An optimization on the stacking line of low-pressure axial-flow fan using the surrogate-assistant optimization method

The shape of the blade stacking line greatly influences the low-pressure axial-flow fan’s operational efficiency, but there is still no fast and effective way to determine the stacking line’s optimal shape. This paper presents an automatic optimization design procedure for the blade stacking line in the low-pressure axial-flow fan based on the FINE/Design3D™ platform. The procedure combines computational fluid dynamics (CFD), surrogate model method, and genetic algorithm (GA) to execute a secondary optimization on a composite skewed-swept rotor-only axial fan blade. The results show that the static efficiency and the static pressure rise of the optimized fan respectively increase by 3.76 % and 5.82 % without stall margin decrease. The blade shape variation in skew and sweep direction reduces the tip leakage flow loss, improves the blade loading distribution, and contributes to the efficiency increment. This research provides a useful reference for the blade stacking line’s automatic optimization for the axial-flow fan.