Skip to main content
Log in

Comparing various machine learning approaches in modeling the dynamic viscosity of CuO/water nanofluid

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

Abstract

Nanofluids are broadly employed in heat transfer mediums to enhance their efficiency and heat transfer capacity. Thermophysical properties of nanofluids play a crucial role in their thermal behavior. Among various properties, the dynamic viscosity is one of the most crucial ones due to its impact on fluid motion and friction. Applying appropriate models can facilitate the design of nanofluidics thermal devices. In the present study, various machine learning methods including MPR, MARS, ANN-MLP, GMDH, and M5-tree are used for modeling the dynamic viscosity of CuO/water nanofluid based on the temperature, concentration, and size of nanostructures. The input data are extracted from various experimental studies to propose a comprehensive model, applicable in wide ranges of input variables. Moreover, the relative importance of each variable is evaluated to figure out the priority of the variables and their influences on the dynamic viscosity. Finally, the accuracy of the models is compared by employing the statistical criteria such as R-squared value. The models’ outputs disclosed that employing ANN-MLP approach leads to the most precise model. R-square value and average absolute percent relative error (AAPR) value of the model by using ANN-MLP model are 0.9997 and 1.312%, respectively. According to these values, ANN-MLP is a reliable approach for predicting the dynamic viscosity of the studied nanofluid. Additionally, based on the relative importance of the input variables, it is concluded that concentration has the highest relative importance; while the influence of size is the lowest one.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Abbreviations

A :

Heat transfer area (m2)

Cp:

Specific heat (kj kg-1 °C−1)

d :

Nanoparticle diameter (m)

h :

Convective heat transfer coefficient (W m−2 °C−1)

h E :

Enhanced convective heat transfer coefficient

h NE :

Non-enhanced convective heat transfer coefficient

k :

Thermal conductivity (W m−1 °C−1)

L :

Tube length (m)

M :

Mass flow rate (kg s−1)

Q :

Heat transfer rate (W)

Re :

Reynolds number (dimensionless)

T :

Temperature (°C)

U :

Overall heat transfer coefficient (W m-2oc-1)

V :

Velocity (m2 s−1)

n :

Flow behavior index

\(\Delta T_{\text{lm}}\) :

Logarithmic mean temperature difference (°C)

\(\Delta P_{\text{nf}}\) :

Pressure drop

α :

Thermal diffusivity (m2 s−1)

\(\rho\) :

Density (kg m−3)

\(\vartheta\) :

Kinematic viscosity (m2 s−1)

\(\eta\) :

Performance evaluation analysis

\(\varphi_{\text{V}}\) :

Nanoparticle volume concentration (dimensionless)

f:

Fluid

i:

Inside

nf:

NANOFLUID

m:

Mean

out:

Outlet

p:

Particles

AAPRE:

Average absolute percent relative error

MLP:

Multi-layer perceptron

LM:

Levenberg–Marquardt

RMSE:

Root mean square error

MPR:

Multivariable polynomial regression

MARS:

Multivariate adaptive regression splines

GMDH:

Group method of data handling

References

  1. Karimipour A, Bagherzadeh SA, Taghipour A, Abdollahi A, Safaei MR. A novel nonlinear regression model of SVR as a substitute for ANN to predict conductivity of MWCNT-CuO/water hybrid nanofluid based on empirical data. Phys A Stat Mech Appl. 2019;521:89–97.

    CAS  Google Scholar 

  2. Nafchi PM, Karimipour A, Afrand M. The evaluation on a new non-Newtonian hybrid mixture composed of TiO2/ZnO/EG to present a statistical approach of power law for its rheological and thermal properties. Phys A Stat Mech Appl. 2019;516:1–18.

    CAS  Google Scholar 

  3. 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. Phys A Stat Mech Appl. 2019;519:209–16.

    CAS  Google Scholar 

  4. 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. Phys A Stat Mech Appl. 2019;521:138–45.

    CAS  Google Scholar 

  5. Ahmadi MH, Mohseni-Gharyehsafa B, Farzaneh-Gord M, Jilte RD, Kumar R, Chau K. Applicability of connectionist methods to predict dynamic viscosity of silver/water nanofluid by using ANN-MLP, MARS and MPR algorithms. Eng Appl Comput Fluid Mech. 2019;13:220–8.

    Google Scholar 

  6. Nazari MA, Ghasempour R, Ahmadi MH, Heydarian G, Shafii MB. Experimental investigation of graphene oxide nanofluid on heat transfer enhancement of pulsating heat pipe. Int Commun Heat Mass Transf. 2018;91:90–4.

    CAS  Google Scholar 

  7. Mohamadian F, Eftekhar L, Haghighi Bardineh Y. Applying GMDH artificial neural network to predict dynamic viscosity of an antimicrobial nanofluid. Nanomedicine J. Mashhad Univ Med Sci. 2018;5:217–21.

    CAS  Google Scholar 

  8. Akbarianrad N, Mohammadian F, Alhuyi Nazari M, Rahbani Nobar B. Applications of nanotechnology in endodontic: a review. Nanomed J Mashhad Univ Med Sci. 2018;5:121–6.

    CAS  Google Scholar 

  9. Plata S. A note on Fisher’s correlation coefficient. Appl Math Lett. 2006;19:499–502.

    Google Scholar 

  10. Lawal AS. Prediction of vapor and liquid viscosities from the Lawal-Lake-Silberberg equation of state. In: SPE enhanced oil recovery symposium. 1986.

  11. Mohseni-Gharyehsafa B, Ebrahimi-Moghadam A, Okati V, Farzaneh-Gord M, Ahmadi MH, Lorenzini G. Optimizing flow properties of the different nanofluids inside a circular tube by using entropy generation minimization approach. J Therm Anal Calorim. 2018;135:1–11.

    Google Scholar 

  12. Olia H, Torabi M, Bahiraei M, Ahmadi MH, Goodarzi M, Safaei MR, et al. Application of nanofluids in thermal performance enhancement of parabolic trough solar collector: state-of-the-art. Appl Sci. 2019;9:463.

    CAS  Google Scholar 

  13. Ahmadi MH, Alhuyi Nazari M, Ghasempour R, Madah H, Shafii MB, Ahmadi MA. Thermal conductivity ratio prediction of Al2O3/water nanofluid by applying connectionist methods. Colloids Surf A Physicochem Eng Asp. 2018;541:154–64.

    CAS  Google Scholar 

  14. Ahmadi MH, Mirlohi A, Alhuyi Nazari M, Ghasempour R. A review of thermal conductivity of various nanofluids. J Mol Liq. 2018;265:181–8.

    CAS  Google Scholar 

  15. Maddah H, Ghazvini M, Ahmadi MH. Predicting the efficiency of CuO/water nanofluid in heat pipe heat exchanger using neural network. Int Commun Heat Mass Transf. 2019;104:33–40.

    CAS  Google Scholar 

  16. Jahangir MH, Ghazvini M, Pourfayaz F, Ahmadi MH. A numerical study into effects of intermittent pump operation on thermal storage in unsaturated porous media. Appl Therm Eng. 2018;138:110–21.

    Google Scholar 

  17. Shadloo MS, Hadjadj A. Laminar-turbulent transition in supersonic boundary layers with surface heat transfer: a numerical study. Numer Heat Transf Part A Appl. 2017;72:40–53. https://doi.org/10.1080/10407782.2017.1353380.

