dr. sven schmitz university of california, davis computational modeling of wind turbine aerodynamics...
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Dr. Sven SchmitzDr. Sven Schmitz
University of California, DavisUniversity of California, Davis
Computational Modeling of Wind Turbine AerodynamicsComputational Modeling of Wind Turbine Aerodynamics and Helicopter Hover Flow Using Hybrid CFD and Helicopter Hover Flow Using Hybrid CFD
Pennsylvania State University
April 21st, 2010
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OutlineOutline
Wind EnergyWind Energy
The NREL Phase VI ExperimentThe NREL Phase VI Experiment
Hybrid CFD for Wind TurbinesHybrid CFD for Wind Turbines
Hybrid CFD for Helicopter Hover FlowHybrid CFD for Helicopter Hover Flow
Future Research DirectionsFuture Research Directions
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Wind EnergyWind Energy
“Alternative Sunrise”
Windkraftanlage Holzweiler mit Braunkohlekraftwerk Grevenbroich, Germany, April 2010.
Free energy source
Emission free
No water use
Scalability, i.e. ‘local’ & ‘wind power plant’
Less dependence on fossil fuels
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Wind Energy - U.S. Wind Energy - U.S. MarketMarket
Over 10,000 MW installed in 2009 - U.S. world Over 10,000 MW installed in 2009 - U.S. world leaderleader
Top U.S. Wind Turbine Supplier : Top U.S. Wind Turbine Supplier : GE EnergyGE Energy
Wind industry supports 85,000 jobs in 50 statesWind industry supports 85,000 jobs in 50 states
Now 9 wind turbine manufacturers in U.S.Now 9 wind turbine manufacturers in U.S.
www.awea.org/reports (April (April 2010)2010)
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Wind Energy - Wind Energy - IncentivesIncentives
US DOE – Energy Efficiency and Renewable US DOE – Energy Efficiency and Renewable EnergyEnergy 20% Wind Energy by 203020% Wind Energy by 2030
Pennsylvania - Alternative Energy Investment Pennsylvania - Alternative Energy Investment Act (2009)Act (2009) Wind Energy Supply Chain Initiative (WESCI)Wind Energy Supply Chain Initiative (WESCI)
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Wind Energy - Power Wind Energy - Power CurveCurve
P
23 C 4
2
1
DWP
and W site specific
CP ≈ 0.52 at Wrated (CP,Betz = 0.59)
Rotor Diameter D driving factor
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Wind Energy - Cost of Energy Wind Energy - Cost of Energy (COE)(COE)
O & M estimated at 10%-20% of total COE.
Availability & Loss are site & design specific.
Aerodynamics &
Aeroelasticity
[Walford, C., SAND2006-1100][Walford, C., SAND2006-1100]
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Wind Energy - Cost Wind Energy - Cost ReductionReduction
Maximize Availability, Minimize LossMaximize Availability, Minimize Loss Improved designs for Region IIImproved designs for Region II Reduce fatigue loadsReduce fatigue loads
Minimize Operation and Maintenance (O & Minimize Operation and Maintenance (O & M)M) Reduce # turbines to maintain by increasing Reduce # turbines to maintain by increasing
turbine powerturbine power Reduce fatigue loadsReduce fatigue loads
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Wind EnergyWind EnergyChallenges in Computational ModelingChallenges in Computational Modeling
Unsteady AerodynamicsUnsteady Aerodynamics Blade load response to wind gustBlade load response to wind gust
AeroelasticityAeroelasticity Blade tip deflections of several metersBlade tip deflections of several meters Twist changes > 10degTwist changes > 10deg
Airfoil SoilingAirfoil Soiling Performance loss caused by dirt, insects, etc.Performance loss caused by dirt, insects, etc.
