Abstract
In this study, a Large Eddy Simulation (LES) of the aluminum-smelting process is performed using OpenFOAM. To understand the coupled behavior of heat transfer, mass transfer, and flow of the smelting process, a multi-physics computational fluid dynamics (CFD) model based on the Eulerian–Eulerian multi-fluid approach is adopted. The model accounts for CO2 bubble and magnetohydrodynamics (MHD)-driven flow, along with alumina dissolution, transport, and bath temperature evolution. The simulation predictions show small-scale turbulent vortical structures in the anode–cathode space caused by combined effect of MHD and CO2 bubble-bath interactions and relatively large-scale asymmetric vortices in the inter-anode space caused by the CO2 bubble-bath interactions. The vortex formation at the edges of the anodes evidently aids in transporting alumina from the central channel to the bottom of the anodes and prevents accumulation of gas bubbles in the periphery of the anode bottom. Symmetric bath cold spots are observed in the vicinity of the feeder. Cold spots are also observed in the anode–cathode distance space below the anode bottom due to the transport of undissolved solid to this region by the flow. The findings from the work are useful in developing and designing alumina-feeding strategy leading to reduced anode effects and smooth operation of the cell. The work also highlights the important flow structures in conventional aluminum-smelting cell.
| Original language | English |
|---|---|
| Pages (from-to) | 2407-2426 |
| Number of pages | 20 |
| Journal | Metallurgical and Materials Transactions B: Process Metallurgy and Materials Processing Science |
| Volume | 53 |
| Issue number | 4 |
| DOIs | |
| State | Published - Aug 2022 |
Funding
This research was supported by a US Department of Energy High Performance Computing for Energy Innovation (HPC4EI) Grant. This research used compute resources of Compute and Data Environment for Science (CADES) at the Oak Ridge National Laboratory, which is supported by the Office of Science of the US Department of Energy under Contract No. DE-AC05-00OR22725. This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). This research was supported by a US Department of Energy High Performance Computing for Energy Innovation (HPC4EI) Grant. This research used compute resources of Compute and Data Environment for Science (CADES) at the Oak Ridge National Laboratory, which is supported by the Office of Science of the US Department of Energy under Contract No. DE-AC05-00OR22725. This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan ).
| Funders | Funder number |
|---|---|
| DOE Public Access Plan | |
| US Department of Energy High Performance Computing for Energy Innovation | HPC4EI |
| U.S. Department of Energy | DE-AC05-00OR22725 |
| Office of Science |