Error estimation and adaptive model reduction applied to offshore wind turbine modeling

Recently developed error estimation methods provide a powerful tool for the efficient creation of component wise reduced models. Error estimation methods consist in estimating the contribution of each component to the reduction error of the assembled system, without evaluation of the full solution. This allows applying component model reduction in an adaptive manner, meaning that component models are refined based on their error contribution. By doing so, one can obtain optimal reduced models of the assembled structure in terms of accuracy with respect to model size. In this paper these methods are applied to the modeling of an offshore wind turbine. In practice the dynamics of offshore wind turbines are analyzed using aero-elastic codes based on geometrically simplified structural models. As thousands of simulations are required per wind farm for verification purposes, such coarse models allow reasonable computation times while still capturing the overall dynamic behavior. However, the wind industry is moving towards more complex offshore foundation structures, such as jackets and tripods. Even the simplest models of such structures have many more DoF than the complete wind turbine model, leading to excessive computational cost. In this paper it is therefore proposed to apply error estimation and adaptive model reduction to obtain compact models of the total offshore wind turbine. Through application on a realistic model of a wind turbine and complex offshore foundation, it will be shown that this approach gives very compact yet accurate models of the combined structure.