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The NREL Phase VI The NREL Phase VI ExperimentExperiment
NREL = NREL = NNational ational RRenewable enewable EEnergy nergy LLaboratoryaboratory
NREL Phase VI Rotor, April 2000NREL Phase VI Rotor, April 2000
R = 5.03mR = 5.03m2 Blades, Twist, Taper2 Blades, Twist, TaperStall-controlled, S809 Stall-controlled, S809
Airfoil Airfoil [Somers, NREL/SR-[Somers, NREL/SR-440-6918]440-6918]
5m/s < V5m/s < VWindWind < 25m/s < 25m/s = 72rpm= 72rpmP ≈ 10KWP ≈ 10KW
NREL Phase VI Rotor in NASA Ames 120’ x 80’ wind
tunnel
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The NREL Phase VI The NREL Phase VI ExperimentExperiment
Blind Comparison Run, December 2000Blind Comparison Run, December 2000
Comparison of computational Comparison of computational modelsmodels
Performance Codes (BEMs)Performance Codes (BEMs)Aeroelastic CodesAeroelastic CodesWake CodesWake CodesCFD CodesCFD Codes
NREL Phase VI Rotor in NASA Ames 120’ x 80’ wind
tunnel
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The NREL Phase VI The NREL Phase VI ExperimentExperiment
No-Yaw, Steady-State, No-Stall conditions …No-Yaw, Steady-State, No-Stall conditions … Turbine Power Prediction : 25% - 175% of measuredTurbine Power Prediction : 25% - 175% of measured
Blade Bending Prediction : 85% - 150% of measuredBlade Bending Prediction : 85% - 150% of measured
CFD Codes -> Overall best predictions of turbine power and blade CFD Codes -> Overall best predictions of turbine power and blade loads.loads.Wake Codes -> Good performance for attached flow.Wake Codes -> Good performance for attached flow.
Main Results from Blind Comparison Run Main Results from Blind Comparison Run [NREL/TP-500-29494][NREL/TP-500-29494]
Conclusions from Blind Comparison Run Conclusions from Blind Comparison Run [NREL/TP-500-29494][NREL/TP-500-29494]
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Difficulties of computational modelsDifficulties of computational models
CFD CodesCFD Codes : High Computational Cost & Artificial : High Computational Cost & Artificial Dissipation Dissipation
Wake Codes Wake Codes : Prediction of strong 3D effects close to : Prediction of strong 3D effects close to the rotor bladethe rotor blade
Reduce cost and dissipation.
Near-Field RANS + Far-Field Wake Code
=
Hybrid CFD for Wind Turbines
Hybrid CFD for Wind Hybrid CFD for Wind TurbinesTurbines
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Parallelized Coupled Solver (PCS)Parallelized Coupled Solver (PCS)
Navier-Stokes
Vortex Method
)()( 1 jjj yy Vortex Filament
Biot-Savart Law (discrete)
j
Bound
Vortex
j
j
Vortex
Filament
j
r
rl
r
rlv
3
_
3
4
4
Boundary of Navier-Stokes Zone
Converged for …
51 10)()( njnj yy
j jL Aj dAdsvy ..)( Bound Vortex
Hybrid CFD for Wind Hybrid CFD for Wind TurbinesTurbines
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Average uB from power estimate using actuator disc theory
Biot-Savart Law
3
__
4
)(
r
rldrv
R
uUadv T
T
R
uUadv B
B
05.0 TB uu
0)(2 22 BB uuURP
Hybrid CFD for Wind Hybrid CFD for Wind TurbinesTurbinesVortex MethodVortex Method
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C
Accuracy of straight-line Vortex Segmentation :
=> [Gupta & Leishman, AIAA-2004-0828]
ΔΘ = 10˚ => Error < 10%
ΔΘ < 2.5˚ => Error < 1%
Parameters for accurate
calculation of induced velocities :
Minimum Number of Vortex Filaments : 39
Trefftz Plane Location : 20 blade radii behind the rotor disc
Vortex Segmentation ΔΘ : 0.02˚ at the blade, 12˚ after 1 revolution
Accuracy achieved in Induced Velocities at representative points : < 1%
Hybrid CFD for Wind Hybrid CFD for Wind TurbinesTurbinesVortex MethodVortex Method
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Optimum Wind TurbineOptimum Wind Turbine
Inviscid Flow :PCS = Parallelized Coupled Solver
VLM = Vortex Line Method [J.J. Chattot]
8.8048.8048.8798.879Power [kW]Power [kW]
-583.80-583.80-588.82-588.82Torque [Nm]Torque [Nm]
1814.81814.81803.11803.1Bending Moment [Nm]Bending Moment [Nm]
-179.89-179.89-183.63-183.63Tangential Force [N]Tangential Force [N]
508.31508.31509.62509.62Thrust [N]Thrust [N]
PCSPCSVLMVLM
Difference in Power : 0.84 %
Hybrid CFD for Wind Hybrid CFD for Wind TurbinesTurbines
[S. Schmitz, J. J. Chattot, Computers & Fluids (2006)]
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Optimum Wind TurbineOptimum Wind Turbine
Viscous Flow :
7.3217.3217.8357.835Power [kW]Power [kW]
-485.50-485.50-519.58-519.58Torque [Nm]Torque [Nm]
1636.41636.41670.21670.2Bending Moment [Nm]Bending Moment [Nm]
-150.80-150.80-163.26-163.26Tangential Force [N]Tangential Force [N]
458.60458.60472.41472.41Thrust [N]Thrust [N]
PCSPCSVLMVLM
Difference in Power : 6.6 %
PCS = Parallelized Coupled Solver
VLM = Vortex Line Method [J.J. Chattot]
Hybrid CFD for Wind Hybrid CFD for Wind TurbinesTurbines
[S. Schmitz, J. J. Chattot, Computers & Fluids (2006)]
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Rotating, S-Sequence
Fully Attached Flow : U=7m/s
NREL Phase VI RotorNREL Phase VI Rotor
Hybrid CFD for Wind Hybrid CFD for Wind TurbinesTurbines
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Very good agreement w/ measured surface pressure coefficient.
