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This paper reviews the state of the art in offshore wind farm operations and maintenance with a focus on decision support models for the scheduling of maintenance. Factors influential to maintenance planning are collected from the literature and their inclusion in state-of-the-art models is discussed. Methods for modeling and optimization are presented. The methods currently used and possible alternatives are discussed. The existing models are already able to aid the decision-making process. They can be improved by applying more advanced mathematical methods, including uncertainties in the input, regarding more of the influential factors, and by collecting, analyzing, and subsequently using more accurate data.

Modern wind turbines already represent a tightly optimized confluence of materials science and aerodynamic engineering. Veers et al. review the challenges and opportunities for further expanding this technology, with an emphasis on the need for interdisciplinary collaboration. They highlight the need to better understand atmospheric physics in the regions where taller turbines will operate as well as the materials constraints associated with the scale-up. The mutual interaction of turbine sites with one another and with the evolving features of the overall electricity grid will furthermore necessitate a systems approach to future development. Science , this issue p. eaau2027 Harvested by advanced technical systems honed over decades of research and development, wind energy has become a mainstream energy resource. However, continued innovation is needed to realize the potential of wind to serve the global demand for clean energy. Here, we outline three interdependent, cross-disciplinary grand challenges underpinning this research endeavor. The first is the need for a deeper understanding of the physics of atmospheric flow in the critical zone of plant operation. The second involves science and engineering of the largest dynamic, rotating machines in the world. The third encompasses optimization and control of fleets of wind plants working synergistically within the electricity grid. Addressing these challenges could enable wind power to provide as much as half of our global electricity needs and perhaps beyond.

Optimization of the maintenance policies for offshore wind parks is an important step in lowering the costs of energy production from wind. The yield from wind energy production is expected to fall, which will increase the need to be cost efficient. In this article, the Markov decision process is presented and how it can be applied to evaluate different policies for corrective maintenance planning. In the case study, we show an alternative to the current state-of-the-art policy for corrective maintenance that will achieve a cost-reduction when energy production prices drop below the current levels. The presented method can be extended and applied to evaluate additional policies, with some examples provided.

The ultimate capacity of support structures is checked with extreme loads. This is straightforward when the limit state equations depend on a single load component, and it has become common to report maxima for each load component. However, if more than one load component is influential, e.g., both axial force and bending moments, it is not straightforward how to define an extreme load. The combination of univariate maxima can be too conservative, and many different combinations of load components can result in the worst value of the limit state equations. The use of contemporaneous load vectors is typically non-conservative. Therefore, in practice, limit state checks are done for each possible load vector, from each time step of a simulation. This is not feasible when performing reliability assessments and structural optimization, where additional, time-consuming computations are involved for each load vector. We therefore propose to use Pareto-optimal loads, which are a small set of loads that together represent all possible worst case scenarios. Simulations with two reference wind turbines show that this approach can be very useful for jacket structures, whereas the design of monopiles is often governed by the bending moment only. Even in this case, the approach might be useful when approaching the structural limits during optimization.

To maintain the increasing interest and development in offshore wind energy, novel training tools for engineers and researchers are needed. Concurrently, educational outreach activities are in demand to inform the public about the importance of offshore wind energy. In this paper, the development of a serious game about the design and management of offshore wind farms is presented to address such demands. Such a serious game may enable a new audience to explore the field of offshore wind as well as provide researchers entering the field a better understanding of the intricacies of the industry. This requires a simulation that is realistic but also effective in teaching information and engaging outreach. Ultimately, increased public support and expanded training tools are desired to improve decision-making and to provide opportunities to test and integrate innovative solutions. The work presented here includes the game design and implementation of a prototype game. The game design involves building a game framework and developing a simplified simulation. This simulation addresses weather prediction, offshore wind farm design, operation and maintenance, energy demand, climate change, and finance. Playtesting of the prototype demonstrated immersion and informed decision-making of the players and surveys revealed that knowledge had increased while playing the game. Recommendations for future versions of the game are listed.