    Article  CAS  Google Scholar 

  18. Hopp-Hirschler M, Shadloo MS, Nieken U. A smoothed particle hydrodynamics approach for thermo-capillary flows. Comput Fluids. 2018;176:1–19.

    Google Scholar 

  19. Toghyani S, Afshari E, Baniasadi E, Shadloo MS. Energy and exergy analyses of a nanofluid based solar cooling and hydrogen production combined system. Renew Energy. 2019;141:1013–25.

    CAS  Google Scholar 

  20. Nasiri H, Abdollahzadeh Jamalabadi MY, Sadeghi R, Safaei MR, Nguyen TK, Safdari Shadloo M. A smoothed particle hydrodynamics approach for numerical simulation of nano-fluid flows. J Therm Anal Calorim. 2019;135:1733–41.

    CAS  Google Scholar 

  21. Rashidi MM, Nasiri M, Shadloo MS, Yang Z. Entropy generation in a circular tube heat exchanger using nanofluids: effects of different modeling approaches. Heat Transf Eng. 2017;38:853–66. https://doi.org/10.1080/01457632.2016.1211916.

    Article  CAS  Google Scholar 

  22. Sarafraz MM, Hormozi F. Application of thermodynamic models to estimating the convective flow boiling heat transfer coefficient of mixtures. Exp Therm Fluid Sci. 2014;53:70–85.

    CAS  Google Scholar 

  23. Sadeghi R, Shadloo MS, Hopp-Hirschler M, Hadjadj A, Nieken U. Three-dimensional lattice Boltzmann simulations of high density ratio two-phase flows in porous media. Comput Math Appl. 2018;75:2445–65.

    Google Scholar 

  24. Safdari Shadloo M. Numerical simulation of compressible flows by lattice Boltzmann method. Numer Heat Transf Part A Appl. 2019;75:167–82. https://doi.org/10.1080/10407782.2019.1580053.

    Article  Google Scholar 

  25. Niazi S, Beni MN. Numerical study of the effect of a nanofluid with nanoparticles of nonuniform size on natural convection in an inclined enclosure. Nanosci Technol Int J. 2017;8:261–308.

    Google Scholar 

  26. Rahmati AR, Niazi S, Naderi Beni M. An incompressible generalized lattice Boltzmann method for increasing heat transfer with nanofluids in a square cavity. In: Proceedings of the 7th international conference on computational heat mass transfer 2011. p. 352.

  27. 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.

    Google Scholar 

  28. Maheshwary PB, Handa CC, Nemade KR. A comprehensive study of effect of concentration, particle size and particle shape on thermal conductivity of titania/water based nanofluid. Appl Therm Eng. 2017;119:79–88. https://doi.org/10.1016/j.applthermaleng.2017.03.054.

    Article  CAS  Google Scholar 

  29. Ramezanizadeh M, Alhuyi Nazari M, Ahmadi MH, Açıkkalp E. Application of nanofluids in thermosyphons: a review. J Mol Liq. 2018;272:395–402.

    CAS  Google Scholar 

  30. Karimipour A, Bagherzadeh SA, Goodarzi M, Alnaqi AA, Bahiraei M, Safaei MR, et al. Synthesized CuFe2O4/SiO2 nanocomposites added to water/EG: evaluation of the thermophysical properties beside sensitivity analysis & EANN. Int J Heat Mass Transf. 2018;127:1169–79.

    CAS  Google Scholar 

  31. Karimipour A, Hemmat Esfe M, Safaei MR, Toghraie Semiromi D, Jafari S, Kazi SN. Mixed convection of copper–water nanofluid in a shallow inclined lid driven cavity using the lattice Boltzmann method. Phys A Stat Mech Appl. 2014;402:150–68.

    CAS  Google Scholar 

  32. Karimipour A, Hossein Nezhad A, D’Orazio A, Hemmat Esfe M, Safaei MR, Shirani E. Simulation of copper–water nanofluid in a microchannel in slip flow regime using the lattice Boltzmann method. Eur J Mech B Fluids. 2015;49:89–99.

    Google Scholar 

  33. Nikkhah V, Sarafraz MM, Hormozi F. Application of spherical copper oxide (II) water nano-fluid as a potential coolant in a boiling annular heat exchanger. Chem Biochem Eng Q. 2015;29:405–15.

    CAS  Google Scholar 

  34. Sarafraz MM, Hormozi F, Peyghambarzadeh SM, Vaeli N. Upward flow boiling to di-water and cuo nanofluids inside the concentric annuli. J Appl Fluid Mech (JAFM). 2015;8:651–9.

    Google Scholar 

  35. Sarafraz MM, Nikkhah V, Madani SA, Jafarian M, Hormozi F. Low-frequency vibration for fouling mitigation and intensification of thermal performance of a plate heat exchanger working with CuO/water nanofluid. Appl Therm Eng. 2017;121:388–99.

    CAS  Google Scholar 

  36. Sarafraz MM, Hormozi F, Kamalgharibi M. Sedimentation and convective boiling heat transfer of CuO-water/ethylene glycol nanofluids. Heat Mass Transf. 2014;50:1237–49.

    CAS  Google Scholar 

  37. Hemmat Esfe M, Abbasian Arani AA, Niroumand AH, Yan W-M, 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.

    CAS  Google Scholar 

  38. Safaei MR, Safdari Shadloo M, Goodarzi MS, Hadjadj A, Goshayeshi HR, Afrand M, et al. A survey on experimental and numerical studies of convection heat transfer of nanofluids inside closed conduits. Adv Mech Eng. 2016;8:168781401667356. https://doi.org/10.1177/1687814016673569.

    Article  CAS  Google Scholar 

  39. Salari E, Peyghambarzadeh M, Sarafraz MM, Hormozi F. Boiling heat transfer of alumina nano-fluids: role of nanoparticle deposition on the boiling heat transfer coefficient. Period Polytech Chem Eng. 2016;60:252–8.

    CAS  Google Scholar 

  40. Sarafraz MM, Peyghambarzadeh SM, Alavi Fazel SA, Vaeli N. Nucleate pool boiling heat transfer of binary nano mixtures under atmospheric pressure around a smooth horizontal cylinder. Period Polytech Chem Eng. 2013;57:71.

    CAS  Google Scholar 

  41. Tahmasebi Sulgani M, Karimipour A. Improve the thermal conductivity of 10w40-engine oil at various temperature by addition of Al2O3/Fe2O3 nanoparticles. J Mol Liq. 2019;283:660–6.

    CAS  Google Scholar 

  42. 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.

    CAS  Google Scholar 

  43. Salari E, Peyghambarzadeh SM, Sarafraz MM, Hormozi F, Nikkhah V. Thermal behavior of aqueous iron oxide nano-fluid as a coolant on a flat disc heater under the pool boiling condition. Heat Mass Transf. 2017;53:265–75. https://doi.org/10.1007/s00231-016-1823-4.

    Article  CAS  Google Scholar 

  44. Safaei M, Ahmadi G, Goodarzi M, Safdari Shadloo M, Goshayeshi H, Dahari M, et al. Heat transfer and pressure drop in fully developed turbulent flows of graphene nanoplatelets–silver/water nanofluids. Fluids. 2016;1:20.

    Google Scholar 

  45. Sarafraz MM, Nikkhah V, Nakhjavani M, Arya A. Thermal performance of a heat sink microchannel working with biologically produced silver-water nanofluid: experimental assessment. Exp Therm Fluid Sci. 2018;91:509–19.

