Numerical Research on Optimization and Flow Mechanism in Lift Fan Stator

Aiming at a large-scale lift axial flow fan, the optimization design of blade camber curves is implemented with artificial neural network and genetic algorithm for the axial flow fan stator. The used grid is proven to be independent and the numerical results of fan performance match the test results well. The performance and stator blade profiles of the fan are analyzed for the original and optimized situations. As the mass flow coefficient is 0.28, 0.35 and 0.45, the flow mechanism is discussed to demonstrate the effect on stability, hence a method for reducing flow loss in the stator is proposed. The results show that reducing the attack angle and slightly increasing backward angle can suppress the secondary flow within the flow channel under main operating conditions by optimizing inlet and outlet angles of the stator. The optimized structure is able to maintain the pressure rise, improve the efficiency and reduce the power. At the design point, the efficiency is increased by over 7%. For the stator blade, reducing the inlet angle and increasing the outlet angle can adjust load distributions on the blade surface, reduce flow loss and enhance gas flow capacity.