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Open AccessArticle

Prediction of Thermo-Physical Properties of TiO2-Al2O3/Water Nanoparticles by Using Artificial Neural Network

1
Department of Renewable Energy and Environmental Engineering, University of Tehran, Tehran 1439957131, Iran
2
Department of Chemistry, Payame Noor University (PNU), Tehran P.O. Box, 19395-3697, Iran
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Faculty of Mechanical Engineering, Shahrood University of Technology, POB- Shahrood 3619995161, Iran
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Department of Ocean and Mechanical Engineering, Florida Atlantic University, 777 Glades Road Boca Raton, FL 33431, USA
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Kalman Kando Faculty of Electrical Engineering, Obuda University, 1034 Budapest, Hungary
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Institute of Structural Mechanics (ISM), Bauhaus-Universität Weimar, 99423 Weimar, Germany
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Thuringian Institute of Sustainability and Climate Protection, 07743 Jena, Germany
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Department of Mathematics and Informatics, J. Selye University, 94501 Komarno, Slovakia
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Institute of Research and Development, Duy Tan University, Da Nang 550000, Viet Nam
*
Authors to whom correspondence should be addressed.
Nanomaterials 2020, 10(4), 697; https://doi.org/10.3390/nano10040697
Received: 28 December 2019 / Revised: 14 March 2020 / Accepted: 29 March 2020 / Published: 7 April 2020
(This article belongs to the Special Issue Applications of Nanofluids)
In this paper, an artificial neural network is implemented for the sake of predicting the thermal conductivity ratio of TiO2-Al2O3/water nanofluid. TiO2-Al2O3/water in the role of an innovative type of nanofluid was synthesized by the sol–gel method. The results indicated that 1.5 vol.% of nanofluids enhanced the thermal conductivity by up to 25%. It was shown that the heat transfer coefficient was linearly augmented with increasing nanoparticle concentration, but its variation with temperature was nonlinear. It should be noted that the increase in concentration may cause the particles to agglomerate, and then the thermal conductivity is reduced. The increase in temperature also increases the thermal conductivity, due to an increase in the Brownian motion and collision of particles. In this research, for the sake of predicting the thermal conductivity of TiO2-Al2O3/water nanofluid based on volumetric concentration and temperature functions, an artificial neural network is implemented. In this way, for predicting thermal conductivity, SOM (self-organizing map) and BP-LM (Back Propagation-Levenberq-Marquardt) algorithms were used. Based on the results obtained, these algorithms can be considered as an exceptional tool for predicting thermal conductivity. Additionally, the correlation coefficient values were equal to 0.938 and 0.98 when implementing the SOM and BP-LM algorithms, respectively, which is highly acceptable. View Full-Text
Keywords: thermal conductivity; TiO2-Al2O3/water; nanofluid; artificial neural network thermal conductivity; TiO2-Al2O3/water; nanofluid; artificial neural network
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MDPI and ACS Style

Sadeghzadeh, M.; Maddah, H.; Ahmadi, M.H.; Khadang, A.; Ghazvini, M.; Mosavi, A.; Nabipour, N. Prediction of Thermo-Physical Properties of TiO2-Al2O3/Water Nanoparticles by Using Artificial Neural Network. Nanomaterials 2020, 10, 697.

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