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 …
Leveraging machine learning in porous media
The emergence of artificial intelligence (AI) and, more particularly, machine learning (ML),
has had a significant impact on engineering and the fundamental sciences, resulting in …
has had a significant impact on engineering and the fundamental sciences, resulting in …
Computationally efficient multiscale neural networks applied to fluid flow in complex 3D porous media
The permeability of complex porous materials is of interest to many engineering disciplines.
This quantity can be obtained via direct flow simulation, which provides the most accurate …
This quantity can be obtained via direct flow simulation, which provides the most accurate …
Investigation on the lubrication heat transfer mechanism of the multilevel gearbox by the lattice boltzmann method
Q Li, P Xu, L Li, W Xu, D Tan - Processes, 2024 - mdpi.com
In a gear transmission system in a closed space, the heat transfer between gears and fluids
presents highly nonlinear characteristics due to the complex physical processes involved in …
presents highly nonlinear characteristics due to the complex physical processes involved in …
From computational fluid dynamics to structure interpretation via neural networks: an application to flow and transport in porous media
The modeling of flow and transport in porous media is of the utmost importance in many
chemical engineering applications, including catalytic reactors, batteries, and CO2 storage …
chemical engineering applications, including catalytic reactors, batteries, and CO2 storage …
Deep-learning-based workflow for boundary and small target segmentation in digital rock images using UNet++ and IK-EBM
Abstract Three-dimensional (3D) X-ray micro-computed tomography (μCT) has been widely
used in petroleum engineering because it can provide detailed pore structural information …
used in petroleum engineering because it can provide detailed pore structural information …
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 …
Evaluation of geometric tortuosity for 3D digitally generated porous media considering the pore size distribution and the A-star algorithm
Porous materials are of great interest in multiple applications due to their usefulness in
energy conversion devices and their ability to modify structural and diffusive properties …
energy conversion devices and their ability to modify structural and diffusive properties …
Review of shale gas transport prediction: Basic theory, numerical simulation, application of ai methods, and perspectives
The gas transport mechanism in shale reservoirs is extremely complex and is a typical
multiscale and multiphysics coupled transport process, considering the complex shale rock …
multiscale and multiphysics coupled transport process, considering the complex shale rock …
[HTML][HTML] Prediction of local concentration fields in porous media with chemical reaction using a multi scale convolutional neural network
The study of solute transport in porous media is of interest in many chemical engineering
systems. Some example applications include packed bed catalytic reactors, filtration …
systems. Some example applications include packed bed catalytic reactors, filtration …