Articles | Volume 11, issue 9
https://doi.org/10.5194/wes-11-3653-2026
https://doi.org/10.5194/wes-11-3653-2026
Research article
 | 
22 Sep 2026
Research article |  | 22 Sep 2026

Lifetime reassessment of offshore wind turbines considering different operating conditions using Kriging meta-models

Franziska Schmidt, Clemens Hübler, and Raimund Rolfes

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Kriging meta-models for damage equivalent load assessment of idling offshore wind turbines
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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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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
Dimitrov, N., Kelly, M. C., Vignaroli, A., and Berg, J.: From wind to loads: wind turbine site-specific load estimation with surrogate models trained on high-fidelity load databases, Wind Energ. Sci., 3, 767–790, https://doi.org/10.5194/wes-3-767-2018, 2018. a, b, c, d, e
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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.
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