Andy P. Broeren

Glenn Research Center, Cleveland, Ohio

Sam Lee

ASRC Aerospace Corporation, Cleveland, Ohio

Gautam H. Shah and Patrick C. Murphy

Langley Research Center, Hampton, Virginia

Aerodynamic Effects of Simulated Ice 

Accretion on a Generic Transport Model

NASA/TM—2012-217246

February 2012

SAE–2011–38–0065
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 7115 Standard Drive

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Andy P. Broeren

Glenn Research Center, Cleveland, Ohio

Sam Lee

ASRC Aerospace Corporation, Cleveland, Ohio

Gautam H. Shah and Patrick C. Murphy

Langley Research Center, Hampton, Virginia

Aerodynamic Effects of Simulated Ice 

Accretion on a Generic Transport Model

NASA/TM—2012-217246

February 2012

SAE–2011–38–0065

National Aeronautics and

Space Administration

Glenn Research Center

Cleveland, Ohio 44135

Prepared for the

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Level of Review: This material has been technically reviewed by technical management. 

Acknowledgments

This research was supported by the NASA Aviation Safety Program. The authors thank Gene Addy and Colin Bidwell at the 

NASA Glenn Research Center for their important contributions in research planning and ice-accretion analysis. In addition, Sue 

Grafton, Earl Harris and Gary Wainwright at the NASA Langley Research Center are gratefully acknowledged for their efforts 

in the wind-tunnel model design, fabrication and aerodynamic testing.
NASA/TM—2012-217246 1

Aerodynamic Effects of Simulated Ice Accretion on a

Generic Transport Model

Andy P. Broeren

National Aeronautics and Space Administration

Glenn Research Center

Cleveland, Ohio 44135

Sam Lee

ASRC Aerospace Corporation

Cleveland, Ohio, 44135

Gautam H. Shah and Patrick C. Murphy

National Aeronautics and Space Administration

Langley Research Center

Hampton, Virginia 23681–2199

Abstract

An experimental research effort was begun to develop a 

database of airplane aerodynamic characteristics with 

simulated ice accretion over a large range of incidence and 

sideslip angles. Wind-tunnel testing was performed at the 

NASA Langley 12-ft Low-Speed Wind Tunnel using a 3.5

percent scale model of the NASA Langley Generic Transport 

Model. Aerodynamic data were acquired from a sixcomponent force and moment balance in static-model sweeps 

from α = –5° to 85° and β = –45° to 45° at a Reynolds number 

of 0.24×106 and Mach number of 0.06. The 3.5 percent scale 

GTM was tested in both the clean configuration and with fullspan artificial ice shapes attached to the leading edges of the 

wing, horizontal and vertical tail. Aerodynamic results for the 

clean airplane configuration compared favorably with similar 

experiments carried out on a 5.5 percent scale GTM. The 

addition of the large, glaze-horn type ice shapes did result in 

an increase in airplane drag coefficient but had little effect on 

the lift and pitching moment. The lateral-directional characteristics showed mixed results with a small effect of the ice 

shapes observed in some cases. The flow visualization images 

revealed the presence and evolution of a spanwise-running 

vortex on the wing that was the dominant feature of the 

flowfield for both clean and iced configurations. The lack of 

ice-induced performance and flowfield effects observed in this 

effort was likely due to Reynolds number effects for the clean 

configuration. Estimates of full-scale baseline performance 

were included in this analysis to illustrate the potential icing 

effects.

Nomenclature

b Wing span

c Local chord, streamwise direction

cmac Mean aerodynamic chord 

CD Airplane drag coefficient, stability axis

Cl Airplane rolling moment coefficient, body axis, 

0.25cmac

CL Airplane lift coefficient, stability axis

Cm Airplane pitching moment coefficient, stability axis, 

0.25cmac

Cn Airplane yawing moment coefficient, body axis, 

0.25cmac

M Mach Number

Re Reynolds number based upon mean aerodynamic 

chord

S Wing reference area

x Airfoil section coordinate in chordwise direction, x = 0 

at section leading edge

y Spanwise location on airplane, y = 0 corresponds to 

plane of symmetry

z Airfoil section coordinate in normal direction, z = 0 at 

section leading edge

α Angle of attack or incidence angle

β Angle of sideslip or yaw angle

CFD Computational fluid dynamics

GTM Generic Transport Model

IPS Ice-protection system

IRAC Integrated Resilient Aircraft Control 

LWC Liquid Water Content

MVD Median Volumetric Diameter

Introduction

The Integrated Resilient Aircraft Control (IRAC) Project of 

the NASA Aviation Safety Program has sought to develop 

aircraft control design tools and techniques for enabling safe 

flight in the presence of adverse conditions. These adverse 

conditions, such as those caused by environmental factors, 

sensor faults or airframe damage, include upsets leading to 

aircraft loss-of-control situations (Ref. 1). Airframe ice 

accretion has been identified in a number of studies as a causal 

factor leading to loss of control. Reehorst et al. (Ref. 2)
NASA/TM—2012-217246 2

reviewed icing-related accidents as identified in a recent report 

on aircraft loss of control. Ratvasky and Lee (Ref. 3) also 

identified the potential for aircraft loss of control resulting 

from ice accretion. This provided motivation for the consideration of icing effects for development of advanced modeling 

and simulation toward flight safety improvement under the 

auspices of the IRAC project. The advanced flight simulation 

models are being developed for high angle of attack and 

sideslip conditions. There is a lack of steady and unsteady 

aerodynamic data for airplanes operating at extreme conditions consistent with the loss-of-control accident database.

These data are useful for the development of high-fidelity 

flight simulators at high α and β for upset recovery training, 

accident reconstruction and validation of advanced flight 

control (Ref. 4).

A closely related effort has been carried out for a commercial transport category airplane configuration subjected to 

simulated airframe damage conditions. Shah et al. (Refs. 5 and 

6) compiled an extensive database of static and dynamic 

aerodynamic characteristics for the NASA Langley Generic 

Transport Model (GTM) with and without simulated damage.

The GTM was designed to represent a large, commercial, 

twin-engine, transport-category airplane and was developed as 

a platform for flight dynamics modeling and simulation. Static 

and dynamic wind-tunnel tests of a 5.5 percent scale model 

were performed over a large range of incidence and sideslip at 

the NASA Langley 14- by 22-ft wind tunnel at a Reynolds 

number based on mean aerodynamic chord of 0.54×106

.

Airframe damage was simulated by systematically removing 

portions of the wing and tailplane in addition to other 

scenarios. Shah (Ref. 6) documented the changes in the 

airplane stability and control characteristics and noted the 

potential modeling challenges associated with the asymmetries 

and nonlinearities for the simulated damage configurations.

