Determining the best structure for an artificial neural network to model the dynamic viscosity of MWCNT-ZnO (25:75)/SAE 10W40 oil nano-lubricant

dc.authorid Eftekhari, SeyedAli/0000-0002-9730-4232
dc.authorscopusid 55767855700
dc.authorscopusid 57222872672
dc.authorscopusid 22136195900
dc.authorscopusid 55375146900
dc.authorscopusid 23028598900
dc.authorscopusid 57211509514
dc.authorwosid Eftekhari, SeyedAli/AAG-3342-2019
dc.contributor.author Esfe, Mohammad Hemmat
dc.contributor.author Eftekhari, S. Ali
dc.contributor.author Sajadi, S. Mohammad
dc.contributor.author Hashemian, Mohammad
dc.contributor.author Salahshour, Soheil
dc.contributor.author Motallebi, Seyed Majid
dc.date.accessioned 2024-05-25T11:38:55Z
dc.date.available 2024-05-25T11:38:55Z
dc.date.issued 2024
dc.department Okan University en_US
dc.department-temp [Esfe, Mohammad Hemmat; Motallebi, Seyed Majid] Nanofluid Adv Res Team, Tehran, Iran; [Eftekhari, S. Ali; Hashemian, Mohammad] Islamic Azad Univ, Dept Mech Engn, Khomeinishahr Branch, Khomeinishahr, Iran; [Sajadi, S. Mohammad] Cihan Univ Erbil, Dept Nutr, Erbil, Kurdistan, Iraq; [Salahshour, Soheil] Istanbul Okan Univ, Fac Engn & Nat Sci, Dept Genet & Bioengn, Istanbul, Turkiye; [Salahshour, Soheil] Bahcesehir Univ, Fac Engn & Nat Sci, Istanbul, Turkiye; [Salahshour, Soheil] Lebanese Amer Univ, Dept Comp Sci & Math, Beirut, Lebanon en_US
dc.description Eftekhari, SeyedAli/0000-0002-9730-4232 en_US
dc.description.abstract In this paper, an artificial neural network (ANN) was utilized to examine the dynamic viscosity of MWCNT-ZnO (25:75)/SAE 10W40 oil nano-lubricant. The effect of temperature, shear rate (SR) and solid volume fraction (SVF) on dynamic viscosity is studied at a temperature ranging from T = 5-55 degrees C, SR varying SR= 50-900 rpm, and SVF= 0.05-1%. A set of 172 experimental data is determined and applied as a training dataset of ANNs with various structures. A two-layer ANN with 17 neurons in the hidden layer is selected with R2 = 0.9999 and MSE= 7.77e-5 to predict the dynamic viscosity. Results show that SR is the most influential parameter having an inverse effect on the dynamic viscosity, i.e. by increasing this parameter from 50 to 900 rpm, the viscosity reduces from 600 cP to 40 cP. en_US
dc.identifier.citationcount 0
dc.identifier.doi 10.1016/j.mtcomm.2023.107607
dc.identifier.issn 2352-4928
dc.identifier.scopus 2-s2.0-85181751760
dc.identifier.scopusquality Q2
dc.identifier.uri https://doi.org/10.1016/j.mtcomm.2023.107607
dc.identifier.uri https://hdl.handle.net/20.500.14517/1307
dc.identifier.volume 38 en_US
dc.identifier.wos WOS:001127699100001
dc.identifier.wosquality Q2
dc.institutionauthor Salahshour S.
dc.language.iso en
dc.publisher Elsevier 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 6
dc.subject Nanofluid en_US
dc.subject Lubricant en_US
dc.subject Viscosity en_US
dc.subject ANN en_US
dc.title Determining the best structure for an artificial neural network to model the dynamic viscosity of MWCNT-ZnO (25:75)/SAE 10W40 oil nano-lubricant en_US
dc.type Article en_US
dc.wos.citedbyCount 5

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