Impeller geometry optimization using a machine learning-based algorithm with physics embedded dynamic sampling method for a high-speed miniature pump - Song, Xueyi; Zheng, Kexin; Luo, Xianwu (Journal of mechanical science and technology, 2025)
This paper proposes an optimization approach leveraging machine learning, integrated with physics-informed dynamic sampling, to improve the hydraulic efficiency of highspeed pumps utilized in aerospace. These pumps are difficult to optimize due to their sensitivity to various impeller geometrical parameters. To address the large design space and reduce computational cost, the study introduces fundamental design physics equations and integrates a distance criterion within the optimization algorithm. The optimization process focuses on 13 key design variables and utilizes CFD simulations to predict hydraulic performance, with the goal of maximizing efficiency while ensuring the pump head within specified limits. The results show a 4.35 % increase in hydraulic efficiency and improved flow uniformity. Analysis of entropy generation rates and boundary vorticity flux reveals more uniform flow along the blades’ suction side and reduced vorticity near the trailing edge, indicating reduced flow separation and turbulence. This study offers an effective tool for optimizing high-speed miniature pumps, providing insights for future pump designs.