Analysis of Radial Hydraulic Forces in Centrifugal Pump Operation via Hierarchical Clustering (HC) Algorithms
Hehui Zhang, Kang Li, Ting Liu, Yichu Liu, Jianxin Hu, Qingsong Zuo, Liangxing Jiang
Abstract
As critical industrial equipment, the operational stability of a centrifugal pump is profoundly affected by hydraulic radial forces acting on the impeller. However, existing research has limitations in systematically characterizing time-varying force patterns, elucidating the correlations between fluid–structure interaction (FSI) and vibration and noise, and developing multi-operating condition analysis methodologies. This study focuses on a horizontal end-suction centrifugal pump, integrating computational fluid dynamics (CFD) simulations to develop a transient radial force dataset covering nine operating conditions ranging from 0.4 Qn to 1.2 Qn. Feature engineering was utilized to extract 23 time-frequency domain features. Through Pearson correlation analysis and agglomerative hierarchical clustering (AHC) algorithms, multi-operating condition classification patterns of hydraulic radial forces were unveiled. Key findings include: (1) the X/Y directional force components exhibit distinct anisotropic correlations with the flow rate; (2) hierarchical clustering based on cosine distance and average linkage divides operating conditions into low, medium, and high flow regimes; (3) feature redundancy elimination requires balancing statistical metrics with physical interpretability. This work proposes an unsupervised learning framework, offering a data-driven approach for the hydraulic optimization of centrifugal pumps and intelligent diagnostics, with engineering significance for improving equipment reliability and operational efficiency.