A framework to perform quantification and reduction of uncertainties in a wind turbine numerical model using a global sensitivity analysis and a recursive Bayesian inference method is developed in this article. We explain how a prior probability distribution on the model parameters is transformed into a posterior probability distribution, by incorporating a physical model and real field noisy observations. Nevertheless, these approaches suffer from the so‐called curse of dimensionality. In order to reduce the dimension, Sobol' indices approach for global sensitivity analysis, in the context of wind turbine modeling, is presented. A major issue arising for such inverse problems is identifiability, that is, whether the observations are sufficient to unambiguously determine the input parameters that generated the observations. Global sensitivity analysis is also used in the context of identifiability.

中文翻译：

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基于全局灵敏度分析和递归贝叶斯推理方法的风力涡轮机数值模型中的不确定性量化和减少

本文开发了一种框架，该框架使用全局灵敏度分析和递归贝叶斯推断方法来执行风力涡轮机数值模型中的量化和减少不确定性。我们解释了如何通过合并物理模型和实地噪声观测，将模型参数上的先验概率分布转换为后验概率分布。然而，这些方法遭受了所谓的维度诅咒。为了减小尺寸，在风力涡轮机建模的背景下，提出了用于整体灵敏度分析的Sobol指数方法。这种反问题产生的主要问题是可识别性，即观察是否足以确定生成观察的输入参数。