4.7 Article

Mathematical framework for predicting solar thermal build-up of spectrally selective coatings at the Earth's surface

期刊

APPLIED MATHEMATICAL MODELLING
卷 31, 期 8, 页码 1635-1651

出版社

ELSEVIER SCIENCE INC
DOI: 10.1016/j.apm.2006.05.007

关键词

finite element heat transfer; spectrally selective coatings; solar loading

向作者/读者索取更多资源

A transient finite element thermal model is formulated valid for surface coatings on any substrate material and based on the continuum conduction equations with solar loading as a heat source. The model allows cooling to be applied at outer surfaces of the body, by natural convection and accounts for ambient radiative heat loss. Hemispherical spectral reflectivities are obtained for various polymer-based coatings on a steel substrate using spectrophotometers in the 0.1 mu m to 25 mu m wavelengths. A time-dependent solar irradiation energy source (blackbody equivalent) is applied to an object with spectrally diffuse outer surfaces, and the incoming heat flux is split by a band approximation into reflected and absorbed energy and finally integrated over the complete spectrum to provide thermal source terms for the finite element model. Results show that cyclic diurnal thermal build-up of temperature can be predicted for a body with different spectrally selective coatings. While the model exhibits the classic relationship for thermal build-up with colour, i.e., dark colours absorb more heat and lighter colours remain cooler, it also shows that colours, which appear similar, can have very different thermal build-ups, depending on the infrared reflectivity of the coating. The general suitability of the finite element method to describe geometrically complex bodies coupled with additional parameters such as latitude, longitude and a variable ambient temperature can be used to simulate a variety of scenarios for a diverse number of applications. (c) 2006 Elsevier Inc. 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