Taking a self-designed single-stage adjustable rotor blade axial flow fan model as the research object

a three-dimensional aerodynamic optimization design model based on artificial neural network(ANN) and genetic algorithm(GA) is established. The 3D aerodynamic optimization for outlet guide vane is carried out to generate optimized blade profile at the design point

and the overall performances of the fan before and after the optimization of the outlet guide vane are numerically verified using the full-channel 3D numerical calculation method. The simulation results show that

compared with the original blade profile

for the optimized blade profile at design point

the efficiency value of the fan increases by 1.1%

and the total pressure value of the fan increases by 2.1%.For the optimized blade profile at off-design point

the efficiency value of the fan increases by 8.6%

and the total pressure value of the fan increases by 20.3%. Through further analysis on internal flow field of the impeller stage

conclusions can be made that whether it is at the design point or at the off-design point

the optimized blade profile not only can exhibit stronger resistance to airflow separations than the original blade profile

but also can effectively suppress the radial migration of low-energy fluid in the cascade channel

and thus the optimized blade profile significantly reduces the flow losses inside the cascade channel.