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A complete fatigue assessment for operational conditions for offshore wind turbines involves simulating thousands of environmental states. For applications such as optimization, where this assessment needs to be repeated many times, that presents a significant computational problem. Here, we propose a novel way of reducing the number of simulated environmental states (load cases) while maintaining an acceptable accuracy. From one full fatigue analysis of a base design, the OC3 monopile (with the NREL 5MW turbine), the distribution of fatigue damage per load case can be used to estimate the lifetime fatigue damage of a range of modified designs. Using importance sampling and a specially adapted two‐stage filtering procedure, we obtain pseudo‐optimal sets of load cases from which the fatigue damage is estimated. This is applied to seven different designs that have been modified to emulate iterations of an optimization loop. For several of these designs, sampling less than 1% of all load cases can give damage estimates with median errors of less than 2%. Even for the most severe cases, using 3% of the environmental states yields a maximum error of 10%. While further refinement is possible, the method is considered viable for applications within design optimization and preliminary design. [ABSTRACT FROM AUTHOR]

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. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Time domain analyses of Offshore Wind Turbines involve a lot of computational effort. In this paper, we present a method for reducing this effort by using results from analyses with shorter simulation lengths to predict the results of the longer simulations lengths required by the standards. This involves using simple statistical treatment of the first 10 minutes of simulation data, and a subsequent linear regression, to predict the damage equivalent load of the full 60 minutes simulation. With some reservations about the general applicability of the method, the results are promising. Some suggestions for further investigations and developments of the presented methods are discussed. [ABSTRACT FROM AUTHOR]

Considerable variations in environmental site conditions can exist within large offshore wind farms leading to divergent fatigue loads on support structures. An efficient frequency-domain method to calculate wave-induced fatigue loads on offshore wind turbine monopile foundations was developed. A verification study with time-domain simulations for a 4MW offshore wind turbine showed result accuracies of more than 90% for equivalent bending moments at mudline and interface level. This accuracy and computation times in the order of seconds make the frequency-domain method ideal for preliminary design and support structure optimization. The model is applied in sensitivity analysis of fatigue loads using Monte-Carlo simulations. Local and global sensitivity studies and a probabilistic assessment give insight into the importance of site parameters like water depth, soil stiffness, wave height, and wave period on fatigue loads. Results show a significant influence of water depth and wave period. This provides a basis for design optimization, load interpolation and uncertainty analysis in large wind farms. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]