基于数据驱动的复杂进气下风扇转子叶根损失模型

A data-driven based hub loss prediction model was developed for the fan rotor under complex inflow conditions. The key aerodynamic parameters were extracted as input parameters and the entropy loss as output parameter. The sample database was constructed based on computation-efficient single-blade-passage steady computational method. Different boundary conditions were set and combined to make the database samples cover a wide range of complex inflows as far as possible. The RBF neural network was used to construct the mapping between input and output parameters to realize rapid prediction of hub loss. Results showed that the loss model can accurately capture the radial distributions of hub loss and significantly improve the prediction accuracy. Meanwhile，the averaged loss prediction error in the rotor hub region was mostly lower than 10% under different inlet mass flow，inlet swirl angle and inflow distortion conditions.