Multi-objective Optimization by Using Modified PSO Algorithm for Axial Flow Pump Impeller

Axial flow pumps are one type of blade pump with great flux, lower head, highly fluids flow. This type of pump can be used in agriculture, irrigation and massive water project widely. Impellers are the main and highly sensitive part of the pumps which performs the function by transferring energy to the fluid there by increasing pressure and velocity. In axial flow pump design process, in order to get high performance pump, designers usually try to increase the efficiency (.) and decrease the required net positive suction head (NPSHr) simultaneously. In this paper, multi-objective optimization of axial flow pump based on modified Particle Swarm Optimization (MPSO) is performed. At first, the NPSHr and. in a set of axial flow pump are numerically investigated using commercial software ANSYS with the design variables concerning hub angle beta(h), chord angle beta(c), cascade solidity of chord sigma(c), maximum thickness of blade H. And then, using the Group Method of Data Handling (GMDH) type neural networks in commercial software DTREG, the corresponding polynomial representation for NPSHr and eta with respect to design variables are obtained. Finally, multi objective optimization based on modified Particle Swarm Optimization (MPSO) approach is used for Pareto based optimization. The result shows that an optimal solution of the axial flow pump impeller was obtained: NPSHr was decreased by 11.68 % and efficiency was increased by 4.24 % simultaneously. It means by using this method, better performance pump with higher efficiency and lower NPSHr can be got and this optimization is feasible.