For the first time, machine learning methods are applied to the gas diffusion layer (GDL) to
characterize structural and transport properties. In the first investigation, a benchmark study …

In recent years, computational fluid dynamics (CFD) has become increasingly important and
has proven to be an effective method for assessing environmental conditions in poultry …

Using machine learning models to predict the permeability of porous media is one of the key
research directions of current pore-scale models. Since three-dimensional porous media …

The objective of this work is to predict grid-level flow fields in porous media as a priori to
determining the permeability of porous media. A physics-informed ML model is developed …

Machine learning is a form of artificial intelligence that is applicable in all fields of study. It
incorporates many algorithms used in carrying out various tasks such as classification …

The relationships between macroscopic properties and microstructural characteristics are of
great significance for natural porous rocks, based on which transport properties can be …

Geologic cores are rock samples that are extracted from deep under the ground during the
well drilling process. They are used for petroleum reservoirs' performance characterization …

Global warming is an important environmental issue, and carbon capture and storage (CCS)
is a major technology to reduce the emission of greenhouse gases. Captured carbon …

The main objective of this study was to assess the performance of cold recycled mixtures
(CRMs) at:(1) binder level through evaluating the rheological and mechanical properties of …

Porosity, absolute permeability and diffusion coefficient are important characteristics of the
flow of fluids in the pore space of rocks, the determination of which is resource-intensive and …