Skip to main content
Log in

ANN modeling, cost performance and sensitivity analyzing of thermal conductivity of DWCNT–SiO2/EG hybrid nanofluid for higher heat transfer

An experimental study

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

Abstract

In this study, the thermal conductivity of SiO2–DWCNT/ethylene glycol hybrid nanofluid has been experimentally investigated on 0.03–1.71% solid volume fraction and temperatures from 30 to 50 °C. SiO2 and DWCNT’s nanoparticles dispersed in EG as base fluid, and its thermal conductivity was measured. The thermal conductivity was obtained 38% more than ethylene glycol thermal conductivity at some temperatures. A new correlation (R 2 = 0.9925) was proposed to predict experimental thermal conductivity ratio as a function of volume concentration and temperature. Also an artificial neural network was designed for thermal conductivity ratio data predicting. The best artificial neural network topology has two hidden layers with five neurons in each layer. Comparing the experimental thermal conductivity ratio with artificial neural network outputs and the correlation shows the high capacity and accuracy of artificial neural network in thermal conductivity ratio data predicting.

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
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

Abbreviations

T :

Temperature (°C)

W :

Mass (gr)

K :

Thermal conductivity (W m−1 °C−1)

ρ :

Density (kg m−3)

φ :

Particle volume fraction

nf:

Nanofluid

bf:

Base fluid

References

  1. Maxwell JC. A treatise on electricity and magnetism Dover Publications. Oxford: Clarendon press; 1954.

    Google Scholar 

  2. Li Q, Xuan Y, Wang J. Investigation on convective heat transfer and flow features of nanofluids. J Heat Transf. 2003;125:151–5.

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  5. 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 

  6. Li H, Wang L, He Y, Hu Y, Zhu J, Jiang B. Experimental investigation of thermal conductivity and viscosity of ethylene glycol based ZnO nanofluids. Appl Therm Eng. 2015;88:363–8.

    Article  CAS  Google Scholar 

  7. Hemmat Esfe M, Saedodin S, Mahian O, Wongwises S. Efficiency of ferromagnetic nanoparticles suspended in ethylene glycol for applications in energy devices: effects of particle size, temperature, and concentration. Int Commun Heat Mass Transf. 2014;58:138–46. doi:10.1016/j.icheatmasstransfer.2014.08.035.

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  9. Esfe MH, Hajmohammad H, Toghraie D, Rostamian H, Mahian O, Wongwises S. Multi-objective optimization of nanofluid flow in double tube heat exchangers for applications in energy systems. Energy. 2017. doi:10.1016/j.energy.2017.06.104.

  10. Meibodi SS, Kianifar A, Mahian O, Wongwises S. Second law analysis of a nanofluid-based solar collector using experimental data. J Therm Anal Calorim. 2016;126(2):617–25.

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

  13. Behrangzade A, Heyhat MM. The effect of using nano-silver dispersed water based nanofluid as a passive method for energy efficiency enhancement in a plate heat exchanger. Appl Therm Eng. 2016;102:311–7.

    Article  CAS  Google Scholar 

  14. Oliveira GA, Contreras EM, Bandarra Filho EP. Experimental study on the heat transfer of MWCNT/water nanofluid flowing in a car radiator. Appl Therm Eng. 2017;111:1450–6.

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  16. Bahrami M, Akbari M, Karimipour A, Afrand M. An experimental study on rheological behavior of hybrid nanofluids made of iron and copper oxide in a binary mixture of water and ethylene glycol: non-Newtonian behavior. Exp Therm Fluid Sci. 2016;79:231–7.

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  19. Amiri M, Movahedirad S, Manteghi F. Thermal conductivity of water and ethylene glycol nanofluids containing new modified surface SiO2–Cu nanoparticles: experimental and modeling. Appl Therm Eng. 2016;108:48–53.

    Article  CAS  Google Scholar 

  20. Abdolbaqi MK, Azmi WH, Mamat R, Sharma KV, Najafi G. Experimental investigation of thermal conductivity and electrical conductivity of BioGlycol–water mixture based Al2O3 nanofluid. Appl Therm Eng. 2016;102:932–41.

