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Ji-Youn Arns

Researcher at University of New South Wales

Publications -  34
Citations -  922

Ji-Youn Arns is an academic researcher from University of New South Wales. The author has contributed to research in topics: Relative permeability & Porous medium. The author has an hindex of 16, co-authored 33 publications receiving 699 citations. Previous affiliations of Ji-Youn Arns include Australian National University.

Papers
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Journal ArticleDOI

Effect of Network Topology on Relative Permeability

TL;DR: In this article, the role of topology on drainage relative permeabilities derived from network models is investigated and the importance of accurately reproducing sample topology when deriving relative permeability curves from the model networks is discussed.
Journal ArticleDOI

Relative Permeability from Tomographic Images: Effect of Correlated Heterogeneity

TL;DR: In this paper, the authors examine the calculation of relative permeability and residual saturation using networks derived from tomographic images of Fontainebleau sandstone previously used to successfully calculate single-phase transport properties.
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High-fidelity replication of thermoplastic microneedles with open microfluidic channels.

TL;DR: A novel manufacturing method that may overcome the complexity of hollow microneedle fabrication is reported that involves direct printing from computer-aided design (CAD) drawing without the constraints imposed by subtractive machining or etching processes.
Proceedings ArticleDOI

Permeability Upscaling for Carbonates from the Pore-Scale Using Multi-Scale Xray-CT Images

TL;DR: In this paper, a key part of the study is the establishment of porosity transforms between high-resolution and low-resolution images to arrive at a calibrated porosity map to constraint permeability estimates for the whole core.
Journal ArticleDOI

Porous Structure Reconstruction Using Convolutional Neural Networks

TL;DR: A convolutional neural network reconstruction method is introduced to reconstruct high-resolution porous structures based on low-resolution µ-CT images and high- resolution scanning electron microscope (SEM) images to achieve state-of-the-art performance.