The static and dynamic, pre- and post-stall aerodynamics of 

the GTM configuration have been investigated using a variety 

of experimental capabilities. This has included the NASA 

Langley 14- by 22-ft wind tunnel described above with 

conventional static testing capabilities and a forced-oscillation 

system for dynamic testing. Dynamic experiments on a 

3.5 percent scale GTM have also been conducted at the NASA 

Langley Vertical Spin Tunnel using its rotary balance rig. In 

addition to these ground-based facilities, NASA Langley has 

also developed a 5.5 percent scale remotely piloted flying 

testbed that is dynamically scaled and designed to be flown 

into and recover from extreme upset flight conditions (Ref. 4)

Data from these sources can be subsequently combined to 

model the airplane behavior for flight simulation and control 

research (Ref. 7). However, as noted by Foster et al. (Ref. 4)

and Owens et al., (Ref. 8). significant challenges remain in 

scaling the subscale, low-Reynolds number data into the flight 

scale and Reynolds number regime. While flight testing of the 

actual airplane does provide full-scale, flight-Reynolds 

number data, it remains cost prohibitive, and is unsafe in high 

α and β conditions. Another increasingly important tool in the 

modeling effort is computational fluid dynamics (CFD). Frink 

et al. (Ref. 9) have demonstrated the potential capabilities of 

CFD in providing useful flight dynamics information using the 

subscale, low-Reynolds number experimental database for 

validation. The authors have noted that the CFD methods may 

be applied with more certainty at higher-Reynolds number 

flight conditions, thus providing another path for estimating 

full-scale vehicle response.

Analogous research programs have also been carried out in 

order to investigate the effects of ice accretion on flight 

dynamics and develop flight training simulation devices.

Ratvasky et al. (Ref. 10) describe the development of a piloted 

flight simulator for icing effects training or Ice Contamination 

Effects Flight Training Device (ICEFTD). The ICEFTD was 

based upon the modified DeHavilland DHC-6 Twin Otter 

airplane used for icing flight research at NASA Glenn. This 

development program was a comprehensive effort that 

included icing aerodynamics, geometry and Reynolds number 

scaling studies, static and dynamic wind-tunnel testing and 

flight validation for both the baseline airplane configuration 

and with a series of artificial ice shapes (Refs. 11 and 12). The 

ICEFTD was subsequently used in a variety of settings for 

pilot evaluation and training (Ref. 13). More recent research 

conducted with the ICEFTD has been used to develop a 

knowledge-based, envelope-protection system that provides 

cues to the pilot for maintaining safe flight in simulated icing 

conditions (Refs. 14 and 16).

The twin-engine turboprop ICEFTD development served as 

a model for a follow-on program with a twin-engine business 

jet airplane. As before, a series of geometry and Reynolds 

number scaling studies were performed with various models 

of the semi-span wing panel including the aileron. This 

provided scaling information for static and dynamic data on a 

subscale, complete-airplane model. The scaled data were 

subsequently used to build the icing effects training simulator

(Refs. 17 and 18). Finally, a modest flight validation program 

was performed using artificial ice shapes attached to the 

airplane (Ref. 19). All of this research was carried out for the 

baseline (clean) configuration and with three iced configurations based upon ice-protection system (IPS) pre-activation 

roughness, failed IPS and runback ice shapes. The combined 

results showed that high-fidelity flight simulation for icing 

effects can be performed, provided the necessary scaling and 

validation has been accomplished.

The lessons learned from these research programs and 

others are summarized in a recent review of icing effects 

simulation and modeling. Ratvasky et al. (Ref. 20) build on 

the foundations of two-dimensional experiments that clearly 

show traditional icing effects such as maximum lift degradation and increased drag, to changes in control surface effectiveness, hinge moment reversals and reduced pitch damping 

derived from dynamic three-dimensional model experiments.

Ratvasky et al. (Ref. 20) reviewed the current state of icing 

effects modeling and discuss the need for this work to 

continue on to other platforms. For example, the past efforts 

involving the twin-engine turboprop and business jet cannot 

be applied across the entire spectrum of airplanes. More 
NASA/TM—2012-217246 3

research is required for other classes including regional jets, 

large transports and future designs such as the Blended-WingBody.

The overall goal of the current work is to extend the aforementioned modeling and simulation efforts to an iced-GTM 

configuration. The specific objective of the research described 

in this paper was to obtain the subscale, low-Reynolds number 

wind-tunnel data that can be used for preliminary model 

comparisons and CFD validation. As noted above, developing 

a high-fidelity flight simulation model requires the synthesis 

of many research efforts. The work presented here represents 

the initial stages of the overall effort. In order to accomplish 

the specific objectives, a 3.5 percent scale GTM was tested at 

the NASA Langley 12-ft Low-Speed Wind Tunnel in both the 

clean and iced configuration. Artificial ice shapes were 

designed and built based upon computational analysis.

Aerodynamic data were acquired over a large range of angle 

of attack and sideslip at a Reynolds number of 0.24×106

.

Surface-oil flow visualization was also performed for selected 

static cases to provide qualitative flowfield information. These 

results are presented and discussed in the context of past work 

where appropriate.

Experimental Methods

The experiments were carried out at the NASA Langley 

12-ft Low-Speed Wind Tunnel. This atmospheric facility is 

comprised of a regular octagonal test section measuring 12-ft 

between the parallel surfaces and approximately 20-ft long.

Originally designed for free-flight simulation testing (see 

http://gis.larc.nasa.gov/historic/12-Foot_Low_Speed_Tunnel), 

the tunnel has truncated inlet and diffuser sections as shown in 

Figure 1. The wind tunnel is driven by a fan capable of 

generating test-section dynamic pressure up to 7 psf. Most of 

the testing was conducted at a dynamic pressure of 5 psf 

corresponding to a Reynolds number based upon mean 

aerodynamic chord of approximately 0.24×106 and a Mach 

number of approximately 0.06. A small number of trials were 

performed at lower Reynolds and Mach number. The data 

acquisition and tunnel control software were programmed to 

maintain the desired value of dynamic pressure as the model 

was positioned through the various α and β sweeps. The static 

aerodynamic data were acquired from a six-component 

balance located inside the model. As shown in Figure 2, the 

model was sting mounted to a C-sector positioning system for 

the static tests. The 3.5 percent scale GTM was designed 

based upon the 5.5 percent scale model used in numerous tests 

at the Langley 14- by 22-ft wind tunnel (Refs. 5 and 6). The 

model was constructed in sections using fused deposition 

modeling, a rapid-prototyping method. The pieces were then 

fitted together to form the completed model. The model was 

sanded smooth and painted flat black. Important geometry 

information for the 3.5 percent scale GTM is listed in Table I.

