January 2002 • NREL/CP-500-31243

The Nebulous Art of Using Wind-Tunnel Data for Predicting Rotor Performance: Preprint

James L. Tangler

To be presented at the 21st ASME Wind Energy Conference Reno, Nevada January 14-17, 2002

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Printed on paper containing at least 50% wastepaper, including 20% postconsumer waste The Nebulous Art of Using Wind-Tunnel Airfoil Data for Predicting Rotor Performance

James L. Tangler National Renewable Energy Laboratory 1617 Cole Boulevard Golden, Colorado, 80401-3393

Abstract should help advance the state of the art of more accurately predicting the aerodynamic performance of a The objective of this study was threefold: to evaluate wind turbine rotor. different two-dimensional S809 airfoil data sets in the prediction of rotor performance; to compare blade- A recent comparison2 of predictions to measurements element momentum rotor predicted results to lifting- for the NASA Ames data set showed that in general surface, prescribed- results; and to compare the blade-element momentum (BEM) theory overpredicts NASA Ames combined experiment rotor measured peak power. Reasons for this overprediction are also data with the two different performance prediction addressed in this study. Using this unique steady state methods. The S809 airfoil data sets evaluated included performance database and two-dimensional wind tunnel those from Delft University of Technology, Ohio State data, two aerodynamic performance prediction methods University, and Colorado State University. The were compared to the NASA data. One of these codes performance prediction comparison with NASA Ames was the basic BEM method, WTPERF3, while the data documents shortcomings of these performance second code was a more analytically rigorous lifting- prediction methods and recommends the use of the surface, prescribed-wake approach called lifting- lifting-surface, prescribed-wake method over blade- surface wind turbine (LSWT)4. element momentum theory for future analytical improvements. Performance Prediction Codes

Introduction Blade-Element Momentum Because of its simplicity, steady state performance Improvement to aerodynamic performance prediction prediction using BEM theory has been the mainstay of codes based on comparisons with field-measured power the wind industry for predicting rotor performance. curves has inherent limitations. In an unsteady field Various versions of BEM exist, beginning with PROP5 environment, induces error and wind shear and followed by many other versions, such as alters the power curve relative to the steady state PROP936, PROPID7, and WTPERF3. For this paper, assumption on which the performance prediction rotor performance predictions were acquired using a methods are based. Turbulence-induced errors occur recent version of BEM theory, WTPERF. when using the method of bins for measuring power. For each wind speed bin, the sum of the wind speeds Some limitations of BEM that affect its accuracy are cubed is greater than the cube of the mean wind speed. related to simplifications that are not easily corrected. This relationship results in the power curve rotating These error-producing simplifications begin with the about some mean wind speed value, yielding too high a assumption of uniform inflow over each rotor disc power value at low wind speeds and too low a value at annulus and no interaction between annuluses. Also, high wind speeds as is encountered. Compounding the tip loss model accounts for blade number effects, this error is the hub-height wind speed measurement but not effects due to differences in blade planform, that, in the presence of wind shear, is not representative which must be modeled with lifting-surface theory. of the rotor disc average. Finally, a two-dimensional (2-D) assumption relates effective angle of attack to local blade loads for a three- The need for an accurate measured steady state power dimensional (3-D) environment. In addition, some curve for correlating with predicted performance has versions of BEM numerically model the blade with been a research priority difficult to achieve outside of a equally spaced radial segments, which results in poor large wind tunnel. The opportunity to test a full-scale, resolution of loading in the tip region where it should 10-m (33-ft) diameter, wind turbine in the NASA Ames rapidly drop to zero. Insufficient resolution of the tip 24.4- x 36.6-m (80- x 120-ft) wind tunnel1 represented region typically leads to an overprediction of the tip a opportunity to acquire a unique data set. This data set loading and peak power.

