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.

1. Introduction

Since the very start of the widespread adoption of computers as tools, engineers have been using their power to simplify their work or, even, perform tasks that were deemed impossible before. One such task is solving problems related to fluid dynamics problems: aerodynamics, thermal energy transfer, liquid and gas behavior, etc. Those problems have a wide range of applications in real life, from plane building to thermal energy storage systems designs and from oil pipeline design to wave resilience for ships and boats. It is no surprise, then, that the approaches to solving these problems have captured the attention of several researchers and engineers, thereby allowing them to build and design increasingly complex and capable algorithms and methods to provide engineers with ever-improving tools. One such approach is computational fluid dynamics (CFD); it is a relatively recent tool that, along with algorithms, numerical methods, and computational technology, enables the modeling, solving, and analysis of complex problems related to fluid dynamics, heat transfer, and the behavior of liquids and gases. Currently, its use is widely adopted across various fields, including the design and testing of propulsion systems, vehicles, and aircrafts; medicine, with applications such as analyzing blood flow or air circulation in the lungs; and disciplines like architecture and renewable energy, among many others [1,2,3,4].

2. AI in CFD: Overview and Perspective

2.1. CFD History and Overview

According to [1,2,3,4], CFD technology is implemented as computational software, offering a broad range of options from commercial solutions to open-source programs. Some of the most recognized CFD software include ANSYS Fluent [5], COMSOL Multiphysics [6], Open FOAM [7], Autodesk CFD [8], and Simcenter STAR-CD [9], each designed to address different approaches and scales of simulations in fluid dynamics. These tools enable the application of diverse analysis and design alternatives to complex problems through algorithms that employ advanced numerical methods and the discretization of equations governing fluid behavior.

Among the most commonly used numerical methods are the finite volume method (FVM) [10], which calculates the flow through the surfaces of small cells dividing the domain; the finite difference method (FDM) [11], which approximates the derivatives using differences at discrete points in a mesh representing the domain; and the finite element method (FEM), which tries to approximate the solution by solving simple equations (such as polynomials) on a domain divided into subdomains [11]. Other more specific techniques, such as discrete particle dynamics (DPM), which interprets the behavior of discrete elements of a system, such as particles or granular solids [12], and adaptive mesh refinement (AMR), which modifies the mesh of a domain to improve accuracy in specific regions [13], have emerged. These techniques, combined with advances in hardware and software, have significantly improved the accuracy and efficiency of simulations over time.

In its early stages, during the second half of the 20th century, CFD was limited to solving simplified equations, such as linearized Navier–Stokes equations, due to the computational constraints of the time. It initially emerged within the aerospace and military contexts, with applications focused on basic flows. As computer capabilities improved during the 1970s and 1980s, more advanced numerical methods, such as the finite volume method, were developed, and the first specialized programs, such as ANSYS Fluent, among others, appeared [2,3,4].

2.2. Using AI in CFD

With the advancement of the 20th century and the increasing use of artificial intelligence (AI), researchers began to implement this new technology in CFD for various purposes. Among the most common uses of AI in CFD are the acceleration of calculations, whether equations or algorithms, through neural networks that replace or complement parts of the traditional process; the generation of CFD simulations via AI; or the creation of hybrid models that combine CFD and AI for different aspects [14,15,16,17,18].

Although it is a relatively recent field of research, many of the most common AI techniques were applied for its use in CFD. There are researchers leveraging the capabilities of genetic algorithms to produce optimal or quasi-optimal designs as in works by Hacioğlu et al. [19] and Lira et al. [20], who combine genetic algorithms with neural networks and apply that hybrid approach to optimization in aerodynamic designs and micro photoreactor designs, respectively. Both studies concluded that, for their specific areas, these new implementations achieved optimal results compared to conventional CFD tools, improving efficiency and result accuracy [19,20].

Artificial neural networks (ANNs) are one of the most commonly used AI techniques to complement or substitute CFD. The nature of most CFD problems, represented as bidimensional data point matrices evolving along the time axis, kind of image-like or video-like, suits perfectly with most of the deep neural network (DNN) approaches, such as convolutional neural networks (CNNs), autoencoders (AEs), and recurrent neural networks (RNNs).

For example, the study by Usman et al. [21] proposed the use of spectral convolutional neural networks to optimize the solution of partial differential equations (used in CFD); the results indicate that the predictions were accurate in transient flow patterns around submerged bodies, although with limitations that subsequent studies aim to improve. Studies by Zhou et al. [22] and Chickerur et al. [23] propose using artificial neural networks [24] and convolutional neural networks [25], respectively, as alternatives to conventional CFD methods. In both studies, CFD simulations were used to train and validate the neural networks, achieving results and demonstrating that neural networks were an optimal and accurate alternative that can reduce computational costs. One of the most promising neural network-based approaches is the use of physically informed neural networks (PINNs); they are neural networks constrained during the learning (building) phase by the governing equations behind the physical model they are representing. That approach combines the generalization power of neural networks with the precision of the physical model equations. Works like [26,27,28] use PINN to approach the solutions of CFD problems by using those neural network models as a surrogate model of the Navier–Stokes equations governing CFD. Although neural network models require high volumes of data and time to be trained (built), once that phase is completed, they can infer (solve) CFD problems in a fraction of the time it takes a CFD solver. (Most neural networks have inference times that are nearly instantaneous).

