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

  • 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 coefficientIn 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-Textshow moreshow less

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Metadaten
Document Type:Article
Author: Milad SadeghzadehORCiD, Heydar Maddah, Mohammad Hossein Ahmadi, Amirhosein Khadang, Mahyar Ghazvini, Amirhosein MosaviORCiD, Narjes NabipourORCiD
DOI (Cite-Link):https://doi.org/10.3390/nano10040697Cite-Link
URN (Cite-Link):https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20200421-41308Cite-Link
URL:https://www.mdpi.com/2079-4991/10/4/697
Parent Title (English):Nanomaterials
Publisher:MDPI
Place of publication:Basel
Language:English
Date of Publication (online):2020/04/07
Date of first Publication:2020/04/07
Release Date:2020/04/21
Publishing Institution:Bauhaus-Universität Weimar
Institutes:Fakultät Bauingenieurwesen / Institut für Strukturmechanik
Volume:2020
Issue:Volume 10, Issue 4, 697
Tag:Artificial neural network; Nanofluid; Thermal conductivity
GND Keyword:Wärmeleitfähigkeit; Fluid; Neuronales Netz
Dewey Decimal Classification:500 Naturwissenschaften und Mathematik
BKL-Classification:35 Chemie
Licence (German):License Logo Creative Commons 4.0 - Namensnennung (CC BY 4.0)