Article: Doubrawa, P; Barthelmie, RJ; Wang, H; Churchfield, MJ; “A Stochastic Wind Turbine Wake Model Based on New Metrics for Wake Characterization”, Wind Energy, 20 (3):449-463
Abstract: Understanding the detailed dynamics of wind turbine wakes is critical to predicting the performance and maximizing the efficiency of wind farms. This knowledge requires atmospheric data at a high spatial and temporal resolution, which are not easily obtained from direct measurements. Therefore, research is often based on numerical models, which vary in fidelity and computational cost. The simplest models produce axisymmetric wakes and are only valid beyond the near wake. Higher-fidelity results can be obtained by solving the filtered Navier-Stokes equations at a resolution that is sufficient to resolve the relevant turbulence scales. This work addresses the gap between these two extremes by proposing a stochastic model that produces an unsteady asymmetric wake. The model is developed based on a large-eddy simulation (LES) of an offshore wind farm. Because there are several ways of characterizing wakes, the first part of this work explores different approaches to defining global wake characteristics. From these, a model is developed that captures essential features of a LES-generated wake at a small fraction of the cost. The synthetic wake successfully reproduces the mean characteristics of the original LES wake, including its area and stretching patterns, and statistics of the mean azimuthal radius. The mean and standard deviation of the wake width and height are also reproduced. This preliminary study focuses on reproducing the wake shape, while future work will incorporate velocity deficit and meandering, as well as different stability scenarios. Copyright (c) 2016 John Wiley & Sons, Ltd.
Funding Acknowledgement: U.S. Department of Energy [DE-EE0005379]; National Science Foundation [NSF 1464383]; Cooperative Research and Development Agreement [CRD-15-590]
Funding Text: This work was partly funded by the U.S. Department of Energy DE-EE0005379, National Science Foundation NSF 1464383, and Cooperative Research and Development Agreement CRD-15-590. The authors appreciate the comments of the reviewers, which substantially improved the quality of the manuscript. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to so, for U.S. Government purposes.