Stochastic representations of turbulent wind and irregular waves are used in time domain simulations of offshore wind turbines. The variability due to finite sampling of this input loading is an important source of simulation error. For the OC4 reference jacket structure with a 5 MW wind turbine, an error of 12–34% for ultimate loads and 6–12% for fatigue loads can occur with a probability of 1%, for simulations with a total simulation length of 60 min and various load cases. In terms of fatigue life, in the worst case, the lifetime of a joint was thereby overestimated by 29%. The size of this error can be critical, i.e., ultimate or fatigue limits can be exceeded, with probability depending on the choice of number of random seeds and simulation length. The analysis is based on a large simulation study with about 30,000 time domain simulations. Probability density functions of response variables are estimated and analyzed in terms of confidence intervals; i.e., how probable it is to obtain results significantly different from the expected value when using a finite number of simulations. This simulation error can be reduced to the same extent, either using several short simulations with different stochastic representations of the wind field or one long simulation with corresponding total length of the wind field. When using several short‐term simulations, it is important that ultimate and fatigue loads are calculated based on the complete, properly combined set of results, in order to prevent a systematic bias in the estimated loads. Copyright © 2014 John Wiley & Sons, Ltd.

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The estimation of fatigue lifetime for an offshore wind turbine support structure requires a large number of time-domain simulations. It is an important question whether it is possible to reduce the number of load cases while retaining a high level of accuracy of the results. We present a novel method for simplified fatigue load assessments based on statistical regression models that estimate fatigue damage during power production. The main idea is to predict the total fatigue damage only and not also the individual damage values for each load case . We demonstrate the method for a jacket-type support structure. Reducing the number of simulated load cases from 21 to 3, the total fatigue damage estimate exhibited a maximum error of about 6% compared with the complete assessment. As a consequence, a significant amount of simulation time can be saved, in the order of a factor of seven. This quick fatigue assessment is especially interesting in the application of structural optimization, with a large number of iterations.

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