Low-pass filtering of meandering scales

Abstract The Dynamic Wake Meandering model is becoming a de facto standard for cost-efficient simulation of non-stationary wind power plant (WPP) flow fields to be used as input for aeroelastic WPP design computations. The fundamental conjecture behind this model is to address the massive modeling challenge – caused by the wide range of flow scales represented in WPP flow fields – by introducing a simplifying ‘binary’ split of flow scales, and consider wake deficits as passive tracers driven by large scale turbulence structures. This split in scales is in the original paper formulated in terms of a spatial wake scale dictated flow average, which in turn, adopting Taylor’s hypothesis, is re-casted into a low-pass filter with a cut-off frequency, f c , equal to U ∞ /(2 D w ), where U ∞ is the undisturbed ambient mean wind speed, and D w is the wake diameter. Proper determination of f c is crucial to obtain reliable predictions of both production- and load simulations of WPP turbines, since the modeling of wake meandering energy is directly associated with f c . Various attempts have been performed to validate or reject the originally conjectured value of f c , which essentially represents an upper limit value of f c potentially allowing all parts of a wake organized flow structure to be displaced in the same direction by any eddy size of the large scale turbulence field. Using a full-scale test campaign, combining Lidar observations of near-wake flow field dynamics with an advanced adaptive wake tracking procedure based on extended Kalman filtering, a remarkably good agreement between the originally conjectured cut-off frequency and the measured (dominating) scales contributing to wake meandering is demonstrated.