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Materials 2017, 10(5), 552; doi:10.3390/ma10050552

Experiment and Artificial Neural Network Prediction of Thermal Conductivity and Viscosity for Alumina-Water Nanofluids

College of Power and Energy Engineering, Harbin Engineering University, Harbin 150001, China
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Academic Editor: Thomas Fiedler
Received: 15 April 2017 / Revised: 11 May 2017 / Accepted: 12 May 2017 / Published: 19 May 2017
(This article belongs to the Section Materials for Energy Applications)
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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. View Full-Text
Keywords: nanofluids; thermal conductivity; viscosity; artificial neural network; experimental data nanofluids; thermal conductivity; viscosity; artificial neural network; experimental data
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zhao, N.; Li, Z. Experiment and Artificial Neural Network Prediction of Thermal Conductivity and Viscosity for Alumina-Water Nanofluids. Materials 2017, 10, 552.

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