Skip to main content
SpringerLink
Log in

Estimation of ship roll damping and natural frequency using an extended Kalman filter applied to URANS output

  • Published:
Sādhanā Aims and scope Submit manuscript

Abstract

This article discusses an alternative way of evaluating the roll damping coefficient and natural frequency of a floating body. Experimental values are required for the data assimilation process. In this study rather than performing an actual experiment, pseudo-experimental values were derived through computational fluid dynamics (CFD). An extended Kalman filtering (EKF) technique with CFD for estimation of the equivalent linear damping coefficient and natural frequency of free roll decay motion was determined. For the free roll decay motion, the roll angle values obtained from the CFD simulations were given as input to the EKF, and the parameter estimation was performed. CFD analyses were performed to simulate free roll decay by using the Unsteady Reynolds-Averaged Navier–Stokes (URANS) approach with success. Using calculated data of roll response inverse analyses were carried out to identify roll damping and natural frequency for the 3-D floating body. The effects of uncertainty in the process and measurement noise statistics on performance were examined. The measured residual was compared to the theoretical estimate for filtering correctness. It is found that the variation of the damping coefficient and natural frequency with time was determined by EKF within very small error limits.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure14
Figure 15
Figure 16
Figure 17
Figure 18
Figure 19

Similar content being viewed by others

References

  1. Wassermann S, Feder D F and Abdel-Maksoud M 2016 Estimation of ship roll damping - A comparison of the decay and the harmonic excited roll motion technique for a post panamax container ship. Ocean Eng. 120: 371–382

    Article  Google Scholar 

  2. Froude W 1872 On the influence of resistance upon the rolling of ships. Nav. Sci. 411–429

  3. Spouge J 1988 Non-linear analysis of large-amplitude rolling experiments. Int. Shipbuild. Prog. 35: 271–324

    Google Scholar 

  4. Igbadumhe J F, Sallam O, Fürth M and Feng R 2020 Experimental determination of non-linear roll damping of an FPSO pure roll coupled with liquid sloshing in two-row tanks. J. Mar. Sci. Eng. 8(8): 582

    Article  Google Scholar 

  5. Sun J, James Hu S L and Li H 2021 Nonlinear roll damping parameter identification using free-decay data. Ocean Eng. 219: 108425

    Article  Google Scholar 

  6. Oliveira A C and Fernandes A C 2013 The nonlinear roll damping of a FPSO hull. J. Offshore Mech. Arct. Eng. 136(1): 011106

    Article  Google Scholar 

  7. Reina G, Paiano M and Blanco-Claraco J L 2017 Vehicle parameter estimation using a model-based estimator. Mech. Syst. Signal Process. 87: 227–241

    Article  Google Scholar 

  8. Zhi L, Yu P, Li Q S, Chen B and Fang M 2018 Identification of wind loads on super-tall buildings by Kalman filter. Comput. Struct. 208: 105–117

    Article  Google Scholar 

  9. Reina G and Messina A 2019 Vehicle dynamics estimation via augmented extended Kalman filtering. Meas. J. Int. Meas. Confed. 133: 383–395

    Article  Google Scholar 

  10. Tanaka M, Matsumoto T and Yamamura H 2004 Application of BEM with extended Kalman filter to parameter identification of an elastic plate under dynamic loading. Eng. Anal. Bound. Elem. 28(2): 213–219

    Article  MATH  Google Scholar 

  11. Ojima Y and Kawahara M 2009 Estimation of river current using reduced Kalman filter finite element method. Comput. Methods Appl. Mech. Eng. 198(9–12): 904–911

    Article  MATH  Google Scholar 

  12. Song M, Astroza R, Ebrahimian H, Moaveni B and Papadimitriou C 2020 Adaptive Kalman filters for nonlinear finite element model updating. Mech. Syst. Signal Process. 143: 106837

    Article  Google Scholar 

  13. Araki M, Sadat-Hosseini H, Sanada Y, Tanimoto K, Umeda N and Stern F 2012 Estimating maneuvering coefficients using system identification methods with experimental, system-based, and CFD free-running trial data. Ocean Eng. 51: 63–84

    Article  Google Scholar 

  14. Avalos G O G, Wanderley J B V, Fernandes A C and Oliveira A C 2014 Roll damping decay of a FPSO with bilge keel. Ocean Eng. 87: 111–120

    Article  Google Scholar 

  15. Mancini S, Begovic E, Day A H and Incecik A 2018 Verification and validation of numerical modelling of DTMB 5415 roll decay. Ocean Eng. 162: 209–223

