Skip to main content
SpringerLink
Log in

On the evaluation of the dynamic viscosity of non-Newtonian oil based nanofluids

Experimental investigation, predicting, and data assessment

  • Published:
Journal of Thermal Analysis and Calorimetry Aims and scope Submit manuscript

Abstract

The present study has evaluated the rheological behavior of an oil-based (5W50) hybrid nanofluid [magnesium oxide (65%)–multi-walled carbon nanotubes (35%)]. MgO nanoparticles with a diameter of 50 nm and multi-walled carbon nanotube nanoparticles with a diameter of 3–5 nm were selected as dispersed particles in the oil base fluid. Viscosity of nanofliud was examined at the temperature range of 5–55 °C (6 cases) and concentration of 0.05–1% (6 cases). The effect of these two parameters (temperature and volume fraction) on the viscosity was studied. The results of the curve fitting on the experimental results and achieving the proper coefficient of determination (R2) showed non-Newtonian behavior of nanofluid in all volume fractions. The impact of shear rate changes in the range of 665.5–11,997 s−1 on the viscosity was studied. A new experimental correlation was proposed in order to predict the viscosity at different temperatures, volume fractions, and shear rates. The R-squared value was 0.999, which shows the precision of the proposed correlation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Abbreviations

T :

Temperature (°C)

w :

Weight (g)

k :

Thermal conductivity (W m−1 °C−1)

m :

Consistency index

n :

Power index

ρ :

Density (kg m−3)

φ :

Solid volume fraction (%)

\( \tau \) :

Shear stress

µ :

Viscosity

nf:

Nanofluid

bf:

Base fluid

MSE:

Mean square error

R 2 :

Coefficient of determination

Rel.:

Relative

Exp.:

Experimental

Pred.:

Predicted

MWCNT:

Multi-walled carbon nanotube

References

  1. Hemmat Esfe M, Rostamian H, Shabani-samghabadi A, Arani AA. Application of three-level general factorial design approach for thermal conductivity of MgO/water nanofluids. Appl Therm Eng. 2017;127:1194–9.

    Article  CAS  Google Scholar 

  2. Salari M, Malekshah EH, Hemmat Esfe M. Three dimensional simulation of natural convection and entropy generation in an air and MWCNT/water nanofluid filled cuboid as two immiscible fluids with emphasis on the nanofluid height ratio’s effects. J Mol Liq. 2017;227:223–33.

    Article  CAS  Google Scholar 

  3. Hemmat Esfe M, Arani AA, Yan WM, Aghaei A. Natural convection in T-shaped cavities filled with water-based suspensions of COOH-functionalized multi walled carbon nanotubes. Int J Mech Sci. 2017;121:21–32.

    Article  Google Scholar 

  4. Hemmat Esfe M, Abbasian Arani AA, Rezaee M, Yazdeli RD, Wongwises S. An inspection of viscosity models for numerical simulation of natural convection of Al2O3-water nanofluid with variable properties. Curr Nanosci. 2017;13(5):449–61.

    Google Scholar 

  5. Illbeigi M, Solaimany Nazar A. Numerical simulation of laminar convective heat transfer and pressure drop of water based-Al2O3 nanofluid as a non newtonian fluid by computational fluid dynamic (CFD). Transp Phenom Nano Micro Scales. 2017.

  6. Hemmat Esfe M, Hajmohammad H, Moradi R, Arani AA. Multi-objective optimization of cost and thermal performance of double walled carbon nanotubes/water nanofluids by NSGA-II using response surface method. Appl Therm Eng. 2017;112:1648–57.

    Article  CAS  Google Scholar 

  7. Hemmat Esfe M, Arani AA, Azizi T, Mousavi SH, Wongwises S. Numerical study of laminar-forced convection of Al2O3-water nanofluids between two parallel plates. J Mech Sci Technol. 2017;31(2):785–96.

