In the early design phase for a 15 MW spar-type Floating Offshore Wind Turbine (FOWT), lack of environmental details heightens uncertainty in mooring fatigue predictions. Sensitivity analysis (SA) is employed to investigate the importance of wind and wave on this FOWT, based on a site-specific model from EU H2020 funded project COREWIND. Numerical simulations of wind-only, wave-only and wind-wave tests are conducted in OpenFAST. The SA results prove that among wind speed, significant wave height and peak wave period, wind speed is the most influential parameter and wave impact is limited on mooring fatigue loads. The wind-only mooring fatigue loads are generally higher than the integrated wind-wave results, indicating that we can apply wind-only tests for efficient and reliable predictions of mooring tension fatigue for this site-specific FOWT. The findings of SA decrease the prediction uncertainty and promotes efficient mooring designs.
Over the lifetime of floating offshore wind turbines (FOWTs), complex environmental conditions cause cyclic loadings and increase mooring failure risks. Although there is little public data on mooring failures of FOWTs, lessons can be learnt from the oil and gas industry that fatigue is one of the main reasons for mooring failures (Ma et al., 2019). What is more, the fault tree analysis for FOWTs shows a high failure rate for mooring system fatigue (Kang et al., 2019). Mooring failures are harmful to mooring structural strength and result in unacceptable movements of the floater, which affects the wind power generation. Therefore, a feasible mooring design and a reliable fatigue prediction are critical for mooring integrity and in-service performance of FOWTs.
Recent mooring fatigue studies on FOWTs investigate the effect of mooring line dynamics (Azcona et al., 2017), wave nonlinearity (Xu et al., 2019) and mooring designs (Pan et al., 2021). Different procedures are proposed for reliable mooring fatigue predictions (Müller and Cheng 2018, Barrera et al., 2020). In mentioned studies, sea states are described by site measurements, empirical joint probability functions and Monte Carlo samplings, which are common methods for the reconstruction of environmental conditions. In the early design stage, the site-specific joint distributions of wind speed, wave height and wave period are not provided for a 15 MW spar-type FOWT (Vigara et al., 2019). And the empirical functions are not applicable for the specific site. Meanwhile, the Monte Carlo method generates a large scale of samplings and the high computational effort is not preferred for preliminary designs. Lack of environmental details heightens uncertainty in predictions, therefore it is essential to identify the most important environmental parameters for reliable and efficient mooring fatigue predictions.
