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

dc.authorid A. Hamoodi, Karrar/0000-0002-5719-864X
dc.authorid Li, Zhixiong/0000-0002-7265-0008
dc.authorid Eftekhari, SeyedAli/0000-0002-9730-4232
dc.authorid Jasim, Dheyaa Jumaah/0000-0001-7259-3392
dc.authorscopusid 58777209100
dc.authorscopusid 57219805679
dc.authorscopusid 22136195900
dc.authorscopusid 57474867200
dc.authorscopusid 57225906716
dc.authorscopusid 57222062476
dc.authorscopusid 16416765400
dc.authorwosid A. Hamoodi, Karrar/M-8021-2019
dc.authorwosid Li, Zhixiong/G-8418-2018
dc.authorwosid Eftekhari, SeyedAli/AAG-3342-2019
dc.authorwosid Jasim, Dheyaa Jumaah/GPS-5013-2022
dc.contributor.author Zhang, Yuelei
dc.contributor.author Hammoodi, Karrar A.
dc.contributor.author Sajadi, S. Mohammad
dc.contributor.author Li, Z.
dc.contributor.author Jasim, Dheyaa J.
dc.contributor.author Nasajpour-Esfahani, Navid
dc.contributor.author Khabaz, Mohamad Khaje
dc.date.accessioned 2024-05-25T11:28:25Z
dc.date.available 2024-05-25T11:28:25Z
dc.date.issued 2024
dc.department Okan University en_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, Iran en_US
dc.description A. Hamoodi, Karrar/0000-0002-5719-864X; Li, Zhixiong/0000-0002-7265-0008; Eftekhari, SeyedAli/0000-0002-9730-4232; Jasim, Dheyaa Jumaah/0000-0001-7259-3392 en_US
dc.description.abstract This 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.sponsorship Science Foundation of Donghai Lab- oratory [DH-2022KF0302] en_US
dc.description.sponsorship This work is supported by the Science Foundation of Donghai Lab- oratory (No. DH-2022KF0302) . en_US
dc.identifier.citationcount 3
dc.identifier.doi 10.1016/j.triboint.2023.109185
dc.identifier.issn 0301-679X
dc.identifier.issn 1879-2464
dc.identifier.scopus 2-s2.0-85180478982
dc.identifier.scopusquality Q1
dc.identifier.uri https://doi.org/10.1016/j.triboint.2023.109185
dc.identifier.uri https://hdl.handle.net/20.500.14517/1154
dc.identifier.volume 191 en_US
dc.identifier.wos WOS:001137978800001
dc.identifier.wosquality Q1
dc.institutionauthor Salahshour S.
dc.language.iso en
dc.publisher Elsevier Sci Ltd en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.scopus.citedbyCount 9
dc.subject Titanium Dioxide en_US
dc.subject SAE50 oil en_US
dc.subject ANN prediction en_US
dc.subject Model en_US
dc.subject Multi-Walled Carbon Nanotube (MWCNT) en_US
dc.title Obtaining an accurate prediction model for viscosity of a new nano-lubricant containing multi-walled carbon nanotube-titanium dioxide nanoparticles with oil SAE50 en_US
dc.type Article en_US
dc.wos.citedbyCount 7

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