Skip to main content

Feasibility of least-square support vector machine in predicting the effects of shear rate on the rheological properties and pumping power of MWCNT–MgO/oil hybrid nanofluid based on experimental data


The main objective of the present paper was to investigate the feasibility of the least-square support vector machine (LSSVM) in predicting the effects of shear rate on the dynamic viscosity of a hybrid oil-based nanolubricant containing MWCNT and MgO nanoparticles in different solid concentrations and temperatures. Firstly, measuring the dynamic viscosity of the nanofluid revealed that the nanofluid is a non-Newtonian fluid at the temperatures of 10 °C and 20 °C in all the studied shear rates and solid concentrations while it showed Newtonian behavior at the rest of the studied temperatures. Then the effects of solid concentration and temperature on the dynamic viscosity have been experimentally studied, and it is found that the dynamic viscosity increased as the solid concentration increased; the maximum increase has been observed at the solid concentration of 1.5% and temperature of 60 °C by 52 vol.%, while the minimum increase has been observed at the solid concentration of 0.125 vol.% and temperature of 10 °C by 11%. Based on the experimental data, a new correlation to predict the dynamic viscosity of the nanofluid in terms of shear rate, solid concentration, and the temperature has been proposed. Then, the LSSVM has been employed to predict the dynamic viscosity behavior of the nanofluid considering the shear rate, temperature, and solid concentration as the input variables and the dynamic viscosity as the output variable and the results showed the excellent capability of the LSSVM in predicting the dynamic viscosity. Finally, the effects of adding the hybrid nanoparticles on the pumping power have been studied.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14


µ :

Dynamic viscosity (mPa s)

\( \dot{\gamma } \) :

Shear rate (s−1)

ρ :

Density (kg m−3)

φ :

Solid concentration (vol.%)

T :

Temperature (°C)

γ :

Shear strain


Least-square support vector machine


Fanning friction factor ratio


  1. 1.

    Choi JA, Eastman SUS. Enhancing thermal conductivity of fluids with nanoparticles, ASME FED, 231: 99–103, International Mechanical Engineering Congress and Exhibition, San Francisco, CA (United States); 1995.

  2. 2.

    Minakov AV, Rudyak VY, Pryazhnikov MI. Rheological behavior of water and ethylene glycol based nanofluids containing oxide nanoparticles. Colloids Surf A. 2018;554:279–85.

    CAS  Article  Google Scholar 

  3. 3.

    Bhattacharyya A, Seth GS, Kumar R, Chamkha AJ. Simulation of Cattaneo–Christov heat flux on the flow of single and multi-walled carbon nanotubes between two stretchable coaxial rotating disks. J Therm Anal Calorim. 2019.

    Article  Google Scholar 

  4. 4.

    Seth GS, Bhattacharyya A, Kumar R, Chamkha AJ. Entropy generation in hydromagnetic nanofluid flow over a non-linear stretching sheet with Navier’s velocity slip and convective heat transfer. Phys Fluids. 2018.

    Article  Google Scholar 

  5. 5.

    Yıldız Ç, Arıcı M, Karabay H. Comparison of a theoretical and experimental thermal conductivity model on the heat transfer performance of Al2O3–SiO2/water hybrid-nanofluid. Int J Heat Mass Transf. 2019;140:598–605.

    CAS  Article  Google Scholar 

  6. 6.

    Elumalai PV, Annamalai K, Dhinesh B. Effects of thermal barrier coating on the performance, combustion and emission of DI diesel engine powered by biofuel oil–water emulsion. J Therm Anal Calorim. 2019;137:593–605.

    CAS  Article  Google Scholar 

  7. 7.

    Asadi A, Aberoumand S, Moradikazerouni A, Pourfattah F, Żyła G, Estellé P, Mahian O, Wongwises S, Nguyen HM, Arabkoohsar A. Recent advances in preparation methods and thermophysical properties of oil-based nanofluids: a state-of-the-art review. Powder Technol. 2019.

