1. Dr. Sven Schmitz University of California, Davis Computational Modeling of Wind Turbine Aerodynamics and Helicopter Hover Flow Using Hybrid CFD Pennsylvania State University April 21 st, 2010

2. 2 Outline Wind Energy Wind Energy The NREL Phase VI Experiment The NREL Phase VI Experiment Hybrid CFD for Wind Turbines Hybrid CFD for Wind Turbines Hybrid CFD for Helicopter Hover Flow Hybrid CFD for Helicopter Hover Flow Future Research Directions Future Research Directions

3. 3 Wind 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

4. 4 Wind Energy - U.S. Market Over 10,000 MW installed in 2009 - U.S. world leader Over 10,000 MW installed in 2009 - U.S. world leader Top U.S. Wind Turbine Supplier : GE Energy Top U.S. Wind Turbine Supplier : GE Energy Wind industry supports 85,000 jobs in 50 states Wind industry supports 85,000 jobs in 50 states Now 9 wind turbine manufacturers in U.S. Now 9 wind turbine manufacturers in U.S. (April 2010) www.awea.org/reports (April 2010)

5. 5 Wind Energy - Incentives US DOE – Energy Efficiency and Renewable Energy US DOE – Energy Efficiency and Renewable Energy  20% Wind Energy by 2030 Pennsylvania - Alternative Energy Investment Act (2009) Pennsylvania - Alternative Energy Investment Act (2009)  Wind Energy Supply Chain Initiative (WESCI)

6. 6 Wind Energy - Power Curve   and W site specific  C P ≈ 0.52 at W rated (C P,Betz = 0.59)  Rotor Diameter D driving factor

7. 7 Wind Energy - Cost of Energy (COE) O & M estimated at 10%-20% of total COE. Availability & Loss are site & design specific. Aerodynamics & Aeroelasticity [Walford, C., SAND2006-1100]

8. 8 Wind Energy - Cost Reduction Maximize Availability, Minimize Loss Maximize Availability, Minimize Loss  Improved designs for Region II  Reduce fatigue loads Minimize Operation and Maintenance (O & M) Minimize Operation and Maintenance (O & M)  Reduce # turbines to maintain by increasing turbine power  Reduce fatigue loads

9. 9 Wind Energy Challenges in Computational Modeling Unsteady Aerodynamics Unsteady Aerodynamics  Blade load response to wind gust Aeroelasticity Aeroelasticity  Blade tip deflections of several meters  Twist changes > 10deg Airfoil Soiling Airfoil Soiling  Performance loss caused by dirt, insects, etc.

10. 10 The NREL Phase VI Experiment NREL = National Renewable Energy Laboratory NREL Phase VI Rotor, April 2000 NREL Phase VI Rotor, April 2000  R = 5.03m  2 Blades, Twist, Taper  Stall-controlled, S809 Airfoil [Somers, NREL/SR-440-6918]  5m/s < V Wind < 25m/s   = 72rpm  P ≈ 10KW NREL Phase VI Rotor in NASA Ames 120’ x 80’ wind tunnel

11. 11 The NREL Phase VI Experiment Blind Comparison Run, December 2000 Blind Comparison Run, December 2000 Comparison of computational models  Performance Codes (BEMs)  Aeroelastic Codes  Wake Codes  CFD Codes NREL Phase VI Rotor in NASA Ames 120’ x 80’ wind tunnel

12. 12 The NREL Phase VI Experiment No-Yaw, Steady-State, No-Stall conditions … No-Yaw, Steady-State, No-Stall conditions … Turbine Power Prediction : 25% - 175% of measured Turbine Power Prediction : 25% - 175% of measured Blade Bending Prediction : 85% - 150% of measured Blade Bending Prediction : 85% - 150% of measured CFD Codes -> Overall best predictions of turbine power and blade loads. Wake Codes -> Good performance for attached flow. Main Results from Blind Comparison Run [NREL/TP-500-29494] Conclusions from Blind Comparison Run [NREL/TP-500-29494]

13. 13 Difficulties of computational models CFD Codes: High Computational Cost & Artificial Dissipation CFD Codes: High Computational Cost & Artificial Dissipation Wake Codes : Prediction of strong 3D effects close to the rotor blade Wake Codes : Prediction of strong 3D effects close to the rotor blade Reduce cost and dissipation. Near-Field RANS + Far-Field Wake Code = Hybrid CFD for Wind Turbines

14. 14 Parallelized Coupled Solver (PCS) Navier-Stokes Vortex Method Vortex Filament Biot-Savart Law (discrete) Boundary of Navier-Stokes Zone Converged for … Bound Vortex Hybrid CFD for Wind Turbines

15. 15 Average u B from power estimate using actuator disc theory Biot-Savart Law Hybrid CFD for Wind Turbines Vortex Method

16. 16 C Accuracy of straight-line Vortex Segmentation : => [Gupta & Leishman, AIAA-2004-0828] ΔΘ = 10˚ => Error < 10% ΔΘ 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 Turbines Vortex Method

17. 17 Optimum Wind Turbine Inviscid Flow : PCS= Parallelized Coupled Solver VLM =Vortex Line Method [J.J. Chattot] 8.8048.879 Power [kW] -583.80-588.82 Torque [Nm] 1814.81803.1 Bending Moment [Nm] -179.89-183.63 Tangential Force [N] 508.31509.62 Thrust [N] PCSVLM Difference in Power : 0.84 % Hybrid CFD for Wind Turbines [S. Schmitz, J. J. Chattot, Computers & Fluids (2006)]