Figure 1.—Cutaway schematic of NASA Langley 12-ft LowSpeed Wind Tunnel.

Figure 2.—3.5 percent Scale GTM installed in the NASA 

Langley 12-ft Low-Speed Wind Tunnel C-sector mounting 

and positioning system.

TABLE I.—1 GEOMETRY INFORMATION

FOR THE 3.5 PERCENT SCALE GTM

Dimension Value

Wing span, b...................................................................4.36 ft

Wing reference area, S.................................................. 2.39 ft2

Mean aerodynamic chord, cmac ......................................................... 0.58 ft

Wing root chord..............................................................0.84 ft

Wing tip chord................................................................0.20 ft

Wing aspect ratio ................................................................. 8.0

Wing taper ratio ................................................................. 0.24

Wing quarter-chord sweep...................................................25°

Fuselage length ...............................................................5.43 ft

Horizontal tail span.........................................................1.73 ft

Horizontal tail reference area........................................ 0.66 ft2

Vertical tail span.............................................................0.86 ft

Vertical tail reference area ............................................ 0.45 ft2
NASA/TM—2012-217246 4

The static aerodynamic data sweeps were performed over a 

90° angle of attack range (α = –5° to 85°) and a 90° angle of 

sideslip range (β = –45° to 45°). Data were acquired from the 

force balance at discrete, pre-set values of angle of attack and 

sideslip. These data were filtered at 10 Hz and 500 samples 

per channel were averaged over a 10-sec interval. The force 

and moment coefficients were calculated from the balance 

loads and tunnel calibration data for dynamic pressure. The 

balance moment center was located at 0.25cmac so no moment 

transfers were performed. The tunnel calibration results in a 

ratio of dynamic pressure measured at the upstream pitot to 

one measured at the model location. This was the only 

correction applied to the dynamic pressure. Further tunnel 

qualification tests with calibration models having similar 

frontal area to the 3.5 percent scale GTM at low to moderate 

angle of attack yielded good agreement in known performance 

data without additional blockage corrections. 

The experimental uncertainty in the performance coefficients was estimated using the methods of Kline and McClintock (Ref. 21) and Coleman and Steele (Ref. 22) for 20:1 

odds. The values listed in Table II were calculated for the 

clean-model configuration at Re = 0.24×106

, M = 0.06 and 

α = β = 8°. These uncertainties take in account uncertainty in 

the forces and moments determined from the balance calibration and the uncertainty in the dynamic-pressure measurement.

They do not include potential uncertainties in the model 

reference area, mean aerodynamic chord and wing span as 

these were assumed to be negligibly small. All of these 

uncertainties were acceptable for the purposes of this investigation. In addition to this analysis, several repeat runs were 

performed for both clean and iced configurations and these 

run-to-run variations in the coefficients were much smaller 

than these uncertainties listed in Table II. 

TABLE II.—ESTIMATED EXPERIMENTAL UNCERTAINTIES 

IN AERODYNAMIC DATA FOR 3.5 PERCENT SCALE 

CLEAN GTM AT α = 8° AND β = 8° IN NASA 

LANGLEY 12-ft LOW-SPEED WIND TUNNEL

Aerodynamic 

quantity

Reference 

value

Absolute 

uncertainty

Relative 

uncertainty

CL 0.7387 ±0.0068 ±0.92%

CD 0.0632 ±0.0030 ±4.83%

Cm –0.1431 ±0.0023 ±1.62%

Cl –0.0252 ±0.0003 ±1.03%

Cn 0.0315 ±0.0006 ±1.93%

Surface-oil flow visualization was performed on the left and 

right wings for selected static angles of attack and sideslip 

based upon the force and moment data. The wings were first 

covered with self-adhesive Ultracote Plus surface covering.

This covering material provided an exceptionally smooth 

surface for the oil to flow. A base coat of SAE 30 weight 

motor oil was applied to the surface covering. Any excess was 

wiped clean prior to the application of a mixture of baby oil 

and UV fluorescent dye. The oil-dye mixture was applied with 

a small sponge roller used for trim painting applications. UV 

blacklights were used to illuminate the test surface. The 

appropriate angle of attack and sideslip were set and the 

airflow was set to a dynamic pressure of 5 psf for approximately 10 min. The oil flow was monitored via video camera 

during the run and recorded on 8 mm tape. Thus, ambiguities 

were avoided by watching the development of the oil patterns 

during the runs. Run times of 10 min were sufficiently long 

for steady state conditions to develop in the oil patterns. At the 

conclusion of the run, the fan was shut down and the tunnel 

was entered to take photographs with a digital camera. Usually 

the oil-dye mixture could be redistributed for the next run 

using the sponge roller. After about four runs, it was necessary 

to wipe off the oil-dye mixture and apply a fresh coating.

Artificial Ice Shapes

It was determined at the beginning stages of this program 

that large, glaze-horn type ice accretion (with the iceprotection system not activated) should be used for aerodynamic testing on the 3.5 percent scale GTM. This was 

primarily due to the practical limitations of the 3.5 percent

model scale. Because of the small model size relative to full 

scale, smaller ice shapes would have been impractical to 

fabricate and install accurately. Also, previous research has 

shown that geometric scaling is appropriate for large, leadingedge ice accretion. For example, Lee et al. (Ref. 17) tested 

various artificial ice shapes on a full-scale and corresponding 

8.3 percent scale semi-span business jet wing over a Reynolds 

number range of 0.15×06 to 4.2×106

. They found little 

variation in the iced-model performance over this range with 

the large, leading-edge ice shape. Since time and resources did 

not allow for a suitable geometry and Reynolds number 

scaling study on a wing or tail surface component, a large ice 

shape was selected since it required neither.

The artificial ice shapes tested on the 3.5 percent GTM were 

developed through a series of computational analysis efforts.

A flowfield solution on the full-scale airplane was obtained 

using the USM3D code for an approximate hold condition at 

an aircraft angle of attack of 6° at M = 0.33 and Re = 17×106

.

Local flow characteristics taken from the flowfield solution 

were used to analyze selected wing section cuts using 

LEWICE (Ref. 23), the NASA two-dimensional ice growth 

software. This process was used to rapidly survey a large 

variety of potential icing-cloud conditions to produce a large 

glaze-horn ice shape. The selected conditions were: total 

temperature = 27 °F, a droplet MVD = 20 μm, a cloud LWC = 

0.5 g/m3 for a 45-min exposure. These conditions along with 

the flowfield solution were used as inputs to LEWICE3D

(Ref. 24) to generate ice accretion on the entire wing and 

horizontal and vertical tail.

