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Statistical study and a complete overview of nanofluid viscosity correlations: a new look

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Abstract

Nanofluids are considered the top candidates to replace surface cooling systems, making it essential to study the effect of nanoparticles on thermophysical properties of the base fluid when it is added. Viscosity is a crucial factor in heat transfer, especially convection heat transfer. In most of the studies published, the correlations obtained from experiments were performed without examining statistical tests, and the effect of different parameters, including temperature, volume (mass) fraction, etc., on the viscosity of nanofluid in the proposed correlations was not specified. Moreover, some correlations it was shown that the elimination of one of the parameters had no effect on the response of that correlation. For statistical analysis, analysis of variance and sensitivity analysis were used to determine the relationship of the correlation with its variable parameters. The results showed that approximately 27.2% of the correlations presented for the ethylene glycol-based nanofluid and 27.7% of the correlations presented for the water-based nanofluid are reliable. Finally, as until now, no accurate correlation has been provided for the viscosity in a wide temperature and volume fraction range. According to the R-square statistical index, viscosity models were obtained in this study with an accuracy of 97.01% and 96.08% for water- and ethylene glycol-based nanofluids, regardless of the nanoparticle type. Also, the RMSE value was improved by 35.82% and 49.84% compared to the best correlation presented by the researchers for estimating the viscosity of water-based nanofluid and ethylene glycol-based nanofluid, respectively.

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Abbreviations

T:

Temperature (°C or K)

\(\varphi\) :

Concentration (%)

\(\mu\) :

Dynamic viscosity (mPa.s)

d:

Diameter (nm)

\(\dot{\gamma }\) :

Shear rate (s−1)

N:

Number of data

\({\uptheta }\) :

Dimensionless temperature

a, b:

Constant values

bf:

Base fluid

nf:

Nanofluid

p:

Particles

max:

Maximum

min:

Minimum

o:

Reference value

exp:

Experimental data

pre:

Predicted data

w:

Mass concentration

References

  1. Mondal S, et al. A theoretical nanofluid analysis exhibiting hydromagnetics characteristics employing CVFEM. J Braz Soc Mech Sci Eng. 2020;42(1):1–12.

    Article  CAS  Google Scholar 

  2. Seyyedi SM, Dogonchi A, Hashemi-Tilehnoee M, Ganji D, Chamkha AJ. Second law analysis of magneto-natural convection in a nanofluid filled wavy-hexagonal porous enclosure. Int J Numer Methods Heat Fluid Flow. 2020;30:4811.

    Article  Google Scholar 

  3. Dogonchi A, Waqas M, Seyyedi SM, Hashemi-Tilehnoee M, Ganji D. A modified Fourier approach for analysis of nanofluid heat generation within a semi-circular enclosure subjected to MFD viscosity. Int Commun Heat Mass Trans. 2020;111:104430.

    Article  CAS  Google Scholar 

  4. Tlili I, Seyyedi SM, Dogonchi A, Hashemi-Tilehnoee M, Ganji D. Analysis of a single-phase natural circulation loop with hybrid-nanofluid. Int Commun Heat Mass Trans. 2020;112:104498.

    Article  CAS  Google Scholar 

  5. Dogonchi A, Waqas M, Gulzar MM, Hashemi-Tilehnoee M, Seyyedi SM, Ganji D. Simulation of Fe3O4-H2O nanoliquid in a triangular enclosure subjected to Cattaneo-Christov theory of heat conduction. Int J Numer Methods Heat Fluid Flow. 2019;29:4430.

    Article  Google Scholar 

  6. Seyyedi SM, Dogonchi A, Hashemi-Tilehnoee M, Waqas M, Ganji D. Investigation of entropy generation in a square inclined cavity using control volume finite element method with aided quadratic Lagrange interpolation functions. Int Commun Heat Mass Transf. 2020;110:104398.

    Article  Google Scholar 

  7. Abdelmalek Z, Tayebi T, Dogonchi A, Chamkha A, Ganji D, Tlili I. Role of various configurations of a wavy circular heater on convective heat transfer within an enclosure filled with nanofluid. Int Commun Heat Mass Trans. 2020;113:104525.

    Article  CAS  Google Scholar 

  8. Sadeghi M, Tayebi T, Dogonchi A, Nayak M, Waqas M. Analysis of thermal behavior of magnetic buoyancy-driven flow in ferrofluid–filled wavy enclosure furnished with two circular cylinders. Int Commun Heat Mass Trans. 2021;120:104951.

