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Thermal radiation correction formula of the scaling criteria for film cooling of turbine blades

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  • Li, Haiwang
  • Wang, Meng
  • You, Ruquan
  • Liu, Song

Abstract

Adiabatic effectiveness (η) is the most important parameter for evaluating film cooling capacity. For turbine blades, it is usually based on the scaling criteria to achieve the mapping of effectiveness from low temperature conditions in the laboratory to real application scenarios. However, with the increase of gas temperature and humidity, the thermal radiation effect is obvious, resulting in the inapplicability of the traditional scaling criteria. In this study, a radiation correction formula was proposed to modify the scaling criteria. Firstly, based on the wide band K-distribution (WBK) model, the absorption coefficients of typical aviation kerosene gas products were calculated. Secondly, the dataset was constructed based on computational fluid dynamics (CFD) assisted design, and Python codes were introduced to automate the post-processing of coefficients. Thirdly, radiation related dimensionless parameters (εw, Tbi, TR, mCO2, mH2O, mO2, mN2) were selected as independent variables. Finally, an explicit function between based on a nonlinear fitting algorithm was obtained. The correlation formula of the scaling criteria proposed was compatible with reasonable flow parameters and model parameters. Compared with the traditional approach, which must be tested at high temperature to fully consider the effects of radiation, the formula proposed in this study is energy-saving and innovative.

Suggested Citation

  • Li, Haiwang & Wang, Meng & You, Ruquan & Liu, Song, 2023. "Thermal radiation correction formula of the scaling criteria for film cooling of turbine blades," Energy, Elsevier, vol. 282(C).
    • Handle: RePEc:eee:energy:v:282:y:2023:i:c:s0360544223023599
    DOI: 10.1016/j.energy.2023.128965
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    References listed on IDEAS

    as
    1. Jiang, Chiju & Zhang, Weihao & Li, Ya & Li, Lele & Wang, Yufan & Huang, Dongming, 2023. "Multi-scale Pix2Pix network for high-fidelity prediction of adiabatic cooling effectiveness in turbine cascade," Energy, Elsevier, vol. 265(C).
    2. Wang, Zhiduo & Feng, Zhenping & Zhang, Xiaobo & Peng, Jingbo & Zhang, Fei & Wu, Xing, 2022. "Improving cooling performance and robustness of NGV endwall film cooling design using micro-scale ribs considering incidence effects," Energy, Elsevier, vol. 253(C).
    3. Wang, Qi & Yang, Li & Huang, Kang, 2022. "Fast prediction and sensitivity analysis of gas turbine cooling performance using supervised learning approaches," Energy, Elsevier, vol. 246(C).
    4. Park, Jun Su & Lee, Dong Hyun & Rhee, Dong-Ho & Kang, Shin Hyung & Cho, Hyung Hee, 2014. "Heat transfer and film cooling effectiveness on the squealer tip of a turbine blade," Energy, Elsevier, vol. 72(C), pages 331-343.
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