4.7 Article

Natural convection heat transfer of nanofluids in annular spaces between horizontal concentric cylinders

期刊

APPLIED THERMAL ENGINEERING
卷 31, 期 17-18, 页码 4055-4063

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.applthermaleng.2011.08.010

关键词

Nanofluids; Natural convection; Horizontal annuli; Theoretical analysis; Optimal particle loading

向作者/读者索取更多资源

Natural convection heat transfer of nanofluids in annular spaces between long horizontal concentric cylinders maintained at different uniform temperatures is investigated theoretically. The main idea upon which the present work is based is that nanofluids behave more like single-phase fluids rather than like conventional solid liquid mixtures. This assumption implies that all the convective heat transfer correlations available in the literature for single-phase flows can be extended to nanoparticle suspensions, provided that the thermophysical properties appearing in them are the nanofluid effective properties calculated at the reference temperature. In this connection, two empirical equations, based on a wide variety of experimental data reported in the literature, are used for the evaluation of the nanofluid effective thermal conductivity and dynamic viscosity. Conversely, the other effective properties are computed by the traditional mixing theory. The heat transfer enhancement that derives from the dispersion of nano-sized solid particles into the base liquid is calculated for different operating conditions, nanoparticle diameters, and combinations of solid and liquid phases. The fundamental result obtained is the existence of an optimal particle loading for maximum heat transfer. In particular, for any assigned combination of suspended nanoparticles and base liquid, it is found that the optimal volume fraction increases as the nanofluid average temperature increases and the nanoparticle size decreases. (C) 2011 Elsevier Ltd. All rights reserved.

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