The offshore industry has been trending towards larger wind turbines, often located far offshore. The degradation of turbine support structures is accentuated in harsh marine environments whereas inspections and maintenance tasks are more complex and cost demanding. Therefore, an optimal and rational management of offshore wind substructures is becoming increasingly important.

 

This research aims to develop a decision-making framework for optimal management (inspection, monitoring, and maintenance) of offshore wind turbine support structures using digital twins. A “digital twin” is a virtual replica of physical assets on which simulations can be run to predict the behavior of the real structure. A wind turbine “digital twin” may become less and less accurate over time due to behavioral changes of the physical turbine. However, a “digital twin” can also be updated through on-site monitoring data. Since the uncertainties are significantly reduced, the “digital twin” again represents the real turbine more accurately and helps the decision maker to make more rational and informed decisions. In this context, the developed decision-making framework will not only provide optimal management (inspection, monitoring, and maintenance) policies but also identify when the “digital twin” needs to be updated.

 

This PhD research will minimize the total expected life-cycle cost of offshore wind turbines by controlling structural failure risk of support structures through optimal management (inspection, monitoring, and maintenance) policies.