4.8 Article

Impact of size and thermal gradient on supercooling of phase change materials for thermal energy storage

Journal

APPLIED ENERGY
Volume 290, Issue -, Pages -

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2021.116635

Keywords

Phase change materials; PCM; Thermal energy storage; Subcooling; Supercooling; Crystallization

Funding

  1. Energy Efficiency and Renewable Energy, Building Technologies Program, of the U.S. Department of Energy [DE-AC02-05CH11231]

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This research demonstrates the characterization of supercooling behavior in phase change materials using common lab scale thermal analysis techniques, followed by the development of a statistical model to predict supercooling performance in thermal energy storage applications of varying sizes. By validating the model's accuracy, successful predictions of supercooling temperature changes were achieved.
Phase change material based thermal energy storage has many current and potential applications in the heating and cooling of buildings, battery and electronics thermal management, thermal textiles, and dry cooling of power plants. However, connecting lab scale thermal data obtained with differential scanning calorimetry (DSC) to the performance of large-scale practical systems has been a major challenge primarily due to the dependence of supercooling on the size and temperature gradient of the system. In this work we show how a phase change material's supercooling behavior can be characterized experimentally using common lab scale thermal analysis techniques. We then develop a statistics based theoretical model that uses the lab-scale data on small samples to quantitatively predict the supercooling performance for a general thermal energy storage application of any size, including also allowing for the possibility of temperature gradients. Finally, we validate the modeling methodology by comparing to experimental results for solid-solid phase change in neopentyl glycol, which shows how the model successfully predicts the changes in supercooling temperature across a large range of cooling rates (2 orders of magnitude) and volumes (3 orders of magnitude). By accounting for thermal gradients, the model avoids similar to 2x error incurred by lumped approximations.

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