Impact of Size and Thermal Gradient on Supercooling of Phase Change Materials for Thermal Energy Storage

TL;DR

This paper develops a statistical model to predict supercooling behavior of phase change materials in thermal energy storage systems, bridging lab-scale data and large-scale practical applications by accounting for size and thermal gradients.

Contribution

The authors introduce a novel statistical model that accurately predicts supercooling in large-scale systems based on lab-scale thermal analysis data, considering size and temperature gradients.

Findings

Model accurately predicts supercooling across various sizes and cooling rates.

Incorporating thermal gradients reduces prediction errors by approximately 2x.

Validated with experimental data on neopentyl glycol, demonstrating practical applicability.

Abstract

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 on 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 with 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 ~2x error incurred by lumped approximations.