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Proposing new hybrid nano-engine oil for lubrication of internal combustion engines: Preventing cold start engine damages and saving energy

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  • Hemmat Esfe, Mohammad
  • Abbasian Arani, Ali Akbar
  • Esfandeh, Saeed
  • Afrand, Masoud

Abstract

In this paper, viscosity of SAE 5W50 oil enriched by MWCNT and ZnO nanoparticles with combination ratio of 1:4 is experimentally investigated in solid volume fractions of 0.05, 0.1, 0.25, 0.5, 0.75, and 1% and temperature range of 5 to 55 ˚C. It is done with the aim of feasibility study on achieving a modified nano-engine oil that can minimize cold start engine damages using nanoparticles. According to results, produced nano oil with concentrations less than 0.25% selected as modified engine oil. Almost 9% reduction in viscosity of nano-oil with solid volume fraction of 0.05% compared to ordinary engine oil in 5 °C and shear rate of 666.5 (1/sec) was one of the very interesting results of this investigation which makes pumping easier and oil will enter the lubrication cycle faster and this minimizes cold start damages. Moreover, Presence of nanoparticles improves heat transfer from engine parts. The result showed that in temperature range of 35–55 °C, and solid volume fractions less than 0.25%, our proposed nano-oil is more appropriate for high temperature usage too, because of lower dependency of its viscosity to temperature in comparison to pure 5W50 engine oil. A mathematical correlation was proposed to predict viscosity of nano-engine oils used in this research. R-Squared = 0.9685 of this correlation shows its high accuracy.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:170:y:2019:i:c:p:228-238
    DOI: 10.1016/j.energy.2018.12.127
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    References listed on IDEAS

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    1. Hemmat Esfe, Mohammad & Hajmohammad, Hadi & Toghraie, Davood & Rostamian, Hadi & Mahian, Omid & Wongwises, Somchai, 2017. "Multi-objective optimization of nanofluid flow in double tube heat exchangers for applications in energy systems," Energy, Elsevier, vol. 137(C), pages 160-171.
    2. 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.
    3. Karimipour, Arash & Hemmat Esfe, Mohammad & Safaei, Mohammad Reza & Toghraie Semiromi, Davood & Jafari, Saeed & Kazi, S.N., 2014. "Mixed convection of copper–water nanofluid in a shallow inclined lid driven cavity using the lattice Boltzmann method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 402(C), pages 150-168.
    4. 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.
    5. 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.
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    Cited by:

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    2. Hemmat Esfe, Mohammad & Sadati Tilebon, Seyyed Mohamad, 2020. "Statistical and artificial based optimization on thermo-physical properties of an oil based hybrid nanofluid using NSGA-II and RSM," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    3. Wang, Xin & Liu, Xiang & Bai, Yun, 2024. "Prediction of the temperature of diesel engine oil in railroad locomotives using compressed information-based data fusion method with attention-enhanced CNN-LSTM," Applied Energy, Elsevier, vol. 367(C).
    4. Arani, Ali Akbar Abbasian & Alirezaie, Ali & Kamyab, Mohammad Hassan & Motallebi, Sayyid Majid, 2020. "Statistical analysis of enriched water heat transfer with various sizes of MgO nanoparticles using artificial neural networks modeling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
    5. 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).
    6. Javaid, Usman & Mehmood, Adeel & Iqbal, Jamshed & Uppal, Ali Arshad, 2023. "Neural network and URED observer based fast terminal integral sliding mode control for energy efficient polymer electrolyte membrane fuel cell used in vehicular technologies," Energy, Elsevier, vol. 269(C).
    7. Rocha, Déborah Domingos da & de Castro Radicchi, Fábio & Lopes, Gustavo Santos & Brunocilla, Marcello Francisco & Gomes, Paulo César de Ferreira & Santos, Nathalia Duarte Souza Alvarenga & Malaquias, , 2021. "Study of the water injection control parameters on combustion performance of a spark-ignition engine," Energy, Elsevier, vol. 217(C).
    8. Hemmat Esfe, Mohammad & Afrand, Masoud, 2020. "Mathematical and artificial brain structure-based modeling of heat conductivity of water based nanofluid enriched by double wall carbon nanotubes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    9. Aprea, C. & Greco, A. & Maiorino, A. & Masselli, C., 2020. "The use of barocaloric effect for energy saving in a domestic refrigerator with ethylene-glycol based nanofluids: A numerical analysis and a comparison with a vapor compression cooler," Energy, Elsevier, vol. 190(C).
    10. Hemmat Esfe, Mohammad & Rostamian, Seyed Hadi, 2020. "Rheological behavior characteristics of MWCNT-TiO2/EG (40%–60%) hybrid nanofluid affected by temperature, concentration, and shear rate: An experimental and statistical study and a neural network simu," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).
    11. Ruhani, Behrooz & Toghraie, Davood & Hekmatifar, Maboud & Hadian, Mahdieh, 2019. "Statistical investigation for developing a new model for rheological behavior of ZnO–Ag (50%–50%)/Water hybrid Newtonian nanofluid using experimental data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 741-751.

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