0
Research Papers

Calibration and Validation of the Dynamic Wake Meandering Model for Implementation in an Aeroelastic Code

[+] Author and Article Information
H. Aa. Madsen, G. C. Larsen, T. J. Larsen, N. Troldborg

Wind Energy Division, Risø National Laboratory for Sustainable Energy, Technical University of Denmark (DTU), P.O. Box 49, DK-4000 Roskilde, Denmarkhama@risoe.dtu.dk

R. Mikkelsen

Department of Mechanical Engineering, MEK, Fluid Mechanics Section, Technical University of Denmark (DTU), Nils Koppels Alle, 2800 Lyngby, Denmark

J. Sol. Energy Eng 132(4), 041014 (Oct 14, 2010) (14 pages) doi:10.1115/1.4002555 History: Received July 13, 2009; Revised September 02, 2010; Published October 14, 2010; Online October 14, 2010

As the major part of new wind turbines are installed in clusters or wind farms, there is a strong need for reliable and accurate tools for predicting the increased loadings due to wake operation and the associated reduced power production. The dynamic wake meandering (DWM) model has been developed on this background, and the basic physical mechanisms in the wake—i.e., the velocity deficit, the meandering of the deficit, and the added turbulence—are modeled as simply as possible in order to make fast computations. In the present paper, the DWM model is presented in a version suitable for full integration in an aeroelastic model. Calibration and validation of the different parts of the model is carried out by comparisons with actuator disk and actuator line (ACL) computations as well as with inflow measurements on a full-scale 2 MW turbine. It is shown that the load generating part of the increased turbulence in the wake is due almost exclusively to meandering of the velocity deficit, which causes “apparent” turbulence when measuring the flow in a fixed point in the wake. Added turbulence, originating mainly from breakdown of tip vortices and from the shear of the velocity deficit, has only a minor contribution to the total turbulence and with a small length scale in the range of 10–25% of the ambient turbulence length scale. Comparisons of the calibrated DWM model with ACL results for different downstream positions and ambient turbulence levels show good correlation for both wake deficits and turbulence levels. Finally, added turbulence characteristics are compared with correlation results from literature.

Copyright © 2010 by American Society of Mechanical Engineers
Your Session has timed out. Please sign back in to continue.

References

Figures

Grahic Jump Location
Figure 18

The upper figure shows the added turbulence computed with the DWM model for the NM80 turbine at three different wind speeds and two different ambient turbulence levels. The lower figure is from Jiminez (31) and shows added turbulence computed with a LES model in comparison with empirical correlation curves from literature as well as experimental results.

Grahic Jump Location
Figure 1

The main elements of the DWM model

Grahic Jump Location
Figure 2

Wake expansion of an annular BEM stream tube due to pressure changes in the near wake region

Grahic Jump Location
Figure 3

Layout of the turbulence box used for generating the meander turbulence. A coarse grid resolution (size of 1 rotor diameter) is used in the in-plane (v,w) directions, whereas the resolution in the u-direction is chosen to be fine. The symbol “Y” indicates the rotor position.

Grahic Jump Location
Figure 4

Left: the initial deficit and the modified initial deficit used as input for the TL model compared with the AD model 2D downstream. Right: comparison of TL and AD model for different loadings.

Grahic Jump Location
Figure 5

Comparison of AD computations with calibrated TL model predictions

Grahic Jump Location
Figure 6

The filter function F2 derived on basis of results in Fig. 5 and F1 derived on basis of the results in Figs.  89

Grahic Jump Location
Figure 7

Computations on the NM80 turbine with the calibrated TL model and compared with AD results. Left: downstream development of the velocity deficit at different radial positions; right: distribution of axial and radial velocity 6D downstream.

Grahic Jump Location
Figure 8

Final calibrated influence of ambient turbulence on development of axial velocity deficit downstream by comparing the TL results (to the left) with ACL simulations (to the right) with simulations representing different ambient turbulence levels. Computations on the NM80 turbine at 8 m/s at radius 0.8.

Grahic Jump Location
Figure 9

Final calibrated influence of ambient turbulence on development of the axial velocity deficit downstream by comparing the TL results (to the left) with ACL simulations (to the right) with simulations representing different ambient turbulence levels. Computations on the NM80 turbine at 8 m/s at radius 0.0.

Grahic Jump Location
Figure 10

Left: distribution of the turbulence generated by meandering of the deficits computed with the DWM model at two different downstream positions and for two different values of the parameter kamb. Right: downstream development of turbulence from meandering at a radius of 0.75 for two different values of kamb.

Grahic Jump Location
Figure 11

Left: spectra of the u-turbulence component at different radial positions 14R downstream simulated with the ACL model in conditions corresponding to no ambient turbulence. Right: the same for the v turbulence component.

Grahic Jump Location
Figure 12

Ratio of PSD spectra of the u, v, and w turbulence components computed with the ACL model 14R downstream

Grahic Jump Location
Figure 13

Comparison of added turbulence predicted by the ACL and DWM model after calibration of the two parameters km1 and km2 in Eq. 19. The ACL simulations were conducted without ambient turbulence. Left: results at different downstream positions for a 5 m/s inflow situation. Right: the same for a 10 m/s inflow situation.

Grahic Jump Location
Figure 14

Simulations on the NM80 turbine in three inflow conditions at low ambient turbulence (2–3%) with the DWM model with calibrated added turbulence as shown in Fig. 1. Wind speed around 5–6 m/s. Standard deviation of the relative velocity at radius 24 m on the blade is shown. To the left, free inflow and 1/3 wake operation at 3.5D downstream and to the right, free inflow and 2/3 wake operation at 3.5D downstream.

Grahic Jump Location
Figure 15

Left: total turbulence intensity 3D downstream predicted with the DWM model for the NM80 at 8 m/s and Iamb=10%. Model runs with and without added turbulence. Right: power density spectra of the axial turbulence component of the ambient wind, the wake at radius r=R and the wake simulated without added turbulence.

Grahic Jump Location
Figure 16

Comparison of the DWM model with ACL results for the NM80 turbine at 8 m/s, downstream positions of 3D, 6D, and 10D (from left to right in the figure) and for 5%, 10%, and 15% ambient turbulence (from top to bottom). The nondimensional axial velocities are shown.

Grahic Jump Location
Figure 17

Comparison of the DWM model with ACL results for the NM80 turbine at 8 m/s, downstream positions of 3D, 6D, and 10D (from left to right in the figure) and for 5%, 10%, and 15% ambient turbulence (from top to bottom). The total turbulence intensity is shown.

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In