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Sandia 2014 Wind Turbine Blade Workshop- Van Dam

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Sandia 2014 Wind Turbine Blade Workshop- Van Dam

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Sandia 2014 Wind Turbine Blade Workshop- Van Dam

  1. 1. 1 Surface Erosion and Roughness Effects on Airfoil and Wind Turbine Performance C.P. (Case) van Dam Department of Mechanical & Aerospace Engineering University of California, Davis 2014 Wind Turbine Blade Workshop Albuquerque, NM 27 August 2014
  2. 2. 2 Contributors • Sandia National Laboratories – David Maniaci – Mark Rumsey – Matt Barone • Texas A&M – Ed White – Robert Ehrmann – Ben Wilcox • UC Davis – Chris Langel – Ray Chow – Owen Hurley
  3. 3. 3 Outline • Background • Project outline – Field Measurements – Wind tunnel testing – Computational modeling • Model validation • Airfoil results • Turbine performance effects – NREL 5-MW rotor • Conclusions & Next steps Source: Mayda
  4. 4. 4 Windplant Loss Categories Walls & Kline (2012) • Power losses can be as much as 20-30% in state of the art windplants: – Wake losses – Turbine availability – Balance of Plant (BOP) availability – Electrical – Environmental – Turbine performance – Curtailment
  5. 5. 5 Windplant Loss Categories Walls & Kline (2012) • Power losses can be as much as 20-30% in state of the art windplants: – Wake losses – Turbine availability – Balance of Plant (BOP) availability – Electrical – Environmental – Turbine performance – Curtailment
  6. 6. Environmental - Turbine Performance 6 Losses • These losses typically range from 1% to 10% • Impact on rotor aerodynamics: – Icing • Glaze • Hoar – Blade soiling – Blade erosion – Drop in air density (high temperature) – Turbulence, shear, etc.
  7. 7. 7 Blade Contamination and Erosion Examples Spruce (2006) Kanaby (2007)
  8. 8. 8 Background - I • Early stall controlled, constant speed wind turbines were severely affected by blade surface contamination and erosion. Large performance losses resulted (40% in peak power, ≥ 20% in energy capture). • Development and introduction of blade section shapes that were less roughness sensitive mitigated this issue. • Issue was focus of Wind Energy Conversion System Blade-Surface Roughness Workshop at NREL on April 20-21,1993.
  9. 9. 9 Blade Contamination Moroz & Eggleston (1993) • Surface soiling induced loss in power for fixed pitch, stall controlled rotors was big problem • Surface contamination caused by insect contamination, dust, erosion of gel coat • Surface roughness causes reduced sectional lift curve slope and maximum lift coefficient, and increased sectional drag • Effect greater for stall controlled rotors than pitch controlled rotors
  10. 10. 10 Blade Contamination Tangler (1993) • Surface contamination induced loss in power was problem for stall controlled rotors • Aerostar blade uses NACA 4415-4424 airfoils • NREL blade uses S805A, 806A, 807 airfoils • NREL airfoils designed to have less (maximum) lift sensitivity to surface roughness • Tests show reduced loss in turbine power due to surface roughness for NREL blade
  11. 11. 11 Background - II • Effect of (small) roughness: – It may cause premature transition from laminar to turbulent boundary layer state – It may cause boundary layer separation – It may cause flow unsteadiness – It removes energy from flow (increased skin friction) – Effect depends on: • Roughness height • Roughness chordwise location • Roughness density • Pressure gradient • Unit Reynolds number • Mach number
  12. 12. 12 Background - III • Variable speed, variable pitch turbines started to supersede the constant speed, fixed pitch turbines and this significantly mitigated the problem. • However, a resurgence of the surface roughness problem has occurred: – More awareness as a result of improved windplant performance analysis methods – Higher maximum thickness-to-chord ratio (t/c) blade sections – Higher lift-to-drag ratio (L/D) blade sections – Higher Reynolds numbers • Combination of high density altitude and blade surface roughness can be especially troublesome. • Because of size of turbines, blade washing is often cost prohibitive. • Detailed knowledge of loss mechanisms is still missing. • Computational tools to analyze roughness sensitivity of airfoils are missing.
