Quantification and reduction of uncertainties in a wind turbine numerical model based on a global sensitivity analysis and a recursive Bayesian inference approach

A framework to perform quantification and reduction of uncertainties in a wind turbine numerical model using global sensitivity analysis and recursive Bayesian inference method is developed in this paper. 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 modelling, is presented. A major issue arising for such inverse problems is identifiabil-ity, i.e. whether the observations are sufficient to unambiguously determine the input parameters that generated the observations. Hereafter, global sensitivity analysis is also used in the context of identifiability.

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