    Article  Google Scholar 

  8. Karimipour A, Hemmat Esfe M, Safaei MR, Semiromi DT, Jafari S, Kazi SN. Mixed convection of copper–water nanofluid in a shallow inclined lid driven cavity using the lattice Boltzmann method. Physica A. 2014;402:150–68.

    Article  CAS  Google Scholar 

  9. Hemmat Esfe M, Saedodin S, Wongwises S, Toghraie D. An experimental study on the effect of diameter on thermal conductivity and dynamic viscosity of Fe/water nanofluids. J Therm Anal Calorim. 2015;119(3):1817–24.

    Article  CAS  Google Scholar 

  10. Raei B, Shahraki F, Jamialahmadi M, Peyghambarzadeh SM. Experimental investigation on the heat transfer performance and pressure drop characteristics of γ-Al2O3/water nanofluid in a double tube counter flow heat exchanger. Transp Phenom Nano Micro Scales. 2016;5(1):64–75.

    Google Scholar 

  11. Hemmat Esfe M, Abbasian Arani AA, Mon Yan W, Aghaei A. Numerical study of mixed convection inside a Γ-shaped cavity with Mg (OH2)-EG nanofluids. Curr Nanosci. 2017;13(4):354–63.

    Google Scholar 

  12. Hemmat Esfe M. The investigation of effects of temperature and nanoparticles volume fraction on the viscosity of copper oxide-ethylene glycol nanofluids. Period Polytech Chem Eng. 2017.

  13. Hemmat Esfe M, Wongwises S, Rejvani M. Prediction of thermal conductivity of carbon nanotube-EG nanofluid using experimental data by ANN. Curr Nanosci. 2017;13(3):324–9.

    Article  Google Scholar 

  14. Hemmat Esfe M, Behbahani PM, Arani AA, Sarlak MR. Thermal conductivity enhancement of SiO2–MWCNT (85: 15%)–EG hybrid nanofluids. J Therm Anal Calorim. 2017;128(1):249–58.

    Article  CAS  Google Scholar 

  15. Hemmat Esfe M, Rostamian H, Toghraie D, Yan WM. Using artificial neural network to predict thermal conductivity of ethylene glycol with alumina nanoparticle. J Therm Anal Calorim. 2016;126(2):643–8.

    Article  CAS  Google Scholar 

  16. Hemmat Esfe M, Karimipour A, Yan WM, Akbari M, Safaei MR, Dahari M. Experimental study on thermal conductivity of ethylene glycol based nanofluids containing Al2O3 nanoparticles. Int J Heat Mass Transf. 2015;88:728–34.

    Article  CAS  Google Scholar 

  17. Hemmat Esfe M, Saedodin S, Naderi A, Alirezaie A, Karimipour A, Wongwises S, Goodarzi M, bin Dahari M. Modeling of thermal conductivity of ZnO-EG using experimental data and ANN methods. Int Commun Heat Mass Transf. 2015;63:35–40.

    Article  CAS  Google Scholar 

  18. Goudarzi K, Jamali H. Heat transfer enhancement of Al2O3-EG nanofluid in a car radiator with wire coil inserts. Appl Therm Eng. 2017;118:510–7.

    Article  CAS  Google Scholar 

  19. Hemmat Esfe M, Saedodin S, Bahiraei M, Toghraie D, Mahian O, Wongwises S. Thermal conductivity modeling of MgO/EG nanofluids using experimental data and artificial neural network. J Therm Anal Calorim. 2014;118(1):287–94.

    Article  CAS  Google Scholar 

  20. Hemmat Esfe M, Saedodin S, Asadi A, Karimipour A. Thermal conductivity and viscosity of Mg (OH) 2-ethylene glycol nanofluids. J Therm Anal Calorim. 2015;120(2):1145–9.

