Research Trend of Dynamic Fluid in Learning: A Bibliometric Analysis
DOI:
https://doi.org/10.21009/1.09209Keywords:
bibliometric analysis, dynamic fluid, physics learningAbstract
This article provides an extensive bibliometric literature review on dynamic fluid in learning. This study aims to analyze research trends related to dynamic fluid in learning topics in 2019-2023 through bibliometric analysis with the Scopus database. Based on the criteria obtained, 327 articles were obtained from 1598 documents. The articles have already been analyzed from the journal and conference proceedings indexed in Scopus. Dynamic fluid research trends in learning are also reviewed based on the number of articles published each year, sources of publication (both journals and proceedings), the most productive countries, the most productive authors based on the number of documents, and co-occurrences using VOSviewer. The results show that the dynamics of learning are increasing every year. JPCS has published the most dynamic fluid articles in learning. The United States, as the most productive country, researches this topic. In addition, most of the prolific authors come from the US. A visualization of dynamic fluid research trends in learning using VOSviewer software obtained five clusters. The results of this study provide direction for further research on fluid dynamics in learning.
References
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Ahmed, SE, Rahman, SM, San, O, Rasheed, A & Navon, IM 2019, ‘Memory embedded non-intrusive reduced order modeling of non-ergodic flows’, Physics of Fluids, vol. 31, no. 12, p. 126602.
AlSaleem, SS, Al-Qadami, E, Korany, HZ, Shafiquzzaman, M, Haider, H, Ahsan, A, Alresheedi, M, AlGhafis, A & AlHarbi, A 2022, ‘Computational fluid dynamic applications for solar stills efficiency assessment: A review’, Sustainability, vol. 14, no. 17, p. 10700.
Ansari, M, Gandhi, HA, Foster, DG & White, AD 2022, ‘Iterative symbolic regression for learning transport equations’, AIChE Journal, vol. 68, no. 6, p. 17695.
Astra, IM, Nurjanah, I, Raihanati, R & Fitri, LHA 2022, ‘Development of electronic module using TAI to improve HOTS of high school students in fluid dynamic materials’, Journal of Physics: Conference Series, vol. 2377, no. 1, p. 12071.
Babanezhad, M, Marjani, A & Shirazian, S 2020, ‘Multidimensional machine learning algorithms to learn liquid velocity inside a cylindrical bubble column reactor’, Scientific Reports, vol. 10, no. 1, pp. 1-14.
Benner, P, Goyal, P, Heiland, J & Duff, IP 2020, ‘Operator inference and physics-informed learning of low-dimensional models for incompressible flows’, arXiv preprint arXiv:2010.06701 [Preprint].
Buhendwa, AB, Bezgin, DA & Adams, NA 2022, ‘Consistent and symmetry preserving data-driven interface reconstruction for the level-set method’, Journal of Computational Physics, vol. 457, p. 111049.
Corrochano, A, D’Alessio, G, Parente, A & Le Clainche, S 2023, ‘Higher order dynamic mode decomposition to model reacting flows’, International Journal of Mechanical Sciences, vol. 249, p. 108219.
Dabaghian, PH, Ahmed, SE & San, O 2022, ‘Nonintrusive reduced order modeling of convective Boussinesq flows’, arXiv preprint arXiv:2212.07522 [Preprint].
Diansah, I & Asyhari, A 2020, ‘Effectiveness of physics electronic modules based on Self Directed Learning Model (SDL) towards the understanding of dynamic fluid concept’, Journal of Physics: Conference Series, vol. 1572, no. 1, p. 12024.
Fathurohman, C, Wibowo, FC & Iswanto, BH 2021, ‘Development of Android Physics Applications (APA) as learning media on dynamic fluid concepts’, Journal of Physics: Conference Series, vol. 2019, no. 1, p. 12059.
Halim, A, Safitri, R & Nurfadilla, E 2020, ‘Impact of Problem-based Learning (PBL) model through Science Technology Society (STS) approach on students’ interest’, Journal of Physics: Conference Series, vol. 1460, no. 1, p. 12145.