LEWICE3D generated ice accretion in a series of twodimensional spanwise section cuts. These were lofted into a 

quasi-three-dimensional geometry along the span of the wing 

and tail surfaces. Cross-sections of the ice shapes at various 

spanwise locations on the wing are shown in Figure 3. These 

cross-sections were taken in the streamwise direction and 

normalized by the corresponding local chord. So the large 
NASA/TM—2012-217246 5

Figure 3.—Cross-section view of wing ice shape at three 

spanwise locations on the 3.5 percent scale GTM.

horn-shape near the wing tip (2y/b = 0.92) was over 6 percent

of the local chord in length. This corresponds to a length of 

0.18 in. on the 3.5 percent scale GTM and 5.0 in. at full scale.

The other spanwise locations shown in Figure 3 are near 

midspan (2y/b = 0.51) and approximately halfway between the 

fuselage and the engine pylon (2y/b = 0.22). These cross 

sections show that the glaze ice shape becomes smaller at 

locations farther inboard. This is explained by the smaller total

Figure 4.—Cross-section view of horizontal and vertical ice 

shapes on the 3.5 percent scale GTM.

collection efficiencies associated with the larger physical 

dimension of the leading-edge geometry moving inboard on 

the wing. The ice shapes also tend to angle downward from 

the midspan station inboard because of the increased sectional 

lift values moving inboard on the wing. The ice-shape cross 

sections for the horizontal and vertical tail are shown in Figure 

4 for the midspan stations of each surface. For these figures 

the local chord length was also normalized by the wing mean 

aerodynamic chord length. The ice shape on the vertical tail 

was nearly symmetric since there was no net lift on this 

surface. The cross-sections shown in Figure 3 and Figure 4 are 

fairly smooth. This smoothing resulted from the lofting 

process that was used to develop the fully three-dimensional 

shapes from the LEWICE3D cross-sections. These threedimensional shapes were manufactured using the fused 

deposition modeling technique and were fixed to the leading 

edges using a combination of alignment pins and tape as 

shown in Figure 5. No roughness was applied to the smooth 

shapes. Note that there was no ice shape on the wing behind 

the engines. This occurred because the engine prevented 

droplet impingement in this region, sometimes referred to as a 

“shadow zone.” This was the only iced-airplane configuration 

tested in this effort.

x/c

z/c

-0.08 -0.06 -0.04 -0.02 0.00 0.02 0.04 0.06 0.08 0.10 -0.06

-0.04

-0.02

0.00

0.02

0.04

0.06 2y/b = 0.92

c/cmac = 0.42

x/c

z/c

-0.08 -0.06 -0.04 -0.02 0.00 0.02 0.04 0.06 0.08 0.10 -0.06

-0.04

-0.02

0.00

0.02

0.04

0.06 2y/b = 0.51 c/cmac = 0.88

x/c

z/c

-0.08 -0.06 -0.04 -0.02 0.00 0.02 0.04 0.06 0.08 0.10 -0.06

-0.04

-0.02

0.00

0.02

0.04

0.06 2y/b = 0.22

c/cmac = 1.37

x/c

z/c

-0.08 -0.06 -0.04 -0.02 0.00 0.02 0.04 0.06 0.08 0.10 -0.06

-0.04

-0.02

0.00

0.02

0.04

0.06 Midspan Horizontal Tail

c/cmac = 0.58

x/c

z/c

-0.08 -0.06 -0.04 -0.02 0.00 0.02 0.04 0.06 0.08 0.10 -0.06

-0.04

-0.02

0.00

0.02

0.04

0.06 Midspan Verticall Tail

c/cmac = 0.86
NASA/TM—2012-217246 6

Figure 5.—Artificial ice shapes installed on the 3.5 percent

scale GTM, top—wing and tail leading edges; bottom—

horizontal tail.

Results and Discussion

Clean Model

The static aerodynamic measurements were carried out for a 

large range of incidence and yaw angles to document the clean 

performance of the baseline 3.5 percent scale GTM. These 

data were compared to previous results for the 5.5 percent

scale GTM that was tested in the NASA Langley 14- by 22-ft

wind tunnel (Ref. 5). The 5.5 percent scale GTM data were for 

the clean configuration with boundary-layer trip strips applied 

to the upper and lower surfaces of the wing and tail (Ref. 5)

while the clean-model data for the 3.5 percent scale GTM had 

no boundary-layer trip strips. A comparison of airplane lift, 

drag and pitching-moment coefficients is plotted in Figure 6

for β = 0°. In absolute terms, the lift-curve and pitchingmoment slopes for the 5.5 percent scale model were slightly 

lower than for the 3.5 percent scale model in the curves 

leading up to stall. This could be explained by the lack of 

blockage correction in the present data. However, the drag 

data would be similarly affected, while the data in Figure 6

match very well in the pre-stall region. This agreement in drag 

also suggests minimal effect of the boundary-layer trips 

applied to the 5.5 percent scale model. The differences in 

pitching moment may also be due to a small offset in the 

moment center or horizontal tail incidence between the two

Figure 6.—Comparison of baseline (clean) GTM longitudinal 

characteristics at β = 0°; 5.5 percent GTM data after Shah et 

al. (Ref. 5).

models. At α ≈ 11°, there was a clear stall break in lift 

coefficient for the 5.5 percent scale GTM, accompanied by a 

large change in pitching-moment slope for α > 14°. Stall for 

the 3.5 percent scale model was not as well defined, but 

occurred at an angle of attack that was at least 2° or 3° higher, 

corresponding to the break in the pitching-moment curve. This 

is consistent with the drag coefficient that was lower for the 

3.5 percent scale model for incidence angles greater than 11°.

α (deg.) -5 0 5 10 15 20 25

0.0

CL

Cm

α (deg.) -5 0 5 10 15

0.0

5.5% GTM Clean, Re = 0.54×10

6

, M = 0.08

3.5% GTM Clean, Re = 0.24×106

, M = 0.06

CD
NASA/TM—2012-217246 7

Figure 7.—Comparison of baseline (clean) GTM lateraldirectional characteristics at α = 0°; 5.5 percent GTM data 

after Shah et al. (Ref. 5).

For this β = 0° condition, similar agreement between the data 

sets was observed in the roll and yaw axes. Of course, there 

was very little variation with angle of attack due to the model

symmetry.