  13. 13. 13 Surface Roughness and Erosion Project • Effects of Surface Contamination and Erosion on Wind Turbine Performance • Project started in April 2012 • Team: – Sandia National Laboratories, Albuquerque – Texas A&M University – University of California, Davis • Tasks: – Field measurements of surface roughness and erosion – Wind tunnel testing of effect of surface roughness and erosion on airfoil performance – Development of computational roughness model to account for effect on aerodynamic performance of airfoils, blades, rotors – Correlate wind tunnel and CFD results
  14. 14. 14 Wind Tunnel • Oran W. Nicks Low Speed Wind Tunnel at Texas A&M • Closed return tunnel • Test section 7 ft × 10 ft • Maximum velocity of 90 m/s • Blockage of 4.8% • Turbulence intensity of 0.25% • Maximum Rec = 3.6 × 106 based on loading at maximum lift conditions • Maximum Rec = 5.0 × 106 to α = 4° Model installed in wind tunnel freestream
  15. 15. 15 Configurations • Model chord = 0.813 m • Airfoil NACA 633-418 • Clean • Tripped • Forward Facing Steps • Chipped paint 157μm • Straight step 157μm • Distributed Roughness • 100μm: 3, 9, 15% coverage • 140μm: 3, 6, 9, 12, 15% cov. • 200μm: 3% cov. • Distributed and 2D roughness Simulated insect roughness (140 μm, 3% coverage) on NACA 633-418.
  16. 16. pressure side suction side pressure side suction side 16 Distributed Roughness Random insect distribution with 3% coverage. Random insect distribution with 15% coverage.
  17. 17. 17 Drag Polar at Rec = 3.2 × 106 L/D=106 CL,max=1.36 L/D=72 CL,max=1.28
  18. 18. 18 Transition, α = 0°, Rec = 3.2 × 106 CP,min XFOIL, N=5.5
  19. 19. 19 Eroded Leading Edge Model
  20. 20. 20 Computational Modeling - I • OVERFLOW-2 – Overset, multigrid, compressible Reynolds-averaged Navier-Stokes flow solution method – Semi-public domain – Method newly developed Roughness Model has been coded into • Reynolds averaged Navier-Stokes Equations – Remove turbulent fluctuations from flow equations. All eddy scales are ignored and mean flow can then be resolved with coarser computational grid. • Turbulence Modeling – To properly account for turbulent fluctuations, there must be a way to approximate the effect of the removed scales. In RANS methods, these fluctuations are accounted for in the Reynolds stress terms – Surface roughness has a prominent effect on this process • Transition Modeling – Baseline turbulence models must either assume fully laminar or “fully” turbulent. Need additional correlation to automate switch between laminar and turbulent. – Surface roughness has a prominent effect on this process
  21. 21. 21 Computational Modeling - II
  22. 22. 22 Computational Modeling - III • Existing transition model Langtry-Menter: – Recently developed – Two variable model • Local momentum thickness parameter, transition onset when local momentum thickness ≥ critical momentum thickness • Intermittency parameter governs growth turbulent kinetic energy from transition onset to fully turbulent • Roughness model adds 3rd variable to Langtry- Menter transition model: – Roughness amplification parameter (Ar) • Turbulence model modified to account for surface roughness effects – Currently based on Wilcox
  23. 23. 23 Roughness Variable (Ar) Distribution • There is a direct correlation between distribution of Ar and skin friction due to dependence on wall shear stress (τw) Flat plate flow, Re = 1.34 million, Ma = 0.30 Top: Distribution of Ar variable along flat plate Bottom: Corresponding skin friction distribution Ar Rough Wall Boundary = f (k+ ) k+ = U!ks " ks = Roughness Height U! = !w # Cf = !w 1 2 #U2
  24. 24. 24 Initial Validation Cases • Flat plate with distributed sand-grain roughness of varying heights (Feindt, 1956) – Zero pressure gradient – Adverse pressure gradient • NACA 0012 with leading edge roughness (Kerho & Bragg, 1997) • Texas A&M tunnel, NACA 633-418 – Clean – Distributed roughness
  25. 25. 25 Effect of Roughness Height on Skin Friction Flat plate, zero-pressure gradient, Feindt (1956) Re k = !U k k μ
  26. 26. 26 Comparison of Measured and Predicted Effect of Roughness on Transition Flat plate, zero-pressure gradient, Feindt (1956) Re k = !U k k μ
