A CFD-based surrogate model for predicting slurry pipe flow pressure drops. (3rd April 2023)

• Record Type: Journal Article
• Authors: Elkarii, Marwane; Boukharfane, Radouan; Benjelloun, Saad; Bouallou, Chakib
• Is Part Of: Particulate science and technology. Volume 41:Number 3(2023)
• Page Start: 432
• Page End: 442
• Publication Date: 2023-04-03
• Subjects: Slurry flows -- CFD -- Two fluid model -- Surrogate models -- Horizontal pipelines; Particles -- Periodicals; 620.43
• DOI: 10.1080/02726351.2022.2110341
• Languages: English
• ISSNs: 0272-6351

Abstract

Abstract: Slurry pipelines are extensively employed in most mining operations to transport raw materials and tailings. The aim of the paper is twofold: on the one hand, to develop a generalized slurry flow model using computational fluid dynamics (CFD) to develop a better insight into the complexity of slurry pipe flows, and on the other hand, to develop a surrogate model for pressure drop predictions, which can be used in operations simulation and monitoring tools. A two-fluid model based on the Eulerian-Eulerian approach along with the SST k - ω turbulence model was used. The motion of the solid phase was modeled by the kinetic theory of granular flow (KTGF) to account for both particle-particle and particle-wall interactions. The model was validated by comparison with experimental data available in literature. A new two-phase pressure drop correlation is proposed based on the numerical computations of horizontal pipes featuring 525 data points.