Andy P. Broeren and Harold E. Addy, Jr.

Glenn Research Center, Cleveland, Ohio

Michael B. Bragg and Greg T. Busch

University of Illinois at Urbana-Champaign, Urbana, Illinois

Didier Guffond and Emmanuel Montreuil

Offi ce National d’Etudes et Recherches Aérospatiales, Châtillon, France

Aerodynamic Simulation of Ice Accretion on Airfoils

NASA/TP—2011-216929

June 2011
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Andy P. Broeren and Harold E. Addy, Jr.

Glenn Research Center, Cleveland, Ohio

Michael B. Bragg and Greg T. Busch

University of Illinois at Urbana-Champaign, Urbana, Illinois

Didier Guffond and Emmanuel Montreuil

Offi ce National d’Etudes et Recherches Aérospatiales, Châtillon, France

Aerodynamic Simulation of Ice Accretion on Airfoils

NASA/TP—2011-216929

June 2011

National Aeronautics and

Space Administration

Glenn Research Center

Cleveland, Ohio 44135
Acknowledgments

The NASA Aviation Safety Program provided the support for this research. In addition, the authors acknowledge several individuals 

representing the three organizations involved for their signifi cant contributions to the success of this large collaborative effort. 

Former NASA Icing Branch Chief Tom Bond, now at the Federal Aviation Administration, deserves credit for building and 

maintaining programmatic support for this research. Mark Potapczuk, Sam Lee, and Colin Bidwell of the NASA Glenn Research 

Center provided technical assistance in this research. The authors thank the staff of the NASA Glenn Icing Research Tunnel (IRT) 

for their assistance in the two IRT ice-accretion test campaigns completed as part of this program. Tammy Langhalls, formerly 

at NASA Glenn, deserves special thanks for digitizing all of the ice tracings from the IRT campaigns. Frédéric Moëns of Offi ce 

National d’Etudes et Recherches Aérospatiales (ONERA) Châtillon, now at ONERA Meudon, provided technical assistance in 

this research. Philippe Desplas, Denis CassouDesalle, and the rest of the ONERA F1 wind-tunnel personnel provided excellent 

support in planning and executing the full-scale model aerodynamic testing. Leia Blumenthal of the University of Illinois, now at 

Harvard University, made important technical contributions in the early phases of this research. Finally, the University of Illinois 

Aerospace Engineering Machine Shop staff facilitated the manufacture and instrumentation of many of the ice-shape simulations 

used for wind-tunnel testing. David Foley deserves special recognition for fabricating and instrumenting nearly all of the highfi delity three-dimensional castings used in the Phase III and V research.

Available from

NASA Center for Aerospace Information

7115 Standard Drive

Hanover, MD 21076–1320

National Technical Information Service

5301 Shawnee Road

Alexandria, VA 22312

Available electronically at http://www.sti.nasa.gov

Trade names and trademarks are used in this report for identifi cation 

only. Their usage does not constitute an offi cial endorsement, 

either expressed or implied, by the National Aeronautics and 

Space Administration.

Level of Review: This material has been technically reviewed by expert reviewer(s). 
NASA/TP—2011-216929 iii

Contents

Summary........................................................................................................................................................................1

1.0 Introduction.........................................................................................................................................................1

2.0 Symbols ..............................................................................................................................................................3

3.0 Phase I: Ice-Shape Classification........................................................................................................................3

4.0 Phase II: Subscale-Model Ice-Accretion Testing................................................................................................5

5.0 Phase III: Subscale-Model Aerodynamic Testing...............................................................................................6

5.1 Ice Accretions and Simulation Methods.................................................................................................6

5.2 Experimental Methods............................................................................................................................8

5.3 Results and Discussion .........................................................................................................................10

5.3.1 ED0762 Roughness...................................................................................................................10

5.3.2 ED0735 Horn Ice.......................................................................................................................11

5.3.3 ED0730 Streamwise Ice ............................................................................................................14

5.3.4 ED0760 Spanwise-Ridge Ice.....................................................................................................16

5.4 Summary of Simulation Effectiveness for Phase III.............................................................................19

6.0 Phase IV: Full-Scale-Model Ice-Accretion Testing ..........................................................................................21

7.0 Phase V: Full-Scale-Model Aerodynamic Testing............................................................................................21

7.1 Ice Accretions Tested ...........................................................................................................................22

7.2 Experimental Methods..........................................................................................................................25

7.3 Results and Discussion .........................................................................................................................27

7.3.1 Clean- Versus Iced-Airfoil Aerodynamics................................................................................27

7.3.2 Reynolds and Mach Number Effects.........................................................................................32

8.0 Phase VI: Simulation Validation Testing..........................................................................................................39

8.1 Experimental and Ice-Shape Simulation Methods................................................................................39

8.2 Results and Discussion .........................................................................................................................39

8.2.1 Clean Model Comparison..........................................................................................................39

8.2.2 Horn Ice Simulation ..................................................................................................................40

8.2.3 Ice Roughness Simulation .........................................................................................................43

8.2.4 Streamwise Ice Simulation ........................................................................................................48

8.2.5 Spanwise-Ridge Ice Simulation.................................................................................................54

8.3 Summary of Simulation Effectiveness for Phase VI.............................................................................59

9.0 Subscale Iced-Airfoil Aerodynamic Simulation Methodology.........................................................................61

9.1 Ice Roughness.......................................................................................................................................61

9.2 Streamwise Ice......................................................................................................................................62

9.3 Horn Ice ................................................................................................................................................62

9.4 Tall Spanwise-Ridge Ice.......................................................................................................................62

9.5 Short Spanwise-Ridge Ice.....................................................................................................................62

10.0 Summary, Conclusions, and Recommendations...............................................................................................62

10.1 Summary...............................................................................................................................................62

10.2 Conclusions ..........................................................................................................................................64

10.3 Recommendations.................................................................................................................................64

References ...................................................................................................................................................................65

NASA/TP—2011-216929 1

Aerodynamic Simulation of Ice Accretion on Airfoils

Andy P. Broeren and Harold E. Addy, Jr.

