Abstract
This bibliometric study examines the use of artificial intelligence (AI) methods, such as machine learning (ML) and deep learning (DL), in the design of thermal energy storage (TES) tanks. TES tanks are essential parts of energy storage systems, and improving their design has a big impact on how effectively and sustainably energy is used. With the increasing availability and advancements in AI techniques, researchers explored their application within the field of TES tank design to address challenges related to structural analysis, material selection, and optimization. However, a comprehensive analysis of the current state of research, trends, and potential areas for improvement is lacking. This study aims to bridge this gap by analysing scientific papers, patents, and publications in conference proceedings from the Scopus database to identify trends, research gaps, and areas of importance in this domain. The novelty of this study lies in its focused examination of AI methods specifically applied to TES tank design, providing a unique perspective on the intersection of these two important fields. By conducting a thorough bibliometric analysis, this study offers original insights into the current landscape and future directions of AI-enabled TES tank design research. The methodology involves a systematic search query and the use of VOSviewer software for keyword analysis. The results indicate a notable increase in publications during recent years (2020-August 2023), aligning with the growing focus on sustainable energy solutions. The most frequent keywords, such as TES, genetic algorithm (GA), and phase change material (PCM), suggest a focus on improving optimization of TES tanks with AI, while the least common keywords highlight specific areas with limited work and opportunities for future studies. In conclusion, this bibliometric analysis underscores the potential of AI to enhance the design and performance of TES tanks, emphasizes the value of further research in this area, and identifies opportunities for future advancements in AI-enabled TES tank design to contribute to energy efficiency and sustainability efforts.
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