Artificial Intelligence Applied to Computational Fluid Dynamics and Its Application in Thermal Energy Storage: A Bibliometric Analysis

Abstract

Computational fluid dynamics became an essential tool for analyzing complex fluid behavior, with applications ranging from aerospace engineering to renewable energy systems. Recent advancements in artificial intelligence further enhanced computational fluid dynamics capabilities, improving computational efficiency and predictive accuracy. However, despite its widespread adoption, the integration of artificial intelligence in computational fluid dynamics for thermal energy storage remained an underexplored research area. This study presented a bibliometric analysis of the existing literature on artificial intelligence applications in computational fluid dynamics, with a specific focus on thermal energy storage systems. By comparing two research domains—artificial intelligence in computational fluid dynamics and artificial intelligence in computational fluid dynamics applied to thermal energy storage—this paper identified a significant gap in the latter, as reflected in the low number of publications, limited collaboration networks, and weak citation relationships. While artificial intelligence-driven computational fluid dynamics research expanded across multiple disciplines, its application in thermal energy storage is still in its early stages, highlighting the need for further investigations. The results indicated a growing interest in artificial intelligence-enhanced computational fluid dynamics models for thermal energy storage optimization, particularly in areas such as heat transfer, phase change materials, and system efficiency improvements. The results also included an analysis of leading contributors to this field, along with emerging countries’ contributions. A study of the key publication sources with a high impact in this domain was also included.

Keywords

• artificial intelligence
• computational fluid dynamics (CFD)
• thermal energy storage (TES)