Entwicklung eines Modells zur Vorhersage stationärer Transitionsmechanismen in Niederdruckturbinen unter dem Einsatz von skalenauflösenden Simulationen

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Müller-Schindewolffs, Christoph: Entwicklung eines Modells zur Vorhersage stationärer Transitionsmechanismen in Niederdruckturbinen unter dem Einsatz von skalenauflösenden Simulationen. Hannover : Institut für Turbomaschinen und Fluid-Dynamik, 2021 (Berichte aus dem Institut für Turbomaschinen und Fluid-Dynamik ; 28), xviii, 203 S.

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To cite the version in the repository, please use this identifier: https://doi.org/10.15488/11982

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Abstract: 
Steady RANS simulations form the backbone of the aerodynamic turbomachinery design tool chain. They combine a fast response with the ability to describe three-dimensional geometries. However, they are based on approximations through modeling approaches.This is especially relevant for predicting boundary layer transition correctly which has a high impact on low-pressure turbine efficiency.Due to the high standard of modern low-pressure turbine efficiency, it is important to resolve complex three-dimensional geometries including small features accurately. This includes the appropriate transition prediction on airfoils and becomes increasingly relevant for endwalls as well. The transition prediction on these three-dimensional geometries requires locally formulated transition models, such as the γ-Reθ model. These models are required to be robust but also sensitive to environmental impacts, particularly in the two-dimensional mean flow.Within the present work, potentials of the established γ-Reθ model are presented and transferred to a reformulation of the model. This includes the consideration of further influencing parameters on the transition process and a more detailed modeling of transition phases. However, the essential improvement of the new model variant, called MSC2020, is a more precise determination of the pressure gradient parameter.This requires fundamental reformulation of model equations on the one hand, but it allows acquiring a more sensitively calibrated model that shows dynamic behavior with increased accuracy on the other hand.The model development is accompanied by an iLES study of the MTU-T161 low-pressure turbine cascade. Characteristic transitional structures are analyzed and brought into relation to the transition modeling by the MSC2020 model. As it turns out, the large amounts of turbulent kinetic energy predicted by the iLES do not agree with the modeled turbulence predicted by two-equation models. Only the turlulent energy resulting from the three-dimensional breakdown of the Kelvin-Helmholtz Instability can be related to modeled turbulence.The elaborated and calibrated model predicts transition on a variety of turbine and compressor cascades precisely which results in a significant improvement in comparison to the γ-Reθ reference model. This is also valid for turbine cascades that are operated far-off design point, where the model is robust and reliable. Besides the intention to improve the transition prediction within the two-dimensional flow regions, the loss prediction driven by secondary flows close to the endwalls of the MTU-T161 cascade is improved as well.
License of this version: Es gilt deutsches Urheberrecht. Das Dokument darf zum eigenen Gebrauch kostenfrei genutzt, aber nicht im Internet bereitgestellt oder an Außenstehende weitergegeben werden.
Document Type: DoctoralThesis
Publishing status: publishedVersion
Issue Date: 2022
Appears in Collections:Fakultät für Maschinenbau
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pos. country downloads
total perc.
1 image of flag of Germany Germany 264 78.57%
2 image of flag of United States United States 11 3.27%
3 image of flag of France France 11 3.27%
4 image of flag of China China 10 2.98%
5 image of flag of Australia Australia 10 2.98%
6 image of flag of Austria Austria 5 1.49%
7 image of flag of Iran, Islamic Republic of Iran, Islamic Republic of 4 1.19%
8 image of flag of No geo information available No geo information available 2 0.60%
9 image of flag of Russian Federation Russian Federation 2 0.60%
10 image of flag of Greece Greece 2 0.60%
    other countries 15 4.46%

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