2020/05/08 by Xinlei Liu, Chong Peng, Hongxin Bai +4 · 23 citations
Chemistry · Engineering · #Artificial intelligence #Chemistry #Chromatography #Computational fluid dynamics #Computer science #Drop (telecommunication) #Engineering #Enhanced Oil Recovery Techniques #Flow (mathematics) #Geology #Heat and Mass Transfer in Porous Media #Lattice Boltzmann Simulation Studies #Materials science #Mechanical engineering #Mechanics #Network model #Packed bed #Particle (ecology) #Physics #Pressure drop #Simulation #Work (physics)
paper · doi:10.1002/aic.16258
published in AIChE Journal 66(9) (Wiley)
openalex publication_date 2020/05/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/22
Abstract 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.