Optimization of rotor blade in stage circumstance for transonic axial flow compressor

Construct optimization platform. Adopt artificial neural network approximate model with global and local combinatorial optimization method. Then optimize the rotor of transonic axial flow compressor Stage35 in stage circumstance. The optimization variables are axial movement distance, circumference movement distance and the stagger angle of the hub, middle and tip section. With control of the mass flow and the pressure ratio, maximum efficiency is set as the optimization goal, the result is reached as the margin remains unchanged and the adiabatic efficiency increases by 0.3% over the whole range of incidence conditions at the designed rotation speed.