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Leading-edge redesign of a turbomachinery blade and its effect on aerodynamic performance

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  • Zhang, Weihao
  • Zou, Zhengping
  • Ye, Jian

Abstract

A simple and practical polynomial-based turbomachinery profile leading edge geometry design method is proposed in this paper. A mature blade profile – Hodson–Dominy (HD) blade has been selected to verify the effectiveness of the method. Comprehensive analyses on original HD blade and the redesigned blade were conducted by both Large Eddy Simulations (LES) and Reynolds Average Navier–Stokes (RANS) simulations. The results showed that the optimization on HD blade can depress the pressure spike and separation near the leading edge effectively. Meanwhile the separation bubble on suction surface near the trailing edge was reduced partly. The above changes made a 1/3 decrease in suction side leading edge loss and a 10% decrease in the whole profile loss at the design condition. Also, this method was testified to be effective at the conditions with the attack angle from −12° to +9°. Further investigations were carried out on a 5-stage low-pressure turbine, which indicated that the performance was improved in a wide range of working conditions, and a 0.5% increase in efficiency was reported near the design point.

Suggested Citation

  • Zhang, Weihao & Zou, Zhengping & Ye, Jian, 2012. "Leading-edge redesign of a turbomachinery blade and its effect on aerodynamic performance," Applied Energy, Elsevier, vol. 93(C), pages 655-667.
    • Handle: RePEc:eee:appene:v:93:y:2012:i:c:p:655-667
    DOI: 10.1016/j.apenergy.2011.12.091
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    References listed on IDEAS

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    1. Benini, Ernesto & Biollo, Roberto & Ponza, Rita, 2011. "Efficiency enhancement in transonic compressor rotor blades using synthetic jets: A numerical investigation," Applied Energy, Elsevier, vol. 88(3), pages 953-962, March.
    2. Hamakhan, I.A. & Korakianitis, T., 2010. "Aerodynamic performance effects of leading-edge geometry in gas-turbine blades," Applied Energy, Elsevier, vol. 87(5), pages 1591-1601, May.
    3. Ghorbanian, K. & Gholamrezaei, M., 2009. "An artificial neural network approach to compressor performance prediction," Applied Energy, Elsevier, vol. 86(7-8), pages 1210-1221, July.
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    1. Galindo, J. & Fajardo, P. & Navarro, R. & García-Cuevas, L.M., 2013. "Characterization of a radial turbocharger turbine in pulsating flow by means of CFD and its application to engine modeling," Applied Energy, Elsevier, vol. 103(C), pages 116-127.
    2. Gabl, Roman & Innerhofer, Daniel & Achleitner, Stefan & Righetti, Maurizio & Aufleger, Markus, 2018. "Evaluation criteria for velocity distributions in front of bulb hydro turbines," Renewable Energy, Elsevier, vol. 121(C), pages 745-756.
    3. Wang, Xiaojing & Zou, Zhengping, 2019. "Uncertainty analysis of impact of geometric variations on turbine blade performance," Energy, Elsevier, vol. 176(C), pages 67-80.

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