National Aeronautics and Space Administration

Glenn Research Center

Cleveland, Ohio 44135

Michael B. Bragg and Greg T. Busch

University of Illinois at Urbana-Champaign

Urbana, Illinois 61801

Didier Guffond and Emmanuel Montreuil

Office National d’Etudes et Recherches Aérospatiales

Châtillon, France F–92322

Summary

This report presents the results of a collaborative research 

program among NASA, Office National d’Etudes et 

Recherches Aérospatiales (ONERA), and the University of 

Illinois that was designed to improve the current state of the 

art in aerodynamic scaling and simulation of ice accretion on 

airfoils. Ice accretions were classified into four types on the 

basis of aerodynamic effects: roughness, horn, streamwise, 

and spanwise ridge. The NASA Icing Research Tunnel (IRT) 

was used to generate ice accretions within these four types 

using both subscale and full-scale models. Aerodynamic 

testing was performed at the ONERA F1 pressurized wind 

tunnel using a 72-in.- (1.83-m-) chord, full-span, NACA 

23012 airfoil model with high-fidelity, three-dimensional 

castings of the IRT ice accretions. Performance data were 

recorded over Reynolds numbers from 4.5×106 to 15.9×106

and Mach numbers from 0.10 to 0.28. Lower fidelity iceaccretion simulation methods were developed and tested on an 

18-in.- (0.46-m-) chord NACA 23012 airfoil model at the 

University of Illinois wind tunnel at Mach 0.18 and a Reynolds number of 1.8×106

. The aerodynamic accuracy of the 

lower fidelity, subscale ice simulations was validated against 

the full-scale results for a factor of 4 reduction in model scale 

and a factor of 8 reduction in Reynolds number. The results 

show that geometric scaling of the gross ice-shape features is 

appropriate for horn and large spanwise-ridge ice shapes. 

Geometrically scaled simulations of streamwise ice and 

roughness on the subscale model tended to produce conservative aerodynamic performance relative to the full-scale model. 

The results presented in this report provide guidance regarding 

the effect of this roughness size in the subscale model testing. 

The completed program has defined the level of geometric 

fidelity required for artificial ice shapes to yield aerodynamic 

performance results to within a known level of uncertainty and 

has culminated in a proposed methodology for subscale icedairfoil aerodynamic simulation.

1.0 Introduction

In many applications, simulation of the ice-accretion geometry on a wing or other surface is required for aerodynamic 

evaluation. The measurement of the aerodynamic performance 

of an aircraft or wing with actual ice accretions is very difficult 

and expensive and seldom a practical solution. Aircraft 

performance data with ice accreted in flight are available

(Refs. 1 to 7), but limited, because of the cost and difficulty of

acquiring these data. In flight, it is difficult to document the iceaccretion geometry accurately, particularly because shedding

and sublimation often occur. In addition, the accretions are 

difficult to attribute to a particular icing condition because of the 

natural atmospheric variation through which the airplane 

traverses. For accretions formed in an icing wind tunnel, the 

inability to provide adequate instrumentation, the nonuniformity 

of the cloud over the model, poor aerodynamic flow quality, 

sublimation, and increased cost also make aerodynamic 

measurements difficult and rare (Ref. 8). The most common 

way to acquire iced-airfoil and wing data is to use geometric 

representations of ice accretion in a dry-air wind tunnel or in 

flight. These geometric representations are often referred to as 

“artificial ice shapes” or “ice-accretion simulations.”

The best current technology for creating an accurate iceaccretion simulation is the mold and casting method developed at the NASA Glenn Research Center (Ref. 9). In this 

method, molds are made from an ice accretion generated in an 

icing wind tunnel, such as Glenn’s Icing Research Tunnel 

(IRT). From these molds, ice-accretion castings are made that 

maintain the major features of the ice, including the detailed 

surface roughness and the spanwise and chordwise variations.

Typically, these castings are attached to wings and airfoils and 

are instrumented to obtain high-fidelity aerodynamic data

(Refs. 10 to 15). This is an expensive process and is not 

practical for many situations, but it does generate benchmark 

data for iced-airfoil and wing research.
NASA/TP—2011-216929 2

Although much of our understanding of ice-accretion aerodynamics is anchored by cast-ice simulation data, there have 

been very few studies employing full-scale, ice-accretion cast 

shapes aerodynamically tested at full-scale Reynolds and 

Mach numbers. The second Federal Aviation Administration

(FAA) and NASA Tailplane Icing Program provides full-scale 

cast-ice performance data at full-scale Reynolds and Mach 

numbers, but the ice shapes were applied to a fully threedimensional tailplane model (Ref. 16). The NASA Modern 

Airfoils Program (Ref. 17) currently provides the best airfoil 

performance data set at flight Reynolds and Mach numbers;

but because of size limitations in the NASA Low-Turbulence 

Pressure Tunnel used for the aerodynamic testing, the ice 

accretions were obtained on a 36-in.- (914.4-mm-) chord 

airfoil. Ice-accretion scaling methods can provide scaling of 

the gross ice shape for moderate scales (Ref. 18), but not of 

the roughness and ice-shape details known to be aerodynamically important in some cases.

Simulations of ice accretions on airfoils and wings are

needed or desirable for many applications. For flight tests or 

wind-tunnel tests of iced wings and airfoils for certification or 

research, cast ice shapes are seldom available, and lower

fidelity, artificial ice shapes are required. Often wind-tunnel 

testing requires scale models for which no castings are 

available, and small-scale simulations are needed. Computational methods must geometrically model ice on airfoils and 

wings, and it is either impossible or impractical to model a 

complete, rough three-dimensional accretion. Simpler

geometries that accurately represent the key features important 

to the aerodynamics are needed. Currently, there is only a 

limited understanding of how to accurately simulate the iceaccretion geometry to reproduce the aerodynamic effect of ice 

accretion on lifting surfaces. For the artificial ice shapes to be 

robust under a wide range of geometries and flow conditions, 

accurate artificial ice shapes need to be based on an understanding of the fundamental flow physics. Although there are 

commonly accepted practices supported by some research in 

this area, there are also remaining questions about the

aerodynamic accuracy of artificial ice shapes (Ref. 19).

There are several potential sources of uncertainty in the 

aerodynamic results of subscale simulations of airfoil ice 

accretions. As discussed by Busch and Bragg (Ref. 20), these 

sources include ice-accretion geometry uncertainty, icedairfoil performance measurement uncertainty, and Reynolds

and Mach number effects. The uncertainties associated with 

ice-accretion geometry arise from its surface complexities and 

roughness. These geometric features are difficult to measure 

and document. There can be significant spanwise variations in 

gross ice shape and roughness features like “feathers.” The 

difficulty in quantifying important ice features necessarily 

yields uncertainties in lower fidelity simulations, particularly 

on subscale models, where the features may be geometrically 

scaled to smaller sizes. The uncertainties associated with 

measuring iced-airfoil aerodynamic performance include the 

typical instrument uncertainties associated with any airfoil test 

along with others that may be amplified by the presence of 

artificial ice on the airfoil.

Busch and Bragg (Ref. 20) address two primary sources: 

pressure tap placement and spanwise drag variation.

