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Thermal conductivity modeling of MgO/EG nanofluids using experimental data and artificial neural network

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Abstract

The application of nanofluids in energy systems is developing day by day. Before using a nanofluid in an energy system, it is necessary to measure the properties of nanofluids. In this paper, first the results of experiments on the thermal conductivity of MgO/ethylene glycol (EG) nanofluids in a temperature range of 25–55 °C and volume concentrations up to 5 % are presented. Different sizes of MgO nanoparticles are selected to disperse in EG, including 20, 40, 50, and 60 nm. Based on the results, an empirical correlation is presented as a function of temperature, volume fraction, and nanoparticle size. Next, the model of thermal conductivity enhancement in terms of volume fraction, particle size, and temperature was developed via neural network based on the measured data. It is observed that neural network can be used as a powerful tool to predict the thermal conductivity of nanofluids.

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References

  1. Lorenzini G, Medici M, Alberto Oliveira Rocha L. Convective analysis of constructal T-shaped fins. J Eng Therm. 2014;23(2):98–104.

    Article  CAS  Google Scholar 

  2. Mahian O, Kianifar A, Kalogirou SA, Pop I, Wongwises S. A review of the applications of nanofluids in solar energy. Int J Heat Mass Transf. 2013;57:582–94.

    Article  CAS  Google Scholar 

  3. Rashidi I, Mahian O, Lorenzini G, Biserni C, Wongwises S. Natural convection of Al2O3/water nanofluid in a square cavity: effects of heterogeneous heating. Int J Heat Mass Transf. 2014;74:391–402.

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  5. Witharana S, Palabiyik I, Musina Z, Ding Y. Stability of glycol nanofluids—the theory and experiment. Powder Technol. 2013;239:72–7.

    Article  CAS  Google Scholar 

  6. Barbés B, Páramo R, Blanco E, Pastoriza-Gallego MJ, Piñeiro MM, Legido JL, Casanova C. Thermal conductivity and specific heat capacity measurements of Al2O3 nanofluids. J Therm Anal Calorim. 2013;11:1615–25.

    Article  Google Scholar 

  7. Hemmat Esfe M, Saedodin S, Mahian O, Wongwises W. Thermal conductivity of Al2O3/water nanofluids: measurement, correlation, sensitivity analysis, and comparisons with literature reports. J Therm Anal Calorim. 2014;. doi:10.1007/s10973-014-3771-x.

    Google Scholar 

  8. Salehi JM, Heyhat MM, Rajabpour A. Enhancement of thermal conductivity of silver nanofluid synthesized by a one-step method with the effect of polyvinylpyrrolidone on thermal behavior. Appl Phys Lett. 2013;102:231907.

    Article  Google Scholar 

  9. Halelfadl S, Maré T, Estellé P. Efficiency of carbon nanotubes water based nanofluids as coolants. Exp Therm Fluid Sci. 2014;53:104–10.

    Article  CAS  Google Scholar 

  10. Yiamsawasd T, Selim Dalkilic A, Wongwises S. Measurement of the thermal conductivity of titania and alumina nanofluids. Thermochim Acta. 2012;545:48–56.

    Article  CAS  Google Scholar 

  11. Longo GA, Zilio C. Experimental measurement of thermophysical properties of oxide–water nano-fluids down to ice-point. Exp Therm Fluid Sci. 2011;35:1313–24.

    Article  CAS  Google Scholar 

  12. Kleinstreuer C, Feng Y. Experimental and theoretical studies of nanofluid thermal conductivity enhancement: a review. Nanoscale Res Lett. 2011;6:229.

    Article  Google Scholar 

  13. Longo GA, Zilio C. Experimental measurements of thermophysical properties of Al2O3– and TiO2–ethylene glycol nanofluids. Int J Thermophys. 2013;34:1288–307.

    Article  CAS  Google Scholar 

  14. Xie H, Yu W, Chen W. MgO nanofluids: higher thermal conductivity and lower viscosity among ethylene glycol-based nanofluids containing oxide nanoparticles. J. Exp Nanosci. 2010;5:463–72.

    Article  CAS  Google Scholar 

  15. 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 

  16. Hemmat Esfe M, Saedodin S, Mahmoodi M. Experimental studies on the convective heat transfer performance and thermophysical properties of MgO-Water nanofluid under turbulent flow. Exp Therm Fluid Sci. 2013;52:68–78.

