Prediction of penetration depth in a plunging water jet using soft computing approaches

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dc.contributor.authorOnen, Fevzi
dc.date.accessioned2024-04-24T16:01:55Z
dc.date.available2024-04-24T16:01:55Z
dc.date.issued2014
dc.departmentDicle Üniversitesien_US
dc.description.abstractThe flow characteristics of the plunging water jets can be defined as volumetric air entrainment rate, bubble penetration depth, and oxygen transfer efficiency. In this study, the bubble penetration depth is evaluated based on four major parameters that describe air entrainment at the plunge point: the nozzle diameter (D (N)), jet length (L (j)), jet velocity (V (N)), and jet impact angle (theta). This study presents artificial neural network (ANN) and genetic expression programming (GEP) model, which is an extension to genetic programming, as an alternative approach to modeling of the bubble penetration depth by plunging water jets. A new formulation for prediction of penetration depth in a plunging water jets is developed using GEP. The GEP-based formulation and ANN approach are compared with experimental results, multiple linear/nonlinear regressions, and other equations. The results have shown that the both ANN and GEP are found to be able to learn the relation between the bubble penetration depth and basic water jet properties. Additionally, sensitivity analysis is performed for ANN, and it is found that D (N) is the most effective parameter on the bubble penetration depth.en_US
dc.identifier.doi10.1007/s00521-013-1475-y
dc.identifier.endpage227en_US
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-84902528847
dc.identifier.scopusqualityQ1
dc.identifier.startpage217en_US
dc.identifier.urihttps://doi.org/10.1007/s00521-013-1475-y
dc.identifier.urihttps://hdl.handle.net/11468/14496
dc.identifier.volume25en_US
dc.identifier.wosWOS:000338191300020
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherSpringer London Ltden_US
dc.relation.ispartofNeural Computing & Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectPenetration Depthen_US
dc.subjectGenetic Expression Programming (Gep)en_US
dc.subjectArtificial Neural Network (Ann)en_US
dc.subjectRegression Analysisen_US
dc.titlePrediction of penetration depth in a plunging water jet using soft computing approachesen_US
dc.titlePrediction of penetration depth in a plunging water jet using soft computing approaches
dc.typeArticleen_US

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