Multi-objective optimization of dual-arc blades in a squirrel-cage fan using modified non-dominated sorting genetic algorithm

Abstract

In this study, a dual-arc profile parameterized by four geometric variables was designed to replace the original single-arc profile of a squirrel-cage fan used in a range hood, in order to improve the efficiency of the entire machine and the fan pressure. A modified Non-dominated Sorting Genetic algorithm coupled with a three-dimensional Reynolds-averaged Navier–Stokes computation is applied to search the optimum blade shape. Moreover, a relatively coarse but proven reliable grid model is employed to accelerate the optimization process, and a dynamic crowding distance is applied to improve the broad diversity of the Pareto front. The optimization results show that the optimal dual-arc blades are formed by a leading arc with a relatively smaller curvature and a trailing arc with a larger curvature, and the shape of the leading arc dominates the aerodynamic performance of the dual-arc blade. The blade schemes at two end of the Pareto front have increased the fan pressure and efficiency at the optimization point by 5.3% and 1.5%, respectively, but also result in a decline in another performance indicator. The best compromised solution in the middle of the Pareto front has improved the pressure by 2.6% without reducing the efficiency in the numerical calculation. Compared with the single-arc blade with the same inlet and outlet angle, the dual-arc blade has a higher fan pressure, but at the same time, the efficiency is negatively affected. Finally, the new impeller with optimized dual-arc blades is manufactured and tested, and the experimental results show an increment exceeds 2% in pressure and an unexpected slightly improvement in fan efficiency.