Obtaining an accurate prediction model for viscosity of a new nano-lubricant containing multi-walled carbon nanotube-titanium dioxide nanoparticles with oil SAE50

dc.authoridA. Hamoodi, Karrar/0000-0002-5719-864X
dc.authoridLi, Zhixiong/0000-0002-7265-0008
dc.authoridEftekhari, SeyedAli/0000-0002-9730-4232
dc.authoridJasim, Dheyaa Jumaah/0000-0001-7259-3392
dc.authorscopusid58777209100
dc.authorscopusid57219805679
dc.authorscopusid22136195900
dc.authorscopusid57474867200
dc.authorscopusid57225906716
dc.authorscopusid57222062476
dc.authorscopusid16416765400
dc.authorwosidA. Hamoodi, Karrar/M-8021-2019
dc.authorwosidLi, Zhixiong/G-8418-2018
dc.authorwosidEftekhari, SeyedAli/AAG-3342-2019
dc.authorwosidJasim, Dheyaa Jumaah/GPS-5013-2022
dc.contributor.authorZhang, Yuelei
dc.contributor.authorSalahshour, Soheıl
dc.contributor.authorSajadi, S. Mohammad
dc.contributor.authorLi, Z.
dc.contributor.authorJasim, Dheyaa J.
dc.contributor.authorNasajpour-Esfahani, Navid
dc.contributor.authorKhabaz, Mohamad Khaje
dc.date.accessioned2024-05-25T11:28:25Z
dc.date.available2024-05-25T11:28:25Z
dc.date.issued2024
dc.departmentOkan Universityen_US
dc.department-temp[Zhang, Yuelei] Wuchang Univ Technol, Sch Artificial Intelligence, Wuhan 430223, Hubei, Peoples R China; [Hammoodi, Karrar A.] Univ Warith Al Anbiyaa, Dept Air Conditioning & Refrigerat, Fac Engn, Karbala, Iraq; [Sajadi, S. Mohammad] Cihan Univ Erbil, Dept Nutr, Erbil, Kurdistan Regio, Iraq; [Li, Z.] Donghai Lab, Zhoushan 316021, Peoples R China; [Li, Z.] Opole Univ Technol, Fac Mech Engn, PL-45758 Opole, Poland; [Jasim, Dheyaa J.] Al Amarah Univ Coll, Dept Petr Engn, Maysan, Iraq; [Nasajpour-Esfahani, Navid] Georgia Inst Technol, Dept Mat Sci & Engn, Atlanta, GA 30332 USA; [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, Lebanon; [Eftekhari, S. A.; Khabaz, Mohamad Khaje] Islamic Azad Univ, Khomeinishahr Branch, Dept Mech Engn, Khomeini Shahr, Iranen_US
dc.descriptionA. Hamoodi, Karrar/0000-0002-5719-864X; Li, Zhixiong/0000-0002-7265-0008; Eftekhari, SeyedAli/0000-0002-9730-4232; Jasim, Dheyaa Jumaah/0000-0001-7259-3392en_US
dc.description.abstractThis study aims to investigate the viscosity behavior of multi-walled carbon nanotube (MWCNT) - titanium dioxide (TiO2) (40-60) - SAE50 oil nanofluid using an Artificial Neural Network (ANN) modeling approach. The main objective is to develop a highly accurate predictive model for viscosity by considering three input parameters: temperature, solid volume fraction (SVF), and shear rate (SR). Rheological measurements provide experimental data used to train and validate the ANN model. The ANN model's architecture, activation functions, and training algorithms are carefully chosen. Data are divided to three subsets including train, validation and test. ANN is trained using trainlm algorithm for 50 times to vanish the effect of random nature of ANN weight initialization. The trained ANN model is then utilized to predict the viscosity of the nanofluid under varying conditions. The results demonstrate the efficacy of the proposed ANN model in capturing the complex relationship between viscosity and the input parameters, providing accurate viscosity predictions for the MWCNT-TiO2-oil SAE50 nanofluid. Furthermore, the influence of temperature, SVF, and SR on viscosity is analyzed, offering valuable insights into the flow behavior of the nanofluid. According to the obtained results, the developed ANN model presents a reliable and efficient approach to estimate the viscosity of the MWCNTTiO2-SAE50 oil nanofluid, eliminating the need for costly and extensive experimental measurements within the analyzed range. ANN could model the nanofluid viscosity with R2 = 0.9998 and MSE= 0.000189 that is quite acceptable. Also, the experimental data revealed that for the investigated nanofluid, temperature and shear rate have impressive effect on the viscosity (changing viscosity more than 100% for the analyzed margin), on the other hand, the nanoparticle volume fraction effect is much lower, to be more precise, increasing the nanoparticle percentage will increase the viscosity mean value around 30%.en_US
dc.description.sponsorshipScience Foundation of Donghai Lab- oratory [DH-2022KF0302]en_US
dc.description.sponsorshipThis work is supported by the Science Foundation of Donghai Lab- oratory (No. DH-2022KF0302) .en_US
dc.identifier.citation3
dc.identifier.doi10.1016/j.triboint.2023.109185
dc.identifier.issn0301-679X
dc.identifier.issn1879-2464
dc.identifier.scopus2-s2.0-85180478982
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.triboint.2023.109185
dc.identifier.urihttps://hdl.handle.net/20.500.14517/1154
dc.identifier.volume191en_US
dc.identifier.wosWOS:001137978800001
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.subjectTitanium Dioxideen_US
dc.subjectSAE50 oilen_US
dc.subjectANN predictionen_US
dc.subjectModelen_US
dc.subjectMulti-Walled Carbon Nanotube (MWCNT)en_US
dc.titleObtaining an accurate prediction model for viscosity of a new nano-lubricant containing multi-walled carbon nanotube-titanium dioxide nanoparticles with oil SAE50en_US
dc.typeArticleen_US
dspace.entity.typePublication
relation.isAuthorOfPublicationf5ba517c-75fb-4260-af62-01c5f5912f3d
relation.isAuthorOfPublication.latestForDiscoveryf5ba517c-75fb-4260-af62-01c5f5912f3d

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