Bridging the gap between academic research and industrial development of floating offshore wind farms

Abstract

First, the presentation will show how digitalisation and machine learning techniques can be used to develop engineering design tools capable of accounting for dynamic unsteady effects. This is highlighted as a key industrial need to increase the commercial readiness level of floating offshore wind turbines. High-fidelity numerical tools are required to analyse the detailed system dynamics. However, there is still a big gap in integrating this knowledge into design tools. STEP4WIND proposes to use machine-learning techniques to train reduced-order models using high-fidelity and on-site data. This can for example be used to identify dominant flow patterns and the associated structural forcing, and feed these reduced set of load cases and responses into design optimisation. The improved structural design parameters can be further integrated into new multi-variable turbine controller algorithms. Second, the talk will present ongoing development in test and validation methodologies for floating offshore wind turbines. On the one hand, the need to better combine hardware-in-the-loop in both wind tunnel and wave basin will be shown, with the objective to advance the reliability of complementary wind tunnel/wave basin HIL testing. On the other hand, a world-first cable test rig will be used to test electrically and mechanically dynamic cables and help the industry converge to a design process for dynamic cables, improving their efficiency and resilience. New floating-specific strategies for operation & maintenance, electrical infrastructures, logistics, and blue economy activities will also be briefly presented. Finally, a multidisciplinary design, analysis and optimization framework will be discussed aiming at identifying the main drivers for cost reductions in FOWT farms and performing integrated design optimisation on the whole innovation chain.