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RANS simulations of wind flow at the Bolund experiment

RANS simulations of wind flow at the Bolund experiment. D. Cabezón 1) , J. Sumner 2) , B. Garcia 1) , J. Sanz Rodrigo 1) , C. Masson 2). 1) Department of Wind Energy, National Renewable Energy Centre (CENER), Spain.

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RANS simulations of wind flow at the Bolund experiment

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  1. RANS simulations of wind flow at the Bolund experiment D. Cabezón1), J. Sumner2), B. Garcia1), J. Sanz Rodrigo1), C. Masson2) 1) Department of Wind Energy, National Renewable Energy Centre (CENER), Spain 2) Department of Mechanical Engineering, École de Technologie Supérieure (ETS), Montreal, Canada EWEC 2011, Brussels, 16th March 2011

  2. OVERVIEW • Introduction • Bolund blind comparison • CFD wind flow models • CENER SBL model CFDWind 1.0 • CENER ABL model CFDWind 2.0 • ETS SBL model • Results • Speed-up factors • Normalized turbulent kinetic energy • Conclusions

  3. 1. INTRODUCTION • As part of the development of wind flow models, uncertainties must be: • Identified and evaluated • Minimized as much as possible • Method: Validation through field measurement campaigns • Since Askervein (1983), no other detailed experiment for code validation purposes carried out • Bolund blind comparison: • Extensive measurement campaign over a 12m high coastal island • Validation of wind flow models in complex terrain • Measurement of uncertainty for present state-of-the art models • Summary of 3 CFD wind flow models

  4. 2. BOLUND BLIND COMPARISON • Joint project of RISOe-DTU and Vestas during 3 years (2007-2009) • Complete dataset for validation of wind flow models in complex terrain • Open call to research centres, universities and industry • Wide variety of wind flow models: • Experimental methods • Linearized models • Non-linear CFD models: RANS (1 equation) + RANS (2 equations) + LES • Bolund hill located to the north of RISOe National Laboratory (DK) Images property of RISOe-DTU

  5. 2. BOLUND BLIND COMPARISON • Steep escarpment • Complex geometry: • 12m high • 75m width • 130m long • Roughness change: sea (z0=0.0003m) - land (z0=0.015m) • Well-defined inflow conditions • Common information to all the participants: • Topography and roughness description • Inflow velocity and turbulence profiles • Coriolis and thermal effects neglected Images property of RISOe-DTU

  6. 2. BOLUND BLIND COMPARISON • 3 months of measurements • 2 lines (line A and line B) with 10 meteorologicalmastsincluding: • 12 cups, 23 sonics, 2 LIDARs (M2 and M9 metmasts) • Mastscovering 4 characteristicregions: • Upstream, edge, hill centre and hillwake • Referencemastsforinflowconditions: • M0: western winddirections • M9: easternwinddirection • 4 flow cases: 270º, 255º, 239º, 90º (resultsonlyshownfor WD=270º)

  7. 3. CFD WIND FLOW MODELS • CENER surface boundary layer (SBL) model CFDWind 1.0 • RANS equations with turbulence closure based on kε model • Coefficients calibrated for SBL flows (Panofsky): Cµ =0.033, C1ε=1.176, C2ε =1.92, σk=1.0,σε =1.3 • Adapted for the simulation of wind speed and turbulence based on: • Monin–Obukhov theory • Richards and Hoxey computational approach • Turbulent viscosity computed as: • Mixing length strictly increasing with heigth: [κ~0.4] • Coriolis and thermal effects neglected • Computational model widely used by most of wind flow models based on the RANS approach with 2 equations turbulence closures [k] [ε] lm=κz

  8. 3. CFD WIND FLOW MODELS • CENER atmosphericboundarylayer (ABL) modelCFDWind 2.0 • Based on thelimited-lengthscalekεmodel of Apsley and Castro • Windflowsolvedfromtheground up tothegeostrophiclevel • 2 maindifferences: • Activation of Corioliseffect • Limitation of mixinglength lmto a maximumvaluelmax (avoidingtoodeep ABL) Substitution of εproductionterm at theε equationby: where: , [Ug=geostroficwind, f=Coriolis factor] • Mixinglengthaffectingturbulentviscosity and turbulenttransport • Coefficientscalibratedfor ABL flows (Detering and Etling): Cµ =0.0256, C1ε=1.13, C2ε =1.9, σk=0.74,σε =1.3

  9. 3. CFD WIND FLOW MODELS • ETS surface boundary layer (SBL) model • RANS equations with turbulence closure based on the RNG kε model • Additional ε source term by: where η = RNG coefficient, function of the magnitude of the mean strain tensor S β, η0 = constant RNG coefficients • Improvement of flow predictions where recirculation is present • Coriolis and thermal effects neglected • Coefficients calibrated for SBL flows (El Kasmi and Masson): Cµ =0.0333, C1ε=0.47, C2ε =1.68, σk=1.0,σε =1.3, β=0.012, η0 =4.38

  10. 3. CFD WIND FLOW MODELS • Computational features of the models for the simulations at Bolund

  11. 4. RESULTS • Speed-up factor (U/Uref) • Results only for 270º inflow direction • Along line B at 2m and 5m high • Fairly good agreement with predictions • Influence of RNG downstream the first escarpment • Influence of lm limiting effect in the wake of the hill Z=2m agl Z=5m agl

  12. 4. RESULTS • Normalized turbulent kinetic energy (k/U2ref ) • Results only for 270º inflow direction • Along line B at 2m and 5m high • Overestimation (except first edge -2m high) • One single zone of elevated k from experiments • Biggest overestimation produced by CFDWind1.0

  13. 4. RESULTS • Turbulence length scale lm • Axial evolution for 270º inflow direction (line B at 5m high) • Related to turbulent viscosity according to: • Downstreamof thefirstescarpment: peak in k leadingtopeak in lm • In thewake of thehill, CFDWind2 shows: • Quickerreduction of εnearthewall • Rapid increase of turbulentviscosity and fasterflowrecovery • Eventhat, furtherinvestigationrequired

  14. 4. CONCLUSIONS • CFD wind flow models based on RANS approach and 2-equation kε closure: • State-of-the-art in wind flow modeling • Properly modified to represent SBL / ABL • Accurate predictions of mean wind speed [mae(speed-up factors ~ 10-1)] • Further improvement needed in turbulence modeling [mae(normalized k ~ 10-2)] • Full ABL models with respect to SBL models • Improve predictions for big hub heigths outside the SBL • Consequences of the limiting-length–scale effect near the ground to be investigated • Further work and research needs: • Improve turbulence modelling as much as possible (RSM, LES, DES, etc.) • Mesoscale-microscale coupling (generation of boundary conditions, high resolution wind maps, etc.) -> european wind atlas! • Validate at new sites based on massive measurement campaigns • Create collaborative research networks • …

  15. ACKNOWLEDGEMENTS We would like to acknowledge A.Bechmann, P.E.Rethore, N.N. Sørensen, J. Berg, H.E. Jørgensen, J. Mann, M. Courtney, P. Hansen and the rest of the team at RISOe-DTU and Vestas for organizing and funding the Bolund blind comparison and supplying the database for the validation of models Image property of RISOe-DTU

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