    Article  Google Scholar 

  8. 8.

    Babar H, Ali HM. Towards hybrid nanofluids: preparation, thermophysical properties, applications, and challenges. J Mol Liq. 2019;281:598–633.

    CAS  Article  Google Scholar 

  9. 9.

    Asadi A, Pourfattah F, Miklós Szilágyi I, Afrand M, Żyła G, Seon Ahn H, Wongwises S, MinhNguyen H, Arabkoohsar A, Mahian O. Effect of sonication characteristics on stability, thermophysical properties, and heat transfer of nanofluids: a comprehensive review. Ultrason Sonochemistry. 2019;58:104701.

    CAS  Article  Google Scholar 

  10. 10.

    Sajid MU, Ali HM. Thermal conductivity of hybrid nanofluids: a critical review. Int J Heat Mass Transf. 2018;126:211–34.

    CAS  Article  Google Scholar 

  11. 11.

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

    CAS  Article  Google Scholar 

  12. 12.

    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.

    CAS  Article  Google Scholar 

  13. 13.

    Mahian O, Kolsi L, Amani M, Estellé P, Ahmadi G, Kleinstreuer C, Marshall JS, Siavashi M, Taylor RA, Niazmand H, Wongwises S, Hayat T, Kolanjiyil A, Kasaeian A, Pop I. Recent advances in modeling and simulation of nanofluid flows—part I: fundamentals and theory. Phys Rep. 2019;790:1–48.

    CAS  Article  Google Scholar 

  14. 14.

    Mahian O, Kolsi L, Amani M, Estellé P, Ahmadi G, Kleinstreuer C, Marshall JS, Taylor RA, Abu-Nada E, Rashidi S, Niazmand H, Wongwises S, Hayat T, Kasaeian A, Pop I. Recent advances in modeling and simulation of nanofluid flows—part II: applications. Phys Rep. 2018.

    Article  Google Scholar 

  15. 15.

    Asadi A. A guideline towards easing the decision-making process in selecting an effective nanofluid as a heat transfer fluid. Energy Convers Manag. 2018;175:1–10.

    CAS  Article  Google Scholar 

  16. 16.

    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.

    CAS  Article  Google Scholar 

  17. 17.

    Asadi A, Asadi M, Rezaniakolaei A, Rosendahl LA, Wongwises S. An experimental and theoretical investigation on heat transfer capability of Mg (OH)2/MWCNT-engine oil hybrid nano-lubricant adopted as a coolant and lubricant fluid. Appl Therm Eng. 2018.

    Article  Google Scholar 

  18. 18.

    Bashirnezhad K, Bazri S, Safaei MR, Goodarzi M, Dahari M, Mahian O, Dalkiliça AS, Wongwises S. Viscosity of nanofluids: a review of recent experimental studies. Int Commun Heat Mass Transfer. 2016;73:114–23.

    CAS  Article  Google Scholar 

  19. 19.

    Kumar V, Sarkar J. Experimental hydrothermal behavior of hybrid nanofluid for various particle ratios and comparison with other fluids in minichannel heat sink. Int Commun Heat Mass Transf. 2020;110:104397.

    CAS  Article  Google Scholar 

  20. 20.

    Thiyagarajan S, Sonthalia A, Edwin Geo V, Ashok B, Nanthagopal K, Karthickeyan V, Dhinesh B. Effect of electromagnet-based fuel-reforming system on high-viscous and low-viscous biofuel fueled in heavy-duty CI engine. J Therm Anal Calorim. 2019;138:633–44.

    CAS  Article  Google Scholar 

  21. 21.

    Ranjbarzadeh R, Moradikazerouni A, Bakhtiari R, Asadi A, Afrand M. An experimental study on stability and thermal conductivity of water/silica nanofluid: eco-friendly production of nanoparticles. J Clean Prod. 2019;206:1089–100.

    CAS  Article  Google Scholar 

  22. 22.