2.3. 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 Hathal 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 Bahlekeh 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 Chekifi et al. [33] and Abidi 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 Zheng 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 Rabienataj 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. Thus, the first field of analysis provides a general context that facilitated the approach to the second field, also highlighting the existing gap in terms of the current literature in the latter. The aim of this research is to offer a review of the state of the art that provides a general overview of advancements in AI use in CFD and other fields to fill the current gaps for implementing AI in CFD for TES.

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 Mongeon 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 Falagas 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. Queries used in the Scopus database.

Topic	Query
IA on CFD	(“computational fluid dynamic*” OR “fluid dynamic* simulation” OR “CFD software” OR “comsol” OR “ansys” OR “openfoam” OR “solidWorks” OR “simulation software” OR “CFD”)
IA on CFD implemented on TES	(“thermal energy storage” OR “TES tank*” OR “heat storage*” OR “phase change material* storage” OR “latent heat storage*” OR “sensible heat storage*” OR “thermal energy reservoir*” OR “sorption heat storage “) AND (“computational fluid dynamic*” OR “fluid dynamic* simulation” OR “CFD software” OR “comsol” OR “ansys” OR “openfoam” OR “solidWorks” OR “simulation software” OR “CFD”) AND (“artificial* intelligence*” OR “neur* net*” OR “machine learning” OR “deep learning” OR “deep reinforcement learning” OR “artificial intelligence algorithm*” OR “artificial intelligence model*” OR “artificial intelligence technique*”)

.

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 query and 36 for the second. The extracted data included citation details, bibliographic information, keywords, and funding details. Duplication of the literature in this case was not detected since only Scopus database was used; nevertheless similar keywords used in the documents that might create overlaps in the visual maps in the software VOSviewer v.1.6.20 were grouped using a thesaurus file.

To analyze the data obtained from Table 1 queries, Python [54] was used for visualization and extracting relevant insights. Additionally, VOSviewer [55] facilitated an in-depth bibliometric analysis. This software employs the VOS (visualization of similarities) method, a unidimensional method distinct from other visualization methods such as multidimensional scaling, and a similarity measure known as association strength or the proximity index [56,57,58]. This allowed us to assign scalable values to these relationships. VOSviewer enables the formation of “clusters” that highlight shared characteristics or distinctions [56,57]; in terms of bibliometric analysis, the relationships examined include citations, co-authorship, term co-occurrence, and other relevant connections [59].

The analysis included geographic distribution, language distribution, publication trends by year, funding sources, institutional affiliations of authors, trending contributing authors, and keyword analysis for both queries. This approach aimed to provide a comparative framework for the second query, identifying potential research gaps and opportunities for further scientific advancements in AI applications for CFD and TES.

4. Results and Discussion

To present the results of this study, the findings from the first query, which focused on AI and CFD, were introduced first. Next, the results and analysis of the second query, which examined AI and CFD applications in TES, were presented. Finally, a brief comparative analysis of both queries was provided.

4.1. First Query: Use of AI on CFD

The Scopus database query retrieved a total of 9364 publications from 1984 to 2024. Despite this extensive time range, the number of annual publications remained below 50 until 2004 and did not exceed 100 until 2009. As illustrated in Figure 1, the past two decades had witnessed a more than 3500% increase in publications related to artificial intelligence in computational fluid dynamics, with the most significant surge occurring in the last decade, reaching 1898 publications in 2024.

The fast rise in AI-related CFD publications was likely driven by multiple technological advancements that have facilitated the broader adoption of artificial intelligence across various disciplines. The expansion of training datasets and improvements in computational hardware were key enablers of large-scale AI model development. For instance, in 2010, most models required fewer than 100 petaflops of computational power, whereas by 2023, the majority demanded over 1 million petaflops on average [60]. This trend also aligns with the continued evolution of computational modeling, which has historically progressed alongside these technological advancements, further contributing to the increasing integration of artificial intelligence [61,62].

Regarding the language distribution of the retrieved publications, Figure 2 shows that English was the predominant language (94.5%), as it was consistently used since the earliest recorded publication in the query. It was followed by Chinese (5.1%), which has gained prominence since the early 21st century, experiencing a sharp increase in 2023 when the number of publications in this language doubled compared to 2022. The remaining languages, accounting for 0.48% of publications, were Korean, followed by French. While French presented in publications since 2002, Korean exhibited notable growth, with six articles published in this language in 2024.

