Flow-rate estimation in PVC and carbon-steel pipes using flow-induced vibrations and data-driven models
DOI:
https://doi.org/10.24036/teknomekanik.v9i2.53072Keywords:
frequency-domain analysis, flow-induced vibrations, machine learning, neural network, non-intrusive flow measurementAbstract
Non-intrusive flow measurement methods are increasingly required in pipeline systems to eliminate pressure losses, prevent contamination, and avoid structural modifications. Flow-induced vibration (FIV) offers a promising alternative; however, its applicability to standard industrial carbon-steel pipelines and its integration with data-driven modeling remain limited. This study experimentally investigates FIV-based flow estimation in 1-inch PVC and carbon-steel pipes conveying water under controlled conditions using regression, and machine learning models, while examining the influence of pipe material and vibration-response characteristics on flow-rate prediction performance. Vibration responses were measured using a tri-axial accelerometer and analyzed to identify flow-sensitive frequency bands. Regression and machine-learning models were developed to relate vibration characteristics to flow rate. The results demonstrate a predominantly monotonic relationship between band-averaged vibration amplitude and flow rate, with material-dependent sensitivity observed between PVC and carbon-steel pipes. Data-driven models improved prediction performance and robustness on the dynamic behavior of flow-induced vibrations, The findings demonstrate the potential of combining FIV analysis with intelligent modeling as a non-intrusive approach for flow measurement in industrial pipelines. Neural time-series modeling was used for training purpose only. Open-loop training provides a stable and efficient way for the network to learn the underlying dynamic relationship between inputs and outputs. A meaningful assessment of the model's predictive capability requires closed-loop testing, where the network relies on its own previous predictions. This was not conducted in the present study.
References
M. Pereira, “Flow meters: Part 1,” Instrumentation & Measurement Magazine, IEEE, vol. 12, pp. 18–26, Dec. 2009, https://doi.org/10.1109/MIM.2009.4762948
P. Mohindru, “Recent advancements in volumetric flow meter for industrial application,” Heat and Mass Transfer, vol. 59, pp. 1–18, May 2023, https://doi.org/10.1007/s00231-023-03413-4
R. C. . Baker, Flow measurement handbook : industrial designs, operating principles, performance, and applications. Cambridge University Press, 2009. https://doi.org/10.1017/CBO9780511471100
M. Al Sawwafi, M. Zarog, R. Zaier, and A. Al Yahmedi, “Modeling Flow-Induced Vibration of Pipes in Oil Industry: A Case Study,” Romanian Journal of Acoustics and Vibration, vol. 22, pp. 111–115, Dec. 2025. https://rjav.sra.ro/index.php/rjav/article/view/449
F. Villa, C. Sánchez, M. Vallejo, J. S. Botero-Valencia, and E. Delgado-Trejos, “Dataset of Flow-Induced Vibrations on a Pipe Conveying Cold Water,” Data., vol. 6, no. 9, 2021, https://doi.org/10.3390/data6090100
J. Wang, S. Li, and C. Bose, “Effect of a fixed downstream cylinder on the flow-induced vibration of an elastically supported primary cylinder,” Physics of Fluids, vol. 36, May 2024, https://doi.org/10.1063/5.0207136
X. Fu, S. Fu, N. Zhibo, B. Zhao, J. Shen, and P. Deng, “A validated fluid-structure interaction simulation model for vortex-induced vibration of a flexible pipe in steady flow.” May 2025. https://doi.org/10.48550/arXiv.2502.05748
M. I. Mohd Ismail et al., “A review of vibration detection methods using accelerometer sensors for water pipeline leakage,” IEEE Access, vol. 7. Institute of Electrical and Electronics Engineers Inc., pp. 51965–51981, 2019. https://doi.org/10.1109/ACCESS.2019.2896302
J. Shen et al., “Experimental Investigation on Vortex Induced Vibration of a Rigidly Coupled Twin-Tube Model,” May 2024. https://doi.org/10.1115/OMAE2024-122890
R. P. Evans, J. D. Blotter, and A. G. Stephens, “Flow rate measurements using flow-induced pipe vibration,” in Journal of Fluids Engineering, Transactions of the ASME, Mar. 2004, pp. 280–285. https://doi.org/10.1115/1.1667882
J. E. Urrea, L. E. Escobar-Correa, J. P. Morán-Zabala, A. F. Ramirez-Barrera, J. I. Padilla-Buritica, and E. Delgado-Trejos, “Flow rate estimation from flow-induced vibration using signal decomposition and advanced regression models,” Flow Measurement and Instrumentation, vol. 107, p. 103084, 2026, https://doi.org/https://doi.org/10.1016/j.flowmeasinst.2025.103084