Halim, A, Ulandari, S, Hamid, A, Wahyuni, A, Syukri, M & Irwandi, I 2021, ‘The Development of student worksheets based on a scientific approach in the dynamic fluid concepts’, Journal of Physics: Conference Series, vol. 1882, no. 1, p. 12025.
Hasbi, JE, Samsudin, A & Chandra, DT 2022, ‘Measuring science process skills of K-11 students in dynamic fluid’, AIP Conference Proceedings, vol. 2468, no. 1, p. 20042.
Herdayanti, A & Manurung, SR 2020, ‘Development of aid tool using arduino uno sensor for dynamic fluid at senior high school’, Journal of Physics: Conference Series, vol. 1485, no. 1, p. 12003.
Hong, SW, Park, J, Jeong, H, Lee, S, Choi, L, Zhao, L & Zhu, H 2021, ‘Fluid dynamic approaches for prediction of spray drift from ground pesticide applications: A review’, Agronomy, vol. 11, no. 6, p. 1182.
Khan, U, Pao, W, Sallih, N & Hassan, F 2022, ‘Identification of horizontal gas-liquid two-phase flow regime using deep learning’, CFD Letters, vol. 14, no. 10, pp. 68-78.
Kholiq, A & Khoiriah, M 2021, ‘Online learning during the covid-19 pandemic with ELISA assistance on the concept of dynamic fluid: Student response analysis’, Journal of Physics: Conference Series, vol. 1805, no. 1, p. 12042.
Kiener, A & Bekemeyer, P 2022, ‘Correcting the discretization error of coarse grid CFD simulations with machine learning’, in World Congress in Computational Mechanics and ECCOMAS Congress.
Kim, JH, Roh, MI, Kim, KS, Yeo, IC, Oh, MJ, Nam, JW, Lee, SH & Jang, YH 2022, ‘Prediction of the superiority of the hydrodynamic performance of hull forms using deep learning’, International Journal of Naval Architecture and Ocean Engineering, vol. 14, p. 100490.
Koes-H, S, Nisa, IK, Faiqatul, HE, Wahyuni, T, Yuenyong, C, Sutaphan, S & Yuenyong, J 2021, ‘The development of lesson plan of the water pressure booster pump STEM education’, Journal of Physics: Conference Series, vol. 1835, no. 1, p. 12049.
Lu, L, Pecha, MB, Wiggins, GM, Xu, Y, Gao, X, Hughes, B & Parks II, JE 2022, ‘Multiscale CFD simulation of biomass fast pyrolysis with a machine learning derived intra-particle model and detailed pyrolysis kinetics’, Chemical Engineering Journal, vol. 431, p. 133853.
Malik, S 2019, ‘Assessing the teaching and learning process of an introductory programming course with bloom’s taxonomy and Assurance of Learning (AOL)’, International Journal of Information and Communication Technology Education, vol. 15, no. 2, pp. 130-145, Available at: https://doi.org/10.4018/IJICTE.2019040108.
Maulik, R, San, O, Jacob, JD & Crick, C 2019, ‘Sub-grid scale model classification and blending through deep learning’, Journal of Fluid Mechanics, vol. 870, pp. 784-812.
Maulik, R, Sharma, H, Patel, S, Lusch, B & Jennings, E 2021, ‘A turbulent eddy-viscosity surrogate modeling framework for Reynolds-averaged Navier-Stokes simulations’, Computers & Fluids, vol. 227, p. 104777.
Mortezazadeh, M, Zou, J, Hosseini, M, Yang, S & Wang, L 2022, ‘Estimating urban wind speeds and wind power potentials based on machine learning with city fast fluid dynamics training data’, Atmosphere, vol. 13, no. 2, p. 214.
Muliyati, D, Fatih, FHA, Permana, AH, Sari, NLK & Purwahida, R 2022, ‘Fire Phyghter”-the development of educational games for exploring dynamic fluids topic’, Journal of Physics: Conference Series, vol. 2377, no. 1, p. 12069.
