Impact of the temperature dependency of fiberglass insulation R-value on cooling energy use in buildings

TitleImpact of the temperature dependency of fiberglass insulation R-value on cooling energy use in buildings
Publication TypeConference Proceedings
Year of Publication1996
AuthorsRonnen M Levinson, Hashem Akbari, Lisa M Gartland
Conference Name1996 ACEEE Summer Study on Energy Efficiency in Buildings
Date Published1996/august 25-3
Keywordsbuilding design, Heat Island

Building energy models usually employ a constant, room-temperature-measured value for the thermal resistance of fiberglass roof insulation. In summer, however, the mean temperature of roof insulation can rise significantly above room temperature, lowering the insulation`s thermal resistance by 10% to 20%. Though the temperature dependence of the thermal resistance of porous materials like fiberglass has been extensively studied, it is difficult to theoretically predict the variation with temperature of a particular fiberglass blanket, from first principles. Heat transfer within fiberglass is complicated by the presence of three significant mechanisms - conduction through air, conduction through the glass matrix, and radiative exchange within the matrix - and a complex, unknown internal geometry. Purely theoretical models of fiberglass heat transfer assume highly simplified matrix structures and require typically-unavailable information about the fiberglass, such as its optical properties. There is also a dearth of useful experimental data. While the thermal resistances of many individual fiberglass samples have been measured, there is only one practical published table of thermal resistance vs. both temperature and density. Data from this table was incorporated in the DOE-2 building energy model. DOE-2 was used to simulate the roof surface temperature, roof heat flux, and cooling energy consumption of a school bungalow whose temperature and energy use had been monitored in 1992. The DOE-2 predictions made with and without temperature variation of thermal conductivity were compared to measured values. Simulations were also run for a typical office building. Annual cooling energy loads and annual peak hourly cooling powers were calculated for the office building using both fixed and variable thermal conductivities, and using five different climates. The decrease in the R-value of the office building`s roof led to a 2% to 4% increase in annual cooling energy load.


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