Scopus
🔓 Açık Erişim YÖKSİS Eşleşti
Machine Learning-Based Prediction of NPSH, Noise, and Vibration Levels in Radial Pumps Under Cavitation Conditions
Journal of Tekirdag Agricultural Faculty · Mart 2024
YÖKSİS Kayıtları
Machine Learning-Based Prediction of NPSH, Noise, and Vibration Levels in Radial Pumps Under Cavitation Conditions
Tekirdağ Ziraat Fakültesi Dergisi · 2024 TR DİZİN
ÖĞRETİM GÖREVLİSİ MEHMET KURT →
Machine Learning-Based Prediction of NPSH, Noise, and Vibration Levels in Radial Pumps Under Cavitation Conditions
Tekirdağ Ziraat Fakültesi Dergisi · 2024 ESCI
ÖĞRETİM GÖREVLİSİ HASAN KIRILMAZ →
Machine Learning-Based Prediction of NPSH, Noise, and Vibration Levels in Radial Pumps Under Cavitation Conditions
Tekirdağ Ziraat Fakültesi Dergisi · 2024 ESCI
DOÇENT NURİ ORHAN →
Machine Learning-Based Prediction of NPSH, Noise, and Vibration Levels in Radial Pumps Under Cavitation Conditions
Tekirdağ Ziraat Fakültesi Dergisi · 2024 ESCI
ÖĞRETİM GÖREVLİSİ MEHMET KURT →
Makale Bilgileri
DergiJournal of Tekirdag Agricultural Faculty
Yayın TarihiMart 2024
Cilt / Sayfa21 · 533-546
Scopus ID2-s2.0-85191361018
Erişim🔓 Açık Erişim
Özet
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 (R<sup>2</sup> = 0.86 and R<sup>2</sup> = 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.
Yazarlar (4)
1
Nuri Orhan
ORCID: 0000-0002-9987-1695
2
Kurt Mehmet
ORCID: 0000-0002-9566-6627
3
Kirilmaz Hasan
ORCID: 0000-0002-0263-6200
4
Ertuğrul Murat
ORCID: 0000-0001-9582-1546
Anahtar Kelimeler
Centrifugal pumps
Machine learning
Net positive suction head (NPSH)
Noise
Vibration
Kurumlar
Bozok Üniversitesi
Yozgat Turkey
Selçuk Üniversitesi
Selçuklu Turkey
Metrikler
1
Atıf
4
Yazar
5
Anahtar Kelime