Multi-objective optimization of pump turbine based on improved partical swarm optimization algorithm

In order to improve the pump-turbine's pumping efficiency with long and short blade structure during operation, the partical swarm optimization (PSO) algorithm was improved by linearly changing inertial weights and changing learning factors, and then the improved PSO algorithm was used to optimize the pump turbine structure. A combination of test and numerical simulation was adopted, and the improved PSO algorithm of the learning factor was used to optimize the nine structural parameters to improve the pump turbine working condition efficiency and head. It is founded that under the guide vane opening of 9.8°, each working condition's efficiency and head have been improved to a certain extent. The rated working point's efficiency value is increased by 0.56%, and the head is increased by 2.10%, with the rated working point efficiency of 17.5° increased by 0.55%, lifted by 0.018%. And the high-efficiency area is broadened to a certain extent. Except for small flow rate, the efficiency value and head of the other operating points of the 24.8° opening are significantly improved. © 2021, Editorial Board of Journal of Huazhong University of Science and Technology. All right reserved.