Machine Learning for Feature Identification and Characterization of Fluid Flow Around a Cylinder Using Airborne Acoustic Signature

Document Type : Research Paper

Authors
Zahra Shah Hosseini
Arman Mohseni
Faculty of Mechanical and Energy Engineering, Shahid Beheshti University (SBU), Tehran, 1983969411, Iran
10.22055/jacm.2025.48497.5277
Abstract
This study employs machine learning (random forest, adaptive boosting, and multilayer perceptron) to identify flow features around a cylinder using airborne acoustic signatures. Acoustic data, including sound pressure levels, are derived from numerical simulations. The studied machine learning models effectively distinguish between different flow states, classified based on the values of Reynolds number. Furthermore, this study investigates the impact of observer position on the accuracy of machine learning models for flow differentiation. The results show that random forest detects 7.5° rotations of observation point with 66.63% accuracy at Re = 30000, outperforming visual methods. Notably, the detection performance of the models remains consistent regardless of the observer’s distance from the sound source, in both the near and far fields. It is worth noting that this study integrates numerical simulations with practical applications, such as wind turbine noise monitoring, where deviations in acoustic sensors can impact the performance of machine learning classification.
Keywords
Aeroacoustics
Machine learning
External flow over a cylinder
Airborne acoustic signature
Numerical simulation
Subjects

Publisher’s Note Shahid Chamran University of Ahvaz remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