These findings underscored the strong preference for English as the primary language of scientific publications. However, they also suggested a gradual shift towards Chinese, likely driven by the increasing number of articles published by Chinese researchers and the country’s substantial investment in research through initiatives such as Made in China 2025 [63,64].

4.1.1. Analysis of Countries

Additionally, an analysis of the country of origin of the retrieved publications was conducted. After excluding entries without a defined country (497 publications), administrative regions of other nations, and non-UN-recognized entities [65], a total of 103 countries were identified as contributors to AI and CFD research. Figure 3 presents the ten countries with the highest number of publications, with China leading (34% of the total), followed by the United States (16%). Below 1000 publications, India and the United Kingdom rank next.

Among the top 10 countries, 3 belong to the European Union [66]: Germany (541 publications), Italy (263 publications), and France (237 publications). If the European Union was considered a single entity in terms of publication output in this field, it would rank second, with a total of 1812 publications.

Based on publication volume in this research area, China and the United States emerged as the leading countries in the field. These results aligned with previous studies, highlighting their dominance in AI investment and model development in recent years [60,67].

Figure 4a illustrates the co-authorship relationships among the top 10 publishing countries. It revealed a strong collaboration between the United States and China in joint publications, as well as their capacity to engage with any of the ten most productive countries. Within Europe, the close cooperation among the European Union Member States was evident, but there was also a lack of stronger collaborations with Asian countries. Additionally, Figure 4a shows the temporal evolution with respect to the average year of publication of co-authorship for each country, highlighting China’s emergence as a modern leader in this field, the increasing contributions of Asian countries in recent years [60], and the continued relevance of Europe as a significant research force.

Figure 4b provides a broader perspective on collaboration networks, measured through co-authorship, including countries with at least ten publications. The generated clusters generated using the VOS method are displayed in different colors, emphasizing the strongest collaborations. Three major clusters stand out: the yellow cluster, including China and the United States; the red cluster, comprising India, Turkey, and other nations; and the green cluster, featuring the United Kingdom, Germany, and Italy.

Unlike the yellow cluster, where intercontinental collaboration is evident, the red and green clusters exhibited a more regional focus. The green cluster included many European countries, with the United Kingdom and Germany maintaining strong collaborations with non-European countries, while nations with fewer publications, such as Italy and Spain, had more limited international connections. Additionally, the green cluster demonstrated stronger collaboration with Latin American countries, such as Chile and Ecuador, than with Asian or Oceanian nations. The red cluster, led by India, predominantly consisted of Asian countries but also included African and Oceanian nations, reflecting robust collaboration across these regions.

4.1.2. Analysis of Institutions

This query also included an analysis of the institutions affiliated with each publication. As shown in

Table 2. Top 10 affiliated institutions with the highest number of publications for the first query made.

Affiliation Institutions	# of Publications	Country
Ministry of Education of the People’s Republic of China	206	China
Chinese Academy of Sciences	119	China
Xi’an Jiao tong University	110	China
Zhejiang University	108	China
Northwestern Polytechnical University	105	China
Shanghai Jiao Tong University	97	China
Tsinghua University	95	China
Beihang University	82	China
Harbin Institute of Technology	82	China
Tianjin University	79	China

, the ten institutions with the highest number of publications were all located in China. The first institution outside of China appears at rank eleven: Imperial College London in the United Kingdom, with a total of 78 publications.

Considering this criterion, the countries with the highest number of affiliated institutions are China (59), the United States (28), the United Kingdom (9), and Iran (9). These figures once again highlight China’s significant efforts in research and innovation, particularly in the field of artificial intelligence.

A key observation from Table 2 is the dominance of China with two leading research institutions: the Ministry of Education of the People’s Republic of China (MOE) [68] and the Chinese Academy of Sciences [69]. These public institutions represent the most significant entities for education and scientific research in China. Both were established in 1949, with the MOE focused on overseeing education at all levels and the Chinese Academy of Sciences dedicated to advancing scientific research.

As a governmental institution, the MOE is primarily funded by the Chinese central government. In addition to managing research centers and universities, it is responsible for all aspects of education nationwide. Meanwhile, the Chinese Academy of Sciences, although also a public institution, receives a higher proportion of private funding compared to the MOE. It is more research-oriented than its counterpart and operates over 100 research centers across China [70,71,72].

4.1.3. Analysis of Funding Sponsor

Both publications and research institutions require financial support to conduct investigations. Using data retrieved from the Scopus database, an analysis was conducted on the ten funding sponsors with the highest number of publications in this query. Figure 5 presents a relational graph illustrating the connection between funding sources, the number of publications they financed, and their country of origin, proportional to other nations with top 10 funding sources.