    Article  CAS  Google Scholar 

  21. Alirezaie A, Saedodin S, Hemmat Esfe M, Rostamian SH. Investigation of rheological behavior of MWCNT (COOH-functionalized)/MgO-engine oil hybrid nanofluids and modelling the results with artificial neural networks. J Mol Liq. 2017.

  22. Hemmat Esfe M, Saedodin S, Rejvani M, Shahram J. Experimental investigation, model development and sensitivity analysis of rheological behavior of ZnO/10W40 nano-lubricants for automotive applications. Physica E. 2017;90:194–203.

    Article  CAS  Google Scholar 

  23. Hemmat Esfe M, Tatar A, Ahangar MR, Rostamian H. A comparison of performance of several artificial intelligence methods for predicting the dynamic viscosity of TiO2/SAE 50 nano-lubricant. Physica E. 2017.

  24. Amiri A, Shanbedi M, Ahmadi G, Rozali S. Transformer oils-based graphene quantum dots nanofluid as a new generation of highly conductive and stable coolant. Int Commun Heat Mass Transf. 2017;83:40–7.

    Article  CAS  Google Scholar 

  25. Hemmat Esfe M, Rostamian H, Sarlak MR, Rejvani M, Alirezaie A. Rheological behavior characteristics of TiO2–MWCNT/10w40 hybrid nano-oil affected by temperature, concentration and shear rate: an experimental study and a neural network simulating. Physica E. 2017;94:231–40.

    Article  CAS  Google Scholar 

  26. Hemmat Esfe M, Sarlak MR. Experimental investigation of switchable behavior of CuO–MWCNT (85%–15%)/10W-40 hybrid nano-lubricants for applications in internal combustion engines. J Mol Liq. 2017;242:326–35.

    Article  CAS  Google Scholar 

  27. Attari H, Derakhshanfard F, Darvanjooghi MH. Effect of temperature and mass fraction on viscosity of crude oil-based nanofluids containing oxide nanoparticles. Int Commun Heat Mass Transf. 2017;82:103–13.

    Article  CAS  Google Scholar 

  28. Hemmat Esfe M, Rostamian H. Non-Newtonian power-law behavior of TiO2/SAE 50 nano-lubricant: an experimental report and new correlation. J Mol Liq. 2017;232:219–25.

    Article  CAS  Google Scholar 

  29. Hemmat Esfe M, Bahiraei M, Hajmohammad MH, Afrand M. Rheological characteristics of MgO/oil nanolubricants: experimental study and neural network modeling. Int Commun Heat Mass Transf. 2017;86:245–52.

    Article  CAS  Google Scholar 

  30. Hemmat Esfe M, Afrand M, Gharehkhani S, Rostamian H, Toghraie D, Dahari M. An experimental study on viscosity of alumina-engine oil: effects of temperature and nanoparticles concentration. Int Commun Heat Mass Transf. 2016;76:202–8.

    Article  CAS  Google Scholar 

  31. Hemmat Esfe M, Alirezaie A, Rejvani M. TTTd alterations gradient of thermal conductivity increases with the rise of volume fraction of up to 1%, and emmmy then, the sensitivity decreases. Generally, the current study is a combination of empirical studies along. Appl Therm Eng. 2017;111:1202–10.

    Article  CAS  Google Scholar 

  32. Barbés B, Páramo R, Blanco E, Casanova C. Thermal conductivity and specific heat capacity measurements of CuO nanofluids. J Therm Anal Calorim. 2014;115(2):1883–91.

    Article  Google Scholar 

  33. Hemmat Esfe M, Yan WM, Akbari M, Karimipour A, Hassani M. Experimental study on thermal conductivity of DWCNT-ZnO/water-EG nanofluids. Int Commun Heat Mass Transf. 2015;68:248–51.

    Article  CAS  Google Scholar 

  34. Alirezaie A, Hajmohammad MH, Ahangar MR, Hemmat Esfe M. Price-performance evaluation of thermal conductivity enhancement of nanofluids with different particle sizes. Appl Therm Eng. 2018;128:373–80.

