A Review of Machine Learning Methods in Turbine Cooling Optimization

Liang Xu, Shenglong Jin, Weiqi Ye (), Yunlong Li and Jianmin Gao
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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

Energies, 2024, vol. 17, issue 13, 1-26

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.

Keywords: turbine cooling; optimization method; machine learning; thermal performance enhancement (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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