    Article  Google Scholar 

  16. Hashimoto H, Omura T, Matsuda A, Yoneda S, Stern F and Tahara Y 2019 Several remarks on EFD and CFD for ship roll decay. Ocean Eng. 186: 106082

    Article  Google Scholar 

  17. Cakici F and Kahramanoglu E 2022 A RANS approach for transfer function plot based on discrete fourier transform. Ships Offshore Struct. 17(5): 1–13

    Article  Google Scholar 

  18. ITTC 2011 Numerical Estimation of Roll Damping. Recomm. Proced. p. 33

  19. Zarchan B P 2011 Fundamentals of Kalman Filtering. Practical Approach-Second Edition. American Institute of Aeronautics and Astronautics (Progress in Astronautics and Aeronautics), Virginia

  20. Gelb A 2001 Applied optimal estimation. M.I.T Press, Massachusetts. 64(4)

  21. Hermann R and Krener A J 1977 Nonlinear Controllability and Observability. IEEE Trans. Automat. Contr. 22(5): 728–740

    Article  MathSciNet  MATH  Google Scholar 

  22. Villaverde A F 2018 Observability and structural identifiability of nonlinear biological systems arXiv

  23. Jung K H, Chang K A and Jo H J 2006 Viscous effect on the roll motion of a rectangular structure. J. Eng. Mech. 132(2): 190–200

    Article  Google Scholar 

  24. Wilcox D C 1998 Turbulence Modeling for CFD. 2nd edn. La Canada Flintridge, CA, USA, DCW Industries Inc, California

    Google Scholar 

  25. Espinoza Haro M P, Park J C, Kim D H and Lee S B 2020 CFD simulation on workability of a seaweed harvesting boat due to wake-wash. J. Mar. Sci. Eng. 8(8): 544

    Article  Google Scholar 

  26. Kim M, Jung K H, Park S B, Lee G N, Duong T T, Suh S B and Park I R 2020 Experimental and numerical estimation on roll damping and pressure on a 2-D rectangular structure in free roll decay test. Ocean Eng. 196: 106801

    Article  Google Scholar 

  27. Irkal M A R, Nallayarasu S and Bhattacharyya S K 2016 CFD approach to roll damping of ship with bilge keel with experimental validation. Appl. Ocean Res. 55: 1–17

    Article  Google Scholar 

  28. Irkal M A R, Nallayarasu S and Bhattacharyya S K 2019 Numerical prediction of roll damping of ships with and without bilge keel. Ocean Eng. 179: 226–245

    Article  Google Scholar 

  29. ITTC 2011 Practical Guidelines for Ship CFD Applications,” ITTC – Recomm. Proced. Guidel. ITTC. 1–8

  30. Liu Y J, Dou C H, Shen F and Sun Q Y 2021 Vehicle state estimation based on unscented Kalman filtering and a genetic-particle swarm algorithm. J. Inst. Eng. Ser. 102(2): 447–469

    Google Scholar 

  31. Simon D 2006 Optimal State Estimation: Kalman, H∞, and Nonlinear Approaches

  32. Mehra R K 1970 On the identification of variances and adaptive Kalman filtering. IEEE Transactions on Automatic Control. 15(10): 175–184

    Article  MathSciNet  Google Scholar 

  33. Wang Z, Dong M, Qin Y, Du Y, Zhao F and Gu L 2017 Suspension system state estimation using adaptive Kalman filtering based on road classification. Veh. Syst. Dyn. 55(3): 371–398

    Article  Google Scholar 

  34. Jwo D J and Cho T S 2007 A practical note on evaluating Kalman filter performance optimality and degradation. Appl. Math. Comput. 193(2): 482–505

    Article  MathSciNet  MATH  Google Scholar 

  35. Solonen A, Hakkarainen J, Ilin A, Abbas M and Bibov A 2014 Estimating model error covariance matrix parameters in extended Kalman filtering. Nonlinear Process. Geophys. 21(5): 919–927

    Article  Google Scholar 

Download references

Acknowledgement

The computational work presented in this paper was conducted at National Centre for High-Performance Computing (UHeM) of Istanbul Technical University. The author thanks for this help.

Author information

Authors and Affiliations

  1. Department of Motor Vehicles and Transportation Technologies, Canakkale Onsekiz Mart University, Canakkale, Turkey

    Yavuz Hakan Ozdemir

Authors
  1. Yavuz Hakan Ozdemir
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yavuz Hakan Ozdemir.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ozdemir, Y.H. Estimation of ship roll damping and natural frequency using an extended Kalman filter applied to URANS output. Sādhanā 48, 168 (2023). https://doi.org/10.1007/s12046-023-02232-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12046-023-02232-x

Keywords

Search

Navigation