    Article  CAS  Google Scholar 

  35. Hemmat Esfe M, Hajmohammad MH. Thermal conductivity and viscosity optimization of nanodiamond-Co3O4/EG (40: 60) aqueous nanofluid using NSGA-II coupled with RSM. J Mol Liq. 2017;238:545–52.

    Article  CAS  Google Scholar 

  36. Shamaeil M, Firouzi M, Fakhar A. The effects of temperature and volume fraction on the thermal conductivity of functionalized DWCNTs/ethylene glycol nanofluid. J Therm Anal Calorim. 2016;126(3):1455–62.

    Article  CAS  Google Scholar 

  37. Hemmat Esfe M, Hajmohammad MH, Razi P, Ahangar MR, Arani AA. The optimization of viscosity and thermal conductivity in hybrid nanofluids prepared with magnetic nanocomposite of nanodiamond cobalt-oxide (ND-Co3O4) using NSGA-II and RSM. Int Commun Heat Mass Transf. 2016;79:128–34.

    Article  CAS  Google Scholar 

  38. Hemmat Esfe M, Razi P, Hajmohammad MH, Rostamian SH, Sarsam WS, Arani AA, Dahari M. Optimization, modeling and accurate prediction of thermal conductivity and dynamic viscosity of stabilized ethylene glycol and water mixture Al2O3 nanofluids by NSGA-II using ANN. Int Commun Heat Mass Transf. 2017;82:154–60.

    Article  CAS  Google Scholar 

  39. Hemmat Esfe M. Designing an artificial neural network using radial basis function (RBF-ANN) to model thermal conductivity of ethylene glycol–water-based TiO2 nanofluids. J Therm Anal Calorim. 2017;127(3):2125–31.

    Article  CAS  Google Scholar 

  40. Hemmat Esfe M, Ahangar MR, Toghraie D, Hajmohammad MH, Rostamian H, Tourang H. Designing artificial neural network on thermal conductivity of Al2O3–water–EG (60–40%) nanofluid using experimental data. J Therm Anal Calorim. 2016;126(2):837–43.

    Article  CAS  Google Scholar 

  41. Abbasian Arani AA, Abbaszadeh M, Ardeshiri A. Mixed convection fluid flow and heat transfer and optimal distribution of discrete heat sources location in a cavity filled with nanofluid. Transp Phenom Nano Micro Scales. 2016;5(1):30–43.

    Google Scholar 

  42. Hemmat Esfe M. Designing a neural network for predicting the heat transfer and pressure drop characteristics of Ag/water nanofluids in a heat exchanger. Appl Therm Eng. 2017;126:559–65.

    Article  CAS  Google Scholar 

  43. Hemmat Esfe M, Akbar Abbasian Arani A, Aghaie A, Wongwises S. Mixed convection flow and heat transfer in an up-driven, inclined, square enclosure subjected to DWCNT-water nanofluid containing three circular heat sources. Curr Nanosci. 2017;13(3):311–23.

    Article  Google Scholar 

  44. Hemmat Esfe M, Arani AA, Niroumand AH, Yan WM, Karimipour A. Mixed convection heat transfer from surface-mounted block heat sources in a horizontal channel with nanofluids. Int J Heat Mass Transf. 2015;89:783–91.

    Article  CAS  Google Scholar 

  45. Raei B, Shahraki F, Jamialahmadi M, Peyghambarzadeh SM. Experimental study on the heat transfer and flow properties of γ-Al2O3/water nanofluid in a double-tube heat exchanger. J Therm Anal Calorim. 2017;127(3):2561–75.

    Article  CAS  Google Scholar 

  46. Hemmat Esfe M, Saedodin S, Mahian O, Wongwises S. Thermophysical properties, heat transfer and pressure drop of COOH-functionalized multi walled carbon nanotubes/water nanofluids. Int Commun Heat Mass Transf. 2014;58:176–83.

