Offshore Wind Foundation Diagnostic Monitoring System (OFDiMoS)

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Offshore Wind Turbines (OWTs) are a scientifically-mature renewable energy technology with potential to significantly lower society's carbon emissions. There have been rapid recent developments in this sector, with more efficient and larger turbines being fabricated, requiring higher support towers and larger foundations. These new structures are increasingly unlike previous models and the design methods used to estimate their foundation behaviour are becoming less applicable and reliable. New OWTs are also being installed further offshore in less certain and more challenging ground conditions. This means that these developments might behave differently than expected, increasing the risk of damage occurrence due to poorly understood interactions between harsh wind and waves with these structures. Foundation construction and maintenance constitute 20-40% of overall windfarm cost and therefore any effort made to increase their reliability has direct financial benefits for UK consumers, by lowering the cost of energy production.This project will develop a computer tool to identify damage and real-time changes in OWT behaviour by analysing data from sensors installed on these machines. The tool creates a digital twin of the structure-foundation system, which can carry out real-time damage diagnosis. A digital twin is a virtual representation of an object that spans its lifecycle, is updated from real-time data, and uses simulation, machine learning and reasoning to assist decision-making. For OWTs, this represents an exact counterpart (or twin) of the physical structure. The tool will use advanced geotechnical models to estimate the soil-structure interaction behaviour, updated in real-time, which is highly novel. This system will inform how the real structure is performing, and will also enable future performance predictions be made, with estimation of remaining useful life. This is timely given the recent rapid development of the sector and the increasing uncertainty surrounding the behaviour of new OWT installations. Digital twins have been used in other industries but have not yet been successfully applied to OWT asset management. A specific innovation is the use of installed sensors on the target structure that will feedback real-time dynamic data, enabling near-instantaneous condition assessments be obtained, facilitating rapid maintenance interventions should damage be detected during transient events (storms). Longer term trends over days/weeks can be analysed to identify slower damage accumulation such as corrosion. Long-term performance can be benchmarked against design cases used to size the foundations, facilitating informed decision-making surrounding service life extension with minimised risk, which ultimately lowers energy costs.