  27. 27. 27 Comparison of Measured and Predicted Boundary Layer Profiles NACA 0012, Re = 1.25 × 106, α = 0° 1/2 in. roughness strip applied at s = 4 mm (x/c = 0.0018 - 0.0191) • Wind tunnel measurement from Kerho & Bragg (1997) • Slight lag in boundary layer development at early stations • Profiles match well at later stations
  28. 28. 28 Comparison of Measured and Predicted Boundary Layer States NACA 0012, Re = 1.25 × 106, α = 0°
  29. 29. 29 Comparison of Measured and Predicted Drag Polars NACA 633-418, Clean surface, Re = 1.6 × 106, Texas A&M tunnel
  30. 30. 30 Comparison of Measured and Predicted Transition Location NACA 633-418, k/c = 170 × 10-6 @ x/c = -0.12:0.04, Re = 1.6 × 106, Texas A&M tunnel
  31. 31. 31 Comparison of Measured and Predicted Transition Location NACA 633-418, k/c = 170 × 10-6 @ x/c = -0.12:0.04, Re = 2.4 × 106, Texas A&M tunnel
  32. 32. 32 NREL 5-MW Rotor • Geometry based on 6MW DOWEC rotor – Conceptual off-shore turbine design – ECN (Energy Research Centre of the Netherlands) • Rotor diameter truncated and hub diameter reduced
  33. 33. 33 NREL 5-MW Rotor • Rotor diameter =126 m • Specific power = 401 W/m2 • 12.1 RPM • 3 m hub diameter • 61.5 m blade length • 4.7 m max chord • 13.3° inboard twist • 3 m/s cut-in speed • 25 m/s cut-out • 12 m/s rated speed
  34. 34. 34 Performance Prediction Using Computational Roughness Model • Six different airfoil profiles • Airfoils analyzed using OVERFLOW-2 in both “clean” and “rough” configuration • Roughness applied from 5% chord on lower to 5% chord on upper surface • Height of roughness set at k/c = 240 × 10-6 ( k = 0.24 mm or 0.001 in. for a chord of 1 m) • Corresponds to relatively heavy soiling
  35. 35. 35 Airfoil Performance with Roughness • Midspan DU-91-W210 airfoil Re = 7.24 × 106 , k/c = 240 × 10-6 roughness applied x/c = -0.05:0.05
  36. 36. Effect of Blade Roughness on Turbine 36 Power WT-Perf, NREL 5-MW turbine, Roughness height k/c = 240 × 10-6 Percent power loss due to degradation Gross power loss due to degradation
  37. 37. Effect of Blade Roughness on Turbine 37 Performance NREL 5 MW turbine, Roughness height k/c = 240 × 10-6 Change in Annual Energy Capture (%)* Turbine Capacity Factor (rough)* Turbine Capacity Factor (clean)* Mean wind speed at hub height (m/s) 5.5 0.194 0.186 -4.26 6.0 0.241 0.231 -3.82 6.5 0.287 0.278 -3.43 7.0 0.334 0.323 -3.08 8.0 0.420 0.409 -2.80 8.5 0.459 0.449 -2.52 * = based on Raleigh distribution
  38. 38. 38 NACA 633-418 Performance at Rec=3.2×106 Configuration dCL/dα L/Dmax CL,max Rek,crit Clean 6.71/rad 106 1.36 - 100-03 -0.3% -18% -3.4% 316±12 100-09 -1.6% -24% -4.8% 271±13 100-15 -3.1% -32% -6.0% 254±13 140-03 -3.4% -35% -4.0% 240±19 140-03ext -2.8% -37% -5.6% 222±19 140-06 -3.7% -37% -5.6% 207±19 140-09 -3.6% -39% -7.4% 178±18 140-12 -3.6% -40% -7.8% 178±18 140-15 -3.7% -41% -8.7% 178±18 200-03 -2.7% -35% -0.6% 227±28 ELE -7.3% -52% -16.9% -
  39. 39. 39 NREL 5MW AEP Losses
  40. 40. 40 Conclusions • Comprehensive study on effect of blade surface erosion and soiling on wind turbine performance is being conducted: – Field measurements of blade erosion – Wind tunnel testing (NACA 633-418) – Computational modeling of surface roughness • Study is providing significant aerodynamic insight into surface roughness effects • Newly developed model allows for specifying roughness and analyzing impact on airfoil/blade/rotor performance • Computational modeling and wind tunnel studies will be published in two Sandia reports in fall 2014
  41. 41. 41 Next Steps • Near term: – Implement improvements in computational roughness model: • Pressure gradient effect • Distributed roughness density effect – Calibrate/validate computational roughness model against Texas A&M wind tunnel results • Longer term: – Evaluate (experimentally and computationally) roughness sensitivity of higher t/c and higher L/D section shapes – 3D RANS modeling of roughness effect on rotor performance – Implement boundary modifiers (VGs) in RANS and study their effectiveness mitigating surface roughness effects – Develop lower-order tool to evaluate surface roughness effects and optimize boundary layer modifiers (size, location)
  42. 42. 42 Acknowledgements • U.S. Department of Energy • Sandia National Laboratories • Warren and Leta Giedt Endowment • National Science Foundation GK-12 RESOURCE program

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