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

Basit Öğe Kaydı
dc.contributor.authorDurdu, Akif
dc.contributor.authorÇeltek, Seyit Alperen
dc.contributor.authorOrhan, Nuri
dc.date.accessioned2024-10-29T17:53:09Z
dc.date.available2024-10-29T17:53:09Z
dc.date.issued2021
dc.departmentTekirdağ Namık Kemal Üniversitesi
dc.description.abstractNowadays submersible deep well pumps are the most used irrigation systems in agriculture field. Efficient operation and economical life of pumps is an important issue. One of the most important parameters affecting pump efficiency and life is cavitation The cavitation is one of the problems frequently faced in the pump systems that widely used in the agriculture field. The cavitation could cause more undesired effects such as loss of hydraulic performance, erosion, vibration and noise. This paper presents a novel model for the detection of vortex cavitation in the deep well pump used in the agriculture system using adaptive neural fuzzy networks. The data submergence, flow rate, power consumption, pressure values, and noise values used for training the ANFIS (Adaptive-Network Based Fuzzy Inference Systems) network are acquired from an experimental pump. In this study, we use to the sixty-seven data for training process, while the fifteen data have used for testing of our model. The average percentage error (APE) has obtained as 0.08 % and as 0.34 % respectively for 67 training data and for 15 test data. The performance of the implemented model shows the advantages of ANFIS. The result of this study shows that ANFIS can be successfully used to detect vortex cavitation. This paper has two novel contributions which are the usage of noise value on cavitation detection and find out cavitation by using adaptive neural fuzzy networks. During the cavitation, the pump parameters must change by controller for prevent unwanted pump errors. The strategy proposed could be preliminary study of automatic pump control. Also proposed novel control strategy can be used for cavitation control in agriculture irrigation pumps, because of easy set up and no need extra cost. The ANFIS based model has real-time applicable thanks to rapid and easy control. It is possible to set safe boundaries in submergence in this model. Thus, users by adjusting controllable parameters can prevent cavitation and increase pump efficiency.
dc.identifier.doi10.33462/jotaf.769037
dc.identifier.endpage624
dc.identifier.issn1302-7050
dc.identifier.issn2146-5894
dc.identifier.issue4en_US
dc.identifier.startpage613
dc.identifier.trdizinid1144612
dc.identifier.urihttps://doi.org/10.33462/jotaf.769037
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1144612
dc.identifier.urihttps://hdl.handle.net/20.500.11776/13417
dc.identifier.volume18
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.relation.ispartofTekirdağ Ziraat Fakültesi Dergisi
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectAdaptive fuzzy neural networks
dc.subjectCavitation
dc.subjectSubmergence
dc.subjectVortex cavitation
dc.subjectDeep well pumps
dc.titleDetection of Vortex Cavitation With The Method Adaptive Neural Fuzzy Networks in the Deep Well Pumps
dc.typeArticle

Dosyalar

Koleksiyon

TR-Dizin İndeksli Yayınlar Koleksiyonu
Tekirdağ Ziraat Fakültesi Dergisi