• Journal of the Korean Geotechnical Society
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• v.32 no.1
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• pp.55-62
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• 2016

This study presents the numerical results of soil-water characteristic curve for sandy soil by pore network model. The Jumunjin sand is subjected to the high resolution 3D X-ray computed tomographic imaging and its pore structure is constructed by the web of pore body and pore channel. The channel radius, essential to the computation of capillary pressure, is obtained based on the skeletonization and Euclidean Distance transform. The experimentally obtained soil-water characteristic curve corroborates the numerically estimated one. The pore channel radius defined by minimum radii of pore throat results in the slightly overestimation of air entry value, while the overall evolution of capillary pressure resides in the acceptable range. The relative permeability computed by a series of suggested models runs above that obtained by pore network model at high degree of saturation.

• Clean Technology
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• v.12 no.4
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• pp.224-231
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• 2006

Collection efficiencies and pressure drops for the removal of small particles from dilute liquid suspensions by granular bed filter were calculated using network model. The network model is composed of a number of nodes connected with cylindrical bond and particles are deposited on the bond surface. The collection efficiency of each cylindrical bond was predicted using unit cell model corresponding to the pore volume of cylindrical pore both at the initial and transient states. Deposited particles on the collector surface may act as additional collector and reduce the pore size of the collector. As a result, the collection efficiency was improved and pressure drop increased with deposition. Even though the stochastic nature of network requires a large number of simulation work, the model proposed in this study can be used in investigating collection efficiency and pressure drop.

• Smart Structures and Systems
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• v.34 no.1
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• pp.51-62
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• 2024

Application of biopolymers to improve the mechanical properties of soils has been extensively reported. However, a comprehensive understanding of various engineering applications is necessary to enhance their effectiveness. While numerous experimental studies have investigated the use of biopolymers as injection materials, a detailed understanding of their injection behavior in soil through numerical analyses is lacking. This study aimed to address this gap by employing pore network modeling techniques to analyze the injection characteristics of biopolymer solutions in soil. A pore network was constructed from computed tomography images of Ottawa 20-30 sand. Fluid flow simulations incorporated power-law parameters and governing equations to account for the viscosity characteristics of biopolymers. Agar gum was selected as the biopolymer for analysis, and its injection characteristics were evaluated in terms of concentration and pore-size distribution. Results indicate that the viscosity properties of biopolymer solutions significantly influence the injection characteristics, particularly concerning concentration and injection pressure. Furthermore, notable trends in injection characteristics were observed based on pore size and distribution. Importantly, in contrast to previous studies, meaningful correlations were established between the viscosity of the injected fluid, injection pressure, and injection distance. Thus, this study introduces a novel methodology for integrating pore network construction and fluid flow characteristics into biopolymer injections, with potential applications in optimizing field injections such as permeation grouting.

• Geomechanics and Engineering
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• v.39 no.1
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• pp.73-85
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• 2024

There is growing interest in introducing artificial ground freezing (AGF) as a method to temporarily secure unstable ground during tunnel construction. In order to efficiently operate an artificial ground freezing system, basic modeling research is needed on the changes in freezing behavior according to various soil environmental conditions as well as design conditions. In this study, a thermal-hydraulic coupled analysis was performed to simulate the artificial ground freezing process of ground containing salt water. The effect of major variables, including pore water salinity, on artificial ground freezing test performance was investigated. Additionally, an artificial neural network-based prediction model was proposed to estimate the time required to achieve the desired arch thickness. The artificial neural network model demonstrated reliable accuracy (R² = 0.9942) in predicting the time it would take to reach the desired arch thickness. Among the major input variables considered, pore water salinity appeared to be the most influential input variable, and initial soil temperature showed the least importance.

• Journal of IKEEE
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• v.26 no.4
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• pp.647-652
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• 2022

In this paper, we propose a prediction system for skin pore labeling based on a CNN(Convolution Neural Network) model, where a data set is constructed by processing skin images taken by users, and a pore feature image is generated by the proposed image processing algorithm. The skin image data set was labeled for pore characteristics based on the visual classification criteria of skin beauty experts. The proposed image processing algorithm was applied to generate pore feature images from skin images and to train a CNN model that predicts pore feature ratings. The prediction results with pore features by the proposed CNN model is similar to experts visual classification results, where less learning time and higher prediction results were obtained than the results by the comparison model (Resnet-50). In this paper, we describe the proposed image processing algorithm and CNN model, the results of the prediction system and future research plans.

• 한국전산유체공학회:학술대회논문집
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• 2011.05a
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• pp.46-53
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• 2011

Water is continuously produced in polymer electrolyte membrane fuel cell (PEMFC), and is transported and exhausted through polymer electrolyte membrane (PEM), catalyst layer (CL), microporous layer (MPL), and gas diffusion layer (GDL). The low operation temperatures of PEMFC lead to the condensation of water, and the condensed water hinders the transport of reactants in porous layers (MPL and GDL). Thus, water flooding is currently one of hot issues that should be solved to achieve higher performance of PEMFC. This research aims to study liquid water transport in porous layers of PEMFC by using pore-network model, while the microscale pore structure and hydrophilic/hydrophobic surface properties of GDL and MPL were fully considered.

