A quantitative and generalized assessment of bubble-induced turbulence models for gas-liquid systems
Name
1-s2.0-S2590140019300164-main.pdf
Description
Published version
Size
2.39 MB
Format
Adobe PDF
Checksum (MD5)
a7662f709d8fb60674f8aa56c3be66f8
Author(s)
Magolan, Ben
Lubchenko, Nazar
Baglietto, Emilio
Date Issued
May 2019
Journal
Chemical Engineering Science: X
Publisher
Elsevier BV
Citation
Magolan, Ben, Nazar Lubchenko, and Emilio Baglietto. "A quantitative and generalized assessment of bubble-induced turbulence models for gas-liquid systems." Chemical Engineering Science: X 2 (2019): 100009 © 2019 The Author(s)
Version
Final published version
Abstract
In gas-liquid systems, bubble motion and interaction with the surrounding liquid medium serves to dramatically modify the liquid turbulent kinetic energy profile. While several two-equation bubble-induced turbulence (BIT) models have been advanced to predict this phenomenon, the intrinsic non-linearities that accompany the solution of the governing equations, interfacial forces, and turbulence models complicate their assessment. This hinders understanding of model performance and obstructs necessary model improvements. Here, the mathematical formulation of existing BIT models is investigated, and selected models are quantitatively assessed through simulation of the entire Liu (1989) air/water pipe flow experimental database in OpenFOAM. Critical to this work is the approach adopted to decouple the connection between turbulence and momentum closures, which ensures physically consistent volume fraction profiles and enables fair comparison between models. The assessment reveals that existing closures struggle with reliably predicting the turbulent kinetic energy profile as well as routinely worsen mean flow predictions. These observations are used to propose a pathway for the assembly of new BIT model formulations.
MIT Department
Massachusetts Institute of Technology. Department of Nuclear Science and Engineering
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.cesx.2019.100009
