In recent times, goals for industry standards and national mandates have resulted in attempts to reduce the environmental impact of transient thermal processes (e.g., thermal management) in a multitude of applications ranging from industry to domestic use (consumer markets). A potential cheap, efficient and reliable solution is the implementation of a thermal energy storage (TES) unit which can serve as a primary or supplemental option, i.e., as a source of heating and/or cooling. Phase change materials (PCMs) can be used in TES due to their high latent heat storage capacity during phase transformation. Inorganic PCMs typically have the highest latent heat capacity and are attractive for their ability to store the highest amounts of thermal energy in small form factors while conferring respectable power ratings (however, they suffer from compromised reliability issues, that often arise from the need for subcooling). Subcooling (also known as supercooling) is a phenomenon where the temperature needs to be reduced substantially below the melting point to initiate solidification.

A technique for obviating subcooling issues is to allow a small portion of the PCM to remain unmelted, which then allows the PCM to solidify and nucleate — starting from the unmelted portion of PCM (this is termed as the “cold finger” technique). A fundamental challenge for using the cold finger technique is to reliably control the amount of melt fraction in the total volume of the PCM (such that a target amount of the PCM remains solidified or unmelted). Thus, reliability is enhanced at the expense of substantial reduction in storage capacity. However, using Machine Learning (ML) techniques, this deficiency can be addressed by reliably predicting and thus controlling the amount of melt fraction in the total volume of the PCM with a higher accuracy than conventional techniques. Conventional techniques for predicting transient characteristics in real time control typically use physics-based prediction strategies that are effective for a narrow range of operating conditions with concomitant disadvantages: they suffer from high measurement uncertainties corresponding to large levels of error in the real time predictions and unreliability for a variety of operating conditions.

In this study, significant technological advances were achieved by utilizing nearest neighbor search processes (such as radial basis functions) along with an artificial neural network (ANN) algorithm. This technique is simple to implement, device independent and was demonstrated successfully for predicting the melt fraction of a PCM with high accuracy and robustness. With this method, the melt fraction of a PCM can be accurately determined, which allows the maximum thermal capacity of a PCM to be utilized while mitigating reliability issues (such as subcooling) and enhancing the thermodynamic efficiencies of the TES platforms. Melting experiments were performed using a digital camera (for video recording) and a graduated cylinder containing PCM for monitoring the transient values of the melt fraction based on the height of the liquid phase of the PCM in the cylinder. An array of 3 thermocouples was placed at predetermined heights within the body of the PCM to monitor the transient propagation of the melting process within the PCM. In the final stages of the melting process, the predictions from the ML algorithm was found to be more accurate (90∼95% accuracy) than that of the conventional techniques based on physics-based solvers (∼60% accuracy). The accuracy of the ML algorithm was low at smaller melt fractions (∼30%) and improved substantially at higher melt fractions (∼95%).

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