    Article  CAS  Google Scholar 

  47. Hemmat Esfe M, Saedodin S. Turbulent forced convection heat transfer and thermophysical properties of MgO–water nanofluid with consideration of different nanoparticles diameter, an empirical study. J Therm Anal Calorim. 2015;119(2):1205–13.

    Article  CAS  Google Scholar 

  48. Hemmat Esfe M, Saedodin S, Mahian O, Wongwises S. Thermal conductivity of Al2O3/water nanofluids. J Therm Anal Calorim. 2014;117(2):675–81.

    Article  CAS  Google Scholar 

  49. Saedodin S, Biglari M, Hemmat Esfe M, Noroozi MJ. Mixed convection heat transfer performance in a ventilated inclined cavity containing heated blocks: effect of dispersing Al2O3 in water and aspect ratio of the block. J Comput Theor Nanosci. 2013;10(11):2663–75.

    Article  CAS  Google Scholar 

  50. Hemmat Esfe M, Firouzi M, Afrand M. Experimental and theoretical investigation of thermal conductivity of ethylene glycol containing functionalized single walled carbon nanotubes. Phys E Low-dimension Syst Nanostruct. 2018;95:71–7.

    Article  CAS  Google Scholar 

  51. Hemmat Esfe M, Wongwises S, Naderi A, Asadi A, Safaei MR, Rostamian H, Dahari M, Karimipour A. Thermal conductivity of Cu/TiO2–water/EG hybrid nanofluid: experimental data and modeling using artificial neural network and correlation. Int Commun Heat Mass Transf. 2015;66:100–4.

    Article  CAS  Google Scholar 

  52. Hemmat Esfe M, Alirezaie A, Rejvani M. An applicable study on the thermal conductivity of SWCNT–MgO hybrid nanofluid and price-performance analysis for energy management. Appl Therm Eng. 2017;111:1202–10.

    Article  CAS  Google Scholar 

  53. Ahmadi Nadooshan A, Hemmat Esfe M, Afrand M. Prediction of rheological behavior of SiO2-MWCNTs/10W40 hybrid nanolubricant by designing neural network. J Therm Anal Calorim. 2017. https://doi.org/10.1007/s10973-017-6688-3.

    Article  Google Scholar 

  54. Hemmat Esfe M, Saedodin S, Biglari M, Rostamian H. Experimental investigation of thermal conductivity of CNTs-Al2O3/water: a statistical approach. Int Commun Heat Mass Transf. 2015;69:29–33.

    Article  CAS  Google Scholar 

  55. Hemmat Esfe M, Esfandeh S, Rejvani M. Modeling of thermal conductivity of MWCNT–SiO2 (30:70%)/EG hybrid nanofluid, sensitivity analyzing and cost performance for industrial applications. J Therm Anal Calorim. https://doi.org/10.1007/s10973-017-6680-y.

  56. Ghadikolaei SS, Yassari M, Sadeghi H, Hosseinzadeh K, Ganji DD. Investigation on thermophysical properties of TiO2–Cu/H2O hybrid nanofluid transport dependent on shape factor in MHD stagnation point flow. Powder Technol. 2017;322:428–38.

    Article  CAS  Google Scholar 

  57. Hemmat Esfe M, Arani AA, Firouzi M. Empirical study and model development of thermal conductivity improvement and assessment of cost and sensitivity of EG-water based SWCNT–ZnO (30%: 70%) hybrid nanofluid. J Mol Liq. 2017;244:252–61.

    Article  CAS  Google Scholar 

  58. Hemmat Esfe M, Esfandeh S, Saedodin S, Rostamian H. Experimental evaluation, sensitivity analyzation and ANN modeling of thermal conductivity of ZnO–MWCNT/EG-water hybrid nanofluid for engineering applications. Appl Therm Eng. 2017.

