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

Adrien Hirvoas 1, 2 Clémentine Prieur 2 Elise Arnaud 2 Fabien Caleyron 1 Miguel Zuniga 1
2 AIRSEA - Mathematics and computing applied to oceanic and atmospheric flows
Inria Grenoble - Rhône-Alpes, UGA - Université Grenoble Alpes, LJK - Laboratoire Jean Kuntzmann, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology
Abstract : 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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https://hal.inria.fr/hal-02877403
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Submitted on : Monday, June 22, 2020 - 12:40:36 PM
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Adrien Hirvoas, Clémentine Prieur, Elise Arnaud, Fabien Caleyron, Miguel Zuniga. Quantification and reduction of uncertainties in a wind turbine numerical model based on global sensitivity analysis and recursive Bayesian inference approach. 2020. ⟨hal-02877403v1⟩

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