TY  - EJOU
AU  - Zhai, Yuling 
AU  - Li, Long 
AU  - Xuan, Zihao 
AU  - Ma, Mingyan 
AU  - Wang, Hua 

TI  - Experimental Performance Evaluation and Artificial-Neural-Network Modeling of ZnO-CuO/EG-W Hybrid Nanofluids
T2  - Fluid Dynamics \& Materials Processing

PY  - 2022
VL  - 18
IS  - 3
SN  - 1555-2578

AB  - 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 (R<sup>2</sup>) was 0.9979 and 0.9989, respectively.
KW  - Hybrid nanofluids; base fluid ratio; viscosity; thermal conductivity; ANN model

DO  - 10.32604/fdmp.2022.017485