Acta Scientific Computer Sciences

Research Article Volume 3 Issue 8

A Data Model for In-stream Forces on a Cylinder Using Neural Networks and Linear Prediction Filters

Erdem Aktosun1,2, Nikolaos I Xiros1* and Jason M Dahl2

1Naval Architecture and Marine Eng., University of New Orleans, USA
2Ocean Engineering, University of Rhode Island, USA

*Corresponding Author: Nikolaos I Xiros, Naval Architecture and Marine Eng., University of New Orleans, USA.

Received: May 20, 2021; Published: July 29, 2021

Abstract

Hydrodynamic force and error estimation model are developed based on experimental data. Force model is developed to model the dynamic forces with Artificial Neural Network (ANN) on an oscillating circular cylinder for flow conditions where Vortex-Induced-Vibrations (VIV) are known to occur and data error estimation model is developed for this existing neural network time dependent hydrodynamic force model. The force and error estimation model are developed to use in potential control systems to improve VIV based energy harvesting. The dynamic model is empirical, utilizing force measurements obtained for a large set of forced motion experiments, spanning a range of parametric values that prescribe the kinematics of the cylinder motion. The model includes the dynamics of a circular cylinder undergoing forced combined in-line and cross-flow motion in a free stream. The experiments were conducted in a fully automated towing tank where parameters of in-line amplitude of motion, cross-flow amplitude of motion, reduced velocity, and phase difference between in-line and cross-flow motion were varied over nearly 10,000 experiments. All experiments were carried out at a constant Reynolds number of 7620. A feed forward neural network is trained using the force database to develop a time dependent model of forces on the cylinder for given kinematic conditions. The time series error between the measured and feed forward Artificial Neural Network (ANN) model is found for the lift and drag force time histories. An autoregressive (AR) error predictor is developed from the existing neural network time dependent model of forces on the cylinder for given kinematic conditions. This error predictor is developed based on the error between the measured signal and artificial neural network model and can be used to improve predictions from the model.

Keywords: Vortex Induced Vibrations; Neural Networks; Data Analysis; Autoregressive Filter; Linear Prediction

