Hydraulic Performance Optimization of Pump Impeller Based on a Joint of Particle Swarm Algorithm and Least-Squares Support Vector Regression

The hydraulic performance of centrifugal pumps is considerably affected by the impeller, and an effective optimization method for centrifugal pump impeller has been developed in the current study. A combination of the least-squares support vector regression machine (LSSVR) and particle swarm optimization algorithm (PSO) is proposed to redesign the impeller and improve the hydraulic performance. In the case study, four key geometric parameters of the impeller, namely, inlet angle, outlet angle, wrap angle and number of blades are selected for optimization. Maximum efficiency and constant head are selected as the optimization targets. During the optimization design, the required database for the LSSVR agent model is designed according to design of experiments. The optimal solution is then found in the established agent model space by the particle swarm algorithm and then verified by computational fluid dynamics. Ultimately, an improved impeller structure with an improved efficiency is provided. Numerical results show that the optimized impeller's efficiency is increased by 1.29% under the condition that the head is essentially unchanged. Then, the reason for the improvement of impeller hydraulic efficiency is explained by the entropy production method. The conclusions show that the PSO-LSSVR method can be used to optimize the pump impeller and achieve higher pump performance.