The Horizon Europe research project TAILWIND aims to advance station-keeping system technologies for floating offshore wind farms. This paper marks the first steps in the project putting forward economic, environmental, and technological key performance indicators to assist the selection and development of anchors. Scenarios in terms of location, soil profile, and floater are defined. Anchor loading histories are obtained through coupled load simulations and used to design typical anchors. Key performance indicators describing three sustainability aspects – economic, environmental, and technological – are proposed to assess the industrial feasibility of each anchor type. The study has two outcomes. Firstly, sustainability key performance indicators that can guide the selection of anchor technologies for future floating offshore wind development are proposed. Secondly, guidance on the most promising anchor for further development via experimental testing and numerical modelling within the TAILWIND project is provided.

The application of Deep Reinforcement Learning (DRL) for the management of engineering systems has shown very promising results in terms of optimality and scalability. The interpretability of these policies by decision-makers who are so far mostly familiar with traditional approaches is also needed for implementation. In this work, we address this topic by providing a comprehensive overview of POMDP- and DRL-based management policies, along with simulation-based implementation details, for facilitating their interpretation. By mapping a sufficient statistic, namely a belief state, to the current optimal action, POMDP-DRL strategies are able to automatically adapt in time considering long-term sought objectives and the prior history. Through simulated policy realizations, POMDP-DRL-based strategies identified for representative inspection and maintenance planning settings are thoroughly analyzed. The results reveal that if the decision-maker opts for an alternative, even suboptimal, action other than the one suggested by the DRL-based policy, the belief state will be accordingly updated and can still be used as input for the remainder of the planning horizon, without any requirements for model retraining.