This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States. Lifting-Surface, Prescribed-Wake Computer execution time for LSWT is about 10 times Modeling the rotor blades with a lifting surface and the greater than a comparable case with BEM. Using a 700 resulting wake (Fig.1) eliminates errors resulting MHz Pentium III required about 7 seconds for a 15- from the simplifications mentioned for BEM theory. point wind speed sweep versus less than a second for The local inflow for each annulus is now greater at BEM. each blade than the average of the annulus because of induced effects from the blade trailing vorticity. NASA/CER Experimental Data Greater local induction leads to lower angle of attack distributions and greater induced drag. Lifting-surface Wind-Tunnel CER Test wake theory also allows interaction between the rotor Rotor test data were acquired in the NASA Ames 24.4- annuluses and blade surface chordwise panels. This x 36.6-m (80- x 120-ft), wind-tunnel test section. The formulation eliminates the need for a tip loss model and test configuration for the comparison with predictions provides a more accurate radial load distribution. A was a constant-speed (72 rpm), two-bladed, upwind, lifting surface that includes chordwise panels8 results in stall-regulated rotor. Rotor blades10 for this test had a lower outboard loading relative to a simpler lifting line linear chord taper with a nonlinear twist distribution as formulation. shown in Fig. 2, and operated with a 3 degree tip pitch toward feather relative to the airfoil chord line. The radius from the center of rotation, which includes both blade and hub, was 5.03-m (16.5-ft). The S809 airfoil was used from blade root to tip for simplicity and because of the availability of 2-D wind-tunnel data from several wind tunnels.

Fig. 1. Blade and wake model for LSWT 8.

With lifting surface, the 3-D relationship between effective angle of attack and local blade loads is reflected through an inner-loop iteration that modifies the 2-D linear curve slope to ensure compatibility Fig. 2. Chord and twist distribution for the CER between the resulting effective 3-D linear lift curve blade10. slope and the potential flow circulation. Rotor Performance Data Numerically modeling the blade tip and root regions of This unique data set is considered to be the only the blade with the cosine radial segment distribution comprehensive, steady state, wind-tunnel data set in option in LSWT better follows the large gradients in existence for a 10-m (33-ft) diameter rotor. loading that are present as a result of the shed tip and Comparisons in this paper were limited to rotor power, root vorticity. Probably the biggest unknown of the inflow distributions, and normal and tangential force LSWT method is how closely the prescribed wake coefficients (C , C ). These force coefficients are 1,9 n t geometry represents reality. Recent wake studies perpendicular and parallel to the airfoil chord line. should provide better calibration of the wake equations, Measured rotor power used for these comparisons was which in turn influences the predicted performance. based on low-speed-shaft torque measurements. The inflow measurements at five spanwise stations (r/R = This study focused on axis-symmetric, steady state 0.30, 0.47, 0.63, 0.80, 0.95) were acquired using five- performance prediction. The LSWT method also hole pressure probes. Although no correction was includes inputs for a wind shear profile, tower shadow, applied for converting the inflow angle to angle of and off-axis rotor shaft alignment. These influences add attack in this study, a 3-D correction11 is recommended additional asymmetric displacement to the wake model.

2 1.4 0.35 in lieu of a 2-D correction. Values of Cn and Ct at the 1.2 five spanwise stations were derived from 22 pressure t

n 1 0.25

taps per station. Integration of the average pressure cie

fi 0.8

between adjacent taps projected onto the chord line ef 0.6 0.15 nt provided values of Cn. Integration of the same average Co ft i

0.4 cei pressure projected onto an axis orthogonal to the chord L

12 0.2 0.05 effi line provided values of Ct. Rae and Pope describe this o procedure in Low-Speed Wind Tunnel Testing. 0 -20 -15 -10 -5 0 5 10 15 20 25 30

-0.2 -0.05 Drag C OSU Lift, 1,000,000 Prediction and Measurement Comparisons -0.4 Delft Lift, Reynolds number 1,000,000 CSU Lift, Reynolds number 650,000 -0.6 -0.15 OSU Drag S809 Airfoil Data Sets -0.8 Delft Drag A comparison of three, 2-D, S809 airfoil data sets of CSU Drag -1 -0.25 section lift and profile drag coefficients (Cl, Cd) are Angle of Attack shown in Fig. 3. Two of these data sets, the Delft13 and the Ohio State University14 (OSU) data, are for a Fig. 3. Comparison of S809 wind tunnel data sets. Reynolds number of 1,000,000, while the Colorado 15 State University (CSU) data set is for a Reynolds 14 number of 650,000. The tip-region Reynolds number for the NASA Ames test was close to 1,000,000. 12 Noticeable differences are seen between these 2-D airfoil data sets that will have a significant influence on 10 W

