Ogholaja, T, Njobuenwu, DO orcid.org/0000-0001-6606-1912 and Fairweather, M (2018) LES of Particle Collision and Agglomeration in Vertical Channel Flows. In: Computer-Aided Chemical Engineering. 28th European Symposium on Computer Aided Process Engineering, 10-13 Jun 2018, Graz, Austria. Elsevier , pp. 555-560.
Abstract
Large eddy simulation in a four-way coupled system is used to simulate particle collisions and agglomeration in turbulent vertical channel flows. The particle phase is modelled using Lagrangian particle tracking, ensuring that individual particle behaviour is effectively monitored by solving the particle equation of motion. Particle collisions are described using the hard-sphere collision model, with agglomeration tested based on the pre-collision kinetic energy, restitution coefficient and the van der Waals interactions between particles. The conditions influencing collision and agglomeration are studied for a fluid of Reτ = 300 with 125 μm spherical particles at volume fraction ϕv ~ O(10-³). Comparing flows in upward and downward directions reveals the influence of the various forces acting on the particles, with the drag and lift forces being dominant in both flows, although the latter is found to govern particle behaviour in downflow, driving the particles towards the wall regions where increased collisions and agglomeration occur. The particle distribution in upflow is more symmetric, with fewer collisions and agglomerations, due to the increased effects of drag. The fluid flow is also slightly modified by the presence of the particles.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Keywords: | Eulerian-Lagrangian; particles; collision; agglomeration; vertical channel |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Chemical & Process Engineering (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 30 Aug 2018 11:40 |
Last Modified: | 05 Sep 2018 11:34 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/B978-0-444-64235-6.50098-X |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:134968 |