    Article  CAS  Google Scholar 

  21. Agarwal R, Verma K, Agrawal NK, Duchaniya RK, Singh R. Synthesis, characterization, thermal conductivity and sensitivity of CuO nanofluids. Appl Therm Eng. 2016;102:1024–36.

    Article  CAS  Google Scholar 

  22. Esfahani MA, Toghraie D. Experimental investigation for developing a new model for the thermal conductivity of Silica/Water–Ethylene glycol (40–60%) nanofluid at different temperatures and solid volume fractions. J Mol Liquids. 2017;232:105–12.

    Article  CAS  Google Scholar 

  23. Hemmat Esfe M, Saedodin S, Yan WM, 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(1):455–60.

    Article  CAS  Google Scholar 

  24. Teng TP, Cheng CM, Cheng CP. Performance assessment of heat storage by phase change materials containing MWCNTs and graphite. Appl Therm Eng. 2013;50(1):637–44.

    Article  CAS  Google Scholar 

  25. Huang D, Wu Z, Sunden B. Effects of hybrid nanofluid mixture in plate heat exchangers. Exp Therm Fluid Sci. 2016;72:190–6.

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  27. Hemmat Esfe M, Afrand M, Yan WM, Akbari M. Applicability of artificial neural network and nonlinear regression to predict thermal conductivity modeling of Al2O3–water nanofluids using experimental data. Int Commun Heat Mass Transf. 2015;66:246–9.

    Article  CAS  Google Scholar 

  28. Hemmat Esfe M, Afrand M, Wongwises S, Naderi A, Asadi A, Rostami S, Akbari M. Applications of feedforward multilayer perceptron artificial neural networks and empirical correlation for prediction of thermal conductivity of Mg (OH)2–EG using experimental data. Int Commun Heat Mass Transf. 2015;67:46–50.

    Article  CAS  Google Scholar 

  29. Meybodi MK, Naseri S, Shokrollahi A, Daryasafar A. Prediction of viscosity of water-based Al2O3, TiO2, SiO2, and CuO nanofluids using a reliable approach. Chemom Intell Lab Syst. 2015;149:60–9.

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  34. Hemmat Esfe M, Naderi A, Akbari M, Afrand M, Karimipour A. Evaluation of thermal conductivity of COOH-functionalized MWCNTs/water via temperature and solid volume fraction by using experimental data and ANN methods. J Therm Anal Calorim. 2015;121(3):1273–8.

    Article  CAS  Google Scholar 

  35. Harandi SS, Karimipour A, Afrand M, Akbari M, D’Orazio A. An experimental study on thermal conductivity of F-MWCNTs–Fe3O4/EG hybrid nanofluid: effects of temperature and concentration. Int Commun Heat Mass Transf. 2016;76:171–7.

    Article  Google Scholar 

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

  37. Xing M, Yu J, Wang R. Experimental investigation and modelling on the thermal conductivity of CNTs based nanofluids. Int J Therm Sci. 2016;104:404–11.

    Article  CAS  Google Scholar 

  38. Pang C, Jung JY, Lee JW, Kang YT. Thermal conductivity measurement of methanol-based nanofluids with Al2O3 and SiO2 nanoparticles. Int Commun Heat Mass Transf. 2012;55(21):5597–602.

    Article  CAS  Google Scholar 

  39. Chen L, Xie H, Li Y, Yu W. Nanofluids containing carbon nanotubes treated by mechanochemical reaction. Thermochim Acta. 2008;477(1):21–4.

    Article  CAS  Google Scholar 

  40. Sun C, Bai B, Lu WQ, Liu J. Shear-rate dependent effective thermal conductivity of H2O + SiO2 nanofluids. Phys Fluids. 2013;25(5):052002.

    Article  Google Scholar 

  41. Liu MS, Lin MC, Huang IT, Wang CC. Enhancement of thermal conductivity with carbon nanotube for nanofluids. Int Commun Heat Mass Transf. 2005;32(9):1202–10.

