4.7 Article

Numerical and statistical study on melting of nanoparticle enhanced phase change material in a shell-and-tube thermal energy storage system

期刊

APPLIED THERMAL ENGINEERING
卷 111, 期 -, 页码 950-960

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.applthermaleng.2016.09.133

关键词

Heat transfer; Nanofluids; Nanoparticle enhanced phase change; material (NePCM); Thermal energy storage; Response surface methodology; Factorial design

资金

  1. Natural Science and Engineering Council of Canada (NSERC) Discovery Grant

向作者/读者索取更多资源

This paper presents a numerical and statistical study on heat transfer and energy storage performance of a vertical shell and tube thermal energy storage unit, where CuO - water nanofluid is used as heat transfer fluid (HTF) in the tube and CuO - paraffin nanoparticle enhanced phase change material (NePCM) is used on the shell side for latent thermal energy storage. A computational fluid dynamics (CFD) model is developed to simulate the laminar nanofluid flow, melting of NePCM and the overall heat transfer between the HTF and NePCM, where an enthalpy porosity technique is used in modeling the melting process. With this numerical model, the response surface methodology (RSM) is applied to statistically investigate the effects of three main operational parameters on the NePCM melting process: (1) concentration of CuO nanoparticles in the HTF, with a range of 0-4 vol.%; (2) concentration of CuO nanoparticles in the NePCM, with a range of 0-7 vol.%; and (3) inlet temperature of the HTF. Thermophysical properties of the WePCM are considered to be temperature dependent in the analysis. Face-centered central composite design (CCF) is employed to find the significant factors and their interactions in affecting liquid fraction rate in the melting process. It is found that changes in all these three factors are responsible for 99.8% liquid fraction variation, with HTF inlet temperature as the most significant parameter. Moreover, all three factors show a parabolic behavior on the liquid fraction variation. Finally, a regression model is developed for prediction of liquid fraction in the NePCM. (C) 2016 Elsevier Ltd. All rights reserved.

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