Further comparison of the baseline data in roll and yaw is 

shown in Figure 7 for a sideslip angle sweep at α = 0°. The 

data show the large variation in these coefficients and change 

in sign with sideslip as expected for the lateral-directional

Figure 8.—Surface-oil flow visualization on the clean right wing 

for α = 4° and β = 0° Flow is from left to right.

characteristics. The agreement in rolling-moment coefficient 

between the two data sets is fairly good over the range shown.

It is unclear as to why there are larger differences for yawing 

moment. In both cases the absolute slope is again lower for the 

5.5 percent scale model which could have resulted from 

potential uncorrected blockage effects on the 3.5 percent scale 

model. Similar comparisons were observed for the numerous 

incidence and sideslip angle sweeps performed. Shah et al.

(Ref. 5) documented the variation in longitudinal and lateral 

characteristics for the 5.5 percent scale GTM over a large αand β-range. These plots are not reproduced here because 

similar results were observed for the present series of tests.

Surface-oil flow visualization was performed on the baseline configuration for α = 4°, 8°, 10°, 12° and 14° documenting the time-averaged surface flowfield on the wings leading 

up to stall for β = 0°. A flow visualization image of the right 

wing is shown in Figure 8 at α = 4°. At this angle of attack, the 

surface flow was fairly uniform in the streamwise direction 

everywhere on the wing, except near the trailing-edge tip 

region. In this location, oil accumulated and appeared to flow 

upstream right at the trailing edge of the wing tip; thus 

indicating local boundary-layer separation as indicated in the 

figure.

β (deg.) -25 -20 -15 -10 -5 0 5 10 15 20 25

0.0

Cl

β (deg.) -25 -20 -15 -10 -5 0 5 10 15 20 25

0.0

5.5% GTM Clean, Re = 0.54×10

6

, M = 0.08

3.5% GTM Clean, Re = 0.24×106

, M = 0.06

Cn
NASA/TM—2012-217246 8

Figure 9.—Surface-oil flow visualization on the clean right wing 

for α = 8° and β = 0° Flow is from left to right.

Increasing the angle of attack to 8° led to the formation of a 

spanwise-running vortex centered along the leading-edge 

separation bubble reattachment location illustrated in Figure 9.

This complex flowfield is also described with the aid of Figure 

10 which is a schematic representation adapted from Poll 

(Ref. 25). Poll refers to the leading-edge separation location as 

the “primary” separation (c.f., Figure 10) that led to a vortex 

sheet rolling up to form a strong vortex very close to the wing 

surface. This vortex drew free-stream fluid down to the 

surface along the reattachment line indicated in Figure 9 and 

Figure 10. This formed a closed, leading-edge separation 

bubble with surface flow moving upstream between the 

reattachment location and the leading edge. Poll’s model 

suggests that initially this reverse flow experiences a favorable 

pressure gradient, but then moves into a region of adverse 

pressure gradient leading to the “secondary” separation of the 

reversed flow. This is indicated in Figure 10 and can be seen 

in Figure 9 as the line of oil accumulation upstream of and 

approximately parallel to the reattachment line. Downstream 

of the reattachment location, the surface flow was uniform and 

Figure 10.—Fundamental representation of flowfield features 

observed in Figure 9. Adapted from Poll (Ref. 25).

generally in the streamwise direction to the wing trailing edge.

The apparent separation near the wing tip that was observed at 

α = 4°, was not observed at α = 8°, perhaps due to the 

influence of the spanwise vortex. Finally, Figure 9 shows an 

increased effect of the engine nacelle and pylon that formed a 

distinct “wake” in the surface flow as well as an increased 

effect of the wing-fuselage juncture flow.

Increasing the angle of attack to 10° resulted in no significant changes to the surface-flow topology. The only noteworthy difference from α = 8° was that the reattachment location 

of the spanwise vortex appeared to have moved closer to the 

leading edge, while the secondary separation location 

remained in approximately the same location. Thus indicating 

a slight decrease in the size of the leading-edge separation 

bubble. Continuing to α = 12° brought significant changes to 

the surface flow as was expected based upon the impending 

stall break in the lift curve (c.f. Figure 6). The flow visualization in Figure 11 indicates that the spanwise vortex had broken 

down in to two separate structures. There appears to be a small 

spanwise-running vortex from the fuselage-junction region 

forming a partial-span separation bubble. This structure 

apparently interacted with flow emanating from the inboard 

side of the engine-nacelle pylon/wing junction. Then on the 

outboard side of the pylon another partial-span vortex 

appeared to have formed. Unlike at lower angles of attack, the 

vortex in this case appears to have lifted off the surface in a 

downstream direction near the wing midspan section. From 

the midspan section inboard to the root, the surface flow is 

generally in the streamwise direction to the trailing edge, 

indicating significant loading on this inboard half of the wing.

Meanwhile on the outboard portion of the wing, on the other 

S S

R

R S S

S = Separation

R = Reattachment
NASA/TM—2012-217246 9

Figure 11.—Surface-oil flow visualization on the clean right 

wing for α = 12° and β = 0° Flow is from left to right.

side of the partial-span vortex, the flow appeared to be rotating 

backward (in the upstream direction). This is seen most clearly 

on the outer 25 percent of the span where the oil flowed 

upstream from the trailing edge of the wing. Still visible near 

the leading edge is the secondary separation of the reverse 

flow on the outboard half of the wing. Poll (Ref. 25) also 

observed this flow topology for increasing angles of attack on 

a 30° swept wing. He noted the formation of the “part-span 

spiral vortex” that was only capable of inducing relative weak 

flow at the wing surface.

Increasing the angle of attack to 14° resulted in no significant changes to the surface flow topology from 12°. However, 

the partial-span vortex did appear to leave the surface of the 

wing farther upstream than at 12°. This had the effect of 

increasing slightly the portions of the wing with reverse 

surface flow. The flow between the engine-nacelle pylon and 

the fuselage was nearly identical to that at α = 12°. All of the 

images shown in Figure 8, Figure 9 and Figure 10 were for the 

right wing. Flow visualization was also performed simultaneously on the left wing. No significant differences were 

observed. This was consistent with the roll and yaw moment 

data that also showed no significant asymmetries with 

increasing angle of attack at β = 0°.

Iced Model

The iced aerodynamics of the GTM was investigated with 

the artificial ice shapes installed on the wing and tail leading 

edges. The effect on airplane lift, drag and pitching moment 

coefficient is summarized in Figure 12 for β = 0°. The 

incremental increase in drag due to the ice shapes is clearly 

seen over the angle of attack range in the figure. In contrast, 

the lift data showed virtually no effect due to the ice shapes. In 

fact, there may have been a slight increase in maximum lift 

and stalling angle in the iced configuration. Little difference 

was also observed in the pitching moment.

