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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), Spain2) Department of Mechanical Engineering, École de Technologie Supérieure (ETS), Montreal, Canada
EWEC 2011, Brussels, 16th March 2011
OVERVIEW1. Introduction
2. Bolund blind comparison
3. CFD wind flow models
1. CENER SBL model CFDWind 1.0
2. CENER ABL model CFDWind 2.0
3. ETS SBL model
4. Results1. Speed-up factors
2. Normalized turbulent kinetic energy
5. Conclusions
1. INTRODUCTIONAs 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
2. BOLUND BLIND COMPARISONJoint project of RISOe-DTU and Vestas during 3 years (2007-2009)Complete dataset for validation of wind flow models in complex terrainOpen 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
2. BOLUND BLIND COMPARISONSteep escarpmentComplex geometry: 12m high 75m width 130m long
Roughness change: sea (z0=0.0003m) - land (z0=0.015m)Well-defined inflow conditionsCommon information to all the participants: Topography and roughness description Inflow velocity and turbulence profiles
Coriolis and thermal effects neglected
Images property of RISOe-DTU
2. BOLUND BLIND COMPARISON3 months of measurements2 lines (line A and line B) with 10 meteorological masts including: 12 cups, 23 sonics, 2 LIDARs (M2 and M9 met masts)
Masts covering 4 characteristic regions: Upstream, edge, hill centre and hill wake
Reference masts for inflow conditions: M0: western wind directions M9: eastern wind direction
4 flow cases: 270º, 255º, 239º, 90º (results only shown for WD=270º)
3. CFD WIND FLOW MODELSCENER 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
kjk
t
ii
i
Gxk
xku
x
kCG
kC
xxu
x kj
t
ji
i
2
21
2kCt
lm=κz
[ε][k]
3. CFD WIND FLOW MODELSCENER atmospheric boundary layer (ABL) model CFDWind 2.0 Based on the limited-length scale kε model of Apsley and Castro Wind flow solved from the ground up to the geostrophic level 2 main differences:
Activation of Coriolis effect Limitation of mixing length lm to a maximum value lmax (avoiding too deep ABL)
Substitution of ε production term at the ε equation by:
where: , [Ug=geostrofic wind, f=Coriolis factor]
Mixing length affecting turbulent viscosity and turbulent transport Coefficients calibrated for ABL flows (Detering and Etling):
Cµ =0.0256, C1ε=1.13, C2ε =1.9, σk=0.74,σε =1.3
k
CGk
Cxx
ux k
j
t
ji
i
2
21
kG
ll
CCC km
max121 )(
2/34/3 kC
lm c
g
fU
l
00027.0max
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 Sβ, 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
3. CFD WIND FLOW MODELS
k
CGk
Cxx
ux k
j
t
ji
i
2
21
kC
P2
30
3
1
)/1(
Computational features of the models for the simulations at Bolund
3. CFD WIND FLOW MODELS
Model DomainExtents
Grid Boundary conditions Numericalmethod
Inlet / outlet
Wall Sides / top
CFDWind1.0 (SBL)
1260m (E-W)1170m (N-S)300m (vertical)
ICEM CFD3M cells∆x=∆y=0.8mZ1=0.025m
MO profiles/ zeropressure
Modifiedwall funct. (Blockenet.al)
Symmetry/fixed shearstress
FLUENT 12.0Second-orderupwind
CFDWind2.0 (ABL)
1260m (E-W)1170m (N-S)1700m (vertical)
ICEM CFD4.25M cells∆x=∆y=0.8mZ1=0.025m
1D ABL profiles/ zeropressure
Periodic / symmetry
ETS (SBL) 990m (X-axial)650m (Y-cross)120m (Z-vertical)
BlockMesh8M cells∆x=0.5m∆y=2mZ1<=0.1m
MO profiles/ zerogradient
z0-based wallfunctions
Symmetry/fixed shearstress
OPENFOAM 1.5.x
Central-differencing / First-orderupwind
4. RESULTSSpeed-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 (m) CFDWind1 CFDWind2 ETSave 0.087 0.109 0.126min 0.001 0.001 0.034max 0.313 0.297 0.196ave 0.051 0.058 0.100min 0.021 0.001 0.003max 0.081 0.184 0.186
2m
5m
Z=2m agl Z=5m agl
4. RESULTSNormalized turbulent kinetic energy (k/U2
ref ) 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
Z (m) CFDWind1 CFDWind2 ETSave 0.024 0.026 0.024min 0.001 0.001 0.006max 0.048 0.059 0.060ave 0.011 0.008 0.010min 0.001 0.000 0.000max 0.036 0.026 0.027
2m
5m
4. RESULTSTurbulence length scale lm Axial evolution for 270º inflow direction (line B at 5m high) Related to turbulent viscosity according to:
5.025.0 kCl tm
Downstream of the first escarpment: peak in k leading to peak in lm
In the wake of the hill, CFDWind2 shows: Quicker reduction of ε near the
wall Rapid increase of turbulent
viscosity and faster flowrecovery
Even that, further investigationrequired
4. CONCLUSIONSCFD 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
…
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