Wind Energy Science
Wind Energy Science
WES
Articles | Volume 7, issue 2
Articles | Volume 7, issue 2
https://doi.org/10.5194/wes-7-759-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-7-759-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 7, issue 2
Research article
 | 
31 Mar 2022
Research article |  | 31 Mar 2022

Efficient Bayesian calibration of aerodynamic wind turbine models using surrogate modeling

Benjamin Sanderse, Vinit V. Dighe, Koen Boorsma, and Gerard Schepers

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Short summary
An accurate prediction of loads and power of an offshore wind turbine is needed for an optimal design. However, such predictions are typically performed with engineering models that contain many inaccuracies and uncertainties. In this paper we have proposed a systematic approach to quantify and calibrate these uncertainties based on two experimental datasets. The calibrated models are much closer to the experimental data and are equipped with an estimate of the uncertainty in the predictions.
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