There is a lot of uncertainty around modelling the foundations of offshore wind turbines under complex loading regimes. As a result, the design of offshore wind turbine foundations are often conservative leading to excess costs. However, relatively mature methods from the field of structural health monitoring and machine learning offer the ability to provide accurate feedback on the efficacy and shortcomings of offshore wind foundation and structural modelling, leading to cost reduction in design, operation and life extension. Research is therefore required to deliver and validate data driven methods for OWF structural property estimation, which can inform and be embedded within industrial foundation and structural design processes.
The key research questions are: How can system identification methods be applied to deliver accurate estimation of design critical offshore wind foundation and structural properties? How are these methods, including the algorithms and sensing systems, best applied to field monitored data? How can the estimated properties be utilised to deliver feedback to the design, maintenance and lifetime reassessment of offshore windfarms? How might sensor systems be optimally designed to provide data for structural assessment or design method development.