Modeling and data analytics options for the fatigue analysis of a floating offshore wind turbine

In recent years, renewable energy has gained in importance due to increasing concerns about climate change. In the offshore environment, wind turbines supported on floating platforms offer an efficient solution for renewable energy generation. However, floating structures are sensitive to environment loading from wind and waves; their response can be difficult to predict due to the complex and nonlinear motion characteristics. Accurate estimation of the response of such structures and an ability to predict service life at a desired location can help to reduce costs and reduce downtime for repair and maintenance. This study is concerned with the assessment of fatigue damage of a floating offshore wind turbine and evaluation of the failure probability over specified periods of exposure. Using platform-turbine response simulations over a wide range of sea states that define contrasting wind and wave conditions, "damage-equivalent" loads for each sea state are computed using three different approaches. A Rayleigh-based prediction computed in the frequency domain assumes narrow-band response characteristics and, while easy to compute, is less accurate than a Dirlik-based approach, which in turn overestimates fatigue loads relative to the time-domain approach of rainflow cycle counting which serves as a reference. Trends in the fatigue loads across sea states are similar with all the methods. From sensitivity analyses using Monte Carlo simulations, it is found that uncertainty connected to S-N curve definition and to the load factor used for fatigue are influential parameters when evaluating the fatigue life of the selected floating offshore wind turbine.