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

Abstract

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...

Validation of Improved Sampling Concepts for Offshore Wind Turbine Fatigue Design

“…As offshore conditions are scattering, some uncertainty of the extrapolated design lifetimes that depends on the number of used samples is introduced. In literature, the number of simulations required to achieve acceptable uncertainty levels was investigated in various computational studies [6,18,19]. However, first, this number depends significantly on the sampling concept [10,18].…”

“…), used simulation code, dimension of the input space, etc.? This is the reason the objective of this study is to validate two sampling concepts that were previously developed by Hübler et al [18]. In this context, "validation" does not mean that their benefit is proven mathematically, but their performance is assessed using a broad range of wind turbine specific applications.…”

“…The most frequently applied probabilistic sampling concept is Monte Carlo sampling (MCS). In the context of fatigue design of offshore wind turbine substructures, MCS has recently been investigated by various authors [10,11,16,18,19]. On the one hand, probabilistic approaches can reproduce measurement results more precisely [20] and MCS is more suitable for high-dimensional input spaces [10,11].…”

“…On the one hand, probabilistic approaches can reproduce measurement results more precisely [20] and MCS is more suitable for high-dimensional input spaces [10,11]. On the other hand, fatigue damage approximations using probabilistic approaches and limited samples are quite uncertain [18,19] and MCS becomes inefficient (i.e., converges slowly) for highly non-linear model functions [10]. Therefore, to limit computing times while uncertainties are kept on an adequate level, improved sampling techniques are needed.…”

“…Therefore, to limit computing times while uncertainties are kept on an adequate level, improved sampling techniques are needed. For example, Hübler et al [18] and Stieng and Muskulus [16] concentrate their sampling on the input subspace leading to high damages or Müller and Cheng [21] apply quasi-random sampling based on Sobol' sequences.…”