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Quantification and reduction of uncertainties in a wind turbine numerical model based on a global sensitivity analysis and a recursive Bayesian inference approach
Author(s) -
Hirvoas Adrien,
Prieur Clémentine,
Arnaud Elise,
Caleyron Fabien,
Munoz Zuniga Miguel
Publication year - 2021
Publication title -
international journal for numerical methods in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.421
H-Index - 168
eISSN - 1097-0207
pISSN - 0029-5981
DOI - 10.1002/nme.6630
Subject(s) - sobol sequence , identifiability , uncertainty quantification , sensitivity (control systems) , dimensionality reduction , bayesian inference , context (archaeology) , probability distribution , posterior probability , prior probability , bayesian probability , computer science , turbine , curse of dimensionality , mathematics , mathematical optimization , machine learning , artificial intelligence , statistics , engineering , mechanical engineering , electronic engineering , paleontology , biology
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.