Assessment of predictive capability of hybrid URANS/LES methods in residence time calculation

Mehdizadeh, A., Doost, S., Sadiki, A., Janicka, J. and Karimi, N. (2018) Assessment of predictive capability of hybrid URANS/LES methods in residence time calculation. Chemical Engineering Science, 183, pp. 47-59. (doi:10.1016/j.ces.2018.02.035)

157676.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.



The present study aims to assess capability of mostly used hybrid URANS/LES methods in dealing with a complex swirled configuration/reactor when the residence time characteristics need to be predicted at acceptable level of accuracy and fidelity. The configuration is quite complex and out of reach of the classical RANS turbulence models as it consists of different, partly swirled inlet channels and a large variety of time and length scales. In this work only the flow field is considered and is investigated using three different hybrid URANS/LES simulation methods. The models: the Scale Adaptive Simulation (SAS), the Improved Delayed Detached Eddy Simulation(SA-IDDES) and the k-ω-DES, use different triggering mechanisms and underlying RANS models. The results of the flow field, the residence time characteristics and all related quantities are compared with both the Large Eddy Simulation (LES) and experimental data reported in Doost et al. (2016). It turns out that none of the considered hybrid methods is able to predict the residence time characteristics as well as LES does mainly due to the inaccurate prediction of the flow field. It was found that there is a need to improve the hybrid approaches by addressing the shortcomings, particularly those regarding triggering mechanism to make hybrid approaches a reliable computational tool for study complex turbulent flows inside full scale configurations where LES can be prohibitively expensive.

Item Type:Articles
Additional Information:The authors acknowledge the funding (partially) of this work by Deutsche Forschungsgemeinschaft (DFG) under Grant No. SFB/TRR 129.
Glasgow Author(s) Enlighten ID:Karimi, Dr Nader
Authors: Mehdizadeh, A., Doost, S., Sadiki, A., Janicka, J., and Karimi, N.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:Chemical Engineering Science
ISSN (Online):1873-4405
Published Online:08 March 2018
Copyright Holders:Copyright © 2018 Elsevier Ltd.
First Published:First published in Chemical Engineering Science 183:47-59
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

University Staff: Request a correction | Enlighten Editors: Update this record