Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 3069-3089 |
| Seitenumfang | 21 |
| Fachzeitschrift | Wind Energy Science |
| Jahrgang | 10 |
| Ausgabenummer | 12 |
| Publikationsstatus | Veröffentlicht - 22 Dez. 2025 |
Abstract
The use of meta-models (e.g. Kriging, artificial neural networks, and polynomial chaos expansion) as surrogate models of aeroelastic simulation models offers a good opportunity to perform lifetime calculations with a feasible computational effort. Meta-models for the approximation of fatigue loads, i.e. damage equivalent loads, of wind turbines in normal operation have been researched comprehensively in recent years. For offshore wind turbines in particular, however, downtimes, i.e. the times when wind turbines idle, also have a significant impact the lifetime. Currently, there are no meta-models of idling wind turbines available. However, it cannot simply be assumed that the findings from normal operation can be directly transferred to idling, as the structural behaviour differs significantly from normal operation due to the lack of aerodynamic damping and the resulting larger impact of the wave loads. For this reason, for the first time, the creation of meta-models, more precisely Kriging meta-models, for an idling offshore wind turbine is investigated comprehensively in this paper. The investigation of meta-modelling shows that for the approximation of the rotor blade root bending moments, two additional input parameters have to be considered in addition to the input parameters that are used for the creation of a meta-model for the same offshore wind turbine in normal operation. The comprehensive investigation of the Kriging meta-models shows that the meta-models trained with 2500 data points represent the simulation model with an acceptable approximation quality when choosing suitable Kriging settings.
ASJC Scopus Sachgebiete
- Energie (insg.)
- Erneuerbare Energien, Nachhaltigkeit und Umwelt
- Energie (insg.)
- Energieanlagenbau und Kraftwerkstechnik
Ziele für nachhaltige Entwicklung
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in: Wind Energy Science, Jahrgang 10, Nr. 12, 22.12.2025, S. 3069-3089.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Kriging meta-models for damage equivalent load assessment of idling offshore wind turbines
AU - Schmidt, Franziska
AU - Hübler, Clemens
AU - Rolfes, Raimund
N1 - Publisher Copyright: © 2025 Franziska Schmidt et al.
PY - 2025/12/22
Y1 - 2025/12/22
N2 - The use of meta-models (e.g. Kriging, artificial neural networks, and polynomial chaos expansion) as surrogate models of aeroelastic simulation models offers a good opportunity to perform lifetime calculations with a feasible computational effort. Meta-models for the approximation of fatigue loads, i.e. damage equivalent loads, of wind turbines in normal operation have been researched comprehensively in recent years. For offshore wind turbines in particular, however, downtimes, i.e. the times when wind turbines idle, also have a significant impact the lifetime. Currently, there are no meta-models of idling wind turbines available. However, it cannot simply be assumed that the findings from normal operation can be directly transferred to idling, as the structural behaviour differs significantly from normal operation due to the lack of aerodynamic damping and the resulting larger impact of the wave loads. For this reason, for the first time, the creation of meta-models, more precisely Kriging meta-models, for an idling offshore wind turbine is investigated comprehensively in this paper. The investigation of meta-modelling shows that for the approximation of the rotor blade root bending moments, two additional input parameters have to be considered in addition to the input parameters that are used for the creation of a meta-model for the same offshore wind turbine in normal operation. The comprehensive investigation of the Kriging meta-models shows that the meta-models trained with 2500 data points represent the simulation model with an acceptable approximation quality when choosing suitable Kriging settings.
AB - The use of meta-models (e.g. Kriging, artificial neural networks, and polynomial chaos expansion) as surrogate models of aeroelastic simulation models offers a good opportunity to perform lifetime calculations with a feasible computational effort. Meta-models for the approximation of fatigue loads, i.e. damage equivalent loads, of wind turbines in normal operation have been researched comprehensively in recent years. For offshore wind turbines in particular, however, downtimes, i.e. the times when wind turbines idle, also have a significant impact the lifetime. Currently, there are no meta-models of idling wind turbines available. However, it cannot simply be assumed that the findings from normal operation can be directly transferred to idling, as the structural behaviour differs significantly from normal operation due to the lack of aerodynamic damping and the resulting larger impact of the wave loads. For this reason, for the first time, the creation of meta-models, more precisely Kriging meta-models, for an idling offshore wind turbine is investigated comprehensively in this paper. The investigation of meta-modelling shows that for the approximation of the rotor blade root bending moments, two additional input parameters have to be considered in addition to the input parameters that are used for the creation of a meta-model for the same offshore wind turbine in normal operation. The comprehensive investigation of the Kriging meta-models shows that the meta-models trained with 2500 data points represent the simulation model with an acceptable approximation quality when choosing suitable Kriging settings.
UR - http://www.scopus.com/inward/record.url?scp=105026234640&partnerID=8YFLogxK
U2 - 10.5194/wes-10-3069-2025
DO - 10.5194/wes-10-3069-2025
M3 - Article
VL - 10
SP - 3069
EP - 3089
JO - Wind Energy Science
JF - Wind Energy Science
SN - 2366-7443
IS - 12
ER -