The blade shape optimization of a low-pressure axial fan using the surrogate-based multi-objective optimization method

As a core component of a fan, the blade has a decisive impact on the aerodynamic performance of a low-pressure axial flow fan. Due to the limitations of the classical fan design theory on considering the complex 3D internal flow, the spanwise distribution of blade stacking line and section profiles are usually hard to reach the best state. This paper builds a surrogate-assistant multi-objective optimization flow combined with CFD method to explore the optimum blade shape under two typical working conditions. A total of 16 parameters were selected for demonstrating the blade stacking line and section profiles, according to Morris one-at-a-time sensitivity analysis. The objective and constraint functions were the fan’s total-to-static efficiency and static pressure rise, respectively. During the optimization, the surrogate models of all response functions were built using kriging models, on which the multi-objective genetic algorithm takes the exploration. The optimization results indicate that the maximum improvement of the efficiency is 1.26 % for low mass flow working condition and 5.47 % for high mass flow working condition. The optimized models tend to make the low-pressure zone distributed along the blade leading edge in the meridian view and to reduce the tip leakage vortex intensity. This paper provides a good practical demonstration of multi-objective fine optimization on the blade shape of a low-pressure axial flow fan.