The chart in Figure 5 once again highlights China’s dominance in research, this time in terms of financial support, with 4 of the top 10 funding entities (39 in total). Among these, one key institution stands out: the National Natural Science Foundation of China [73], administered by the Ministry of Science and Technology of the People’s Republic of China (MOST) [74]. In 2017 alone, MOST allocated over USD 4.7 billion to the National Natural Science Foundation of China for research projects [75].

Figure 5 also presents funding sponsors from the United States. In this case, both U.S. funding agencies are federal institutions dedicated to innovation and research. The U.S. Department of Energy (DOE) [76], as its name suggests, oversees a broad range of initiatives beyond research. However, in 2022, it accounted for 30% of all research funding in the United States [77]. Meanwhile, the National Science Foundation (NSF) [78], a research-focused agency, received USD 9.6 billion in 2024 to fulfill its mission [79]. Although not all these funds were specifically allocated to the research area of this study, the data demonstrated the United States’ commitment to maintaining its global competitiveness in scientific research.

The remaining countries represented in Figure 5 include the United Kingdom, South Korea, and the European Union. Notably, the European Commission managed the Horizon 2020 funding program, an initiative designed for the period of 2014–2020 to drive European innovation in fields such as renewable energy, artificial intelligence, and other emerging technologies. This program allocated over EUR 80 billion over its six-year duration, of which EUR 7 billion were invested in more than 1900 AI-focused projects. Following its conclusion, the European Commission extended the program for the 2021–2027 period under the Horizon Europe initiative [80,81,82].

An example of Horizon 2020 investment in the research area of this study was the CoE RAISE project, which focuses on AI applications and computational modeling in high-performance computing systems [83].

4.1.4. Analysis of Publication Source

Another analysis conducted in this query focuses on the publication sources with the highest number of publications. This analysis is particularly valuable for future researchers in this field, as it provides useful insights for making informed decisions when selecting where to publish their findings to maximize reach and impact. From a total of 161 potential journals, the analysis focused on the top 10 journals with the highest number of publications. Additionally, their impact was assessed using the h-index [84] and the SJR metric, which is based on data from Scopus [85].

Figure 6 presents a scatter plot of the top 10 publication sources with the highest number of publications. The x-axis represents the SJR value, while the y-axis represents the number of publications. Additionally, the h-index is visually represented through the color and size of each data point corresponding to the publication sources.

In general, the most prominent sources in the graph correspond to those with the highest number of publications, either due to their longevity or their relevance in the field. The sources positioned furthest to the right exhibit the highest impact according to the SJR metric, which evaluates citation quality rather than quantity, based on the last three years of data. Additionally, the size and color of each element in the graph represent the h-index, which reflects the historical impact of the journal. The quartile ranking (2024) of each publication source is also included.

Among the top ten publication sources, the four journals that stand out due to their high impact are Energy (published by Elsevier), Applied Thermal Engineering (published by Elsevier), Physics of Fluids (published by AIP Publishing) [86], and Lecture Notes in Computer Science (including the subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, published by Springer).

Lecture Notes in Computer Science had a high number of publications and the highest h-index among the listed sources. However, its current impact, as measured by SJR, was relatively low (below 1), suggesting that despite its large volume of publications, its overall influence may be limited.

In contrast, Energy and Applied Thermal Engineering had fewer publications but exhibit respectable h-index values and the highest SJR scores among the analyzed sources. These journals represent viable options for researchers seeking to maximize the impact of their publications. However, given their higher impact, publishing in these journals may pose greater challenges. They are particularly relevant for studies integrating the topics of this query with broader research domains.

Another critical aspect in evaluating publication sources is their citation impact. Two analyses were conducted to determine the most optimal journals for publication, based on citation frequency and interconnections.

Figure 7a presents an analysis of citation relationships among the publication sources. In this regard, Physics of Fluids, Energy, Energies, and SAE Technical Paper (yellow cluster) stood out as the most highly cited sources of publication, demonstrating strong citation interactions with most other sources. However, within the scope of this study, publication sources did not exhibit strong mutual citation relationships.

Figure 7b provides a bibliographic coupling analysis of the top 20 publication sources. In this case, “Physics of Fluids” emerged as the most frequently cited source of publication, often appearing in co-citations with sources such as “Ocean Engineering” and “IEEE Access”. The visualization revealed two distinct clusters: the green cluster, which focused on engineering and computing, and the red cluster, which focused on energy and construction.

This classification may serve as a valuable tool for researchers when selecting an appropriate publication source, depending on the specific focus of their study.

4.1.5. Analysis About Author

Table 3. Authors with the highest number of publications in the first query, the institution they belong to, and the country of this institution.

