1

Surface Erosion andRoughness Effects onAirfoil and WindTurbine Performance

C.P. (Case) van Dam

Department of Mechanical &Aerospace EngineeringUniversity of California, Davis

2014 Wind Turbine Blade WorkshopAlbuquerque, NM27 August 2014

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

Outline

• Background• Project outline

– Field Measurements– Wind tunnel testing– Computational modeling

• Model validation• Airfoil results• Turbine performance

effects– NREL 5-MW rotor

• Conclusions & Next stepsSource: Mayda

4

Windplant Loss CategoriesWalls & Kline (2012)

• Power losses can be as much as 20-30% instate of the art windplants:– Wake losses– Turbine availability– Balance of Plant (BOP) availability– Electrical– Environmental– Turbine performance– Curtailment

5

Windplant Loss CategoriesWalls & Kline (2012)

• Power losses can be as much as 20-30% instate of the art windplants:– Wake losses– Turbine availability– Balance of Plant (BOP) availability– Electrical– Environmental– Turbine performance– Curtailment

6

Environmental - Turbine PerformanceLosses

• 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

Blade Contamination and ErosionExamples

Spruce (2006) Kanaby (2007)

8

Background - I

• Early stall controlled, constant speed windturbines were severely affected by bladesurface contamination and erosion. Largeperformance losses resulted (40% in peakpower, ≥ 20% in energy capture).

• Development and introduction of bladesection shapes that were less roughnesssensitive mitigated this issue.

• Issue was focus of Wind Energy ConversionSystem Blade-Surface Roughness Workshopat NREL on April 20-21,1993.

9

Blade ContaminationMoroz & Eggleston (1993)

• Surface soiling induced lossin power for fixed pitch, stallcontrolled rotors was bigproblem

• Surface contaminationcaused by insectcontamination, dust, erosionof gel coat

• Surface roughness causesreduced sectional lift curveslope and maximum liftcoefficient, and increasedsectional drag

• Effect greater for stallcontrolled rotors than pitchcontrolled rotors

10

Blade ContaminationTangler (1993)

• Surface contaminationinduced loss in power wasproblem for stall controlledrotors

• Aerostar blade uses NACA4415-4424 airfoils

• NREL blade uses S805A,806A, 807 airfoils

• NREL airfoils designed tohave less (maximum) liftsensitivity to surfaceroughness

• Tests show reduced loss inturbine power due tosurface roughness forNREL blade

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

Background - III• Variable speed, variable pitch turbines started to supersede the

constant speed, fixed pitch turbines and this significantlymitigated the problem.

• However, a resurgence of the surface roughness problem hasoccurred:– 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 surfaceroughness can be especially troublesome.

• Because of size of turbines, blade washing is often costprohibitive.

• Detailed knowledge of loss mechanisms is still missing.• Computational tools to analyze roughness sensitivity of airfoils

are missing.

13

Surface Roughness and ErosionProject

• Effects of Surface Contamination and Erosion onWind 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

Wind Tunnel

• Oran W. Nicks Low Speed WindTunnel 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 liftconditions

• Maximum Rec = 5.0 × 106 to α =4°

Model installed in wind tunnel

freestream

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

Distributed Roughness

Random insect distribution with 3% coverage. Random insect distribution with 15% coverage.

suction sidepressure side suction sidepressure side

17

Drag Polar at Rec = 3.2 × 106

L/D=106CL,max=1.36

L/D=72CL,max=1.28

18

Transition, α = 0°, Rec = 3.2 × 106

CP,min

XFOIL, N=5.5

19

Eroded Leading Edge Model

20

Computational Modeling - I• OVERFLOW-2

– Overset, multigrid, compressible Reynolds-averaged Navier-Stokes flowsolution 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 computationalgrid.

• Turbulence Modeling– To properly account for turbulent fluctuations, there must be a way to

approximate the effect of the removed scales. In RANS methods, thesefluctuations 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 laminarand turbulent.

