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Using artificial neural network for investigating of concurrent effects of multi-walled carbon nanotubes and alumina nanoparticles on the viscosity of 10W-40 engine oil

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  • Hemmat Esfe, Mohammad
  • Kamyab, Mohammad Hassan
  • Afrand, Masoud
  • Amiri, Mahmoud Kiannejad

Abstract

The present study used artificial neural networks (ANNs) and experimental data to model the viscosity of the MWCNT (50%)–Al2O3 (50%)/10W40 hybrid nanofluid at different temperatures from 5 to 55 °C and for nanoparticle volume fractions of 0.05% to 1%. An ANN and a new correlation as function of solid volume fraction, temperature and shear rate. The multidimensional MLP-ANN was employed and investigated as the learning algorithm. The R-squared values for the proposed correlation and ANN were obtained to be 0.9973 and 0.9944, respectively. In order to analyze the importance of each term of correlation, p-value of parameters is determined. Also one factor and two factor analysis of viscosity are presented in this study. According to one and two factor analysis results, temperature changes has the highest effect on viscosity. Shear rate and solid volume fraction effects on viscosity are in next level of importance.

Suggested Citation

  • Hemmat Esfe, Mohammad & Kamyab, Mohammad Hassan & Afrand, Masoud & Amiri, Mahmoud Kiannejad, 2018. "Using artificial neural network for investigating of concurrent effects of multi-walled carbon nanotubes and alumina nanoparticles on the viscosity of 10W-40 engine oil," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 610-624.
    • Handle: RePEc:eee:phsmap:v:510:y:2018:i:c:p:610-624
    DOI: 10.1016/j.physa.2018.06.029
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    11. Al-Rashed, Abdullah A.A.A. & Ranjbarzadeh, Ramin & Aghakhani, Saeed & Soltanimehr, Mehdi & Afrand, Masoud & Nguyen, Truong Khang, 2019. "Entropy generation of boehmite alumina nanofluid flow through a minichannel heat exchanger considering nanoparticle shape effect," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 724-736.
    12. Rasti, Ehsan & Talebi, Farhad & Mazaheri, Kiumars, 2019. "Improvement of drag reduction prediction in viscoelastic pipe flows using proper low-Reynolds k-ε turbulence models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 412-422.
    13. Alsarraf, Jalal & Moradikazerouni, Alireza & Shahsavar, Amin & Afrand, Masoud & Salehipour, Hamzeh & Tran, Minh Duc, 2019. "Hydrothermal analysis of turbulent boehmite alumina nanofluid flow with different nanoparticle shapes in a minichannel heat exchanger using two-phase mixture model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 275-288.
    14. Hemmat Esfe, Mohammad & Reiszadeh, Mahdi & Esfandeh, Saeed & Afrand, Masoud, 2018. "Optimization of MWCNTs (10%) – Al2O3 (90%)/5W50 nanofluid viscosity using experimental data and artificial neural network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 731-744.
    15. Al-Rashed, Abdullah A.A.A., 2019. "Optimization of heat transfer and pressure drop of nano-antifreeze using statistical method of response surface methodology," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 531-542.
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