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https://hdl.handle.net/11499/6245
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DC Field | Value | Language |
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dc.contributor.author | Gölcü, Mustafa | - |
dc.contributor.author | Pancar, Y. | - |
dc.contributor.author | Sevil Ergür, H. | - |
dc.contributor.author | Göral, E.O. | - |
dc.date.accessioned | 2019-08-16T12:05:18Z | - |
dc.date.available | 2019-08-16T12:05:18Z | - |
dc.date.issued | 2010 | - |
dc.identifier.issn | 1300-686X | - |
dc.identifier.uri | https://hdl.handle.net/11499/6245 | - |
dc.description.abstract | Artificial Neural Network (ANN) was used to predict the effects of splitter blades in a semi-open impeller on centrifugal pump performance. The characteristics of this impeller were compared with those of impellers without splitter blades. Experimental results for lengths of splitter blades in ratio of 1/3, 2/3, and 3/3 of the main blade length were evaluated by different ANN training algorithm. Training and test data were obtained from experimental studies. The best training algorithm and number of neurons were determined. The values of head, efficiency, and effective power were estimated in a semi-open impeller with splitter blades in ratio of 3/6 and 5/6 of the main blade length at the best efficiency point (b.e.p.). Here, as the splitter blade length increases; the flow rate and power increases, the efficiency decrease. All of the estimated values of performance in a semi-open impeller with splitter blades indicate the model works in line with expectations. Experimental studies to determine head, efficiency and effective power consumption in different types of pumps are complex, time consuming, and costly. It also requires specific measurement tools to obtain the characteristics values of pump. To overcome these difficulties, an ANN can be used for prediction of pump performance in semi open impeller. © Association for Scientific Research. | en_US |
dc.language.iso | en | en_US |
dc.relation.ispartof | Mathematical and Computational Applications | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | Artificial neural-network | en_US |
dc.subject | Performance | en_US |
dc.subject | Semi-open impeller | en_US |
dc.subject | Splitter blade | en_US |
dc.subject | Artificial Neural Network | en_US |
dc.subject | Best efficiency point | en_US |
dc.subject | Characteristics values | en_US |
dc.subject | Experimental studies | en_US |
dc.subject | In-line | en_US |
dc.subject | Measurement tools | en_US |
dc.subject | Power characteristic | en_US |
dc.subject | Power Consumption | en_US |
dc.subject | Power increase | en_US |
dc.subject | Pump performance | en_US |
dc.subject | Test data | en_US |
dc.subject | Training algorithms | en_US |
dc.subject | Centrifugal pumps | en_US |
dc.subject | Forecasting | en_US |
dc.subject | Hydraulic machinery | en_US |
dc.subject | Neural networks | en_US |
dc.subject | Well pumps | en_US |
dc.subject | Blowers | en_US |
dc.title | Prediction of head, efficiency, and power characteristics in a semi-open impeller | en_US |
dc.type | Article | en_US |
dc.identifier.volume | 15 | en_US |
dc.identifier.issue | 1 | en_US |
dc.identifier.startpage | 137 | en_US |
dc.identifier.endpage | 147 | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.identifier.scopus | 2-s2.0-78149268886 | en_US |
dc.identifier.trdizinid | 98922 | en_US |
dc.identifier.wos | WOS:000276584100014 | en_US |
dc.identifier.scopusquality | Q3 | - |
dc.owner | Pamukkale University | - |
item.grantfulltext | open | - |
item.openairetype | Article | - |
item.fulltext | With Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.cerifentitytype | Publications | - |
item.languageiso639-1 | en | - |
crisitem.author.dept | 20.01. Automotive Engineering | - |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection Teknik Eğitim Fakültesi Koleksiyonu TR Dizin İndeksli Yayınlar Koleksiyonu / TR Dizin Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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File | Size | Format | |
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Prediction of head.pdf | 277.63 kB | Adobe PDF | View/Open |
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