Downwind wind turbine blades are subjected to tower wake forcing at every rotation, which can lead to structural fatigue. Accurate characterisation of the unsteady aeroelastic forces in the blade design phase requires detailed representation of the aerodynamics, leading to computationally expensive simulation codes, which lead to intractable uncertainty analysis and Bayesian updating. In this paper, a framework is developed to tackle this problem. Full, detailed aeroelastic model of an experimental wind turbine system based on 3-D Reynolds-averaged Navier-Stokes is developed, considering all structural components including nacelle and tower. This model is validated against experimental measurements of rotating blades, and a detailed aeroelastic characterisation is presented. Aerodynamic forces from prescribed forced-motion simulations are used to train a time-domain autoregressive with exogenous input (ARX) model with a localised forcing term, which provides accurate and cheap aeroelastic forces. Employing ARX, prior uncertainties in the structural and rotational parameters of the wind turbine are introduced and propagated to obtain probabilistic estimates of the aeroelastic characteristics. Finally, the experimental validation data are used in a Bayesian framework to update the structural and rotational parameters of the system and thereby reduce uncertainty in the aeroelastic characteristics.

The growing demand for energy worldwide has resulted in the exploration and development of sustainable forms of energy, such as wind energy. Wind turbines are typically used to extract power from the wind through the rotational motion of blades, which are aeroelastic structures. Among other practical examples, aircraft wings are also aeroelastic in nature. Aeroelastic structures suffer from inherent instabilities and fatigue, and hence their design process requires characterisation of safe operating regimes in order to prevent failure. In this dissertation, we present a methodology for predicting dynamic aeroelastic behaviour, and additionally employing data from experiments to improve predictions. The methodology is demonstrated on three test-cases: a 2-DoF airfoil, the Goland wing and an experimental, downwind, wind turbine. The presented method is generic in terms of applicability to any aeroelastic problem, however considering the engineering and societal relevance, the wind turbine problem is extensively investigated. The dissertation contributes to three broad scientific domains - aeroelasticity, reduced ordermodelling and uncertainty quantification.