    CAS  Google Scholar 

  46. 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.

    CAS  Google Scholar 

  47. Bagherzadeh SA, D’Orazio A, Karimipour A, Goodarzi M, Bach Q-V. A novel sensitivity analysis model of EANN for F-MWCNTs–Fe3O4/EG nanofluid thermal conductivity: outputs predicted analytically instead of numerically to more accuracy and less costs. Phys A Stat Mech Appl. 2019;521:406–15.

    CAS  Google Scholar 

  48. Karimipour A, Taghipour A, Malvandi A. Developing the laminar MHD forced convection flow of water/FMWNT carbon nanotubes in a microchannel imposed the uniform heat flux. J Magn Magn Mater. 2016;419:420–8.

    CAS  Google Scholar 

  49. Sarafraz MM, Hormozi F, Silakhori M, Peyghambarzadeh SM. On the fouling formation of functionalized and non-functionalized carbon nanotube nano-fluids under pool boiling condition. Appl Therm Eng. 2016;95:433–44.

    CAS  Google Scholar 

  50. Arya A, Sarafraz MM, Shahmiri S, Madani SAH, Nikkhah V, Nakhjavani SM. Thermal performance analysis of a flat heat pipe working with carbon nanotube-water nanofluid for cooling of a high heat flux heater. Heat Mass Transf. 2018;54:985–97. https://doi.org/10.1007/s00231-017-2201-6.

    Article  CAS  Google Scholar 

  51. Arunkumar T, Anish M, Jayaprabakar J, Beemkumar N. Enhancing heat transfer rate in a car radiator by using Al2O3 nanofluid as a coolant. Int J Ambient Energy. 2017;40:1–7.

    Google Scholar 

  52. Godson L, Raja B, Lal DM, Wongwises S. Experimental investigation on the thermal conductivity and viscosity of silver-deionized water nanofluid. Exp Heat Transf. 2010;23:317–32. https://doi.org/10.1080/08916150903564796.

    Article  CAS  Google Scholar 

  53. Alipour H, Karimipour A, Safaei MR, Semiromi DT, Akbari OA. Influence of T-semi attached rib on turbulent flow and heat transfer parameters of a silver-water nanofluid with different volume fractions in a three-dimensional trapezoidal microchannel. Phys E Low Dimens Syst Nanostruct. 2017;88:60–76.

    CAS  Google Scholar 

  54. Gandomkar A, Saidi MH, Shafii MB, Vandadi M, Kalan K. Visualization and comparative investigations of pulsating ferro-fluid heat pipe. Appl Therm Eng. 2017;116:56–65.

    CAS  Google Scholar 

  55. Ahmadi MH, Ahmadi MA, Nazari MA, Mahian O, Ghasempour R. A proposed model to predict thermal conductivity ratio of Al2O3/EG nanofluid by applying least squares support vector machine (LSSVM) and genetic algorithm as a connectionist approach. J Therm Anal Calorim. 2018;135:1–11.

    Google Scholar 

  56. Mahyari AA, Karimipour A, Afrand M. Effects of dispersed added graphene oxide-silicon carbide nanoparticles to present a statistical formulation for the mixture thermal properties. Phys A Stat Mech Appl. 2019;521:98–112.

    CAS  Google Scholar 

  57. 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. Phys A Stat Mech Appl. 2019;514:36–45.

    CAS  Google Scholar 

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

    CAS  Google Scholar 

  59. Hemmat Esfe M, Rostamian H, Esfandeh S, Afrand M. Modeling and prediction of rheological behavior of Al2O3-MWCNT/5W50 hybrid nano-lubricant by artificial neural network using experimental data. Phys A Stat Mech Appl. 2018;510:625–34.

    CAS  Google Scholar 

  60. Alnaqi AA, Sayyad Tavoos Hal S, Aghaei A, Soltanimehr M, Afrand M, Nguyen TK. Predicting the effect of functionalized multi-walled carbon nanotubes on thermal performance factor of water under various Reynolds number using artificial neural network. Phys A Stat Mech Appl. 2019;521:493–500.

    CAS  Google Scholar 

  61. Hemmat Esfe M, Kamyab MH, Afrand M, Amiri MK. Using artificial neural network for investigating of concurrent effects of multi-walled carbon nanotubes and alumina nanoparticles on the viscosity of 10 W-40 engine oil. Phys A Stat Mech Appl. 2018;510:610–24.

    CAS  Google Scholar 

  62. Longo GA, Zilio C, Ortombina L, Zigliotto M. Application of artificial neural network (ANN) for modeling oxide-based nanofluids dynamic viscosity. Int Commun Heat Mass Transf. 2017;83:8–14.

    CAS  Google Scholar 

  63. Aminian A. Predicting the effective viscosity of nanofluids for the augmentation of heat transfer in the process industries. J Mol Liq. 2017;229:300–8.

    CAS  Google Scholar 

  64. Nojoomizadeh M, D’Orazio A, Karimipour A, Afrand M, Goodarzi M. Investigation of permeability effect on slip velocity and temperature jump boundary conditions for FMWNT/Water nanofluid flow and heat transfer inside a microchannel filled by a porous media. Phys E Low Dimens Syst Nanostruct. 2018;97:226–38.

    CAS  Google Scholar 

  65. 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  Google Scholar 

  66. 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 & ANN for Fe–CuO/Eg–water nanofluid. Phys A Stat Mech Appl. 2019;519:159–68.

    CAS  Google Scholar 

  67. Rahmati AR, Niazi S. Entropic lattice Boltzmann method for microflow simulation. Nanosci Technol Int J. 2014;5:153–67.

    Google Scholar 

  68. Rahmati AR, Niazi S. Simulation of microflows using the lattice Boltzmann method on nonuniform meshes. Nanosci Technol Int J. 2012;3:77–97.

    Google Scholar 

  69. Rahmati AR, Niazi S, Beni MN. Natural convection flow simulation of nanofluid in a square cavity using an incompressible generalized lattice Boltzmann method. Defect Diffus Forum. 2012;329:69–79.

    CAS  Google Scholar 

  70. Rahmati AR, Niazi S. Application and comparison of different lattice Boltzmann methods on non-uniform meshes for simulation of micro cavity and micro channel flow. Comput Methods Eng. 2015;34:97–118.

    Google Scholar 

  71. Ahmadi MH, Hajizadeh F, Rahimzadeh M, Shafii MB, Chamkha AJ. Application GMDH artificial neural network for modeling of Al2O3/water and Al2O3/Ethylene glycol thermal conductivity. Int J Heat Technol. 2018;36:773–82.

    Google Scholar 

  72. Rezaei MH, Sadeghzadeh M, Alhuyi Nazari M, Ahmadi MH, Astaraei FR. Applying GMDH artificial neural network in modeling CO2 emissions in four nordic countries. Int J Low Carbon Technol. 2018;13:266–71.

    CAS  Google Scholar 

  73. Hemmat Esfe M, Bahiraei M, Mahian O. Experimental study for developing an accurate model to predict viscosity of CuO–ethylene glycol nanofluid using genetic algorithm based neural network. Powder Technol. 2018;338:383–90.

    CAS  Google Scholar 

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

    CAS  Google Scholar 

  75. Hassan MA, Banerjee D. A soft computing approach for estimating the specific heat capacity of molten salt-based nanofluids. J Mol Liq. 2019;281:365–75.

