A Review of Machine Learning Methods in Turbine Cooling Optimization

被引:1
|
作者
Xu, Liang [1 ]
Jin, Shenglong [1 ]
Ye, Weiqi [1 ]
Li, Yunlong [1 ]
Gao, Jianmin [1 ]
机构
[1] Xi An Jiao Tong Univ, Sch Mech Engn, State Key Lab Mfg Syst Engn, Xian 710049, Peoples R China
基金
中国博士后科学基金;
关键词
turbine cooling; optimization method; machine learning; thermal performance enhancement; GENETIC ALGORITHM OPTIMIZATION; HEAT-TRANSFER; MULTIOBJECTIVE OPTIMIZATION; NEURAL-NETWORKS; TRANSFER-COEFFICIENTS; SHAPE-OPTIMIZATION; THERMAL OPTIMIZATION; STAGGERED ARRAYS; LEADING-EDGE; PIN-FIN;
D O I
10.3390/en17133177
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
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.
引用
收藏
页数:26
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