基于数据驱动的可控变形叶型优化方法

The optimization design method of controllable morphing blade profile considering both morphing cost and aerodynamic benefits was discussed. A machine learning algorithm was used to build a prediction model to predict key aerodynamic parameters of morphing blade profiles. The morphing cost and aerodynamic benefits were quantified， and a Bayesian optimization framework was built for optimization. Results showed that the prediction and optimization framework based on machine learning can accurately predict the aerodynamic performance of the fan after morphing， and evaluate the profit boundary of the blade profile morphing considering the morphing cost. The main conclusion indicated that using machine learning algorithm and Bayesian optimization framework can obtain a morphing scheme taking into account both morphing cost and aerodynamic benefits. This scheme can reduce the maximum stress of blade by 14.17% and the energy consumption of piezoelectric actuator by 67.45% while ensuring the improvement of aerodynamic performance compared with the scheme only considering aerodynamic benefits.