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 recursive Bayesian inference method is developed this paper. We explain how prior probability distribution on the parameters transformed into posterior distribution, by incorporating physical real field noisy observations. Nevertheless, these approaches suffer from so-called curse dimensionality. In order reduce dimension, Sobol' indices approach for analysis, context modelling, presented. major issue arising such inverse problems identifiabil-ity, i.e. whether observations are sufficient unambiguously determine input that generated Hereafter, also used identifiability.