Predicting supercooling of water for large-scale volumes using differential scanning calorimetry data

Thermal energy storages (TES) have become essential to the renewable energy ecosystem, tackling the intermittent nature of renewable energy sources and enhancing energy efficiency. This makes TES a cornerstone for decarbonization and energy optimization. Phase change materials (PCMs) are a fundamental element of a latent heat TES system, which provides a higher energy storage density than sensible heat storages. However, the supercooling (delay in the crystallization) of PCMs is a crucial factor to consider when developing a latent heat TES. Due to its considerable volume dependence, supercooling that is characterized at the laboratory scale cannot be applied to large-scale TES systems. Designing a reliable and effective TES system becomes highly challenging if the degree of supercooling corresponding to the system's volume is not precisely predicted. In this work, a statistical model is developed using Differential scanning calorimetry (DSC) data to predict the degree of supercooling for larger volumes. DSC is used to obtain supercooling experimental data for two distinct volumes and cooling rates for type 2 pure water. From these data, an extrapolation is made using a statistical model, and the model's predictions are validated by comparing them with experimental supercooling results for type 2 pure water at two larger volumes (3 mL and 500 mL). The model demonstrated high accuracy, with errors of 0.82 % and 5.36 % for 3 mL and 500 mL volumes at a cooling rate of 1 °C/min, respectively. Finally, data analysis was conducted on the DSC results to establish an optimal protocol by determining the minimum number of DSC experiments to accurately predict the degree of supercooling. In this study, the minimum number of total combined samples is found to be 40, and the minimum number of cycles (repetitions) is found to be 6 for each sample.