What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Thermodynamics
- Mechanics
His main research concerns Mechanics, Multiphase flow, Capillary action, Porous medium and Coal mining.
His Mechanics research incorporates elements of Saturation, Geotechnical engineering, Displacement and Capillary number.
As a part of the same scientific family, Ryan T. Armstrong mostly works in the field of Geotechnical engineering, focusing on Image segmentation and, on occasion, Permeability.
The study of Capillary action is intertwined with the study of Capillary pressure in a number of ways.
His Porous medium study combines topics from a wide range of disciplines, such as Two-phase flow and Hysteresis.
His Coal mining research incorporates themes from Mineralogy and Fracture.
His most cited work include:
- From connected pathway flow to ganglion dynamics (131 citations)
- Linking pore-scale interfacial curvature to column-scale capillary pressure (124 citations)
- Porosity and permeability characterization of coal: A micro-computed tomography study (123 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Porous medium, Mechanics, Multiphase flow, Coal and Mineralogy.
His Porous medium study is related to the wider topic of Porosity.
His study in Mechanics is interdisciplinary in nature, drawing from both Saturation, Displacement and Capillary action, Capillary number.
His Capillary action research is multidisciplinary, incorporating elements of Geotechnical engineering and Capillary pressure.
The study incorporates disciplines such as Wetting, Statistical physics, Two-phase flow and Relative permeability in addition to Multiphase flow.
His Mineralogy research is multidisciplinary, relying on both Tomography, Oil shale and Petrophysics, Permeability.
He most often published in these fields:
- Porous medium (27.88%)
- Mechanics (27.27%)
- Multiphase flow (18.79%)
What were the highlights of his more recent work (between 2019-2021)?
- Porous medium (27.88%)
- Artificial intelligence (11.52%)
- Mechanics (27.27%)
In recent papers he was focusing on the following fields of study:
Ryan T. Armstrong mainly focuses on Porous medium, Artificial intelligence, Mechanics, Relative permeability and Convolutional neural network.
His Porous medium study incorporates themes from Flow, Permeability, Grayscale, Fluid dynamics and Capillary action.
His Capillary action research is multidisciplinary, incorporating perspectives in Detailed balance and Capillary pressure.
His work in the fields of Mechanics, such as Multiphase flow, Lattice Boltzmann methods, Steady state and Computer simulation, overlaps with other areas such as Sorption.
Ryan T. Armstrong has researched Multiphase flow in several fields, including Wetting, Topology, State function and Classification of discontinuities.
In his study, Porosity is inextricably linked to Tomography, which falls within the broad field of Pore scale.
Between 2019 and 2021, his most popular works were:
- Machine learning for predicting properties of porous media from 2d X-ray images (26 citations)
- Accelerated computation of relative permeability by coupled morphological and direct multiphase flow simulation (17 citations)
- Boosting Resolution and Recovering Texture of 2D and 3D Micro‐CT Images with Deep Learning (16 citations)
In his most recent research, the most cited papers focused on:
- Thermodynamics
- Artificial intelligence
- Mechanics
The scientist’s investigation covers issues in Artificial intelligence, Relative permeability, Multiphase flow, Porous medium and Convolutional neural network.
The Artificial intelligence study combines topics in areas such as Micro ct and Computer vision.
His Relative permeability research includes themes of Voxel and Finite volume method.
Multiphase flow is a subfield of Mechanics that Ryan T. Armstrong tackles.
Mechanics is often connected to Capillary action in his work.
His work carried out in the field of Porous medium brings together such families of science as Image, Grayscale and Binary image.
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