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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Interactive discussion

Status: closed

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
    • AC1: 'Response to reviewer comments on wes-2021-58', Benjamin Sanderse, 01 Sep 2021
  • RC2: 'Comment on wes-2021-58', Emmanuel Branlard, 08 Aug 2021
    • AC1: 'Response to reviewer comments on wes-2021-58', Benjamin Sanderse, 01 Sep 2021
  • AC1: 'Response to reviewer comments on wes-2021-58', Benjamin Sanderse, 01 Sep 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Benjamin Sanderse on behalf of the Authors (20 Sep 2021)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (25 Nov 2021) by Michael Muskulus
RR by Anonymous Referee #3 (04 Jan 2022)
ED: Publish subject to minor revisions (review by editor) (31 Jan 2022) by Michael Muskulus
AR by Benjamin Sanderse on behalf of the Authors (09 Feb 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (26 Feb 2022) by Michael Muskulus
ED: Publish as is (27 Feb 2022) by Jakob Mann (Chief editor)
AR by Benjamin Sanderse on behalf of the Authors (28 Feb 2022)
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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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