    Article  CAS  Google Scholar 

  9. Hashemi-Tilehnoee M, Dogonchi A, Seyyedi SM, Sharifpur M. Magneto-fluid dynamic and second law analysis in a hot porous cavity filled by nanofluid and nano-encapsulated phase change material suspension with different layout of cooling channels. J Energy Storage. 2020;31:101720.

    Article  Google Scholar 

  10. Dogonchi A, Asghar Z, Waqas M. CVFEM simulation for Fe3O4-H2O nanofluid in an annulus between two triangular enclosures subjected to magnetic field and thermal radiation. Int Commun Heat Mass Trans. 2020;112:104449.

    Article  CAS  Google Scholar 

  11. Dogonchi A, Selimefendigil F, Ganji D. Magneto-hydrodynamic natural convection of CuO-water nanofluid in complex shaped enclosure considering various nanoparticle shapes. Int J Numer Methods Heat Fluid Flow. 2019;5:1663.

    Article  Google Scholar 

  12. Selimefendigil F. Natural convection in a trapezoidal cavity with an inner conductive object of different shapes and filled with nanofluids of different nanoparticle shapes. Iran J Sci Technol Transact Mech Eng. 2018;42(2):169–84.

    Article  Google Scholar 

  13. Selimefendigil F, Öztop HF (2020) Effects of a rotating tube bundle on the hydrothermal performance for forced convection in a vented cavity with Ag–MgO/water hybrid and CNT–water nanofluids. J Therm Anal Calorim, pp. 1–18, 2020

  14. Chamkha AJ, Molana M, Rahnama A, Ghadami F. On the nanofluids applications in microchannels: a comprehensive review. Powder Technol. 2018;332:287–322.

    Article  CAS  Google Scholar 

  15. Izadi S, Armaghani T, Ghasemiasl R, Chamkha AJ, Molana M. A comprehensive review on mixed convection of nanofluids in various shapes of enclosures. Powder Technol. 2019;343:880–907.

    Article  CAS  Google Scholar 

  16. Molana M. A comprehensive review on the nanofluids application in the tubular heat exchangers. Am J Heat Mass Transf. 2016;3(5):352–81.

    CAS  Google Scholar 

  17. Pandya NS, Shah H, Molana M, Tiwari AK. Heat transfer enhancement with nanofluids in plate heat exchangers: a comprehensive review. Eur J Mech B/Fluids. 2020;81:173–90.

    Article  Google Scholar 

  18. Einstein A (1905) Eine neue bestimmung der moleküldimensionen, ETH Zurich

  19. Brinkman H. The viscosity of concentrated suspensions and solutions. J Chem Phys. 1952;20(4):571–571.

    Article  CAS  Google Scholar 

  20. Batchelor G. The effect of Brownian motion on the bulk stress in a suspension of spherical particles. J Fluid Mech. 1977;83(1):97–117.

    Article  Google Scholar 

  21. Duangthongsuk W, Wongwises S. Measurement of temperature-dependent thermal conductivity and viscosity of TiO2-water nanofluids. Exp Thermal Fluid Sci. 2009;33(4):706–14.

    Article  CAS  Google Scholar 

  22. Esfe MH, 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 

  23. Sharifpur M, Adio SA, Meyer JP. Experimental investigation and model development for effective viscosity of Al2O3–glycerol nanofluids by using dimensional analysis and GMDH-NN methods. Int Commun Heat Mass Transfer. 2015;68:208–19.

    Article  CAS  Google Scholar 

  24. 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 

  25. Akbari M, Afrand M, Arshi A, Karimipour A. An experimental study on rheological behavior of ethylene glycol based nanofluid: proposing a new correlation as a function of silica concentration and temperature. J Mol Liq. 2017;233:352–7.

    Article  CAS  Google Scholar 

  26. Li W, Zou C. Experimental investigation of stability and thermo-physical properties of functionalized β-CD-TiO2-Ag nanofluids for antifreeze. Powder Technol. 2018;340:290–8.

    Article  CAS  Google Scholar 

  27. Yu L, Bian Y, Liu Y, Xu X. Experimental investigation on rheological properties of water based nanofluids with low MWCNT concentrations. Int J Heat Mass Transf. 2019;135:175–85.

