Progresses on the Application of Data-driven Methods in Turbine Blade Cooling Design

From the perspective of gas turbine blade cooling design, the study and application progresses of data-driven methods of domestic and foreign researchers in recent years was summarized. Three types of data-driven methods including mathematical statistics, machine learing and deep learning were introduced. The advantages of data-driven methods over experimental and numerical simulations were expounded. And the application of data-driven methods was emphatically summarized, mainly including predicition and uncertainty quantification of turbine blade cooling characteristics, improvement of turbulence models in numerical simulation, and data fusion of existing data and knowledge. Based on the current research hotspots and development tendency, further studies focus on data-driven methods in gas turbine blade cooling design were proposed, including exploring the potential of data fusion approches, improving generalization ability, reducing data cost, studying data processing methods, comparing the advantages and disadvantages of different data-driven methods, and developing data-driven approaches for complex cooling designs.