Articles | Volume 7, issue 2
https://doi.org/10.5194/wes-7-759-2022
https://doi.org/10.5194/wes-7-759-2022
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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Cited articles

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Bak, C., Madsen, H. A., Gaunaa, M., Paulsen, U. S., Fuglsang, P., Romblad, J., Olesen, N. A., Enevoldsen, P., Laursen, J., and Jensen, L.: DAN-AERO MW: Comparisons of airfoil characteristics for two airfoils tested in three different wind tunnels, Torque 2010: The science of making torque from wind, EWEA, 2010, 59–70, 2010. a
Bayes, T.: LII. An essay towards solving a problem in the doctrine of chances. By the late Rev. Mr. Bayes, FRS communicated by Mr. Price, in a letter to John Canton, AMFR S, Philos. T. R. Soc. Lond., 53, 370–418, 1763. a
Blatman, G.: Adaptive sparse polynomial chaos expansions foruncertainty propagation and sensitivity analysis, PhD thesis, Universite Blaise Pascal, Clermont-Ferrand, France, https://sudret.ibk.ethz.ch/research/publications/doctoralTheses/g--blatman.html, 2009. a
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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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