Cross, M., Croft, T. N., Djambazov, G. ORCID: https://orcid.org/0000-0001-8812-1269 and Pericleous, K. ORCID: https://orcid.org/0000-0002-7426-9999 (2006) Computational modelling of bubbles, droplets and particles in metals reduction and refining. Applied Mathematical Modelling, 3. pp. 1445-1458. ISSN 0307-904X (doi:10.1016/j.apm.2006.03.007)

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Abstract

A multi-phase framework is typically required for the CFD modelling of metals reduction processes. Such processes typically involve the interaction of liquid metals, a gas (often air) top space, liquid droplets in the top space and injection of both solid particles and gaseous bubbles into the bath. The exchange of mass, momentum and energy between the phases is fundamental to these processes. Multi-phase algorithms are complex and can be unreliable in terms of either or both convergence behaviour or in the extent to which the physics is captured. In this contribution, we discuss these multi-phase flow issues and describe an example of each of the main “single phase” approaches to modelling this class of problems (i.e., Eulerian–Lagrangian and Eulerian–Eulerian). Their utility is illustrated in the context of two problems – one involving the injection of sparging gases into a steel continuous slab caster and the other based on the development of a novel process for aluminium electrolysis. In the steel caster, the coupling of the Lagrangian tracking of the gas phase with the continuum enables the simulation of the transient motion of the metal–flux interface. The model of the electrolysis process employs a novel method for the calculation of slip velocities of oxygen bubbles, resulting from the dissolution of alumina, which allows the efficiency of the process to be predicted.

Item Type:	Article
Additional Information:	Article originally presented as a paper at the Third International Conference on CFD in the Minerals and Process Industries, CSIRO, Melbourne, Australia, 10-12 December 2003.
Uncontrolled Keywords:	bubbles, computational modelling, metals reduction process,
Subjects:	Q Science > QA Mathematics T Technology > TN Mining engineering. Metallurgy
Pre-2014 Departments:	School of Computing & Mathematical Sciences School of Computing & Mathematical Sciences > Centre for Numerical Modelling & Process Analysis School of Computing & Mathematical Sciences > Centre for Numerical Modelling & Process Analysis > Computational Science & Engineering Group School of Computing & Mathematical Sciences > Department of Computer Systems Technology School of Computing & Mathematical Sciences > Department of Mathematical Sciences
Related URLs:	Publisher Organisation Organisation
Last Modified:	02 Mar 2019 15:51
URI:	http://gala.gre.ac.uk/id/eprint/981

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