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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Rosario Nastasi & Giovanni Labrini & Simone Salvadori & Daniela Anna Misul, 2024. "Shape Optimization of a Diffusive High-Pressure Turbine Vane Using Machine Learning Tools," Energies, MDPI, vol. 17(22), pages 1-21, November.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:22:p:5642-:d:1518630
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    References listed on IDEAS

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    1. Gérard Biau & Erwan Scornet, 2016. "A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 197-227, June.
    2. Gérard Biau & Erwan Scornet, 2016. "Rejoinder on: A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 264-268, June.
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