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 fl...

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Veröffentlicht in:Applied sciences Jg. 15; H. 18; S. 10251
Hauptverfasser: Zhang, Hehui, Li, Kang, Liu, Ting, Liu, Yichu, Hu, Jianxin, Zuo, Qingsong, Jiang, Liangxing
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.09.2025
Schlagworte:
Acoustics
Algorithms
Cavitation
centrifugal pump
Clustering
Correlation analysis
Datasets
Design optimization
Efficiency
feature engineering
Fluid dynamics
Fourier transforms
hierarchical clustering
hydraulic radial force
Hydraulics
Investigations
Liu, Timothy
Machine learning
Sensors
Signal processing
Simulation
Turbulence models
Vibration
ISSN:2076-3417, 2076-3417
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Zusammenfassung: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.
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ISSN:2076-3417
2076-3417
DOI:10.3390/app151810251