Artificial intelligence approach for energy and entropy analyses of NiFe2O4/H2O nanofluid flow in a tube with vortex generator

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dc.authoridGurdal, Mehmet/0000-0003-2209-3394
dc.contributor.authorGurdal, Mehmet
dc.date.accessioned2024-09-29T15:55:20Z
dc.date.available2024-09-29T15:55:20Z
dc.date.issued2023
dc.departmentKarabük Üniversitesien_US
dc.description.abstractThe base purpose of the study is to examine the influence of vortex generator geometry and nanofluid on thermo-hydraulic and irreversibility behavior by using a numerical and predictable approach under a laminar flow regime. The selection of an original magnetic nanofluid, the handling of forced convection in a tube including a vortex generator with the artificial neural network approach, and the 2nd law analysis of thermodynamics reflect the novelty of the study. In this context, it was explored numerically for heat transfer and flow profiles of NiFe2O4/H2O flowing with 1% volume fraction in a tube with different vortex generator geometries. This study has been carried out with the solutions under constant heat flux conditions of 2000 W/m2 along the tube. The analyzes have been applied in the range of 500<2000. The Laminar model and single-phase approach in all analyses have been taken into account. Heat convection coefficient, pressure drop, and entropy generation were analyzed for the smooth tube and tube with wave ratio (WR=h/w) of 2, 3, and 4. Levenberg-Marquardt as train algorithm and Learngdm and Tansig as transfer function was used as the MLP network model in the present study. As a result, The highest heat transfer ratio, pressure drop, frictional entropy, and total entropy values are obtained for the tube with a wave ratio of 2. The highest deviation rates between the predicted and numerical results for WT1 and WT2 cases (the supreme among all cases) have been seen as 4.67% and 1.73% for the heat convection coefficient and pressure drop values, respectively.en_US
dc.identifier.doi10.1016/j.enganabound.2023.04.016
dc.identifier.endpage292en_US
dc.identifier.issn0955-7997
dc.identifier.issn1873-197X
dc.identifier.scopus2-s2.0-85152632110en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage277en_US
dc.identifier.urihttps://doi.org/10.1016/j.enganabound.2023.04.016
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4606
dc.identifier.volume152en_US
dc.identifier.wosWOS:000990605900001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofEngineering Analysis With Boundary Elementsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial neural networken_US
dc.subjectForced heat convectionen_US
dc.subjectWavy tape inserten_US
dc.subjectNanofluid flowen_US
dc.subjectEntropyen_US
dc.titleArtificial intelligence approach for energy and entropy analyses of NiFe2O4/H2O nanofluid flow in a tube with vortex generatoren_US
dc.typeArticleen_US

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