World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
55
Citations
8922
World Ranking
3077
National Ranking
161

Overview

Ryan T. Armstrong is affiliated with the University of New South Wales in Australia and focuses their research primarily within the field of Engineering. Their work covers several subfields including Ocean Engineering, Mechanics of Materials, Mechanical Engineering, Environmental Engineering, and Geophysics.

Their research topics encompass a variety of areas relevant to energy and environmental sciences. Notable topics include Enhanced Oil Recovery Techniques, Hydrocarbon Exploration and Reservoir Analysis, Hydraulic Fracturing and Reservoir Analysis, Groundwater Flow and Contamination Studies, Seismic Imaging and Inversion Techniques, Coal Properties and Utilization, and CO2 Sequestration and Geologic Interactions.

Among recent publications authored or co-authored by Ryan T. Armstrong are:

  • Deep learning in pore scale imaging and modeling, 2021, Earth-Science Reviews
  • In-situ hydrogen wettability characterisation for underground hydrogen storage, 2022, International Journal of Hydrogen Energy
  • Automated lithology classification from drill core images using convolutional neural networks, 2020, Journal of Petroleum Science and Engineering
  • Large-scale physically accurate modelling of real proton exchange membrane fuel cell with deep learning, 2023, Nature Communications
  • Deep neural networks for improving physical accuracy of 2D and 3D multi-mineral segmentation of rock micro-CT images, 2021, Applied Soft Computing

Ryan T. Armstrong regularly collaborates with several researchers, including:

  • Peyman Mostaghimi
  • Ying Da Wang
  • James E. McClure
  • Kunning Tang
  • Chenhao Sun

Common venues for publishing their work include:

  • arXiv (Cornell University)
  • Water Resources Research
  • Transport in Porous Media
  • Zenodo (CERN European Organization for Nuclear Research)
  • Fuel

Best Publications

  • From connected pathway flow to ganglion dynamics

    M. Rücker;M. Rücker;S. Berg;R. T. Armstrong;A. Georgiadis

  • Porosity and permeability characterization of coal: A micro-computed tomography study

    Hamed Lamei Ramandi;Peyman Mostaghimi;Ryan T. Armstrong;Mohammad Saadatfar

  • Beyond Darcy's law: The role of phase topology and ganglion dynamics for two-fluid flow

    Ryan T. Armstrong;James E. McClure;Mark A. Berrill;Maja Rücker

  • Interfacial velocities and capillary pressure gradients during Haines jumps.

    Ryan T. Armstrong;Steffen Berg

  • Deep learning in pore scale imaging and modeling

    Ying Da Wang;Martin J. Blunt;Ryan T. Armstrong;Peyman Mostaghimi

  • Linking pore-scale interfacial curvature to column-scale capillary pressure

    Ryan T. Armstrong;Mark L. Porter;Dorthe Wildenschild

  • Pore-scale displacement mechanisms as a source of hysteresis for two-phase flow in porous media

    S. Schlüter;S. Schlüter;S. Berg;M. Rücker;M. Rücker;R. T. Armstrong

  • Critical capillary number: Desaturation studied with fast X‐ray computed microtomography

    Ryan T. Armstrong;Apostolos Georgiadis;Holger Ott;Denis Klemin

  • Porous Media Characterization Using Minkowski Functionals: Theories, Applications and Future Directions

    Ryan T. Armstrong;James E. McClure;Vanessa Robins;Zhishang Liu

  • In-situ hydrogen wettability characterisation for underground hydrogen storage

    Unknown

  • Large-scale physically accurate modelling of real proton exchange membrane fuel cell with deep learning

    Unknown

  • Cleat-scale characterisation of coal: An overview

    Peyman Mostaghimi;Ryan T. Armstrong;Alireza Gerami;Yibing Hu

  • Connected pathway relative permeability from pore-scale imaging of imbibition

    S. Berg;M. Rücker;M. Rücker;H. Ott;H. Ott;A. Georgiadis

  • Fast X-ray Micro-Tomography of Multiphase Flow in Berea Sandstone: A Sensitivity Study on Image Processing

    L. Leu;L. Leu;S. Berg;F. Enzmann;R. T. Armstrong;R. T. Armstrong

  • Modeling the velocity field during Haines jumps in porous media

    Ryan T. Armstrong;Nikolay Evseev;Dmitry Koroteev;Steffen Berg

  • Automated lithology classification from drill core images using convolutional neural networks

    Fatimah Alzubaidi;Peyman Mostaghimi;Pawel Swietojanski;Stuart R. Clark

  • Machine learning for predicting properties of porous media from 2d X-ray images

    Naif Alqahtani;Fatimah Alzubaidi;Ryan T. Armstrong;Pawel Swietojanski

  • Coal cleat reconstruction using micro-computed tomography imaging

    Yu Jing;Ryan T. Armstrong;Hamed Lamei Ramandi;Peyman Mostaghimi

  • Enhancing Resolution of Digital Rock Images with Super Resolution Convolutional Neural Networks

    Ying Da Wang;Ryan T. Armstrong;Peyman Mostaghimi

  • Deep neural networks for improving physical accuracy of 2D and 3D multi-mineral segmentation of rock micro-CT images

    Ying Da Wang;Mehdi Shabaninejad;Ryan T. Armstrong;Peyman Mostaghimi

  • Rough-walled discrete fracture network modelling for coal characterisation

    Yu Jing;Ryan T. Armstrong;Peyman Mostaghimi

  • Geometric state function for two-fluid flow in porous media

    James E. McClure;Ryan T. Armstrong;Mark A. Berrill;Steffen Schlüter

  • Trapping and hysteresis in two‐phase flow in porous media: A pore‐network study

    V Joekar-Niasar;Florian Doster;R. T. Armstrong;D. Wildenschild

Frequent Co-Authors

Peyman Mostaghimi
Peyman Mostaghimi University of New South Wales
Steffen Berg
Steffen Berg Shell (Netherlands)
Dorthe Wildenschild
Dorthe Wildenschild Oregon State University
Michael Kersten
Michael Kersten Johannes Gutenberg University of Mainz
Steffen Schlüter
Steffen Schlüter Helmholtz Centre for Environmental Research
Christoph H. Arns
Christoph H. Arns University of New South Wales
William G. Gray
William G. Gray University of North Carolina at Chapel Hill
Majid Ebrahimi Warkiani
Majid Ebrahimi Warkiani University of Technology Sydney
Klaus Regenauer-Lieb
Klaus Regenauer-Lieb University of New South Wales
Alexander G. Schwing
Alexander G. Schwing University of Illinois at Urbana-Champaign

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