Reducing Uncertainty of Monte Carlo Estimated Fatigue Damage in Offshore Wind Turbines Using FORM

Uncertainties related to fatigue damage estimation of
non-linear systems are highly dependent on the tail behaviour
and extreme values of the stress range distribution. By using
a combination of the First Order Reliability Method (FORM)
and Monte Carlo simulations (MCS), the accuracy of the fatigue
estimations may be improved for the same computational efforts.
The method is applied to a bottom-fixed, monopile-supported large
offshore wind turbine, which is a non-linear and dynamically sensitive
system. Different curve fitting techniques to the fatigue damage
distribution have been used depending on the sea-state dependent
response characteristics, and the effect of a bi-linear S-N curve is
discussed. Finally, analyses are performed on several environmental
conditions to investigate the long-term applicability of this multistep
method. Wave loads are calculated using state-of-the-art theory, while
wind loads are applied with a simplified model based on rotor thrust
coefficients.

Authors:

• Jan-Tore H. Horn
• Jørgen Juncher Jensen

Keywords:

• Fatigue damage
• FORM
• monopile
• monte carlo simulation
• reliability
• wind turbine.

References:

[1] D. Zwick and M. Muskulus, “The simulation error caused by input
loading variability in offshore wind turbine structural analysis,” Wind
Energy, vol. 18, no. 8, aug 2015.
[2] J. J. Jensen, “Fatigue damage estimation in non-linear systems using a
combination of Monte Carlo simulation and the First Order Reliability
Method,” Marine Structures, vol. 44, pp. 203–210, dec 2015.
[3] P.-L. Liu and A. Der Kiureghian, “Optimization algorithms
for structural reliability,” Structural Safety, vol. 9,
no. 3, pp. 161–177, feb 1991. (Online). Available:
http://www.sciencedirect.com/science/article/pii/0167473091900417
[4] H. Madsen, S. Krenk, and N. Lind, Methods of Structural Safety.
Prentice-Hall, Inc., 1986.
[5] DNV GL, “RP-C203 Fatigue design of offshore steel structures,” Tech.
Rep. April, 2005.
[6] WAFO-group, “WAFO - A Matlab Toolbox for Analysis
of Random Waves and Loads,” 2000. (Online). Available:
http://www.maths.lth.se/matstat/wafo/
[7] T. Moan and A. Naess, Stochastic Dynamics of Marine Structures.
Cambridge University Press, 2013.
[8] C. Bak, F. Zahle, R. Bitsche, A. Yde, L. C. Henriksen, A. Nata, and
M. H. Hansen, “Description of the DTU 10 MW Reference Wind
Turbine,” no. July, 2013.
[9] E. Smilden and L. Eliassen, “Wind Model for Simulation of Thrust
Variations on a Wind Turbine,” Energy Procedia, 2016.
[10] O. M. Faltinsen, J. N. Newman, and T. Vinje, “Nonlinear
wave loads on a slender vertical cylinder,” Journal of Fluid
Mechanics, vol. 289, p. 179, apr 1995. (Online). Available:
http://journals.cambridge.org/abstract S0022112095001297
[11] X. Y. Zheng, T. Moan, and S. T. Quek, “Numerical simulation of
non-Gaussian wave elevation and kinematics based on two-dimensional
fourier transform,” pp. 1–6, 2006.
[12] M. Tucker, P. Challenor, and D. Carter, “Numerical simulation of a
random sea: a common error and its effect upon wave group statistics,”
Applied Ocean Research, vol. 6, no. 2, pp. 118–122, apr 1984.
[13] J. T. H. Horn, J. R. Krokstad, and J. Amdahl, “Hydro-Elastic
Contributions to Fatigue Damage on a Large Monopile,” Energy
Procedia, 2016.
[14] DNV GL, “OS-J101 Design of Offshore Wind Turbine Structures,” Tech.
Rep., 2014.
[15] T. Burton, D. Sharpe, N. Jenkins, and E. Bossanyi, Wind Energy
Handbook. John Wiley & Sons, Ltd, 2002. (Online). Available:
http://dx.doi.org/10.1002/0470846062.ch4
[16] J. J. Jensen, “Extreme value predictions using Monte Carlo simulations
with artificially increased load spectrum,” Probabilistic Engineering
Mechanics, vol. 26, no. 2, pp. 399–404, apr 2011. (Online). Available:
http://www.sciencedirect.com/science/article/pii/S0266892010000767