    Asadi A, Alarifi IM, Ali V, Nguyen HM. An experimental investigation on the effects of ultrasonication time on stability and thermal conductivity of MWCNT–water nanofluid: finding the optimum ultrasonication time. Ultrason Sonochem. 2019.

    Article  PubMed  Google Scholar 

  23. 23.

    Okonkwo EC, Wole-Osho I, Kavaz D, Abid M. Comparison of experimental and theoretical methods of obtaining the thermal properties of alumina/iron mono and hybrid nanofluids. J Mol Liq. 2019.

    Article  Google Scholar 

  24. 24.

    Syam Sundar L, Venkata Ramana E, Singh MK, Sousa ACM. Thermal conductivity and viscosity of stabilized ethylene glycol and water mixture Al2O3 nanofluids for heat transfer applications: an experimental study. Int Commun Heat Mass Transf. 2014;56:86–95.

    CAS  Article  Google Scholar 

  25. 25.

    Hemmat Esfe M, Saedodin S, Asadi A, Karimipour A. Thermal conductivity and viscosity of Mg(OH)2–ethylene glycol nanofluids: finding a critical temperature. J Therm Anal Calorim. 2015.

    Article  Google Scholar 

  26. 26.

    Asadi A, Asadi M, Rezaniakolaei A, Rosendahl LA, Afrand M, Wongwises S. Heat transfer efficiency of Al2O3−MWCNT/thermal oil hybrid nanofluid as a cooling fluid in thermal and energy management applications: an experimental and theoretical investigation. Int J Heat Mass Transf. 2018;117:474–86.

    CAS  Article  Google Scholar 

  27. 27.

    Hamzah MH, Sidik NAC, Ken TL, Mamat R, Najafi G. Factors affecting the performance of hybrid nanofluids: a comprehensive review. Int J Heat Mass Transf. 2017;115:630–46.

    CAS  Article  Google Scholar 

  28. 28.

    Hemmat Esfe M, Abbasian Arani AA, Rezaie M, Yan W-M, 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.

    CAS  Article  Google Scholar 

  29. 29.

    Allahyar HR, Hormozi F, ZareNezhad B. Experimental investigation on the thermal performance of a coiled heat exchanger using a new hybrid nanofluid. Exp Thermal Fluid Sci. 2016;76:324–9.

    CAS  Article  Google Scholar 

  30. 30.

    Soltanimehr M, Afrand M. Thermal conductivity enhancement of COOH-functionalized MWCNTs/ethylene glycol–water nanofluid for application in heating and cooling systems. Appl Therm Eng. 2016;105:716–23.

    CAS  Article  Google Scholar 

  31. 31.

    Fakoor Pakdaman M, Akhavan-Behabadi MA, Razi P. An experimental investigation on thermo-physical properties and overall performance of MWCNT/heat transfer oil nanofluid flow inside vertical helically coiled tubes. Exp Therm Fluid Sci. 2012;40:103–11.

    CAS  Article  Google Scholar 

  32. 32.

    Chen LF, Cheng M, Yang DJ, Yang L. Enhanced thermal conductivity of nanofluid by synergistic effect of multi-walled carbon nanotubes and Fe2O3 nanoparticles. Appl Mech Mater. 2014;548–549:118–23.

    CAS  Article  Google Scholar 

  33. 33.

    Eshgarf H, Afrand M. An experimental study on rheological behavior of non-Newtonian hybrid nano-coolant for application in cooling and heating systems. Exp Thermal Fluid Sci. 2016;76:221–7.

    CAS  Article  Google Scholar 

  34. 34.

    Hemmat Esfe M, Saedodin S, Yan W-M, Afrand M, Sina N. Study on thermal conductivity of water-based nanofluids with hybrid suspensions of CNTs/Al2O3 nanoparticles. J Therm Anal Calorim. 2016;124:455–60.

    CAS  Article  Google Scholar 

  35. 35.

    Afrand M, Nazari Najafabadi K, 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.

    CAS  Article  Google Scholar 

  36. 36.

