Analysis of Radial Hydraulic Forces in Centrifugal Pump Operation via Hierarchical Clustering (HC) Algorithms

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.