Investigating the hydraulic performance of slanted axial flow pumps using an enstrophy dissipation-based hybrid optimization approach
Zheming Tong, Zhongqin Yang, Shuiguang Tong, Zekui Shu, Xiangkun Elvis Cao
Abstract
Axial flow pumps (AFPs) are widely employed in urban flood control and drainage systems due to its high discharge at relatively low heads. As off-design condition becomes more common in real operations, we proposed an enstrophy dissipation-based hybrid optimization (EDHO) approach, which combines both the advantage of sparrow search algorithm and Non-dominated Sorting Genetic Algorithm III (NSGA-ΙΙΙ) to enlarge the Preferred Operating Range (POR) of a slanted axial flow pump (SAFP). The overall hydraulic performance was optimized with the proposed EDHO approach with a special focus on energy loss mechanism. According to the analysis, eddy dissipation occupied the most energy loss under partial loads, while shear dissipation also contributed a lot under overload conditions especially around impellers. It is demonstrated that the POR of SAFP was significantly improved after optimization. In particular, the available operation interval was broadened, and the corresponding head and efficiency were remarkably increased by refining the impeller and diffuser profiles. In contrast with NSGA-II, NSGA-III, and multi-objective evolutionary algorithm, the novel hybrid algorithm showed significantly better convergence performance, solution diversity, and stability.