the predicted performance with WTPERF as seen in k , Fig. 4. For these predictions 2-D airfoil data was used r 8 we only up to an angle of attack of 16 degrees without any

r Po 6 o stall delay model. t tip pitch = 3 degrees toward feather Ro 4

The zero angle of attack, lift-coefficient of the OSU CER/NASA data Delft airfoil data, flat plate data is noticeably lower than the other two data sets and 2 16 OSU airfoil data, flat plate the Eppler code prediction. This leads to a lower CSU airfoil data, flat plate predicted power at 5 m/s (16 ft/s) compared to that 0 predicted with the Delft and CSU data sets. The CSU 0.0 5.0 10.0 15.0 20.0 25.0 30.0 data have a maximum lower than the Wind Speed, m/s other two data sets, largely as a result of its lower Reynolds number. The predicted peak power is also the Fig. 4. Predicted performance using WTPERF and lowest largely as a result of the low maximum lift different wind-tunnel data sets. coefficient. The minimum drag of the CSU data is unreasonably low relative to the other two data sets, power it does illustrate the significant differences due and relative to Eppler code predictions. The low to the three airfoil data tables. minimum drag results in a higher predicted power at 5 to 7 m/s (16 to 23 ft/s). Of these three data sets, the OSU data set was chosen for the comparison between the BEM and LSWT The deficiency in predicted power from 7 to 10 m/s performance prediction codes, and their comparison (23 to 33 ft/s) is largely due to the omission of a stall with NASA Ames data. This choice does not imply the delay model for modifying the 2-D wind tunnel data. OSU data set to be more accurate than the Delft data. Differences in the three airfoil data sets clearly For this study, the absolute values of the predictions are manifest themselves in different predicted power less important than the relative differences that were curves, particularly around peak power. The over used to draw most conclusions. prediction in peak power for all three airfoil data set is largely due to using 2-D data only up to 16 degrees Most experts agree a stall delay model is needed for the without the following rapid drop in Cl resulting from highly 3-D inboard region, which normally precludes flow separation. After 16 degrees flat plate theory is the use of the rapid drop in Cl that is associated with 2- used for determining values of Cl and Cd. Although D data. However, over the outboard part of the blade, this procedure results in an over prediction of peak 2-D data including the rapid drop in Cl after 16 degrees