    Article  CAS  Google Scholar 

  28. Yan SR, Kalbasi R, Nguyen Q, Karimipour A (2020) Rheological behavior of hybrid MWCNTs-TiO2/EG nanofluid: a comprehensive modeling and experimental study. J Mol Liq. p. 113058

  29. Molana M, Ghasemiasl R, Armaghani T (2021) A different look at the effect of temperature on the nanofluids thermal conductivity: focus on the experimental-based models. J Therm Anal Calorim, pp. 1–25

  30. Fisher RA (1992) Statistical methods for research workers. In: Breakthroughs in statistics: Springer, 1992, pp. 66–70

  31. Scheffe H. The analysis of variance. New Jersey: John Wiley & Sons; 1999.

    Google Scholar 

  32. Dalkılıç AS, et al. Experimental investigation on the viscosity characteristics of water based SiO2-graphite hybrid nanofluids. Int Commun Heat Mass Transfer. 2018;97:30–8.

    Article  CAS  Google Scholar 

  33. Li L, Zhai Y, Jin Y, Wang J, Wang H, Ma M. Stability, thermal performance and artificial neural network modeling of viscosity and thermal conductivity of Al2O3-ethylene glycol nanofluids. Powder Technol. 2020;363:360–8.

    Article  CAS  Google Scholar 

  34. Alarifi IM, Alkouh AB, Ali V, Nguyen HM, Asadi A. On the rheological properties of MWCNT-TiO2/oil hybrid nanofluid: An experimental investigation on the effects of shear rate, temperature, and solid concentration of nanoparticles. Powder Technol. 2019;355:157–62.

    Article  CAS  Google Scholar 

  35. Ruhani B, Barnoon P, Toghraie D. Statistical investigation for developing a new model for rheological behavior of Silica–ethylene glycol/Water hybrid Newtonian nanofluid using experimental data. Physica A. 2019;525:616–27.

    Article  CAS  Google Scholar 

  36. Huminic A, Huminic G, Fleacă C, Dumitrache F, Morjan I. Thermo-physical properties of water based lanthanum oxide nanofluid. An experimental study. J Mol Liq. 2019;287:111013.

    Article  CAS  Google Scholar 

  37. Esfe MH, Esfandeh S, Niazi S. An experimental investigation, sensitivity analysis and RSM analysis of MWCNT (10)-ZnO (90)/10W40 nanofluid viscosity. J Mol Liq. 2019;288:111020.

    Article  CAS  Google Scholar 

  38. Esfe MH, Abad ATK, Fouladi M. Effect of suspending optimized ratio of nano-additives MWCNT-Al2O3 on viscosity behavior of 5W50. J Mol Liq. 2019;285:572–85.

    Article  CAS  Google Scholar 

  39. Esfe MH, Esfandeh S. The statistical investigation of multi-grade oil based nanofluids: Enriched by MWCNT and ZnO nanoparticles. Phys A Stat Mech Appl. 2019;554:122159.

    Article  CAS  Google Scholar 

  40. Li Z, Asadi S, Karimipour A, Abdollahi A, Tlili I. Experimental study of temperature and mass fraction effects on thermal conductivity and dynamic viscosity of SiO2-oleic acid/liquid paraffin nanofluid. Int Commun Heat Mass Trans. 2020;110:104436.

    Article  CAS  Google Scholar 

  41. Sahoo RR, Kumar V. Development of a new correlation to determine the viscosity of ternary hybrid nanofluid. Int Commun Heat and Mass Trans. 2020;111:104451.

    Article  CAS  Google Scholar 

  42. Tian Z, et al. Prediction of rheological behavior of a new hybrid nanofluid consists of copper oxide and multi wall carbon nanotubes suspended in a mixture of water and ethylene glycol using curve-fitting on experimental data. Phys A Stat Mech Appl. 2020;554:124101.

    Article  CAS  Google Scholar 

  43. Li Z, et al. Nanofluids as secondary fluid in the refrigeration system: Experimental data, regression, ANFIS, and NN modeling. Int J Heat Mass Trans. 2019;144:118635.

    Article  CAS  Google Scholar 

  44. Kumar PG, Sakthivadivel D, Meikandan M, Vigneswaran V, Velraj R. Experimental study on thermal properties and electrical conductivity of stabilized H2O-solar glycol mixture based multi-walled carbon nanotube nanofluids: developing a new correlation. Heliyon. 2019;5(8):e02385.