Author	# of Publications	Institution	Country
Babanezhad, Meisam	33	Duy Tan University	Vietnam
Shirazian, Saeed	28	Ton Duc Thang University	Iran
Li, Wei	25	Southwest Jiao Tong University	China
Wang, Wei	21	-	China
Hang, Weiwei	20	Northwestern Polytechnical University	China
Marjani, Azam	20	Ton Duc Thang University	Vietnam
Pal, Pinaki	20	Argonne National Laboratory	United States
Liu, Jie	19	National University of Defence Technology	China
Rezakazemi, Mashallah	18	Shah rud University of Technology	Iran
Liu, Wei	18	-	China

presents the top 10 authors relevant to this query, ranked by the number of publications. The data, obtained using the VOSviewer tool, considered authors with more than five publications and display the latest institution to which each author was affiliated, along with the country of that institution. In Table 3, two authors, Wang Wei and Liu Wei, were listed without an institutional affiliation. This occurs because the VOSviewer tool aggregates results for multiple authors that sharing the same name, generating multiple possible institutions for each. This naming ambiguity also led to inaccuracies in the reported number of documents and citations associated with these authors.

The author with the most publications was Babanezhad, Meisam from Duy Tan University, Vietnam. His most cited paper was “Prediction of fluid pattern in a shear flow on intelligent neural nodes using ANFIS and LBM” [87] (52 citations); in this study Babanezhad used a computational method and a hybrid system of neural networks and fuzzy logic to predict the fluid behavior in shear flow. The study concluded that integrating AI-driven inference with physics-based simulations enhances predictive capabilities in complex fluid dynamics problems. The second most cited paper was titled “Liquid-phase chemical reactors: Development of 3D hybrid model based on CFD-adaptive network-based fuzzy inference system” [88] (46 citations); in this case the author described the development of a 3D hybrid model for liquid-phase chemical reactors by integrating CFD and the hybrid system of neural networks and fuzzy logic. The model enhanced accuracy, reduced computational cost, and improved reactor performance analysis.

Many of the most cited documents by Babanezhad focus on the application of the hybrid model known as ANFIS (adaptive neuro-fuzzy inference system) [89], which integrates artificial neural networks and fuzzy logic to enable learning and prediction in complex systems, particularly in fluid dynamics applications [90,91,92,93].

Following Babanezhad, the next most prolific authors were Shirazian, Saeed from Ton Duc Thang University, Iran, and Li, Wei from Southwest Jiao tong University, China. In this case, both authors, Shirazian and Babanezhad, had frequently collaborated, and their research areas were closely related. The most cited article of Shirazian was “ANFIS pattern for molecular membranes separation optimization” [94] (99 citations); in this study the author applied ANFIS to optimize molecular membrane separation processes. The model enhanced accuracy, reduced error experiments, and improved membrane performance.

Meanwhile, Li’s papers are specialized in heat transfer, fluid dynamics, and artificial intelligence [35,95]. His most cited paper was “Convolutional neural networks for steady flow approximation” [96] (632 citations), where he used convolutional neural networks (CNNs) to approximate steady flow solutions. The results showed that CNNs reduced computational costs while maintaining precision. A collaboration analysis among the authors identified in this query was also conducted. The results were visualized using VOSviewer.

Figure 8 shows the 500 most prolific authors (with more than five publications), highlighting extensive contributions and collaborations among a relatively small group of researchers. However, as seen in Figure 9, the two most published authors (Babanezhad, Meisam and Shirazian, Saeed) primarily collaborated only with each other (within the top 10 authors). This creates a distinct cluster (orange cluster) characterized by a high volume of publications but isolated from the core collaborative network.

Figure 9 further emphasizes the collaborative “core” identified in Figure 8. This visualization reveals the strong interconnections among many highly published authors while also illustrating the ambiguity issues with the names Liu Wei and Wang Wei. Additionally, Li, Wei (the third most published author) holds a central position in the graph, indicating a significant number of connections with most of the authors represented.

4.1.6. Keyword Analysis

The final analysis focused on the co-occurrence of keywords identified in the retrieved publications. Figure 10 presents the co-occurrence graph, generated from the query data, considering keywords with a minimum occurrence of five. This visualization highlights the 500 most frequently occurring keywords, revealing the presence of four distinct clusters: the green cluster, which encompasses terms related to fluid dynamics and its various applications; the blue cluster, which includes keywords associated with artificial intelligence and neural networks, particularly in the context of optimization; the red cluster, which focuses on medical and biomedical applications related to fluid flow; and the yellow cluster, which comprises terms associated with energy and heat transfer.

Figure 10 shows the strong interconnection between computational modeling and artificial intelligence methodologies, with AI primarily applied for optimization and error correction. Notably, the medical component appears more distantly related to CFD and AI applications, as its keywords, while present in the dataset, exhibit weaker direct associations with the core focus of this study.

4.2. AI Applied to CFD with a Focus on the TES Field

The second query can be considered a more specific search within the broader scope of the first query. The objective of this approach was to assess the current state of research in this area while establishing a comparative reference point. This query retrieved a total of 37 publications, including journal articles, conference papers, and book series, spanning from 2000 (the earliest recorded publication) to 2024.

