Abstract
To effectively predict the thermal conductivity and viscosity of alumina (Al2O3)-water nanofluids, an artificial neural network (ANN) approach was investigated in the present study. Firstly, using a two-step method, four Al2O3-water nanofluids were prepared respectively by dispersing different volume fractions (1.31%, 2.72%, 4.25%, and 5.92%) of nanoparticles with the average diameter of 30 nm. On this basis, the thermal conductivity and viscosity of the above nanofluids were analyzed experimentally under various temperatures ranging from 296 to 313 K. Then a radial basis function (RBF) neural network was constructed to predict the thermal conductivity and viscosity of Al2O3-water nanofluids as a function of nanoparticle volume fraction and temperature. The experimental results showed that both nanoparticle volume fraction and temperature could enhance the thermal conductivity of Al2O3-water nanofluids. However, the viscosity only depended strongly on Al2O3 nanoparticle volume fraction and was increased slightly by changing temperature. In addition, the comparative analysis revealed that the RBF neural network had an excellent ability to predict the thermal conductivity and viscosity of Al2O3-water nanofluids with the mean absolute percent errors of 0.5177% and 0.5618%, respectively. This demonstrated that the ANN provided an effective way to predict the thermophysical properties of nanofluids with limited experimental data.
Highlights
Considering the higher thermal conductivity of many solid materials, including Cu, CuO, TiO2, ZnO, Fe3 O4, MgO, Al2 O3 and graphite, dispersing solid particles in the conventional coolants is one of the most efficient ways to enhance the heat transfer process [1]
Based on the experimental data, the measurement uncertainty of thermal conductivity is less than 5% for water
All the results show that the suspension of Al2 O3 nanoparticles can increase the viscosity of water, and there is a slight non-linear relationship between the viscosity of nanofluids and nanoparticle volume fraction
Summary
Considering the higher thermal conductivity of many solid materials, including Cu, CuO, TiO2 , ZnO, Fe3 O4 , MgO, Al2 O3 and graphite, dispersing solid particles in the conventional coolants (such as water, oil, ethylene glycol, refrigerant, etc.) is one of the most efficient ways to enhance the heat transfer process [1]. Over the last two decades, various experimental investigations have been published to evaluate the effects of nanoparticles on thermal conductivity and the viscosity characteristics of base fluids. References [4,5,6,7,8,9,10,11,12,13] respectively reviewed the experimental and theoretical developments of various nanofluids’ thermophysical parameters. According to their analysis, it could be found that the addition of nanoparticles did enhance the thermal conductivity and viscosity of base fluids in varying degrees. It was unfortunate that there were still many differences in the Materials 2017, 10, 552; doi:10.3390/ma10050552 www.mdpi.com/journal/materials
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