[S. Schmitz, J. J. Chattot, ASME JSEE (2005)]
NREL Phase VI RotorNREL Phase VI Rotor
Hybrid CFD for Wind Hybrid CFD for Wind TurbinesTurbines
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Influence of Vortex Sheet Revolutions on Rotor Torque : VWind = 7m/s
Collaboration with GE Wind
Wind Aero Design Tool Development
(2007-2009)
UCD Award #08003057, #700163655
Routine Design Use
Hybrid CFD for Wind Hybrid CFD for Wind TurbinesTurbinesNREL Phase VI RotorNREL Phase VI Rotor
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Hybrid CFD for Wind Hybrid CFD for Wind TurbinesTurbinesNREL Phase VI RotorNREL Phase VI Rotor
Other CFD Results
[Duque et al, AIAA-1999-0037]
[Sezer-Uzol, Long, AIAA-2006-0394]
[Potsdam, Mavriplis, AIAA-2009-1221]
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NREL Phase VI RotorNREL Phase VI Rotor
Application of PCS to the NREL Phase VI Rotor :
Steady (no yaw), Fully Turbulent, k-ε and k-ω turbulence models
VLM = Vortex Line Model
[J. J. Chattot , CFD Journal (2002)]
PCS = Parallelized Coupled Solver
[S. Schmitz, J. J. Chattot, ASME JSEE (2005)]
Hybrid CFD for Wind Hybrid CFD for Wind TurbinesTurbines
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Distribution of Bound Circulation(Parked, L – Sequence, U = 20.1 m/s)
Trailing Vortex @ r/R=0.40
Attached FlowSeparated Flow
Stalled Flow
Good agreement between VLM and PCS for attached flow.
Apparent Differences for separated flow (3D effects)
A ‘Trailing Vortex’ is attached to a region of stalled flow.
[Schreck, AIAA-2005-0776]
[Tangler, AIAA-2005-0591]
Hybrid CFD for Wind Hybrid CFD for Wind TurbinesTurbinesNREL Phase VI RotorNREL Phase VI Rotor
[S. Schmitz, J. J. Chattot, ASME JSEE (2006)]
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(a) 47 = 3.53deg (b) 47 = 13.46deg (c) 47 = 23.49deg (d) 47 = 33.50deg
Iso-Vorticity Surface behind Parked NREL PhaseVI Blade (=19s-1) (L – Sequence, U = 20.1 m/s)
Visualization of ‘Trailing Vortex’ by an Iso-Vorticity Surface
Hybrid CFD for Wind Hybrid CFD for Wind TurbinesTurbinesNREL Phase VI RotorNREL Phase VI Rotor
[S. Schmitz, J. J. Chattot, ASME JSEE (2006)]
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complex physicsneed for high accuracya recurring engineering needmany methods developed, few validatedlittle data that supports complete physical models
Hybrid CFD for Helicopter Hybrid CFD for Helicopter Hover FlowHover Flow
Collaboration with US Army AFDD
A New CFD Approach for the Computation of General Rotorcraft Flows (2006-2010)
UCD Award #NNX08AU38A, #NNA0CB79A
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Coupling UMTURNS w/ HELIX-IA
i. HELIX-IA provides wake structure and
induced inflow.
ii. Interpolate HELIX-IA velocity to UMTURNS
boundary.
iii. Impose Blade Circulation from
UMTURNS to HELIX-IA Wake.
