Articles | Volume 11, issue 4
https://doi.org/10.5194/wes-11-1227-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-11-1227-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Damage identification on a large-scale wind turbine rotor blade using sample-based deterministic model updating
Marlene Wolniak
CORRESPONDING AUTHOR
Leibniz University Hannover/ForWind, Institute of Structural Analysis, Appelstr. 9A, 30167 Hannover, Germany
Jasper Ragnitz
Leibniz University Hannover/ForWind, Institute of Structural Analysis, Appelstr. 9A, 30167 Hannover, Germany
Clemens Jonscher
Leibniz University Hannover/ForWind, Institute of Structural Analysis, Appelstr. 9A, 30167 Hannover, Germany
Benedikt Hofmeister
Ruhr-University Bochum, Photonics and Terahertz Technology, Universitätsstr. 150, 44801 Bochum, Germany
Helge Jauken
Leibniz University Hannover/ForWind, Institute of Structural Analysis, Appelstr. 9A, 30167 Hannover, Germany
Clemens Hübler
Technical University of Darmstadt, Institute of Structural Mechanics and Design, Franziska-Braun-Str. 3, 64287 Darmstadt, Germany
Raimund Rolfes
Leibniz University Hannover/ForWind, Institute of Structural Analysis, Appelstr. 9A, 30167 Hannover, Germany
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Susanne Könecke, Clemens Jonscher, Tobias Bohne, and Raimund Rolfes
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-44, https://doi.org/10.5194/wes-2026-44, 2026
Preprint under review for WES
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This paper presents a framework to identify wind turbine noise in long-term field measurements. By combining statistical criteria, turbine operating data, and physics-based signal analysis, periods dominated by wind turbine noise and its key components are detected. The framework is validated using a structured listening test and applied to a one month dataset. The framework, listening-test platform, and anonymized audio data are publicly available to support further research.
Franziska Schmidt, Clemens Hübler, and Raimund Rolfes
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-38, https://doi.org/10.5194/wes-2026-38, 2026
Preprint under review for WES
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A lifetime reassessment of an offshore wind turbine using Kriging meta-models is performed. This method is compared to a full lifetime reassessment using aeroelastic simulations considering all actually occurred combinations of environmental parameters and to the approach according to IEC 61400-3. By using the meta-models, the computing time can be significantly reduced compared to the other two methods, while ensuring a high approximation quality in the prediction of the lifetime fatigue loads.
Jonathan Thurn, Clemens Jonscher, Benedikt Hofmeister, Gianluca Zorzi, and Raimund Rolfes
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-5, https://doi.org/10.5194/wes-2026-5, 2026
Revised manuscript under review for WES
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Load monitoring for wind turbine support structures is essential as turbines approach the end of their design life. Typically, load monitoring involves estimating strain from vibrational measurements. This paper introduces an approach that uses a single acceleration sensor to estimate strains across all fatigue-relevant excitation frequencies on the entire support structure, enabled by compensating for gravity-induced biases in acceleration measurements.
Franziska Schmidt, Clemens Hübler, and Raimund Rolfes
Wind Energ. Sci., 10, 3069–3089, https://doi.org/10.5194/wes-10-3069-2025, https://doi.org/10.5194/wes-10-3069-2025, 2025
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In this work, a Kriging meta-model of an idling offshore wind turbine is analysed in detail for the first time. It becomes clear that the findings regarding meta-modelling of the idling wind turbine are generally similar to the findings regarding meta-modelling of the same wind turbine in normal operation. However, for the approximation of the rotor blade root bending moments, two additional input parameters have to be included compared to the same wind turbine in operation.
Clemens Jonscher, Paula Helming, David Märtins, Andreas Fischer, David Bonilla, Benedikt Hofmeister, Tanja Grießmann, and Raimund Rolfes
Wind Energ. Sci., 10, 193–205, https://doi.org/10.5194/wes-10-193-2025, https://doi.org/10.5194/wes-10-193-2025, 2025
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This study investigates dynamic displacement estimation using double-time-integrated acceleration signals for future application in load monitoring based on accelerometers. To estimate displacements without amplitude distortion, a tilt error compensation method for low-frequency vibrations of tower structures using the static bending line without the need for additional sensors is presented. The method is validated using a full-scale onshore wind turbine tower and a terrestrial laser scanner.
Susanne Könecke, Jasmin Hörmeyer, Tobias Bohne, and Raimund Rolfes
Wind Energ. Sci., 8, 639–659, https://doi.org/10.5194/wes-8-639-2023, https://doi.org/10.5194/wes-8-639-2023, 2023
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Extensive measurements in the area of wind turbines were performed in order to validate a sound propagation model. The measurements were carried out under various environmental conditions and included the acquisition of acoustical, meteorological and wind turbine performance data. By processing and analysing the measurement data, validation cases and input parameters for the propagation model were derived. Comparing measured and modelled propagation losses, generally good agreement is observed.
Clemens Hübler and Raimund Rolfes
Wind Energ. Sci., 7, 1919–1940, https://doi.org/10.5194/wes-7-1919-2022, https://doi.org/10.5194/wes-7-1919-2022, 2022
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Offshore wind turbines are beginning to reach their design lifetimes. Hence, lifetime extensions are becoming relevant. To make well-founded decisions on possible lifetime extensions, fatigue damage predictions are required. Measurement-based assessments instead of simulation-based analyses have rarely been conducted so far, since data are limited. Therefore, this work focuses on the temporal extrapolation of measurement data. It is shown that fatigue damage can be extrapolated accurately.
Clemens Jonscher, Benedikt Hofmeister, Tanja Grießmann, and Raimund Rolfes
Wind Energ. Sci., 7, 1053–1067, https://doi.org/10.5194/wes-7-1053-2022, https://doi.org/10.5194/wes-7-1053-2022, 2022
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This work presents a method to use low-noise IEPE sensors in the low-frequency range down to 0.05 Hz. In order to achieve phase and amplitude accuracy with this type of sensor in the low-frequency range, a new calibration procedure for this frequency range was developed. The calibration enables the use of the low-noise IEPE sensors for large structures, such as wind turbines. The calibrated sensors can be used for wind turbine monitoring, such as fatigue monitoring.
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Short summary
This study investigates how finite-element (FE) models of different fidelity affect the damage identification in a 31 m wind turbine rotor blade tested under edgewise fatigue loading. Different design variable configurations are compared, whereby the FE model updating is based on modal parameters identified from measured vibration data.
This study investigates how finite-element (FE) models of different fidelity affect the damage...
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