Aerodynamic Optimization Design of Weight Reduction Turbine Blade Based on Dynamic XGBoost and MaxLIPO Trust Region Algorithm [基于动态XGBoost 与MaxLIPO 置信域算法的减重涡轮叶型气动优化设计]

作者：Zhang Z.-Q.; Song Y.-K.; Wang M.-Y.; Lu X.-G.; Zhang Y.-F.

来源：Journal of Engineering Thermophysics, 2021, 42(11): 2804-2815.

• B-spline
• Machine learning
• Optimization algorithm
• Weight reduction turbine blade
• XGBoost

This research first proposed a new aerodynamic optimization method that using machine learning XGBoost algorithm as the regression model, and global optimization algorithm MaxLIPO Trust Region method as the optimization method on regression model. In order to ensure the accuracy of the regression model near the optimal value, a dynamic recognition add point method is used to construct a dynamically updated regression model, and a double convergence criterion is used to determine the convergence of the optimization process. After constructing the optimization process, a new type of weight reduction high-pressure turbine blade was optimized. The results show that the optimization process can achieve rapid and effective optimization of aerodynamic performance compared to traditional optimization methods, and the regression prediction accuracy near the optimal value reaches a level with a minimum error from the CFD result. Meanwhile, the influence weight factor of geometrical parameters on the aerodynamic performance could be analyzed. Finally, the effective aerodynamic optimization design of the weight reduction turbine blade is achieved.

• 单位
  中国科学院大学