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Fast prediction and sensitivity analysis of gas turbine cooling performance using supervised learning approaches

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  • Wang, Qi
  • Yang, Li
  • Huang, Kang

Abstract

Fast prediction tools for turbine cooling performance have been demanded by industry for decades to support the iterative design process and the comprehensive response analysis and sensitivity analysis. This study aimed at establishing a comprehensively evaluated deep learning-based data modeling tool for the design of gas turbine blades. The geometry focused on was an air-cooled blade with ribbed channels and film cooling holes, which deformed globally within a wide range of geometrical parameters. A Conditional Generative Adversarial Network was constructed to model the distribution of the internal heat transfer coefficient and the external adiabatic film cooling effectiveness under any in-range geometry and boundary conditions. A series of single-point tests, response analysis, and sensitivity analysis were conducted using the trained model and compared with the Computational Fluid Dynamics results to comprehensively evaluate the model performance. The results showed that the model provided accurate predictions for cooling performance distributions, and also possessed the ability to obtain reasonable response and sensitivity. This study was a successful case of using deep learning approaches to model complex heat transfer problems. For practical applications, the proposed model could serve as an aid to designers to reduce the design burden.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:246:y:2022:i:c:s0360544222002766
    DOI: 10.1016/j.energy.2022.123373
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    References listed on IDEAS

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    1. Wang, Qi & Yang, Li & Rao, Yu, 2021. "Establishment of a generalizable model on a small-scale dataset to predict the surface pressure distribution of gas turbine blades," Energy, Elsevier, vol. 214(C).
    2. Yin, Linfei & Lu, Yuejiang, 2021. "Expandable deep width learning for voltage control of three-state energy model based smart grids containing flexible energy sources," Energy, Elsevier, vol. 226(C).
    3. Sun, Lei & Liu, Tianyuan & Xie, Yonghui & Zhang, Di & Xia, Xinlei, 2021. "Real-time power prediction approach for turbine using deep learning techniques," Energy, Elsevier, vol. 233(C).
    4. Liu, Zuming & Karimi, Iftekhar A., 2020. "Gas turbine performance prediction via machine learning," Energy, Elsevier, vol. 192(C).
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    Citations

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    Cited by:

    1. Chen, Zhimin & Chen, Xuejiao & Yang, XuFei & Yu, Bo & Wang, Bohong & Zhu, Jianqin & Chen, Yujie & Cai, Weihua, 2024. "Numerical study on cooling characteristics of turbine blade based on laminated cooling configuration with clapboards," Energy, Elsevier, vol. 299(C).
    2. Li, Jinxing & Li, Yunzhu & Liu, Tianyuan & Zhang, Di & Xie, Yonghui, 2023. "Multi-fidelity graph neural network for flow field data fusion of turbomachinery," Energy, Elsevier, vol. 285(C).
    3. 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).
    4. Li, Lele & Zhang, Weihao & Li, Ya & Zhang, Ruifeng & Liu, Zongwang & Wang, Yufan & Mu, Yumo, 2024. "A non-parametric high-resolution prediction method for turbine blade profile loss based on deep learning," Energy, Elsevier, vol. 288(C).
    5. 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).

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