## 3 Methodology  
To conduct this study, a database with extensive studies on the relevant topics is required. Currently, numerous academic databases are available, with Google Scholar [39], Web of Science [40], and Scopus [41] being among the most prominent. The literature includes comparative studies assessing the effectiveness of these databases across various research fields. For instance, the research carried out by [PERSON] et al. [42] validated the effectiveness of Web of Science and Scopus in disciplines such as natural sciences and engineering while also highlighting their limitations in fields like social sciences and the arts. Additionally, they highlight that although alternatives such as Google Scholar exist, its reliability is often questioned due to inconsistencies in its database. A further notable example was the study by [PERSON] et al. [43] on medical and biomedical research. Their analysis compared Scopus, PubMed [44], Web of Science, and Google Scholar, evaluating factors such as database size, research coverage, document languages, and other key characteristics. The study concluded that, while PubMed is highly valuable for biomedical research, databases like Scopus and Web of Science provide greater data volume and, consequently, broader citation coverage. Regarding Google Scholar, concerns were raised about its reliability due to the lack of transparency in its content. Considering both the literature and the scope of this study, the most appropriate databases are Web of Science by Clarivate Analytics and Scopus by Elsevier. In this case, Scopus was selected due to its broader database, greater coverage of publications, availability in multiple languages, and ease of bibliographic tracking and citation [45; 46]. Currently, the Scopus database includes more than 26,000 journals, 292,000 books, and references from publishers such as Springer [47], IEEE [48], and others [49]. After selecting Scopus as the used database, the available literature was filtered to assess existing research on \"computational fluid dynamics\", \"artificial intelligence\", and \"thermal energy storage\", the key topics of this study. To ensure a comprehensive analysis, two queries were performed. The first query, with a broader scope, focused on \"computational fluid dynamics\" and \"artificial intelligence\". The second query was more specific, incorporating the previous topics and \"thermal energy storage\", to study the impact of these technologies in the specific area. The queries performed are presented in Table 1.  
To maximize relevant literature inclusion, the queries incorporated various related terms. Below, some of these terms and the reason for their inclusion are detailed:  
The category of computational fluid dynamics included terms related to widely used CFD software, such as \"COMSOL\", \"ANSYS\", \"Open FOAM\", and \"SolidWorks\" [50]. Additionally, the commonly used abbreviation CFD was included, as it frequently appears in research keywords. Terms corresponding with artificial intelligence such as \"machine learning\" [15], \"neural networks\", and \"deep learning\" [51] were selected. These terms directly relate to AI development, and the academic literature typically emphasizes specific AI methodologies rather than the general term \"artificial intelligence\". To ensure a comprehensive search while maintaining relevance to the previous topics, a final term related to thermal energy storage was used; terms such as \"phase change material storage\", \"latent heat storage\", \"sensible heat storage\", \"thermal energy reservoir\", and \"sorption heat storage\" were incorporated [30; 52; 53]. Several attempts were made to find the best query that reflected the objective of the paper. The quality of the query was checked from the outcome of the Scopus database and the keyword analysis. Data for both queries was collected on 1 January 2025, covering publications from 1985 to 2024. A total of 9623 publications were retrieved for the first