Experimental study and modeling of hydraulic jump for a suddenly expanding stilling basin using different hybrid algorithms

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dc.authoridGUL, ENES/0000-0001-9364-9738
dc.authoridDursun, Omer Faruk/0000-0003-3923-5205
dc.authoridMohammadian, Abdolmajid/0000-0001-5381-8189
dc.authorwosidGUL, ENES/AAH-6191-2021
dc.authorwosidDursun, Omer Faruk/AAA-8464-2020
dc.authorwosidMohammadian, Abdolmajid/A-2995-2015
dc.contributor.authorGul, Enes
dc.contributor.authorDursun, O. Faruk
dc.contributor.authorMohammadian, Abdolmajid
dc.date.accessioned2024-08-04T20:50:51Z
dc.date.available2024-08-04T20:50:51Z
dc.date.issued2021
dc.departmentİnönü Üniversitesien_US
dc.description.abstractHydraulic jump is a highly important phenomenon for dissipation of energy. This event, which involves flow regime change, can occur in many different types of stilling basins. In this study, hydraulic jump characteristics such as relative jump length and sequent depth ratio occurring in a suddenly expanding stilling basin were estimated using hybrid Extreme Learning Machine (ELM). To hybridize ELM, Imperialist Competitive Algorithm (ICA), Firefly Algorithm (FA) and Particle Swarm Optimization (PSO) metaheuristic algorithms were implemented. In addition, six different models were established to determine effective dimensionless (relative) input variables. A new dataset was constructed by adding the data obtained from the experimental study in the present research to the data obtained from the literature. The performance of each model was evaluated using k-fold cross validation. Results showed that ICA hybridization slightly outperformed FA and PSO methods. Considering relative input parameters, Froude number (Fr), expansion ratio (B) and relative sill height (S), and effective input combinations were Fr - B- S and Fr - B for the prediction of the sequent depth ratio (Y) and relative hydraulic jump length (L-j/h(1)), respectively.en_US
dc.description.sponsorshipIUBAP (Inonu University Scientific Projects Unit) [FCD-2018-1324, FBG-2018-1474]en_US
dc.description.sponsorshipThis research was supported by IUBAP (Inonu University Scientific Projects Unit) under the project numbers FCD-2018-1324 and FBG-2018-1474.en_US
dc.identifier.doi10.2166/ws.2021.139
dc.identifier.endpage3771en_US
dc.identifier.issn1606-9749
dc.identifier.issn1607-0798
dc.identifier.issue7en_US
dc.identifier.scopus2-s2.0-85119180071en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage3752en_US
dc.identifier.urihttps://doi.org/10.2166/ws.2021.139
dc.identifier.urihttps://hdl.handle.net/11616/100309
dc.identifier.volume21en_US
dc.identifier.wosWOS:000651892300001en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIwa Publishingen_US
dc.relation.ispartofWater Supplyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectcross-validationen_US
dc.subjectevolutionary algorithmen_US
dc.subjectextreme learning machineen_US
dc.subjecthybrid modelen_US
dc.subjecthydraulic jumpen_US
dc.subjectmachine learningen_US
dc.subjectoptimizationen_US
dc.titleExperimental study and modeling of hydraulic jump for a suddenly expanding stilling basin using different hybrid algorithmsen_US
dc.typeArticleen_US

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