Muliyati, D, Rahmah, A, Sunaryo, S & Susanti, D 2021, ‘The development of Android-based physics teaching materials on static fluids’, AIP Conference Proceedings, vol. 2331, no. 1, p. 30038.
Permana, GA, Parno, P, Hidayat, A & Ali, M 2021, ‘Improving creative thinking skill of fluid dynamic through IBL-STEM with formative assessment’, AIP Conference Proceedings, vol. 2330, no. 1
Ramli, Z & Serevina, V 2021, ‘E-book static fluid and dynamic fluid web-based with a problem-based learning model to improve students physics problem-solving skills’, Journal of Physics: Conference Series, vol. 1, p. 12001.
Saharuddin, KD, Ariff, MHM, Mohmad, K, Bahiuddin, I, Ubaidillah, Mazlan, SA, Nazmi, N & Fatah, AYA 2021, ‘Prediction model of magnetorheological (MR) fluid damper hysteresis loop using extreme learning machine algorithm’, Open Engineering, vol. 11, no. 1, pp. 584-591.
San, O, Pawar, S & Rasheed, A 2022, ‘Variational multiscale reinforcement learning for discovering reduced order closure models of nonlinear spatiotemporal transport systems’, Scientific Reports, vol. 12, no. 1, p. 17947
Sani, RA 2021, ‘Development of dynamic fluid props with ADDIE design at SMA’, Journal of Physics: Conference Series, vol. 1811, no. 1, p. 012069.
Sari, S & Suyatna, A 2021, ‘Need assesment and design of e-modules to stimulate HOTS on dynamic fluid materials with the STEM approach’, Journal of Physics: Conference Series, vol. 1796, no. (1), p. 012003.
Sholihat, FN, Zulfikar, A, Setyadin, AH, Jubaedah, DS, Muhaemin, MH, Afif, NF, Fratiwi, NJ, Bhakti, SS, Amalia, SA, Hidayat, SR & Nugraha, MG 2019, ‘The effectiveness of ALBICI model in diagnosing K-11 students’ conceptions on debit concept’, Journal of Physics: Conference Series, vol. 1204, no. 1, p. 12035.
Strönisch, S, Meyer, M & Lehmann, C. (2022). Flow field prediction on large variable sized 2D point clouds with graph convolution, Proceedings of the Platform for Advanced Scientific Computing Conference, pp. 1–10.
Sumo, M, Jatmiko, B, Supardi, ZAI, Arifin, S & Mukit, A 2022, ‘Development of physics learning instrument using guided inquiry model as effort to increase student learning result on dynamic fluid learning material at senior high school’, Journal of Physics: Conference Series, vol. 2392, no. 1, p. 12007.
Tabib, MV, Pawar, S, Ahmed, SE, Rasheed, A & San, O 2021, ‘A non-intrusive parametric reduced order model for urban wind flow using deep learning and Grassmann manifold’, Journal of Physics: Conference Series, vol. 2018, no. 1, p. 12038.
Vikara, D & Khanna, V 2022, ‘Application of a deep learning network for joint prediction of associated fluid production in unconventional hydrocarbon development’, Processes, vol. 10, no. 4, p. 740.
Wati, M, Safiah, S & Misbah, M 2021. How to train problem-solving skills in physics using authentic learning’, Journal of Physics: Conference Series, vol. 1760, no. 1, p. 12009.
Wulandari, D, Roza, D, Pulungan, ASS, Rangkuti, MA, Brata, WWW, Tanjung, YI, Ramadhani, I & Hasim LR 2022, ‘The implementation of teaching material based on stem in fluid for biology student’, International Journal of Research-Granthaalayah, vol. 10, no. 2, pp. 61-70.
Zeng, Q, Gao, Y, Guan, K, Liu, J & Feng, Z 2021, ‘ Machine learning and a computational fluid dynamic approach to estimate phase composition of chemical vapor deposition boron carbide’, Journal of Advanced Ceramics, vol. 10, no. 3, pp. 537–550.
