Application of data-driven models to predict the dimensions of flow separation zone

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dc.authoridGHAREHBAGHI, AMIN/0000-0002-2898-3681
dc.authoridLatif, Sarmad/0000-0002-0417-3545
dc.authoridGhasemlounia, Redvan/0000-0003-1796-4562
dc.authorwosidGhasemlounia, Redvan/GQZ-7676-2022
dc.authorwosidGHAREHBAGHI, AMIN/JVD-7375-2023
dc.authorwosidParsaie, Abbas/E-6976-2016
dc.authorwosidLatif, Sarmad/ABD-4755-2020
dc.contributor.authorGharehbaghi, Amin
dc.contributor.authorGhasemlounia, Redvan
dc.contributor.authorLatif, Sarmad Dashti
dc.contributor.authorHaghiabi, Amir Hamzeh
dc.contributor.authorParsaie, Abbas
dc.date.accessioned2024-06-13T20:17:50Z
dc.date.available2024-06-13T20:17:50Z
dc.date.issued2023
dc.departmentİstanbul Gedik Üniversitesi
dc.description.abstractIn this research, the effect of a submerged multiple-vane system on the dimensions of flow separation zone (DFSZ) is assessed via 192 measured datasets. The vanes' shape comprised two segments, curved and flat plates which are located in the connection of main channel to the lateral intake channel with an angle of 55 degrees. In this direction, a butterfly's array for the vanes' arrangement along with different main controlling factors such as distances of vanes along the flow (delta(l)), degree of curvature (beta), and angles of attack to the local primary flow direction (theta) is utilized. Through capturing photos and utilizing AutoCAD and SURFER software, maximum relative length and width are calculated. Based on the experimental measurements, maximum percentage reduction of DFSZ, in comparison with the controlled test (without submerged vanes), is obtained with theta =30 degrees, beta = 34 degrees, and delta(l) = 10 cm with value of 78 and 76%, respectively. Moreover, several data-driven models, namely, gene expression programming (GEP), support vector regression (SVR), and a robust hybrid SVR with an ant colony optimization algorithm (ACO) (i.e., hybrid SVR-ACO model), are developed in order to predict DFSZ via the operative dimensionless variables realized by Spearman's rho and Pearson's coefficient processes. In accordance with the statistical metrics, model grading process, scatter plot, and the hybrid SVR(RBF)-ACO model are preferred as the best and most precise model to predict maximum relative length and width with a total grade (TG) of 6.75 and 5.8, respectively. The generated algebraic formula for DFSZ under the optimal scenario of GEP is equated with the corresponding measured ones and the results are within 0-10%.
dc.description.sponsorshipShahid Chamran University of Ahvaz
dc.description.sponsorshipThe authors would like to thank Shahid Chamran University of Ahvaz for their support.
dc.identifier.doi10.1007/s11356-023-27024-y
dc.identifier.endpage65586
dc.identifier.issn0944-1344
dc.identifier.issn1614-7499
dc.identifier.issue24
dc.identifier.pmid37085682
dc.identifier.scopus2-s2.0-85153204456
dc.identifier.scopusqualityQ1
dc.identifier.startpage65572
dc.identifier.urihttps://doi.org/10.1007/s11356-023-27024-y
dc.identifier.urihttps://hdl.handle.net/11501/1110
dc.identifier.volume30
dc.identifier.wosWOS:000975912800001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherSpringer Heidelberg
dc.relation.ispartofEnvironmental Science and Pollution Research
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectLateral Intake
dc.subjectSubmerged Vanes
dc.subjectDimensions Of Flow Separation Zone
dc.subjectButterflies Array
dc.subjectData-Driven Models
dc.subjectSupport Vector Regression
dc.subjectSediment Management
dc.subjectSubmerged Vanes
dc.subjectOptimization
dc.titleApplication of data-driven models to predict the dimensions of flow separation zone
dc.typeArticle

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