Articles | Volume 11, issue 9
https://doi.org/10.5194/wes-11-3653-2026
© 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-11-3653-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Lifetime reassessment of offshore wind turbines considering different operating conditions using Kriging meta-models
Franziska Schmidt
CORRESPONDING AUTHOR
Leibniz University Hannover, Institute of Structural Analysis, ForWind, Appelstr. 9A, 30167 Hannover, Germany
Clemens Hübler
TU Darmstadt, Institute of Structural Mechanics and Design, Franziska-Braun-Str. 3, 64287 Darmstadt, Germany
Raimund Rolfes
Leibniz University Hannover, Institute of Structural Analysis, ForWind, Appelstr. 9A, 30167 Hannover, Germany
Related authors
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
Short summary
Short summary
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.
Jonathan Thurn, Clemens Jonscher, Benedikt Hofmeister, Gianluca Zorzi, and Raimund Rolfes
Wind Energ. Sci., 11, 3359–3376, https://doi.org/10.5194/wes-11-3359-2026, https://doi.org/10.5194/wes-11-3359-2026, 2026
Short summary
Short summary
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 biaxial and DC-capable 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.
Susanne Könecke, Clemens Jonscher, Tobias Bohne, and Raimund Rolfes
Wind Energ. Sci., 11, 1771–1789, https://doi.org/10.5194/wes-11-1771-2026, https://doi.org/10.5194/wes-11-1771-2026, 2026
Short summary
Short summary
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 1-month dataset. The framework, listening-test platform, and anonymized audio data are publicly available to support further research.
Marlene Wolniak, Jasper Ragnitz, Clemens Jonscher, Benedikt Hofmeister, Helge Jauken, Clemens Hübler, and Raimund Rolfes
Wind Energ. Sci., 11, 1227–1249, https://doi.org/10.5194/wes-11-1227-2026, https://doi.org/10.5194/wes-11-1227-2026, 2026
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Cited articles
American Petrolium Institute (API): Recommended Practice for planning, designing and constructing fixed offshore platforms – working stress design – Errata and supplement 3: RP 2A WSD, https://www.api.org/publications-standards-and-statistics/standards-addenda-and-errata/standards-addenda-and-errata/~/media/f86ed982222e44c3b6ee42dc9ba39833.ashx (last access: 6 February 2026), 2007. a
American Society for Testing and Materials (ASTM): Standard practices for cycle counting in fatigue analysis, ASTM E1049-85, https://doi.org/10.1520/E1049-85R17, 2017. a
Avendaño-Valencia, L. D., Abdallah, I., and Chatzi, E.: Virtual fatigue diagnostics of wake-affected wind turbine via Gaussian Process regression, Renew. Energ., 170, 539–561, https://doi.org/10.1016/j.renene.2021.02.003, 2021. a
Bouty, C., Schafhirt, S., Ziegler, L., and Muskulus, M.: Lifetime extension for large offshore wind farms: Is it enough to reassess fatigue for selected design positions?, Enrgy. Proced., 137, 523–530, https://doi.org/10.1016/j.egypro.2017.10.381, 2017. a
Goodman, J.: Mechanics applied to engineering, Longmans, Green & Co., London, UK, https://archive.org/details/cu31924004025338/mode/2up (last access: 6 February 2026), 1914. a
Haghi, R. and Crawford, C.: Data-driven surrogate model for wind turbine damage equivalent load, Wind Energ. Sci., 9, 2039–2062, https://doi.org/10.5194/wes-9-2039-2024, 2024. a
Häfele, J., Hübler, C., Gebhardt, C. G., and Rolfes, R.: An improved two-step soil-structure interaction modeling method for dynamical analyses of offshore wind turbines, Appl. Ocean Res., 55, 141–150, https://doi.org/10.1016/j.apor.2015.12.001, 2016. a, b
Hübler, C., Gebhardt, C. G., and Rolfes, R.: Hierarchical four-step global sensitivity analysis of offshore wind turbines based on aeroelastic time domain simulations, Renew. Energ., 111, 878–891, https://doi.org/10.1016/j.renene.2017.05.013, 2017a. a, b
