Porous Media Flows: General
ORAL · T23 · ID: 681488
Presentations
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Characterizing flow through artificial deformable porous media
ORAL
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Presenters
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Raunak Basak
University of British Columbia
Authors
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Raunak Basak
University of British Columbia
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Boris Stoeber
University of British Columbia
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Prediction of 3D Velocity and Temperature Field of Reticulated Foams using Deep Learning
ORAL
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Presenters
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Danny Ko
University of California, Los Angeles
Authors
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Danny Ko
University of California, Los Angeles
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Hangjie Ji
North Carolina State University
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Y. Sungtaek Ju
University of California, Los Angeles
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A Hybrid Experimental-Numerical Approach to Study the Evolution of Porous Media during Biomineralization.
ORAL
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Publication: Evolution of Porosity-Permeability relationship in bio-mediated processes for ground improvement: a pore-scale computational study.
Presenters
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Sina Nassiri
University of Akron
Authors
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Sina Nassiri
University of Akron
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SeyedArmin MotahariTabari
PhD Student, University of Akron, Akron, OH, PhD student, Department of Civil Engineering, University of Akron, Akron, OH
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Nariman Mahabadi
University of Akron, Assistant Professor, Department of Civil Engineering, University of Akron, Akron, OH
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Prediction of the velocity distribution in porous media from the pore volume distribution
ORAL
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Publication: RELATIONSHIP BETWEEN PORE FLUID VELOCITY DISTRIBUTION AND PORE SIZE DISTRIBUTION<br>Vi T. Nguyen, Ngoc H. Pham, Dimitrios V. Papavassiliou (submitted).<br>
Presenters
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Vi Nguyen
University of Oklahoma
Authors
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Vi Nguyen
University of Oklahoma
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Dimitrios V Papavassiliou
University of Oklahoma
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Formation and collapse of gas cavities in a soft porous medium
ORAL
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Publication: Fluid–fluid phase separation in a soft porous medium. OW Paulin, LC Morrow, MG Hennessy, and CW MacMinn. Journal of the Mechanics and Physics of Solids, 164:104892, 2022.
Presenters
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Oliver Paulin
University of Oxford
Authors
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Oliver Paulin
University of Oxford
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Liam Morrow
University of Oxford
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Matthew Hennessy
University of Bristol
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Chris W MacMinn
University of Oxford
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Finite PINN Net: Physics-informed deep convolutional neural networks for learning 3D transient Darcy flows in heterogeneous porous media
ORAL
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Presenters
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Mohammad Sarabian
OriGen.ai, Inc
Authors
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Mohammad Sarabian
OriGen.ai, Inc
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Pablo Ruiz Mataran
OriGen.AI, Inc
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Ruben Rodriguez Torrado
OriGen.AI, Inc
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Net Flow Through Soft Porous Media Generated by Periodic Mean-Zero Pressure Gradient
ORAL
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Presenters
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Jacob Stein
University of Minnesota
Authors
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Jacob Stein
University of Minnesota
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Jeffrey Tithof
University of Minnesota, U Minnesota
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Mass transport in Channels with Porous Walls
ORAL
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Presenters
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Alex J Warhover
Georgia Institute of Technology
Authors
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Alex J Warhover
Georgia Institute of Technology
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Marc A Guasch
Georgia Institute of Technology
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Michael F F Schatz
Georgia Institute of Technology
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Roman O Grigoriev
Georgia Institute of Technology
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Super resolution-assisted pore flow field prediction using neural networks
ORAL
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Publication: Neural network–based pore flow field prediction in porous media using super resolution, Physical Review Fluids 7, 074302 (2022)
Presenters
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Xu-Hui Zhou
Virginia Tech
Authors
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Xu-Hui Zhou
Virginia Tech
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James McClure
Virginia Tech
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Cheng Chen
Stevens Institute of Technology
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Heng Xiao
Virginia Tech
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Dynamics of fluid-driven fractures in the viscous-dominated regime
ORAL
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Presenters
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Sri Savya Tanikella
University of California, Santa Barbara
Authors
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Sri Savya Tanikella
University of California, Santa Barbara
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Emilie Dressaire
University of California, Santa Barbara
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Low-resolution magnetic resonance velocimetry in porous media: comparison with Navier Stokes
ORAL
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Publication: Bruschewski, M.; Flint, S.; Becker, S. Magnetic Resonance Velocimetry Measurement of Viscous Flows through Porous Media: Comparison with Simulation and Voxel Size Study. Physics 2021, 3, 1254-1267. https://doi.org/10.3390/physics3040079
Presenters
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Sid BECKER
University of Canterbury
Authors
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Sid BECKER
University of Canterbury
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Martin Bruschewski
University of Rostock
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Sam Flint
University of Canterbury
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Fluid Flow Prediction in Porous Media using Sparse Data and Physics-Informed PointNet
ORAL
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Presenters
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Ali Kashefi
Stanford University
Authors
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Ali Kashefi
Stanford University
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Tapan Mukerji
Stanford University
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