Gas turbine are used for aircraft propulsion and in land-based power generation or industrial application. One of the key to increase turbine efficiency (or to reduce fuel consumption) is to increase turbine inlet temperature (TIT). It is crucial to design an efficient cooling system for turbine blade, to avoid penalties in the overall turbine efficiency. There is still an unsatisfactory comprehension of the mechanisms of turbulence and heat transfer in those configurations, considering also that, most of the times, they are designed using simple empirical correlations. The objective of this thesis is to analyse flow configurations representing the most complex parts of a turbine cooling system, using high-fidelity approaches (i.e. DNS and LES). These approaches are unique tools for a clear understanding of the physics behind the cooling process, but are unlikely to be appealing in the following years for industries, since they require large computational power and a lot of time to be performed (usually months). Increasing computing power does not necessarily make DNS or LES attractive, since some other issues may arise (e.g. the high storage required for the simulated data). Starting from the above considerations, URANS models, tailored to account for rotation and non linear turbulent flow features, were developed and validated in this research, to overcome all the issues related to DNS and LES.

Flow and heat transfer mechanism for gas turbine internal cooling: DNS & LES study / Salvagni, Alessandro. - (2017 Feb 22).

Flow and heat transfer mechanism for gas turbine internal cooling: DNS & LES study

SALVAGNI, ALESSANDRO
22/02/2017

Abstract

Gas turbine are used for aircraft propulsion and in land-based power generation or industrial application. One of the key to increase turbine efficiency (or to reduce fuel consumption) is to increase turbine inlet temperature (TIT). It is crucial to design an efficient cooling system for turbine blade, to avoid penalties in the overall turbine efficiency. There is still an unsatisfactory comprehension of the mechanisms of turbulence and heat transfer in those configurations, considering also that, most of the times, they are designed using simple empirical correlations. The objective of this thesis is to analyse flow configurations representing the most complex parts of a turbine cooling system, using high-fidelity approaches (i.e. DNS and LES). These approaches are unique tools for a clear understanding of the physics behind the cooling process, but are unlikely to be appealing in the following years for industries, since they require large computational power and a lot of time to be performed (usually months). Increasing computing power does not necessarily make DNS or LES attractive, since some other issues may arise (e.g. the high storage required for the simulated data). Starting from the above considerations, URANS models, tailored to account for rotation and non linear turbulent flow features, were developed and validated in this research, to overcome all the issues related to DNS and LES.
22-feb-2017
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/936598
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