Zhao, Y, Zhong, C, Wang, F & Wang, Y 2022, ‘Visual explainable convolutional neural network for aerodynamic coefficient prediction’, International Journal of Aerospace Engineering, pp. 1-12.
Zupic, I & Čater, T 2015, ‘Bibliometric methods in management and organization’, Organizational research methods, vol. 18, no. 3, pp. 429-472.
Ahmed, SE, Rahman, SM, San, O, Rasheed, A & Navon, IM 2019, ‘Memory embedded non-intrusive reduced order modeling of non-ergodic flows’, Physics of Fluids, vol. 31, no. 12, p. 126602.
AlSaleem, SS, Al-Qadami, E, Korany, HZ, Shafiquzzaman, M, Haider, H, Ahsan, A, Alresheedi, M, AlGhafis, A & AlHarbi, A 2022, ‘Computational fluid dynamic applications for solar stills efficiency assessment: A review’, Sustainability, vol. 14, no. 17, p. 10700.
Ansari, M, Gandhi, HA, Foster, DG & White, AD 2022, ‘Iterative symbolic regression for learning transport equations’, AIChE Journal, vol. 68, no. 6, p. 17695.
Astra, IM, Nurjanah, I, Raihanati, R & Fitri, LHA 2022, ‘Development of electronic module using TAI to improve HOTS of high school students in fluid dynamic materials’, Journal of Physics: Conference Series, vol. 2377, no. 1, p. 12071.
Babanezhad, M, Marjani, A & Shirazian, S 2020, ‘Multidimensional machine learning algorithms to learn liquid velocity inside a cylindrical bubble column reactor’, Scientific Reports, vol. 10, no. 1, pp. 1-14.
Benner, P, Goyal, P, Heiland, J & Duff, IP 2020, ‘Operator inference and physics-informed learning of low-dimensional models for incompressible flows’, arXiv preprint arXiv:2010.06701 [Preprint].
Buhendwa, AB, Bezgin, DA & Adams, NA 2022, ‘Consistent and symmetry preserving data-driven interface reconstruction for the level-set method’, Journal of Computational Physics, vol. 457, p. 111049.
Corrochano, A, D’Alessio, G, Parente, A & Le Clainche, S 2023, ‘Higher order dynamic mode decomposition to model reacting flows’, International Journal of Mechanical Sciences, vol. 249, p. 108219.
Dabaghian, PH, Ahmed, SE & San, O 2022, ‘Nonintrusive reduced order modeling of convective Boussinesq flows’, arXiv preprint arXiv:2212.07522 [Preprint].
Diansah, I & Asyhari, A 2020, ‘Effectiveness of physics electronic modules based on Self Directed Learning Model (SDL) towards the understanding of dynamic fluid concept’, Journal of Physics: Conference Series, vol. 1572, no. 1, p. 12024.
Fathurohman, C, Wibowo, FC & Iswanto, BH 2021, ‘Development of Android Physics Applications (APA) as learning media on dynamic fluid concepts’, Journal of Physics: Conference Series, vol. 2019, no. 1, p. 12059.
Halim, A, Safitri, R & Nurfadilla, E 2020, ‘Impact of Problem-based Learning (PBL) model through Science Technology Society (STS) approach on students’ interest’, Journal of Physics: Conference Series, vol. 1460, no. 1, p. 12145.
Halim, A, Ulandari, S, Hamid, A, Wahyuni, A, Syukri, M & Irwandi, I 2021, ‘The Development of student worksheets based on a scientific approach in the dynamic fluid concepts’, Journal of Physics: Conference Series, vol. 1882, no. 1, p. 12025.
Hasbi, JE, Samsudin, A & Chandra, DT 2022, ‘Measuring science process skills of K-11 students in dynamic fluid’, AIP Conference Proceedings, vol. 2468, no. 1, p. 20042.
Herdayanti, A & Manurung, SR 2020, ‘Development of aid tool using arduino uno sensor for dynamic fluid at senior high school’, Journal of Physics: Conference Series, vol. 1485, no. 1, p. 12003.
