Offshore Wind Foundation Diagnostic Monitoring System (OFDiMoS)

海上风力涡轮机(OWTs)是一种科学成熟的可再生能源技术，具有显著降低社会碳排放的潜力。随着更高效和更大的涡轮机的制造，该部门最近有了快速的发展，需要更高的支撑塔和更大的基础。这些新结构与以前的模型越来越不同，用于估计其地基性能的设计方法正变得越来越不适用和可靠。新的OWTs也被安装在更不确定和更具挑战性的地面条件下的更远的近海。这意味着这些事态发展的表现可能与预期不同，增加了由于对恶劣风浪与这些结构之间的相互作用知之甚少而造成损害发生的风险。基础建设和维护占风电场总成本的20%-40%，因此任何提高其可靠性的努力都会通过降低能源生产成本为英国消费者带来直接的经济利益。该项目将开发一个计算机工具，通过分析安装在这些机器上的传感器的数据来识别损坏和OWT行为的实时变化。该工具创建了结构-基础系统的数字孪生兄弟，可以进行实时损伤诊断。数字孪生兄弟是一个对象的虚拟表示，它跨越了对象的生命周期，根据实时数据进行更新，并使用模拟、机器学习和推理来辅助决策。对于OWTs来说，这代表了物理结构的完全对应(或孪生)。该工具将使用先进的岩土工程模型来估计土壤-结构相互作用行为，并实时更新，这是非常新颖的。该系统将告知实际结构的性能如何，并将能够进行未来性能预测，以及对剩余使用寿命的估计。考虑到该部门最近的快速发展，以及围绕新的自来水厂安装行为的不确定性日益增加，这是及时的。数字双胞胎已经在其他行业使用，但尚未成功应用于OWT资产管理。一项具体的创新是在目标结构上使用安装的传感器，这些传感器将反馈实时动态数据，从而能够获得近瞬时的状态评估，便于在瞬变事件(风暴)期间检测到损坏时进行快速维护干预。可以分析几天/几周的较长期趋势，以确定较慢的损害累积，如腐蚀。长期性能可以根据用于调整基础尺寸的设计案例进行基准，从而促进围绕延长使用寿命的明智决策，将风险降至最低，最终降低能源成本。

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.