Multi-Objective Optimization of Helico-Axial Multiphase Pump Impeller Based on NSGA-II

This paper presented an approach based on non-dominated sorting of genetic algorithm (NSGA-II) for the multi-objective optimization of boost cell of helico-axial multiphase pump impeller. The pressure rise and efficiency were optimized with design variables concerning flange inlet angle β1, flange outlet angle β2, inlet top-hub ratio htr and hub half cone angle γ. RANS(Reynolds-averaged Navier-Stokes equations) with the standard k -e turbulence model were applied to obtain the performance of the impeller as the fitness value at every step of the optimization process. At the end of the procedure, a global Pareto-optimal set was gotten. Comparing the inner flow field characters with original impeller, the optimized one has higher pressure rise and efficiency and more proper distribution of gas void fraction (GVF). By this optimization platform, maximum efficiency and pressure rise were increased by 12.112% and 10.774%, respectively. The optimal result showed that NSGA-II combined the RANS could provide a valuable tool for the optimization of multiphase pump impeller.