The placement of static pressure taps on an artificial ice shape 

must be done with some care to avoid anomalous or unrepresentative surface pressures due to flow variations in and around 

roughness elements. In typical two-dimensional airfoil performance tests, drag measurements are performed via a wake 

survey. The authors document iced-airfoil cases that show a 

large dependence of drag coefficient on the spanwise location of 

the wake-survey station. This type of uncertainty can make it 

difficult to evaluate the effectiveness of subscale ice-shape 

simulations in terms of drag coefficient. Finally, the effects of 

Reynolds and Mach numbers can introduce uncertainty when 

subscale ice-airfoil data are being compared with full-scale 

results. The present research program was developed, in part, to 

address these uncertainties. The results presented in this report 

attempt to quantify these uncertainties in terms of the iced-airfoil 

maximum lift coefficient, stalling angle, and drag coefficient.

The overall goal of this work was to provide, for the first 

time, high-fidelity, full-scale, iced-airfoil aerodynamic data 

and validated subscale-model simulation methods that produce 

the essential full-scale aerodynamic characteristics. As 

originally described by Bragg et al. (Ref. 21), the research 

program was organized into six phases that included both 

subscale and full-scale model experiments. The subscalemodel testing was important to minimizing overall cost while 

providing a validation vehicle for the subscale ice-accretion 

simulations. The full-scale model trials were required to obtain 

heretofore nonexistent benchmark aerodynamic data.

In Phase I: Ice-Shape Classification, the existing iced-airfoil 

aerodynamic literature was reviewed to classify ice shapes 

according to their aerodynamic effects. In Phase II: SubscaleModel Ice-Accretion Testing, high-fidelity ice shapes were 

obtained for the subscale model having the characteristics 

developed in Phase I. In Phase III: Subscale-Model Aerodynamic Testing, the high-fidelity ice shapes obtained in 

Phase II were used to develop aerodynamic simulation 

methods on the subscale model. In Phase IV: Full-ScaleModel Ice-Accretion Testing, high-fidelity ice shapes were 

obtained for the full-scale model having the characteristics 

developed in Phase I. In Phase V: Full-Scale-Model Aerodynamic Testing, the ice shapes acquired in Phase IV were 

used for aerodynamic testing on the full-scale model to obtain 

a benchmark data set for the validation of subscale simulation 

methods. Finally, in Phase VI: Simulation Validation Testing, 

the methods developed in Phase III were used to scale and 

simulate the full-scale ice shapes for testing on the subscale 

model at a lower Reynolds number. The objective of this 

phase was to “close the loop” by using the subscale model

data to reproduce the aerodynamic effects of the ice shapes 

tested on the full-scale model at high Reynolds numbers.
NASA/TP—2011-216929 3

The purpose of this comprehensive final report is to 

describe the research and key results conducted in each phase.

This research program has produced a number of papers and 

reports that document the work conducted during each phase 

in much more detail. These works are cited throughout this 

report in the context of the appropriate phase of the program.

2.0 Symbols

α airfoil angle of attack

αstall stalling angle of attack, coincident with the maximum lift coefficient

c airfoil chord length

Cd drag coefficient

Cd, casting drag coefficient for the three-dimensional casting

Cd, sim drag coefficient for the simulation

ΔCd,rms root-mean-square percent difference in drag 

coefficient between the lower fidelity simulation 

and the three-dimensional casting simulation over 

a given angle-of-attack range

ΔCd,rms,bal root-mean-square percent difference in drag 

coefficient from the force balance between the lower

fidelity simulation and the three-dimensional casting 

simulation over a given angle-of-attack range

Cl lift coefficient

Cl,α lift-curve slope

Cl,max maximum lift coefficient, coincident with stalling 

angle

Cm quarter-chord pitching-moment coefficient

Cp pressure coefficient

k ice-roughness height or thickness

LWC liquid water content

M freestream Mach number

MVD median volume diameter

N number of angles of attack

Re Reynolds number based on chord

s surface length along airfoil profile

x chordwise position along airfoil 

y normal position from airfoil chord line

δ boundary-layer thickness

3.0 Phase I: Ice-Shape Classification

In the first phase of the research, ice-shape classifications

were developed on the basis of the aerodynamics unique to 

each type. There are many types of ice accretions documented 

in the literature that are characterized by the accretion process.

These include rime ice, glaze ice, mixed ice, beak ice, runback 

ice, and intercycle ice. Although these classifications are 

appropriate for thinking about the accretion of ice, they may 

not be as useful when the objective is aerodynamic simulation.

In an initial stage of this research, Bragg et al. (Refs. 22 and 

23) examined the icing aerodynamics literature and developed 

four fundamental types based on the flowfield physics. These 

ice-shape classifications are (1) roughness, (2) horn ice, 

(3) streamwise ice, and (4) spanwise-ridge ice. The important 

aerodynamics associated with each of these classifications is 

summarized in the following paragraphs. Much of this 

discussion is taken from Bragg et al. (Refs. 22 and 23), and 

more details can be found therein.

Ice roughness occurs during the initial stages of the accretion process before a significant ice shape, such as a horn, is 

accreted. The other three ice types are also “rough,” but here 

the initial surface roughness does not significantly alter the 

airfoil contour or the inviscid flowfield. Ice roughness may be 

associated with glaze, rime, or mixed conditions and also

results from the operation of ice-protection systems such as 

pneumatic deicers. A key aerodynamic feature of ice roughness is that it is usually much larger than the local boundarylayer thickness even at the very early stages of development.

These roughness elements each act as bluff bodies with their 

own three-dimensional separated flowfield. In fact, the 

distinguishing characteristic of ice roughness and the other 

types of ice can be described in terms of the extent of 

boundary-layer separation. Horn, streamwise, and spanwiseridge ice shapes are characterized by separated flow regions 

that are primarily two dimensional with characteristic lengths 

that are often large in comparison to the height of the ice 

accretion. In contrast, the separated flow regions generated by 

ice roughness are fundamentally three dimensional, are very 

local to the roughness elements, and are similar in scale to the 

roughness itself.

Roughness is characterized by its height, concentration (or

density), and surface location and distribution. Its effect on 

airfoil performance depends on all of these parameters.

Roughness shape can also be significant, but the irregular 

shapes seen in ice roughness are not thought to be as important

as, and are certainly less well understood than, the other three 

parameters. Roughness can affect airfoil performance in several 

ways. The roughness elements themselves can extract momentum from the flow and cause boundary-layer separation near the 

trailing edge (Refs. 22 to 24). Trailing-edge separation can
NASA/TP—2011-216929 4

contribute to reduced lift coefficients at lower stalling angles

of attack. Roughness usually promotes bypass transition in the 

boundary layer that can lead to increased skin friction. These 

effects manifest themselves as performance degradation—

increased drag and decreased maximum lift.