    Article  Google Scholar 

  45. Asadi A, Pourfattah F. Heat transfer performance of two oil-based nanofluids containing ZnO and MgO nanoparticles; a comparative experimental investigation. Powder Technol. 2019;343:296–308.

    Article  CAS  Google Scholar 

  46. Shahsavar A, Khanmohammadi S, Karimipour A, Goodarzi M. A novel comprehensive experimental study concerned synthesizes and prepare liquid paraffin-Fe3O4 mixture to develop models for both thermal conductivity & viscosity: a new approach of GMDH type of neural network. Int J Heat Mass Transf. 2019;131:432–41.

    Article  CAS  Google Scholar 

  47. Li F, Li L, Zhong G, Zhai Y, Li Z. Effects of ultrasonic time, size of aggregates and temperature on the stability and viscosity of Cu-ethylene glycol (EG) nanofluids. Int J Heat Mass Transf. 2019;129:278–86.

    Article  CAS  Google Scholar 

  48. Esfe MH, Raki HR, Emami MRS, Afrand M. Viscosity and rheological properties of antifreeze based nanofluid containing hybrid nano-powders of MWCNTs and TiO2 under different temperature conditions. Powder Technol. 2019;342:808–16.

    Article  CAS  Google Scholar 

  49. Soman DP, Karthika S, Kalaichelvi P, Radhakrishnan T. Impact of viscosity of nanofluid and ionic liquid on heat transfer. J Mol Liq. 2019;291:111349.

    Article  CAS  Google Scholar 

  50. Ruhani B, Toghraie D, Hekmatifar M, Hadian M. Statistical investigation for developing a new model for rheological behavior of ZnO–Ag (50%–50%)/Water hybrid Newtonian nanofluid using experimental data. Physica A. 2019;525:741–51.

    Article  CAS  Google Scholar 

  51. Elcioglu EB, Yazicioglu AG, Turgut A, Anagun AS. Experimental study and Taguchi analysis on alumina-water nanofluid viscosity. Appl Therm Eng. 2018;128:973–81.

    Article  CAS  Google Scholar 

  52. Ghasemi S, Karimipour A. Experimental investigation of the effects of temperature and mass fraction on the dynamic viscosity of CuO-paraffin nanofluid. Appl Therm Eng. 2018;128:189–97.

    Article  CAS  Google Scholar 

  53. Moldoveanu GM, Ibanescu C, Danu M, Minea AA. Viscosity estimation of Al2O3, SiO2 nanofluids and their hybrid: an experimental study. J Mol Liq. 2018;253:188–96.

    Article  CAS  Google Scholar 

  54. Moldoveanu GM, Minea AA, Iacob M, Ibanescu C, Danu M. Experimental study on viscosity of stabilized Al2O3, TiO2 nanofluids and their hybrid. Thermochim Acta. 2018;659:203–12.

    Article  CAS  Google Scholar 

  55. Saeedi AH, Akbari M, Toghraie D. An experimental study on rheological behavior of a nanofluid containing oxide nanoparticle and proposing a new correlation. Phys E. 2018;99:285–93.

    Article  CAS  Google Scholar 

  56. Karimipour A, Ghasemi S, Darvanjooghi MHK, Abdollahi A. A new correlation for estimating the thermal conductivity and dynamic viscosity of CuO/liquid paraffin nanofluid using neural network method. Int Commun Heat Mass Transfer. 2018;92:90–9.

    Article  CAS  Google Scholar 

  57. Alrashed AA, Gharibdousti MS, Goodarzi M, de Oliveira LR, Safaei MR, Bandarra Filho EP. Effects on thermophysical properties of carbon based nanofluids: experimental data, modelling using regression, ANFIS and ANN. Int J Heat Mass Trans. 2018;125:920–32.

    Article  CAS  Google Scholar 

  58. Khodadadi H, Toghraie D, Karimipour A. Effects of nanoparticles to present a statistical model for the viscosity of MgO-Water nanofluid. Powder Technol. 2019;342:166–80.

    Article  CAS  Google Scholar 

  59. Esfe MH, Arani AAA. An experimental determination and accurate prediction of dynamic viscosity of MWCNT (% 40)-SiO2 (% 60)/5W50 nano-lubricant. J Mol Liq. 2018;259:227–37.

