Researchers at Texas A&M University have made a significant breakthrough in the field of thermal energy storage. Their work, published in the International Journal of Heat and Mass Transfer, has established key design principles for composite phase change materials that can rapidly store thermal energy. This development is expected to simplify the design process and enable the calculation of near-optimal composite phase change materials without the need for complex computational fluid dynamic calculations or extensive iterative design.
While previous studies have explored thermal energy storage systems, none have offered insights into enhancing rate performance, optimization, and performance prediction as this research has done.
Phase change materials are capable of storing thermal energy as latent heat and are often combined with high-thermal conductivity metals to create composites with high power density and significant energy storage capacity. The research team aimed to address the fundamental question of how to design a composite phase change material that achieves a balance between energy density (the amount of energy stored, akin to the range of an electric vehicle before requiring a recharge) and power density (the speed at which energy can be stored, comparable to the time needed to charge an electric vehicle), while avoiding excessive mass or volume.
The researchers have established a theoretical framework for designing and optimizing cylindrical composites with three important criteria: minimizing temperature rise, maximizing effective volumetric heat capacity, and maximizing effective heat capacity based on mass. These criteria can be used to evaluate the performance of most composite phase change material systems and assist in the design of future cylindrical composites, while considering the specific thermal loads associated with the thermal storage application.
Crucially, the team conducted experimental demonstrations that treating the system as an effective composite allowed them to simplify calculations and predict structures that are close to optimal.