Longer icing exposures under glaze and mixed icing conditions usually result in the formation of horn ice. The horn ice 

shape can be characterized by its height, the angle it makes 

with the chord line, and its location indicated by s/c (the 

nondimensional surface length along the airfoil profile,

distance/chord). The most pervasive feature of the horn ice 

flowfield is the large separation bubble that forms downstream 

of the horn. The stagnation point is usually located on the ice

shape, and the boundary layer cannot negotiate the large 

adverse pressure gradient encountered at the tip of the horn.

Thus the separation location remains approximately fixed at 

the horn tip over a large angle-of-attack range. The separated 

shear layer undergoes transition to turbulent flow and then 

usually reattaches to the airfoil surface downstream. The 

separation bubble causes a large redistribution of pressure that 

results in pitching moment changes and decreased lift; it also

greatly increases the airfoil drag. This flowfield is also known 

to be unsteady and three dimensional. These factors make 

computational simulation extremely challenging. Therefore, 

understanding the behavior of the separation bubble is a key to 

understanding horn ice aerodynamics.

For this shape, it is somewhat paradoxical that the separation bubble features that make the flow complicated for 

analysis and computational modeling simplify the effects on 

lift, drag, and pitching moment. For example, several studies 

(Refs. 16 and 25 to 28) have shown that very simple geometries, such as a leading-edge “spoiler,” can reproduce the 

performance characteristics of a horn ice shape. A simple 

geometry representing the height, angle, and location of the 

ice horn, essentially generates an equivalent separation bubble 

on the airfoil and, hence, very similar performance results.

Also, the addition of surface roughness is shown in some

studies (Refs. 11, 25, 27, and 29) to have only minor effects 

on the integrated performance.

Streamwise ice has the smallest effect of the nonroughness 

ice accretions and has received the least attention in the 

literature. Therefore, an understanding of the aerodynamics of 

these accretions is not as developed as for the horn and 

spanwise-ridge ice. Streamwise ice is often formed as a result 

of rime icing conditions that occur at cold temperatures when 

the incoming water droplets freeze on the surface upon

impingement. As a result, the initial ice accretion that forms 

follows the contour of the airfoil surface. At large accretion 

times, or when the icing conditions are otherwise appropriate, 

streamwise ice shapes can occur that are not as conformal to 

the original airfoil surface and may grow a hornlike feature 

into the flow. In some cases, flow separation may result as 

with horn ice. However, these separation bubbles tend to be 

much smaller and, therefore, have less of an effect on overall 

flowfield and aerodynamic performance. This means that 

other flowfield features, such as trailing-edge separation, play 

at least an equal role in the aerodynamics. For streamwise ice, 

the specific ice geometry and surface roughness characteristics

can be important factors in the aerodynamics.

For the streamwise ice geometries that are conformal to the 

airfoil leading edge, the stagnation point at moderate lift 

coefficients occurs on the ice shape, and the boundary layer 

remains attached as it flows around the leading edge of the ice 

and downstream on the upper surface. Since the streamwise 

ice/airfoil intersection is not smooth, an adverse pressure 

gradient may exist in this area and flow separation may occur

in the junction region. The flow separation location is not 

fixed to a specific point on the ice shape like it is fixed to the 

tip for a horn shape, but it can move upstream or downstream 

depending upon the angle of attack and incoming boundarylayer state, which depend on the surface roughness, Reynolds 

number, and other factors. For a streamwise ice shape having 

a hornlike feature oriented into the flow direction, a larger 

separated flow region may exist especially at higher angles of 

attack. These separation bubbles are typically much smaller 

than for horn ice and thus do not have as large of an effect on 

the stall mechanisms (Refs. 11 and 30).

Spanwise-ridge ice accretions are perhaps most often associated with supercooled large-droplet (SLD) icing conditions.

Usually these accretions form downstream of leading-edge iceprotection systems and, despite their association with SLD, can 

occur for all drop-size ranges. Runback icing can form ridge 

accretions and usually occurs when there is a heated leadingedge ice-protection system that is not evaporating all of the 

impinging water. Some water flows downstream on the surface 

from the heated section and freezes on the cooler, unheated 

surface. Because of the formation mechanisms just described, 

ridges often exhibit extensive spanwise variation in their 

geometry. These properties, and the associated flowfield, make 

the spanwise ridge-type accretion different from the horn shapes 

discussed previously. Spanwise-ridge ice can have more severe 

aerodynamic effects, and the shapes themselves are typically 

more three-dimensional than horn shapes. Spanwise ridges are 

generally located farther downstream than horns or streamwise 

shapes. This distance allows the boundary layer to develop,

perhaps transition, or become transitional because of small ice 

roughness upstream of the spanwise ridge. Thus the spanwise 

ridge acts as a flow obstacle.

The unique characteristics of the ridge—a large spanwise 

variation and a downstream location—make the resulting 

aerodynamics and performance different from those for horn

ice. The chief similarity is the large separation bubble 

downstream of the spanwise ridge, which can be complex,

three dimensional, and highly unsteady. Spanwise ridges also 

have a separation bubble upstream of the ridge because the 

ridge is located well downstream of the stagnation point. This 

upstream separation can be a further challenge for computational simulation. The fact that the ridge is located down-
NASA/TP—2011-216929 5

stream of the stagnation point also amplifies the effect of 

airfoil geometry, which is characterized by the clean airfoil 

pressure distribution, much more so than in the horn case.

Other important features are the geometry, size, and location 

of the ridge on the airfoil (Ref. 31).

Later research conducted for spanwise ridges has revealed 

some potentially important differences in the fundamental 

aerodynamics between “short” and “tall” ridges. Broeren et al. 

(Refs. 32 and 33) distinguish between short and tall spanwiseridge shapes on the basis of their effect on the airfoil pressure 

distribution and resulting aerodynamics. In the case of 

spanwise-ridge ice, this distinction recently led Broeren et al. 

(Refs. 32 and 33) to propose this subsclassification of 

spanwise-ridge ice. In the case of short ridges, the separation 

bubble is small and stable and does not substantially increase 

in size with increasing angle of attack. The effect on the 

pressure distribution is minimal, thus resulting in significantly 

lower aerodynamic penalties relative to tall spanwise ridges.

Although much has been learned from this recent work, more 

research is required to further develop the short-ridge 

aerodynamic characteristics.

