Abstract A methodology for estimating extreme response statistics for marine structures, that takes both the long-term variability of the metocean environment and the short-term variability of response into account is presented. The proposed methodology uses Gaussian process regression to estimate parameters of the short-term response distribution, based on output from computationally expensive hydrodynamic simulations. We present an adaptive design strategy for sequential updating of the model, focusing on the metocean conditions that contribute the most to the long-term extreme. With this approach, only a limited number of hydrodynamic simulations are needed. The suggested approach is demonstrated on the problem of estimating the 25-year extreme vertical bending moment on a ship. We show that a relatively small number of iterations (full hydrodynamic simulations) are needed to converge toward the “exact” results obtained by running a large number of simulations covering the entire range of sea states. The results suggest that the proposed method can be used as an alternative to contour-based methods or other methods that consider a few sea states using accurate numerical simulations, with little or no added complexity or computational effort.

中文翻译：

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使用高斯过程回归估计极端结构响应的顺序采样方法

摘要 提出了一种估计海洋结构极端响应统计数据的方法，该方法考虑了大气海洋环境的长期变化和响应的短期变化。所提出的方法使用高斯过程回归来估计短期响应分布的参数，基于计算成本高昂的流体动力学模拟的输出。我们提出了一种用于模型顺序更新的自适应设计策略，重点关注对长期极端影响最大的海洋气象条件。使用这种方法，只需要有限数量的流体动力学模拟。建议的方法在估计船上 25 年极端垂直弯矩的问题上得到了证明。我们表明，通过运行覆盖整个海况范围的大量模拟，需要相对较少的迭代次数（完整的流体动力学模拟）才能收敛到“精确”的结果。结果表明，所提出的方法可用作基于轮廓的方法或其他使用精确数值模拟考虑一些海况的方法的替代方法，而几乎不增加复杂性或计算工作量。