Hu, Zhihua; (1995) Spectral fatigue analysis techniques. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

This thesis presents a practical design tool for wind turbine blades which was developed from existing theory on spectral fatigue analysis previously used for offshore platform design. The usual aim with spectral fatigue analysis techniques is to estimate fatigue damage or some related function such as rainflow ranges from spectral statistics. Monitored structural responses from different wind turbines were used to assess these existing techniques. The best two methods (suitable only for Gaussian stationary and random responses) were found to be Dirlik's empirical formula and Bishop's theoretical solution. Various parameters involved in the computation such as cutoff frequency and clipping ratio were examined. Guidelines for selection of these parameters are also given. A method based on Bishop's theoretical solution is extended to include the influence of mean stress. The joint PDF of rainflow cycle and mean stress can be obtained from the response PSD using this method. Because the global mean level information is usually not provided by the PSD, only the relative mean of each rainflow cycle is calculated using this method. The global mean level can then be provided by the designer during the structural analysis stage. This new method was used to analyse the mean stress influence for wind turbine blades using the two monitored structural response histories mentioned above. A number of possible approaches for the spectral fatigue analysis of non- Gaussian response histories are discussed. A method based on Bishop's theoretical solution is extended to calculate the PDF of rainflow ranges from non- Gaussian response histories specified as a peak trough transition matrix. Although this is only a partial solution to the overall problem it still represents a significant breakthrough. It may, for instance, be of use for estimating rainflow ranges from standardised load sequences specified as turning point matrices. Restricted by the complexity of non-Gaussian processes, especially the limited information provided by PSD's, a universal solution for the transition matrix and the peak number of the process in unit time is currently not available. As part of the continual development of wind turbines, blade diameters are continuing to increase. The blade response can then sometimes contain a huge deterministic component, caused by gravity, which makes the edgewise response process not only non-Gaussian but also not random. Existing methods can not deal with this situation. By numerical simulation from selected spectra and deterministic component parameters, a mathematical model for the rainflow cycle PDF has been established. Least square techniques were employed for curve fitting to obtain a set of model parameters. These parameters were used to train a back propagation neural network. Finally, a neural network toolbox was developed for the fatigue analysis of wind turbine blades subjected to both Gaussian stationary random flapwise responses and edgewise responses with a deterministic component. In principle, the method can easily be extended to cover more then one deterministic components. Verification of the technique has been carried out using measured responses from a Howden HWP330 wind turbines.

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