Model updating of an offshore wind turbine support structure based on modal identification and Bayesian inference

Offshore wind turbine support structures are in harsh and unsteady marine environment, and their dynamic characteristics could change gradually after long-term service. To better understand the status and improve remaining life estimation, it is essential to conduct in-situ measurement and update the numerical models of these support structures. In this paper, the modal properties of a 5.5 MW offshore wind turbine were first identified by the widely used operational modal analysis technique, frequency domain decomposition, given the acceleration data obtained from a vibration measurement campaign. Then a finite element model was created in MATLAB and a set of model parameters including scour depth, foundation stiffness, hydrodynamic added mass and damping coefficients was updated in a Bayesian inference frame. It is found the posterior distributions of most parameters significantly differ from their prior distributions, except for the hydrodynamic added mass coefficient. The predicted modal properties with the updated parameters are generally close to those identified values, especially for natural frequencies and damping ratios. Specifically, it is found different combinations of the scour depth and foundation stiffness coefficient can reach very similar modal property predictions, meaning that model updating results are not unique.