This brief summary of the salient characteristics of each 

type of ice shape presents a convenient classification of icedairfoil aerodynamics. Real ice shapes may not fit neatly into 

only one classification, and of course, it is not realistic to think 

of these classifications as rigid, or unchanging. Undoubtedly 

more research will yield greater insight into the aerodynamics 

and may lead to further development of these concepts, as has 

already occurred in the case of spanwise ridges. Perhaps an 

additional classification will be added in the future. Future 

considerations notwithstanding, the present analysis formed 

the foundation for the research described in this report.

4.0 Phase II: Subscale-Model Ice- Accretion Testing

The objective of this phase was to obtain high-fidelity ice 

shapes having characteristics of the four types developed in 

Phase I. To obtain these ice shapes, a subscale model was 

subjected to simulated, in-flight icing conditions in an icing 

wind tunnel. The icing conditions were based on the in-flight

icing environment that a commuter aircraft might encounter.

Molds were acquired of the ice accreted on the model under 

these conditions. Castings were then made from these molds 

and used as the high-fidelity ice shapes for the tests in 

Phase III. The purpose here was not to produce scaled iceaccretion geometry, but to generate ice accretions that were 

representative of the four ice classifications to use for 

aerodynamic simulation development in Phase III. 

The subscale ice accretions were acquired in Glenn’s IRT.

The IRT is a closed-return, refrigerated wind tunnel that 

contains a system of spray nozzles that are used to simulate 

in-flight icing cloud conditions (Ref. 34). The test section is 

72 in. (1.83 m) high by 108 in. (2.74 m) wide by 240 in. 

(6.10 m) long. It has a maximum speed of 570 ft/sec (174 m/s) 

with no model installed. The total temperature can be 

controlled from –20 to 33 oF (–29 to 0.5 oC) via a heat 

exchanger located in the settling chamber supplied by an 

external refrigeration plant. Also located in the settling 

chamber are 10 horizontal spray bars that contain 251 

individually controlled spray nozzles used to generate icing 

clouds within Code of Federal Regulations (CFR) Part 25, 

Appendix C icing certification envelopes (see Ref. 35).

The NACA 23012 airfoil model used in this phase of the 

testing had an 18-in. (0.46-m) chord and spanned the 72-in. 

(1.83-m) height of the IRT test section. The NACA 23012 

airfoil was chosen for this research program because it is 

representative of a family of airfoils commonly used in the 

airframe industry and has been shown to be aerodynamically 

sensitive to ice accretion. Thus, aerodynamic penalties are 

amplified, resulting in a more severe testing of the simulation 

methods. The model was equipped with pressure taps that 

facilitated model alignment and a removable leading-edge 

section that facilitated the molding of selected ice accretions.

For the spanwise-ridge ice accretion, an electric foil heater 

was attached to the removable leading edge. Icing conditions 

and heater settings were adjusted to create conditions where 

water flowed downstream over the heater and then froze as a 

spanwise ridge aft of the heater on both the upper and lower 

surfaces of the model. Figure 1 shows a photograph of the 

model in the IRT.

Selecting the icing conditions for this phase of the program 

began with the consideration of a full-scale commuter aircraft 

operating in various natural icing environments. As described 

by Blumenthal (Ref. 36), a representative commuter aircraft 

with a wing section similar to the NACA 23012 airfoil was 

reviewed and analyzed in terms of aircraft weight, airspeeds, lift 

requirements, and other factors. The analysis provided information on the desired airspeeds and model angles of attack to

Figure 1.—Subscale NACA 23012 ice-accretion model 

installed in the NASA Icing Research Tunnel.
NASA/TP—2011-216929 6

TABLE 1.—ICING CONDITIONS FOR SUBSCALE ICING TESTS—ICE ACCRETIONS 

SELECTED FOR AERODYNAMIC TESTING IN PHASE III

[IRT, Icing Research Tunnel; α, angle of attack; MVD, median volume diameter; LWC, liquid water content.]

Ice-shape

classification

IRT

run

Airspeed,

kn

α,

deg

MVD,

μm

LWC,

g/m3

Total temperature Static temperature Spray time,

min °F °C °F °C

Roughness ED0762 200 2.0 15.4 0.75 28.0 –2.2 18.5 –7.5 0.5

Horn ED0735 200 2.0 15.4 0.75 28.0 –2.2 18.5 –7.5 5.0

Streamwise ED0730 175 5.3 15.0 0.30 0.0 –17.8 –7.4 –21.8 5.0

Spanwise ridge ED0760 175 0.9 15.0 0.64 24.0 –4.4 16.8 –8.5 5.0

use in the testing. Icing cloud conditions were initially selected 

from the CFR Part 25, Appendix C, icing envelopes. Icing 

scaling methods (Ref. 18) were then applied to help attain 

reasonable similarity in impingement limits, the collection 

efficiency, the freezing fraction, the accumulation parameter, 

and other scale factors. In addition, for some cases, analysis was 

conducted using the LEWICE ice-accretion software program 

(Ref. 37). This analysis helped to ensure that the icing conditions would generate representative ice accretions. Further 

adjustments to the icing conditions were made so that the test

conditions fell within the IRT’s operating envelope.

The ice accretions produced in the IRT were recorded using 

photographs, ice tracings, and ice-depth measurements. When 

a particular ice accretion was selected for Phase III testing, the 

accretion was repeated and a mold was made for that run.

Molds were made of 16 ice accretions: 6 of these were horn 

accretions, 4 were streamwise accretions, and 3 each were 

roughness and spanwise-ridge accretions (see Ref. 36).

Castings were subsequently made from each of these molds.

The best representatives of the four classifications of ice 

shapes determined in Phase I formed the reference ice shapes 

for aerodynamic testing in Phase III. Table 1 provides the IRT

test conditions for these four ice accretions. These ice shapes 

and the resulting aerodynamic effects are described in the 

following section.

5.0 Phase III: Subscale-Model

Aerodynamic Testing

Phase III of the program explored methods for geometrically simulating ice accretions in each of the four classifications. Since each of the four types has different fundamental 

aerodynamics, if techniques can be developed to simulate 

these four shapes, then most ice accretions can be simulated.

A variety of simulation methods can be found in the literature 

and in practice, and these were categorized for the subscale 

testing. The simulation categories or methods considered were 

three-dimensional casting, two-dimensional smooth, simple 

geometry, and simple geometry with spanwise variation. The 

roughness associated with ice accretion can be accounted for 

by adding distributed grit roughness to each of the noncasting 

simulation methods. The highest fidelity simulation method is 

considered to be the three-dimensional casting, since this is 

manufactured directly from a mold of the ice accretion. It is 

considered to be the benchmark for aerodynamic data that the 

other simulation methods are evaluated against. The purpose 

of this subscale testing was to explore many variations of the 

simulation methods in order to evaluate their ability to 

reproduce the proper iced-airfoil aerodynamics. In this way, 

the accuracy of the various simulation methods was systematically quantified. This phase of the program also addressed 

uncertainties associated with the ice-shape geometry and 

aerodynamic performance measurements. Quantifying this 

accuracy at small scale and understanding the aerodynamic 

differences of the simulation methods was a key to successfully executing Phase VI of this program.

