66 on average) and correlated with the compressionrelated features in the first group (1)(2)(3)(4)(5)(6)(7)(8)(9). This demonstrates that the connectivity in the samples is broadly interlinked with the degree of compression.…”

Section: Resultsmentioning

confidence: 99%

“…Our discrete-element model is a simple proxy for sands and sedimentary rocks (i.e., Ottawa sand and sandstone) [2,5,6]. Batches of 400 soft spheres are dropped in a rectangular container with periodic boundary conditions for Figure 1.…”

Section: Discrete Element Modelingmentioning

confidence: 99%

Testing Occam’s razor to characterize high-order connectivity in pore networks of granular media: Feature selection in machine learning

2017

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A perennial challenge for the characterization and modelling of phenomena involving granular media is that the internal connectivity of, and interactions between, the pores and the particles exhibit hallmarks of complexity: multi-scale and nonlinear interactions that lead to a plethora of patterns at the mesoscale, including fluid flow patterns that ultimately render a permeability of the granular media at the macroscale. A multitude of physical parameters exist to characterize geometry and structure, including pore/particle shape, volume and surface area, while a rich class of complex network parameters quantifies internal connectivity of the pore and particles in the material. A large collection of such variables is likely to exhibit a high degree of redundancy.Here we demonstrate how to use feature selection in machine learning theory to identify the most informative and non-redundant, yet parsimonious set of features that optimally characterizes the interstitial flow properties of porous, granular media, e.g., permeability, from high resolution data.

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“…Existing studies on the pore structure based on CT and NMR could only examine several rock or soil samples simultaneously, and were therefore unable to obtain general rules. Certain studies have used the discrete element method (DEM) to generate granular media and investigate their pore structure [24,45,46,47,48,49,50]. These DEM studies focused on the pore structures of mono-sized packing, bi-dispersed packing, and packing with different gradation parameters.…”

Section: Introductionmentioning

confidence: 99%

Pore Structure of Grain-Size Fractal Granular Material

Liu

Jeng

2019

Materials

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Numerous studies have proven that natural particle-packed granular materials, such as soil and rock, are consistent with the grain-size fractal rule. The majority of existing studies have regarded these materials as ideal fractal structures, while few have viewed them as particle-packed materials to study the pore structure. In this study, theoretical analysis, the discrete element method, and digital image processing were used to explore the general rules of the pore structures of grain-size fractal granular materials. The relationship between the porosity and grain-size fractal dimension was determined based on bi-dispersed packing and the geometric packing theory. The pore structure of the grain-size fractal granular material was proven to differ from the ideal fractal structure, such as the Menger sponge. The empirical relationships among the box-counting dimension, lacunarity, succolarity, grain-size fractal dimension, and porosity were provided. A new segmentation method for the pore structure was proposed. Moreover, a general function of the pore size distribution was developed based on the segmentation results, which was verified by the soil-water characteristic curves from the experimental database.

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Characterisation of conduction phenomena in soils at the particle-scale: Finite element analyses in conjunction with synthetic 3D imaging

Cited by 30 publications

References 29 publications

Machine learning framework for analysis of transport through complex networks in porous, granular media: A focus on permeability

Machine learning framework for analysis of transport through complex networks in porous, granular media: A focus on permeability

Testing Occam’s razor to characterize high-order connectivity in pore networks of granular media: Feature selection in machine learning

Pore Structure of Grain-Size Fractal Granular Material

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