    CAS  Google Scholar 

  76. Baghban A, Kahani M, Nazari MA, Ahmadi MH, Yan W-M. Sensitivity analysis and application of machine learning methods to predict the heat transfer performance of CNT/water nanofluid flows through coils. Int J Heat Mass Transf. 2019;128:825–35.

    CAS  Google Scholar 

  77. Gholami E, Vaferi B, Ariana MA. Prediction of viscosity of several alumina-based nanofluids using various artificial intelligence paradigms—comparison with experimental data and empirical correlations. Powder Technol. 2018;323:495–506.

    CAS  Google Scholar 

  78. Firouzi M, Bagherzadeh SA, Mahmoudi B, Hajizadeh A, Afrand M, Nguyen TK. Robust weighted least squares support vector regression algorithm to estimate the nanofluid thermal properties of water/graphene oxide-silicon carbide mixture. Phys A Stat Mech Appl. 2019;525:1418–28.

    Google Scholar 

  79. Ahmadi M-A, Ahmadi MH, Fahim Alavi M, Nazemzadegan MR, Ghasempour R, Shamshirband S. Determination of thermal conductivity ratio of CuO/ethylene glycol nanofluid by connectionist approach. J Taiwan Inst Chem Eng. 2018;91:383–95.

    CAS  Google Scholar 

  80. Sekulic S, Kowalski BR. MARS: a tutorial. J Chemom. 1992;6:199–216.

    CAS  Google Scholar 

  81. Yilmaz I, Kaynar O. Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils. Expert Syst Appl. 2011;38:5958–66.

    Google Scholar 

  82. Attoh-Okine NO, Cooger K, Mensah S. Multivariate adaptive regression (MARS) and hinged hyperplanes (HHP) for doweled pavement performance modeling. Constr Build Mater. 2009;23:3020–3.

    Google Scholar 

  83. Nguyen CT, Desgranges F, Roy G, Galanis N, Maré T, Boucher S, et al. Temperature and particle-size dependent viscosity data for water-based nanofluids—hysteresis phenomenon. Int J Heat Fluid Flow. 2007;28:1492–506.

    CAS  Google Scholar 

  84. Pastoriza-Gallego MJ, Casanova C, Legido JL, Piñeiro MM. CuO in water nanofluid: influence of particle size and polydispersity on volumetric behaviour and viscosity. Fluid Phase Equilib. 2011;300:188–96.

    CAS  Google Scholar 

  85. Fridedman JH. Multivariate adaptive regression splines (with discussion). Ann Stat. 1991;19:79–141.

    Google Scholar 

  86. Abraham A, Steinberg D. Is neural network a reliable forecaster on earth? A MARS Query!. Berlin: Springer; 2001. p. 679–86.

    Google Scholar 

  87. Friedman JH. Estimating functions of mixed ordinal and categorical variables using adaptive splines. 1991.

  88. Ebrahimi-Moghadam A, Mohseni-Gharyehsafa B, Farzaneh-Gord M. Using artificial neural network and quadratic algorithm for minimizing entropy generation of Al2O3-EG/W nanofluid flow inside parabolic trough solar collector. Renew Energy. 2018;129:473–85.

    CAS  Google Scholar 

  89. Sanjari E, Lay EN. Estimation of natural gas compressibility factors using artificial neural network approach. J Nat Gas Sci Eng. 2012;9:220–6.

    Google Scholar 

  90. Pourkiaei SM, Ahmadi MH, Hasheminejad SM. Modeling and experimental verification of a 25 W fabricated PEM fuel cell by parametric and GMDH-type neural network. Mech Ind. 2016;17:105. https://doi.org/10.1051/meca/2015050.

    Article  CAS  Google Scholar 

  91. Loni R, Asli-Ardeh EA, Ghobadian B, Ahmadi MH, Bellos E. GMDH modeling and experimental investigation of thermal performance enhancement of hemispherical cavity receiver using MWCNT/oil nanofluid. Sol Energy. 2018;171:790–803.

    CAS  Google Scholar 

  92. Ivakhnenko AG. The group method of data handling—a rival of the method of stochastic approximation. Sov Autom Control. 1966;13:43–55.

    Google Scholar 

  93. Alberg D, Last M, Kandel A. Knowledge discovery in data streams with regression tree methods. Wiley Interdiscip Rev Data Min Knowl Discov. 2012;2:69–78.

    Google Scholar 

  94. Quinlan JR. Learning with continuous classes. In: Proceedings of the Australian joint conference on artificial intelligence. 1992. p. 343–8.

  95. Farzaneh-Gord M, Mohseni-Gharyehsafa B, Ebrahimi-Moghadam A, Jabari-Moghadam A, Toikka A, Zvereva I. Precise calculation of natural gas sound speed using neural networks: an application in flow meter calibration. Flow Meas Instrum. 2018;64:90–103.

    Google Scholar 

  96. Farzaneh-Gord M, Mohseni-Gharyehsafa B, Toikka A, Zvereva I. Sensitivity of natural gas flow measurement to AGA8 or GERG2008 equation of state utilization. J Nat Gas Sci Eng. 2018;57:305–21.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Mohammad Hossein Ahmadi, Marjan Goodarzi or Ravinder Kumar.

Additional information

Publisher's Note

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

Appendix

Appendix

The coefficients of Eq. 3 are obtained based on the equations presented as follows:

