A Review of Machine Learning Methods in Turbine Cooling Optimization

被引:2
|
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
相关论文
共 50 条
  • [1] Machine learning methods for wind turbine condition monitoring: A review
    Stetco, Adrian
    Dinmohammadi, Fateme
    Zhao, Xingyu
    Robu, Valentin
    Flynn, David
    Barnes, Mike
    Keane, John
    Nenadic, Goran
    RENEWABLE ENERGY, 2019, 133 : 620 - 635
  • [2] Optimization of cooling structures in gas turbines: A review
    Zhang, Guohua
    Zhu, Rui
    Xie, Gongnan
    Li, Shulei
    Sunden, Bengt
    CHINESE JOURNAL OF AERONAUTICS, 2022, 35 (06) : 18 - 46
  • [3] Machine Learning Methods in CFD for Turbomachinery: A Review
    Hammond, James
    Pepper, Nick
    Montomoli, Francesco
    Michelassi, Vittorio
    INTERNATIONAL JOURNAL OF TURBOMACHINERY PROPULSION AND POWER, 2022, 7 (02)
  • [4] Review of Turbine Cooling Technologies
    Olczak, Dariusz
    Jaworski, Maciej
    JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER-TRANSACTIONS OF THE ASME, 2023, 145 (09):
  • [5] Nonlinear Optimization of Turbine Conjugate Heat Transfer with Iterative Machine Learning and Training Sample Replacement
    Dutta, Sandip
    Smith, Reid
    ENERGIES, 2020, 13 (17)
  • [6] Automatic Optimization-Based Methods in Machine Learning: A Systematic Review
    Shahrabadi, Somayeh
    Adao, Telmo
    Alves, Victor
    Magalhaes, Luis G.
    INTELLIGENT SYSTEMS AND APPLICATIONS, VOL 2, INTELLISYS 2023, 2024, 823 : 309 - 326
  • [7] A Survey of Optimization Methods From a Machine Learning Perspective
    Sun, Shiliang
    Cao, Zehui
    Zhu, Han
    Zhao, Jing
    IEEE TRANSACTIONS ON CYBERNETICS, 2020, 50 (08) : 3668 - 3681
  • [8] Hybrid approaches to optimization and machine learning methods: a systematic literature review
    Azevedo, Beatriz Flamia
    Rocha, Ana Maria A. C.
    Pereira, Ana I.
    MACHINE LEARNING, 2024, 113 (07) : 4055 - 4097
  • [9] Reservoir optimization and machine learning methods
    Warin, Xavier
    EURO JOURNAL ON COMPUTATIONAL OPTIMIZATION, 2023, 11
  • [10] Machine learning methods for the study of cybersickness: a systematic review
    Yang, Alexander Hui Xiang
    Kasabov, Nikola
    Cakmak, Yusuf Ozgur
    BRAIN INFORMATICS, 2022, 9 (01)