-
Indexed by:
Article
-
First Author:
Na Zhu
-
Correspondence Author:
Shengwei Wang
-
Co-author:
Xinhua Xu,Zhenjun Ma
-
Journal:
International Journal of Thermal Science
-
Included Journals:
SCI
-
Affiliation of Author(s):
The Hong Kong Polytechnic University
-
Place of Publication:
India
-
Discipline:
Engineering
-
First-Level Discipline:
Civil Engineering
-
Funded by:
Hong Kong Research Grants Council
-
Document Type:
J
-
Volume:
49
-
Page Number:
1722-1731
-
ISSN No.:
1290-0729
-
Key Words:
Simplified model; Phase change material; Building structure; Parameter identification; Genetic algorithm
-
DOI number:
10.1016/j.ijthermalsci.2010.03.020
-
Date of Publication:
2010-09-01
-
Impact Factor:
4.779
-
Abstract:
The use of phase change materials (PCM) to enhance the building energy performance has attracted increasing attention of researchers and practitioners over the last few years. Thermodynamic models of building structures using PCMs are essential for analyzing their impacts on building energy performance at different conditions and using different control strategies. There are few PCM models of detailed physics providing good accuracy in simulating thermodynamic behavior of building structures integrated with PCM layers. However, simplified models with acceptable accuracy and good reliability are preferable in many practical applications concerning computation speed and program size particularly when involving large buildings or models are used for online applications. A simplified physical dynamic model of building structures integrated with SSPCM (shaped-stabilized phase change material) is developed and validated in this study. The simplified physical model represents the wall by 3 resistances and 2 capacitances and the PCM layer by 4 resistances and 2 capacitances respectively while the key issue is the parameter identification of the model. The parameters of the simplified model are identified using genetic algorithm (GA) on the basis of the basic physical properties of the wall and PCM layer. Two GA-based preprocessors are developed to identify the optimal parameters (resistances and capacitances) of the model by frequency-domain regression and time-domain regression respectively. Validation results show that the simplified model can represent light walls and median walls integrated with SSPCM with good accuracy. (C) 2010 Elsevier Masson SAS