$$N_{2} = C_{4} + N_{94} \times N_{3} \times C_{5} + N_{94}^{2} \times C_{6} + N_{3} \times C_{7} + N_{3}^{2} \times C_{8}$$
$$N_{3} = C_{9} + N_{58} \times C_{10} + N_{58} \times N_{4} \times C_{11} + N_{58}^{2} \times C_{12} + N_{4} \times C_{13} + N_{4}^{2} \times C_{14}$$
$$N_{4} = C_{15} + N_{122} \times C_{16} + N_{122} \times N_{5} \times C_{17} + N_{122}^{2} \times C_{18} + N_{5} \times C_{19} + N_{5}^{2} \times C_{20}$$
$$N_{5} = C_{21} + N_{67} \times C_{22} + N_{67} \times N_{10} \times C_{23} + N_{67}^{2} \times C_{24} + N_{10} \times C_{25} + N_{10}^{2} \times C_{26}$$
$$N_{10} = C_{27} + N_{108} \times C_{28} + N_{108} \times N_{14} \times C_{29} + N_{14} \times C_{30} + N_{14}^{2} \times C_{31}$$
$$N_{14} = C_{32} + N_{27} \times N_{29} \times C_{33} + N_{27}^{2} \times C_{34} + N_{29} \times C_{35}$$
$$N_{29} = C_{36} + N_{142} \times C_{37} + N_{142}^{2} \times C_{38} + N_{40} \times C_{39}$$
$$N_{40} = C_{40} + N_{49} \times C_{41} + N_{49}^{2} \times C_{42} + N_{93} \times C_{43}$$
$$N_{93} = C_{44} + N_{134} \times C_{45} + N_{134}^{2} \times C_{46} + N_{162} \times C_{47}$$
$$N_{162} = C_{48} + N_{182} \times C_{49} + N_{182} \times N_{265} \times C_{50} + N_{182}^{2} \times C_{51} + N_{265} \times C_{52} + N_{265}^{2} \times C_{53}$$
$$N_{182} = C_{54} + d \times C_{55} + d \times N_{237} \times C_{56} + d^{2} \times C_{57} + N_{237} \times C_{58} + N_{237}^{2} \times C_{59}$$
$$N_{49} = C_{60} + N_{242} \times C_{61} + N_{242} \times N_{110} \times C_{62} + N_{242}^{2} \times C_{63} + N_{110} \times C_{64} + N_{110}^{2} \times C_{65}$$
$$N_{27} = C_{66} + N_{37} \times C_{67} + N_{45} \times C_{68} + N_{45}^{2} \times C_{69}$$
$$N_{45} = C_{70} + N_{231} \times C_{71} + N_{231} \times N_{78} \times C_{72} + N_{231}^{2} \times C_{73} + N_{78} \times C_{74} + N_{78}^{2} \times C_{75}$$
$$N_{78} = C_{76} + N_{135} \times N_{157} \times C_{77} + N_{135}^{2} \times C_{78} + N_{157} \times C_{79} + N_{157}^{2} \times C_{80}$$
$$N_{135} = C_{81} + N_{178} \times C_{82} + N_{178} \times N_{200} \times C_{83} + N_{178}^{2} \times C_{84} + N_{200}^{2} \times C_{85}$$
$$N_{178} = C_{86} + N_{245} \times C_{87} + N_{245} \times N_{250} \times C_{88} + N_{245}^{2} \times C_{89} + N_{250} \times C_{90} + N_{250}^{2} \times C_{91}$$
$$N_{37} = C_{92} + N_{42} \times C_{93} + N_{126} \times C_{94}$$
$$N_{126} = C_{95} + N_{195} \times C_{96} + N_{195} \times N_{154} \times C_{97} + N_{154} \times C_{98} + N_{154}^{2} \times C_{99}$$
$$N_{154} = C_{100} + N_{183} \times C_{101} + N_{183} \times N_{244} \times C_{102} + N_{183}^{2} \times C_{103} + N_{244} \times C_{104} + N_{244}^{2} \times C_{105}$$
$$N_{42} = C_{106} + N_{110} \times C_{107} + N_{110} \times N_{231} \times C_{108} + N_{110}^{2} \times C_{109} + N_{231} \times C_{110} + N_{231}^{2} \times C_{111}$$
$$N_{110} = C_{112} + N_{200} \times C_{113} + N_{200} \times N_{208} \times C_{114} + N_{200}^{2} \times C_{115} + N_{208} \times C_{116} + N_{208}^{2} \times C_{117}$$
$$N_{108} = C_{118} + N_{192} \times C_{119} + N_{192} \times N_{145} \times C_{120} + N_{192}^{2} \times C_{121} + N_{145} \times C_{122} + N_{145}^{2} \times C_{123}$$
$$N_{145} = C_{124} + N_{208} \times C_{125} + N_{208} \times N_{244} \times C_{126} + N_{208}^{2} \times C_{127} + N_{244} \times C_{128} + N_{244}^{2} \times C_{129}$$
$$N_{192} = C_{130} + N_{246} \times C_{131} + N_{246} \times N_{262} \times C_{132} + N_{246}^{2} \times C_{133} + N_{262} \times C_{134}$$
$$N_{67} = C_{135} + N_{267} \times C_{136} + N_{267} \times N_{80} \times C_{137} + N_{80} \times C_{138} + N_{80}^{2} \times C_{139}$$
$$N_{80} = C_{140} + N_{139} \times N_{150} \times C_{141} + N_{139}^{2} \times C_{142} + N_{150} \times C_{143}$$
$$N_{150} = C_{144} + N_{261} \times C_{145} + N_{261} \times N_{185} \times C_{146} + N_{261}^{2} \times C_{147} + N_{185} \times C_{148}$$
$$N_{185} = C_{149} + N_{248} \times C_{150} + N_{248} \times N_{262} \times C_{151} + N_{248}^{2} \times C_{152} + N_{262} \times C_{153}$$
$$N_{139} = C_{154} + N_{188} \times C_{155} + N_{188} \times N_{208} \times C_{156} + N_{188}^{2} \times C_{157} + N_{208} \times C_{158} + N_{208}^{2} \times C_{159}$$
$$N_{188} = C_{160} + N_{250} \times C_{161} + N_{250} \times N_{260} \times C_{162} + N_{250}^{2} \times C_{163} + N_{260} \times C_{164} + N_{260}^{2} \times C_{165}$$
$$N_{122} = C_{166} + N_{149} \times N_{152} \times C_{167} + N_{149}^{2} \times C_{168} + N_{152} \times C_{169} + N_{152}^{2} \times C_{170}$$
$$N_{152} = C_{171} + N_{203} \times C_{172} + N_{203} \times N_{244} \times C_{173} + N_{203}^{2} \times C_{174} + N_{244} \times C_{175} + N_{244}^{2} \times C_{176}$$
$$N_{244} = C_{177} + T \times N_{262} \times C_{178} + T^{2} \times C_{179} + N_{262} \times C_{180}$$
$$N_{203} = C_{181} + N_{237} \times C_{182} + N_{237} \times N_{267} \times C_{183} + N_{237}^{2} \times C_{184} + N_{267} \times C_{185} + N_{267}^{2} \times C_{186}$$
$$N_{267} = C_{187} + N_{268} \times C_{188}$$
$$N_{149} = C_{189} + N_{181} \times C_{190} + N_{181} \times N_{208} \times C_{191} + N_{181}^{2} \times C_{192} + N_{208} \times C_{193} + N_{208}^{2} \times C_{194}$$
$$N_{181} = C_{195} + N_{246} \times C_{196} + N_{246} \times N_{250} \times C_{197} + N_{246}^{2} \times C_{198} + N_{250} \times C_{199} + N_{250}^{2} \times C_{200}$$
