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


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2014-10

AUTHORS

Mohammad Hemmat Esfe, Seyfolah Saedodin, Mehdi Bahiraei, Davood Toghraie, Omid Mahian, Somchai Wongwises

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. More... »

PAGES

287-294

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10973-014-4002-1

DOI

http://dx.doi.org/10.1007/s10973-014-4002-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1020050434


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