Skip to main content
SpringerLink
Log in

Modeling of thermal conductivity of MWCNT-SiO2 (30:70%)/EG hybrid nanofluid, sensitivity analyzing and cost performance for industrial applications

An experimental based study

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

Abstract

In the present study, the thermal conductivity of SiO2-MWCNT/EG hybrid nanofluid has been investigated experimentally at solid volume fraction range from 0.025 to 0.86% and temperatures range from 30 to 50 °C. SiO2 particles and multi wall carbon nanotubes (MWCNTs) dispersed with the ratio of 70:30% by mass in ethylene glycol (EG) as the base fluid. The thermal conductivity ratio of mentioned hybrid nanofluid increased to 20.1% more than EG thermal conductivity at 50 °C and the solid volume fraction of 0.86%. Also in the present study, a new correlation was proposed to predict experimental TCR (thermal conductivity ratio) based on the solid volume fraction and the temperature. The R-squared for the proposed correlation is equal to 0.9864. The sensitivity of nanofluid’s thermal conductivity was increased with temperature and solid volume fraction increasing. Also, an ANN was designed for TCR data modeling and forecasting. The most optimal topology was an ANN contains two hidden layers and four neurons in each hidden layer. The R-squared, MSE, and AARD for proposed ANN are equal to 0.9989, 6.8344e−06, and 0.0105, respectively. The results indicated that the neural network is stronger than the correlation in the estimating and predicting experimental thermal conductivity ratio.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

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

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. Xing M, Yu J, Wang R. Thermo-physical properties of water-based single-walled carbon nanotube nanofluid as advanced coolant. Appl Therm Eng. 2015;87:344–51.

    Article  CAS  Google Scholar 

  2. Goudarzi K, Shojaeizadeh E, Nejati F. An experimental investigation on the simultaneous effect of CuO–H2O nanofluid and receiver helical pipe on the thermal efficiency of a cylindrical solar collector. Appl Therm Eng. 2014;73:1236–43.

    Article  CAS  Google Scholar 

  3. Halelfadl S, Adham AM, Mohd-Ghazali N, Maré T, Estellé P, Ahmad R. Optimization of thermal performances and pressure drop of rectangular microchannel heat sink using aqueous carbon nanotubes based nanofluid. Appl Therm Eng. 2014;62:492–9.

    Article  CAS  Google Scholar 

  4. Moghaddam MA, Motahari K. Experimental investigation, sensitivity analysis and modeling of rheological behavior of MWCNT-CuO (30–70)/SAE40 hybrid nano-lubricant. Appl Therm Eng. 2017;123:1419–33.

    Article  CAS  Google Scholar 

  5. Motahari K, Abdollahi Moghaddam M, Moradian M. Experimental investigation and development of new correlation for influences of temperature and concentration on dynamic viscosity of MWCNT-SiO2 (20–80)/20W50 hybrid nano-lubricant. Chin J Chem Eng. 2017. doi:10.1016/j.cjche.2017.06.011.

    Google Scholar 

  6. Esfe MH, 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 Comm Heat Mass Transf. 2016;75:192–6.

    Article  Google Scholar 

  7. Esfe MH, 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;125:673–85.

    Article  Google Scholar 

  8. 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. doi:10.1007/s10973-016-5469-8.

    Article  CAS  Google Scholar 

  9. 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 

  10. Esfe MH, 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  Google Scholar 

  11. 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;118:287–94. doi:10.1007/s10973-014-4002-1.

    Article  CAS  Google Scholar 

  12. 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 Liq. 2017;232:105–12.

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  14. Hemmat M, Saedodin S, Rejvani M, Shahram J. Experimental investigation, model development and sensitivity analysis of rheological behavior of ZnO/10W40 nano-lubricants for automotive applications. Phys E Low-Dimens Syst Nanostruct. 2017;90:194–203. doi:10.1016/j.physe.2017.02.015.

    Article  Google Scholar 

  15. Vajjha RS, Das DK, Chukwu GA. An experimental determination of the viscosity of propylene glycol/water based nanofluids and development of new correlations. J Fluids Eng Am Soc Mech Eng. 2015;137:81201.

    Article  Google Scholar 

  16. Esfe MH. The investigation of effects of temperature and nanoparticles volume fraction on the viscosity of copper oxide-ethylene glycol nanofluids. Period Polytech Chem Eng. 2017. doi:10.3311/PPch.9741.

  17. 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 

  18. 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 

  19. Mena JB, Ubices De Moraes AA, Benito YR, Ribatski G. Parise JAR. Extrapolation of Al2O3-water nanofluid viscosity for temperatures and volume concentrations beyond the range of validity of existing correlations. Appl Therm Eng. 2013;51:1092–7.

    Article  CAS  Google Scholar 

  20. Hemmat Esfe M, Wongwises S, Rejvani M. Prediction of Thermal Conductivity of Carbon Nanotube-EG Nanofluid Using Experimental Data by ANN. Curr Nanosci. 2017;13(3):324–9.

