Detection of Vortex Cavitation With The Method Adaptive Neural Fuzzy Networks in the Deep Well Pumps

Durdu, Akif; Çeltek, Seyit Alperen; Orhan, Nuri

doi:10.33462/jotaf.769037

Cited by 1 publication

(1 citation statement)

References 16 publications

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“…The detection and prevention of cavitation in pumps require a thorough understanding of the onset and full development of this phenomenon. A considerable number of studies have focused on investigating cavitation in kinetic pumps, as well as water turbines, as revealed by the literature (Al-Obaidi and Towsyfyan, 2019;Bordoloi and Tiwari, 2017;Čdina, 2003;Durdu et al, 2021;Kan et al, 2022;Panda et al, 2018). Recently, researchers have attempted to identify cavitation by utilizing machine learning models (Arendra et al, 2020;Bordoloi and Tiwari, 2017;Matloobi and Riahi, 2021;Panda et al, 2018;Wang, et al, 2019;Wang et al, 2020) .…”

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confidence: 99%

Machine Learning-Based Prediction of NPSH, Noise, and Vibration Levels in Radial Pumps Under Cavitation Conditions

ORHAN,

KURT,

KIRILMAZ

et al. 2024

Tekirdağ Ziraat Fakültesi Dergisi

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Cavitation, a physical phenomenon that detrimentally affects pump performance and reduces pump life, can cause wear on pump elements. Various engineering methods have been developed to identify the initiation and full development of the cavitation process. One such method is the determination of the net positive suction head (NPSH) through a 3% decrease in total head (Hm) at a constant flow rate. In radial pumps, commonly used in agricultural irrigation and industry, cavitation conditions result in a sudden drop in the Hm-Q curve, making it challenging to detect the 3% Hm value drop. This study differs from others in the literature by modelling NPSH, noise, and vibration levels using three machine learning models, specifically artificial neural networks (ANN), support vector machines (SVM), and decision tree regression (DTR). The best-performing model predicts NPSH, noise, and vibration levels corresponding to a 3% decrease in Hm level. The present study determined the NPSH values of a horizontal shaft centrifugal pump at different flow rates and constant operating speed, and the vibration and noise levels were measured for these NPSH values. For each of the NPSH, noise, and vibration levels, ANN, SVM and DTR models were created. The performances of these models were evaluated using criteria such as root mean squared error (RMSE), Mean Absolute Error (MAE) and mean absolute percentage error (MAPE). In addition, Taylor and error box diagrams were created. The ANN model and DTR yielded high accuracy predictions for NPSH values (R2 = 0.86 and R2 = 0.8, respectively). The ANN model provided the best prediction performance for noise and vibration levels. By entering the level of 3% drop in the Hm value of the pump as external data input to the ANN model, NPSH3, noise, and vibration levels were determined. The ANN models can be effectively employed to determine NPSH3, noise, and vibration levels, particularly in radial flow pumps, where detecting 3% reductions in manometric height value is challenging.

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