S. K. Venkata and B. R. Navada, “Estimation of flow rate through analysis of pipe vibration,” Acta Mechanica et Automatica, vol. 12, no. 4, pp. 294–300, Dec. 2018, https://doi.org/10.2478/ama-2018-0045
M. T. Pittard, R. P. Evans, R. D. Maynes, and J. D. Blotter, “Experimental and numerical investigation of turbulent flow induced pipe vibration in fully developed flow,” Review of Scientific Instruments, vol. 75, no. 7, pp. 2393–2401, Jul. 2004, https://doi.org/10.1063/1.1763256
G. Dinardo, L. Fabbiano, and G. Vacca, “Fluid flow rate estimation using acceleration sensors,” in Proceedings of the International Conference on Sensing Technology, ICST, 2013, pp. 221–225. https://doi.org/10.1109/ICSensT.2013.6727646
S. Chun, S. Lee, and H. Yoon, “Use of Laser Doppler Vibrometry for Measuring Flow-Induced Vibration of a Thermowell in a Pipe Flow,” Proceedings of the ASME 2021 Fluids Engineering Division Summer Meeting. Volume 2: Fluid Applications and Systems; Fluid Measurement and Instrumentation. Virtual, Online. August 10–12, 2021. V002T04A014. ASME. https://doi.org/10.1115/FEDSM2021-64609
M. M. Campagna, G. Dinardo, L. Fabbiano, and G. Vacca, “Fluid flow measurements by means of vibration monitoring,” Meas. Sci. Technol., vol. 26, no. 11, Oct. 2015, https://doi.org/10.1088/0957-0233/26/11/115306
K. A. R. Medeiros, C. R. H. Barbosa, and E. C. De Oliveira, “Flow measurement by piezoelectric accelerometers: Application in the oil industry,” Pet. Sci. Technol., vol. 33, no. 13–14, pp. 1402–1409, 2015, https://doi.org/10.1080/10916466.2015.1044613
A. Awawdeh, S. T. S. Bukkapatnam, S. R. T. Kumara, C. Bunting, and R. Komanduri, “Wireless sensing of flow-induced vibrations for pipeline integrity monitoring,” in Fourth IEEE Workshop on Sensor Array and Multichannel Processing, 2006., 2006, pp. 114–117. https://doi.org/10.1109/SAM.2006.1706103
Y. Shang, C. Wang, Y. Zhang, W. Zhao, J. Ni, and G. Peng, “Non-Intrusive Pipeline Flow Detection Based on Distributed Fiber Turbulent Vibration Sensing,” Sensors, vol. 22, no. 11, Jun. 2022, https://doi.org/10.3390/s22114044
S. Wang, D. Xu, G. Liu, T. Xue, and Y. Liu, “Application of Distributed Acoustic Sensing Technology in Pipeline Leakage Monitoring,” Journal of Energy and Natural Resources, vol. 13, pp. 81–89, May 2024, https://doi.org/10.11648/j.jenr.20241302.14
H. Ding and J. C. Ji, “Vibration control of fluid-conveying pipes: a state-of-the-art review,” Applied Mathematics and Mechanics (English Edition), vol. 44, pp. 1423–1456, Sep. 2023, https://doi.org/10.1007/s10483-023-3023-9
M. H. Bin Abdul Hamid, “Experimental study on pipe fluid induced vibration,” Universiti Teknologi PETRONAS, Tronoh (MY), 2013. https://utpedia.utp.edu.my/id/eprint/10608/1/Dissertation%20Report_14593.pdf
S. M. Khot, P. Khaire and A. S. Naik, "Experimental and simulation study of flow induced vibration through straight pipes," 2017 International Conference on Nascent Technologies in Engineering (ICNTE), Vashi, India, 2017, pp. 1-6. https://doi.org/10.1109/ICNTE.2017.7947938
Y.-J. Cha, R. Ali, J. Lewis, and O. Büyükӧztürk, “Deep learning-based structural health monitoring,” Autom. Constr., vol. 161, p. 105328, 2024, https://doi.org/https://doi.org/10.1016/j.autcon.2024.105328
O. Matania, I. Dattner, J. Bortman, R. S. Kenett, and Y. Parmet, “A systematic literature review of deep learning for vibration-based fault diagnosis of critical rotating machinery: Limitations and challenges,” J. Sound Vib., vol. 590, p. 118562, 2024, https://doi.org/https://doi.org/10.1016/j.jsv.2024.118562
B. A. Tama, M. Vania, S. Lee, and S. Lim, “Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals,” Artif. Intell. Rev., vol. 56, no. 5, pp. 4667–4709, 2023, https://doi.org/10.1007/s10462-022-10293-3
F. Saleem, Z. Ahmad, and J.-M. Kim, “Real-Time Pipeline Leak Detection: A Hybrid Deep Learning Approach Using Acoustic Emission Signals,” Applied Sciences, vol. 15, no. 1, 2025, https://doi.org/10.3390/app15010185
S. Heydari, R. Gao, and R. K. Jaiman, “Predicting flow-induced vibration in isolated and tandem cylinders using hypergraph neural networks,” Comput. Fluids, vol. 307, p. 106930, 2026, https://doi.org/https://doi.org/10.1016/j.compfluid.2025.106930
“RECOVIB® Tiny by Micromega Dynamics.” Accessed: Nov. 16, 2024. [Online]. Available: https://micromega-dynamics.com/products/recovib/miniature-vibration-recorder/
L. Meirovitch, Elements of Vibration Analysis, 2nd ed. New York: McGraw-Hill, 1986.
R. D. Blevins, Flow-Induced Vibration, 2nd ed. New York: Van Nostrand Reinhold, 1990.
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Copyright (c) 2026 Khalid Alnabhani, Musaab Zarog, Hadj Bourdoucen (Author)
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