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C056 -- Innovative Evaluation, Optimization and Design Tool for Assessing Enclosed Reflective Airspace Performance

M00008058

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C056 -- Innovative Evaluation, Optimization and Design Tool for Assessing Enclosed Reflective Airspace Performance

Conference Proceeding by ASHRAE, 2022

Hamed H. Saber, PhD; Saleh A. Alshehri, PhD; David W. Yarbrough, PhD, PE, Member ASHRAE

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Enclosed airspaces of different dimensions and inclinations that exist in building components include double-pane windows, curtain walls, skylight devices and reflective insulation assemblies. Assessing the energy performance of buildings requires accurate determination of the thermal resistance (R-value) of the enclosed airspaces in different climatic conditions. The parameters that affect the R-value of an enclosed airspace include the physical properties of air, the temperature and emittance of surfaces in the airspace, the airspace dimensions, and the heat-flow direction. The current methods such as ISO-6946 provide R-values for horizontal and vertical enclosed airspaces. Additionally, thermal test results from the U.S. National Bureau of Standards were used in the ASHRAE Handbook of Fundamentals to provide R-values for horizontal, vertical and 45o airspaces. As such, ASHRAE R-values do not cover the various climatic conditions and construction techniques that exist around the globe. The effect of aspect ratio on airspace R-values is not included in the ISO-6946 or ASHRAE methods. Many studies have shown that the complicated airflow inside an enclosed airspace has a significant impact on R-value. This has motivated the development of a tool for accurately determining R-values of enclosed airspaces for a wide variety of conditions. A validated numerical model along with Neural Networks were used to develop a “Reflective Airspace Tool”. The capabilities of this tool for evaluation, optimization and design are explored in this paper. Keywords: Reflective insulations; enclosed airspace; airspace aspect ratio; low-emittance material; effective emittance; thermal resistance; R-value; Neural Networks