## 2 AI in CFD: Overview and Perspective
### CFD for Thermal Energy Storage  
Among the applications that have benefited from advances in CFD is thermal energy storage (TES). In this field, materials often analyzed using fluid dynamics are employed to transport or store thermal energy. Within these TES systems, storage tanks require CFD analysis to simulate scenarios, materials, and geometries; optimize designs; or evaluate their efficiency and performance [29; 30]. In this context, CFD played a crucial role in TES, enabling the modeling of phenomena such as heat transfer, flow distribution, and the design of advanced thermal systems. One example of CFD analysis in TES tank systems was conducted by [PERSON] et al. [31], where the COMSOL software was used for CFD analysis to study the performance of various phase change materials in a TES tank focused on latent heat TES [30]. In the study, CFD analysis helped identify the optimal material among those analyzed by examining the temperature in the used topology. Another example demonstrating the advantages of this tool was the research by [PERSON] et al. [32], which employed CFD to model and validate a latent heat TES tank model. The study included several comparisons of different variations in the same design. Finally, to illustrate the importance of CFD in TES systems, the research of [PERSON] et al. [33] and [PERSON] et al. [34] can be mentioned, where a comprehensive review of various studies in two different TES areas using CFD analysis to study geometries, materials, and other aspects in the application of sensible thermal energy and latent thermal energy, respectively, was conducted. However, the use of CFD in TES, as in other areas of study, still faces challenges due to system complexity, the high computational costs associated with detailed simulations, and the costs derived from its use. A solution to these problems was the incorporation of artificial intelligence, which, as mentioned earlier, has begun to transform the overall landscape of CFD by offering innovative solutions to overcome these limitations. In the literature, some examples of AI use in CFD in the field of thermal energy storage can be found. One example is the research conducted by [PERSON] et al. [35], which seeks to optimize the design process of latent heat storage devices, maximizing energy storage capacity and heat transfer rates for different device characteristics. The combination of ML and CFD techniques significantly reduced computational costs to find optimal configurations. Another example in the literature was the study by [PERSON] et al. [36], which proposed a hybrid system of CFD and AI tools to optimize TES units based on phase change materials (PCMs). The proposal aims to optimize units with fins (used for thermal energy transmission) by modifying parameters such as the length and location within the unit. The results highlight significant improvements in the design of the system and efficiency. Despite these examples and the optimal results of using AI in CFD in other fields, the literature on AI use in CFD in the TES context remains relatively scarce. To more clearly understand the current impact of AI in the field of CFD, and more specifically in using CFD for TES, this article conducted a bibliometric analysis of the existing literature up to late 2024 to provide a comprehensive context in the mentioned areas. Bibliometric analysis is a recognized tool in academia for identifying new approaches, listing high-interest research and researchers, and providing information on the current state of a field or area of interest [37; 38]. In this study, two fields were analyzed through bibliometric methods: The first was a generalization of CFD technology and its integration with AI. The second focused specifically on the use of CFD in TES and its combination with AI. This approach was chosen due to the familiarity between the two fields, with the second being a branch of the first.