Assessment of environmental contour methods in prediction of long-term extreme responses of conceptual multi-rotor wind turbine
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Abstract
Determination of long-term extreme responses caused by environmental conditions is vital for the assessment of the design of offshore systems. The full long-term analysis (FLTA) is considered the most accurate approach for this. In principle, this approach directly integrates the probability distribution of all short-term extreme responses and the related environmental conditions. However, the FLTA is also very computational demanding due to all possible environmental conditions, thus making the method inefficient. Therefore, simplified methods have previously been proposed as more efficient alternatives to predict the long-term extreme responses for offshore structures.One method is the environmental contour method (ECM), which predicts the long-term extreme response by considering the short-term extreme distribution of only important conditions lying on the environmental contour surface with the same return period as the long-term extreme response. However, for systems with active survival strategies for mitigation of the extreme response, such as offshore wind turbines, the method might be insufficient. A method based on the ECM but accounts for the discontinuity in the response is the modified environmental contour method (MECM). In addition to the contour used in the ECM, MECM also considers additional contours within the operational range. The result of MECM is the largest response of conditions found on both contours.In this thesis, the applicability of two simplified environmental contour methods is investigated for the prediction of 50-year extreme responses of an inhouse two-rotor floating wind turbine (2WT) for different offshore environments. The results from these methods are then benchmarked against the results of the FLTA. It was found that the ECM is not suitable for this system due to large underpredictions of some responses governed by the wind. However, the MECM was able to improve the results significantly while significantly reduce the computational effort.