Improving the energy performance of vortex pump based on whale optimization algorithm
Peijian Zhou, Zaipeng Wen, Yang Wang, Yanzhao Wu, Denghao Wu, Renfang Huang, Zhifeng Yao
Abstract
This study introduced a multi-objective optimization framework for vortex pumps, utilizing the whale optimization algorithm (WOA) and Gaussian process regression (GPR) to enhance energy performance under various operating conditions. Initially, 12 design parameters were analysed using the Plackett-Burman test, identifying five critical hydraulic parameters. These parameters formed the basis for optimizing the pump's head and weighted efficiency. A surrogate model database was created using Latin hypercube sampling, and GPR facilitated the optimization process. The application of WOA resulted in a 1.94 m increase in head, a 1.72% rise in efficiency, and a 1.69% improvement in weighted efficiency. Entropy production and rigid vorticity analysis further showed a significant reduction in energy loss across pump components. This research offers a robust framework for the efficient and energy-saving design of vortex pumps.