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A Review of Machine Learning Methods in Turbine Cooling Optimization

Author

Listed:
  • Liang Xu

    (State Key Laboratory for Manufacturing System Engineering, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

  • Shenglong Jin

    (State Key Laboratory for Manufacturing System Engineering, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

  • Weiqi Ye

    (State Key Laboratory for Manufacturing System Engineering, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

  • Yunlong Li

    (State Key Laboratory for Manufacturing System Engineering, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

  • Jianmin Gao

    (State Key Laboratory for Manufacturing System Engineering, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

Abstract

In the current design work, turbine performance requirements are getting higher and higher, and turbine blade design needs multiple rounds of iterative optimization. Three-dimensional turbine optimization involves multiple parameters, and 3D simulation takes a long time. Machine learning methods can make full use of historically accumulated data to train high-precision data models, which can greatly reduce turbine blade performance evaluation time and improve optimization efficiency. Based on the data model, the advanced intelligent combinatorial optimization technology can effectively reduce the number of iterations, find the better model faster, and improve the optimization calculation efficiency. Based on the different cooling parts of turbine blades and machine learning, this research explores the potential of implementing different machine learning algorithms in the field of turbine cooling design.

Suggested Citation

  • Liang Xu & Shenglong Jin & Weiqi Ye & Yunlong Li & Jianmin Gao, 2024. "A Review of Machine Learning Methods in Turbine Cooling Optimization," Energies, MDPI, vol. 17(13), pages 1-26, June.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:13:p:3177-:d:1424229
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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. Hu, Kaibin & Wang, Xiaobo & Zhong, Shengquan & Lu, Cheng & Yu, Bocheng & Yang, Li & Rao, Yu, 2024. "Optimization of turbine blade trailing edge cooling using self-organized geometries and multi-objective approaches," Energy, Elsevier, vol. 289(C).
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