  59. Rostamian SH, Biglari M, Saedodin S, Hemmat Esfe M. An inspection of thermal conductivity of CuO–SWCNTs hybrid nanofluid versus temperature and concentration using experimental data, ANN modeling and new correlation. J Mol Liq. 2017;231:364–9.

    Article  CAS  Google Scholar 

  60. Hemmat Esfe M, Rejvani M, Karimpour R, Arani AA. Estimation of thermal conductivity of ethylene glycol-based nanofluid with hybrid suspensions of SWCNT–Al2O3 nanoparticles by correlation and ANN methods using experimental data. J Therm Anal Calorim. 2017;128(3):1359–71.

    Article  Google Scholar 

  61. Arani AA, Ababaei A, Sheikhzadeh GA, Aghaei A. Numerical simulation of double-diffusive mixed convection in an enclosure filled with nanofluid using Bejan’s heatlines and masslines. Alex Eng J. 2017.

  62. Hemmat Esfe M, Arani AA, Rezaie M, Yan WM, Karimipour A. Experimental determination of thermal conductivity and dynamic viscosity of Ag–MgO/water hybrid nanofluid. Int Commun Heat Mass Transf. 2015;66:189–95.

    Article  CAS  Google Scholar 

  63. Arefmanesh A, Arani AA, Niroumand A. Thermally developing flow of Al2O3-water nanofluid through regular N-sided polygonal ducts: a semi-analytic weighted residuals approach. Int J Ref. 2017;78:136–56.

    Article  CAS  Google Scholar 

  64. Hemmat Esfe M, Saedodin S, Asadi A. An empirical investigation on the dynamic viscosity of Mg(OH)2–ethylene glycol in different solid concentrations and proposing new correlation based on experimental data. Int J Nat Eng Sci. 2014;8(3):29–34.

    Google Scholar 

  65. Hemmat Esfe M, Ahangar MR, Rejvani M, Toghraie D, Hajmohammad MH. Designing an artificial neural network to predict dynamic viscosity of aqueous nanofluid of TiO2 using experimental data. Int Commun Heat Mass Transf. 2016;75:192–6.

    Article  CAS  Google Scholar 

  66. Hemmat Esfe M, Saedodin S. An experimental investigation and new correlation of viscosity of ZnO–EG nanofluid at various temperatures and different solid volume fractions. Exp Therm Fluid Sci. 2014;55:1–5.

    Article  CAS  Google Scholar 

  67. Hemmat Esfe M, Zabihi F, Rostamian H, Esfandeh H. Experimental investigation and model development of the non-Newtonian behavior of CuO-MWCNT-10w40 hybrid nano-lubricant for lubrication purposes. J Mol Liq. 2018;249:677–87.

    Article  CAS  Google Scholar 

  68. Hemmat Esfe M, Saedodin S, Mahmoodi M. Experimental studies on the convective heat transfer performance and thermophysical properties of MgO–water nanofluid under turbulent flow. Exp Therm Fluid Sci. 2014;52:68–78.

    Article  CAS  Google Scholar 

  69. Vakili-Nezhaad GR, Dorany A. Investigation of the effect of multiwalled carbon nanotubes on the viscosity index of lube oil cuts. Chem Eng Commun. 2009;196(9):997–1007.

    Article  CAS  Google Scholar 

  70. Rashidi A, Ahmadi H, Mohtasebi SS, Pourkhalil M. Thermal and rheological properties of oil-based nanofluids from different carbon nanostructures. Int Commun Heat Mass Transf. 2013;48:178–82.

    Article  Google Scholar 

  71. Hemmat Esfe M, Karimpour R, Arani AA, Shahram J. Experimental investigation on non-Newtonian behavior of Al2O3–MWCNT/5W50 hybrid nano-lubricant affected by alterations of temperature, concentration and shear rate for engine applications. Int Commun Heat Mass Transf. 2017;82:97–102.

