4.7 Article

Improving the thermal conductivity of ethylene glycol by addition of hybrid nano-materials containing multi-walled carbon nanotubes and titanium dioxide: applicable for cooling and heating

期刊

JOURNAL OF THERMAL ANALYSIS AND CALORIMETRY
卷 143, 期 2, 页码 1701-1712

出版社

SPRINGER
DOI: 10.1007/s10973-020-09921-3

关键词

Hybrid nanofluid; Thermal conductivity; Ethylene glycol; RSM; Artificial intelligence

向作者/读者索取更多资源

This research investigated the thermal conductivity of a hybrid nanofluid containing MWCNTs and TiO2, showing that loading nanoparticles can enhance the sensitivity of thermal conductivity to temperature, with the positive effects amplified as temperature rises. Statistical analysis and machine learning algorithms were used for prediction and comparison.
In this research, thermal conductivity of a hybrid nanofluid containing multi-walled carbon nanotubes (MWCNTs) and titanium dioxide (TiO2) has been examined. Many samples were prepared by loading MWCNTs and TiO(2)at 50:50 mass% into the ethylene glycol (EG) to measure the thermal conductivity at temperatures within 25-50 degrees C and solid volume fractions of 0-1%. The amount of positive nanoparticles incorporation efficacy on the thermal conductivity was influenced by the volume fraction as well as the temperature. As the temperature augmented from 25 to 50 degrees C, the EG thermal conductivity was intensified by 9.6%, while this figure for nanofluid at 1 vol.% was 18.2%. Therefore, it is concluded that the loading nanoparticles amplified the thermal conductivity sensitivity to temperature. Statistical analysis showed that the positive effects of loading nanoparticles on thermal conductivity are amplified by rising temperature. At 25 degrees C, loading nanoparticles (1 vol.%) can amplify the thermal conductivity by 16%, while at 50 degrees C, this figure was 25.18% (maximum thermal conductivity enhancement). A correlation was developed using response surface methodology (RSM), and the input parameters significance was specified applying analysis of variance (ANOVA). Finally, using 28 machine learning-based algorithms, the hybrid nanofluid thermal conductivity has been predicted and compared with the RSM outputs.

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

4.7
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