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28.01.2020 | Research Article-Chemical Engineering

Experimental and Theoretical Investigation of Thermophysical Properties of Synthesized Hybrid Nanofluid Developed by Modeling Approaches

Zeitschrift:
Arabian Journal for Science and Engineering
Autoren:
Fatemeh Nasirzadehroshenin, Alireza pourmozafari, Heydar Maddah, Hossein Sakhaeinia

Abstract

Although, titanium oxide (TiO2) has appropriate mechanical and chemical stability used in different applications, its thermal conductivity slightly increases with an increasing temperature and concentration compared with other metal oxides such as aluminum oxide (Al2O3). Thus, synthesized aluminum oxide nanoparticles were incorporated on the surfaces of titanium oxide in ultrasonication condition with purpose of thermophysical properties modification. The scanning electron microscopy and X-ray diffraction were used to investigate the structure and morphology of synthesized nanocomposite. The impact of variables (temperature, volume fraction and nanoparticle size) on the thermal conductivity and viscosity of prepared hybrid nanofluid was investigated using KD2Pro instrument and Brookfield DVII viscometer, respectively. Results showed a significant improvement of thermophysical properties of prepared hybrid nanofluid, compared to water or untreated titanium oxide–water. The results showed that three mentioned variables considerably affect the thermophysical properties of hybrid nanofluid; as an increasing volume fraction, reducing nanoparticle size and temperature led to an increasing viscosity while enhanced thermal conductivity was resulted from an increasing nanofluid volume fraction and temperature, and a decreasing nanoparticle size. This was confirmed using two computer-modeling approaches, which allow optimization of the thermophysical properties of hybrid nanofluid. Modifying Response Surface Methodology-Central Composite Design (RSM-CCD) estimated accurately the optimal conditions for thermal conductivity and viscosity. The best artificial neural network model was chosen based on its predictive accuracy for estimation of thermophysical properties; having seven neurons in hidden layer and minimum error, demonstrated the most accurate approach for modeling the considered task.

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