$$N_{246} = C_{201} + N_{259} \times C_{202} + N_{259} \times N_{268} \times C_{203} + N_{259}^{2} \times C_{204} + N_{268} \times C_{205} + N_{268}^{2} \times C_{206}$$
$$N_{58} = C_{207} + N_{72} \times C_{208} + N_{72} \times N_{99} \times C_{209} + N_{72}^{2} \times C_{210} + N_{99} \times C_{211} + N_{99}^{2} \times C_{212}$$
$$N_{99} = C_{213} + N_{265} \times C_{214} + N_{265} \times N_{134} \times C_{215} + N_{265}^{2} \times C_{216} + N_{134} \times C_{217} + N_{134}^{2} \times C_{218}$$
$$N_{134} = C_{219} + N_{186} \times C_{220} + N_{186} \times N_{200} \times C_{221} + N_{186}^{2} \times C_{222} + N_{200}^{2} \times C_{223}$$
$$N_{186} = C_{224} + N_{248} \times N_{250} \times C_{225} + N_{248}^{2} \times C_{226} + N_{250} \times C_{227} + N_{250}^{2} \times C_{228}$$
$$N_{265} = C_{229} + N_{266} \times C_{230}$$
$$N_{266} = C_{231} + d \times C_{232} + d \times N_{268} \times C_{233} + d^{2} \times C_{234} + N_{268} \times C_{235}$$
$$N_{72} = C_{236} + N_{248} \times C_{237} + N_{248} \times N_{131} \times C_{238} + N_{248}^{2} \times C_{239} + N_{131} \times C_{240} + N_{131}^{2} \times C_{241}$$
$$N_{131} = C_{242} + N_{200} \times C_{243} + N_{200} \times N_{221} \times C_{244} + N_{200}^{2} \times C_{245} + N_{221} \times C_{246} + N_{221}^{2} \times C_{247}$$
$$N_{221} = C_{248} + T \times C_{249} + T \times N_{245} \times C_{250} + N_{245} \times C_{251} + N_{245}^{2} \times C_{252}$$
$$N_{94} = C_{253} + N_{208} \times C_{254} + N_{208} \times N_{161} \times C_{255} + N_{208}^{2} \times C_{256} + N_{161} \times C_{257} + N_{161}^{2} \times C_{258}$$
$$N_{161} = C_{259} + N_{195} \times C_{260} + N_{195} \times N_{211} \times C_{261} + N_{195}^{2} \times C_{262} + N_{211} \times C_{263} + N_{211}^{2} \times C_{264}$$
$$N_{211} = C_{265} + N_{245} \times C_{266} + N_{245} \times N_{262} \times C_{267} + N_{245}^{2} \times C_{268} + N_{262} \times C_{269} + N_{262}^{2} \times C_{270}$$
$$N_{245} = C_{271} + \phi \times C_{272} + \phi \times N_{259} \times C_{273} + \phi^{2} \times C_{274} + N_{259} \times C_{275} + N_{259}^{2} \times C_{276}$$
$$N_{195} = C_{277} + N_{249} \times C_{278} + N_{249} \times N_{250} \times C_{279} + N_{249}^{2} \times C_{280} + N_{250} \times C_{281}$$
$$N_{208} = C_{282} + \phi \times C_{283} + \phi \times N_{248} \times C_{284} + N_{248} \times C_{285} + N_{248}^{2} \times C_{286}$$
$$N_{248} = C_{287} + T \times C_{288} + T \times N_{259} \times C_{289} + N_{259}^{2} \times C_{290}$$
$$N_{44} = C_{291} + N_{231} \times C_{292} + N_{231} \times N_{70} \times C_{293} + N_{70} \times C_{294} + N_{70}^{2} \times C_{295}$$
$$N_{70} = C_{296} + N_{142} \times C_{297} + N_{142} \times N_{157} \times C_{298} + N_{142}^{2} \times C_{299} + N_{157} \times C_{300}$$
$$N_{157} = C_{301} + \phi \times C_{302} + \phi \times N_{183} \times C_{303} + \phi^{2} \times C_{304} + N_{183} \times C_{305} + N_{183}^{2} \times C_{306}$$
$$N_{183} = C_{307} + T \times C_{308} + T \times N_{237} \times C_{309} + T^{2} \times C_{310} + N_{237} \times C_{311} + N_{237}^{2} \times C_{312}$$
$$N_{237} = C_{313} + N_{259} \times N_{264} \times C_{314} + N_{259}^{2} \times C_{315} + N_{264} \times C_{316} + N_{264}^{2} \times C_{317}$$
$$N_{142} = C_{318} + N_{200} \times C_{319} + N_{200} \times N_{230} \times C_{320} + N_{200}^{2} \times C_{321} + N_{230} \times C_{322} + N_{230}^{2} \times C_{323}$$
$$N_{230} = C_{324} + \phi \times C_{325} + \phi \times N_{249} \times C_{326} + N_{249} \times C_{327} + N_{249}^{2} \times C_{328}$$
$$N_{200} = C_{329} + N_{250} \times C_{330} + N_{250} \times N_{262} \times C_{331} + N_{250}^{2} \times C_{332} + N_{262} \times C_{333} + N_{262}^{2} \times C_{334}$$
$$N_{262} = C_{335} + \phi \times C_{336} + \phi \times N_{264} \times C_{337} + \phi^{2} \times C_{338} + N_{264} \times C_{339} + N_{264}^{2} \times C_{340}$$
$$N_{250} = C_{341} + T \times N_{264} \times C_{342} + T^{2} \times C_{343} + N_{264} \times C_{344} + N_{264}^{2} \times C_{345}$$
$$N_{231} = C_{345} + N_{242} \times C_{346} + N_{242}^{2} \times C_{347} + N_{256} \times C_{348} + N_{256}^{2} \times C_{349}$$
$$N_{256} = C_{350} + N_{261} \times C_{351} + N_{261} \times N_{263} \times C_{352} + N_{261}^{2} \times C_{353} + N_{263} \times C_{354} + N_{263}^{2} \times C_{355}$$
$$N_{263} = C_{356} + N_{264} \times C_{357} + N_{264}^{2} \times C_{358}$$
$$N_{261} = C_{359} + \phi \times C_{360} + \phi \times N_{268} \times C_{361} + \phi^{2} \times C_{362} + N_{268}^{2} \times C_{363}$$
$$N_{242} = C_{364} + N_{249} \times C_{365} + N_{249} \times N_{260} \times C_{366} + N_{249}^{2} \times C_{367} + N_{260} \times C_{368}$$
$$N_{260} = C_{369} + N_{264} \times C_{370} + N_{264} \times N_{268} \times C_{371} + N_{268} \times C_{372}$$
$$N_{268} = C_{373} + d \times C_{374} + d^{2} \times C_{375} + T \times C_{376} + T^{2} \times C_{377}$$
$$N_{264} = C_{378} + d \times C_{379} + d \times \phi \times C_{380} + \phi \times C_{381} + \phi^{2} \times C_{381}$$
$$N_{249} = C_{382} + d \times C_{383} + d \times N_{259} \times C_{384} + N_{259} \times C_{385} + N_{259}^{2} \times C_{386}$$
$$N_{259} = C_{387} + T \times C_{388} + T \times \phi \times C_{389} + T^{2} \times C_{390} + \phi \times C_{391} + \phi^{2} \times C_{392}$$

