A comprehensive review and statistical analysis of studies on neural networks for predicting nanofluid viscosity: Highlighting top researchers, publications, and countries (2011−2023)

This paper presents a scientometric analysis of nanofluid viscosity research that employs artificial neural network methods for viscosity prediction. A total of 211 documents published between 2011 and 2023 were analyzed to identify leading researchers, top journals, publication trends, frequently used keywords, and common nanoparticles. The analysis revealed that 2021 was the most productive year with 44 publications. Powder Technology, Journal of Molecular Liquids, and Journal of Thermal Analysis and Calorimetry were the top three journals in this field. Among the authors, M. Hemmat Esfe ranked first with 43 publications and 1937 citations. The most frequent keywords were nanofluidics, viscosity, nanofluids, neural networks, and artificial neural networks. The most commonly investigated nanoparticles included Al₂O₃, CuO, SiO₂, TiO₂, MWCNT, MgO, ZnO, Fe₃O₄, carbon, and graphene. These findings provide a comprehensive overview of the development trends and key contributors in artificial neural network-based nanofluid viscosity studies. • This article deals with the scientific analysis of studies that have used ANN method. • Most articles in the field of NF viscosity with ANN are related to 2021 with 44 articles. • Powder Technology is the top three journals in the field of NF viscosity with ANN. • Hemmat Esfe, M, with 43 documents and 1937 citations, is in the first place among active authors. • Nanofluidics, Viscosity, Nanofluids, Neural Networks and ANNs are frequent in studied field.