3 Rotor Power, kW predicted pow rat w cred lif abru OSU airf 5. m F B B v BEM codes T s e reg L m w i sho C B res ov tran in L attrib ev pow m ou A 10 12 14 2 n 0 2 4 6 8 p x e i S S h 0.0 o l l E e i g - a odel eas peak g t coef eeds rsio c iden ade- ade- n t erpredi i T D stall. g e s ll W W u m y ain i e M an . o w e d sitio on 5.Errori ood ltin g h er, larg r part ib pt ssi s p s af u n i T T u e ) lig , ari n . ility r , dropi u t ce of Elem as elem th prediction ter 1 p ed pow ve ed i s pow g A pow v f n n eeds o is d L p h B ag s i e t Fi to o a cien to c on t t cu ares of f b f E dif 5 h il d t reem th .0 e o i en ely clos er th o 0 M i S , en ngl f o rv th s WT g g. 8m , r u m n W t e both er relativ n e P l t at20an h t datacom n e f t DataC f at p a is of w 6 tter co C becau of er bet e o dis h w e e predi er cu ta w T th er s / en can u a e bl ren o s (3 BEM(WT in r . l PER P s o A lt of i / an eory m c s resu n s l t peak llow g ate th f w B repan w predi 10.0 (26f to m o d ce in a issio 2 d ith t bes 3 ade ors w w s l c tl tip s rv ith f i E F - rrelatio th Wind t lts e p e een t t D d o o an t s e e tom h an M an t/s). T of r C eeds a es can d 16deg m th w d perf th c m n p eo c c eas peak pow t e een in t o aris areav / y w k 8t p lo d e d PR th Spee a s u ry betw e m d e m eas aris beex d w u w ) ta o f rv u an 15. , astalld i ss m n in predi at1 d L e om bot o r nd n on P o h i 0 i es ed peak ith orm u ab st m w f pow eas 10m ER on is ten eas d, m/s er. Wh i u O eas o th r t sp s C r e h t ed pow h rm prov o S i r P o ti s 6 n b L u to p ees F u ith e pow u is W a o d een u 93, i BEM m pi c erag o d u r e LSW LSW LSW CE pected tores t n r an m e r t ed data. s er betw tc rm S t e ed pow e d 2 odi ed datareas in T ce res ls. e / i e i R/ h =3 20. th , res d WT s p o d peak on s to 0 g lay e tobein T T T u N (26t f test s ( 0 aris ns a ally pow , O , av , O e en ed tog s d PR r A l d f du ees resu degr er. A ow S er cu i predicted n S S an of e er A m ab cat bot u U d h U p p d g a on d rap u i ectiv g ees erpredict th e t d r gh t t s ru o a a e r een er cu l a d hi ees i o as pow ta, ta, d i o t O er. In d o s po towar n a i t rv L p A o v 20d fl s n 25. m e n ta. s 33 h e g i P t at p t S w di id g d l. u th v gh e all- th t 0 i ely e 93) an o lts in WT p f f t m r d a g d egr oderat e so c l rv h h er w a v of t of f on er th f -sp f lu r r r u a u t eat e f e 2- p e ereof t So er ee o . r adu om delay th e ll o e / v lt in w able tion es s ably p her s Fig T ren tw an e ) i are are an th m m i nd e h h t in a D is i f 30.0 o d d d h e e e e e e e e s f l t . . . 4 F (C in of f betw perf as an attack f How resu cos an w f an A th Am f d betw o t/s o o Rotor Power, kW 10 12 14 i i i com h is strib 0 2 4 6 8 g llo r s rce coef C g d L d n t 0 ), o ), W . i . w l s d e e 0 n orm lts in e o ciated n m w s e een een 6. Predi lp sp e i an d prov v relativ n eas u i S t p in e n d u dis a tio T W eed d C a ari r, as prediction th f g s d t g n attack P a u ap y to p T n i ce predi t f . eq s E r d , ribu e predictedan i eeds s littled t s w 5 ed on com , oth cien . R ed .0 th relativ u u c ith C C e to F of t a r n in e e w tion t n ed predicts tio d f i d perf = C t (C = C B n f isth predi erstan e r l ictio L s n p r th om ow i E i n s aris g i l e to 10 of l c (s n f M d S (cos h s ), an w f t 5t dis .0 s an W i in e t n an orm o f c e d on ren Wi p blade s clo e u th an α t n re th th T eed in o atth e α o t codes n ) s d an ribu eory d m . T 19m d tan r t ) +C a ce isseen d of d e − u in e airf n Spe s n s C reaso e to ce com 1 h i ed h 5 creas f tion eas g 2, e di m .0 e reaso creas d an o e e e tip,w g l (cos d 16 to / rm g e ofat d, eas (s s e o th ti . L u m (16t , p n m/ s n an il in pi r calcu in s ares in tial f s en g ed u α c tc es S e tip α b s p L W PR C l ch r f h =3 g repan S e E ) WT ari ts T o ed ) 20 W abov l O R/ n e f PERF t pow o y d r o P l T u .0 ack tw NA degr o o h 9 , 62f h s r th O of h f 3 late th (r/R=0 d on w S rce coef is ere th , O o s S een ow , i A O e res c attack di ed in g U da r ees er S e lin di als i S an h d U e s U assu t th towar n e 10 i / u s c data ta, f s cu data screp W s (Eq. 1) e, in r an (Eq. 2) bet t repan lts 2 is o d N ). A ri 5 e v l e radial a , L .0 rv s f m , T d t bu fl m Fig n . pl l f at f h at een eat P g o es / ate i pl S w p a i t s A cien t pl a . l E rm g l her a i 9 lu W e T c tio a . n een o t (33 ( h S RF e o t . i 9 to e α h c n 7 In e es of e al A w T ) n y e s s ) r t . 30 .0 50 2.2

45 2.0 tip pitch = 3 degrees toward feather tip pitch = 3 degrees toward feather NASA 19 m/s 1.8

40 N NASA 16 m/s C

NASA 13 m/s , t 1.6 35 NASA 10 m/s

NASA 7 m/s en NASA, 7 m/s i

NASA 5 m/s c 1.4 LSWT, OSU data, flat plate, 7 m/s i degrees 30 WTPERF 19 m/s f f WTPERF, OSU data, flat plate, 7 m/s

