Comparison of Measured and Simulated Structural Loads of an Offshore Wind Turbine at Alpha Ventus

Müller, Kolja; Reiber, Mario; Cheng, Po Wen

doi:10.17736/ijope.2016.fvr01

Cited by 10 publications

(11 citation statements)

References 8 publications

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“…For a more accurate, fully probabilistic design an approach is required which considers the load uncertainties imposed by the varying environmental conditions. In Müller and Cheng (2016) we indicated that a simple lumping of load cases as performed in standard procedures may miss important load variation which is observed on the real turbine. In a follow-up presented in Müller et al (2016) we showed that the spreading of full-scale environmental fatigue loads can be reproduced by state-of-the-art simulation tools when using appropriate methods for the selection of the environmental combinations.…”

Section: Introductionmentioning

confidence: 94%

“…Hence, if looking at damage contribution the results of this study have to be adjusted accordingly and lead to a slower convergence. In Müller and Cheng (2016), we obtained a damage uncertainty (range of 95 percentiles for different wind speeds) for fixed bottom offshore fatigue simulations according to International Electrotechnical Commission (2009) between 10 and 20 %. To reach the same uncertainty in this study, 200-500 samples are required (see Fig.…”

Section: Damage Contribution From High Del Events (Impact Of Outliers)mentioning

confidence: 99%

“…However, it is difficult to know this information a priori if no sensitivity study is performed for the considered structure. This can be carried out in several ways which consider non-monotonous impact of independent parameters such as decision trees, neural networks, chi-square tests, regression analyses, variance-based analyses or extended Fourier amplitude tests, as used in Kusiak andZhang (2010), Faerron Guzmán et al (2018), Müller et al (2016), Hübler et al (2017), andMcKay et al (2014).…”

Section: Introductionmentioning

confidence: 99%

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Application of a Monte Carlo procedure for probabilistic fatigue design of floating offshore wind turbines

Müller

Cheng

2018

Wind Energ. Sci.

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Abstract. Fatigue load assessment of floating offshore wind turbines poses new challenges on the feasibility of numerical procedures. Due to the increased sensitivity of the considered system with respect to the environmental conditions from wind and ocean, the application of common procedures used for fixed-bottom structures results in either inaccurate simulation results or hard-to-quantify conservatism in the system design. Monte Carlo-based sampling procedures provide a more realistic approach to deal with the large variation in the environmental conditions, although basic randomization has shown slow convergence. Specialized sampling methods allow efficient coverage of the complete design space, resulting in faster convergence and hence a reduced number of required simulations. In this study, a quasi-random sampling approach based on Sobol sequences is applied to select representative events for the determination of the lifetime damage. This is calculated applying Monte Carlo integration, using subsets of a resulting total of 16 200 coupled time-domain simulations performed with the simulation code FAST. The considered system is the Danmarks Tekniske Universitet (DTU) 10 MW reference turbine installed on the LIFES50+ OO-Star Wind Floater Semi 10 MW floating platform. Statistical properties of the considered environmental parameters (i.e., wind speed, wave height and wave period) are determined based on the measurement data from the Gulf of Maine, USA. Convergence analyses show that it is sufficient to perform around 200 simulations in order to reach less than 10 % uncertainty of lifetime fatigue damageequivalent loading. Complementary in-depth investigation is performed, focusing on the load sensitivity and the impact of outliers (i.e., values far away from the mean). Recommendations for the implementation of the proposed methodology in the design process are also provided.

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Section: Introductionmentioning

confidence: 94%

Section: Damage Contribution From High Del Events (Impact Of Outliers)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of a Monte Carlo procedure for probabilistic fatigue design of floating offshore wind turbines

Müller

Cheng

2018

Wind Energ. Sci.

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“…That is why there exist several approaches to decrease the computing time by using meta models for fatigue damages. Examples are Zwick and Muskulus, Müller et al, Toft et al, or Stewart . Zwick and Muskulus reduced the number of simulations by a factor of 7 without adding a significant amount of additional uncertainty by applying a multivariate linear regression.…”

Section: Introductionmentioning

confidence: 99%

“…Three existing drawbacks according to the state of the art are treated in this study. Firstly, environmental conditions are no longer treated as constant within each wind speed bin, as Müller et al found out that variable environmental conditions can reproduce measurement variability more precisely. Secondly, so far, mainly jacket and floating substructures were investigated.…”

Section: Introductionmentioning

confidence: 99%

Methodologies for fatigue assessment of offshore wind turbines considering scattering environmental conditions and the uncertainty due to finite sampling

2018

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For substructures of offshore wind turbines, the fatigue limit state is in most cases a decisive design factor. However, to calculate the fatigue lifetime of wind turbines, numerous time domain simulations for different load cases with changing environmental conditions are necessary. According to the state of the art, wind speed bins (of 2 m s −1 ) are employed, while keeping all other environmental states constant. However, assuming constant parameters in each wind speed bin is an unfounded simplification. Therefore, in this study, methodologies for fatigue assessment considering scattering environmental conditions are investigated by assuming statistical distributions for environmental conditions for all wind speeds that are derived using real data measured at the North Sea research platform FINO3. These statistical distributions are used to conduct time domain simulations of an OC3 monopile-with a 5-MW wind turbine-using the aero-servo-hydro-elastic simulation framework FAST. The fatigue lifetime is calculated, and its uncertainty due to finite sampling is assessed. It is shown that if scattering environmental states in each wind speed bin are applied, the uncertainty due to finite sampling is significant. Furthermore, only some wind speed bins contribute to the overall fatigue damage. Based on these findings, in a last step, different Monte Carlo sampling concepts are investigated to reduce the number of simulations needed to calculate the fatigue lifetime with a defined uncertainty. By combining several wind speed bins and by sampling according to the damage distribution, it is proved that the number of simulations can be reduced by more than 30% without increasing the uncertainty. KEYWORDSfatigue limit state, offshore wind energy, scattering environmental conditions, simulation error, uncertainty INTRODUCTIONThe share of offshore wind energy in overall energy production has grown over the last years. However, the cost of offshore wind energy is still high compared with other renewable energies. 1 As it is forecast that substructure improvements can potentially reduce the overall turbine cost by more than 5%, 2 an accurate and reliable simulation of offshore wind turbine substructures is beneficial. For substructures made of steel, mainly the fatigue limit state is decisive. 3 To calculate the fatigue lifetime of wind turbines, numerous time domain simulations are necessary. Current standards 4 define that these simulations should mirror the changing environmental conditions at the precise site of a wind turbine. However, for research purposes, these data are scarcely available, and even in industrial projects, the data quality is frequently poor because information on some parameters or long-term data is missing. This is why several research projects characterized environmental conditions at specific sites or entire areas and published statistical distributions as a reference. Examples for data bases of main conditions (wind speeds, wave height, wave period, and wind and wave directions) are the UPWIND desi...

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Rapid Assessment of Offshore Monopile Fatigue Using Machine Learning

Houseago

Mockute

Cross

et al. 2022

Lecture Notes in Civil Engineering

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Comparison of Measured and Simulated Structural Loads of an Offshore Wind Turbine at Alpha Ventus

Cited by 10 publications

References 8 publications

Application of a Monte Carlo procedure for probabilistic fatigue design of floating offshore wind turbines

Application of a Monte Carlo procedure for probabilistic fatigue design of floating offshore wind turbines

Methodologies for fatigue assessment of offshore wind turbines considering scattering environmental conditions and the uncertainty due to finite sampling

Rapid Assessment of Offshore Monopile Fatigue Using Machine Learning

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