While it appears that the effect of the artificial ice shapes on 

the GTM performance was minimal, this appearance was 

likely due to Reynolds number effects on the clean model. It is 

well known that the performance of two-dimensional airfoils 

and three-dimensional wings is influenced by Reynolds and 

Mach number (Refs. 26 to 28). For Mach numbers less than 

about 0.3, Reynolds-number effects are usually more significant for Re less than 6.0×106

. As discussed later in this 

sections, Reynolds number effects on iced-airfoil and wing 

performance are much smaller in contrast to the clean 

configuration. Kaneshige (Ref. 29) used a combination of 

flight-test data and classical Reynolds number scaling 

techniques (Ref. 30) to estimate the high-Reynolds number lift 

and drag coefficients for the clean GTM. These data are also 

plotted in Figure 12. When comparing the estimated full-scale 

clean GTM data with the subscale iced-GTM data, the 

expected decease in maximum lift and stalling angle for the 

iced configuration is clearly seen. The drag penalties were also 

higher for incidence angles less than about 10°. For incidences 

angles of about 13° and higher, the full-scale clean drag 

coefficient was the same or larger than for the iced configuration. This could have been due to the contribution of induced 

drag resulting from the higher lift coefficients for the clean 

configuration.

The effect of the artificial ice shapes on the lateral-directional 

characteristics of the 3.5 percent scale GTM are summarized in 

Figure 13 for angle of attack sweeps at constant sideslip and in 

Figure 14 for sideslip sweeps at constant angle of attack. There 

was little effect of the ice on rolling moment for both β = 0° and 

8° in Figure 13. Still preserved is the diminished lateral stability 

for α > 12° noted by Shah et al. (Ref. 5) for the 5.5 percent scale 

GTM at nonzero sideslip. According to the data in Figure 13, 

there was an increase in yawing moment due to the artificial ice 

for β = 8°, perhaps due to some effect of the ice shape on the 

vertical stabilizer. At this sideslip angle, Shah et al. (Ref. 5) 

suggested that the decreasing directional stability for α > 15° 

(for both clean and iced configurations) was due to shielding of 

the vertical stabilizer by the fuselage. The results are further 

mixed for the sideslip angle sweeps for the two incidence angles 

shown in Figure 14. For the rolling moment, there does appear 

to have been a decrease in lateral stability with the iced-GTM 

configuration at α = 14°. However, the yawing moment 

variation was mostly unaffected by the artificial ice shapes.
NASA/TM—2012-217246 10

Figure 12.—Effect of artificial ice shapes on GTM longitudinal characteristics at β = 0°, Full-scale GTM data after 

Kaneshige (Ref. 29).

Figure 13.—Effect of artificial ice shapes on 3.5 percent

scale GTM lateral-directional characteristics at fixed 

sideslip angle.

α (deg.) -5 0 5 10 15 20 25

0.0

CL

Cm

α (deg.) -5 0 5 10 15

0.0

3.5% GTM Clean, Re = 0.24×106, M = 0.06

3.5% GTM Iced, Re = 0.24×10

6

, M = 0.06

Full-scale GTM Clean, Re = 36×10

6

, M = 0.3

CD

α (deg.) -5 0 5 10 15 20 25

0.0

Cl

α (deg.) -5 0 5 10 15 20 25

0.0

Clean, β = 0 deg.

Iced, β = 0 deg.

Clean, β = 8 deg.

Iced, β = 8 deg.

Cn
NASA/TM—2012-217246 11

Figure 14.—Effect of artificial ice shapes on 3.5 percent

scale GTM lateral-directional characteristics at fixed 

incidence angle.

In contrast to the clean, or un-iced, configuration, Reynolds 

number effects on lift and drag coefficient for airfoils, wing 

and airplane models with artificial ice shapes have been shown 

to be small. For example, Lee et al. (Ref. 17), investigated 

geometry and Reynolds number scaling effects on a semi-span 

wing panel representative of a twin-engine business jet.

Aerodynamic testing was conducted with three different 

model scales: full scale, 42 percent scale and 8.3 percent scale 

over a Reynolds number range of 4.2×106 down to 0.15×106

.

Three different ice configurations were investigated, including 

a large glaze-horn similar to that used in the present work. Lee 

et al. (Ref. 17) found that there was very little Reynolds 

number dependence in the wing performance with the large 

glaze-horn ice shape over the range tested. In contrast, there 

was a large change in performance of the clean wing with 

Reynolds number as expected. These data are reproduced here 

in Figure 15 and Figure 16. The clean wing data in Figure 15

show the classic effects in the significant increase in maximum lift coefficient and stalling angle with increasing 

Reynolds number. For Reynolds numbers greater than 

4.2×106

, further increases in maximum lift and stalling angle 

would be expected, however, these would likely be incrementally less than those shown in the Figure 15. Typical Reynolds 

number effects were also seen in the pitching moment 

variation with angle of attack that grew more linear in the prestall region with increasing Reynolds number. This behavior is 

contrasted against that shown in Figure 16 for the same wing 

model with a large glaze-horn artificial ice shape similar to 

that used for the present tests. There was considerably less 

dependence of the performance data on Reynolds number for 

the iced-wing configuration. Therefore, it is reasonable to 

conclude that the lift and drag performance of the iced 3.5

percent scale GTM was at least representative of a full-scale 

condition.

The percent decrease in lift coefficient and percent increase 

in drag coefficient due to the artificial ice shapes was computed using the present data for the iced-GTM configuration 

and estimated full-scale clean performance shown in Figure 

12. These increments are plotted versus angle of attack in 

Figure 17. In terms of lift coefficient, the effect of the ice is 

minimal until about 10° angle of attack, while the effect on 

drag coefficient is largest at α = 0°. This behavior is typical of 

icing flight encounters where the primary effect is reduced 

airspeed in cruise or holding conditions. The reduction in 

maximum lift coefficient and stalling angle of course become 

more important as more lift is required for lower airspeed 

conditions. Also shown in Figure 17 are analogous data for the 

semi-span business jet wing tested by Lee et al. (Ref. 17). In 

both cases, a large glaze-horn type ice shape was used in the 

iced-wing configuration. The data for the semi-span wing 

model show similar percent increases in drag, but much larger 

reductions in lift coefficient. (Note that the percent reduction 

in CL for α = 0°, was large since the full-scale reference value 

was near 0.0.) Over 50 percent reduction in CL was observed 

for the semi-span wing compared to the 24 percent reduction 

in CL for the GTM used in the present tests.

