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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}
}
  • Wind turbines within an array do not experience free-stream undisturbed flow fields. Rather, the flow fields on internal turbines are influenced by wakes generated by upwind unit and exhibit different dynamic characteristics relative to the free stream. The International Electrotechnical Commission (IEC) standard 61400-1 for the design of wind turbines only considers a deterministic wake model for the design of a wind plant. This study is focused on the development of a stochastic model for waked wind fields. First, high-fidelity physics-based waked wind velocity fields are generated using Large-Eddy Simulation (LES). Stochastic characteristics of these LES waked wind velocity field,more » including mean and turbulence components, are analyzed. Wake-related mean and turbulence field-related parameters are then estimated for use with a stochastic model, using Multivariate Multiple Linear Regression (MMLR) with the LES data. To validate the simulated wind fields based on the stochastic model, wind turbine tower and blade loads are generated using aeroelastic simulation for utility-scale wind turbine models and compared with those based directly on the LES inflow. The study's overall objective is to offer efficient and validated stochastic approaches that are computationally tractable for assessing the performance and loads of turbines operating in wakes.« less
  • Scanning LiDARs can be used to obtain three-dimensional wind measurements in and beyond the atmospheric surface layer. In this work, metrics characterizing wind turbine wakes are derived from LiDAR observations and from large-eddy simulation (LES) data, which are used to recreate the LiDAR scanning geometry. The metrics are calculated for two-dimensional planes in the vertical and cross-stream directions at discrete distances downstream of a turbine under single-wake conditions. The simulation data are used to estimate the uncertainty when mean wake characteristics are quantified from scanning LiDAR measurements, which are temporally disjunct due to the time that the instrument takes tomore » probe a large volume of air. Based on LES output, we determine that wind speeds sampled with the synthetic LiDAR are within 10% of the actual mean values and that the disjunct nature of the scan does not compromise the spatial variation of wind speeds within the planes. We propose scanning geometry density and coverage indices, which quantify the spatial distribution of the sampled points in the area of interest and are valuable to design LiDAR measurement campaigns for wake characterization. Lastly, we find that scanning geometry coverage is important for estimates of the wake center, orientation and length scales, while density is more important when seeking to characterize the velocity deficit distribution.« less