Journal Screenshot

International Journal of Academic Research in Progressive Education and Development

Open Access Journal

ISSN: 2226-6348

Bibliometric Analysis of Neural Network in improving Computational Fluid Dynamics Education

Nurul Hafizah Zainal Abidin, Ezzah Suraya Sarudin, Wan Noor Hayatie Wan Abdul Aziz, Izzati Khalidah Khalid, Yusrafizal Yusof

http://dx.doi.org/10.6007/IJARPED/v12-i2/17189

Open access

The integration of neural networks into computational fluid dynamics (CFD) has led to significant advancements in fluid dynamics simulations, enhancing accuracy and effectiveness. This convergence presents a promising avenue for studying fluid systems and has the potential to revolutionize our understanding of their complex behaviours. To foster collaboration and drive research in this field, a comprehensive bibliometric review was conducted, examining the intersection of fluid dynamics education and neural networks. From a pool of 999 articles gathered from the Google Scholar database, 779 articles published between 2018 and 2023 were selected using the Publishing or Perish (PoP) software. These articles were analysed and categorized using VOSviewer, revealing three distinct clusters representing the primary research topics in computational fluid dynamics and neural networks. The findings of this study provide valuable insights into the current state of research in this emerging field, offering a comprehensive overview of key themes and their relationships. These insights can guide researchers and practitioners in identifying gaps, exploring collaborations, and advancing interdisciplinary approaches. Moving forward, further exploration of the identified clusters and associated keywords can deepen understanding of specific research topics, while investigations into methodologies and techniques employed in the articles can contribute to the development of advanced computational tools and frameworks for fluid dynamics simulations. Collaborative efforts and interdisciplinary approaches hold great potential for advancing our knowledge of fluid systems and driving progress in this exciting field.

Ahmar, A. S., Kurniasih, N., Irawan, D. E., Sutiksno, D. U., Napitupulu, D., Setiawan, M. I., ... & Abraham
J. (2018). Lecturers’ understanding on indexing databases of SINTA, DOAJ, Google Scholar, SCOPUS, and Web of Science: A study of Indonesians. In Journal of Physics: Conference Series (Vol. 954, No. 1, p. 012026). IOP Publishing.
Ahmi, A., Elbardan, H., & Ali, R. H. R. M. (2019). Bibliometric analysis of published literature on industry 4.0. In 2019 International Conference on Electronics, Information, and Communication (ICEIC) (1-6). IEEE.
Brunton, S. L. (2021). Applying machine learning to study fluid mechanics. Acta Mechanica Sinica, 37(12), 1718-1726.
Brunton, S. L., Hemati, M. S., & Taira, K. (2020). Special issue on machine learning and data-driven methods in fluid dynamics. Theoretical and Computational Fluid Dynamics, 34(4), 333-337.
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285-296.
Ellegaard, O., & Wallin, J. A. (2015). The bibliometric analysis of scholarly production: How great is the impact? Scientometrics, 105, 1809-1831.
Hudha, M. N., Hamidah, I., Permanasari, A., Abdullah, A. G., Rachman, I., & Matsumoto, T. (2020). Low Carbon Education: A Review and Bibliometric Analysis. European Journal of Educational Research, 9(1), 319-329.
Kochkov, D., Smith, J. A., Alieva, A., Wang, Q., Brenner, M. P., & Hoyer, S. (2021). Machine learning–accelerated computational fluid dynamics. Proceedings of the National Academy of Sciences, 118(21), e2101784118.
Li, Z., Lu, J., Wang, G., Feng, L., Broo, D. G., & Kiritsis, D. (2021, September). A bibliometric analysis on model-based systems engineering. In 2021 IEEE International Symposium on Systems Engineering (ISSE) (pp. 1-8). IEEE.
Sudrajat, U., Ardianto, D., & Permana, I. (2022). Engineering design process: A review and bibliometric analysis. International Journal of STEM Education for Sustainability, 2(2), 180-192.
Van Eck, N. J., & Waltman, L. (2011). VOSviewer manual. Manual for VOSviewer version, 1(0).
Vinuesa, R., & Brunton, S. L. (2022). Enhancing computational fluid dynamics with machine learning. Nature Computational Science, 2(6), 358-366.
Yang, Y., Reniers, G., Chen, G., & Goerlandt, F. (2019). A bibliometric review of laboratory safety in universities. Safety Science, 120, 14-24.

In-Text Citation: (Abidin et al., 2023)
To Cite this Article: Abidin, N. H. Z., Sarudin, E. S., Aziz, W. N. H. W. A., Khalid, I. K., & Yusof, Y. (2023). Bibliometric Analysis of Neural Network in improving Computational Fluid Dynamics Education. International Journal of Academic Research in Progressive Education and Development, 12(2), 852–862.