کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6680199 | 1428068 | 2018 | 19 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A review of thermal and optical characterisation of complex window systems and their building performance prediction
ترجمه فارسی عنوان
بررسی ویژگی های حرارتی و نوری سیستم های پیچیده پنجره و پیش بینی عملکرد ساختمان آنها
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی انرژی
مهندسی انرژی و فناوری های برق
چکیده انگلیسی
Window systems play a key role in establishing both the thermal and luminous environments within buildings, as well as the consequent energy required to maintain these for the comfort of their occupants. Various strategies have been employed to improve the thermal and optical performance of window systems. Some of these approaches result in products with relatively complex structures. Thus, it becomes difficult to characterise their optical and thermal properties for use in building performance prediction. This review discusses the experimental and numerical methods used to predict the thermal and optical behaviour of complex window systems. Following a discussion of thermal characterisation methods available in the literature that include experimental test methods, theoretical calculation methods and Computational Fluid Dynamic methods, sophisticated optical methods, such as use of Bidirectional Scatter Distribution Functions (BSDF) to optically characterise complex window systems, are introduced. The application of BSDF allows advanced daylight assessment metrics along with daylight evaluation tools to be used to realise dynamic annual prediction of the luminous environment. Finally, this paper reviews methods that permit the prediction of the combined thermal, daylight and energy behaviour of buildings that make use of complex window systems.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Energy - Volume 222, 15 July 2018, Pages 729-747
Journal: Applied Energy - Volume 222, 15 July 2018, Pages 729-747
نویسندگان
Yanyi Sun, Yupeng Wu, Robin Wilson,