Compared to the 9364 publications retrieved in the first query, this subset represented less than 0.5% of the total publication volume. The dataset included contributions from 132 authors, 81 institutions, and 27 publication sources. These figures highlight a significant “gap” in the literature regarding the application of artificial intelligence (AI) in computational fluid dynamics (CFD) for thermal energy storage (TES).

Of the 37 retrieved publications, 35 were written in English, while 2, both from 2024, were in Korean. The limited linguistic diversity in this dataset could facilitate future research, as fewer language barriers enhance information accessibility in this domain. When compared to Figure 2, which also recorded 10 Korean-language publications (6 from 2024), these findings further reinforce the dominance of English as the primary language of scientific communication.

Unlike the trend observed in Figure 1, where the first query demonstrated steady growth, research within this second query exhibited a much slower increase throughout the second decade of the 21st century. It was only after 2020 that the number of annual publications exceeded four. However, since then, the growth rate has aligned with the trend observed in the first query, as shown in Figure 11. This suggests that although research in this area has been ongoing for several years, it has only recently gained significant interest and growth potential.

4.2.1. Analysis by Countries

Figure 12 presents a map of the top ten countries with the highest number of publications in this research area. Countries that also appeared in the top ten of the first query are marked with yellow stars. The leading contributors in this second query are Iran, Germany, Canada, and Turkey, each with four publications. Unlike the first query, where a single country dominated the publication volume, the second query reflects a more globally distributed research effort.

Interestingly, not all the top ten countries from the first query appear in Figure 12. If the European Union was considered a single territorial entity in this analysis, it would lead the ranking with a total of eight publications. As in the first query, France and Germany play a key role in driving innovation in this field. Additionally, Spain, recognized for its focus on renewable energy, emerges as a significant contributor to thermal energy storage research.

To assess the collaborative capacity of the top ten publishing countries in this query, Figure 13a presents a co-authorship analysis. Iran and Australia, positioned at the center of the network, emerge as key players in research collaboration, having established connections with most of the other top ten countries. The figure also reveals two major clusters of collaboration, cluster 1, with Canada and European countries (excluding Spain), and cluster 2, with Turkey and the United States, which have also formed strong research partnerships.

Additionally, Figure 13b provides a broader perspective, showcasing all countries with publications related to the second query. In contrast to Figure 4b, the second query exhibits significantly lower levels of international collaboration. Apart from the connections between some of the top ten publishing countries, no additional collaborative clusters are observed. Notably, Spain and South Korea do not show visible collaborative links with other countries.

The graphs also illustrate the average publication year for each country, revealing that Iran, Turkey, Spain, India, and South Korea present opportunities for pioneering research in this field. Given this, the low level of international collaboration in this research area may be attributed to its relatively recent emergence as a novel topic of scientific inquiry.

4.2.2. Affiliation Analysis

To identify the key institutions involved in this research area,

Table 4. Top 9 institutions found in the affiliation with the highest number of publications for the second query.

Affiliation Institutions	# of Publications	Country
Concordia University	4	Canada
King Abdulaziz University	2	Saudi Arabia
China Medical University	2	China
CNRS (Centre National de la Recherche scientifique)	2	France
Pusan National University	2	South Korea
Iran University of Science and Technology	2	Iran
China Medical University Hospital	2	China
Universitat de Lleida	2	Spain
Istanbul Ticaret Üniversitesi	2	Turkey

presents the nine institutions with the highest number of publications related to this query. Concordia University (Canada) ranks first with four publications (282 citations in this query), followed by King Abdulaziz University (Saudi Arabia) with 75 citations.

In this case, none of the top 10 institutions from the first query appear in Table 4. This suggests that new universities specializing in AI for CFD have recently developed an interest in researching this emerging field, specifically its application in thermal energy storage systems.

4.2.3. Analysis of Funding Sponsor

Figure 14 presents the funding sponsor identified in the second query. The graph highlights the top five countries with the highest number of sponsors identified. Although it does not reflect the number of publications per country, it provides insight into the number of publications supported by each funding entity. The visualization includes both funding agencies and national funding programs, some of which may be administered by the same institutions.

For the second query, the European Commission was the only entity that has funded more than one publication. All other funding sponsors contributed to a single publication each. Among the countries with the highest number of funding entities, Australia (6) and Spain (6) lead the ranking. This significant number of financial sponsors suggests strong governmental and institutional efforts to support innovative research in this field. Additionally, the European Union reappeared in Figure 14, as represented by the European Commission and its Horizon 2020 program, further demonstrating the broad impact of this initiative. Meanwhile, the United States remains a key contributor in sponsoring research related to this area of study.

4.2.4. Analysis of Source

The next analysis focused on publication sources, considering journals with the highest number of publications in this query. Additionally, sources that were previously identified as high-publication journals in the first query (Energy, Energies, and Lecture Notes in Computer Science) were also examined. As in the first query, two impact indices were considered: the h-index and the SJR metric. Figure 15 also displays the quartile ranking of each source, where applicable.