WTPERF 16 m/s e 1.2 WTPERF 13 m/s o ack, 25

WTPERF 10 m/s C WTPERF 7 m/s 1.0 20 WTPERF 5 m/s ce LSWT 19 m/s

e of Att 0.8

LSWT 16 m/s F o r

15 LSWT 13 m/s al ngl

LSWT 10 m/s m 0.6 A LSWT 7 m/s 10 LSWT 5 m/s o r N 0.4

5 0.2

0 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Radial Station, r/R Radial Station, r/R Fig. 7. Comparison of predicted angle of attack and measured inflow distribution. 2.2 have a much lower angle of attack distribution in the tip 2.0 tip pitch = 3 degrees toward feather region with increasing wind speed, due largely to the 1.8 CN ,

strong tip vortex induced effect. Neglecting this t 1.6 induced effect in BEM leads to additional error in the

cien 1.4 i f

prediction of peak power. f e 1.2 Co

e 1.0 Only a qualitative comparison of the measured inflow c 0.8

distribution can be made with the predicted angle of F o r

attack distributions because no correction has been 0.6

applied to the measured inflow angles in front of the o rmal NASA, 10 m/s N 0.4 LSWT, OSU, flat plate, 10 m/s blade. An interesting observation in the inflow WTPERF, OSU, flat plate, 10 m/s distribution is the high angle of attack or blade induced 0.2 upwash at 50% radius for low wind speeds. This high 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 induced upwash extends toward the hub at higher wind Radial Station, r/R speeds. The cause of the upwash may be due to a vortex that lies just above the blade surface in this region. A delta wing at high angles of attack exhibits 2.2 similar behavior. 2.0 tip pitch = 3 degrees toward feather 1.8 The comparison of predicted and measured Cn is shown N C , in Fig. 8 for wind speeds of 7, 10, and 13 m/s (23, 33, t 1.6 en and 43 ft/s). At 7 m/s agreement between predictions i c 1.4 i f f

and measurements is reasonably good. A noticeable e 1.2 o

discrepancy at all three wind speeds is that the C 1.0 measured Cn outboard of 80% radius is lower than ce 0.8 predictions. An expected observation is the much F o r al

greater measured Cn inboard at 10 and 13 m/s (33 and m 0.6

o r NASA, 13 m/s

43 ft/s), which correspond to angles of attack above N 0.4 LSWT, OSU, flat plate, 13 m/s WTPERF, OSU, flat plate, 13 m/s stall. No stall-delay model was included in the 0.2 predictions that would reduce this discrepancy. 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 The comparison of predicted and measured Ct is shown Radial Station, r/R in Fig. 9 for wind speeds of 7, 10, and 13 m/s (23, 33, and 43 ft/s). Below stall, at 7 m/s (23 ft/s), WTPREP and LSWT are in agreement with the measured C t Fig. 8. Comparison of predicted and measured normal distribution over most of the span. In the root region force coefficients, 7, 10, 13 m/s (23, 33, and 43 ft/s). some difference is seen between prediction and

