This study evaluated, by time-domain simulations, the fatigue life of the jacket support structure of a 3.6 MW wind turbine operating in Fuhai Offshore Wind Farm. The long-term statistical environment was based on a preliminary site survey that served as the basis for a convergence study for an accurate fatigue life evaluation. The wave loads were determined by the Morison equation, executed via the in-house HydroCRest code, and the wind loads on the wind turbine rotor were calculated by an unsteady BEM method. A Finite Element model of the wind turbine was built using Beam elements. However, to reduce the time of computation, the hot spot stress evaluation combined FE-derived Closed-Form expressions of the nominal stresses at the tubular joints and stress concentration factors. Finally, the fatigue damage was assessed using the Rainflow Counting scheme and appropriate SN curves. Based on a preliminary sensitivity study of the fatigue damage prediction, an optimal load setting of 60-min short-term environmental conditions with one-second time steps was selected. After analysis, a sufficient fatigue strength was identified, but further calculations involving more extensive long-term data measurements are required in order to confirm these results. Finally, this study highlighted the sensitivity of the fatigue life to the degree of fluctuation (standard deviation) of the wind loads, as opposed to the mean wind loads, as well as the importance of appropriately orienting the jacket foundations according to prevailing wind and wave conditions.
Calibration of Long-Term Time-Domain Load Generation for Fatigue Life Assessment of Offshore Wind Turbine
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Nelson, B, Quéméner, Y, Lin, T, Huang, H, & Chien, C. "Calibration of Long-Term Time-Domain Load Generation for Fatigue Life Assessment of Offshore Wind Turbine." Proceedings of the ASME 2017 36th International Conference on Ocean, Offshore and Arctic Engineering. Volume 10: Ocean Renewable Energy. Trondheim, Norway. June 25–30, 2017. V010T09A053. ASME. https://doi.org/10.1115/OMAE2017-61747
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