β (deg.) -25 -20 -15 -10 -5 0 5 10 15 20 25

0.0

Cl

β (deg.) -25 -20 -15 -10 -5 0 5 10 15 20 25

0.0

Clean, α = 0 deg.

Iced, α = 0 deg.

Clean, α = 14 deg.

Iced, α = 14 deg.

Cn
NASA/TM—2012-217246 12

Figure 15.—Effect of Reynolds number on clean businessjet, semi-span wing model performance, after Lee et al.

(Ref. 17).

Figure 16.—Effect of Reynolds number on business-jet, 

semi-span wing model performance with glaze-horn 

artificial ice shape, after Lee et al. (Ref. 17).

α (deg.) -5 0 5 10 15 20 25

0.0

0.0

CL Cm

α (deg.) -5 0 5 10 15 20 25

0.0

Re = 0.15×106, M = 0.04

Re = 0.62×106

, M = 0.18

Re = 4.2×106

, M = 0.10

CD

α (deg.) -5 0 5 10 15 20 25

0.0

0.0

CL Cm

α (deg.) -5 0 5 10 15 20 25

0.0

Re = 0.15×106, M = 0.04

Re = 0.62×106

, M = 0.18

Re = 4.2×106

, M = 0.10

CD
NASA/TM—2012-217246 13

Figure 17.—Percent decrease in lift coefficient and percent 

increase in drag coefficient due to the artificial ice shapes on 

the 3.5 percent scale GTM relative to the estimated full-scale 

performance from Figure 12 and on the 8.3 percent scale 

business-jet semi span wing (Re = 0.15×106

) relative to the 

full-scale business jet wing (Re = 4.2×106

) from Figure 15

and Figure 16.

Comparison of the magnitude of wing/airplane lift loss is 

difficult because of the inherent dependencies on configuration, Reynolds number, artificial ice-shape geometry and other 

factors. Such factors notwithstanding, however, further 

comparison does yield some perspective on the present data.

For example, Reehorst et al. (Ref. 31), conducted performance 

measurements on a 12.5 percent scale, twin-engine, short-haul 

transport airplane model in the NASA Langley 14- by 22-ft

wind tunnel. Two full-span, leading-edge ice-shape configurations were tested, although detailed geometry information was 

not provided. For one configuration that appeared to be similar 

to the iced-GTM configuration in the present tests, the 

reduction in CL at stall was only 12 percent based upon the 

low-Reynolds number data. This is low relative to the values 

plotted in Figure 17 and was most likely due to Reynolds 

number effects for the clean airplane model.

Higher Reynolds number, iced-configuration data for the 

twin-engine, commercial transport category airplanes similar 

to the present GTM and that of Reehorst et al. (Ref. 31) are 

largely unavailable in the public domain. Lynch and Khodadoust (Ref. 32) performed a comprehensive review of iced 

aerodynamic performance effects. The available full-scale 

and/or high-Reynolds number airplane data were typically 

from straight-wing, propeller-driven configurations. However, 

Zierten and Hill (Ref. 33) reported on flight and wind-tunnel 

tests conducted to determine the effects of simulated ground 

frost on the wings of Boeing 737-200ADV, 757-200 and 767-

200 airplanes. For the ground frost simulations, the entire 

upper surfaces of the main wings were covered with appropriately sized roughness on the flight test airplanes and windtunnel models. Van Hengst et al. (Ref. 34) reproduced the 

maximum lift data along with data from other airplanes as 

shown in Figure 18. There is a wide range of maximum lift 

reductions from 10 to 35 percent, depending upon the airplane 

configuration and whether the data were from flight or windtunnel test. For the Boeing models, the flight test points and 

wind-tunnel data produced similar maximum lift reductions 

despite large differences in roughness size. Based upon these 

data, the 24 percent lift reduction in the present tests on the 

GTM (cf. Figure 17) may be on the low side, given the large 

size of the artificial ice shape relative to the roughness sizes 

shown in Figure 18. These roughness configurations may be 

useful for benchmarking the iced-GTM performance in any 

follow-on testing, provided suitable simulation methods can 

be implemented. An additional important distinction between 

the present data and that shown in Figure 18 for full-scale 

airplane configurations is the presence and use of the high-lift 

system. Such systems were not incorporated into the 

3.5 percent scale GTM because the small model size and lowReynolds number would likely have negated their effectiveness.

α (deg.)

Percent Red

uctio

n in

CL

-5 0 5 10 15 20 25 -10

0

10

20

30

40

50

60

α (deg.)

Percent Increase in

CD

-5 0 5 10 15

0

25

50

75

100

125

150

175

200

Iced 3.5% GTM relative to full-scale clean

Iced 8.3% semi-span wing relative to full-scale clean
NASA/TM—2012-217246 14

Figure 18.—Percent reductions in maximum lift coefficient for 

various roughness sizes and airplane configurations, after 

van Hengst et al. (Ref. 34) (Used with permission.)

Surface-oil flow visualization was performed on the iced 

configuration for α = 4°, 8°, 10°, 12° and 14° documenting the 

time-averaged surface flowfield on the wings leading up to 

stall for β = 0°. These flow visualization images were 

compared to those from the baseline configuration to note any 

significant differences due to the artificial ice shapes. For α = 

4°, the ice shape caused a spanwise-running vortex to form 

immediately behind the ice shape. The vortex promoted 

surface flow to the trailing edge even near the wing tip, 

mitigating the low-shear/separation region observed on the 

clean wing (cf. Figure 8). This vortex structure was unchanged 

as the angle of attack was increased to α = 8° as illustrated in 

Figure 19. Similar surface-flow topology was observed 

comparing Figure 19 and Figure 9 for the clean model. The 

exception was that the approximate bubble reattachment 

location laid much closer to the leading edge in Figure 19 than 

in Figure 9. Increasing the angle of attack to 10° for the iced 

wing caused the spanwise vortex to angle farther downstream 

and away from the surface in an outboard direction from 

the engine pylon. This was different from the clean wing 

Figure 19.—Surface-oil flow visualization on the right wing with 

artificial ice shape for α = 8° and β = 0° Flow is from left to 

right.

configuration where the surface flow at α = 10° was very 

similar to that at α = 8°.

Increasing the angle of attack to 12° again resulted in similar 

surface-flow topology between the clean and iced-wing cases.

Figure 20 shows that there appeared to be two distinct flow 

regions, one inboard of the engine pylon and another outboard.

