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Stochastic separated flow models with applications in numerical computations of supersonic particle-laden turbulent flows

Bing Wang, Zhaoxin Ren Orcid Logo, Huiqiang Zhang

Advances in Mechanical Engineering, Volume: 7, Issue: 8, Start page: 168781401560133

Swansea University Author: Zhaoxin Ren Orcid Logo

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DOI (Published version): 10.1177/1687814015601335

Abstract

In this article, three stochastic separated flow models were applied to predict the dispersion of inertial fuel particles in the supersonic turbulent flows. The flow field of continuous phase was simulated by means of Reynolds-averaged Navier–Stokes method with a two-equation turbulence model. Clift...

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Published in: Advances in Mechanical Engineering
ISSN: 1687-8140 1687-8140
Published: SAGE Publications 2015
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URI: https://cronfa.swan.ac.uk/Record/cronfa59361
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spelling 2022-02-23T15:34:12.9407118 v2 59361 2022-02-11 Stochastic separated flow models with applications in numerical computations of supersonic particle-laden turbulent flows 62a1a0da0fa78e05c3deafcdee5551ce 0000-0002-6305-9515 Zhaoxin Ren Zhaoxin Ren true false 2022-02-11 AERO In this article, three stochastic separated flow models were applied to predict the dispersion of inertial fuel particles in the supersonic turbulent flows. The flow field of continuous phase was simulated by means of Reynolds-averaged Navier–Stokes method with a two-equation turbulence model. Clift’s expression was used to modify the drag force on the particle considering the compressibility effects. The particle-phase statistics were obtained by a secondary-order time-weighed Eulerian method. The ability of those stochastic separated flow models was then compared for predicting the mean particle velocity and the particle dispersion. For obtaining a statistically stationary solution, the stochastic separated flow model required the largest number of computational particles, whereas the improved stochastic separated flow model was found to need the least. The time-series stochastic separation flow model lay in-between. Compared with the other two models, the particle dispersion was over-predicted by the stochastic separated flow model in the supersonic particle-laden boundary layer flow, while the improved stochastic separated flow model was less predictable for the particle spatial distribution in the particle-laden strut-injection flow. Three models could well predict the mean velocities of the particle phase. This study is valuable for selecting a validated model used for predicting the particle dispersion in supersonic turbulent flows. Journal Article Advances in Mechanical Engineering 7 8 168781401560133 SAGE Publications 1687-8140 1687-8140 Stochastic separated flow model, supersonic turbulent flows, particle-laden flow, numerical simulation 1 8 2015 2015-08-01 10.1177/1687814015601335 COLLEGE NANME Aerospace Engineering COLLEGE CODE AERO Swansea University 2022-02-23T15:34:12.9407118 2022-02-11T01:09:28.8090240 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering Bing Wang 1 Zhaoxin Ren 0000-0002-6305-9515 2 Huiqiang Zhang 3 59361__22443__d6073bfbf0f24b77905527aac26ae279.pdf 59361.pdf 2022-02-23T15:33:19.4682418 Output 4417560 application/pdf Version of Record true This article is distributed under the terms of the Creative Commons Attribution 3.0 License true eng http://www.creativecommons.org/licenses/by/3.0/)
title Stochastic separated flow models with applications in numerical computations of supersonic particle-laden turbulent flows
spellingShingle Stochastic separated flow models with applications in numerical computations of supersonic particle-laden turbulent flows
Zhaoxin Ren
title_short Stochastic separated flow models with applications in numerical computations of supersonic particle-laden turbulent flows
title_full Stochastic separated flow models with applications in numerical computations of supersonic particle-laden turbulent flows
title_fullStr Stochastic separated flow models with applications in numerical computations of supersonic particle-laden turbulent flows
title_full_unstemmed Stochastic separated flow models with applications in numerical computations of supersonic particle-laden turbulent flows
title_sort Stochastic separated flow models with applications in numerical computations of supersonic particle-laden turbulent flows
author_id_str_mv 62a1a0da0fa78e05c3deafcdee5551ce
author_id_fullname_str_mv 62a1a0da0fa78e05c3deafcdee5551ce_***_Zhaoxin Ren
author Zhaoxin Ren
author2 Bing Wang
Zhaoxin Ren
Huiqiang Zhang
format Journal article
container_title Advances in Mechanical Engineering
container_volume 7
container_issue 8
container_start_page 168781401560133
publishDate 2015
institution Swansea University
issn 1687-8140
1687-8140
doi_str_mv 10.1177/1687814015601335
publisher SAGE Publications
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering
document_store_str 1
active_str 0
description In this article, three stochastic separated flow models were applied to predict the dispersion of inertial fuel particles in the supersonic turbulent flows. The flow field of continuous phase was simulated by means of Reynolds-averaged Navier–Stokes method with a two-equation turbulence model. Clift’s expression was used to modify the drag force on the particle considering the compressibility effects. The particle-phase statistics were obtained by a secondary-order time-weighed Eulerian method. The ability of those stochastic separated flow models was then compared for predicting the mean particle velocity and the particle dispersion. For obtaining a statistically stationary solution, the stochastic separated flow model required the largest number of computational particles, whereas the improved stochastic separated flow model was found to need the least. The time-series stochastic separation flow model lay in-between. Compared with the other two models, the particle dispersion was over-predicted by the stochastic separated flow model in the supersonic particle-laden boundary layer flow, while the improved stochastic separated flow model was less predictable for the particle spatial distribution in the particle-laden strut-injection flow. Three models could well predict the mean velocities of the particle phase. This study is valuable for selecting a validated model used for predicting the particle dispersion in supersonic turbulent flows.
published_date 2015-08-01T04:16:36Z
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score 11.012678

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