Optimizing Fatigue Life Predictions for Floating Offshore Wind Turbines: Impact of Binning and Data Duration

Abstract Floating Offshore Wind Turbines (FOWTs) experience dynamic environmental loads over their lifetime, making accurate fatigue assessment crucial for structural reliability and optimised design. Binning methods simplify metocean conditions by grouping environmental inputs into representative cases, reducing computational complexity. However, uncertainties arise from bin size and the length of input data, particularly in long-term fatigue predictions. This study investigates the impact of binning strategies on fatigue life predictions over a 25-year design life, focusing on the effect of metocean input data duration. Using 30 years of the ANEMOC3 hindcast as reference, subsets of 5-, 10- and 15-year data were analyzed. Fatigue damage at key components, such as the tower base and mooring line fairleads of the VolturnUS/IEA 15MW semi-submersible, was calculated. Results show that with 15 years of data, relative errors in tower base fatigue predictions remain below 6%, while heavily loaded mooring lines exhibit errors under 3%. Even with 10 years of data, tower base errors stay within 10%, and mooring line errors below 4%. For the first time, these findings demonstrate that accurate fatigue predictions are achievable without extensive datasets, enabling faster project development in data-scarce regions. This study supports cost reductions and accelerates offshore wind expansion to meet net-zero targets.