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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]