Typical HELIX-IA-hybrid grid topology
91x125x107
193x65x96
Hybrid CFD for Helicopter Hybrid CFD for Helicopter Hover FlowHover Flow
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HELIX-IA : An Iterative Eulerian- / Lagrangian Solution Process
Vorticity Embedding
Hybrid CFD for Helicopter Hybrid CFD for Helicopter Hover FlowHover Flow
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Hybrid CFD for Helicopter Hybrid CFD for Helicopter Hover FlowHover Flow
t = Vorticity Embedding
Roll Up – Vortex Sheet w/ Elliptical Loading (Qv Field)
[S. Schmitz et al, AIAA-2009-3856]
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Hybrid CFD for Helicopter Hybrid CFD for Helicopter Hover FlowHover Flow
t = 0.0
t = /4
t = 2
[S. Schmitz et al, AIAA-2009-3856]
Vorticity Embedding
Roll Up – Pair of Vortex Ring Sheets
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Validation : Model UH-60A Validation : Model UH-60A BladeBlade
Hybrid CFD for Helicopter Hybrid CFD for Helicopter Hover FlowHover Flow
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Axial/Radial Tip Vortex Trajectory Axial/Radial Tip Vortex Trajectory
ComparisonsComparisons
Model UH-60A Rotor – CModel UH-60A Rotor – CTT// = 0.085, = 0.085,
MMtiptip=0.63=0.63Radial Axial
Hybrid CFD for Helicopter Hybrid CFD for Helicopter Hover FlowHover Flow
[S. Schmitz et al, AHS Journal (2009)]
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r/R = 0.865 r/R = 0.92
r/R = 0.945 r/R = 0.965
Hybrid CFD for Helicopter Hybrid CFD for Helicopter Hover FlowHover FlowPressure Coefficient vs. x/c Pressure Coefficient vs. x/c
Model UH-60A Rotor – CModel UH-60A Rotor – CTT// = 0.085, = 0.085,
MMtiptip=0.63=0.63
[S. Schmitz et al, AHS Journal (2009)]
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Hybrid CFD for Helicopter Hybrid CFD for Helicopter Hover FlowHover FlowPressure Coefficient vs. z/c Pressure Coefficient vs. z/c
Model UH-60A Rotor – CModel UH-60A Rotor – CTT// = 0.085, = 0.085,
MMtiptip=0.63=0.63
[S. Schmitz et al, AHS Journal (2009)]
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Hybrid CFD for Helicopter Hybrid CFD for Helicopter Hover FlowHover FlowFigure-of-Merit vs. CFigure-of-Merit vs. CTT
Model UH-60A Rotor – CModel UH-60A Rotor – CTT// = 0.085, = 0.085,
MMtiptip=0.63=0.63
[S. Schmitz et al, AHS Journal (2009)]
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Fast and robust
Accurate wake computation
Suggests that hover data are insufficient
Hybrid CFD for Helicopter Hybrid CFD for Helicopter Hover FlowHover Flow
Typical HELIX-IA-hybrid grid topology
Coupling UMTURNS w/ HELIX-IA
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Combining experiences & resources in Wind Energy and Rotorcraft
HYBRID U-RANS/POTENTIAL SOLVER
Outer Wake Solver
Vorticity-Embedding Potential Solver, HELIX-IA
For steady flow comparable to Biot-Savart
Possibility for efficient free wake computation
Inner U-RANS Solver OverFlow, CFX, UMTURNS, etc.
Future Research DirectionsFuture Research Directions
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=0deg
Solve N blades
Vortex Model
NBj
,
BC – u,v,w
Converged or # subiterations
=+
# Revolutions until solution is periodic.
U-RANS
0,,
xU
t
NB
j
Wind
NB
j
Converged
Understanding the Unsteady Aerodynamics is vital
for future competitiveness of Wind Energy.
HYBRID
U-RANS/POTENTIAL
SOLVER
Future Research DirectionsFuture Research Directions
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HYBRID
U-RANS/POTENTIAL
SOLVER
Aeroelasticity
(PSU VLRCOE)
Acoustics
(Brentner, McLaughlin, Morris)
Mesoscale Modeling
(Brasseur, Haupt)
Airfoil Soiling
(Brasseur, Maughmer)
Future Research DirectionsFuture Research Directions
Current Funding : GE Wind, US Army AFDD
Future Funding : DOE, NSF, NREL, State of Pennsylvania, GE Wind, US Army AFDD
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Wake Interactions at ‘Horns Rev’, Denmark
Hybrid CFD for Wind TurbinesHybrid CFD for Wind TurbinesFuture fast & accurate wind Future fast & accurate wind
turbine/plant designsturbine/plant designs