Coefficients of the obtained equation by GMDH method

Coefficients

\(C_{1} = 0.000101888\)

\(C_{80} = 0.32448\)

\(C_{159} = 0.923782\)

\(C_{238} = 1.68764\)

\(C_{317} = - 0.0766093\)

\(C_{2} = - 0.104363\)

\(C_{81} = - 0.0101023\)

\(C_{160} = - 0.310685\)

\(C_{239} = - 0.824382\)

\(C_{317} = 0.412828\)

\(C_{3} = 1.1043\)

\(C_{82} = 0.987692\)

\(C_{161} = 0.260203\)

\(C_{240} = 1.45509\)

\(C_{318} = - 1.21146\)

\(C_{4} = 0.00463424\)

\(C_{83} = - 1.70049\)

\(C_{162} = 0.624555\)

\(C_{241} = - 0.861041\).

\(C_{319} = 0.63831\)

\(C_{5} = 1.04623\)

\(C_{84} = 0.744123\)

\(C_{163} = - 0.0902504\)

\(C_{242} = - 0.097183\)

\(C_{320} = 0.610329\)

\(C_{6} = - 0.531208\)

\(C_{85} = 0.957725\)

\(C_{164} = 1.12765\)

\(C_{243} = 0.323261\)

\(C_{321} = 0.571365\)

\(C_{7} = 0.99823\)

\(C_{86} = - 0.0479503\)

\(C_{165} = - 0.599282\)

\(C_{244} = - 1.28326\)

\(C_{322} = - 0.0619646\)

\(C_{8} = - 0.514871\)

\(C_{87} = 0.391676\)

\(C_{166} = - 0.0103216\)

\(C_{245} = 0.688311\)

\(C_{323} = 0.11707\)

\(C_{9} = - 0.00646188\)

\(C_{88} = - 0.90133\)

\(C_{167} = - 5.54871\)

\(C_{246} = 0.717225\)

\(C_{324} = - 0.0375356\)

\(C_{10} = 0.128504\)

\(C_{89} = 0.511768\)

\(C_{168} = 2.86973\)

\(C_{247} = 0.590964\)

\(C_{325} = 0.890901\)

\(C_{11} = - 3.91669\)

\(C_{90} = 0.612542\)

\(C_{169} = 0.977683\)

\(C_{248} = 0.852946\)

\(C_{326} = 0.042659\)

\(C_{12} = 1.92619\)

\(C_{91} = 0.387747\)

\(C_{170} = 2.68169\)

\(C_{249} = - 0.0191443\)

\(C_{327} = 0.0252424\)

\(C_{13} = 0.873272\)

\(C_{92} = - 0.00106979\)

\(C_{171} = - 0.0425017\)

\(C_{250} = 0.00977775\)

\(C_{328} = 0.678075\)

\(C_{14} = 1.99033\)

\(C_{93} = 0.779169\)

\(C_{172} = 0.413557\)

\(C_{251} = 0.468342\)

\(C_{329} = - 0.4052\)

\(C_{15} = - 0.00809587\)

\(C_{94} = 0.221451\)

\(C_{173} = - 0.447145\)

\(C_{252} = 0.0315315\)

\(C_{330} = 0.235947\)

\(C_{16} = - 0.0809861\)

\(C_{95} = 0.0418163\)

\(C_{174} = 0.280708\)

\(C_{253} = - 0.0260565\)

\(C_{331} = 0.289462\)

\(C_{17} = - 2.10663\)

\(C_{96} = - 1.29629\)

\(C_{175} = 0.614762\)

\(C_{254} = 0.242299\)

\(C_{332} = 0.15575\)

\(C_{18} = 1.06011\)

\(C_{97} = 0.236447\)

\(C_{176} = 0.161212\)

\(C_{255} = - 2.78707\)

\(C_{333} = 0.0172626\)

\(C_{19} = 1.08355\)

\(C_{98} = 2.26046\)

\(C_{177} = - 0.49192\)

\(C_{256} = 1.42008\)

\(C_{334} = 0.0896242\)

\(C_{20} = 1.04624\)

\(C_{99} = - 0.232186\)

\(C_{178} = - 0.0324272\)

\(C_{257} = 0.732819\)

\(C_{335} = - 0.208469\)

\(C_{21} = 0.00216347\)

\(C_{100} = - 0.018728\)

\(C_{179} = 0.000238997\)

\(C_{258} = 1.37033\)

\(C_{336} = 0.0367259\)

\(C_{22} = - 0.143179\)

\(C_{101} = 0.533494\)

\(C_{180} = 2.24323\)

\(C_{259} = - 0.0650417\)

\(C_{337} = 0.762861\)

\(C_{23} = - 6.2984\)

\(C_{102} = - 0.626631\)

\(C_{181} = - 0.552325\)

\(C_{260} = 1.11735\)

\(C_{338} = 0.264463\)

\(C_{24} = 3.13806\)

\(C_{103} = 0.363571\)

\(C_{182} = 1.16034\)

\(C_{261} = - 1.88654\)

\(C_{339} = - 0.221862\)

\(C_{25} = 1.13621\)

\(C_{104} = 0.472318\)

\(C_{183} = - 0.152476\)

\(C_{262} = 0.78825\)

\(C_{340} = - 0.0320775\)

\(C_{26} = 3.16102\)

\(C_{105} = 0.260478\)

\(C_{184} = 0.0324099\)

\(C_{263} = - 0.101772\)

\(C_{341} = 0.000232005\)

\(C_{27} = 0.00247886\)

\(C_{106} = 0.0519918\)

\(C_{185} = 0.549564\)

\(C_{264} = 1.09707\)

\(C_{342} = 1.94197\)

\(C_{28} = - 0.0939245\)

\(C_{107} = 1.49051\)

\(C_{186} = - 0.0801751\)

\(C_{265} = - 0.0669819\)

\(C_{343} = 0.0482569\)

\(C_{29} = - 0.0377296\)

\(C_{108} = 1.55086\)

\(C_{187} = - 2.45511e - 14\)

\(C_{266} = 0.625866\)

\(C_{344} = - 0.0334375\)

\(C_{30} = 1.09146\)

\(C_{109} = - 0.824814\)

\(C_{188} = 1\)

\(C_{267} = - 0.116374\)

\(C_{345} = 0.730331\)

\(C_{31} = 0.0380288\)

\(C_{110} = - 0.513527\)

\(C_{189} = - 0.0610567\)

\(C_{268} = 0.0851122\)

\(C_{346} = 0.0120662\)

\(C_{32} = - 0.0017645\)

\(C_{111} = - 0.723331\)

\(C_{190} = 0.479185\)

\(C_{269} = 0.430256\)

\(C_{347} = 0.300318\)

\(C_{33} = - 0.30608\)

\(C_{112} = - 0.0757237\)

\(C_{191} = - 2.51891\)

\(C_{270} = 0.0168316\)

\(C_{348} = - 0.0158454\)

\(C_{34} = 0.305889\)

\(C_{113} = 0.33649\)

\(C_{192} = 1.26255\)

\(C_{271} = 0.419449\)

\(C_{349} = 0.824475\)

\(C_{35} = 1.00155\)

\(C_{114} = - 1.01818\)

\(C_{193} = 0.531526\)

\(C_{272} = 0.071373\)

\(C_{350} = - 0.566858\)

\(C_{36} = 0.00811031\)

\(C_{115} = 0.562486\)

\(C_{194} = 1.25509\)

\(C_{273} = - 0.0764732\)

\(C_{351} = - 0.909547\)

\(C_{37} = - 0.316121\)

\(C_{116} = 0.690939\)

\(C_{195} = - 0.105501\)

\(C_{274} = 0.0118725\)

\(C_{352} = 0.719353\)

\(C_{38} = 0.000890796\)

\(C_{117} = 0.453357\)

\(C_{196} = 0.198447\)

\(C_{274} = 0.381078\)

\(C_{353} = 0.487611\)

\(C_{39} = 1.30864\)

\(C_{118} = 0.0405774\)

\(C_{197} = - 1.24815\)

\(C_{275} = 0.170643\)

\(C_{354} = 0.361379\)

\(C_{40} = - 0.00516988\)

\(C_{119} = - 0.682972\)

\(C_{198} = 0.801712\)

\(C_{276} = - 0.0357515\)

\(C_{355} = 0.272552\)

\(C_{41} = 0.726952\)

\(C_{120} = - 1.62233\)

\(C_{199} = 0.88487\)

\(C_{277} = 0.0644234\)

\(C_{356} = 0.699117\)

\(C_{42} = - 0.000557343\)

\(C_{121} = 1.0593\)

\(C_{200} = 0.430317\)

\(C_{278} = - 0.282133\)

\(C_{357} = 0.0501148\)

\(C_{43} = 0.277787\)

\(C_{122} = 1.62942\)

\(C_{201} = 0.203653\)

\(C_{279} = 0.272918\)

\(C_{358} = 1.38111\)

\(C_{44} = - 0.0168319\)

\(C_{123} = 0.569825\)

\(C_{202} = 0.390366\)

\(C_{280} = 0.978144\)

\(C_{359} = - 0.808385\)

\(C_{45} = 0.589344\)

\(C_{124} = - 0.0598019\)

\(C_{203} = - 0.0620935\)