    Article  CAS  Google Scholar 

  60. Esfe MH, Reiszadeh M, Esfandeh S, Afrand M. Optimization of MWCNTs (10%)–Al2O3 (90%)/5W50 nanofluid viscosity using experimental data and artificial neural network. Phys A. 2018;512:731–44.

    Article  CAS  Google Scholar 

  61. Hamid KA, Azmi W, Nabil M, Mamat R, Sharma K. Experimental investigation of thermal conductivity and dynamic viscosity on nanoparticle mixture ratios of TiO2-SiO2 nanofluids. Int J Heat Mass Transf. 2018;116:1143–52.

    Article  CAS  Google Scholar 

  62. Nabil M, Azmi W, Hamid KA, Mamat R, Hagos FY. An experimental study on the thermal conductivity and dynamic viscosity of TiO2-SiO2 nanofluids in water: ethylene glycol mixture. Int Commun Heat Mass Transfer. 2017;86:181–9.

    Article  CAS  Google Scholar 

  63. Żyła G, Fal J. Viscosity, thermal and electrical conductivity of silicon dioxide–ethylene glycol transparent nanofluids: an experimental studies. Thermochim Acta. 2017;650:106–13.

    Article  CAS  Google Scholar 

  64. Amani M, Amani P, Kasaeian A, Mahian O, Kasaeian F, Wongwises S. Experimental study on viscosity of spinel-type manganese ferrite nanofluid in attendance of magnetic field. J Magn Magn Mater. 2017;428:457–63.

    Article  CAS  Google Scholar 

  65. 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 

  66. Ilhan B, Kurt M, Ertürk H. Experimental investigation of heat transfer enhancement and viscosity change of hBN nanofluids. Exp Thermal Fluid Sci. 2016;77:272–83.

    Article  CAS  Google Scholar 

  67. Esfe MH, Ahangar MRH, 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 Transfer. 2016;75:192–6.

    Article  CAS  Google Scholar 

  68. Toghraie D, Alempour SM, Afrand M. Experimental determination of viscosity of water based magnetite nanofluid for application in heating and cooling systems. J Magn Magn Mater. 2016;417:243–8.

    Article  CAS  Google Scholar 

  69. Abdolbaqi MK, et al. Experimental investigation and development of new correlation for thermal conductivity and viscosity of BioGlycol/water based SiO2 nanofluids. Int Commun Heat Mass Transfer. 2016;77:54–63.

    Article  CAS  Google Scholar 

  70. Sundar LS, Hortiguela MJ, Singh MK, Sousa AC. Thermal conductivity and viscosity of water based nanodiamond (ND) nanofluids: An experimental study. Int Commun Heat Mass Transfer. 2016;76:245–55.

    Article  CAS  Google Scholar 

  71. Mostafizur R, Aziz AA, Saidur R, Bhuiyan M. Investigation on stability and viscosity of SiO2–CH3OH (methanol) nanofluids. Int Commun Heat Mass Transfer. 2016;72:16–22.

    Article  CAS  Google Scholar 

  72. 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 Transfer. 2016;76:41–5.

    Article  CAS  Google Scholar 

  73. 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 Transfer. 2016;72:71–83.

    Article  CAS  Google Scholar 

  74. Esfe MH, 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 Transfer. 2016;76:202–8.

    Article  CAS  Google Scholar 

  75. Baratpour M, Karimipour A, Afrand M, Wongwises S. Effects of temperature and concentration on the viscosity of nanofluids made of single-wall carbon nanotubes in ethylene glycol. Int Commun Heat Mass Transfer. 2016;74:108–13.

    Article  CAS  Google Scholar 

  76. Afrand M, Najafabadi KN, Akbari M. Effects of temperature and solid volume fraction on viscosity of SiO2-MWCNTs/SAE40 hybrid nanofluid as a coolant and lubricant in heat engines. Appl Therm Eng. 2016;102:45–54.

    Article  CAS  Google Scholar 

  77. Abdolbaqi MK, et al. An experimental determination of thermal conductivity and viscosity of BioGlycol/water based TiO2 nanofluids. Int Commun Heat Mass Transfer. 2016;77:22–32.