• Korean Journal of Soil Science and Fertilizer
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• v.43 no.4
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• pp.415-423
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• 2010

Pore network models are useful tools to investigate soil pore geometry. These models provide quantitative information of pore geometry from 3D images. This study presents a pore network model to quantify pore structure and hydraulic characteristics. The objectives of this work were to apply the pore network model to characterize pore structure from large images to quantify pore structure, calculate water retention and hydraulic conductivity properties from a three dimensional soil image, and to combine measured hydraulic properties from experiments with calculated hydraulic properties from image. Soil samples were taken from a site located at the Baltimore science center, which is located inside of the city. Undisturbed columns were taken from the site and scanned with a computer tomographer at resolutions of 22 ${\mu}m$. Pore networks were extracted by medial-axis transformation and were used to measure pore geometry from one of the scanned samples. Water retention and unsaturated hydraulic conductivity values were calculated from the soil image. Properties of soil bulk density, water retention and unsaturated hydraulic conductivity were measured from three replicates of scanned soil samples. 3D image analysis provided accurate detailed pore properties such as individual pore volumes, pore length, and tortuosity of all pores. These data made possible to calculate accurate estimations of water retention and hydraulic conductivity. Combination of the calculated and measured hydraulic properties gave more accurate information on pore sizes over wider range than measured or calculated data alone. We could conclude that the hydraulic property computed from soil images and laboratory measurements can describe a full structure of intra- and inter-aggregate pores in soil.

• The Journal of Engineering Geology
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• v.23 no.4
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• pp.447-455
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• 2013

A micro-mechanical pipe network model with the shape of a cube was developed to simulate the behavior of fluid flow through a porous medium. The fluid-flow mechanism through the cubic pipe network channels was defined mainly by introducing a well-known percolation theory (Stauffer and Aharony, 1994). A non-uniform flow generally appeared because all of the pipe diameters were allocated individually in a stochastic manner based on a given pore-size distribution curve and porosity. Fluid was supplied to one surface of the pipe network under a certain driving pressure head and allowed to percolate through the pipe networks. A percolation condition defined by capillary pressure with respect to each pipe diameter was applied first to all of the network pipes. That is, depending on pipe diameter, the fluid may or may not penetrate a specific pipe. Once pore pressures had reached equilibrium and steady-state flow had been attained throughout the network system, Darcy's law was used to compute the resultant permeability. This study investigated the sensitivity of network size to permeability calculations in order to find out the optimum network size which would be used for all the network modelling in this study. Mean pore size and pore size distribution curve obtained from field are used to define each of pipe sizes as being representative of actual oil sites. The calculated and measured permeabilities are in good agreement.

• Journal of the Korean GEO-environmental Society
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• v.10 no.2
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• pp.61-68
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• 2009

This paper presents the numerical investigation for the hydraulic behavior of a fractured rock mass with a hydromechanical interaction which may be considered during the in-situ hydraulic injection test. These experiments consist in a series of flow meter injection tests for fractures existing along an open hole section installed in a borehole, and experimental results are applied for testing a numerical model developed to the analysis and prediction of such hydromechanical interactions. Field experimental results show that conductive fractures form a dynamic and interdependent network, that individual fractures cannot be adequately modeled as independent systems, that new fluid intaking zones generate when pore pressure exceeds the minimum principal stress magnitude in that borehole, and that pore pressures much larger than this minimum stress can be further supported by the circulated fractures. In this study, these characteristics are investigated numerically how to influence the morphology of the natural fracture network in a rock mass by using a discrete fracture ntework model.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.5
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• pp.339-346
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• 2023

The uncertainties of microstructural features affect the properties of materials. Numerous pores that are randomly distributed in materials make it difficult to predict the properties of the materials. The distribution of pores in cementitious materials has a great influence on their mechanical properties. Existing studies focus on analyzing the statistical relationship between pore distribution and material responses, and the correlation between them is not yet fully determined. In this study, the mechanical response of cementitious materials is predicted through an image-based data approach using a convolutional neural network (CNN), and the correlation between pore distribution and material response is analyzed. The dataset for machine learning consists of high-resolution micro-CT images and the properties (tensile strength) of cementitious materials. The microstructures are characterized, and the mechanical properties are evaluated through 2D direct tension simulations using the phase-field fracture model. The attributes of input images are analyzed to identify the spot with the greatest influence on the prediction of material response through CNN. The correlation between pore distribution characteristics and material response is analyzed by comparing the active regions during the CNN process and the pore distribution.