Environmental lumping for efficient fatigue assessment of large-diameter monopile wind turbines

Katsikogiannis, George; Sørum, Stian Høegh; Bachynski, Erin Elizabeth; Amdahl, Jørgen

doi:10.1016/j.marstruc.2021.102939

Cited by 18 publications

(9 citation statements)

References 12 publications

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“…The 5‐MW foundation is based on the OC3 monopile 33 and the 10‐MW foundation is based on Velarde and Bachynski 34 . The 15‐MW tower is based on the original design, 32 while the monopile is designed for a target natural period below 5.5 s and verified using the method in Katsikogiannis et al 35 Each of the three turbines applies its corresponding controller: the NREL Baseline Wind Turbine Controller, 30 the Basic DTU Wind Energy Controller 36 and NREL's Reference OpenSource Controller, 37 respectively. The turbines are assumed located at 30 m water depth on the Norwegian Continental Shelf, at (55.11°N, 3.47°E).…”

Section: Model Descriptionmentioning

confidence: 99%

“…To limit computational requirements, only three wind bins (

N_{U} = 3

) were considered: Close to the rated speed (8–10 m/s), intermediate (14–16 m/s) and high (20–22 m/s) wind speeds. The wave climate is assumed dependent on wind speed through the use of representative sea‐state parameters (

H_{s}

‐

T_{p}

) for each wind bin, based on the lumping method described by Katsikogiannis et al 35 A Pierson–Moskowitz wave spectrum is used at the lowest wind speed, and a JONSWAP spectrum is used for the two higher wind speeds. The wind direction is assumed independent of other environmental parameters, while the wind‐wave misalignment is assumed conditional on the wind speed 48 .…”

Section: Model Descriptionmentioning

confidence: 99%

“…The wind speed‐dependent scatter diagrams of the wave climate are represented by a single lumped sea‐state for each wind speed bin. Following the frequency‐domain lumping method described by Katsikogiannis et al, 35 one damage‐equivalent lumped load case is found for each year of the 60‐year NORA10 database 38 . Therefore, for each wind bin, 60

H_{s} - T_{p}

combinations are found, as shown in Figure 6 for the 14–16 m/s bin.…”

Section: Parameters For Sensitivity Analysismentioning

confidence: 99%

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Fatigue design sensitivities of large monopile offshore wind turbines

et al. 2022

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The input for fatigue analyses of offshore wind turbines is typically chosen based on design values provided by design standards. While this provides a straightforward design methodology, the contribution of different input parameters to the uncertainty in the fatigue damage estimates is usually unknown. This knowledge is important to have when improving current designs and methodologies, and the parameters governing the uncertainty is typically found through a sensitivity analysis. Several sensitivity studies have been performed for monopile‐based offshore wind turbines, typically focusing on specific turbines and engineering disciplines. This paper performs a sensitivity study for three monopile‐based offshore wind turbines (5 MW, 10 MW, 15 MW) using parameters from several engineering disciplines. The results show that the fatigue utilization is primarily governed by the uncertainty in the SN curves and fatigue capacity. Following this, the uncertainty in the environmental conditions is the dominating uncertainty, with wind loads becoming increasingly important as turbine size increases. Additionally, the effect of modelling uncertainties is investigated. The wind‐related model uncertainties dominate in the tower top, while uncertainties in the wave and soil models dominate in the tower base and monopile. Designers wanting to reduce the uncertainty in a design are recommended to focus on the environmental conditions, and using as accurate models as possible. All modelling uncertainties are significant, but research should particularly be focused on wave directionality and soil models.

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Section: Model Descriptionmentioning

confidence: 99%

“…To limit computational requirements, only three wind bins (

N_{U} = 3

H_{s}

‐

T_{p}

Section: Model Descriptionmentioning

confidence: 99%

H_{s} - T_{p}

combinations are found, as shown in Figure 6 for the 14–16 m/s bin.…”

Section: Parameters For Sensitivity Analysismentioning

confidence: 99%

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Fatigue design sensitivities of large monopile offshore wind turbines

et al. 2022

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“…The 5-MW monopile is from the OC3 project [17], while the 10-MW foundation is based on Velarde and Bachynski [18]. The 15-MW tower is the IEA design, and the monopile is designed for a natural period below 5.5 s. The fatigue capacity of the latter was checked using equivalent sea states [19]. Table 1 summarizes the properties of the turbines.…”

Section: Model Descriptionmentioning

confidence: 99%

“…The environmental model assumes the distribution of the wave parameters are conditional on the wind speed, with the wind direction as an independent variable. For each wind speed, a single sea state is used to represent the sea state distribution [19]. This gives the probability of occurrence for an environmental condition as…”

Section: Environmental Modelmentioning

confidence: 99%

Wind and soil model influences on the uncertainty in fatigue of monopile supported wind turbines

Sørum

Bachynski-Polić²,

Amdahl³

2022

J. Phys.: Conf. Ser.

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Several alternative engineering models are available for the use in analysis of offshore wind turbines. However, it is not always clear which of the models will yield the most accurate or sufficiently conservative results. This paper investigates the effect of using two alternative soil-structure interaction models and two wind coherence models. The focus is on assessing how these modelling choices influence the predicted long-term fatigue damage in the support structure. The two soil models are a macro-element model and a p-y-curve model with Rayleigh damping. This gives differences in both the damping and stiffness properties of the turbine model. The differences between the two soil models tend to decrease as the turbine size increases. The wind coherence models considered are the Kaimal spectrum with exponential coherence and the Mann uniform shear turbulence model. The Kaimal model predicts the highest response at low frequencies, while the Mann model gives the highest response predictions at higher frequencies. Which turbulence model predicts the highest long-term fatigue damage is then determined by the natural frequencies, rotor and blade passing frequencies of the different turbines.

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