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Polymorphic uncertainty in met-ocean conditions and the influence on fatigue loads

: Hübler, C.; Müller, F.; Rolfes, R.

Volltext ()

Institute of Physics -IOP-, London:
EERA DeepWind 2020, 17th Deep Sea Offshore Wind R&D Conference : 15 - 17 January 2020, Radisson Blu Royal Garden Hotel, Trondheim, Norway
Bristol: IOP Publishing, 2020 (Journal of physics. Conference series 1669)
Art. 012005, 12 S.
Deep Sea Offshore Wind R&D Conference (DeepWind) <17, 2020, Trondheim>
Deutsche Forschungsgemeinschaft DFG
436547100; ENERGIZE
Effizienzsteigerung unscharfer Strukturanalysen von Windenergieanlagen im Zeitbereich
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IWES ()

An accurate numerical simulation of the structural lifetime of offshore wind turbines is a challenging task due to several reasons. One of them is the uncertainty of met-ocean conditions acting on a turbine, e.g. wind and waves. This uncertainty can be divided into two kinds of uncertainty: aleatory and epistemic uncertainty. If both types of uncertainty occur, this is called polymorphic uncertainty. According to the state of the art, for met-ocean conditions, mainly aleatory uncertainty is considered or both types of uncertainty are modelled using a single probability density function. This leads to a simplification of the actual uncertainty, whose effect on the lifetime estimation has not been analysed so far. In that sense, in this work, the influence of various uncertainty models for met-ocean conditions on long-term damage equivalent loads (DELs) - representing the wind turbine fatigue lifetime - is investigated. For this purpose, different uncertainty models for met-ocean conditions are derived using real measurement data. Not only purely probabilistic models are applied, but imprecise probabilities - here interval random variables - as well. It is shown that the uncertainty models have a considerable influence on the fatigue life of offshore wind turbines. Especially, the large fatigue load intervals, which are determined, clarify the importance of a well-founded decisions regarding uncertainty modelling of met-ocean conditions.