4.3 Article

Analysis of the Non-Newtonian Behavior and Viscosity of GNSs-CuO/Liquid EG Hybrid Nanofluid: An Experimental and Feed-Forward ANN Study

期刊

出版社

SPRINGER/PLENUM PUBLISHERS
DOI: 10.1007/s10765-023-03196-0

关键词

CuO nanoparticles; Experimental setup; Graphene nanosheets; Hybrid nanofluids; Non-Newtonian behavior

向作者/读者索取更多资源

This study investigated the dynamic viscosity of two types of graphene nanosheets-copper oxide/ethylene glycol (GNSs-CuO/liquid EG) hybrid nanofluids, and found that the GNSs-CuO/liquid EG hybrid nanofluids exhibited non-Newtonian behavior and high stability, especially at high temperatures, high mass concentrations, and low shear rates. The GNSs showed a higher increase in viscosity and relative viscosity compared to CuO nanoparticles at similar temperatures and mass fractions.
The knowledge of thermophysical properties of heat transfer systems such as their dynamic viscosity is of interest for their efficiency improvement and miniaturization. Hybrid nanofluids are obtained by the suspension of nanoparticles in base fluids. In this study, the effects of temperature (at four levels, from 5 degrees C to 65 degrees C) and mass percentage (from 0.005 % to 5 %) were investigated on the dynamic viscosity of two types of graphene nanosheets-copper oxide/ethylene glycol (GNSs-CuO/liquid EG) hybrid nanofluids, i.e., 25 %GNSs-75 %CuO/liquid EG and 75 %GNSs-25 %CuO/liquid EG hybrid nanofluids. Transmission electron microscopy (TEM) and Zeta Potential were analyzed to investigate the shape, size, and stability of the nanoparticles. Results showed the non-Newtonian behavior and very high stability of the studied GNSs-CuO/liquid EG hybrid nanofluids. This behavior was more remarkable at high temperatures, high mass concentrations, and low shear rates. In similar temperatures and mass fractions, GNSs resulted in a higher increase in viscosity and relative viscosity compared to CuO nanoparticles. Furthermore, a feed-forward artificial neural network (ANN), as a robust machine learning algorithm, was used to predict the dynamic viscosity of both studied hybrid nanofluids having their mass percentage, temperature, and shear stress as network inputs. The acceptable precision of the ANN model and its generalization were observed through the comparison of experimental and predicted data and the smoothness of the obtained model surfaces, respectively.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.3
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据