Hong, SW, Park, J, Jeong, H, Lee, S, Choi, L, Zhao, L & Zhu, H 2021, ‘Fluid dynamic approaches for prediction of spray drift from ground pesticide applications: A review’, Agronomy, vol. 11, no. 6, p. 1182.
Khan, U, Pao, W, Sallih, N & Hassan, F 2022, ‘Identification of horizontal gas-liquid two-phase flow regime using deep learning’, CFD Letters, vol. 14, no. 10, pp. 68-78.
Kholiq, A & Khoiriah, M 2021, ‘Online learning during the covid-19 pandemic with ELISA assistance on the concept of dynamic fluid: Student response analysis’, Journal of Physics: Conference Series, vol. 1805, no. 1, p. 12042.
Kiener, A & Bekemeyer, P 2022, ‘Correcting the discretization error of coarse grid CFD simulations with machine learning’, in World Congress in Computational Mechanics and ECCOMAS Congress.
Kim, JH, Roh, MI, Kim, KS, Yeo, IC, Oh, MJ, Nam, JW, Lee, SH & Jang, YH 2022, ‘Prediction of the superiority of the hydrodynamic performance of hull forms using deep learning’, International Journal of Naval Architecture and Ocean Engineering, vol. 14, p. 100490.
Koes-H, S, Nisa, IK, Faiqatul, HE, Wahyuni, T, Yuenyong, C, Sutaphan, S & Yuenyong, J 2021, ‘The development of lesson plan of the water pressure booster pump STEM education’, Journal of Physics: Conference Series, vol. 1835, no. 1, p. 12049.
Lu, L, Pecha, MB, Wiggins, GM, Xu, Y, Gao, X, Hughes, B & Parks II, JE 2022, ‘Multiscale CFD simulation of biomass fast pyrolysis with a machine learning derived intra-particle model and detailed pyrolysis kinetics’, Chemical Engineering Journal, vol. 431, p. 133853.
Malik, S 2019, ‘Assessing the teaching and learning process of an introductory programming course with bloom’s taxonomy and Assurance of Learning (AOL)’, International Journal of Information and Communication Technology Education, vol. 15, no. 2, pp. 130-145, Available at: https://doi.org/10.4018/IJICTE.2019040108.
Maulik, R, San, O, Jacob, JD & Crick, C 2019, ‘Sub-grid scale model classification and blending through deep learning’, Journal of Fluid Mechanics, vol. 870, pp. 784-812.
Maulik, R, Sharma, H, Patel, S, Lusch, B & Jennings, E 2021, ‘A turbulent eddy-viscosity surrogate modeling framework for Reynolds-averaged Navier-Stokes simulations’, Computers & Fluids, vol. 227, p. 104777.
Mortezazadeh, M, Zou, J, Hosseini, M, Yang, S & Wang, L 2022, ‘Estimating urban wind speeds and wind power potentials based on machine learning with city fast fluid dynamics training data’, Atmosphere, vol. 13, no. 2, p. 214.
Muliyati, D, Fatih, FHA, Permana, AH, Sari, NLK & Purwahida, R 2022, ‘Fire Phyghter”-the development of educational games for exploring dynamic fluids topic’, Journal of Physics: Conference Series, vol. 2377, no. 1, p. 12069.
Muliyati, D, Rahmah, A, Sunaryo, S & Susanti, D 2021, ‘The development of Android-based physics teaching materials on static fluids’, AIP Conference Proceedings, vol. 2331, no. 1, p. 30038.
Permana, GA, Parno, P, Hidayat, A & Ali, M 2021, ‘Improving creative thinking skill of fluid dynamic through IBL-STEM with formative assessment’, AIP Conference Proceedings, vol. 2330, no. 1
Ramli, Z & Serevina, V 2021, ‘E-book static fluid and dynamic fluid web-based with a problem-based learning model to improve students physics problem-solving skills’, Journal of Physics: Conference Series, vol. 1, p. 12001.