[1] Szepessy, S., On the spanwise correlation of vortex shedding from a circular cylinder at high subcritical Reynolds number, Physics of Fluids, 6(7), 1994, 2406-2416.
[2] Iglesias, E.L., Thompson, D., Smith, M., Experimental study of the aerodynamic noise radiated by cylinders with different cross-sections and yaw angles, Journal of Sound and Vibration, 361, 2016, 108-129.
[3] Inoue, O., Hatakeyama, N., Sound generation by a two-dimensional circular cylinder in a uniform flow, Journal of Fluid Mechanics, 471, 2002, 285-314.
[4] Doolan, C.J., Aeroacoustic simulation of bluff body noise using a hybrid statistical method, Proc. of ACOUSTICS, Adelaide, Australia, 9780975785577, 2009.
[5] Bishop, C., Pattern recognition and machine learning, Springer, New York, 2006.
[6] Zheng, Y., Song, Q., Liu, J., Song, Q., Yue, Q., Research on motion pattern recognition of exoskeleton robot based on multimodal machine learning model, Neural Computing and Applications, 32, 2020, 1869-1877.
[7] Khan, A.A., Laghari, A.A., Awan, S.A., Machine learning in computer vision: A review, EAI Endorsed Transactions on Scalable Information Systems, 8(32), 2021.
[8] Holm, E.A., Cohn, R., Gao, N., Kitahara, A.R., Matson, T.P., Lei, B., Yarasi, S.R., Overview: Computer vision and machine learning for microstructural characterization and analysis, Metallurgical and Materials Transactions A, 51, 2020, 5985-5999.
[9] Dasgupta, D., Akhtar, Z., Sen, S., Machine learning in cybersecurity: a comprehensive survey, The Journal of Defense Modeling and Simulation, 19(1), 2022, 57-106.
[10] Nadeem, A., Vos, D., Cao, C., Pajola, L., Dieck, S., Baumgartner, R., Verwer, S., Sok: Explainable machine learning for computer security applications, IEEE 8th European Symposium on Security and Privacy (EuroS&P), Delft, Netherlands, 221-240, 2023.
[11] Mullainathan, S., Spiess, J., Machine learning: an applied econometric approach, Journal of Economic Perspectives, 31(2), 2017, 87-106.
[12] Nosratabadi, S., Mosavi, A., Duan, P., Ghamisi, P., Filip, F., Band, S.S., Reuter, U., Gama, J., Gandomi, A.H., Data science in economics: comprehensive review of advanced machine learning and deep learning methods, Mathematics, 8(10), 2020, 1799.
[13] Gogas, P., Papadimitriou, T., Machine learning in economics and finance, Computational Economics, 57, 2021, 1-4.
[14] Rajkomar, A., Dean, J., Kohane, I., Machine learning in medicine, New England Journal of Medicine, 380(14), 2019, 1347-1358.
[15] Haug, C.J., Drazen, J.M., Artificial intelligence and machine learning in clinical medicine, New England Journal of Medicine, 388(13), 2023, 1201-1208.
[16] Rana, M., Bhushan, M., Machine learning and deep learning approach for medical image analysis: diagnosis to detection, Multimedia Tools and Applications, 82(17), 2023, 26731-26769.
[17] Brunton, S.L., Noack, B.R., Koumoutsakos, P., Machine learning for fluid mechanics, Annual Review of Fluid Mechanics, 52(1), 2020, 477-508.
[18] Vadrot, A., Yang, X.I., Abkar, M., Survey of machine-learning wall models for large-eddy simulation, Physical Review Fluids, 8(6), 2023, 064603.
[19] Vinuesa, R., Brunton, S.L., Enhancing computational fluid dynamics with machine learning, Nature Computational Science, 2(6), 2022, 358-366.
[20] Lee, S., You, D., Data-driven prediction of unsteady flow over a circular cylinder using deep learning, Journal of Fluid Mechanics, 879, 2019, 217-254.
[21] Guo, X., Li, W., Iorio, F., Convolutional neural networks for steady flow approximation, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, USA, 2016.
[22] Ribeiro, M.D., Rehman, A., Ahmed, S., Dengel, A., DeepCFD: Efficient steady-state laminar flow approximation with deep convolutional neural networks, arXiv preprint arXiv:2004.08826, 2020.
[23] Morimoto, M., Fukami, K., Zhang, K., Fukagata, K., Generalization techniques of neural networks for fluid flow estimation, Neural Computing and Applications, 34(5), 2022, 3647-3669.
[24] Nemati, M., Jahangirian, A., A data-driven machine learning approach for turbulent flow field prediction based on direct computational fluid dynamics database, Journal of Applied Fluid Mechanics, 17(1), 2023, 60-74.
[25] Legendre, C., Ficat-Andrieu, V., Poulos, A., Kitano, Y., Nakashima, Y., Kobayashi, W., Minorikawa, G., A machine learning-based methodology for computational aeroacoustics predictions of multipropeller drones, INTER-NOISE and NOISE-CON Congress and Conference Proceedings, Institute of Noise Control Engineering, Washington, DC, USA, 3467–3478, 2021.
[26] Wang, Y., Wang, K., Abdel-Maksoud, M., noiseNet: A neural network to predict marine propellers’ underwater radiated noise, Ocean Engineering, 236, 2021, 109542.
[27] Kužnar, D., Možina, M., Giordanino, M., Bratko, I., Improving vehicle aeroacoustics using machine learning, Engineering Applications of Artificial Intelligence, 25(5), 2012, 1053-1061.
[28] Bright, I., Lin, G., Kutz, J.N., Compressive sensing based machine learning strategy for characterizing the flow around a cylinder with limited pressure measurements, Physics of Fluids, 25(12), 2013, 127102.
[29] Li, B., Yang, Z., Zhang, X., He, G., Deng, B.-Q., Shen, L., Using machine learning to detect the turbulent region in flow past a circular cylinder, Journal of Fluid Mechanics, 905, 2020, A10.
[30] Sun, X., Cao, W., Liu, Y., Zhu, L., Zhang, W., High Reynolds number airfoil turbulence modeling method based on machine learning technique, Computers & Fluids, 236, 2022, 105298.
[31] Zhu, L., Zhang, W., Kou, J., Liu, Y., Machine learning methods for turbulence modeling in subsonic flows around airfoils, Physics of Fluids, 31(1), 2019, 015105.
[32] Hu, G., Kwok, K.C.S., Predicting wind pressures around circular cylinders using machine learning techniques, Journal of Wind Engineering and Industrial Aerodynamics, 198, 2020, 104099.
[33] Meng, H.-Y., Xu, Z.-X., Yang, J., Liang, B., Cheng, J.-C., Fast prediction of aerodynamic noise induced by the flow around a cylinder based on deep neural network, Chinese Physics B, 31(6), 2022, 545–550.
[34] Li, K., Li, H., Li, S., Chen, Z., Fully convolutional neural network prediction method for aerostatic performance of bluff bodies based on consistent shape description, Applied Sciences, 12(6), 2022, 3147.
[35] Currie, I.G., Fundamental Mechanics of Fluids, McGraw-Hill, 2003.
[36] Lockard, D., A comparison of Ffowcs Williams-Hawkings solvers for airframe noise applications, 8th AIAA/CEAS Aeroacoustics Conference, Breckenridge, Colorado, US, 2002–2580, 2002.
[37] Kinsler, L.E., Frey, A.R., Coppens, A.B., Sanders, J.V., Fundamentals of acoustics, John Wiley & Sons, 2000.
[38] Zuo, Z., Huang, Q., Liu, S., An analysis on the flow field structures and the aerodynamic noise of airfoils with serrated trailing edges based on embedded large eddy flow simulations, Journal of Applied Fluid Mechanics, 12(2), 2019, 327-339.
[39] Norberg, C., Fluctuating lift on a circular cylinder: review and new measurements, Journal of Fluids and Structures, 17(1), 2023, 57-96.
[40] Orselli, R., Meneghini, J., Saltara, F., Two and three-dimensional simulation of sound generated by flow around a circular cylinder, 15th AIAA/CEAS aeroacoustics conference (30th AIAA aeroacoustics conference), American Institute of Aeronautics and Astronautics, Miami, Florida, USA, 3270, 2009.
[41] Yamagata, T., Saito, N., Fujisawa, N., Experimental and numerical studies on Aeolian tone from D-shaped cylinder, Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 232(22), 2018, 4007-4018.
[42] Williamson, C.H.K., Three-dimensional vortex dynamics in bluff body wakes, Experimental Thermal and Fluid Science, 12(2), 1996, 150-168.
[43] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Scikit-learn: Machine learning in Python, The Journal of Machine Learning Research, 12, 2011, 2825-2830.
[44] Breiman, L., Random Forests, Machine Learning, 45, 2001, 5-32.
[45] Ling, J., Templeton, J., Evaluation of machine learning algorithms for prediction of regions of high reynolds averaged Navier Stokes uncertainty, Physics of Fluids, 27(8), 2015, 085103.
[46] Yang, S., Lee, S., Yee, K., Inverse design optimization framework via a two-step deep learning approach: application to a wind turbine airfoil, Engineering with Computers, 39(3), 2023, 2239-2255.
Journal of Applied and Computational Mechanics
Volume 12, Issue 2
Spring 2026
Pages 382-395