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Yann-Aël Muller (1) (2) , Sandrine Aubrun (1) , Stéphane Loyer (1) , Christian Masson (2)

EWEA 2013 - Vienna 5th February. Time-resolved tracking of the far wake meandering of a wind turbine model in wind tunnel conditions. Yann-Aël Muller (1) (2) , Sandrine Aubrun (1) , Stéphane Loyer (1) , Christian Masson (2) (1) Laboratoire PRISME, University of Orleans

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Yann-Aël Muller (1) (2) , Sandrine Aubrun (1) , Stéphane Loyer (1) , Christian Masson (2)

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  1. EWEA 2013 - Vienna 5th February Time-resolved tracking of the far wake meandering of a wind turbine model in wind tunnel conditions Yann-Aël Muller(1) (2), Sandrine Aubrun (1), Stéphane Loyer(1), Christian Masson(2) (1)Laboratoire PRISME, University of Orleans 8 rue Léonard de Vinci, F-45072 Orléans, France (2) École de Technologie Supérieure, 1100 Rue Notre-Dame Ouest, Montréal (Québec) H3C 1K3, Canada *e-mail : yann-ael.muller@etu.univ-orleans.fr

  2. Contents • Wind turbine wake and meandering • Previous wake measurements • Atmospheric boundary layer physical modelling • Wake tracking methodology • Reference case study • Parametric study

  3. Wake and meandering • Wind turbine wakes: Velocity deficit, Increased turbulent intensity. Unsteady trajectory : wake meandering. • Hypothesis (1): Wake is advected by large turbulent eddies (akin to a passive tracer, unlike Von Kàrmàn instability) • Smallest scale involved in meandering : 2D, Larsen et al (2008) • Hypothesis (2): Taylor hypothesis (frozen turbulence) for large eddies • Correlation between upstream turbulence and wake behaviour D Lux >=2D Lux >=2D D J.J. Trujillo, M. Kühn (2009) G.C. Larsen et al. Wake meandering – a pragmatic approach. Wind Energy (2008) 11, 377-395 J.J. Trujillo, M. Kühn, Adaptation of a Lagrangian Dispersion Model for Wind Turbine Wake Meandering, EWEC 2009

  4. Why it matters • Wake meandering suspected to exert strong structural loading on turbines inside farms • Better understanding and modelling could lead to : • Improved rotor design • Improved park layout • Input data for active flow control

  5. Earlier wake characterization : Particle Image Velocimetry • With Particle Image Velocimetry (PIV) : • Wake trajectory is non stationary => meandering • standard deviation is higher on the transverse velocity component (as opposed to the vertical component) Wake meandering Instantaneous PIV snapshot of a porous disk wake G. Espana [3] • However ‘’Classical PIV’’ is slow : no time resolved wake tracking • Need of a different measurement system in order to assess a passive tracer behaviour of the wind turbine wake [3] G. Espana et al. Wind tunnel study of the wake meandering downstream of a modeled wind turbine as an effect of large scale turbulent eddies, J. Wind Eng. Ind. Aerodyn. 101 (2012) 24–33

  6. Experimental modeling of the atmospheric boundary layer Turbulence grid Wind direction • 1/400 scale atmospheric wind tunnel at the University of Orleans • Two boundary layer configurations • Moderately rough (open terrain, few obstacles) • very rough (forest) Porous forest model

  7. Boundary layer comparison 40m turbine Upscaled (x400) velocity profiles for both modelled ABLs, along with the log law function fit for the surface layer * For the D=10cm turbine model (40m at full scale) [4] J.C. Kaimal, J.J. Finnigan, “Atmospheric boundary layer flows, their structure and measurements”, Oxford University Press, 1994.

  8. Wake tracking methodology : goals • Time resolved tracking of the wake transverse displacement at a fixed downstream distance (one dimension) • Correlations with the upstream flow

  9. Wake tracking methodology: Experimental setup Upstream transverse velocity: vupstream downstream transverse velocity: vdownstream Wake position : ywake

  10. Wake position processing Velocity deficit distribution for hot wire data • The function is empirical • Similar to a ‘’weighted average’’ of the probes positions • Weights defined as the exponential of the local velocity deficit • Result: Wake position time series ‘’ywake(t)‘’ • Validated against PIV measurements with 85% agreement (see full paper) Umax Du1=0 Du2 Du5 Du4 Du3 Withyi the position of the ithdatapoint

  11. Result : Wake position time series Sample of the wake position time series obtained by hot wire anemometry • Low frequency signal + high frequency ‘’noise’’

  12. Power spectral density Power spectral density of the transverse velocity upstream and downstream of the wind turbine -2/3 slope Power spectral density of the wake position ‘’Noise’’ from flow turbulence Wake meandering fD/U∞≈0.55 Associatededdyscale≈2D

  13. Cross-spectral analysis Coherence and phase diagram between vupstream and ywake fD/U∞≈0.35 • Phase : • Linear => convective process • Coherence : • Distinct frequency ranges : • Very strong coherence for low frequencies • Decreasing for intermediate frequencies • Uncorrelated range from fD/U∞≈0.35

  14. Cross-spectral analysis Coherence diagram between vupstream, vdownstream and ywake fD/U∞≈0.55 fD/U∞≈0.35 • The vdownstream and ywake coherence overlaps the ‘’uncorrelated range’’ of the vupstream and ywake cross-spectrum • The “cut-off” frequency fD/U∞≈0.55 matches the frequency seen on the PSD for the meandering • Some of the scales involved in the meandering process are decorrelated from the upstream flow

  15. Parametricstudy: Setup • Varying parameters : • Disc diameters: D=10cm and 20cm (No change to the hub height) • Downstream distance: d=3D,4D or 5D • Boundary layer roughness: Moderately rough and very rough (forestry) Upstream transverse velocity: vupstream Porous discs D=10cm and 20cm Wake position : ywake

  16. Cases 5 cases +

  17. Wake meandering PSD Power spectral density of the wake position for each case • All cases display similar features • Meandering range fD/U∞<0.55 • Turbulent inertial range fD/U∞>0.55 (fit shown) • Transition at fD/U∞≈0.55 is the same for all cases

  18. Wake meandering PSD Coherence between upstream transverse velocity and the wake position for each case fD/U∞≈0.35 • Coherence levels are most affected by boundary layer type • Dimensionless cut-off frequency mostly invariant relative to the varying parameters

  19. Conclusion • Time-resolved wake tracking with a hot wire rail is achieved • The dimensionless frequency cut-off for wake meandering appears fairly invariant • Coherence between upstream large scale turbulence and wake meandering is very significant • However the coherent frequencies do not cover all the meandering frequency range • Offshore conditions?

  20. Thank you for your attention

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