Bibliography

  1. Sarpkaya T. “Vortex-Induced Oscillations: A Selective Review”. Journal of Applied Mechanics2 (1979): 241-258.
  2. Bearman PW. “Vortex Shedding from Oscillating Bluff Bodies”. Annual Review of Fluid Mechanics 1 (1984): 195-222.
  3. Parkinson G. “Phenomena and modelling of flow-induced vibrations of bluff bodies”. Progress in Aerospace Sciences 2 (1989): 169-224.
  4. Sarpkaya T. “A critical review of the intrinsic nature of vortex-induced vibrations”. Journal of Fluids and Structures 4 (2004): 389-447.
  5. Williamson CHK and Govardhan R. “Vortex-Induced Vibrations”. Annual Review of Fluid Mechanics1 (2004): 413-455.
  6. Bearman PW. “Circular cylinder wakes and vortex-induced vibrations”. Journal of Fluids and Structures5-6 (2011): 648-658.
  7. Lee JH., et al. “Virtual damper-spring system for VIV experiments and hydrokinetic energy conversion”. Ocean Engineering5-6 (2011): 732-747.
  8. Dahl JM., et al. “Resonant Vibrations of Bluff Bodies Cause Multivortex Shedding and High Frequency Forces”. Physical Review Letters14 (2007): 144503.
  9. Dahl JM., et al. “Dual resonance in vortex-induced vibrations at subcritical and supercritical Reynolds numbers”. Journal of Fluid Mechanics 643 (2010): 395-424.
  10. Jauvtis N and Williamson CHK. “The effect of two degrees of freedom on vortex-induced vibration at low mass and damping”. Journal of Fluid Mechanics509 (2004): 23-62.
  11. Staubli T. “Calculation of the Vibration of an Elastically Mounted Cylinder Using Experimental Data From Forced Oscillation”. Journal of Fluids Engineering2 (1983): 225-229.
  12. Gopalkrishnan R. “Vortex-Induced Forces on Oscillating Bluff Cylinders” (1983).
  13. Morse TL and Williamson CHK. “Employing controlled vibrations to predict fluid forces on a cylinder undergoing vortex-induced vibration”. Journal of Fluids and Structures6-7 (2006): 877-884.
  14. Morse TL and Williamson CHK. “Prediction of vortex-induced vibration response by employing controlled motion”. Journal of Fluid Mechanics 634 (2009): 5-39.
  15. Chaplin JR., et al. “Blind predictions of laboratory measurements of vortex-induced vibrations of a tension riser”. Journal of Fluids and Structures 21 (2005): 25-40.
  16. Bernitsas MM., et al. “Eigen-Solution for Flow Induced Oscillations (VIV and Galloping) Revealed at the Fluid-Structure Interface”. CFD and FSI 2 (2019).
  17. Liu C., et al. “Time-varying hydrodynamics of a flexible riser under multi- frequency vortex-induced vibrations”. Journal of Fluids and Structures 80 (2018): 217-244.
  18. Liu C., et al. “Hydrodynamics of a flexible cylinder under modulated vortex- induced vibrations”. Journal of Fluids and Structures 94 (2020): 102913.
  19. Dahl JJM. “Vortex-induced vibration of a circular cylinder with combined in-line and cross-flow motion” (2008).
  20. Zheng H., et al. “Coupled Inline-Cross Flow VIV Hydrodynamic Coefficients Database”. CFD and VIV 2 (2014).
  21. Aktosun E and Dahl JM. “Experimental Force Database From Controlled In-Line and Cross Flow Cylinder Motion”. The 28th International Ocean and Polar Engineering Conference (2018).
  22. Dahl J and Aktosun E. “Force and wake observations for a circular cylinder undergoing forced 2-DOF motion in a free stream”. APS (2019): C48--004.
  23. Dahl JM., et al. “Two-degree-of-freedom vortex-induced vibrations using a force assisted apparatus”. Journal of Fluids and Structures6-7 (2006): 807-818.
  24. Tsoukalas LH and Uhrig RE. “Fuzzy and neural approaches in engineering” (1997).
  25. Fausett L. “Fundamentals of Neural Networks Prentice Hall”. Englewood Cliffs, NJ (1994): 7632.
  26. Ogunfunmi T. “Adaptive nonlinear system identification: The Volterra and Wiener model approaches” (2007).
  27. Zaknich A. “Principles of adaptive filters and self-learning systems” (2005).
  28. Xiros NI. “Digital Signal Processing”. Springer Handbook of Ocean Engineering (2016): 197-226.
  29. Xiros NI and An P-CE. “Control Theory and Applications”. Springer Handbook of Ocean Engineering (2016): 227-276.
  30. Klamo JT., et al. “On the maximum amplitude for a freely vibrating cylinder in cross-flow”. Journal of Fluids and Structures4 (2005): 429-434.
  31. Hsu HP. “Theory and problems of probability, random variables, and random processes”. 83 (1996).
  32. Castanié F. “Digital Spectral Analysis” (2011).
  33. Orfanidis SJ. “Applied Optimum Signal Processing” (2018).
  34. Bracewell RN and Bracewell RN. “The Fourier transform and its applications”. 31999 (1986).
  35. Wolovich WA. “Linear Multivariable Systems” (1974).
  36. Aktosun E., et al. “A neural network time dependent hydrodynamic force model for forced two-degree-of-freedom sinusoidal motion of a circular cylinder in a free stream”. ASME 2021 40th International Conference on Ocean, Offshore and Arctic Engineering (2021).
  37. Xiros NI., et al. “Dynamic Modeling of Flow Induced Vibration Power-Plants”. International Conference on Offshore Mechanics and Arctic Engineering 51319 (2018): V010T09A009.

Citation

Citation: Nikolaos I Xiros., et al. “A Data Model for In-stream Forces on a Cylinder Using Neural Networks and Linear Prediction Filters". Acta Scientific Computer Sciences 3.8 (2021): 50-64.

Copyright

Copyright: © 2021 Nikolaos I Xiros., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.




Metrics

Acceptance rate35%
Acceptance to publication20-30 days

Indexed In




News and Events


  • Certification for Review
    Acta Scientific certifies the Editors/reviewers for their review done towards the assigned articles of the respective journals.
  • Submission Timeline for Upcoming Issue
    The last date for submission of articles for regular Issues is September 25, 2024.
  • Publication Certificate
    Authors will be issued a "Publication Certificate" as a mark of appreciation for publishing their work.
  • Best Article of the Issue
    The Editors will elect one Best Article after each issue release. The authors of this article will be provided with a certificate of "Best Article of the Issue"
  • Welcoming Article Submission
    Acta Scientific delightfully welcomes active researchers for submission of articles towards the upcoming issue of respective journals.

Contact US