Abstract Physics-Informed Neural Networks (PINN) are a machine learning tool that can be
used to solve direct and inverse problems related to models described by Partial Differential …

Open-cell foams are porous materials characterized by high porosity and large specific
surface, industrially employed as catalyst supports or particulate filters. These materials can …

Derivation of macroscopic models for advection-diffusion processes in the presence of
dominant heterogeneous (eg, surface) reactions using homogenisation theory or volume …

We present a flexible scalable open-source computational framework, named
SECUReFoam, based on the finite-volume library OpenFOAM®, for flow and transport …

Abstract We implement the Multi-Rate Mass Transfer (MRMT) model for mobile–immobile
transport in porous media (Haggerty and Gorelick, 1995; Municchi and Icardi, 2019 [1]) …

We investigate chemical transport in laboratory rock cores using unidirectional pulse tracer
experiments. Breakthrough curves (BTCs) measured at various flow rates in one sandstone …

In this paper, we calculate the heat transfer effective coefficients for a Liquid Metal-Cooled
Fast Reactor. We upscaled the local energy equation of a fuel pin and its surrounding liquid …

Upscaling groundwater flow is a fundamental challenge in hydrogeology. This study
proposed time‐fractional flow equations (t‐FFEs) for upscaling long‐term, transient …

A continuous-time random walk (CTRW) reactive transport model is used to study the impact
of physical heterogeneity on the effective reaction rates in porous media in a sample of …

Transport and particulate processes are ubiquitous in environmental, industrial and
biological applications, often involving complex geometries and porous media. In this work …