4.7 Article

Dynamic mode decomposition of magnetohydrodynamic bubble chain flow in a rectangular vessel

Journal

PHYSICS OF FLUIDS
Volume 33, Issue 8, Pages -

Publisher

AMER INST PHYSICS
DOI: 10.1063/5.0054831

Keywords

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Funding

  1. ERDF project Development of numerical modelling approaches to study complex multiphysical interactions in electromagnetic liquid metal technologies [1.1.1.1/18/A/108]

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This study demonstrates the first application of dynamic mode decomposition (DMD) to bubble flow with resolved dynamic liquid/gas boundaries. By examining the velocity field mode statistics over trajectory time and total flow time, as well as computed mode velocity fields, the study shows how gas flow rate and applied magnetic field affect bubble wake flow and larger-scale flow structures within the liquid metal vessel. The results suggest that DMD can provide unique insights into various momentum transfer and bubble interaction mechanisms, and that mode analysis can be used to explain observed flow patterns, showcasing an implementation of DMD with resilience to data noise, memory efficiency, and special pre-processing for input data.
We demonstrate the first application of dynamic mode decomposition (DMD) to bubble flow with resolved dynamic liquid/gas boundaries. Specifically, we have applied DMD to the output of numerical simulations for a system where chains of bubbles ascend through a rectangular liquid metal vessel. Flow patterns have been investigated in the vessel and bubble reference frames. We show how gas flow rate and applied magnetic affect bubble wake flow and larger-scale flow structures within the liquid metal vessel by examining the velocity field mode statistics over trajectory time and total flow time as well as the computed mode velocity fields. The results of this proof-of-concept study indicate that DMD can yield unique insights into various momentum transfer and bubble interaction mechanisms, and that mode analysis can be used to explain the observed flow patterns. In addition, we showcase our own implementation of DMD that combines resilience to data noise, memory efficiency and special pre-processing for input data.

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