    Article  CAS  Google Scholar 

  72. Soltani O, Akbari M. Effects of temperature and particles concentration on the dynamic viscosity of MgO–MWCNT/ethylene glycol hybrid nanofluid: experimental study. Physica E. 2016;84:564–70.

    Article  CAS  Google Scholar 

  73. Zareie A, Akbari M. Experimental investigation of viscosity of MgO–MWCNTs hybrid nanofluid in water-EG base fluid. Modares Mech Eng. 2016;16(6):199–204 (in Persian).

    Google Scholar 

  74. Hemmat Esfe M, Afrand M, Yan WM, Yarmand H, Toghraie D, Dahari M. Effects of temperature and concentration on rheological behavior of MWCNTs/SiO2 (20–80)-SAE40 hybrid nano-lubricant. Int Commun Heat Mass Transf. 2016;76:133–8.

    Article  CAS  Google Scholar 

  75. Asadi A, Asadi M, Rezaei M, Siahmargoi M, Asadi F. The effect of temperature and solid concentration on dynamic viscosity of MWCNT/MgO (20–80)–SAE50 hybrid nano-lubricant and proposing a new correlation: an experimental study. Int Commun Heat Mass Transf. 2016;78:48–53.

    Article  CAS  Google Scholar 

  76. Hemmat Esfe M, Rostamian H, Afrand M, Wongwises S. Examination of effects of multi-walled carbon nanotubes on rheological behavior of engine oil (10W40). J Nanostruct. 2016;6(4):257–63.

    Google Scholar 

  77. Hemmat Esfe M, Afrand M, Rostamian SH, Toghraie D. Examination of rheological behavior of MWCNTs/ZnO-SAE40 hybrid nano-lubricants under various temperatures and solid volume fractions. Exp Therm Fluid Sci. 2017;80:384–90.

    Article  CAS  Google Scholar 

  78. Adio SA, Mehrabi M, Sharifpur M, Meyer JP. Experimental investigation and model development for effective viscosity of MgO–ethylene glycol nanofluids by using dimensional analysis, FCM-ANFIS and GA-PNN techniques. Int Commun Heat Mass Transf. 2016;72:71–83.

    Article  CAS  Google Scholar 

  79. Sadri R, Ahmadi G, Togun H, Dahari M, Kazi SN, Sadeghinezhad E, Zubir N. An experimental study on thermal conductivity and viscosity of nanofluids containing carbon nanotubes. Nanoscale Res Lett. 2014;9(1):151.

    Article  Google Scholar 

  80. Asadi M, Asadi A. Dynamic viscosity of MWCNT/ZnO–engine oil hybrid nanofluid: an experimental investigation and new correlation in different temperatures and solid concentrations. Int Commun Heat Mass Transf. 2016;76:41–5.

    Article  CAS  Google Scholar 

  81. Ahmadi H, Rashidi A, Nouralishahi A, Mohtasebi SS. Preparation and thermal properties of oil-based nanofluid from multi-walled carbon nanotubes and engine oil as nano-lubricant. Int Commun Heat Mass Transf. 2013;46:142–7.

    Article  Google Scholar 

  82. Aberoumand S, Jafarimoghaddam A, Moravej M, Aberoumand H, Javaherdeh K. Experimental study on the rheological behavior of silver-heat transfer oil nanofluid and suggesting two empirical based correlations for thermal conductivity and viscosity of oil based nanofluids. Appl Therm Eng. 2016;101:362–72.

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Imam Hossein University, Tehran, Iran

    Mohammad Hemmat Esfe

Authors
  1. Mohammad Hemmat Esfe
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad Hemmat Esfe.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hemmat Esfe, M. On the evaluation of the dynamic viscosity of non-Newtonian oil based nanofluids. J Therm Anal Calorim 135, 97–109 (2019). https://doi.org/10.1007/s10973-017-6903-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10973-017-6903-2

Keywords

Search

Navigation