## 4 Results and Discussion
### First Query: Use of AI on CFD
#### 4.1.5 Analysis About Author  
Table 3 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, [PERSON] and [PERSON], 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. Figure 7: Analysis of the most published sources. (**a**) Citation analysis of the top 10 sources. (**b**) Bibliographic matching analysis (20 sources). The author with the most publications was [PERSON] [PERSON] 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 [PERSON] 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 [PERSON] 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 [PERSON], the next most prolific authors were [PERSON], [PERSON] from Ton Duc Thang University, Iran, and [PERSON], [PERSON] from Southwest Jiao Tong University, China. In this case, both authors, [PERSON] and [PERSON], had frequently collaborated, and their research areas were closely related. The most cited article of [PERSON] 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, [PERSON]'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 ([PERSON], [PERSON] and [PERSON], [PERSON]) primarily collaborated only with each other (within the top