Articles | Volume 11, issue 9
https://doi.org/10.5194/wes-11-3171-2026
https://doi.org/10.5194/wes-11-3171-2026
Research article
 | 
02 Sep 2026
Research article |  | 02 Sep 2026

Gaussian process surrogate modeling for efficient controller tuning and fatigue load prediction of the helix wake-mixing method

Daan van der Hoek, Tim Dammann, and Jan-Willem van Wingerden

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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-2026-62', Anonymous Referee #1, 04 May 2026
  • RC2: 'Comment on wes-2026-62', Anonymous Referee #2, 14 May 2026
  • AC1: 'Comment on wes-2026-62', Daan van der Hoek, 17 Jul 2026

Peer review completion

AR – Author's response | RR – Referee report | ED – Editor decision | EF – Editorial file upload
AR by Daan van der Hoek on behalf of the Authors (17 Jul 2026)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (22 Jul 2026) by Majid Bastankhah
RR by Anonymous Referee #1 (29 Jul 2026)
RR by Anonymous Referee #2 (06 Aug 2026)
ED: Publish as is (14 Aug 2026) by Majid Bastankhah
ED: Publish as is (19 Aug 2026) by Paul Fleming (Chief editor)
AR by Daan van der Hoek on behalf of the Authors (21 Aug 2026)  Manuscript 
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Short summary
Wind farms suffer power losses and increased structural loading due to wake interactions. The helix method mitigates this by continuously moving upstream turbine blades to accelerate wake recovery. We developed two surrogate models from high-fidelity simulations: one identifying optimal pitch settings, achieving 7.5 % power gain, and one predicting fatigue loads under various conditions. This enables joint evaluation of power gains and load penalties, supporting informed wind farm control design.
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