In the present study, breaking solitary waves over a sloping seabed and breaking wave forces on a vertically mounted cylinder are simulated with the three-dimensional CFD model REEF3D. The numerical model uses the Reynolds-Averaged Navier–Stokes (RANS) equations together with the level set method (LSM) for the free surface and the k - ω for the turbulence. The numerical model is validated for simulating breaking solitary waves and breaking wave forces against the experimentally measured free surface profiles and vertical and horizontal velocities by Mo et al. (Ocean Eng 74:48–60, 2013 ) and the experimentally measured free surface elevation and breaking wave force by Chakrabarti et al. (Appl Ocean Res 19:113–140, 1997 ). The main purpose of the paper is to examine the effects of the breaking characteristics, the geometric properties, the relative cylinder positions and the incident wave heights on the breaking wave force characteristics. A total of 21 simulations are performed to investigate the characteristics and the geometric properties of solitary waves breaking over a slope and the associated breaking wave forces on a cylinder. First, the characteristics and geometric properties of breaking solitary waves are investigated with two-dimensional simulations. Further, the study explores the effect of the relative distance between the breaking point and the cylinder on breaking wave forces. Finally, the study examines breaking solitary wave forces for different incident waves. This also includes the analysis of breaking wave force characteristics such as the impact duration and rise time, the peak force, the average slamming coefficient and the force impulse. The results of the numerical simulations show that the relative distance between the cylinder and the breaking point plays an important role in obtaining the maximum force. In addition, the numerical model is capable of representing the most important physical flow features related to the breaking solitary waves and the interaction with the vertical slender cylinder.

The fatigue life of dynamic power umbilicals in floating offshore wind systems is strongly influenced by low-frequency platform motions, particularly surge. This study investigates the relationship between global floater dynamics and local cable fatigue using two fatigue estimation methods: rainflow counting and the rarrow-banded Rayleigh approximation. A total of 32 fully coupled time-domain simulations were carried out using a semi-submersible wind turbine model under varying sea states and mooring configurations. Curvature time series were extracted at the fatigue-critical location, and damage was computed using a bilinear strain–life model.The narrow-banded approximation was found to significantly overpredict fatigue under broad-banded excitation, rendering it unsuitable in its uncalibrated form. After numerical calibration of the model parameters, the method showed excellent agreement with rainflow-based estimates. Spectral analysis confirmed that floater surge is dominated by low-frequency resonance, while the umbilical curvature response remains confined to wave-frequency content. This decoupling is attributed to the lazy-wave configuration, which filters out platform-induced low-frequency motion.The results demonstrate that a calibrated narrow-banded model can offer an efficient and accurate alternative to full time-domain fatigue analysis, enabling faster design iteration and system evaluation in floating wind applications.

Fatigue reassessment is required to decide about lifetime extension of aging offshore wind farms. This paper presents a methodology to identify important parameters to monitor during the operational phase of offshore wind turbines. An elementary effects method is applied to analyze the global sensitivity of residual fatigue lifetimes to environmental, structural and operational parameters. Therefore, renewed lifetime simulations are performed for a case study which consists of a 5 MW turbine with monopile substructure in 20 m water depth. Results show that corrosion, turbine availability, and turbulence intensity are the most influential parameters. This can vary strongly for other settings (water depth, turbine size, etc.) making case-specific assessments necessary.

Probabilistic methods in wind turbine design are becoming more important in order to achieve a higher and more economic utilization of the structural resources. A design is thereby evaluated through its reliability by taking uncertainties into account. The influence of uncertainties emerging specifically within the fatigue limit state can be essential on a structure’s reliability. For that reason, this study investigates two different sources for uncertainties within probabilistic fatigue design. The influence of a reduced approach, where the random behavior of a structural failure is only represented through an uncertain critical Miner sum, is compared to an approach, where also the S-N curve is subjected to uncertainties. For this, an offshore wind turbine with monopile support structure is investigated. The reliability analysis is performed through crude Monte Carlo simulations which utilize a Gaussian Process regression model in order to determine the wind turbine’s structural response. The results show that both sources of uncertainties, the critical Miner sum and the S-N curve, contribute differently to the overall reliability. Uncertainties of the critical Miner sum have a rather small influence, whereas the implementation of an uncertain S-N curve leads to a noticeable increase of the reliability.

For the planning of operations and maintenance in offshore wind farms, many simulation models exist. Many rely on artificially generated weather time series to test different strategies. In this paper, we present a novel approach to modeling both the significant wave height and wind speed based on measurements from the site. We use a stochastic process called the Langevin process. First, equations are fitted to the available data, which are then used to generate the artificial weather data. The properties of these artificial weather time series are very close to the properties of the actual weather. Mean and standard deviation as well as the overall distribution and seasonality can be captured by the new model. Additionally, the persistence of waves and winds is replicated. This is especially important, as the length of weather windows is an important factor in operation and maintenance planning.