Saharuddin, KD, Ariff, MHM, Mohmad, K, Bahiuddin, I, Ubaidillah, Mazlan, SA, Nazmi, N & Fatah, AYA 2021, ‘Prediction model of magnetorheological (MR) fluid damper hysteresis loop using extreme learning machine algorithm’, Open Engineering, vol. 11, no. 1, pp. 584-591.
San, O, Pawar, S & Rasheed, A 2022, ‘Variational multiscale reinforcement learning for discovering reduced order closure models of nonlinear spatiotemporal transport systems’, Scientific Reports, vol. 12, no. 1, p. 17947
Sani, RA 2021, ‘Development of dynamic fluid props with ADDIE design at SMA’, Journal of Physics: Conference Series, vol. 1811, no. 1, p. 012069.
Sari, S & Suyatna, A 2021, ‘Need assesment and design of e-modules to stimulate HOTS on dynamic fluid materials with the STEM approach’, Journal of Physics: Conference Series, vol. 1796, no. (1), p. 012003.
Sholihat, FN, Zulfikar, A, Setyadin, AH, Jubaedah, DS, Muhaemin, MH, Afif, NF, Fratiwi, NJ, Bhakti, SS, Amalia, SA, Hidayat, SR & Nugraha, MG 2019, ‘The effectiveness of ALBICI model in diagnosing K-11 students’ conceptions on debit concept’, Journal of Physics: Conference Series, vol. 1204, no. 1, p. 12035.
Strönisch, S, Meyer, M & Lehmann, C. (2022). Flow field prediction on large variable sized 2D point clouds with graph convolution, Proceedings of the Platform for Advanced Scientific Computing Conference, pp. 1–10.
Sumo, M, Jatmiko, B, Supardi, ZAI, Arifin, S & Mukit, A 2022, ‘Development of physics learning instrument using guided inquiry model as effort to increase student learning result on dynamic fluid learning material at senior high school’, Journal of Physics: Conference Series, vol. 2392, no. 1, p. 12007.
Tabib, MV, Pawar, S, Ahmed, SE, Rasheed, A & San, O 2021, ‘A non-intrusive parametric reduced order model for urban wind flow using deep learning and Grassmann manifold’, Journal of Physics: Conference Series, vol. 2018, no. 1, p. 12038.
Vikara, D & Khanna, V 2022, ‘Application of a deep learning network for joint prediction of associated fluid production in unconventional hydrocarbon development’, Processes, vol. 10, no. 4, p. 740.
Wati, M, Safiah, S & Misbah, M 2021. How to train problem-solving skills in physics using authentic learning’, Journal of Physics: Conference Series, vol. 1760, no. 1, p. 12009.
Wulandari, D, Roza, D, Pulungan, ASS, Rangkuti, MA, Brata, WWW, Tanjung, YI, Ramadhani, I & Hasim LR 2022, ‘The implementation of teaching material based on stem in fluid for biology student’, International Journal of Research-Granthaalayah, vol. 10, no. 2, pp. 61-70.
Zeng, Q, Gao, Y, Guan, K, Liu, J & Feng, Z 2021, ‘ Machine learning and a computational fluid dynamic approach to estimate phase composition of chemical vapor deposition boron carbide’, Journal of Advanced Ceramics, vol. 10, no. 3, pp. 537–550.
Zhao, Y, Zhong, C, Wang, F & Wang, Y 2022, ‘Visual explainable convolutional neural network for aerodynamic coefficient prediction’, International Journal of Aerospace Engineering, pp. 1-12.
Zupic, I & Čater, T 2015, ‘Bibliometric methods in management and organization’, Organizational research methods, vol. 18, no. 3, pp. 429-472.
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Published
2023-12-31
How to Cite
Misbah, M., Hamidah, I., Sriyati, S., & Samsudin, A. (2023). Research Trend of Dynamic Fluid in Learning: A Bibliometric Analysis. Jurnal Penelitian & Pengembangan Pendidikan Fisika, 9(2), 263–272. https://doi.org/10.21009/1.09209
