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Title: A stochastic wind turbine wake model based on new metrics for wake characterization: A stochastic wind turbine wake model based on new metrics for wake characterization

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 andmore » 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.« less

Authors:
 [1]; ORCiD logo [1]; ORCiD logo [1];  [2]
  1. Sibley School of Mechanical and Aerospace Engineering, Cornell University, Upson Hall Ithaca 14850 New York USA
  2. National Renewable Energy Laboratory, Golden 80401 Colorado USA
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Wind and Water Technologies Office (EE-4W)
OSTI Identifier:
1341394
Report Number(s):
NREL/JA-5000-66352
Journal ID: ISSN 1095-4244
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Journal Article
Resource Relation:
Journal Name: Wind Energy; Journal Volume: 20; Journal Issue: 3
Country of Publication:
United States
Language:
English
Subject:
17 WIND ENERGY; wind turbine wakes; meandering; large-eddy simulation; wake model

Citation Formats

Doubrawa, Paula, Barthelmie, Rebecca J., Wang, Hui, and Churchfield, Matthew J.. A stochastic wind turbine wake model based on new metrics for wake characterization: A stochastic wind turbine wake model based on new metrics for wake characterization. United States: N. p., 2016. Web. doi:10.1002/we.2015.
Doubrawa, Paula, Barthelmie, Rebecca J., Wang, Hui, & Churchfield, Matthew J.. A stochastic wind turbine wake model based on new metrics for wake characterization: A stochastic wind turbine wake model based on new metrics for wake characterization. United States. doi:10.1002/we.2015.
Doubrawa, Paula, Barthelmie, Rebecca J., Wang, Hui, and Churchfield, Matthew J.. Thu . "A stochastic wind turbine wake model based on new metrics for wake characterization: A stochastic wind turbine wake model based on new metrics for wake characterization". United States. doi:10.1002/we.2015.
@article{osti_1341394,
title = {A stochastic wind turbine wake model based on new metrics for wake characterization: A stochastic wind turbine wake model based on new metrics for wake characterization},
author = {Doubrawa, Paula and Barthelmie, Rebecca J. and Wang, Hui and Churchfield, Matthew J.},
abstractNote = {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.},
doi = {10.1002/we.2015},
journal = {Wind Energy},
number = 3,
volume = 20,
place = {United States},
year = {Thu Aug 04 00:00:00 EDT 2016},
month = {Thu Aug 04 00:00:00 EDT 2016}
}