The partial-span vortex on the inboard portion of the wing is not 

as visible in Figure 20 as in Figure 11, but the flow was 

generally in the streamwise direction toward the trailing edge.

Outboard of the engine pylon, the partial-span vortex was 

angled toward the trailing edge as in Figure 11. The reverse 

flow on the outer 25 percent of the span does not appear to be as 

strong in the iced case (Figure 20) as for the clean case (Figure 

11). The flowfields were qualitatively similar.

The evolution of the spanwise vortex associated with leading-edge ice accretion was investigated on a rectangular swept 

wing by Potapczuk et al. (Ref. 35) and Bragg et al. (Ref. 36)

using experimental and computational methods. They noted 

that at low angle of attack, the spanwise vortex laid parallel to 

the leading edge along the reattachment location showing 

surface-flow topology similar to the flow on the iced-GTM
NASA/TM—2012-217246 15

Figure 20.—Surface-oil flow visualization on the right wing with 

artificial ice shape for α = 12° and β = 0° Flow is from left to 

right.

wing for α = 4° and 8°. Increasing angle of attack led to the 

onset of stall from the tip inboard with the spanwise vortex 

angling downstream and lifting off of the surface. This was 

similar to the observations on the iced-GTM wing for α > 10°.

Increasing the iced GTM angle of attack to 14° did not 

result in fundamental changes to the iced-wing flowfield. The 

partial-span vortex emanating from the engine pylon/wing 

juncture region may have angled slightly more downstream, 

resulting in larger extents of separated or reverse flow on the 

outboard portions of the wing, but this was not markedly 

different from that observed at 12°. The surface flow between 

the fuselage and engine pylon showed some evidence of 

boundary-layer separation, unlike for the clean wing. Flow 

visualization was simultaneously performed on the left wing 

and no significant differences were observed. Any minor 

anomalies between the right and left wings were attributable to 

small differences in the artificial ice-shape mounting and 

attachment. Overall, the flow visualization on the iced-wing 

configuration supported the main results from the force and 

moment measurements where no significant differences were 

observed between the clean and iced configurations.

Summary and Conclusions

An experimental research effort was begun to develop a 

database of airplane aerodynamic characteristics with 

simulated ice accretion over a large range of incidence and 

sideslip angles. These data are important to the development 

of high-fidelity flight dynamics models capable of simulating 

high incidence and sideslip conditions associated with airplane 

loss of control events. The static aerodynamic testing described in this paper represent the initial stages of the overall 

effort.

Wind-tunnel testing was performed at the NASA Langley 

12-ft Low-Speed Wind Tunnel using a 3.5 percent scale model 

of the NASA Langley Generic Transport Model. Aerodynamic 

data were acquired from a six-component force and moment 

balance in static-model sweeps from α = –5° to 85° and 

β = –45° to 45°. The majority of trials were performed at a 

Reynolds number based on mean aerodynamic chord of 

0.24×106 and Mach number of 0.06. Surface-oil flow visualization was also performed on the main wing for selected cases 

and provided a qualitative understanding of the flowfield. The 

3.5 percent scale GTM was tested in both the clean configuration and with full-span artificial ice shapes attached to the 

leading edges of the wing, horizontal and vertical tail. The 

artificial ice shapes were large, glaze-horn type shapes that 

were developed using computational ice growth analysis tools.

Aerodynamic results for the clean airplane configuration 

compared favorably with similar experiments carried out on a 

5.5 percent scale GTM, thus yielding confidence in the model 

quality and wind-tunnel system. The addition of the artificial 

ice shapes did result in an increase in airplane drag coefficient 

but had little effect on the lift and pitching moment. The 

lateral-directional characteristics showed mixed results with a 

small effect of the ice shapes observed in some cases. The 

flow-visualization images revealed the presence and evolution 

of a spanwise-running vortex on the wing that was the 

dominant feature of the flowfield. This vortex was manifested 

in the leading-edge separation bubble in both the clean and 

iced configurations. Prior to stall, the vortex core was close to 

the wing surface and laid approximately parallel to the leading 

edge. For angles of attack near stall, two flow regimes were 

observed: one between the engine and fuselage and one 

outboard of the engine. In the latter regime, the spanwise 

vortex angled downstream and off the surface resulting in 

separated and reverse flow on the outboard portion of the 

wing. This occurred in both configurations, thus supporting 

the comparisons in the force and moment data.

The lack of ice-induced performance and flowfield effects 

observed in this effort was likely due to Reynolds number 

effects for the clean configuration. Based upon observations 

taken from other work, it is likely that the iced-airplane data 
NASA/TM—2012-217246 16

acquired here represents a reasonable simulation of the fullscale behavior. Estimates of full-scale baseline performance 

were included in this analysis to illustrate the potential icing 

effects. However, these estimates are limited. A better 

understanding of the full-scale, high-Reynolds number GTM 

aerodynamics is needed in order to fully evaluate the potential 

icing effects.

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NASA/TM—2012-217246 17

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Aerodynamic Effects of Simulated Ice Accretion on a Generic Transport Model 

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14. ABSTRACT

An experimental research effort was begun to develop a database of airplane aerodynamic characteristics with simulated ice accretion over a 

large range of incidence and sideslip angles. Wind-tunnel testing was performed at the NASA Langley 12-ft Low-Speed Wind Tunnel using 

a 3.5 percent scale model of the NASA Langley Generic Transport Model. Aerodynamic data were acquired from a six-component force 

and moment balance in static-model sweeps from α = -5° to 85° and β = -45° to 45° at a Reynolds number of 0.24×106 and Mach number of 

0.06. The 3.5 percent scale GTM was tested in both the clean configuration and with full-span artificial ice shapes attached to the leading 

edges of the wing, horizontal and vertical tail. Aerodynamic results for the clean airplane configuration compared favorably with similar 

experiments carried out on a 5.5 percent scale GTM. The addition of the large, glaze-horn type ice shapes did result in an increase in 

airplane drag coefficient but had little effect on the lift and pitching moment. The lateral-directional characteristics showed mixed results 

with a small effect of the ice shapes observed in some cases. The flow visualization images revealed the presence and evolution of a 

spanwise-running vortex on the wing that was the dominant feature of the flowfield for both clean and iced configurations. The lack of iceinduced performance and flowfield effects observed in this effort was likely due to Reynolds number effects for the clean configuration. 

Estimates of full-scale baseline performance were included in this analysis to illustrate the potential icing effects. 

15. SUBJECT TERMS

Aircraft icing; Aircraft performance; Aerodynamic characteristics 

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