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Uncertainty analysis of impact of geometric variations on turbine blade performance

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  • Wang, Xiaojing
  • Zou, Zhengping

Abstract

It is important to accurately estimate the impact of manufacturing geometric variations on the turbine aerodynamic performance for the engineering design and manufacture. In this paper, a method to quantify the uncertainty impact of the blade geometric variations was proposed. The principal-component analysis combined with the Kolmogorov-Sminov test and the Sobol sensitivity analysis was used for the uncertainty modeling of the blade geometric variations, and the Kriging surrogate model based on the polynomial chaos expansion (PC-Kriging) was used for the uncertainty quantification in the method. Meanwhile, a Reynolds Average Navier-Stokes (RANS) solver was combined to simulate the aerodynamic performance. This method was applied to estimate the impact on the aerodynamic performance of a low-pressure turbine. The calculation results demonstrated that the aerodynamic performance was significantly influenced, which was manifested as an overall deterioration, a large fluctuation and several extreme cases. Detailed analysis of the mechanisms at the origin of the variations in the aerodynamic performance indicated that the variations of total pressure loss mainly come from the variations of the wake mixing loss, and the 70%–100% axial region on the blade is the sensitive region. The geometric variations, especially the variations of the blade thickness, in the sensitive region are one of the main factors leading to the performance variations. In the engineering manufacture, reasonable formulation of the manufacturing tolerance based on the results of the uncertainty analysis can improve the turbine aerodynamic performance under the influence of the geometric variations.

Suggested Citation

  • Wang, Xiaojing & Zou, Zhengping, 2019. "Uncertainty analysis of impact of geometric variations on turbine blade performance," Energy, Elsevier, vol. 176(C), pages 67-80.
  • Handle: RePEc:eee:energy:v:176:y:2019:i:c:p:67-80
    DOI: 10.1016/j.energy.2019.03.140
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    References listed on IDEAS

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    1. Liu, ZhiYi & Wang, XiaoDong & Kang, Shun, 2014. "Stochastic performance evaluation of horizontal axis wind turbine blades using non-deterministic CFD simulations," Energy, Elsevier, vol. 73(C), pages 126-136.
    2. Lee, Sung Gun & Park, Sang Jun & Lee, Kyung Seo & Chung, Chinwha, 2012. "Performance prediction of NREL (National Renewable Energy Laboratory) Phase VI blade adopting blunt trailing edge airfoil," Energy, Elsevier, vol. 47(1), pages 47-61.
    3. Razaaly, Nassim & Persico, Giacomo & Congedo, Pietro Marco, 2019. "Impact of geometric, operational, and model uncertainties on the non-ideal flow through a supersonic ORC turbine cascade," Energy, Elsevier, vol. 169(C), pages 213-227.
    4. 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.
    5. 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.
    6. Zhou, Min & Wang, Bo & Li, Tiantian & Watada, Junzo, 2018. "A data-driven approach for multi-objective unit commitment under hybrid uncertainties," Energy, Elsevier, vol. 164(C), pages 722-733.
    7. Bornatico, Raffaele & Hüssy, Jonathan & Witzig, Andreas & Guzzella, Lino, 2013. "Surrogate modeling for the fast optimization of energy systems," Energy, Elsevier, vol. 57(C), pages 653-662.
    8. Fu, Xueqian & Zhang, Xiurong, 2018. "Failure probability estimation of gas supply using the central moment method in an integrated energy system," Applied Energy, Elsevier, vol. 219(C), pages 1-10.
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    Cited by:

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