18. 18 Optimum Wind Turbine Viscous Flow : 7.3217.835 Power [kW] -485.50-519.58 Torque [Nm] 1636.41670.2 Bending Moment [Nm] -150.80-163.26 Tangential Force [N] 458.60472.41 Thrust [N] PCSVLM Difference in Power : 6.6 % PCS= Parallelized Coupled Solver VLM =Vortex Line Method [J.J. Chattot] Hybrid CFD for Wind Turbines [S. Schmitz, J. J. Chattot, Computers & Fluids (2006)]

19. 19 Rotating, S-Sequence Fully Attached Flow : U  =7m/s NREL Phase VI Rotor Hybrid CFD for Wind Turbines

20. 20 Very good agreement w/ measured surface pressure coefficient. [S. Schmitz, J. J. Chattot, ASME JSEE (2005)] NREL Phase VI Rotor Hybrid CFD for Wind Turbines

21. 21 Influence of Vortex Sheet Revolutions on Rotor Torque : V Wind = 7m/s Collaboration with GE Wind Wind Aero Design Tool Development (2007-2009) UCD Award #08003057, #700163655 Routine Design Use Hybrid CFD for Wind Turbines NREL Phase VI Rotor

22. 22 Hybrid CFD for Wind Turbines NREL Phase VI Rotor Other CFD Results [ Duque et al, AIAA-1999-0037] [Sezer-Uzol, Long, AIAA-2006-0394] [Potsdam, Mavriplis, AIAA-2009-1221]

23. 23 NREL 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 Turbines

24. 24 Distribution of Bound Circulation (Parked, L – Sequence, U  = 20.1 m/s) Trailing Vortex @ r/R=0.40 Attached Flow Separated 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 Turbines NREL Phase VI Rotor [S. Schmitz, J. J. Chattot, ASME JSEE (2006)]

25. 25 (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 Turbines NREL Phase VI Rotor [S. Schmitz, J. J. Chattot, ASME JSEE (2006)]

26. 26 complex physics need for high accuracy a recurring engineering need many methods developed, few validated little data that supports complete physical models Hybrid CFD for Helicopter Hover Flow Collaboration with US Army AFDD A New CFD Approach for the Computation of General Rotorcraft Flows (2006-2010) UCD Award #NNX08AU38A, #NNA0CB79A

27. 27 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 Hover Flow

28. 28 HELIX-IA : An Iterative Eulerian- / Lagrangian Solution Process Vorticity Embedding Hybrid CFD for Helicopter Hover Flow

29. 29 Hybrid CFD for Helicopter Hover Flow t =  Vorticity Embedding Roll Up – Vortex Sheet w/ Elliptical Loading (Qv Field) [S. Schmitz et al, AIAA-2009-3856]

30. 30 Hybrid CFD for Helicopter Hover Flow t = 0.0 t =  /4 t = 2  [S. Schmitz et al, AIAA-2009-3856] Vorticity Embedding Roll Up – Pair of Vortex Ring Sheets

31. 31 Validation : Model UH-60A Blade Hybrid CFD for Helicopter Hover Flow

32. 32 Axial/Radial Tip Vortex Trajectory Comparisons Model UH-60A Rotor – C T /  = 0.085, M tip =0.63 Radial Axial Hybrid CFD for Helicopter Hover Flow [S. Schmitz et al, AHS Journal (2009)]

33. 33 r/R = 0.865 r/R = 0.92 r/R = 0.945 r/R = 0.965 Hybrid CFD for Helicopter Hover Flow Pressure Coefficient vs. x/c Model UH-60A Rotor – C T /  = 0.085, M tip =0.63 [S. Schmitz et al, AHS Journal (2009)]

34. 34 Hybrid CFD for Helicopter Hover Flow Pressure Coefficient vs. z/c Model UH-60A Rotor – C T /  = 0.085, M tip =0.63 [S. Schmitz et al, AHS Journal (2009)]

35. 35 Hybrid CFD for Helicopter Hover Flow Figure-of-Merit vs. C T Model UH-60A Rotor – C T /  = 0.085, M tip =0.63 [S. Schmitz et al, AHS Journal (2009)]

36. 36 Fast and robust Accurate wake computation Suggests that hover data are insufficient Hybrid CFD for Helicopter Hover Flow Typical HELIX-IA-hybrid grid topology Coupling UMTURNS w/ HELIX-IA

37. 37 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 Directions

38. 38  =0deg Solve N blades Vortex Model BC – u,v,w Converged or # subiterations  =  +  # Revolutions until solution is periodic. U-RANS Converged Understanding the Unsteady Aerodynamics is vital for future competitiveness of Wind Energy. HYBRID U-RANS/POTENTIAL SOLVER Future Research Directions

39. 39 HYBRID U-RANS/POTENTIAL SOLVER Aeroelasticity (PSU VLRCOE) Acoustics (Brentner, McLaughlin, Morris) Mesoscale Modeling (Brasseur, Haupt) Airfoil Soiling (Brasseur, Maughmer) Future Research Directions Current Funding : GE Wind, US Army AFDD Future Funding : DOE, NSF, NREL, State of Pennsylvania, GE Wind, US Army AFDD

40. 40 Wake Interactions at ‘Horns Rev’, Denmark Hybrid CFD for Wind Turbines Future fast & accurate wind turbine/plant designs