Hübler, C., Häfele, J., Gebhardt, C. G., and Rolfes, R.: Experimentally supported consideration of operating point dependent soil properties in coupled dynamics of offshore wind turbines, Mar. Struct., 57, 18–37, https://doi.org/10.1016/j.marstruc.2017.09.002, 2018. a, b
Jonkman, B. J.: TurbSims User's Guide v2.00.00, National Renewable Energy Laboratory, Golden, Colorado, USA, https://openfast.readthedocs.io/en/v4.0.5/_downloads/cb14d3e2d3533d76e405d730fea19846/TurbSim_v2.00.pdf (last access: 01 September 2026), 2016. a
Jonkman, J., Butterfield, S., Musial, W., and Scott, G.: Definition of a 5 MW Reference Wind Turbine for Offshore System Development, National Renewable Energy Laboratory, Golden, Colorado, USA, https://doi.org/10.2172/947422, 2009. a
Jonkman, J. and Musial, W.: Offshore Code Comparison Collaboration (OC3) for IEA Task 23 Offshore Wind Technology and Deployment, National Renewable Energy Laboratory, Golden, Colorado, USA, https://doi.org/10.2172/1004009, 2010. a
Jonkman, J.: The New Modularization Framework for the FAST Wind Turbine CAE Tool, 51st AiAA Aerospace Sciences Meeting, including the New Horizons Forum and Aerospace Exposiotion, 7–10 January 2013, Dallas, Texas, USA, 1–26, https://doi.org/10.2514/6.2013-202, 2013. a
Kallehave, D., Thilsted, C. L., and Liingaard, M.: Modification of the API P–y Formulation of Initial Stiffness of Sand, in: Offshore Site Investigation and Geotechnics: Integrated Technologies – Present and Future, 12–14 September 2012, London, UK, https://onepetro.org/SUTOSIG/proceedings-abstract/OSIG12/OSIG12/SUT-OSIG-12-50/3358?redirectedFrom=PDF (last access: 6 February 2026), 2012. a
Katsikogiannis, G., Sørum, S. H., Bachynski, E. E., and Amdahl, J.: Environmental lumping for efficient fatigue assessment of large-diameter monopile wind turbines, Mar. Struct., 77, 102939, https://doi.org/10.1016/j.marstruc.2021.102939, 2021. a
Müller, K., Dazer, M., and Cheng, P. W.: Damage assessment of floating offshore wind turbines using response surface modeling, Enrgy. Proced., 137, 119–133, https://doi.org/10.1016/j.egypro.2017.10.339, 2017. a
Müller, F., Krabbe, P., Hübler, C., and Rolfes, R.: Assessment of meta-models to estimate fatigue loads of an offshore wind turbine, in: Proceedings of the Thirty-First (2021) International Ocean and Polar Engineering Conference, Rhodos, Greece, 20–25 June 2021, 543–550, https://onepetro.org/ISOPEIOPEC/proceedings-abstract/ISOPE21/ISOPE21/ISOPE-I-21-1214/464471 (last access: 3 December 2025), 2021. a, b
Müller, F., Hübler, C., and Rolfes, R.: Transferability of meta-model configurations for different wind turbine types, in: Proceedings of the ASME 2022, 41st International Conference on Ocean, Offshore and Arctic Engineering, vol. 8: Ocean Renewable Energy, OMAE 2022, Hamburg, Germany, 5–10 June 2022, 79698, https://doi.org/10.1115/OMAE2022-79698, 2022. a, b, c, d, e, f, g, h
Murcia, J. P., Réthoré, P.-E., Dimitrov, N., Natarajan, A., Sørensen, J. D., Graf, P., and Kim, T.: Uncertainty propagation through an aeroelastic wind turbine model using polynomial surrogates, Renew. Energ., 119, 910–922, https://doi.org/10.1016/j.renene.2017.07.070, 2018. a, b
Rasmussen, C. E., and Williams, C. K. I.: Gaussian Processes for Machine Learning, Adaptive Computation and Machine Learning, MIT Press, Cambridge, Massachusetts, USA, https://doi.org/10.7551/mitpress/3206.001.0001, 2006. a, b
Santner, T. J., WIlliams, B. J., and Notz, W. I.: The design and Analysis of Computer Experiments, 2nd edn., Springer Series in Statistics, edited by: Diggle, P., Gather, U., and Zeger, S., Springer, New York, https://doi.org/10.1007/978-1-4939-8847-1, 2018. a, b
