A method based on response-conditioned waves is presented, for the probabilistic assessment of the short-term extreme responses of offshore structures. It is demonstrated that this method has the potential to be an efficient alternative to Monte Carlo simulations, which might be very time-consuming when low probability events are sought. The key element of the proposed approach is the use of the nonlinear wave propagation model HOS-NWT, which allows for the direct and accurate reproduction of those wave episodes in experiments or with high-fidelity numerical simulations. Under these terms, the equivalent fully nonlinear response at a given probability level is obtained and a discrete response distribution can be constructed. The method is experimentally validated for the prediction of the nacelle acceleration of a SPAR-type floating offshore wind turbine.
In the short-term prediction of extreme wave-induced loads and motions of offshore structures, the most prominent approach in terms of accuracy is the Monte Carlo (MC) method using irregular waves. However, evaluating the tail of the distribution requires very long experiments or simulations, due to the rarity of appropriate wave episodes that will induce those extreme responses (van Essen et al., 2023). At the same time, these critical wave sequences, as well as the excited responses of the structure, are very likely to be strongly nonlinear and the use of low-fidelity models, that would allow for some efficiency, is inadequate and might lead to significant underprediction. Therefore, one is obliged to choose between efficiency and accuracy, while the use of model tests or high-fidelity numerical tools, to simulate the entire sea state, becomes either impractical or even cost-prohibitive. Especially in the case of floating offshore wind turbines (FOWT), a vast amount of design load cases (DLC) have to be examined, to assess the behaviour of the structure under various severe conditions and with different combinations of wind and wave loading. For this reason, notable effort has been devoted to finding alternative methods for extreme response prediction (van Essen and Seyffert, 2023). Among them, techniques that consider solely wave episodes of short duration have gained significant ground, due to their efficient implementation in both numerical simulations and experiments (Oberhagemann et al., 2012; Brown et al., 2023).
