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.
Highlights
Active, passive, and combined methods are used for heat transfer enhancement
The results show that the viscosity can be increased up to 50% when W:ethylene glycol (EG) = 60:40 and the thermal conductivity enhancement was found to increase by 2.6% to 12.8%
We found that the synergistic mechanism is caused by dispersing different sizes of nanoparticles to form an ordered arrangement of liquid molecules around nanoparticles, and small size of nanoparticles fills into the space caused by large size of nanoparticles, which results in more compact solid-liquid interface and reasonable heat transfer network [20]
Summary
Passive, and combined methods are used for heat transfer enhancement. Among them, passive method is the best in increasing heat transfer. Similar results were found by Sonawane et al [8] and Cabaleiro et al [9], who investigated the addition of TiO2 nanoparticles in different base fluids (EG, W, and paraffin oil) Whether these same results can be observed in hybrid nanofluids is not known yet. Researchers have used reliable Artificial neural networks (ANN) to predict the thermosphysical properties of nanofluids [11,12]. It can model complex correlations with high speed and acceptable accuracy. The stability, viscosity, thermal conductivity, and modeling of ZnO-CuO/EG-W hybrid nanofluids with different base fluids mixture ratios are studied. The experimental data obtained in the present work were compared with existing models available in the literature as well
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