Optimal design of experiments for computing the fatigue life of an offshore wind turbine based on stepwise uncertainty reduction

The design of an offshore wind turbine to resist fatigue damage during its whole service life requires to estimate an expectation over the pluri-annual joint statistics of wind and wave variables. Using a full factorial-based integration for the estimation of the cumulative fatigue damage represents a tremendous computational cost with aero-servo-hydro-elastic solvers which is generally not affordable by industrial designers. To overcome this limitation, strong approximations with lumping of environmental discretized joint probability (scatter diagram) are generally employed. We present in this paper a new method, called MAKSUR, involving the iterative enrichment of a design of experiments tailored to provide a good approximation of the long term mean damage. This method relies on a Kriging response surface with a learning criterion defined as the variance of the mean damage integral. It is compared to another previous similar approach called AK-DA, also dedicated to damage prediction, but is shown to converge more efficiently and with less numerical parameters to define by the user. The potential of the method for offshore wind turbine is demonstrated by a realistic 6D floating wind turbine case study with six wind and wave input variables.