Affiliation:
1. 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.
Funder
Fundamental Research Funds for the Central Universities
Natural Science Foundation of Shaanxi Province
China Postdoctoral Science Foundation
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