5.1 Ice Accretions and Simulation Methods

The ice accretions selected in each of the four categories are 

shown in Figure 2 to Figure 5. Figure 2 shows the ice 

accretion typical of initial roughness. There is a very smooth 

zone on the leading edge in the stagnation-point region, 

followed by distributed roughness downstream. Tracings for 

this type of ice shape were not obtained because the ice tracing 

method could not adequately capture such geometric features.

The horn shape shown in Figure 3 is a classic glaze ice shape 

with an upper and lower horn structure typical for this icing 

condition. The streamwise shape shown in Figure 4 is typical 

of a rime ice condition, having a smooth zone in the stagnation 

region with rime feathers and roughness downstream. Finally, 

the spanwise-ridge shape is shown in Figure 5. Both an upper 

and lower surface ridge formed because water flowing off of 

the heated surface froze downstream. Casting simulations 

were manufactured for aerodynamic testing from molds of 

each of these accretions.

The various lower fidelity simulation methods are described 

in detail by Busch et al. (Refs. 27, 28, and 38) and are 

summarized here using the horn ice shape as an example.

Two-dimensional smooth simulations have been commonly

used in previous work (Refs. 11 and 15 to 17). Digitized 
NASA/TP—2011-216929 7

Figure 2.—Photograph of subscale model ED0762 roughness 

shape. (No tracing was made for this shape.)

Figure 3.—Photograph and tracing of subscale model ED0735 

horn ice shape.

Figure 4.—Photograph and tracing of subscale 

model ED0730 streamwise ice shape.

Figure 5.—Photograph and tracing of subscale 

model ED0760 spanwise-ridge ice shape.
NASA/TP—2011-216929 8

Figure 6.—Comparison of three-dimensional casting, two-dimensional 

smooth simulation, and simple-geometry simulation for ED0735 

horn ice shape.

pencil tracings of ice shapes were processed using Surface 

Modeling and Grid Generation for Iced Airfoils (SmaggIce,

Ref. 39) to create this type of simulation. This software assists 

in removing the point anomalies that often occur in digitized 

pencil tracings. It can also be used for smoothing; however, no 

smoothing was performed with SmaggIce. This tracing was 

then “extruded” across the model. The resulting shape is 

shown in Figure 6 along with an image of the threedimensional casting for comparison. The simple-geometry 

simulation method entails replacing the major ice features 

with basic geometric shapes. The upper and lower surface 

horn structures were replaced with rectangular shapes that 

mimic the height, angle, and surface location (see Figure 6).

This simulation method is also based on previous work by 

Papadakis et al. (Refs. 16, 25, and 26), who used spoiler ice

simulations and methods by Kim and Bragg (Ref. 40). Other 

variations of the simple-geometry shape also were considered,

including a simulation where the horns had a periodic 

spanwise variation in height based on measurements of the 

three-dimensional casting simulation (Ref. 28). Both the twodimensional smooth and the simple-geometry simulations 

were also tested with various sizes of roughness applied to the 

external surfaces.

5.2 Experimental Methods

The subscale-model aerodynamic testing was carried out at 

the University of Illinois at Urbana-Champaign using the lowspeed, low-turbulence wind tunnel. This facility has a 33.6-in. 

(0.85-m) by 48-in. (1.2-m) test section capable of speeds up to 

Mach 0.20. An 18-in.- (0.46-m-) chord NACA 23012 model 

was designed and built specifically for this program. Figure 7

shows the NACA 23012 model installed in the University of 

Illinois wind tunnel. The model was designed with interchangeable leading edges that accommodated the various ice simulations. There was a baseline leading edge having the NACA 
NASA/TP—2011-216929 9

Figure 7.—Subscale NACA 23012 aerodynamic model 

installed in the University of Illinois wind-tunnel test section.

23012 profile that was used to document the un-iced, or clean, 

airfoil performance, and there were two ice leading edges with 

truncated nose geometry that allowed for the attachment of the 

ice simulations. The three-dimensional ice castings were made 

from a hard, two-part polyurethane material of the very low 

viscosity required to flow into the intricate details of the ice 

molds. The spanwise length of the finished castings was limited 

to one-third of the model span, so that three identical casting 

sections were required to cover the span of the model in the 

Illinois wind tunnel. The simulations bolted on to this leading 

edge and thus had a rigid, repeatable mounting system. The 

model had a main chordwise row of pressure taps, a secondary 

chordwise row, and a set of spanwise taps on the upper surface.

The three-dimensional casting and two-dimensional smooth iceshape simulations also had pressure taps installed at certain

locations that were identified in an earlier study (Refs. 36

and 41). The pressure tap placement was important on the 

irregular surfaces of the ice simulations to avoid unrepresentative static pressure readings.

The model lift and pitching moment data were acquired 

from a force balance and by integration of airfoil surface static 

pressures measured by an electronically scanned pressure 

system. Excellent agreement between these methods was 

obtained for the clean model configuration. The agreement 

was also good in most of the iced-model configurations with 

pressure-tapped ice simulations. There were a number of 

lower fidelity ice simulations, such as the simple-geometry 

shapes, that were not instrumented with pressure taps.

Therefore, the lift and pitching moment data shown in this 

report for the subscale-model tests were obtained from the 

force balance for consistency. Momentum-deficit methods

were used to compute the drag coefficient from total-pressure 

measurements collected by a traversable wake rake. This wake 

rake is shown downstream of the model in Figure 7. Also, 

surface-oil flow visualization was also conducted for selected 

cases. This method involves applying a light coat of mineral 

oil treated with fluorescent dye to the surface of the model in 

the region of interest. The tunnel flow condition is then set and 

maintained for a period of time sufficient to allow the local 

wall shear stress to cause the oil to flow on the surface. The 

resulting oil-flow patterns indicate boundary-layer transition 

and regions of attached and separated flow.

