Multi-objective optimization of a mixed-flow pump impeller using modified NSGA-II algorithm

Abstract

In order to maintain a uniform distribution of pareto-front solutions, a modified NSGA-II algorithm coupled with a dynamic crowding distance (DCD) method is proposed for the multi-objective optimization of a mixed-flow pump impeller. With the pump meridional section fixed, ten variables along the shroud and hub are selected to control the blade load by using a three-dimensional inverse design method. Hydraulic efficiency, along with impeller head, is applied as an optimization objective; and a radial basis neural network (RBNN) is adopted to approximate the objective function with 82 training samples. Local sensitivity analysis shows that decision variables have different impacts on the optimization objectives. Instead of randomly selecting one solution to implement, a technique for ordering preferences by similarity to ideal solution (TOPSIS) is introduced to select the best compromise solution (BCS) from pareto-front sets. The proposed method is applied to optimize the baseline model, i.e. a mixed- flow waterjet pump whose specific speed is 508 min-1·m3s-1·m. The performance of the waterjet pump was experimentally tested. Compared with the baseline model, the optimized impeller has a better hydraulic efficiency of 92% as well as a higher impeller head at the design operation point. Furthermore, the off-design performance is improved with a wider high- efficiency operation range. After optimization, velocity gradients on the suction surface are smoother and flow separations are eliminated at the blade inlet part. Thus, the authors believe the proposed method is helpful for optimizing the mixed-flow pumps.