In recent times, industry goals and national mandates have attempted 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. Organic PCMs such as paraffin waxes provide options with high reliability but suffer from poor thermal characteristics (low storage capacity and poor power ratings). 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 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 (and consequently large levels of error in the real time predictions, are finicky and are often unreliable 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 (requires 2\~3 thermocouples for transient monitoring and real-time predictions) and is device independent. This technique 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 were placed at predetermined heights within the body of the PCM to monitor the transient propagation of the melting process within the PCM. Based on the transient response of just one thermocouple the ML algorithm developed in this study successfully predicted the melt fraction as a function of time. Experimental validation of numerical predictions were also performed in this study to determine the efficacy of the ML algorithm. 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%).


Application of Machine Learning for Enhancing the Transient Performance of Thermal Energy Storage Platforms for Obviating Issues Relating to the  Food-Energy-Water (Few) Nexus

Division / Sponsors: 
Heat Transfer Division (HTD), Fluids Engineering Division (FED)

Heat Transfer Summer Conference
Paper Proceedings: https://asmedigitalcollection.asme.org/HT/HT2020/volume/83709

18th International Conference on Nanochannels, Microchannels, and Minichannels
Paper Proceedings: https://asmedigitalcollection.asme.org/ICNMM/ICNMM2020/volume/83693

Fluids Engineering Division Summer Meeting
Paper Proceedings: https://asmedigitalcollection.asme.org/FEDSM/FEDSM2020/volume/83716

HT-ICNMM2020 - Virtual Conference, SHTC - Virtual Conference, FEDSM2020 - Virtual Conference

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