Busch (Ref. 38) provides more details about the experimental setup, including the experimental uncertainties summarized 

in Table 2. These uncertainties were all considered to be 

acceptable for this investigation. Although the relative 

uncertainty in quarter-chord pitching moment coefficient Cm

appears to be large because the reference value is small, the 

absolute uncertainty is reasonable. The methods of Allen and 

Vincenti (Ref. 42) were used to correct the angle of attack α, 

lift coefficient Cl, pitching-moment coefficient Cm, and drag 

coefficient Cd for wind-tunnel wall boundary effects. All data 

were collected at a Reynolds number of Re = 1.8×106 and a 

corresponding Mach number of M = 0.18.

TABLE 2.—ESTIMATED EXPERIMENTAL UNCERTAINTIES FOR AERODYNAMIC DATA 

FROM SUBSCALE MODEL TESTING IN UNIVERSITY OF ILLINOIS WIND TUNNEL

Aerodynamic quantity Reference 

value

Absolute 

uncertainty

Relative 

uncertainty,

percent

Angle of attack, α 4.16° ±0.02° ±0.48

Lift coefficient, Cl balance 0.548 ±0.00019 ±0.35

Pitching moment coefficient, Cm balance –0.0020 ±0.00023 ±12.1

Pressure coefficient, Cp –0.962 ±0.0045 ±0.47

Drag coefficient, Cd wake 0.0071 ±0.00014 ±1.9
NASA/TP—2011-216929 10

5.3 Results and Discussion

Numerous experimental trials were conducted to investigate 

the aerodynamic effectiveness of the various ice-shape 

simulation methods. Some sample results are provided here 

for each of the ice shapes listed in Table 1 and shown in 

Figures 2 to 5.

5.3.1 ED0762 Roughness

The aerodynamic results presented in Figure 8 show Cl, Cm,

and Cd versus α for the clean airfoil, the airfoil with the threedimensional casting, and three sizes of distributed roughness.

The average ice roughness height divided by the chord length 

on the ED0762 casting was k/c = 0.0025 on the upper surface 

and k/c = 0.0012 on the lower surface. The distributed 

roughness simulations were k/c = 0.0009 glass microbeads 

applied with an 80-percent concentration (80 percent of the 

surface area was covered by roughness elements), and 

k/c = 0.0026 and 0.0033 carborundum grains applied with a 

50-percent concentration. The smooth zone near the stagnation 

point (see Figure 2) was preserved, and the roughness was 

applied evenly to the upper and lower surface at the appropriate locations to mimic the actual ice accretion. Since the 

roughness shape ED0762 was (by definition) fundamentally 

three dimensional, the two-dimensional smooth and simplegeometry simulations were not applicable in this case. A twodimensional smooth simulation would simply be the clean 

airfoil leading-edge geometry. Thus, grit roughness was used 

exclusively to simulate the ice shape. Figure 8 shows the 

detrimental effect of a small amount of roughness on the 

performance of the NACA 23012 airfoil. For the airfoil with 

the three-dimensional casting, the maximum lift coefficient 

Cl,max and stalling angle αstall were reduced to 0.97 and 10.3°

from the clean-airfoil values of 1.48 and 14.4°, respectively.

The minimum drag coefficient based on three-dimensional 

casting data was increased by nearly a factor of 2 with the ice 

roughness.

The roughness altered the shape of the lift curve in the stall 

region as well. The clean NACA 23012 airfoil had a leadingedge stall. McCullough and Gault (Ref. 43) characterized 

airfoil stall types and defined leading-edge stall as, “abrupt 

flow separation from the leading edge, generally without 

subsequent reattachment.” The effect of this is observed in the 

sharp decrease in lift past Cl,max. The presence of the ice 

roughness precipitated a change in the stalling characteristics 

to trailing-edge stall. In trailing-edge stall, boundary-layer 

separation gradually progresses forward on the airfoil upper 

surface with increasing angle of attack (Ref. 43). This 

description matches the fundamental aerodynamics of the 

ice roughness classification. The roughness decreased

boundary-layer momentum, thus leading to early boundarylayer separation. The small changes in lift past Cl,max for the 

airfoil with the various ice simulations is indicative of trailingedge stall.

Figure 8.—Aerodynamic performance comparison for various 

simulations of the ED0762 roughness ice shape at a 

Reynolds number of 1.8×106 and a Mach number of 0.18; 

k/c, ice height per chord length.

Figure 8 also shows the varying effect of the increasing 

roughness size on the performance of the NACA 23012 

airfoil. The lift and pitching moment coefficients were not 

very sensitive to changes in roughness size for the ED0762 

simulations over the range tested. The data show that the most 

challenging aspect of ice roughness simulation lies in the drag 

coefficient. It was difficult to match the drag increase resulting 
NASA/TP—2011-216929 11

from the three-dimensional casting on the airfoil. To facilitate 

comparisons in drag coefficient, Busch et al. (Ref. 44)

developed a parameter to compare Cd over an appropriate α

range. The angle-of-attack range used in this research program 

was that over which Cl varied approximately linearly with α.

The parameter ΔCd,rms is presented as a percentage and is 

computed by determining the root mean square of the percent 

difference between the three-dimensional casting drag coefficient Cd,casting and a given simulation drag coefficient Cd,sim at 

each angle of attack in the predefined linear range (a total of N

angles of attack):

2

,sim ,casting

1 ,casting

,rms

100 percent

N i i

d d

i

i d

d

C C

C

C

N

=

  −   ×

  ∆ =

∑

For the ED0762 ice shape, ΔCd,rms was calculated on the 

interval –4° ≤ α ≤ 8°. The k/c = 0.0009 configuration had a 

ΔCd,rms value of 14.4 percent, whereas the k/c = 0.0026 and 

0.0033 configurations had ΔCd,rms values of 15.5 and 20.6

percent, respectively. The drag data indicate that the 

k/c = 0.0009 roughness simulation offered the closest match to 

the casting for lower angles of attack, whereas the 

k/c = 0.0026 roughness simulation offered the closest match at 

higher angles of attack. These data suggest that further 

experiments should be conducted with different grit sizes used

on the upper and lower surfaces. In this case, using 

k/c = 0.0026 roughness on the upper surface and k/c = 0.0009 

on the lower surface seems like the first logical combination.

Furthermore, estimates of the actual roughness size on the 

casting indicate that this offers the best geometric size match 

as well. It is also known that the concentration, or percent 

surface coverage of roughness is also important to the 

resulting aerodynamic performance. These effects were 

investigated in more detail in Phase VI of the program.

