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The statistical investigation of multi-grade oil based nanofluids: Enriched by MWCNT and ZnO nanoparticles

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  • Hemmat Esfe, Mohammad
  • Esfandeh, Saeed

Abstract

In this experiment, for the first time, nanoparticles were suspended in the 5W30 base oil as a suitable oil for mild climates. Statistical investigation of the temperature effect, volume fraction (VF) and shear rate (SR) for viscosity of MWCNT–ZnO (20%–80%) / 5W30 nanofluid and a new experimental three variable correlation are presented for engineering applications with non-Newtonian fluids as their working fluids. In fact, the effect of SR on viscosity changes is considered for the first time for viscosity prediction of SAE 5W30 engine oil. Viscosity tests were carried out at temperatures between 5–55 °C to check the enriched oil behavior at low temperatures and 0.05%–1% VFs considering economic limitations. The addition of nanoparticles to the 5W30 base fluid at all temperatures increased viscosity. Moreover, the viscosity sensitivity less than 2% on VFs less than one, indicates that this nanofluid is suitable to use in various engineering systems. Low level of sensitivity of proposed nanofluid is a suitable condition for using this nanofluid in industries with a high level of risks of human mistakes.

Suggested Citation

  • Hemmat Esfe, Mohammad & Esfandeh, Saeed, 2020. "The statistical investigation of multi-grade oil based nanofluids: Enriched by MWCNT and ZnO nanoparticles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
    • Handle: RePEc:eee:phsmap:v:554:y:2020:i:c:s037843711931252x
    DOI: 10.1016/j.physa.2019.122159
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    References listed on IDEAS

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    1. Hemmat Esfe, Mohammad & Rostamian, Hossein & Esfandeh, Saeed & Afrand, Masoud, 2018. "Modeling and prediction of rheological behavior of Al2O3-MWCNT/5W50 hybrid nano-lubricant by artificial neural network using experimental data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 625-634.
    2. Sivasankaran, S. & Alsabery, A.I. & Hashim, I., 2018. "Internal heat generation effect on transient natural convection in a nanofluid-saturated local thermal non-equilibrium porous inclined cavity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 275-293.
    3. Hemmat Esfe, Mohammad & Abbasian Arani, Ali Akbar & Esfandeh, Saeed & Afrand, Masoud, 2019. "Proposing new hybrid nano-engine oil for lubrication of internal combustion engines: Preventing cold start engine damages and saving energy," Energy, Elsevier, vol. 170(C), pages 228-238.
    4. Hemmati-Sarapardeh, Abdolhossein & Varamesh, Amir & Husein, Maen M. & Karan, Kunal, 2018. "On the evaluation of the viscosity of nanofluid systems: Modeling and data assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 313-329.
    5. Moradikazerouni, Alireza & Hajizadeh, Ahmad & Safaei, Mohammad Reza & Afrand, Masoud & Yarmand, Hooman & Zulkifli, Nurin Wahidah Binti Mohd, 2019. "Assessment of thermal conductivity enhancement of nano-antifreeze containing single-walled carbon nanotubes: Optimal artificial neural network and curve-fitting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 138-145.
    6. Ghasemi, Ali & Hassani, Mohsen & Goodarzi, Marjan & Afrand, Masoud & Manafi, Sahebali, 2019. "Appraising influence of COOH-MWCNTs on thermal conductivity of antifreeze using curve fitting and neural network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 36-45.
    7. Sharma, Anuj Kumar & Tiwari, Arun Kumar & Dixit, Amit Rai, 2016. "Rheological behaviour of nanofluids: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 779-791.
    8. Mahmoudi, Ahmed & Mejri, Imen & Omri, Ahmed, 2016. "Study of natural convection cooling of a nanofluid subjected to a magnetic field," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 333-348.
    9. Bagherzadeh, Seyed Amin & D’Orazio, Annunziata & Karimipour, Arash & Goodarzi, Marjan & Bach, Quang-Vu, 2019. "A novel sensitivity analysis model of EANN for F-MWCNTs–Fe3O4/EG nanofluid thermal conductivity: Outputs predicted analytically instead of numerically to more accuracy and less costs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 406-415.
    10. Karimipour, Arash & Bagherzadeh, Seyed Amin & Taghipour, Abdolmajid & Abdollahi, Ali & Safaei, Mohammad Reza, 2019. "A novel nonlinear regression model of SVR as a substitute for ANN to predict conductivity of MWCNT-CuO/water hybrid nanofluid based on empirical data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 89-97.
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