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Evaluation of film cooling effect in multi-row hole configurations on turbine blade leading edge

Author

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  • Li, Bingran
  • Liu, Cunliang
  • Ye, Lin
  • Zhou, Tianliang
  • Zhang, Fan

Abstract

The present study evaluated the film cooling performance of turbine blade leading edges with multi-row hole configurations. Numerical models were refined to improve simulation accuracy by adjusting the turbulent viscosity coefficient, enhancing the prediction of jet diffusion in stagnation areas. The film cooling effectiveness distribution was analyzed through pressure-sensitive paint (PSP) experiments, and the numerical method was validated. The Sellers formula, a two-dimensional model, was used to evaluate the impact of upstream jets on downstream film cooling, with comparisons to simulations. The results show that the Sellers formula generally predicts well at low blowing ratios, but strong interactions between the mainstream and secondary jets, along with vortex formation between hole rows, undermine its accuracy at high blowing ratios, creating a concentrated high-effectiveness cooling zone. Changes in hole row layout affect the film cooling effectiveness distribution but have minimal impact on area-average predictions by the Sellers formula. Combining experimental data at high blowing ratios with the Sellers formula provides critical insights for designing leading edge multi-row hole film cooling structures. This study emphasizes the importance of understanding vortex structures and jet interactions to optimize film cooling strategies, offering significant value for engineering designs in turbine blades.

Suggested Citation

  • Li, Bingran & Liu, Cunliang & Ye, Lin & Zhou, Tianliang & Zhang, Fan, 2024. "Evaluation of film cooling effect in multi-row hole configurations on turbine blade leading edge," Energy, Elsevier, vol. 309(C).
    • Handle: RePEc:eee:energy:v:309:y:2024:i:c:s0360544224026823
    DOI: 10.1016/j.energy.2024.132908
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    References listed on IDEAS

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    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. 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).
    3. Sahu, Mithilesh Kumar & Sanjay,, 2016. "Investigation of the effect of air film blade cooling on thermoeconomics of gas turbine based power plant cycle," Energy, Elsevier, vol. 115(P1), pages 1320-1330.
    4. Zhang, Fan & Liu, Cunliang & Ye, Lin & Ran, Yuan & Zhou, Tianliang & Yan, Haonan, 2024. "Study on the film superposition method for dense multirow film Hole layouts," Energy, Elsevier, vol. 293(C).
    5. Sahu, Mithilesh Kumar & Sanjay,, 2017. "Comparative exergoeconomic analysis of basic and reheat gas turbine with air film blade cooling," Energy, Elsevier, vol. 132(C), pages 160-170.
    6. Li, Dike & Qiu, Lu & Tao, Zhi & Zhu, Jianqin, 2024. "Transfer learning neural network for reconstructing temperature field in film cooling with scarce local measurements," Energy, Elsevier, vol. 291(C).
    7. 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).
    8. Xu, Zhi-peng & Liu, Cun-liang & Ye, Lin & Zhu, Hui-ren & Wu, Zhuang, 2024. "Investigation of the effect of combustor swirl flow on turbine vane full coverage film cooling," Energy, Elsevier, vol. 295(C).
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