Bolted ring flanges in offshore-wind support structures - in-situ validation of load-transfer behaviour. (January 2021) - Weijtjens, Wout; Stang, Andre; Devriendt, Christof; Schaumann, Peter
Abstract: Offshore wind turbines are subjected to cyclic loads from wind and waves, ultimately resulting in a fatigue-driven design. These cyclic loads result in cyclic shell forces above the bolted ring flange and ultimately into the cyclic loading of the bolt tension forces. To model the transfer of the shell forces into the bolt, Load Transfer Functions (LTF) are used, of which the tri-linear function according Schmidt/Neuper is the most known approximation but more contemporary approaches exist. As the LTF has a significant impact on the estimated fatigue lifetime of the bolt, it is relevant to investigate the accuracy of the currently used LTF. This paper uses in-situ monitoring data from three offshore wind turbines at the Nobelwind windfarm, each equipped with an instrumented bolt in the monopile to transition piece bolted flange connection and strain-gauges above said flange. Long-term monitoring data is used to derive empirical load transfer coefficients (LTC), i.e. the derivatives of the LTF. In parallel a finite element model (FE model) of the flange connection was developed. The found load transfer coefficients from measurements were compared to the values obtained from the function according to Schmidt/Neuper and the FE model. It is concluded that the observed LTC in the offshore wind farm were favourable compared to the values expected from Schmidt/Neuper. The FE model matched more closely, especially when the MP-flange inclination was included. The inclusion of the MP-flange inclination in the model had a positive effect on the final LTF, which was confirmed by the measurements. Highlights: In-situ tension monitoring of M72 bolts inside the bolted flange connection of an operational offshore wind An empirical load transfer function was determined using long-term monitoring data under different loading conditions FE-based validation of the observations