    Moradikazerouni A, Hajizadeh A, Safaei MR, Afrand M, Yarmand H, Zulkifli NWBM. Assessment of thermal conductivity enhancement of nano-antifreeze containing single-walled carbon nanotubes: optimal artificial neural network and curve-fitting. Physica A. 2019;521:138–45.

    CAS  Article  Google Scholar 

  37. 37.

    Alarifi IM, Nguyen HM, Naderi Bakhtiyari A, Asadi A. Feasibility of ANFIS-PSO and ANFIS-GA models in predicting thermophysical properties of Al2O3−MWCNT/oil hybrid nanofluid. Materials. 2019;12:3628.

    CAS  Article  PubMed Central  Google Scholar 

  38. 38.

    Ramalingam K, Kandasamy A, Balasubramanian D, Palani M, Subramanian T, Varuvel EG, Viswanathan K. Forcasting of an ANN model for predicting behaviour of diesel engine energised by a combination of two low viscous biofuels. Environ Sci Pollut Res. 2019.

    Article  Google Scholar 

  39. 39.

    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.

    CAS  Article  Google Scholar 

  40. 40.

    Asadi A, Asadi M, Siahmargoi M, Asadi T, Gholami Andarati M. The effect of surfactant and sonication time on the stability and thermal conductivity of water-based nanofluid containing Mg(OH)2 nanoparticles: an experimental investigation. Int J Heat Mass Transf. 2017;108:191–8.

    CAS  Article  Google Scholar 

  41. 41.

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

    CAS  Article  Google Scholar 

  42. 42.

    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 Transfer. 2016;78:48–53.

    CAS  Article  Google Scholar 

  43. 43.

    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;120:1145–9.

    CAS  Article  Google Scholar 

  44. 44.

    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.

    Article  Google Scholar 

  45. 45.

    Yang L, Dong H. Robust support vector machine with generalized quantile loss for classification and regression. Appl Soft Comput. 2019;81:105483.

    Article  Google Scholar 

  46. 46.

    Tanveer M, Tiwari A, Choudhary R, Jalan S. Sparse pinball twin support vector machines. Appl Soft Comput. 2019;78:164–75.

    Article  Google Scholar 

  47. 47.

    Boulkaibet I, Belarbi K, Bououden S, Chadli M, Marwala T. An adaptive fuzzy predictive control of nonlinear processes based on Multi-Kernel least squares support vector regression. Appl Soft Comput. 2018;73:572–90.

    Article  Google Scholar 

  48. 48.

    Richhariya B, Tanveer M. A robust fuzzy least squares twin support vector machine for class imbalance learning. Appl Soft Comput. 2018;71:418–32.

    Article  Google Scholar 

  49. 49.

    Zendehboudi A, Baseer MA, Saidur R. Application of support vector machine models for forecasting solar and wind energy resources: a review. J Clean Prod. 2018;199:272–85.

    Article  Google Scholar 

  50. 50.

    Ahmadi MA, Bahadori A. Prediction performance of natural gas dehydration units for water removal efficiency using a least-square support vector machine. Int J Ambient Energy. 2016;37:486–94.

    CAS  Article  Google Scholar 

  51. 51.

    Vapnik VN. An overview of statistical learning theory. IEEE Trans Neural Networks. 1999;10:988–99.

    CAS  Article  PubMed  Google Scholar 

  52. 52.

    Alex JS, Schoelkopf B. A tutorial on support vector regression. Stat Comput. 2004;14:199–222.

    Article  Google Scholar 

  53. 53.

    Ahmadi H, Ahmadi H, Baghban A. Environmental effects modeling vaporization enthalpy of pure hydrocarbons and petroleum fractions using LSSVM approach. 2019.

    Article  Google Scholar 

  54. 54.

    Sun F, Li X, Liao H, Zhang X. A Bayesian least-squares support vector machine method for predicting the remaining useful life of a microwave component. Advances in Mechanical Engineering. 2017;9:1–9.