5 Tangential Force Coefficient, CT -0 0.00 0.05 0.10 0.15 0.20 0.25 Tangential Force Coefficient, CT Tangential Force Coefficient, CT -0 . 0 0.00 0.05 0.10 0.15 0.20 0.25 -0 0. 0. 0. 0. 0. 0. 5 . 0 . 0.0 00 05 10 15 20 25 0 5 0.00 5 0 t 43 f Fig . a 0 n g . 0 9. t LSW W N 0.10 . e 1 / 0 A s T n . SA, 13m 1 PER ). t LS W N T i , O a ASA, 10 L W NA T W 0.2 S l F PE 0.20 T f S W , S T 0 O P U C . A , O R o T E 2 / , S , , s f F R 7 rce coef o U OS S l , O F at pl m , m U m , O f 0 / U , 0.30 S l / .3 s fl at pl s 0 p , U S ate, 13m/s fl .3 a U , aris t fl a pl , ate, 13m/s t R fl a p ate, 10m/s Ra t a Ra a pl l 0.4 a t 0.40 p f d on t 0 e ate, i d l dia . i , a ci 4 a 7 t i e al St l m S , en 10 m/ l of 7 S / 0 s t m 0.50 .5 at t 0 t s .5 ati a / s s io , 7,10,13m t predicted i on, o t n i p n , 0. 0.60 pi , 0 r/ ti ti 6 r . t p pi p pi r 6 c R / / h = R R t tc c 3 h = h = 0 d 0.70 0 .7 .7 e 3degr 3degr g r e an e / s s 0.8 0.80 ees ees 0 t d o (23,33,an . 8 w a tow tow r d m f a 0 a e 0.90 0 eas r .9 r at d d .9 f he eather r u r 1.0 1.00 1 ed . 0 d 6 in accu blade elem w replaces dis poor n ph bot assum p sh an NA s in di abov w th be in drop i th m equ T T N A ou in m as T F i predicted drag di p stall- u drop i A nhe u s r h u h h h o a n u e eas C ad n at e C du s s s ed ction e of o d is t y t en rt e e i k t t t rs o th m i m e t h f u s w SA a ribu ri ri s s e u h t r r h eq t ced ef ictio rately in ically t ld tion en o o e th BEM e od h d dis u e bu bu g E rth e an m BEM t ri 60% m n C p e nt e n r u r clu n u A R e a s em o s r B e h lay tio a cal tion t t m ce of a c en d w e sho th lf i i om rk datais e bladeth e L o s y s s u n te o o repan C p r ex i d aren eri h t o t t in m rf um n f en accu to n n o m u r f m w res E t e ou accou m o ects perf e tiplos en s s etry n d radi ace of f S o es ith M sh cal tip . . v an t th r a larg of o n y o M.Han n f odel w WT . a ld plored. Meas u Ack ag t t res id w r of d c s a c in lt f o d L eachannulusinB A lo e . res e b orm o e ag y th g e y v t m g th eces in l. T u m t beth n rate approach i reem a perf u a o 10an i r e t nd th r s n t e radialblades betw n k at con atef l u clu tiv ss m pport i om r e u ain creas o i S ad a o ngs t a m f o g tco e lts h l h sp o lu e blade. an s WT n w s data.A d of u s e L e v o e f du e isp r m Co d ce predi a d s as orm p en e t in u es ledg re realistican m i t n g e v ry een both an led w lly o t e res o in e o l a as t w e t i d 13m in h nclusio n odel w e o e l f a h S d u ribu ex rm d lu e t e a g of a ack s in an d L e bl e WT predictedan g s e d w m t o u n h r w es im bes bladen rif l. T a a , ith s, ed u r r accu ce predi , h n m s n din t ces f rg th e u ed res tes l o attack d atio . i r h nt ade root n c ccu lt of icted o perf th cation ciated w e el i i er e l odel t o J e p t t easily n g g prov s o s i ith h t n s h / . w o y i p don d h v s e L e e m v u s E a n rate pres t n Fin a (33an lies m C s v l r a e rg bs o rate an an edg o M r to m e u ed e u o orm tiploadin D c rtex g rotor uni lts r n of f m e t tan g p d S t c , an eas th m ictio t b e u O e i lif an ided by h e eed. T o e ber an attach W o e tow t i en rs co fo e d. E a m h g e sults inano p th tial drag n is attach u i d m tin ith u n strib od h l T g m d alarg h ch d t s e e larg r om ts d 43f r rrected ce predi an n g con m i m f ed data.T cribed g r an e pres n s . Equ l in a h e adi t i e o . T g d h eas rd th p d plan t i o u o s d h ely s acos i a ed v t r reg rm h preparin g u i ract tio of s f f o e od. s g e ed toth L n h attack in u e i h rf u l ugh n du . on d is larg t ll- e , u r n t sh / e drop ace stalled a cribed u e root e ed C . attack s r i l . is th l- S w on o m v c c atio s ) t f f o th e to of a cale o v W T s rtex t t e l a o i i u i t th ow i er rm ral o o k h h h n h an ze to re ld i at T g n n n n e e e e e e e e e n - 10Giguere, P., and Selig, M.S., “Design of a Tapered References and Twisted Blade for the NREL Combined Experiment Rotor,” Subcontract No. XAF-4-14076-03, 1Hand, M., et.al., “Unsteady Experiment June 1998. Phase VI: Wind Tunnel Test Configurations and Available Data Campaigns,” NREL Report to be 11Whale, J., et al. “Correcting Inflow Measurements published 2001. from HAWT’s Using a Lifting Surface Code,” ASME Wind Energy Symposium, Reno, NV, January 1999. 2Simms, D., et al., “Unsteady Aerodynamics Experiment in the NASA-Ames Wind Tunnel: A 12Rae, W.H., and Pope, A., “Low-Speed Wind Tunnel Comparison of Predictions to Measurements,” Testing,” Wiley & Sons, 1984. NREL/TP-500-29494, June 2001. 13Somers, D.M., “Design and Experimental Results for 3Buhl, M.L., “WT_PERF User’s Guide,” NREL, 2000. the S809 Airfoil,” NREL/SR-440-6918, 1997.

