High‐throughput computation and the applicability of Monte Carlo integration in fatigue load estimation of floating offshore wind turbines

Gräf, Peter; Stewart, Gordon M.; Lackner, Matthew A.; Dykes, Katherine; Veers, Paul S.

doi:10.1002/we.1870

Cited by 38 publications

(46 citation statements)

References 12 publications

(19 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is noted that some error is introduced in this step due to the introduction of an environmental model (e.g., unrealistically small periods for large wave heights). For the current work, the environmental model is assumed accurate, although other models may be more accurate (see, e.g., Graf et al, 2016). Future studies need to determine the impact of errors introduced due to environmental modeling.…”

Section: −1 Imentioning

confidence: 99%

“…In this study a total of 5400 environmental points are used, which are considered to be sufficient for the convergence of the results (see, e.g., Graf et al, 2016;Müller et al, 2017). For each environmental point, three different wind and wave seeds are applied, resulting in a total of 16 200 simulations.…”

Section: Simulation Settingsmentioning

confidence: 99%

“…Some experience with both approaches has already been established in the past: Graf et al (2016) performed an in-depth comparison between simple Monte Carlo sampling and gridbased analysis. Choe et al (2015) investigated new sampling methods for reliability analysis with black box stochastic simulation tools (e.g., FAST) of wind turbine loads.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Müller

Cheng

2018

Wind Energ. Sci.

View full text Add to dashboard Cite

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.

show abstract

Section: −1 Imentioning

confidence: 99%

Section: Simulation Settingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Müller

Cheng

2018

Wind Energ. Sci.

View full text Add to dashboard Cite

show abstract

“…FAST is a widely-used industry and academic tool for wind turbine loads estimation. NREL's WISDEM software allows for the execution of FAST and its companion tool TurbSim (which generates turbulent wind fields for input to FAST) in a programmatic fashion from python, 5 as has been reported previously (Graf et al, 2016). …”

Section: Turbine Simulationmentioning

confidence: 99%

Adaptive stratified importance sampling: hybridization of extrapolation and importance sampling Monte Carlo methods for estimation of wind turbine extreme loads

Graf¹,

Dykes²,

Damiani³

et al. 2017

Preprint

Self Cite

View full text Add to dashboard Cite

Abstract. Wind turbine extreme loads estimation is especially difficult because turbulent inflow drives nonlinear turbine physics and control strategies, so there can be huge differences in turbine response to essentially equivalent environmental conditions. The two main current approaches, extrapolation and Monte Carlo sampling, are both unsatisfying: extrapolationbased methods are dangerous because by definition they make predictions outside the range of available data, but Monte Carlo methods converge too slowly to routinely reach the desired 50-year return period estimates. Thus a search for a better method 5 is warranted. Here we introduce an adaptive stratified importance sampling approach that allows for treating the choice of environmental conditions at which to run simulations as a stochastic optimization problem that minimizes the variance of unbiased estimates of extreme loads. Furthermore, the framework, built on the traditional "bin"-based approach used in extrapolation methods, provides a close connection between sampling and extrapolation, and thus allows the solution of the stochastic optimization (i.e., the optimal distribution of simulations in different wind speed bins) to guide and recalibrate the extrapolation. 10Results show that indeed this is a promising approach, as the variance of both the Monte Carlo and extrapolation estimates are reduced quickly by the adaptive procedure. We conclude, however, that due to the extreme response variability of turbine loads to the same environmental conditions, our method and any similar method quickly reaches its fundamental limits, and that therefore our efforts going forward are best spent elucidating the underlying causes of the response variability.

show abstract

“…Studies have also been made of various sampling‐based approaches to simplify fatigue assessment for floating support structures. Graf et al used both grid‐based and Monte Carlo sampling on a very large environmental data set. Müller, Dazer, and Cheng used response surface modeling based on Latin Hypercube Sampling and artificial neural networks.…”

Section: Introductionmentioning

confidence: 99%

Load case reduction for offshore wind turbine support structure fatigue assessment by importance sampling with two‐stage filtering

Stieng

Muskulus

2019

Wind Energy

View full text Add to dashboard Cite

A complete fatigue assessment for operational conditions for offshore wind turbines involves simulating thousands of environmental states. For applications such as optimization, where this assessment needs to be repeated many times, that presents a significant computational problem. Here, we propose a novel way of reducing the number of simulated environmental states (load cases) while maintaining an acceptable accuracy. From one full fatigue analysis of a base design, the OC3 monopile (with the NREL 5MW turbine), the distribution of fatigue damage per load case can be used to estimate the lifetime fatigue damage of a range of modified designs. Using importance sampling and a specially adapted two‐stage filtering procedure, we obtain pseudo‐optimal sets of load cases from which the fatigue damage is estimated. This is applied to seven different designs that have been modified to emulate iterations of an optimization loop. For several of these designs, sampling less than 1% of all load cases can give damage estimates with median errors of less than 2%. Even for the most severe cases, using 3% of the environmental states yields a maximum error of 10%. While further refinement is possible, the method is considered viable for applications within design optimization and preliminary design.

show abstract

High‐throughput computation and the applicability of Monte Carlo integration in fatigue load estimation of floating offshore wind turbines

Cited by 38 publications

References 12 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

Adaptive stratified importance sampling: hybridization of extrapolation and importance sampling Monte Carlo methods for estimation of wind turbine extreme loads

Load case reduction for offshore wind turbine support structure fatigue assessment by importance sampling with two‐stage filtering

Contact Info

Product

Resources

About