Optimization of a centrifugal pump with high efficiency and low noise based on fast prediction method and vortex control

This study aims to develop a rapid optimization design method for the centrifugal pump with high efficiency and low noise. The improved delayed detached eddy simulation and Powell's vortex sound method were employed to calculate the flow and sound field. The prediction models of hydraulic loss and noise based on vortex characteristic quantities were built using linear regression (LR) and artificial neural network (ANN) methods respectively. The Kriging surrogate model and the NSGA-II genetic algorithm were utilized for minimizing the entropy production rate and average total sound pressure in the pump, where the objectives of the training dataset for Kriging model were calculated by prediction model (scheme I) and conventional unsteady numerical simulation (scheme II). Results show that the structure and performance of optimized pumps under both schemes are nearly identical, but the computational cost of scheme Iis much lower than that of scheme II. The shear of the optimized pump is significantly reduced and the rigid rotational strength is enhanced, leading to head and efficiency improved by 3.2 % and 3.7 % respectively, under the design flow condition. The average total sound pressure level is reduced by 1.07 % due to the fluctuation suppression of shear and rigid vorticity.