This study is concerned with the estimation of long-term fatigue damage for a floating offshore wind turbine. With the ultimate goal of efficient evaluation of fatigue limit states for floating offshore wind turbine systems, a detailed computational framework is introduced and used to develop a surrogate model using Gaussian process regression. The surrogate model, at first, relies only on a small subset of representative sea states and, then, is supplemented by the evaluation of additional sea states that leads to efficient convergence and accurate prediction of fatigue damage. A 5-MW offshore wind turbine supported by a semi-submersible floating platform is selected to demonstrate the proposed framework. The fore-aft bending moment at the turbine tower base and the fairlead tension in the windward mooring line are used for evaluation. Metocean data provide information on joint statistics of the wind and wave along with their relative likelihoods for the installation site in the Mediterranean Sea, near the coast of Sicily. \textcolor{black}{A coupled frequency-domain model} provides needed power spectra for the desired response processes. The proposed approach offers an efficient and accurate alternative to the exhaustive evaluation of a larger number of sea states and, as such, avoids excessive response simulations.

翻译：本研究聚焦于浮式海上风力发电机长期疲劳损伤的评估问题。为实现浮式风力发电系统疲劳极限状态的高效评价，本文提出了一套详细的计算框架，并基于高斯过程回归构建了替代模型。该替代模型首先仅依赖少量代表性海况数据，随后通过补充评估更多海况数据，实现了疲劳损伤预测的高效收敛与精确计算。选择由半潜式浮式基础支撑的5兆瓦海上风力发电机作为案例验证所提出的框架。评估指标包括风机塔筒底部的俯仰弯矩和迎风系泊缆线的导缆孔张力。海洋气象数据提供了西西里岛附近地中海海域安装点的风浪联合统计信息及其相对概率。采用耦合频域模型获取目标响应过程所需的功率谱。所提方法可替代对大量海况的穷举评估，在避免过度响应模拟的同时，提供了高效精确的替代方案。