作者
Agnese Marcato, Gianluca Boccardo, Daniele Marchisio
发表日期
2022/3/25
期刊
Industrial & Engineering Chemistry Research
卷号
61
期号
24
页码范围
8530-8541
出版商
American Chemical Society
简介
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. The aim of this study is to test the use of fully connected (FCNN) and convolutional neural networks (CNN) for the prediction of crucial properties in porous media systems: the permeability and the filtration rate. The data-driven models are trained on a dataset of computational fluid dynamics (CFD) simulations. To this end, the porous media geometries are created in silico by a discrete element method, and a rigorous setup of the CFD simulations is presented. The models trained have as input both geometrical and operating conditions features so that they could find application in multiscale modeling, optimization problems, and in-line control. The average error on the prediction of the permeability is lower than 2.5%, and that on the …
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A Marcato, G Boccardo, D Marchisio - Industrial & Engineering Chemistry Research, 2022