US Map Visualization of Optimal Properties of Phase Change Materials for Building Efficiency

Mar 27, 2013

Incorporating phase change materials (PCM) in construction materials can reduce the heating and cooling loads of buildings significantly. During the past ten years, many studies have estimated potential reductions of energy consumption of buildings between 10 and 30 percent. This wide range is due to the large number of parameters that affect energy consumption and make the process of selecting the type and amount of PCM challenging. In fact, extensive engineering studies are generally necessary to determine the practicality of PCM in any specific case. As a result, architects and engineers are reluctant to use PCM because of the lack of design guidelines. The International Energy Conservation Code (IECC) and the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) identified eight climate zones in the United States, each determined on the basis of annual degree heating and degree cooling days. Climate zones are further divided into moist, dry and coastal regions leading to 15 specific climates. Phase change materials are defined by their melting temperature, energy storage capacity, i.e., enthalpy, and cost, among other parameters. For a given building in a given climate, there exist an optimal melting temperature and enthalpy that minimize the energy consumption and the payback period. In this research, the optimal properties of PCM are determined for all 15 climates and results are visualized in the form of maps of the United States. Additional topics discussed in this paper are the sensitivity of the optimal properties of PCM and the effect of the average cost of energy on the selection of PCM. Fifteen different maps of the United States were created, from which the most relevant are presented in this paper. The energy consumption is determined numerically using the Department of Energy software EnergyPlus, which calculates the energy consumption for heating and cooling a building under any climate and operation schedule. The software is run on a computer cluster for a wide range of properties from which the optimal values are extracted.

Niraj Poudel (Clemson University)
Vincent Y. Blouin (Clemson University)
Proceedings of the 2013 ARCC Spring Research Conference
Presented at: 
The Visibility of Research
Published & professionally reviewed by: 
University of North Carolina at Charlotte
Architectural Research Centers Consortium

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