    Article  CAS  Google Scholar 

  42. Angayarkanni SA, Philip J. Effect of nanoparticles aggregation on thermal and electrical conductivities of nanofluids. J Nanofluids. 2014;3(1):17–25.

    Article  CAS  Google Scholar 

  43. Glory J, Bonetti M, Helezen M, Mayne-L’Hermite M, Reynaud C. Thermal and electrical conductivities of water-based nanofluids prepared with long multiwalled carbon nanotubes. J Appl Phys. 2008;103(9):094309.

    Article  Google Scholar 

  44. Hemmat Esfe M, Saedodin S, Akbari M, Karimipour A, Afrand M, Wongwises S, Safaei MR, Dahari M. Experimental investigation and development of new correlations for thermal conductivity of CuO/EG–water nanofluid. Int Commun Heat Mass Transf. 2015;65:47–51.

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  46. Xie H, Yu W, Li Y, Chen L. Discussion on the thermal conductivity enhancement of nanofluids. Nanoscale Res Lett. 2011;6(1):124.

    Article  Google Scholar 

  47. Yu W, Choi SU. The role of interfacial layers in the enhanced thermal conductivity of nanofluids: a renovated Maxwell model. J Nanoparticle Res. 2003;5(1–2):167–71.

    Article  CAS  Google Scholar 

  48. Hamilton RL, Crosser OK. Thermal conductivity of heterogeneous two-component systems. Ind Eng Chem Fund. 1962;1(3):187–91.

    Article  CAS  Google Scholar 

  49. Mahian O, Kianifar A, Wongwises S. Dispersion of ZnO nanoparticles in a mixture of ethylene glycol–water, exploration of temperature-dependent density, and sensitivity analysis. J Clust Sci.

  50. Rostamian H, Lotfollahi MN. New functionality for energy parameter of Redlich–Kwong equation of state for density calculation of pure carbon dioxide and ethane in liquid, vapor and supercritical phases. Periodica Polytech, Chem Eng. 2016;60(2):93.

    Google Scholar 

  51. Rostamian H, Lotfollahi MN. A New simple equation of state for calculating solubility of solids in supercritical carbon dioxide. Periodica Polytech, Chem Eng. 2015;59(3):174.

    Article  Google Scholar 

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

    Article  Google Scholar 

  53. Esfe MH, Esfandeh S, Rejvani M. Modeling of thermal conductivity of MWCNT-SiO2 (30:70%)/EG hybrid nanofluid, sensitivity analyzing and cost performance for industrial applications. J Therm Anal Calorim. 2017. doi:10.1007/s10973-017-6680-y.

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

    Article  CAS  Google Scholar 

  55. Nadooshan AA, Esfe MH, Afrand M. Prediction of rheological behavior of SiO2-MWCNTs/10W40 hybrid nanolubricant by designing neural network. J Therm Anal Calorim. 2017. doi:10.1007/s10973-017-6688-3.

  56. Esfe MH, 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(2):837–43.

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  60. Esfe MH, Yan WM, Afrand M, Sarraf M, Toghraie D, Dahari M. Estimation of thermal conductivity of Al2O3/water (40%)–ethylene glycol (60%) by artificial neural network and correlation using experimental data. Int Commun Heat Mass Transf. 2016;74:125–28.

    Article  Google Scholar 

  61. Esfe MH, Motahari K, Sanatizadeh E, Afrand M, Rostamian H, Ahangar MRH. Estimation of thermal conductivity of CNTs-water in low temperature by artificial neural network and correlation. Int Commun Heat Mass Transf. 2016;76:376–81.

    Article  Google Scholar 

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

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mohammad Hemmat Esfe.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hemmat Esfe, M., Abbasian Arani, A.A., Shafiei Badi, R. et al. ANN modeling, cost performance and sensitivity analyzing of thermal conductivity of DWCNT–SiO2/EG hybrid nanofluid for higher heat transfer. J Therm Anal Calorim 131, 2381–2393 (2018). https://doi.org/10.1007/s10973-017-6744-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10973-017-6744-z

Keywords

Navigation