\(C_{281} = - 0.268777\)

\(C_{360} = 0.242336\)

\(C_{46} = - 0.00180901\)

\(C_{125} = 0.487272\)

\(C_{204} = 0.123453\)

\(C_{282} = 0.15697\)

\(C_{361} = 0.0762794\)

\(C_{47} = 0.42606\)

\(C_{126} = - 0.587585\)

\(C_{205} = 0.498564\)

\(C_{283} = - 0.0671583\)

\(C_{362} = - 0.056754\)

\(C_{48} = - 0.461869\)

\(C_{127} = 0.339163\)

\(C_{206} = - 0.11775\)

\(C_{284} = 1.05643\)

\(C_{363} = - 0.117619\)

\(C_{49} = 1.10123\)

\(C_{128} = 0.548575\)

\(C_{207} = - 0.0181805\)

\(C_{285} = 0.053207\)

\(C_{364} = 0.627658\)

\(C_{50} = - 0.102883\)

\(C_{129} = 0.2424\)

\(C_{208} = 0.7355\)

\(C_{286} = 1.17291\)

\(C_{365} = - 0.171821\)

\(C_{51} = 0.0212951\)

\(C_{130} = - 0.244032\)

\(C_{209} = - 3.08926\)

\(C_{287} = - 0.0122227\)

\(C_{366} = 0.14714\)

\(C_{52} = 0.437009\)

\(C_{131} = 0.67702\)

\(C_{210} = 1.5533\)

\(C_{288} = 0.00508263\)

\(C_{367} = 0.506213\)

\(C_{53} = - 0.0565768\)

\(C_{132} = - 0.153503\)

\(C_{211} = 0.270941\)

\(C_{289} = 0.131202\)

\(C_{368} = 1.32753\)

\(C_{54} = - 1.85921\)

\(C_{133} = 0.104918\)

\(C_{212} = 1.53537\)

\(C_{290} = 0.0458628\)

\(C_{369} = - 0.584228\)

\(C_{55} = 0.0123188\)

\(C_{134} = 0.57586\)

\(C_{213} = - 0.247\)

\(C_{291} = - 0.427528\)

\(C_{370} = 0.630552\)

\(C_{56} = - 0.0853752\)

\(C_{135} = - 0.177773\)

\(C_{214} = 0.278115\)

\(C_{292} = 0.121753\)

\(C_{371} = - 0.436317\)

\(C_{57} = 0.00181104\)

\(C_{136} = 0.120413\)

\(C_{215} = - 0.0257353\)

\(C_{293} = 1.38838\)

\(C_{372} = 0.665559\)

\(C_{58} = 3.40407\)

\(C_{137} = - 0.071364\)

\(C_{216} = - 0.046605\)

\(C_{294} = - 0.116904\)

\(C_{373} = 0.034239\)

\(C_{59} = 0.0106707\)

\(C_{138} = 1.09733\)

\(C_{217} = 0.994315\)

\(C_{295} = - 0.00686398\)

\(C_{374} = 0.00141164\)

\(C_{60} = 0.0504876\)

\(C_{139} = 0.0115078\)

\(C_{218} = 0.00778078\)

\(C_{296} = - 0.229736\)

\(C_{375} = 0.0299823\)

\(C_{61} = - 0.466463\)

\(C_{140} = - 0.0153086\)

\(C_{219} = - 0.0172416\)

\(C_{297} = - 0.402938\)

\(C_{376} = - 0.00100677\)

\(C_{62} = 1.59557\)

\(C_{141} = - 0.379483\)

\(C_{220} = 0.968397\)

\(C_{298} = 0.402785\)

\(C_{377} = 0.684088\)

\(C_{63} = - 0.753976\)

\(C_{142} = 0.378227\)

\(C_{221} = - 3.23657\)

\(C_{299} = 1.23205\)

\(C_{378} = 0.0156286\)

\(C_{64} = 1.45038\)

\(C_{143} = 1.01053\)

\(C_{222} = 1.51645\)

\(C_{300} = - 0.167241\)

\(C_{379} = - 0.0408816\)

\(C_{65} = - 0.840024\)

\(C_{144} = - 0.0770843\)

\(C_{223} = 1.72465\)

\(C_{301} = 0.129632\)

\(C_{380} = 0.709821\)

\(C_{66} = - 0.0027522\)

\(C_{145} = - 0.334871\)

\(C_{224} = - 0.11309\)

\(C_{302} = - 0.0204449\)

\(C_{381} = 0.106289\)

\(C_{67} = 0.645283\)

\(C_{146} = - 0.0572972\)

\(C_{225} = - 0.839589\)

\(C_{303} = - 0.00991822\)

\(C_{382} = 0.294528\)

\(C_{68} = 0.357355\)

\(C_{147} = 0.0424457\)

\(C_{226} = 0.611569\)

\(C_{304} = 1.02603\)

\(C_{383} = 0.00952678\)

\(C_{69} = - 0.000333621\)

\(C_{148} = 1.42441\)

\(C_{227} = 1.11301\)

\(C_{305} = 0.0175164\)

\(C_{384} = - 0.0136107\)

\(C_{70} = 0.0509705\)

\(C_{149} = - 0.300251\)

\(C_{228} = 0.206438\)

\(C_{306} = 0.97181\)

\(C_{385} = 0.701702\)

\(C_{71} = - 0.432526\)

\(C_{150} = 0.661217\)

\(C_{229} = 5.20337e - 14\)

\(C_{307} = - 0.0310457\)

\(C_{386} = 0.11125\)

\(C_{72} = 0.59224\)

\(C_{151} = - 0.164548\)

\(C_{230} = 1\)

\(C_{308} = 0.00691791\)

\(C_{387} = 2.0898\)

\(C_{73} = - 0.237332\)

\(C_{152} = 0.108739\)

\(C_{231} = 0.6293\)

\(C_{309} = 0.000166844\)

\(C_{388} = - 0.06113\)

\(C_{74} = 1.39527\)

\(C_{153} = 0.641363\)

\(C_{232} = - 0.00699525\)

\(C_{310} = 0.61922\)

\(C_{389} = - 0.0125589\)

\(C_{75} = - 0.350248\)

\(C_{154} = - 0.0471113\)

\(C_{233} = 0.0286574\)

\(C_{311} = 0.0230335\)

\(C_{390} = 0.000781484\)

\(C_{76} = - 0.0142586\)

\(C_{155} = 0.530502\)

\(C_{234} = - 0.000789405\)

\(C_{312} = 0.425166\)

\(C_{391} = 0.16349\)

\(C_{77} = - 0.89183\)

\(C_{156} = - 1.85411\)

\(C_{235} = 0.273677\)

\(C_{313} = - 0.229788\)

\(C_{392} = 0.0882907\)

\(C_{78} = 0.56676\)

\(C_{157} = 0.930771\)

\(C_{236} = 0.0548395\)

\(C_{314} = 0.248291\)

 

\(C_{79} = 1.00512\)

\(C_{158} = 0.468605\)

\(C_{237} = - 0.472487\)

\(C_{315} = 0.467571\)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ahmadi, M.H., Mohseni-Gharyehsafa, B., Ghazvini, M. et al. Comparing various machine learning approaches in modeling the dynamic viscosity of CuO/water nanofluid. J Therm Anal Calorim 139, 2585–2599 (2020). https://doi.org/10.1007/s10973-019-08762-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10973-019-08762-z

Keywords

Navigation