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

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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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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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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).

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