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

**Authors:** Drew Lilley, Jonathan Lau, Chris Dames, Sumanjeet Kaur, Ravi Prasher  
**arXiv:** [2010.11298](https://arxiv.org/abs/2010.11298)  
**Date:** 2020-10-21  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2010.11298v1/

## Key points

- Phase change materials (PCMs) are a promising technology for thermal energy storage with applications in various industries such as building heating and cooling, battery and electronics thermal management, thermal textiles, and power plant cooling.
- Translating lab-scale thermal data obtained through Differential Scanning Calorimetry (DSC) to large-scale practical systems is challenging due to the impact of supercooling on system size and temperature gradients.
- Researchers led by Drew Lilley, Jonathan Lau, Chris Dames, Sumanjeet Kaur, and Ravi Prasher address this challenge by developing a statistics-based theoretical model that predicts supercooling performance for different sizes and temperature gradients using data from small samples.
- The team validates their modeling methodology by comparing it to experimental results involving solid-solid phase change in neopentyl glycol, demonstrating accurate predictions of changes in supercooling temperature across various cooling rates and volumes.
- By accounting for thermal gradients in their model, the researchers avoid approximately 2 times error typically incurred by lumped approximations.
- This comprehensive approach enhances understanding of supercooling phenomena in PCMs and provides a valuable tool for optimizing the design and performance of thermal energy storage systems across different scales and applications.

## Summary

Phase change materials (PCMs) are a promising technology for thermal energy storage with numerous applications in various industries such as building heating and cooling, battery and electronics thermal management, thermal textiles, and power plant cooling. However, a significant challenge lies in translating lab-scale thermal data obtained through Differential Scanning Calorimetry (DSC) to the performance of large-scale practical systems. This difficulty is due to the impact of supercooling on system size and temperature gradients. In a recent study by Drew Lilley, Jonathan Lau, Chris Dames, Sumanjeet Kaur, and Ravi Prasher, the researchers address this challenge by investigating how the supercooling behavior of PCMs can be experimentally characterized using common lab-scale thermal analysis techniques. They develop a statistics-based theoretical model that leverages data from small samples to predict supercooling performance for thermal energy storage applications of varying sizes and temperature gradients. The team validates their modeling methodology by comparing it to experimental results involving solid-solid phase change in neopentyl glycol. The results demonstrate the model's ability to accurately predict changes in supercooling temperature across a wide range of cooling rates and volumes. Importantly, by accounting for thermal gradients in their model, the researchers avoid approximately 2 times error typically incurred by lumped approximations. This comprehensive approach not only enhances our understanding of supercooling phenomena in PCMs but also provides a valuable tool for optimizing the design and performance of thermal energy storage systems across different scales and applications. The findings contribute significantly to advancing the efficiency and effectiveness of PCM-based technologies in various industrial sectors.