08 Jun 2022
08 Jun 2022
Status: this preprint is currently under review for the journal WES.

Predictive and Stochastic Reduced Order Modelling of Wind Turbine Wake Dynamics

Søren Juhl Andersen1 and Juan Pablo Murcia Leon2 Søren Juhl Andersen and Juan Pablo Murcia Leon
  • 1Department of Wind and Energy Systems, Technical University of Denmark, Anker Engelunds Vej 1, 2800 Kgs Lyngby, Denmark
  • 2Department of Wind and Energy Systems, Technical University of Denmark, Frederiksborgvej 399, 4000 Roskilde, Denmark

Abstract. This article presents a reduced order model of the highly turbulent wind turbine wake dynamics. The model is derived using a database of Large Eddy Simulations (LES), which cover a range of different wind speeds. The model consists of several sub-models: (1) dimensionality reduction using Proper Orthogonal Decomposition (POD) on the global database, (2) projection in modal coordinates to get time series of the dynamics, (3) interpolation over the parameter space enables prediction of unseen cases, and (4) stochastic time series generation to generalize the modal dynamics based on spectral analysis. The model is validated against an unseen LES case in terms of the modal time series properties as well as turbine performance and aero-elastic responses. The reduced order model provides LES accuracy and comparable distributions of all channels. Furthermore, the model provides substantial insights of the underlying flow physics, and how these change with respect to the thrust coefficient CT and whether the model is constructed for single wake or deep array conditions. The predictive and stochastic capabilities of the reduced order model can effectively be viewed as a generalization of LES for statistically stationary flows, and the model framework can be applied to other flow cases than wake dynamics behind wind turbines.

Søren Juhl Andersen and Juan Pablo Murcia Leon

Status: open (until 24 Jul 2022)

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Søren Juhl Andersen and Juan Pablo Murcia Leon

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Short summary
Simulating the turbulent flow inside large wind farms is inherently complex and computational expensive. A new and fast model is developed based on data from high fidelity simulations. The model captures the flow dynamics with correct statistics for a wide range of flow conditions. The model framework provides physical insights and presents a generalization of high fidelity simulation results beyond the case-specific scenarios, which has significant potential for future turbulence modelling.