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A pore network model for calculating pressure drop in packed beds of arbitrary‐shaped particles
Author(s) -
Liu Xinlei,
Peng Chong,
Bai Hongxin,
Zhang Qunfeng,
Ye Guanghua,
Zhou Xinggui,
Yuan Weikang
Publication year - 2020
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.16258
Subject(s) - pressure drop , packed bed , network model , mechanics , computational fluid dynamics , particle (ecology) , work (physics) , drop (telecommunication) , flow (mathematics) , materials science , simulation , chemistry , engineering , computer science , thermodynamics , geology , physics , chromatography , mechanical engineering , artificial intelligence , oceanography
A pore network model is built to predict pressure drop in packed beds of arbitrary‐shaped particles, using a method that consists of particle packing by the rigid body technique, pore network construction by the maximal sphere algorithm, and numerical calculation of fluid flow. The pore network model is firstly validated by comparing with experiments, Ergun‐type equations, and particle‐resolved computational fluid dynamics (CFD). The pore network model is as accurate as the particle‐resolved CFD, and is remarkably two to three orders of magnitude less computationally intensive. Then, the pore network model is used to calculate the pressure drops in the beds packed with particles of different shapes and sizes, as well as using different flow media. These calculation results prove the versatility of the pore network model. This work provides an accurate yet efficient pore network model for predicting pressure drop, which should be a powerful tool for designing packed beds.