24 Jun 2021

24 Jun 2021

Review status: this preprint is currently under review for the journal WES.

Efficient Bayesian calibration of aerodynamic wind turbine models using surrogate modeling

Benjamin Sanderse1, Vinit V. Dighe1,2, Koen Boorsma3, and Gerard Schepers3 Benjamin Sanderse et al.
  • 1Centrum Wiskunde & Informatica, Amsterdam, the Netherlands
  • 2Delft University of Technology, Delft, the Netherlands
  • 3TNO, Petten, the Netherlands

Abstract. This paper presents an efficient strategy for the Bayesian calibration of parameters of aerodynamic wind turbine models. The strategy relies on constructing a surrogate model (based on adaptive polynomial chaos expansions), which is used to perform both parameter selection using global sensitivity analysis and parameter calibration with Bayesian inference. The effectiveness of this approach is shown in two test cases: calibration of airfoil polars based on the measurements from the DanAero MW experiments, and calibration of five yaw model parameters based on measurements on the New MEXICO turbine in yawed conditions. In both cases, the calibrated models yield results much closer to the measurement data, and in addition they are equipped with an estimate of the uncertainty in the predictions.

Benjamin Sanderse et al.

Status: open (until 20 Aug 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wes-2021-58', Anonymous Referee #1, 22 Jul 2021 reply

Benjamin Sanderse et al.

Model code and software

UQ4Wind Benjamin Sanderse, Vinit Dighe, Prashant Kumar

Benjamin Sanderse et al.


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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.