Deep learning in pore scale imaging and modeling
Pore-scale imaging and modeling has advanced greatly through the integration of Deep
Learning into the workflow, from image processing to simulating physical processes. In …
Learning into the workflow, from image processing to simulating physical processes. In …
Large-scale physically accurate modelling of real proton exchange membrane fuel cell with deep learning
Proton exchange membrane fuel cells, consuming hydrogen and oxygen to generate clean
electricity and water, suffer acute liquid water challenges. Accurate liquid water modelling is …
electricity and water, suffer acute liquid water challenges. Accurate liquid water modelling is …
[HTML][HTML] In situ characterization of heterogeneous surface wetting in porous materials
The performance of nano-and micro-porous materials in capturing and releasing fluids, such
as during CO 2 geo-storage and water/gas removal in fuel cells and electrolyzers, is …
as during CO 2 geo-storage and water/gas removal in fuel cells and electrolyzers, is …
Boosting resolution and recovering texture of 2D and 3D micro‐CT images with deep learning
YD Wang, RT Armstrong… - Water Resources …, 2020 - Wiley Online Library
Simulation of flow directly at the pore scale depends on high‐quality digital rock images but
is constrained by detector hardware. A trade‐off between the image field of view (FOV) and …
is constrained by detector hardware. A trade‐off between the image field of view (FOV) and …
DeePore: A deep learning workflow for rapid and comprehensive characterization of porous materials
DeePore 2 is a deep learning workflow for rapid estimation of a wide range of porous
material properties based on the binarized micro–tomography images. By combining …
material properties based on the binarized micro–tomography images. By combining …
Deep neural networks for improving physical accuracy of 2D and 3D multi-mineral segmentation of rock micro-CT images
Segmentation of 3D micro-Computed Tomographic (μ CT) images of rock samples is
essential for further Digital Rock Physics (DRP) analysis, however, conventional methods …
essential for further Digital Rock Physics (DRP) analysis, however, conventional methods …
ML-LBM: predicting and accelerating steady state flow simulation in porous media with convolutional neural networks
Fluid mechanics simulation of steady state flow in complex geometries has many
applications, from the micro-scale (cell membranes, filters, rocks) to macro-scale …
applications, from the micro-scale (cell membranes, filters, rocks) to macro-scale …
Flow-based characterization of digital rock images using deep learning
X-ray imaging of porous media has revolutionized the interpretation of various microscale
phenomena in subsurface systems. The volumetric images acquired from this technology …
phenomena in subsurface systems. The volumetric images acquired from this technology …
The LBPM software package for simulating multiphase flow on digital images of porous rocks
Direct pore scale simulations of two-fluid flow on digital rock images provide a promising tool
to understand the role of surface wetting phenomena on flow and transport in geologic …
to understand the role of surface wetting phenomena on flow and transport in geologic …
Super-resolved segmentation of X-ray images of carbonate rocks using deep learning
Reliable quantitative analysis of digital rock images requires precise segmentation and
identification of the macroporosity, sub-resolution porosity, and solid\mineral phases. This is …
identification of the macroporosity, sub-resolution porosity, and solid\mineral phases. This is …