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Open AccessArticle

Thermal Conductivity Modeling of Nanofluids Contain MgO Particles by Employing Different Approaches

1
Department of Anesthesiology, the First Hospital of Jilin University, Changchun 130021, China
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Faculty of Mechanical Engineering, Shahrood University of Technology, Shahrood 3619995161, Iran
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Department of Renewable Energies, Faculty of New Science & Technologies, University of Tehran, Tehran 5441656498, Iran
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Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City 758307, Vietnam
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Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City 758307, Vietnam
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CORIA-UMR 6614, Normandie University, CNRS-University & INSA, 76000 Rouen, France
*
Author to whom correspondence should be addressed.
Symmetry 2020, 12(2), 206; https://doi.org/10.3390/sym12020206
Received: 26 December 2019 / Revised: 14 January 2020 / Accepted: 16 January 2020 / Published: 1 February 2020
(This article belongs to the Special Issue Nanofluids in Advanced Symmetric Systems)
The existence of solid-phase nanoparticles remarkably improves the thermal conductivity of the fluids. The enhancement in this property of the nanofluids is affected by different items such as the solid-phase volume fraction and dimensions, temperature, etc. In the current paper, three different mathematical models, including polynomial correlation, Multivariate Adaptive Regression Spline (MARS), and Group Method of Data Handling (GMDH), are applied to forecast the thermal conductivity of nanofluids containing MgO particles. The inputs of the model are the base fluid thermal conductivity, volume concentration, and average dimension of solid-phase, and nanofluids’ temperature. Comparing the proposed models revealed higher confidence of GMDH in estimating the thermal conductivity, which is attributed to its complicated structure and more appropriate consideration of the input’s interaction. The values of R-squared for the correlation, MARS, and GMDH are 0.9949, 0.9952, and 0.9991, respectively. In addition, based on the sensitivity analysis, the effect of thermal conductivity of the base fluid on the overall thermal conductivity of nanofluids is more remarkable compared with the other inputs such as volume fraction, temperature, and dimensions of the particles which are used as the inputs of the models. View Full-Text
Keywords: nanofluid; thermal conductivity; MgO nanoparticles; GMDH; MARS nanofluid; thermal conductivity; MgO nanoparticles; GMDH; MARS
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Wang, N.; Maleki, A.; Alhuyi Nazari, M.; Tlili, I.; Safdari Shadloo, M. Thermal Conductivity Modeling of Nanofluids Contain MgO Particles by Employing Different Approaches. Symmetry 2020, 12, 206.

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