Next Article in Journal
Single-Valued Neutrosophic Linguistic-Induced Aggregation Distance Measures and Their Application in Investment Multiple Attribute Group Decision Making
Next Article in Special Issue
Significance of Bioconvective and Thermally Dissipation Flow of Viscoelastic Nanoparticles with Activation Energy Features: Novel Biofuels Significance
Previous Article in Journal
Non-Singular Fast Terminal Sliding Mode Control of High-Speed Train Network System Based on Improved Particle Swarm Optimization Algorithm
Open AccessArticle

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

Department of Anesthesiology, the First Hospital of Jilin University, Changchun 130021, China
Faculty of Mechanical Engineering, Shahrood University of Technology, Shahrood 3619995161, Iran
Department of Renewable Energies, Faculty of New Science & Technologies, University of Tehran, Tehran 5441656498, Iran
Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City 758307, Vietnam
Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City 758307, Vietnam
CORIA-UMR 6614, Normandie University, CNRS-University & INSA, 76000 Rouen, France
Author to whom correspondence should be addressed.
Symmetry 2020, 12(2), 206;
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
Show Figures

Figure 1

MDPI and ACS Style

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.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop