Analyzing Extreme Sea State Conditions by Time-Series Simulation Accounting for Seasonality

Vanem, Erik

doi:10.1115/1.4056786

Cited by 4 publications

(3 citation statements)

References 57 publications

(64 reference statements)

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“…A total of six environmental variables were considered: wind mean speed U , turbulence σ U , significant wave height H s , wave peak period T P , wind direction θ, and wave direction α. A conditional distribution model was fit to all variables, with more details including distribution parameters given in [23,24]. A significant source of uncertainty was found in the conditional distribution models, for combinations of extreme conditions (environmental contours).…”

Section: Uncertainty and Distribution Modelling 31 Environmental Dist...mentioning

confidence: 99%

End-to-end wind turbine design under uncertainties: a practical example

Dimitrov,

Kelly,

McWilliam

et al. 2024

J. Phys.: Conf. Ser.

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This paper illustrates the process of design under uncertainty on a practical case study of an offshore wind farm. We document the entire process through selection and quantification of relevant uncertainties, definition of probabilistic limit states, reliability computation algorithms, as well as illustrating the impacts of the analysis through a design utilization study. The brief introduction in this study draws information and summarizes outcomes from the extensive works that took part within the EU H2020 HIPERWIND project. The results from the study show that significant material savings can be achieved by introducing probabilistic design methodologies, and particularly with the help of an integrated modelling approach where the entire structure (turbine, tower & foundation) is considered as a whole.

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Section: Uncertainty and Distribution Modelling 31 Environmental Dist...mentioning

confidence: 99%

End-to-end wind turbine design under uncertainties: a practical example

Dimitrov,

Kelly,

McWilliam

et al. 2024

J. Phys.: Conf. Ser.

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“…Note that the index 0 is a reference to the fact that these conditions are not perturbed by the wake. A parametric model has been fitted in [27] using conditional probability density functions to capture the dependence structure, with an approach similar to the one presented in [28]. The random vector X is described by the following input random variables:…”

Section: Uncertainty Propagation In a Wake Modelmentioning

confidence: 99%

Quantifying and clustering the wake-induced perturbations within a wind farm for load analysis

Lovera,

Fekhari,

Jézéquel

et al. 2023

J. Phys.: Conf. Ser.

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The main objective of the HIPERWIND project is to reduce the Levelized Cost Of Energy by controlling the uncertainties in a chain of numerical models simulating an offshore wind farm. This paper studies the wake-induced perturbations of the ambient wind conditions inside a wind farm. A theoretical floating offshore wind farm off the coast of South Brittany in France is considered as a test case, and a numerical model simulating the wake of this farm is exploited. This model gives an analytical prediction of wake deficit and added turbulence, also considering the influence of the floaters’ position due to mean wind forces. An uncertainty propagation on the wake model is performed, considering the multivariate joint distribution of ambient wind conditions as input. This results, for each turbine, in a wake-modified probabilistic distribution on the wind speed and turbulence intensity averaged over the rotor. A new approach for clustering these wake-modified distributions is proposed in this paper to regroup the wind turbines exposed to similar environmental conditions, leading to a similar structural response. This analysis is essential to ensure the practical feasibility of the reliability analysis of an overall project with the computational cost of aero-servo-hydro-elastic simulations of offshore wind turbines. Several methods classically used for clustering are compared. Finally, four clusters only are sufficient to represent the whole set of turbines in the wind farm.

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“…Such existing parametric joint distributions often rely on prior data fitting combined with expert knowledge. For example, several parametric approaches have been proposed in the literature to derive such formulations, ranging from fitting conditional distributions (Vanem et al, 2023) to using vine copulas (Li and Zhang, 2020). However, when a considerable amount of environmental data is available, nonparametric approaches may be useful, even if fitting a joint probability distribution with a complex dependence structure may be a challenging task.…”

Section: Introductionmentioning

confidence: 99%

Given-data probabilistic fatigue assessment for offshore wind turbines using Bayesian quadrature

Fekhari,

Chabridon,

Muré

et al. 2024

DCE

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Offshore wind turbines intend to take a rapidly growing share in the electric mix. The design, installation, and exploitation of these industrial assets are regulated by international standards, providing generic guidelines. Constantly, new projects reach unexploited wind resources, pushing back installation limits. Therefore, turbines are increasingly subject to uncertain environmental conditions, making long-term investment decisions riskier (at the design or end-of-life stage). Fortunately, numerical models of wind turbines enable to perform accurate multi-physics simulations of such systems when interacting with their environment. The challenge is then to propagate the input environmental uncertainties through these models and to analyze the distribution of output variables of interest. Since each call of such a numerical model can be costly, the estimation of statistical output quantities of interest (e.g., the mean value, the variance) has to be done with a restricted number of simulations. To do so, the present paper uses the kernel herding method as a sampling technique to perform Bayesian quadrature and estimate the fatigue damage. It is known from the literature that this method guarantees fast and accurate convergence together with providing relevant properties regarding subsampling and parallelization. Here, one numerically strengthens this fact by applying it to a real use case of an offshore wind turbine operating in Teesside, UK. Numerical comparison with crude and quasi-Monte Carlo sampling demonstrates the benefits one can expect from such a method. Finally, a new Python package has been developed and documented to provide quick open access to this uncertainty propagation method.

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Analyzing Extreme Sea State Conditions by Time-Series Simulation Accounting for Seasonality

Cited by 4 publications

References 57 publications

End-to-end wind turbine design under uncertainties: a practical example

End-to-end wind turbine design under uncertainties: a practical example

Quantifying and clustering the wake-induced perturbations within a wind farm for load analysis

Given-data probabilistic fatigue assessment for offshore wind turbines using Bayesian quadrature

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