    Article  Google Scholar 

  55. 55.

    Shahriari B, Swersky K, Wang Z, Adams RP, De Freitas N. Taking the human out of the loop: a review of Bayesian optimization. Proc IEEE. 2016;104:148–75.

    Article  Google Scholar 

  56. 56.

    Law T, Shawe-Taylor J. Practical Bayesian support vector regression for financial time series prediction and market condition change detection. Quant Finance. 2017;17:1403–16.

    Article  Google Scholar 

  57. 57.

    Yu W, Xie H, Chen L, Li Y. Investigation of thermal conductivity and viscosity of ethylene glycol based ZnO nanofluid. Thermochim Acta. 2009;491:92–6.

    CAS  Article  Google Scholar 

  58. 58.

    S. Kabelac, J.F. Kuhnke. Heat transfer mechanisms in nanofluids—experiments and theory—. In: Keynote Papers, Begell House Inc., 2006.

  59. 59.

    Prasher R, Bhattacharya P, Phelan PE. Thermal conductivity of nanoscale colloidal solutions (nanofluids). Phys Rev Lett. 2005;94:025901.

    Article  PubMed  Google Scholar 

  60. 60.

    Einstein A. A new determination of molecular dimensions. Ann Phys. Ci.Nii.Ac.Jp. (n.d.)., 1906. Accessed 4 Mar 2019.

  61. 61.

    Batchelor GK. The effect of Brownian motion on the bulk stress in a suspension of spherical particles. J Fluid Mech. 1977;83:97.

    Article  Google Scholar 

  62. 62.

    Wang X, Xu X, Choi SUS. Thermal conductivity of nanoparticle—fluid mixture. J Thermophys Heat Transfer. 1999;13:474–80.

    CAS  Article  Google Scholar 

  63. 63.

    Blasius H. Das Aehnlichkeitsgesetz bei Reibungsvorgängen in Flüssigkeiten. In: Mitteilungen Über Forschungsarbeiten Auf Dem Gebiete Des Ingenieurwesens. Berlin: Springer, 1913, pp 1–41.

  64. 64.

    Takabi B, Salehi S. Augmentation of the heat transfer performance of a sinusoidal corrugated enclosure by employing hybrid nanofluid. Adv Mech Eng. 2014;6:147059.

    CAS  Article  Google Scholar 

  65. 65.

    Alirezaie A, Saedodin S, Esfe MH, 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;241:173–81.

    CAS  Article  Google Scholar 

  66. 66.

    Bagherzadeh SA, D’Orazio A, Karimipour A, Goodarzi M, Bach QV. A novel sensitivity analysis model of EANN for F-MWCNTs–Fe 3 O 4 /EG nanofluid thermal conductivity: outputs predicted analytically instead of numerically to more accuracy and less costs. Physica A. 2019;521:406–15.

    CAS  Article  Google Scholar 

  67. 67.

    Alrashed AAAA, 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 Transf. 2018;125:920–32.

    CAS  Article  Google Scholar 

  68. 68.

    Bahrami M, Akbari M, Bagherzadeh SA, Karimipour A, Afrand M, Goodarzi M. Develop 24 dissimilar ANNs by suitable architectures & training algorithms via sensitivity analysis to better statistical presentation: measure MSEs between targets and ANN for Fe–CuO/Eg–water nanofluid. Physica A. 2019;519:159–68.

    CAS  Article  Google Scholar 

  69. 69.

    Safaei MR, Hajizadeh A, Afrand M, Qi C, Yarmand H, Zulkifli NWBM. Evaluating the effect of temperature and concentration on the thermal conductivity of ZnO–TiO2/EG hybrid nanofluid using artificial neural network and curve fitting on experimental data. Physica A. 2019;519:209–16.

    CAS  Article  Google Scholar 

  70. 70.

    Ghasemi A, Hassani M, Goodarzi M, Afrand M, Manafi S. Appraising influence of COOH−MWCNTs on thermal conductivity of antifreeze using curve fitting and neural network. Physica A. 2019;514:36–45.

