Loss Breakdown in Axial Turbines: A New Method for Vortex Loss and Wake Detection From 3D RANS SimulationsSource: Journal of Turbomachinery:;2024:;volume( 147 ):;issue: 006::page 61006-1DOI: 10.1115/1.4067033Publisher: 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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contributor author | Raina, Greta | |
contributor author | Bousquet, Yannick | |
contributor author | Luquet, David | |
contributor author | Lippinois, Eric | |
contributor author | Binder, Nicolas | |
date accessioned | 2025-04-21T10:34:52Z | |
date available | 2025-04-21T10:34:52Z | |
date copyright | 11/22/2024 12:00:00 AM | |
date issued | 2024 | |
identifier issn | 0889-504X | |
identifier other | turbo_147_6_061006.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4306485 | |
description 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. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | Loss Breakdown in Axial Turbines: A New Method for Vortex Loss and Wake Detection From 3D RANS Simulations | |
type | Journal Paper | |
journal volume | 147 | |
journal issue | 6 | |
journal title | Journal of Turbomachinery | |
identifier doi | 10.1115/1.4067033 | |
journal fristpage | 61006-1 | |
journal lastpage | 61006-11 | |
page | 11 | |
tree | Journal of Turbomachinery:;2024:;volume( 147 ):;issue: 006 | |
contenttype | Fulltext |