  21. 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 

  22. Hemmat Esfe M, Hassani 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. doi:10.1016/j.icheatmasstransfer.2016.04.002.

    Article  CAS  Google Scholar 

  23. Esfe MH, 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 Comm Heat Mass Transf. 2017;82:154–60.

    Article  Google Scholar 

  24. Colangelo G, Favale E, Miglietta P, Milanese M, de Risi A. Thermal conductivity, viscosity and stability of Al2O3-diathermic oil nanofluids for solar energy systems. Energy. 2016;95:124–36. doi:10.1016/j.energy.2015.11.032.

    Article  CAS  Google Scholar 

  25. Izadi M, Shahmardan MM, Behzadmehr A, Rashidi AM, Amrollahi A. Modeling of effective thermal conductivity and viscosity of carbon structured nanofluid. Transp Phenom Nano Micro Scales. 2015;3:1–13.

    Google Scholar 

  26. 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 

  27. Pang C, Jung J-Y, Lee JW, Kang YT. Thermal conductivity measurement of methanol-based nanofluids with Al2O3 and SiO2 nanoparticles. Int J Heat Mass Transf. 2012;55:5597–602.

    Article  CAS  Google Scholar 

  28. Lee S, Choi SUS, Li S, Eastman JA. Measuring thermal conductivity of fluids containing oxide nanoparticles. J Heat Transfer. 1999;121:280–9.

    Article  CAS  Google Scholar 

  29. Sun C, Bai B, Lu W-Q, Liu J. Shear-rate dependent effective thermal conductivity of H2O+ SiO2 nanofluids. Phys Fluids. 2013;25:52002.

    Article  Google Scholar 

  30. Glory J, Bonetti M, Helezen M, Hermite MM, Reynaud C, Glory J, et al. Thermal and electrical conductivities of water-based nanofluids prepared with long multiwalled carbon nanotubes Thermal and electrical conductivities of water-based nanofluids prepared with long multiwalled carbon nanotubes. J Appl Phys. 2012;94309:94309.

    Google Scholar 

  31. 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 

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

    Article  Google Scholar 

  33. Maxwell JC. A treatise on electricity and magnetism Dover publications. Unabriged Third Ed. Vol. one. Clarendon Press 1954.

  34. Yu W, Choi SUS. The role of interfacial layers in the enhanced thermal conductivity of nanofluids: a renovated Maxwell model. J Nanopart Res. 2003;5:167–71.

    Article  CAS  Google Scholar 

  35. Hamilton R, Crosser O. Thermal conductivity of heterogeneous two-component systems. Ind Eng Chem. 1962;1:187–91.

    CAS  Google Scholar 

  36. 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. 2013;24:1103–14.

    Article  CAS  Google Scholar 

  37. Esfe MH, Arani AAA, 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. doi:10.1016/j.molliq.2017.08.087.

  38. Dehkordi RA, Esfe MH, Afrand M. Effects of functionalized single walled carbon nanotubes on thermal performance of antifreeze: an experimental study on thermal conductivity. Appl Therm Eng. 2017;120:358–66.

    Article  Google Scholar 

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

    Article  Google Scholar 

  40. 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 

  41. 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 

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

    Article  Google Scholar 

  43. Afrand M, Esfe MH, 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. Phys E Low-dimensional Syst Nanostruct. 2017;87:242–7.

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  45. Alirezaie A, Hajmohammad MH, Ahangar MRH, Esfe MH. Price-Performance evaluation of thermal conductivity enhancement of nanofluids with different particle sizes. Appl Therm Eng. 2017. doi:10.1016/j.applthermaleng.2017.08.143.

  46. Esfe MH, Firouzi M, Afrand M. Experimental and theoretical investigation of thermal conductivity of ethylene glycol containing functionalized single walled carbon nanotubes. Phys E Low-dimensional Syst Nanostruct. 2017. doi:10.1016/j.physe.2017.08.017.

  47. Esfe MH, Rostamian H, Shabani-samghabadi A, Arani AAA. Application of three-level general factorial design approach for thermal conductivity of MgO/water nanofluids. Appl Therm Eng. 2017. doi:10.1016/j.applthermaleng.2017.07.211.

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Khomeinishahr Branch, Islamic Azad University, Isfahan, Iran

    Mohammad Hemmat Esfe

  2. Young Researchers and Elite Club, Najafabad Branch, Islamic Azad University, Najafabad, Iran

    Saeed Esfandeh

  3. Young Researchers and Elite Club, Semnan Branch, Islamic Azad University, Semnan, Iran

    Mousa Rejvani

Authors
  1. Mohammad Hemmat Esfe
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Saeed Esfandeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mousa Rejvani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad Hemmat Esfe.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hemmat Esfe, M., 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 131, 1437–1447 (2018). https://doi.org/10.1007/s10973-017-6680-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10973-017-6680-y

Keywords

Search

Navigation