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Experimental and Numerical Analysis of Cavity/Mean-Flow Interaction in Low Pressure Axial Flow Turbines

Authors :
Carlo Costa
Dario Barsi
Davide Lengani
Giulio Venturino
Pietro Zunino
Daniele Simoni
Source :
Journal of Thermal Science. 30:2178-2185
Publication Year :
2021
Publisher :
Springer Science and Business Media LLC, 2021.

Abstract

The increasing performance of modern aeroengines led the research towards the optimization of machine components not deeply analyzed in the past. In this context, the mechanisms driving the interaction process between the secondary flows evolving at the hub of low-pressure turbines with the rotor-stator cavity systems have been poorly investigated in the literature. In this work, an experimental and numerical analysis of the interaction between the endwall near wall flow and the leakage flow of a real cavity system is presented. The experimental results were carried out in the annular low-pressure axial flow turbine of the University of Genova. Experimental blade loading and pressure distributions into the cavity, as well as the measured total pressure loss coefficient, have been used for a proper validation of CFD results. Both steady and unsteady calculations were carried out through the commercial solver Numeca. Particularly, several numerical approaches have been tested into this work: RANS, Non Linear Harmonic (NLH), and URANS. The most promising CFD techniques have been firstly identified by comparison with experimental results and then systematically employed to extend the analysis of secondary flow-cavity flow interaction to positions and quantities not available from the experiments. Losses characterizing the mean flow-cavity flow interaction process will be shown to cover a great amount of the overall stage losses and should be properly accounted for the design of future optimized cavity configurations.

Details

ISSN :
1993033X and 10032169
Volume :
30
Database :
OpenAIRE
Journal :
Journal of Thermal Science
Accession number :
edsair.doi.dedup.....1f3eef150cc05124fda95707b2b62499