Multi-objective optimal design of double-suction centrifugal pump impeller using agent-based models

Double suction centrifugal pumps, which feature large flow and head, are applied in water utility and transportation sectors. The efficiency, sound, and vibration levels are key performance indexes for double suction centrifugal pumps. This study aims to improve the performance of double suction pumps using a multi-objective optimization method. The Latin hypercube sampling (LHS) method is used to randomly generate sample data considering five key geometric parameters of the impeller, and the agent model training samples are generated using numerical computation. Then, the multi-objective optimization design of the impeller, focusing on the head, efficiency, and pressure pulsation energy as the objectives, was carried out by combining the Gaussian process regression (GPR) and non-dominated sorting genetic algorithm II (NSGA-II) algorithms. Results show that the head is increased by 3.91 m, the efficiency is increased by 0.2 %, and the pressure pulsation energy is reduced by 24 % compared with the original model. Meanwhile, the detailed information of energy loss and pressure pulsation in the pump was analyzed to understand the influence of impeller geometry parameters. This study provides a certain reference for the optimized design of double-suction pumps.