    CAS  Article  Google Scholar 

  71. 71.

    Kannaiyan S, Boobalan C, Nagarajan FC, Sivaraman S. Modeling of thermal conductivity and density of alumina/silica in water hybrid nanocolloid by the application of Artificial Neural Networks. Chin J Chem Eng. 2019;27:726–36.

    CAS  Article  Google Scholar 

  72. 72.

    Esfe MH, Esfandeh S, Afrand M, Rejvani M, Rostamian SH. Experimental evaluation, new correlation proposing and ANN modeling of thermal properties of EG based hybrid nanofluid containing ZnO-DWCNT nanoparticles for internal combustion engines applications. Appl Therm Eng. 2018;133:452–63.

    CAS  Article  Google Scholar 

  73. 73.

    Sedaghat F, Yousefi F. Synthesizes, characterization, measurements and modeling thermal conductivity and viscosity of graphene quantum dots nanofluids. J Mol Liq. 2019;278:299–308.

    CAS  Article  Google Scholar 

  74. 74.

    Hemmat Esfe M, Ahangar MRH, 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;126:837–43.

    CAS  Article  Google Scholar 

  75. 75.

    Eshgarf H, Sina N, Esfe MH, Izadi F, Afrand M. Prediction of rheological behavior of MWCNTs–SiO 2 /EG–water non-Newtonian hybrid nanofluid by designing new correlations and optimal artificial neural networks. J Therm Anal Calorim. 2018;132:1029–38.

    CAS  Article  Google Scholar 

  76. 76.

    Vakili M, Karami M, Delfani S, Khosrojerdi S, Kalhor K. Experimental investigation and modeling of thermal conductivity of CuO–water/EG nanofluid by FFBP-ANN and multiple regressions. J Therm Anal Calorim. 2017;129:629–37.

    CAS  Article  Google Scholar 

  77. 77.

    Maddah H, Aghayari R, Ahmadi MH, Rahimzadeh M, Ghasemi N. Prediction and modeling of MWCNT/Carbon (60/40)/SAE 10 W 40/SAE 85 W 90(50/50) nanofluid viscosity using artificial neural network (ANN) and self-organizing map (SOM). J Therm Anal Calorim. 2018;134:2275–86.

    CAS  Article  Google Scholar 

  78. 78.

    Esfe MH, Rejvani M, Karimpour R, Abbasian 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;128:1359–71.

    CAS  Article  Google Scholar 

  79. 79.

    Vafaei M, Afrand M, Sina N, Kalbasi R, Sourani F, Teimouri H. Evaluation of thermal conductivity of MgO−MWCNTs/EG hybrid nanofluids based on experimental data by selecting optimal artificial neural networks. Physica E. 2017;85:90–6.

    CAS  Article  Google Scholar 

  80. 80.

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

    CAS  Article  Google Scholar 

  81. 81.

    Afrand M, Hemmat Esfe M, Abedini E, Teimouri H. Predicting the effects of magnesium oxide nanoparticles and temperature on the thermal conductivity of water using artificial neural network and experimental data. Physica E Low Dimens Syst Nanostruct. 2017;87:242–7.

    CAS  Article  Google Scholar 

Download references


The authors extend their appreciation to the Deanship of Scientific Research at Majmaah University for funding this work under Project Number No (RGP-2019-15).

Author information



Corresponding authors

Correspondence to Ibrahim M. Alarifi or Hossein Moayedi.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Asadi, A., Alarifi, I.M., Nguyen, H.M. et al. Feasibility of least-square support vector machine in predicting the effects of shear rate on the rheological properties and pumping power of MWCNT–MgO/oil hybrid nanofluid based on experimental data. J Therm Anal Calorim 143, 1439–1454 (2021).

Download citation


  • Dynamic viscosity
  • Pumping power
  • Shear rate
  • Temperature
  • Solid concentration