For this query, the most frequently used publication source was the Journal of Energy Storage (published by Elsevier), which exhibits strong impact values in both the h-index and the SJR metric. However, the optimal publication choice would be Applied Energy (currently with three publications and published by Elsevier) due to its high impact scores. Energy (published by Elsevier) and Applied Thermal Engineering (published by Elsevier), both of which also appear in Figure 6, could also be considered. In this case these journals represented the best options for researchers seeking a high impact and increased citation potential.

More accessible alternatives include Energies (published by MDPI) and Lecture Notes in Computer Science (LNCS published by MDPI). While LNCS has a moderate SJR value, it holds the highest H-index among the analyzed sources, making it a strong candidate for publication in Q2 journals.

Figure 16 shows the top 10 of the 27 publication sources obtained from the second query. Among these, Energy Conversion and Management (published by Elsevier) had nearly twice as many citations as the Journal of Cleaner Production (published by Elsevier) and Applied Energy (published by Elsevier), the next most cited publication sources. These three show a significant difference compared to the other publication sources. To further explore publication sources and their interconnections, a bibliographic coupling analysis was performed, where the pairing between publication sources was very limited, forming only two clusters (Figure 17). One possible explanation for this phenomenon is the low number of publications in this query, which results in high-impact sources appearing without any bibliographic pairing with other journals. This effect is not observed in sources with multiple publications in the dataset, such as Applied Energy (three publications) and the Journal of Energy Storage (five publications).

Figure 17 presents the two bibliographic coupling clusters identified in this query. Figure 17a shows a weak connection between Applied Energy (published by Elsevier), Japan Architectural Review (Q3, SJR: 0.31, published by Wiley), and Building and Environment (Q1, SJR: 1.65, published by Elsevier). The weak linkage is attributed to the low number of publications in this query: Applied Energy (published by Elsevier) had only three publications, while the other two sources each have only one. Figure 17b represents the second cluster, containing several interrelated publication sources. The strongest connection is observed between Applied Thermal Engineering (published by Elsevier) and Mathematics (Q2, SJR: 0.48, published by Elsevier), suggesting that these journals frequently cite each other or share similar bibliographic references, positioning them within the same research domain.

The cluster structure reveals that some sources, despite being bibliographically linked, do not necessarily share the same research focus. As a result, three distinct research regions can be identified within the cluster: the first one, Applied Thermal Engineering; the second, the Journal of Energy Storage; and finally, the Transactions of the Korean Society of Mechanical Engineers B.

4.2.5. Authorship Analysis

Like the first query, an analysis was conducted to identify the authors with the highest number of publications in this field. Given the smaller dataset of this query, the number of publications per author is significantly lower than in the first query. However, this analysis further highlights the existing “gap” in the literature regarding the application of AI in CFD for TES. Additionally, it underscores the efforts of emerging institutions and researchers in developing this field.

Table 5. Authors with the highest number of publications in the second query, the institution they belong to, and the country of this institution.

Author	# of Publications	Institution	Country
Haghighat, F.	3	Concordia University	Canada
Akbari, H.	2	Concordia University	Canada
El-Sawi, A.	2	Concordia University	Canada
Ha, M.Y.	2	Pusan Nat’l Univ.	South Korea
Mateu, C.	2	Universitat de Lleida	Spain

presents the top five authors with the highest number of publications, along with their affiliated institution and country. Only 5 authors are listed, as the remaining 126 authors in the dataset have only one publication each.

In comparison with Table 3, beyond the expected difference in publication volume per author, one notable pattern emerges: the first three authors in this query are affiliated with a single institution: Concordia University in Canada. The authors Haghighat, Akbari, and El-Sawi have collaborated on two publications, both focused on the optimization of centralized thermal energy storage systems [97].

The most published author in this dataset is Haghighat, Fariborz, whose most cited paper is “Assessing Long-Term Performance of Centralized Thermal Energy Storage Systems” [97]; in this study, the author employed neural networks and CFD simulations to model and analyze the long-term performance of thermal energy storage (TES) systems. The model was based on latent heat storage using phase change materials (PCMs). The findings indicated that latent heat storage systems with PCMs offer long-term efficiency and that AI-based predictions provide high accuracy in forecasting their long-term behavior. This study is also the most cited article of Akbari, Hashem and El-Sawi, Azeldin.

The fourth author of the list was with Ha, Man Yeong, whose research focused on analyzing the impact of heat source placement and fin arrangement on the melting process of phase change materials (PCMs) in latent heat storage systems. His most cited paper is titled “Numerical Study on the Effect of Aspect Ratio and Center Distance of Elliptical Shell and Tube on the Melting Performance of the Latent Heat Storage” [98]; Ha, Man Yeong used numerical simulations to analyze how the aspect ratio and center distance affect the melting performance in latent heat storage systems. The results show that optimizing these parameters enhances heat transfer and improves energy storage efficiency. The fifth author was Mateu, Carles, whose research is centered on applying artificial intelligence techniques to energy systems. The most cited paper where he appears is titled “Use of Artificial Intelligence Methods in Designing Thermal Energy Storage Tanks: A Bibliometric Analysis” [99]. This study provided a bibliometric analysis of AI-based methodologies, such as machine learning and deep learning, applied to the design of thermal energy storage tanks.

