Abstract The fatigue limit state (FLS) often drives the design of offshore wind turbine (OWT) substructures in European waters. Assessing fatigue damage over the intended design life of an OWT is computationally expensive, primarily as dynamic structural analyses have to be run for a large number of stochastic wind and wave loading conditions. This makes structural reliability assessment for the FLS a challenging task. In addition to evaluating load-induced fatigue damage, simulation-based structural reliability analysis also requires sampling of random variables that model uncertainties in the capacity of OWT structural components. To this aim, we develop and validate a computational framework for OWT fatigue reliability analysis that relies on Gaussian process (GP) regression to build surrogate models of load-induced fatigue damage. We demonstrate that the proposed approach can reduce the computational effort required to evaluate FLS reliability with high accuracy through application to three plausible offshore wind farm sites in Europe. The sensitivity of various goodness-of-fit metrics to different model assumptions is investigated to further reduce the computational effort required to perform GP regression/predictions. The results from this study can provide guidance for practical applications of the proposed framework in OWT projects.

中文翻译：

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海上风力发电机疲劳可靠性分析的高斯过程回归

摘要 疲劳极限状态（FLS）经常驱动欧洲水域海上风力涡轮机（OWT）子结构的设计。在 OWT 的预期设计寿命内评估疲劳损伤在计算上是昂贵的，主要是因为必须针对大量随机风和波浪载荷条件运行动态结构分析。这使得 FLS 的结构可靠性评估成为一项具有挑战性的任务。除了评估载荷引起的疲劳损伤之外，基于仿真的结构可靠性分析还需要对随机变量进行采样，以模拟 OWT 结构部件能力的不确定性。为此，我们开发并验证了一个用于 OWT 疲劳可靠性分析的计算框架，该框架依赖于高斯过程 (GP) 回归来构建负载诱导疲劳损伤的替代模型。我们证明，所提出的方法可以通过应用于欧洲三个合理的海上风电场站点来减少以高精度评估 FLS 可靠性所需的计算工作量。研究了各种拟合优度指标对不同模型假设的敏感性，以进一步减少执行 GP 回归/预测所需的计算工作量。这项研究的结果可以为所提出的框架在 OWT 项目中的实际应用提供指导。研究了各种拟合优度指标对不同模型假设的敏感性，以进一步减少执行 GP 回归/预测所需的计算工作量。这项研究的结果可以为所提出的框架在 OWT 项目中的实际应用提供指导。研究了各种拟合优度指标对不同模型假设的敏感性，以进一步减少执行 GP 回归/预测所需的计算工作量。这项研究的结果可以为所提出的框架在 OWT 项目中的实际应用提供指导。