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Aerothermal performance optimization of uncooled asymmetric cavity bottom blade squealer tip with generative adversarial network-based surrogate model

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  • Tao, Yong
  • Liu, Zhao
  • Feng, Zhenping

Abstract

To improve the aerothermal performance of an uncooled blade squealer tip, an asymmetric cavity bottom blade squealer tip is proposed in this study. A Swin-transformer U-net based generative adversarial network surrogate model is integrated with the NSGA-III optimization algorithm, and two optimization schemes are implemented for aerothermal performance optimization. Training samples are generated using Latin Hypercube Sampling, while numerical simulation data is validated through experimental heat transfer coefficient measurement through the constant wall temperature method. Optimization results show that vortex development in the tip region is effectively controlled through variable cavity depth profiles of the optimized asymmetric cavity bottom blade squealer tips, resulting in reduced heat transfer coefficient and total pressure coefficient. In addition, superior aerothermal performance is confirmed for the optimized asymmetric cavity bottom blade squealer tips with variable cavity depth when compared with a uniform 5 mm-cavity depth squealer tip, indicating that uniformly increasing cavity depth does not represent the optimal approach for aerothermal performance improvement. General guidelines are also established regarding the influence of control point coordinates in asymmetric cavity bottom blade squealer tips on aerothermal performance, with optimal cavity depth variation trends for minimizing area-averaged heat transfer coefficient and area-averaged total pressure loss coefficient being found to be mutually contradictory.

Suggested Citation

  • Tao, Yong & Liu, Zhao & Feng, Zhenping, 2025. "Aerothermal performance optimization of uncooled asymmetric cavity bottom blade squealer tip with generative adversarial network-based surrogate model," Energy, Elsevier, vol. 341(C).
    • Handle: RePEc:eee:energy:v:341:y:2025:i:c:s0360544225050613
    DOI: 10.1016/j.energy.2025.139419
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    References listed on IDEAS

    as
    1. 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).
    2. 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).
    3. Park, Yeseul & Choi, Minsung & Kim, Kibeom & Li, Xinzhuo & Jung, Chanho & Na, Sangkyung & Choi, Gyungmin, 2020. "Prediction of operating characteristics for industrial gas turbine combustor using an optimized artificial neural network," Energy, Elsevier, vol. 213(C).
    4. Qin, Tao-Jie & Tong, Zi-Xiang & Li, Dong & He, Ya-Ling & Hung, Tzu-Chen, 2024. "Aerothermal performance of cavity tip with flow structure effects in a transonic high-pressure turbine blade," Energy, Elsevier, vol. 291(C).
    5. Song, Yu & Liu, Zhao & Han, Yuhui & Zhang, Weixin & Feng, Zhenping, 2025. "Cooperative optimization of cooling units arrangement on gas turbine endwall with generating adversarial network-based surrogate models," Energy, Elsevier, vol. 323(C).
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