    Article  Google Scholar 

  17. Kalogirou SA. Applications of artificial neural-networks for energy systems. Appl Energy. 2000;67:17–35.

    Article  Google Scholar 

  18. Papari MM, Yousefi F, Moghadasi J, Karimi H, Campo A. Modeling thermal conductivity augmentation of nanofluids using diffusion neural networks. Int J Therm Sci. 2011;50:44–52.

    Article  CAS  Google Scholar 

  19. Hojjat M, Etemad SGh, Bagheri R, Thibault J. Thermal conductivity of non-Newtonian nanofluids: experimental data and modeling using neural network. Int J Heat Mass Transf. 2011;54:1017–23.

    Article  CAS  Google Scholar 

  20. Longon GA, Zilio C, Ceseracciu E, Reggiani M. Application of artificial neural network (ANN) for the prediction of thermal conductivity of oxide–water nanofluids. Nano Energy. 2012;1:290–6.

    Article  Google Scholar 

  21. Yoo DH, Hong KS, Yang HS. Study of thermal conductivity of nanofluids for the application of heat transfer fluids. Thermochim Acta. 2007;455:66–9.

    Article  CAS  Google Scholar 

  22. Challoner AR, Powell RW. Thermal conductivities of liquids: new determinations for seven liquids and appraisal of existing values. Proceedings Royal Society London A. 1956;238:90–106.

    Article  CAS  Google Scholar 

  23. Kurt H, Kayfeci M. Prediction of thermal conductivity of ethylene glycol water solutions by using artificial neural networks. Appl Energy. 2009;86:2244–8.

    Article  CAS  Google Scholar 

  24. Czarnetzki W, Roetzel W. Temperature oscillation techniques for simultaneous measurement of thermal diffusivity and conductivity. Int J Thermophys. 1995;16:413–22.

    Article  CAS  Google Scholar 

  25. Cahill DG. Thermal conductivity measurement from 30 to 750 K: the 3w method. Rev Sci Instrum. 1990;61:802–8.

    Article  CAS  Google Scholar 

  26. Iranidokht V, Hamian S, Mohammadi N, Behshad Shafii M. Thermal conductivity of mixed nanofluids under controlled pH conditions. Int J Therm Sci. 2013;74:63–71.

    Article  CAS  Google Scholar 

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Acknowledgements

The authors would like to acknowledge the assistance provided by the nanofluid Laboratory of Semnan university science and technology Park for providing necessary instrumentation to carry out the sample preparation and helping in the analysis of samples to complete the article in time. The sixth author would like to thank the Thailand Research Fund, The National Science and Technology Development Agency, and the National Research University Project for the support. Also, the help and comments of Professor Clement Kleinstreuer at North Carolina State University are appreciated.

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Authors and Affiliations

  1. Faculty of Mechanical Engineering, Semnan University, Semnan, Iran

    Mohammad Hemmat Esfe & Seyfolah Saedodin

  2. Department of Mechanical Engineering, School of Energy, Kermanshah University of Technology, Kermanshah, Iran

    Mehdi Bahiraei

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

    Davood Toghraie

  4. Young Researchers and Elite Club, Mashhad Branch, Islamic Azad University, Mashhad, Iran

    Omid Mahian

  5. Fluid Mechanics, Thermal Engineering and Multiphase Flow Research Lab. (FUTURE), Department of Mechanical Engineering, Faculty of Engineering, King Mongkut’s University of Technology Thonburi (KMUTT), Bangmod, Bangkok, 10140, Thailand

    Somchai Wongwises

Authors
  1. Mohammad Hemmat Esfe
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  2. Seyfolah Saedodin
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  3. Mehdi Bahiraei
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  4. Davood Toghraie
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  5. Omid Mahian
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  6. Somchai Wongwises
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Correspondence to Mohammad Hemmat Esfe or Omid Mahian.

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Hemmat Esfe, M., Saedodin, S., Bahiraei, M. et al. Thermal conductivity modeling of MgO/EG nanofluids using experimental data and artificial neural network. J Therm Anal Calorim 118, 287–294 (2014). https://doi.org/10.1007/s10973-014-4002-1

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  • DOI: https://doi.org/10.1007/s10973-014-4002-1

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