4.2.6. Analysis About Keyword

The final analysis focused on the keywords identified in this second query. A total of 160 keywords were recorded, and

Table 6. Top 10 most frequent keywords in the second query.

Keyword	Occurrences	Keyword	Occurrences
heat storage	28	machine learning	13
artificial neural network	20	forecasting	10
phase change material	16	fins (heat exchange)	9
thermal energy storage	16	optimization	8
computational fluid dynamics	15	learning algorithms	8

presents the top 10 keywords most frequently occurring across the retrieved publications. Among the most frequently occurring terms, several noteworthy cases were identified, including “Fins (heat exchange)”, which refers to the use of fins to enhance heat transfer efficiency in thermal storage systems, and “Forecasting” and “Optimization”, both of which are strongly related to the CFD and AI fields. The papers [36,100,101] published in 0204 indicate that the optimization of TES heat exchangers using artificial intelligence is attracting a lot of interest that could support the overcoming of the actual barriers that TES suffer from especially when using PCMs.

Figure 18 presents a co-occurrence analysis of keywords appearing at least three times, covering a total of 37 terms. Figure 18a includes a temporal analysis, indicating a growing interest in terms such as “heat storage system”, “machine learning”, and “ renewable energies “. In contrast, older topics primarily refer to “air conditioning”, “simulation”, “ventilation”, and “energy utilization” [102]. The optimization of TES in energy systems using artificial intelligence supported by CFD could also be another research field with great potential of application to reduce the energy consumption of energy systems. This does not necessarily imply a decline in research interest. Certain keywords have maintained a steady level of interest since 2014, including “neural networks”, “deep learning”, and “thermal energy storage systems”.

Figure 18a presents the various co-occurrence clusters formed by the keywords extracted from the second query. The central part of the graph highlights key terms that bridge multiple clusters, such as “artificial neural network”, “heat storage”, and “computational fluid dynamics”. These keywords serve as fundamental elements across different types of publications in this query, regardless of their specific research focus. In contrast, terms positioned toward the outer edges of the graph represent more specialized or niche research topics within the broader field.

The red cluster is primarily composed of keywords related to optimization and energy efficiency. The green cluster focuses on “thermal energy storage” and “artificial intelligence”. The blue cluster focuses on “thermal energy”, “simulation”, and “computational fluid dynamics”, reflecting a highly relevant area for this study, particularly in the application and optimization of materials in TES technologies. The yellow cluster is the farthest from the center of the graph, and it contains terms related to “energy” and “storage”. The behavior of this cluster may be because, although these terms appear in the search, they are the least used in the context of the second query.

5. Conclusions

The findings of this study, based on two related queries, reveal a significant gap in the application of artificial intelligence in computational fluid dynamics for thermal energy storage. This gap is evident not only in the numerical disparity between the two queries, with the first focusing on artificial intelligence integration in computational fluid dynamics and the second incorporating thermal energy storage applications (1938 and 36 results), but also in the low levels of collaboration and citation within this emerging field. The lack of connections among authors and publication sources indicates that this research area is still in development, with limited consolidation of research networks.

Despite this gap, there is a gradual increase in the number of publications, suggesting a growing interest in this interdisciplinary domain during the third decade of the 21st century. This pattern of growth highlights the potential of artificial intelligence-driven computational fluid dynamics applications in thermal energy storage as an expanding research field, which may consolidate further in the coming years as new discoveries and methodologies emerge.

From a geographical perspective, China is emerging as a dominant player in this study area, surpassing the United States in scientific production, funding, and technological development. Meanwhile, the European Union continues to lead in investment and research, while new contributors such as South Korea, Iran, and India are gaining relevance in scientific output related to artificial intelligence in computational fluid dynamics.

In terms of research dissemination, the leading publication sources for researchers interested in artificial intelligence applied to computational fluid dynamics and its thermal energy storage-specific applications include “Applied Thermal Engineering”, “Applied Energy”, and “Energy”, which have a high impact within the scientific community. Alternatively, “Lecture Notes in Computer Science” represents a lower-impact but relevant option for computational research.

Finally, to achieve a deeper understanding of the topics covered in the second query (artificial intelligence integration in computational fluid dynamics applied to thermal energy storage), further time and research development are required. There is a pressing need for more extensive investigations, interdisciplinary collaborations, and methodological advancements to bridge the existing knowledge gap and enhance the integration of artificial intelligence techniques in computational fluid dynamics for thermal energy storage applications.