Schmidt, F., Hübler, C., and Rolfes, R.: Lifetime reassessment of offshore wind turbines using meta-models, in: 14th International Conference on Applications of Statistics and Probability in Civil Engineering (ICASP14), Dublin, Ireland, 9–13 July 2023, http://hdl.handle.net/2262/103307 (last access: 6 February 2026), 2023. a, b, c, d
Schmidt, F., Hübler, C., and Rolfes, R: Simulation data and Kriging meta-models of an offshore wind turbine, LUIS [data set], https://doi.org/10.25835/vfc4xy34, 2025b. a
Schmidt, F., Hübler, C., and Rolfes, R.: Simulation data for lifetime reassessment of an offshore wind turbine, LUIS [data set], https://doi.org/10.25835/ubouf7p2, 2026a. a
Schmidt, F., Krabbe, P., Hübler, C., and Rolfes, R: An open-access database for meta-models for fatigue calculations of reference wind turbines, J. Phys. Conf. Ser., 3224, 072003, https://doi.org/10.1088/1742-6596/3224/7/072003, 2026b. a
Schmidt, F., Krabbe, P., Hübler, C., and Rolfes, R: Open-access database for meta-models for fatigue calculations of reference wind turbines, LUIS [data set], https://doi.org/10.25835/eydp1nl2, 2026c. a
Schröder, L., Dimitrov, N. K., Verelst, D. R., and Sørensen, J. A., Wind turbine site-specific load estimation using artificial neural networks calibrated by means of high-fidelity load simulations, J. Phys. Conf. Ser., 1037, 62027, https://doi.org/10.1088/1742-6596/1037/6/062027, 2018. a, b, c
Singh, D., Dwight, R., and Viré, A.: Probabilistic surrogate modeling of damage equivalent loads on onshore and offshore wind turbines using mixture density networks, Wind Energ. Sci., 9, 1885–1904, https://doi.org/10.5194/wes-9-1885-2024, 2024. a, b, c
Singh, D., Haugen, E., Laugesen, K., Dwight, R. P., and Viré, A.: Data-driven probabilistic surrogate model for floating wind turbine lifetime damage equivalent load prediction, Wind Energ. Sci., 10, 2865–2888, https://doi.org/10.5194/wes-10-2865-2025, 2025. a
Stewart, G. M.: Design load analysis of two floating offshore wind turbine concepts, PhD thesis, University of Massachusetts-Amherst, USA, 126 pp., https://doi.org/10.7275/7627466.0, 2016. a, b, c
Stieng, L. E. S. and Muskulus, M.: Reliability-based design optimization of offshore wind turbine support structures using analytical sensitivities and factorized uncertainty modeling, Wind Energ. Sci., 5, 171–198, https://doi.org/10.5194/wes-5-171-2020, 2020. a
Velarde, J., Kramhøft, C., and Sørensen, J. D.: Global sensitivity analysis of offshore wind turbine foundation fatigue loads, Renew. Energ., 140, 177–189, https://doi.org/10.1016/j.renene.2019.03.055, 2019. a, b
Velarde, J., Kramhøft, C., Sørensen, J. D., and Zorzi, G.: Fatigue reliabilty of large monopiles for offshore wind turbines, Int. J. Fatigue, 134, 105487, https://doi.org/10.1016/j.ijfatigue.2020.105487, 2020. a
Yang, H., Zhu, Y., Lu, Q., and Zhang, J.: Dynamic reliability based design optimization of the tripod sub-structure of offshore wind turbines, Renew. Energ., 78, 16–25, https://doi.org/10.1016/j.renene.2014.12.061, 2015. a
Ziegler, L. and Muskulus, M.: Fatigue reassessment for lifetime extension of offshore wind monopile substructures, J. Phys. Conf. Ser., 753, 092010, https://doi.org/10.1088/1742-6596/753/9/092010, 2016. a
Short summary
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 occurring combinations of environmental parameters and to the approach according to International Electrotechnical Commission (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 lifetime fatigue loads.
A lifetime reassessment of an offshore wind turbine using Kriging meta-models is performed. This...
Altmetrics
Final-revised paper
Preprint