Prediction and extensive analysis of MWCNT-MgO/oil SAE 50 hybrid nano-lubricant rheology utilizing machine learning and genetic algorithms to find ideal attributes

dc.authoridBaghoolizadeh, Mohammadreza/0000-0002-3703-0866
dc.authorscopusid57338920800
dc.authorscopusid56388625300
dc.authorscopusid22136195900
dc.authorscopusid23028598900
dc.authorscopusid57449950600
dc.contributor.authorBaghoolizadeh, Mohammadreza
dc.contributor.authorSalahshour, Soheıl
dc.contributor.authorSajadi, S. Mohammad
dc.contributor.authorSalahshour, Soheil
dc.contributor.authorBaghaei, Sh.
dc.date.accessioned2024-05-25T11:37:29Z
dc.date.available2024-05-25T11:37:29Z
dc.date.issued2024
dc.departmentOkan Universityen_US
dc.department-temp[Baghoolizadeh, Mohammadreza] Shahrekord Univ, Dept Mech Engn, Shahrekord 8818634141, Iran; [Pirmoradian, Mostafa; Baghaei, Sh.] Islamic Azad Univ, Dept Mech Engn, Khomeinishahr Branch, Khomeinishahr, Iran; [Sajadi, S. Mohammad] Cihan Univ Erbil, Dept Nutr, Erbil, Kurdistan Regio, Iraq; [Salahshour, Soheil] Istanbul Okan Univ, Fac Engn & Nat Sci, Istanbul, Turkiye; [Salahshour, Soheil] Bahcesehir Univ, Fac Engn & Nat Sci, Istanbul, Turkiye; [Salahshour, Soheil] Lebanese Amer Univ, Dept Comp Sci & Math, Beirut, Lebanonen_US
dc.descriptionBaghoolizadeh, Mohammadreza/0000-0002-3703-0866en_US
dc.description.abstractGenetic algorithms and machine learning methods can accurately anticipate hybrid nanofluids' complicated rheology. Scientists and engineers can understand hybrid materials by using genetic algorithms to optimize and machine learning to discover complicated relationships between input variables and rheological responses. As a continuation of the author's previous research on the rheological properties of a nano-lubricant based on engine oil and hybrid nanoparticles, this study uses machine learning and genetic algorithms to theoretically assess the dynamic viscosity of the MWCNT-MgO/oil SAE 50 hybrid nanofluid and identify optimal properties. MLR, DTree, Ridge, PLR, SVM, Lasso, ECR, GPR, and MPR are used for regression analysis. Best multi-objective issue solutions are represented by the Pareto front. The NSGA-II algorithm determines the Pareto front. The MPR and NSGA-II algorithms provide a Pareto front with the most precise optimal spot boundaries. The Weighted Sum Method (WSM) simplifies multi-objective problems into single-objective problems, making optimal solutions easier to find. The results show that the maximum margin of deviation for mu nf and tau is - 2.5615 and - 5.239, respectively. According to the Taylor chart, the best mu nf mode for R, RMSE and STD is equal to 0.9983, 7.6639, 130.0056. Also, these values for tau are equal to 0.9996, 15.4515, and 516.0219.en_US
dc.identifier.citation0
dc.identifier.doi10.1016/j.triboint.2024.109582
dc.identifier.issn0301-679X
dc.identifier.issn1879-2464
dc.identifier.scopus2-s2.0-85189760812
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.triboint.2024.109582
dc.identifier.urihttps://hdl.handle.net/20.500.14517/1174
dc.identifier.volume195en_US
dc.identifier.wosWOS:001217634500001
dc.identifier.wosqualityQ1
dc.institutionauthorSalahshour S.
dc.language.isoen
dc.publisherElsevier Sci Ltden_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectHybrid nanofluiden_US
dc.subjectRheologyen_US
dc.subjectMachine learningen_US
dc.subjectGenetic algorithmsen_US
dc.titlePrediction and extensive analysis of MWCNT-MgO/oil SAE 50 hybrid nano-lubricant rheology utilizing machine learning and genetic algorithms to find ideal attributesen_US
dc.typeArticleen_US
dspace.entity.typePublication
relation.isAuthorOfPublicationf5ba517c-75fb-4260-af62-01c5f5912f3d
relation.isAuthorOfPublication.latestForDiscoveryf5ba517c-75fb-4260-af62-01c5f5912f3d

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