Shape Optimization of a Diffusive High-Pressure Turbine Vane Using Machine Learning Tools

Rosario Nastasi, Giovanni Labrini, Simone Salvadori () and Daniela Anna Misul
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Rosario Nastasi: Dipartimento Energia, Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10124 Torino, Italy
Giovanni Labrini: Dipartimento Energia, Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10124 Torino, Italy
Simone Salvadori: Dipartimento Energia, Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10124 Torino, Italy
Daniela Anna Misul: Dipartimento Energia, Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10124 Torino, Italy

Energies, 2024, vol. 17, issue 22, 1-21

Abstract: Machine learning tools represent a key methodology for the shape optimization of complex geometries in the turbomachinery field. One of the current challenges is to redesign High-Pressure Turbine (HPT) stages to couple them with innovative combustion technologies. In fact, recent developments in the gas turbine field have led to the introduction of pioneering solutions such as Rotating Detonation Combustors (RDCs) aimed at improving the overall efficiency of the thermodynamic cycle at low overall pressure ratios. In this study, a HPT vane equipped with diffusive endwalls is optimized to allow for ingesting a high-subsonic flow ( M a = 0.6 ) delivered by a RDC. The main purpose of this paper is to investigate the prediction ability of machine learning tools in case of multiple input parameters and different objective functions. Moreover, the model predictions are used to identify the optimal solutions in terms of vane efficiency and operating conditions. A new solution that combines optimal vane efficiency with target values for both the exit flow angle and the inlet Mach number is also presented. The impact of the newly designed geometrical features on the development of secondary flows is analyzed through numerical simulations. The optimized geometry achieved strong mitigation of the intensity of the secondary flows induced by the main flow separation from the diffusive endwalls. As a consequence, the overall vane aerodynamic efficiency increased with respect to the baseline design.

Keywords: turbomachinery; computational fluid dynamics; machine learning; artificial neural network; random forest; aerodynamics; optimization; genetic algorithm; rotating detonation combustion (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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