Shape Optimization of Helico-axial Multiphase Pump Impeller Based on Genetic Algorithm

In this study, a shape optimization of impeller has been performed through a novel genetic algorithm based on optimization technique with pressure rise as objective function while the gas void fraction (GVF) is 30%. The major geometric parameters were selected as the optimizable parameters. Reynolds-averaged Navier - Stokes equations (RANS) with the standard k-epsilon turbulence model were used to obtain the performance of the impeller as the fitness value at every step of the optimization process. Through this optimal method, a set of better geometric parameters were gotten. Comparing of the inner flow field characters with original impeller, the new optimized one has the higher pressure rise, the more proper distribution of GVF. By this optimization, pressure rise was increased by 14.4% while GVF is 30%. The optimal result indicates that genetic algorithms combined the RANS provide a valuable tool for the optimal design of multiphase pump impeller.