Saarblitz, Wind farm in HDR, flickr.com, creative commons by-nc-sa 2.0

DWM

Submitted by Gunner Chr. Larsen on May 4, 2015 - 5:39pm
Main hypothesis

The basic philosophy is to consider wakes as passive tracers continuously emitted from the wind farm turbines. The basic idea is a split of scales in the wake flow field, based on the conjecture that large turbulent eddies are responsible for stochastic wake meandering only, whereas small turbulent eddies are responsible for wake deficit attenuation and expansion in the meandering frame of reference as caused by turbulent mixing.

Software
Solver
Dedicated solver linking the DWM model to an aeroelastic model
Regime
Turbulence
Turbulence closure
Turbulence model

Eddy viscosity approach accounting for wake self induced turbulence as well as for ABL turbulence

Atmospheric boundary layer
Range
Coriolis
No
Atmospheric Stability
Atmospheric Stability
Yes
Stability model
Dedicated model based on Monin-Obukhov stability classification
Canopy
Forest canopy
No
Wind farm
Wind turbine
Yes
Rotor model
Wake model
Wind farm range
Additional information

Wake model based on the principles listed in "Main hypothesis". The applied actuator disc is detailed and based on aeroelastic computations, thus not only accounting for the rotor aerodynamics, but also the wind turbine control algorithm.

Remarks

DWM is likely to be included in ed4 of the IEC 61400-1 standard

References

Model formulation:

[1] Larsen, G.C., Madsen, H.Aa., Thomsen, K., and Larsen, T.J.. Wake meandering - a pragmatic approach. Wind Energy,11, 2008, pp. 377–395.

[2] Madsen, H.Aa., Larsen, G.C., Larsen, T.J., and Troldborg, N.. Calibration and Validation of the Dynamic Wake Meandering Model for Implementation in an Aeroelastic Code. J. Sol. Energy Eng.,132(4), 2010.

Model validation:


   1) Wake flow field characteristics validated in terms of both full scale measurements, CFD

       computations and wind tunnel measurements

[3]  Bingöl F, Mann J, Larsen GC. Light detection and ranging measurements of wake dynamics. Part I: one-dimensional scanning. Wind Energy 2010; 13(1): 51–61.

[4] Trujillo J, Bingöl F, Larsen GC, Mann J. Light detection and ranging measurements on wake dynamics. Wind Energy 2011; 14: 61–75.

[2] Madsen HA, Larsen GC, Larsen TJ, Troldborg N. Calibration and validation of the dynamic wake meandering model for implementation in an aeroelastic code.Journal of Solar Energy Engineering2010;132(4): 041014-1–041014-14. DOI: 10.1115/1.4002555.

[5] España G, Aubrun S, Loyer S, Devinant P. Spatial study of the wake meandering using modeled wind turbines in a wind tunnel.Wind Energy2011;14: 923–937.

[6] España G, Aubrun S, Loyer S, Devinant P.Wind tunnel study of the wake meandering downstream of a modeled wind turbine as an effect of large scale turbulent eddies.Journal of Wind Engineering and Industrial Aerodynamics2012;101: 24–33.

   2) Validation of wind farm wind turbine production

[7] Larsen, T.J.; Madsen, H.Aa.; Larsen, G.C. and Hansen, K.S. (2012). Verification of the Dynamic Wake Meander Model for Loads and Power Production in the Egmond aan Zee Wind Farm.

[8] Larsen, G.C.; Larsen, T.J.; Ott, S.; Hansen, K.S. and Madsen, H.Aa. (2012). FULL SCALE VERIFICATION OFWIND FARM PRODUCTION PREDICTIONS. XXIII ICTAM, 19-24 August 2012, Beijing, China

[9] Larsen, T.J.; Larsen, G.C.; Madsen, H.Aa. and Hansen, K.S. (2012). WIND FARM PRODUCTION ESTIMATES. EWEC 2012, Copenhagen.

   3) Validation of wind farm wind turbine loading

 [10] Larsen, T.J.; Madsen, H.Aa.; Larsen, G.C. and Hansen, K.S. (2012). Verification of the Dynamic Wake Meander Model for Loads and Power Production in the Egmond aan Zee Wind Farm.