Zobrazeno 1 - 10
of 142
pro vyhledávání: '"Ryan T. Armstrong"'
Autor:
Yu Jing, Aaron Uthaia Kumaran, Damion Howard Read Stimson, Karine Mardon, Ljubco Najdovski, Ryan T. Armstrong, Peyman Mostaghimi
Publikováno v:
Scientific Data, Vol 10, Iss 1, Pp 1-7 (2023)
Abstract Positron Emission Tomography (PET) imaging has demonstrated its capability in providing time-lapse fluid flow visualisation for improving the understanding of flow properties of geologic media. To investigate the process of CO2 geo-sequestra
Externí odkaz:
https://doaj.org/article/2640d3936046407990970d7b6d22bb79
Autor:
Ying Da Wang, Quentin Meyer, Kunning Tang, James E. McClure, Robin T. White, Stephen T. Kelly, Matthew M. Crawford, Francesco Iacoviello, Dan J. L. Brett, Paul R. Shearing, Peyman Mostaghimi, Chuan Zhao, Ryan T. Armstrong
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-15 (2023)
Accurate liquid water modelling is challenging. Here the authors use X-ray micro-computed tomography, deep learned super-resolution, multi-label segmentation, and direct multiphase simulation to simulate fuel cell and guide fuel cell design.
Externí odkaz:
https://doaj.org/article/851c8a1d85f14100abb76283ba3a7a9c
Autor:
Maja Rücker, Apostolos Georgiadis, Ryan T. Armstrong, Holger Ott, Niels Brussee, Hilbert van der Linde, Ludwig Simon, Frieder Enzmann, Michael Kersten, Steffen Berg
Publikováno v:
Frontiers in Water, Vol 3 (2021)
Core flooding experiments to determine multiphase flow in properties of rock such as relative permeability can show significant fluctuations in terms of pressure, saturation, and electrical conductivity. That is typically not considered in the Darcy
Externí odkaz:
https://doaj.org/article/3857c11a7eac41d39ab84e58a5e1245a
Akademický článek
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Publikováno v:
Transport in Porous Media. 144:825-847
X-ray micro-computed tomography (micro-CT) has been widely leveraged to characterise the pore-scale geometry of subsurface porous rocks. Recent developments in super-resolution (SR) methods using deep learning allow for the digital enhancement of low
Autor:
Fatimah Alzubaidi, Harikrishnan Nalinakumar, Stuart R. Clark, Jan Erik Lie, Peyman Mostaghimi, Ryan T. Armstrong
Publikováno v:
Mathematical Geosciences.
While machine learning (ML) provides a great tool for image analysis, obtaining accurate fracture segmentation from high-resolution core images is challenging. A major reason is that the segmentation quality of large and detailed objects, such as fra
Autor:
Naif J. Alqahtani, Yufu Niu, Ying Da Wang, Traiwit Chung, Zakhar Lanetc, Aleksandr Zhuravljov, Ryan T. Armstrong, Peyman Mostaghimi
Publikováno v:
Transport in Porous Media. 143:497-525
Reliable quantitative analysis of digital rock images requires precise segmentation and identification of the macroporosity, sub-resolution porosity, and solid\mineral phases. This is highly emphasized in heterogeneous rocks with complex pore size di
Publikováno v:
Rock Mechanics and Rock Engineering. 55:3719-3734
Mineral and hydrocarbon exploration relies heavily on geological and geotechnical information extracted from drill cores. Traditional drill-core characterization is based purely on the subjective expertise of a geologist. New technologies can provide
Autor:
Arash Rabbani, Chenhao Sun, Masoud Babaei, Vahid J. Niasar, Ryan T. Armstrong, Peyman Mostaghimi
Publikováno v:
Geoenergy Science and Engineering. 227:211807
DeepAngle is a machine learning-based method to determine the contact angles of different phases in the tomography images of porous materials. Measurement of angles in 3--D needs to be done within the surface perpendicular to the angle planes, and it
Autor:
Mohammad Ebadi, Ryan T. Armstrong, Peyman Mostaghimi, Ying Da Wang, Naif Alqahtani, Tammy Amirian, Lesley Anne James, Arvind Parmar, David Zahra, Hasar Hamze, Dmitry Koroteev
Publikováno v:
Water Resources Research. 58