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Experimental Performance Evaluation and Artificial-Neural-Network Modeling of ZnO-CuO/EG-W Hybrid Nanofluids

Yuling Zhai*, Long Li, Zihao Xuan, Mingyan Ma, Hua Wang

Engineering Research Center of Metallurgical Energy Conversion and Emission Reduction, Ministry of Education, Kunming University of Science and Technology, Yunnan, 650093, China

* Corresponding Author: Yuling Zhai. Email: email

(This article belongs to this Special Issue: Advanced Technology of Micro and Nano Flow and Structures for Thermal Management of Electronic Components and Energy Storage)

Fluid Dynamics & Materials Processing 2022, 18(3), 629-646. https://doi.org/10.32604/fdmp.2022.017485

Abstract

The thermo-physical properties of nanofluids are highly dependent on the used base fluid. This study explores the influence of the mixing ratio on the thermal conductivity and viscosity of ZnO-CuO/EG (ethylene glycol)-W (water) hybrid nanofluids with mass concentration and temperatures in the ranges 1-5 wt.% and 25-60°C, respectively. The characteristics and stability of these mixtures were estimated by TEM (transmission electron microscopy), visual observation, and absorbance tests. The results show that 120 min of sonication and the addition of PVP (polyvinyl pyrrolidone) surfactant can prevent sedimentation for a period reaching up to 20 days. The increase of EG (ethylene glycol) in the base fluid leads to low thermal conductivity and high viscosity. Thermal conductivity enhancement (TCE) decreases from 21.52% to 11.7% when EG:W is changed from 20:80 to 80:20 at 1 wt.% and 60°C. A lower viscosity of the base fluid influences more significantly the TCE of the nanofluid. An Artificial Neural Network (ANN) has also been used to describe the effectiveness of these hybrid nanofluids as heat transfer fluids. The optimal number of layers and neurons in these models have been found to be 1 and 5 for viscosity, and 1 and 7 for thermal conductivity. The corresponding coefficient of determination (R2) was 0.9979 and 0.9989, respectively.

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Cite This Article

Zhai, Y., Li, L., Xuan, Z., Ma, M., Wang, H. (2022). Experimental Performance Evaluation and Artificial-Neural-Network Modeling of ZnO-CuO/EG-W Hybrid Nanofluids. FDMP-Fluid Dynamics & Materials Processing, 18(3), 629–646.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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