    Article  CAS  Google Scholar 

  78. Dalkilic A, et al. Prediction of graphite nanofluids’ dynamic viscosity by means of artificial neural networks. Int Commun Heat Mass Transfer. 2016;73:33–42.

    Article  CAS  Google Scholar 

  79. Li X, Zou C, Lei X, Li W. Stability and enhanced thermal conductivity of ethylene glycol-based SiC nanofluids. Int J Heat Mass Transf. 2015;89:613–9.

    Article  CAS  Google Scholar 

  80. Saltelli A, et al. Global sensitivity analysis: the primer. New Jersey: John Wiley & Sons; 2008.

    Google Scholar 

  81. Gangadevi R, Vinayagam B. Experimental determination of thermal conductivity and viscosity of different nanofluids and its effect on a hybrid solar collector. J Therm Anal Calorim. 2019;136(1):199–209.

    Article  CAS  Google Scholar 

  82. Topuz A, Engin T, Özalp AA, Erdoğan B, Mert S, Yeter A. Experimental investigation of optimum thermal performance and pressure drop of water-based Al 2 O 3, TiO 2 and ZnO nanofluids flowing inside a circular microchannel. J Therm Anal Calorim. 2018;131(3):2843–63.

    Article  CAS  Google Scholar 

  83. Ghodsinezhad H, Sharifpur M, Meyer JP. Experimental investigation on cavity flow natural convection of Al2O3–water nanofluids. Int Commun Heat Mass Transfer. 2016;76:316–24.

    Article  CAS  Google Scholar 

  84. Sundar LS, Singh MK, Sousa AC. Turbulent heat transfer and friction factor of nanodiamond-nickel hybrid nanofluids flow in a tube: an experimental study. Int J Heat Mass Transf. 2018;117:223–34.

    Article  CAS  Google Scholar 

  85. Zadeh AD, Toghraie D. Experimental investigation for developing a new model for the dynamic viscosity of silver/ethylene glycol nanofluid at different temperatures and solid volume fractions. J Therm Anal Calorim. 2018;131(2):1449–61.

    Article  CAS  Google Scholar 

  86. Sundar LS, Singh MK, Ferro M, Sousa AC. Experimental investigation of the thermal transport properties of graphene oxide/Co3O4 hybrid nanofluids. Int Commun Heat Mass Transfer. 2017;84:1–10.

    Article  CAS  Google Scholar 

  87. Selvam C, Lal DM, Harish S. Heat transport and pressure drop characteristics of ethylene Glycol-based Nano fluid containing silver nanoparticles. IOP Conf Ser Mater Sci Eng. 2018;402(1):012005.

    Article  Google Scholar 

  88. Nadooshan AA, Eshgarf H, Afrand M. Measuring the viscosity of Fe3O4-MWCNTs/EG hybrid nanofluid for evaluation of thermal efficiency: Newtonian and non-Newtonian behavior. J Mol Liq. 2018;253:169–77.

    Article  CAS  Google Scholar 

  89. Wang X, Xu X, Choi SU. Thermal conductivity of nanoparticle-fluid mixture. J Thermophys Heat Transfer. 1999;13(4):474–80.

    Article  CAS  Google Scholar 

  90. Chen H, Ding Y, Tan C. Rheological behaviour of nanofluids. New J Phys. 2007;9(10):367.

    Article  CAS  Google Scholar 

  91. Ho C, Liu W, Chang Y, Lin C. Natural convection heat transfer of alumina-water nanofluid in vertical square enclosures: an experimental study. Int J Therm Sci. 2010;49(8):1345–53.

    Article  CAS  Google Scholar 

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Authors and Affiliations

  1. Department of Mechanical Engineering, West Tehran Branch, Islamic Azad University, Tehran, Iran

    A. Barkhordar & R. Ghasemiasl

  2. Department of Mechanical Engineering, Mahdishahr Branch, Islamic Azad University, Mahdishar, Iran

    T. Armaghani

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  1. A. Barkhordar
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  2. R. Ghasemiasl
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  3. T. Armaghani
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Correspondence to R. Ghasemiasl or T. Armaghani.

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Barkhordar, A., Ghasemiasl, R. & Armaghani, T. Statistical study and a complete overview of nanofluid viscosity correlations: a new look. J Therm Anal Calorim 147, 7099–7132 (2022). https://doi.org/10.1007/s10973-021-10993-y

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