– Surface roughness has a prominent effect on this process

21

Computational Modeling - II

22

Computational Modeling - III• Existing transition model Langtry-Menter:

– Recently developed– Two variable model

• Local momentum thickness parameter, transition onset whenlocal momentum thickness ≥ critical momentum thickness

• Intermittency parameter governs growth turbulent kineticenergy 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 surfaceroughness effects– Currently based on Wilcox

23

• There is a direct correlationbetween distribution of Ar andskin friction due to dependenceon 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!k

s

"

ks= Roughness Height

U!=

!w

#

Cf=

!w

12 #U2

Roughness Variable (Ar) Distribution

24

Initial Validation Cases

• Flat plate with distributed sand-grainroughness 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

Effect of Roughness Height onSkin Friction

Flat plate, zero-pressure gradient, Feindt (1956)

Rek=!U

kk

µ

26

Comparison of Measured andPredicted Effect of Roughness on

TransitionFlat plate, zero-pressure gradient, Feindt (1956)

Rek=!U

kk

µ

27

Comparison of Measured andPredicted 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

Comparison of Measured andPredicted Boundary Layer States

NACA 0012, Re = 1.25 × 106, α = 0˚

29

Comparison of Measured andPredicted Drag Polars

NACA 633-418, Clean surface, Re = 1.6 × 106, Texas A&M tunnel

30

Comparison of Measured andPredicted 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

Comparison of Measured andPredicted 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

NREL 5-MW Rotor

• Geometry based on6MW DOWEC rotor– Conceptual off-shore

turbine design– ECN (Energy Research Centre

of the Netherlands)

• Rotor diametertruncated and hubdiameter reduced

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

Performance Prediction UsingComputational Roughness Model

• Six different airfoil profiles

• Airfoils analyzed using OVERFLOW-2 in both“clean” and “rough” configuration

• Roughness applied from 5% chord on lower to5% chord on upper surface

• Height of roughness set at k/c = 240 × 10-6-6 ( k= 0.24 mm or 0.001 in. for a chord of 1 m)

• Corresponds to relatively heavy soiling

35

Airfoil Performance withRoughness

• Midspan DU-91-W210 airfoil

Re = 7.24 × 106 , k/c = 240 × 10-6 roughness applied x/c = -0.05:0.05

36

Effect of Blade Roughness on TurbinePower

WT-Perf, NREL 5-MW turbine, Roughness height k/c = 240 × 10-6

Percent power loss due todegradation

Gross power loss due todegradation

37

Effect of Blade Roughness on TurbinePerformance

NREL 5 MW turbine, Roughness height k/c = 240 × 10-6

-2.520.4490.4598.5

-2.800.4090.4208.0

-3.080.3230.3347.0

-3.430.2780.2876.5

-3.820.2310.2416.0

-4.260.1860.1945.5

Change inAnnualEnergy

Capture (%)*

TurbineCapacity

Factor (rough)*

TurbineCapacity

Factor (clean)*

Mean windspeed at hubheight (m/s)

* = based on Raleigh distribution

38

NACA 633-418 Performance atRec=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

ELE

-2.7%

-7.3%

-35%

-52%

-0.6%

-16.9%

227±28

-

39

NREL 5MW AEP Losses

40

Conclusions• Comprehensive study on effect of blade surface

erosion and soiling on wind turbine performance isbeing conducted:– Field measurements of blade erosion– Wind tunnel testing (NACA 633-418)– Computational modeling of surface roughness

• Study is providing significant aerodynamic insight intosurface roughness effects

• Newly developed model allows for specifyingroughness and analyzing impact on airfoil/blade/rotorperformance

• Computational modeling and wind tunnel studies willbe published in two Sandia reports in fall 2014

41

Next Steps• Near term:

– Implement improvements in computational roughnessmodel:

• Pressure gradient effect• Distributed roughness density effect

– Calibrate/validate computational roughness model againstTexas 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

Acknowledgements• U.S. Department of Energy• Sandia National Laboratories• Warren and Leta Giedt Endowment• National Science Foundation GK-12 RESOURCE

program