4Kocurek, D., “Lifting Surface Performance Analysis 14Reuss, R.R., et al. “Effects of Grit Roughness and for Horizontal Axis Wind Turbines,” SERI/STR-217- Pitch Oscillations on the S809 Airfoil,” NREL/TP-442- 3163, 1987. 7817, 1995.

5Wilson, R.E., and Walker, S. N., “Performance 15Butterfield, C.P., Musial, W.P., and Simms, D.A., Analysis Program for Type Wind Turbines,” “Combined Experiment Phase I Final Report,” Oregon State University, 1976. NREL/TP-257-4655, 1992.

6McCarty, J., “PROP93 User’s Guide,” Alternative 16Eppler, R., “Airfoil Program System, PROFIL98,” Energy Institute, 1993. User’s Guide, 1998.

7Selig, M.S., and Tangler, J.L., “Development and 17van Bussel, G.J.W., “The Aerodynamics of Horizontal Application of a Multipoint Inverse Design Method for Axis Wind Turbine Rotors Explored with Asymptotic Horizontal Axis Wind Turbines,” Wind Engineering, Expansion Methods,” PhD Thesis, Technical Vol.19, No. 2, 1995, pp 91-105. University Delft, ISBN 90-9008848-2, 1995.

8Kocurek, D.J., “Hover Performance Methodology at Bell Helicopter Textron,” 36th Annual Forum of the American Helicopter Society, Washington D.C., May 1980.

9Fisichella, C.J., “An Improved Prescribed Wake Analysis for Wind Turbine Rotors,” PhD Thesis, M.E. Dept., Univ. of Ill., 2001.

7 Form Approved REPORT DOCUMENTATION PAGE OMB NO. 0704-0188 Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302, and to the Office of Management and Budget, Paperwork Reduction Project (0704-0188), Washington, DC 20503. 1. AGENCY USE ONLY (Leave blank) 2. REPORT DATE 3. REPORT TYPE AND DATES COVERED January 2002 Conference paper

4. TITLE AND SUBTITLE 5. FUNDING NUMBERS The Nebulous Art of Using Wind-Tunnel Airfoil Data for Predicting Rotor Performance: Preprint WER21120

6. AUTHOR(S) James L. Tangler

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) 8. PERFORMING ORGANIZATION National Renewable Energy Laboratory REPORT NUMBER 1617 Cole Blvd. NREL/CP-500-31243 Golden, CO 80401-3393

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12a. DISTRIBUTION/AVAILABILITY STATEMENT 12b. DISTRIBUTION CODE National Technical Information Service U.S. Department of Commerce 5285 Port Royal Road Springfield, VA 22161 13. ABSTRACT (Maximum 200 words) The objective of this study was threefold: to evaluate different two-dimensional S809 airfoil data sets in the prediction of rotor performance; to compare blade-element momentum rotor predicted results to lifting-surface, prescribed-wake results; and to compare the NASA Ames combined experiment rotor measured data with the two different performance prediction methods. The S809 airfoil data sets evaluated included those from Delft University of Technology, Ohio State University, and Colorado State University. The performance prediction comparison with NASA Ames data documents shortcomings of these performance prediction methods and recommends the use of the lifting-surface, prescribed-wake method over blade-element momentum theory for future analytical improvements.

15. NUMBER OF PAGES 14. SUBJECT TERMS wind energy; wind-tunnel airfoil data; rotor performance; wind research 16. PRICE CODE

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