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    Loss Breakdown in Axial Turbines: A New Method for Vortex Loss and Wake Detection From 3D RANS Simulations

    Source: Journal of Turbomachinery:;2024:;volume( 147 ):;issue: 006::page 61006-1
    Author:
    Raina, Greta
    ,
    Bousquet, Yannick
    ,
    Luquet, David
    ,
    Lippinois, Eric
    ,
    Binder, Nicolas
    DOI: 10.1115/1.4067033
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: To enhance turbine efficiency, it is essential to mitigate the loss generated by irreversible phenomena taking place in turbine flows, including boundary layers, shock waves, vortices, and trailing edge wakes. A fast and accurate detection of losses is therefore crucial from the earliest stages of turbine design, in which reduced order models based on oversimplified correlations are employed. Achieving this objective requires a deep comprehension of the physics behind each loss-generating mechanism, a goal attainable through the examination of the 3D flow. While existing criteria allow the identification of various phenomena, accurately quantifying losses generated by vortices remains a challenge: these losses frequently extend beyond the vortical structure. The aim of this paper is to provide a straightforward and effective approach to localize and assess vortex-related losses. This method is grounded in Zlatinov’s decomposition of the entropy generation rate equation into a streamwise and a secondary flow component. A criterion based on the vortex kinematics is used to evaluate the strength of the vortex, thereby enabling the determination of its spatial influence and its contribution to the overall losses. To validate the method, a post-processing code is developed which allows to perform loss breakdown. This tool makes use of existing identification criteria and some new techniques introduced within this work, especially for wake detection. 3D Reynolds-averaged Navier–Stokes simulations are carried out on several configurations, ranging from simple curved ducts to more realistic nozzle guide vanes, to gradually test and validate the computational tool. Results confirm that the highest rates of entropy generation occur outside of the vortical structure, and show good ability to identify both the vortex shape and its area of influence in terms of losses. A drastic improvement in the prediction of vortex losses is especially observed in the case of turbine blades with tip or hub leakage vortices.
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      Loss Breakdown in Axial Turbines: A New Method for Vortex Loss and Wake Detection From 3D RANS Simulations

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4306485
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    contributor authorRaina, Greta
    contributor authorBousquet, Yannick
    contributor authorLuquet, David
    contributor authorLippinois, Eric
    contributor authorBinder, Nicolas
    date accessioned2025-04-21T10:34:52Z
    date available2025-04-21T10:34:52Z
    date copyright11/22/2024 12:00:00 AM
    date issued2024
    identifier issn0889-504X
    identifier otherturbo_147_6_061006.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4306485
    description abstractTo enhance turbine efficiency, it is essential to mitigate the loss generated by irreversible phenomena taking place in turbine flows, including boundary layers, shock waves, vortices, and trailing edge wakes. A fast and accurate detection of losses is therefore crucial from the earliest stages of turbine design, in which reduced order models based on oversimplified correlations are employed. Achieving this objective requires a deep comprehension of the physics behind each loss-generating mechanism, a goal attainable through the examination of the 3D flow. While existing criteria allow the identification of various phenomena, accurately quantifying losses generated by vortices remains a challenge: these losses frequently extend beyond the vortical structure. The aim of this paper is to provide a straightforward and effective approach to localize and assess vortex-related losses. This method is grounded in Zlatinov’s decomposition of the entropy generation rate equation into a streamwise and a secondary flow component. A criterion based on the vortex kinematics is used to evaluate the strength of the vortex, thereby enabling the determination of its spatial influence and its contribution to the overall losses. To validate the method, a post-processing code is developed which allows to perform loss breakdown. This tool makes use of existing identification criteria and some new techniques introduced within this work, especially for wake detection. 3D Reynolds-averaged Navier–Stokes simulations are carried out on several configurations, ranging from simple curved ducts to more realistic nozzle guide vanes, to gradually test and validate the computational tool. Results confirm that the highest rates of entropy generation occur outside of the vortical structure, and show good ability to identify both the vortex shape and its area of influence in terms of losses. A drastic improvement in the prediction of vortex losses is especially observed in the case of turbine blades with tip or hub leakage vortices.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleLoss Breakdown in Axial Turbines: A New Method for Vortex Loss and Wake Detection From 3D RANS Simulations
    typeJournal Paper
    journal volume147
    journal issue6
    journal titleJournal of Turbomachinery
    identifier doi10.1115/1.4067033
    journal fristpage61006-1
    journal lastpage61006-11
    page11
    treeJournal of Turbomachinery:;2024:;volume( 147 ):;issue: 006
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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