Artificial Neural Networking (ANN) Model for Drag Coefficient Optimization for Various Obstacles

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dc.authoridColak, Andac Batur/0000-0001-9297-8134
dc.authoridShatanawi, Wasfi/0000-0001-7492-4933
dc.authoridRehman, Khalil Ur/0000-0002-4218-6582
dc.contributor.authorRehman, Khalil Ur
dc.contributor.authorColak, Andac Batur
dc.contributor.authorShatanawi, Wasfi
dc.date.accessioned2024-11-07T13:35:05Z
dc.date.available2024-11-07T13:35:05Z
dc.date.issued2022
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractFor various obstacles in the path of a flowing liquid stream, an artificial neural networking (ANN) model is constructed to study the hydrodynamic force depending on the object. The multilayer perceptron (MLP), back propagation (BP), and feed-forward (FF) network models were employed to create the ANN model, which has a high prediction accuracy and a strong structure. To be more specific, circular-, octagon-, hexagon-, square-, and triangular-shaped cylinders are installed in a rectangular channel. The fluid is flowing from the left wall of the channel by following two velocity profiles explicitly linear velocity and parabolic velocity. The no-slip condition is maintained on the channel upper and bottom walls. The Neumann condition is applied to the outlet. The entire physical design is mathematically regulated using flow equations. The result is presented using the finite element approach, with the LBB-stable finite element pair and a hybrid meshing scheme. The drag coefficient values are calculated by doing line integration around installed obstructions for both linear and parabolic profiles. The values of the drag coefficient are predicted with high accuracy by developing an ANN model toward various obstacles.
dc.identifier.doi10.3390/math10142450
dc.identifier.issn2227-7390
dc.identifier.issue14
dc.identifier.scopus2-s2.0-85136160183
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.3390/math10142450
dc.identifier.urihttps://hdl.handle.net/11480/16328
dc.identifier.volume10
dc.identifier.wosWOS:000833296200001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofMathematics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241106
dc.subjectliquid stream
dc.subjectregular obstacles
dc.subjectfeed forward
dc.subjectback propagation
dc.subjecthydrodynamic force
dc.subjectartificial neural networking
dc.titleArtificial Neural Networking (ANN) Model for Drag Coefficient Optimization for Various Obstacles
dc.typeArticle

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