This paper considers the problem of retrospectively de-trending wind site data when only statistical moments, in the form of 10-min means and standard deviations in wind speed, are available. Low-frequency trends present in wind speed data are known to bias fatigue damage estimates, and, hence, removal of their influence is important for accurate fatigue life estimation. When raw data is available, this procedure is straightforward; however, for many sites, significant quantities of data are available, which contain only statistical moments. Additional value is therefore unlocked if de-trending can also take place in this context. Existing methods, Models 1 and 2, are introduced, and their performance and viability appraised, respectively. A Gaussian process (GP) regression implementation is also developed, which seeks to incorporate characteristics of real trends extracted from raw data into the fitting procedure via an appropriately chosen lengthscale hyperparameter. Results indicate that Model 2, the recommended method in previous work, suffers from fundamental issues, with the implication that it should no longer be used. Model 1 and GP results are shown to be very similar at the turbulence distribution level. This finding is interpreted as a validation of Model 1 and an indication that it may already be performing as well as can be hoped for, given the information available in the current problem formulation. Theoretical overheads associated with GPs, in addition to the performance similarities mentioned above, lead to Model 1 being recommended as the best approach to moment-based turbulence de-trending at this time.

中文翻译：

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使用统计矩的风场湍流去趋势：评估现有方法并引入高斯过程回归方法

本文考虑了当只有 10 分钟平均值和风速标准偏差等统计矩可用时，追溯去趋势风场数据的问题。众所周知，风速数据中存在的低频趋势会导致疲劳损伤估计产生偏差，因此，消除它们的影响对于准确估计疲劳寿命很重要。当原始数据可用时，此过程很简单；然而，对于许多站点，有大量数据可用，其中仅包含统计矩。因此，如果在这种情况下也可以进行去趋势化，则可以释放额外的价值。介绍了现有方法模型 1 和模型 2，并分别评估了它们的性能和可行性。还开发了高斯过程 (GP) 回归实现，which seeks to incorporate characteristics of real trends extracted from raw data into the fitting procedure via an appropriately chosen lengthscale hyperparameter. 结果表明，模型 2（先前工作中推荐的方法）存在根本性问题，意味着不应再使用它。模型 1 和 GP 结果在湍流分布水平上非常相似。这一发现被解释为对模型 1 的验证，并表明它可能已经按照当前问题公式中可用的信息执行了预期。除了上述性能相似性之外，与 GP 相关的理论开销导致模型 1 被推荐为此时基于矩的湍流去趋势的最佳方法。