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 …

Computational methods in fluid research have been progressing during the past few years,
driven by the incorporation of massive amounts of data, either in textual or graphical form …

Permeability is the key variable for reservoir characterization used for estimating the flow
patterns and volume of hydrocarbons. Modern computer advancement has highlighted the …

In this work we developed an open-source work-flow for the construction of data-driven
models from a wide Computational Fluid Dynamics (CFD) simulations campaign. We …

Permeability is one of the most important properties in subsurface flow problems, which
measures the ability of rocks to transmit fluid. Normally, permeability is determined through …

The formation of water columns inside the gas diffusion layer (GDL) of the proton exchange
membrane fuel cell (PEMFC), which is harmful phenomenon, can be controlled by the GDL's …

Understanding shear behavior is crucial for the design of reinforced concrete beams and
sustainability in construction and civil engineering. Although numerous studies have been …

Carbonate petroleum reservoirs typically have lower permeabilities and recovery factors
than sandstone reservoirs, so the natural fractures they often incorporate have positive …

We propose a novel deep learning framework for predicting the permeability of porous
media from their digital images. Unlike convolutional neural networks, instead of feeding the …

Permeability prediction of porous media from numerical approaches is an important
supplement for experimental measurements with the benefits of being more economical and …