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.
In this study, the blade shape of the squirrel-cage fan system inside the range hood was optimized using the surrogate model to improve the maximum volume flow rate. The influence of computational fluid dynamics (CFD) noise was concerned. The regression Kriging model (RKM) was used as a surrogate model to reflect the relationship between the design parameters of the blade and the volume flow rate. The parallel filling criterion after re-interpolation was used to improve the optimization efficiency further and ensure global optimization. Through experimental verification, we found that the relative error between the volume flow rate of the optimal sample of RKM and the experiment was only 0.4%. Compared with the prototype, the maximum volume flow rate of the optimal sample of RKM was increased by 2.9%, and the efficiency under the corresponding working conditions was increased by 2%. RKM was used to predict the velocity field of the volute and impeller exit section to explore the feasibility of the RKM in the flow field prediction. Research shows that the RKM cannot accurately predict the velocity of each grid on the cross-section. Still, it can accurately predict the changing trend of the velocity.
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