1 / 40

Computational Modeling of Wind Turbine Aerodynamics and Helicopter Hover Flow Using Hybrid CFD

Computational Modeling of Wind Turbine Aerodynamics and Helicopter Hover Flow Using Hybrid CFD. Dr. Sven Schmitz University of California, Davis. Pennsylvania State University April 21 st , 2010. Outline. Wind Energy The NREL Phase VI Experiment Hybrid CFD for Wind Turbines

jefferyb
Télécharger la présentation

Computational Modeling of Wind Turbine Aerodynamics and Helicopter Hover Flow Using Hybrid CFD

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

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

  3. “Alternative Sunrise” Windkraftanlage Holzweiler mit Braunkohlekraftwerk Grevenbroich, Germany, April 2010. Wind Energy • Free energy source • Emission free • No water use • Scalability, i.e. ‘local’ & ‘wind power plant’ • Less dependence on fossil fuels

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

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

  6. Wind Energy - Power Curve • r and W site specific • CP≈ 0.52 at Wrated (CP,Betz = 0.59) • Rotor Diameter D driving factor

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

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

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

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

  11. The NREL Phase VI Experiment • 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. The NREL Phase VI Experiment Main Results from Blind Comparison Run [NREL/TP-500-29494] • No-Yaw, Steady-State, No-Stall conditions … Turbine Power Prediction : 25% - 175% 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. Conclusions from Blind Comparison Run [NREL/TP-500-29494]

  13. Hybrid CFD for Wind Turbines Difficulties of computational models • CFD Codes : High Computational Cost & Artificial Dissipation • 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. Hybrid CFD for Wind Turbines Navier-Stokes Biot-Savart Law (discrete) Boundary of Navier-Stokes Zone Vortex Method Bound Vortex Converged for … Vortex Filament Parallelized Coupled Solver (PCS)

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

  16. Hybrid CFD for Wind Turbines Accuracy of straight-line Vortex Segmentation : => [Gupta & Leishman, AIAA-2004-0828] ΔΘ = 10˚ => Error < 10% ΔΘ < 2.5˚ => Error < 1% C Vortex Method • 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%

  17. Hybrid CFD for Wind Turbines PCS = Parallelized Coupled Solver VLM = Vortex Line Method [J.J. Chattot] Inviscid Flow : VLM PCS Thrust [N] 509.62 508.31 Tangential Force [N] -183.63 -179.89 Bending Moment [Nm] 1803.1 1814.8 Torque [Nm] -588.82 -583.80 Power [kW] 8.879 8.804 Difference in Power : 0.84 % Optimum Wind Turbine [S. Schmitz, J. J. Chattot, Computers & Fluids (2006)]

  18. Hybrid CFD for Wind Turbines VLM PCS Thrust [N] 472.41 458.60 PCS = Parallelized Coupled Solver VLM = Vortex Line Method [J.J. Chattot] Tangential Force [N] -163.26 -150.80 Viscous Flow : Bending Moment [Nm] 1670.2 1636.4 Torque [Nm] -519.58 -485.50 Power [kW] 7.835 7.321 Difference in Power : 6.6 % Optimum Wind Turbine [S. Schmitz, J. J. Chattot, Computers & Fluids (2006)]

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

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

  21. Hybrid CFD for Wind Turbines NREL Phase VI Rotor 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

  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. Hybrid CFD for Wind Turbines 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)]

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

  25. Hybrid CFD for Wind Turbines Visualization of ‘Trailing Vortex’ by an Iso-Vorticity Surface (a) a47 = 3.53deg (b) a47 = 13.46deg (c) a47 = 23.49deg (d) a47 = 33.50deg Iso-Vorticity Surface behind Parked NREL PhaseVI Blade (w=19s-1) (L – Sequence, U = 20.1 m/s) NREL Phase VI Rotor [S. Schmitz, J. J. Chattot, ASME JSEE (2006)]

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

  27. Hybrid CFD for Helicopter Hover Flow Typical HELIX-IA-hybrid grid topology Coupling UMTURNS w/ HELIX-IA • HELIX-IA provides wake structure and induced inflow. • Interpolate HELIX-IA velocity to UMTURNS boundary. • Impose Blade Circulation from UMTURNS to HELIX-IA Wake. 91x125x107 193x65x96

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

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

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

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

  32. Hybrid CFD for Helicopter Hover Flow Radial Axial Axial/Radial Tip Vortex Trajectory ComparisonsModel UH-60A Rotor – CT/s = 0.085, Mtip=0.63 [S. Schmitz et al, AHS Journal (2009)]

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

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

  35. Hybrid CFD for Helicopter Hover Flow Figure-of-Merit vs. CTModel UH-60A Rotor – CT/s = 0.085, Mtip=0.63 [S. Schmitz et al, AHS Journal (2009)]

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

  37. Future Research Directions 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.

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

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

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

More Related