The aerodynamic data are summarized in Table 3 for the 

three ED0762 roughness simulations. The first two columns

list the lower fidelity simulation. The third column lists the 

difference in Cl,max between the airfoil with the given simulation versus the airfoil with the three-dimensional casting. The 

difference in percent of Cl,max for the three-dimensional casting 

configuration also is shown. Similar data are listed in the 

fourth column in terms of αstall, and the fifth column lists the 

corresponding value of ΔCd,rms. A value of zero in the last 

three columns is considered to show perfect agreement. The 

results show that the distributed roughness simulations caused 

lower (more conservative) Cl,max and αstall relative to the threedimensional casting configuration and that the k/c = 0.0009 

roughness gave the best drag comparison in terms of ΔCd,rms.

These data support the conclusion that Cl,max and αstall were 

relatively insensitive to the size of the grit roughness over the 

range tested. The k/c = 0.0026 configuration exhibited the best 

overall aerodynamic simulation of the three-dimensional

casting. This determination was based on that configuration 

having the closest match to the three-dimensional casting data 

in terms of Cl,max, with the values of αstall and ΔCd,rms very 

similar to those for the other roughness cases.

5.3.2 ED0735 Horn Ice

Figure 9 shows the effect of the ED0735 horn shape on the 

performance of the NACA 23012 airfoil. As described in 

Phase I, the horn geometry resulted in a large upper surface 

separation bubble that grew with increasing angle of attack. 

This is consistent with the thin-airfoil stall type (Ref. 43) 

resulting in Cl,max = 0.66 at αstall = 7.2° for the model with the 

three-dimensional casting simulation. The separation bubble 

also caused a significant redistribution of surface static 

pressure relative to the clean configuration. In terms of 

performance, this effect was observed most readily in the large 

dependence of the iced-airfoil pitching moment coefficient on 

angle of attack. 

The large areas of separated flow contributed to large 

increases in drag coefficient as illustrated in Figure 9 (note the 

scale change in Cd from Figure 8). As mentioned in the 

Phase I discussion, the key to the aerodynamic simulation of 

horn ice is to properly represent the size and location of the 

horns themselves. The data in Figure 9 show very little 

difference in the performance coefficients for all of the various 

simulation configurations. The effect of roughness added to 

the two-dimensional smooth and simple-geometry simulations 

TABLE 3.—SUMMARY OF SIMULATION EFFECTIVENESS FOR 

SUBSCALE MODEL ED0762 ROUGHNESS SHAPE

[Cl,max, maximum lift coefficient; αstall, stalling angle; ΔCd,rms, 

root-mean-square percent difference in drag coefficient; α, angle of attack.]

Simulation roughness Simulation Cl,max –

casting Cl,max

Simulation αstall –

casting αstall, 

deg

ΔCd,rms,

percent

(–4° ≤ α ≤ 8°)

Ice roughness height 

per chord length,

k/c

Area covered,

percent

0.0009 80 –0.055 (–5.7 percent) –1.06 14.4

0.0026 50 –0.036 (–3.7 percent) –1.02 15.5

0.0033 50 –0.065 (–6.7 percent) –1.03 20.6
NASA/TP—2011-216929 12

Figure 9.—Aerodynamic performance comparison for various 

simulations of the ED0735 horn ice shape at a Reynolds 

number of 1.8×106 and a Mach number of 0.18; k/c, roughness height per chord length.

was very small, with the possible exception of Cd. The ΔCd,rms

values computed on the interval –4° ≤ α ≤ 6° only ranged 

from 9.4 percent (simple geometry with k/c = 0.0009 roughness) to 11.9 percent (two-dimensional smooth with 

k/c = 0.0009 roughness). Busch et al. (Refs. 27, 28, and 38)

investigated many simulation variants for this particular ice 

shape to explore the effects in more detail. The authors also 

measured and comment on the spanwise variation in drag 

coefficient determined from the wake data. Busch and Bragg 

(Ref. 20) summarize these effects as they contribute to the 

uncertainties in subscale ice-shape simulation. The iced-airfoil 

Cd was measured to vary from 15 to 45 percent in the spanwise direction in some cases and did not appear to correlate 

with ice-shape geometry. The data indicate that comparisons 

of drag coefficient are difficult and that wake surveys should 

be acquired at multiple spanwise locations whenever possible.

Although the bulk of this research was focused on aerodynamic simulation as measured in terms of lift, pitching 

moment, and drag, simulation of the flowfield physics was 

equally important. That is, the low-fidelity simulations of ice 

accretion should replicate the key flowfield features. Since the 

flowfield properties were more difficult to measure and 

quantify, this was accomplished via analysis of chordwise 

pressure distributions and surface-oil flow visualizations.

Busch et al. (Refs. 27, 28, and 38) provide more documentation and descriptions of these results. An illustrative example 

is provided in Figure 10, which is a plot of the pressure 

coefficient Cp for three representations of the ED0735 ice 

shape. The effect of the upper surface separation bubble is 

seen in the region of approximately constant pressure from 

x/c = ‒0.04 to 0.10. A similar pressure signature on the lower 

surface corresponds to the smaller separation bubble due to the 

lower surface horn. The important point for Figure 10 is that 

there is excellent agreement in pressure coefficient for the 

range of simulation fidelities. The data provide some assurance that the key flowfield physics were preserved with the 

two-dimensional smooth and simple-geometry simulations.

The aerodynamic data are summarized in Table 4 for the 

ED0735 horn ice simulations in terms of maximum lift 

coefficient, stalling angle,, and root-mean-square percent 

difference in drag coefficient. Very accurate simulation was 

obtained in this case as quantified by these metrics. The addition 

of roughness to the two-dimensional smooth and simplegeometry configurations did little to improve the simulation 

accuracy. As described in Phase I, accurately representing the 

horn ice geometry is a key to accurate aerodynamic simulation, 

with roughness having little effect.
NASA/TP—2011-216929 13

Figure 10.—Comparison of chordwise pressure distribution for various simulations of the ED0735 horn ice shape at an airfoil angle of attack of 6.2°, a 

Reynolds number of 1.8×106

, and a Mach number of 0.18.

TABLE 4.—SUMMARY OF SIMULATION EFFECTIVENESS FOR SUBSCALE MODEL ED0735 HORN SHAPE

[Cl,max, maximum lift coefficient; αstall, stalling angle; ΔCd,rms, root-mean-square percent difference 

in drag coefficient; α, angle of attack; k/c, ice roughness height per chord length.]

Simulation Simulation Cl,max –

casting Cl,max

Simulation αstall –

casting αstall, 

deg

ΔCd,rms,

percent

(–4° ≤ α ≤ 6°)

Two-dimensional smooth 0.001 (0.2 percent) 0.00 10.7

Simple geometry –0.013 (–2.0 percent) 0.00 11.8

Two-dimensional smooth with 80-percent k/c = 0.0009 –0.017 (–2.6 percent) 0.00 11.9

Simple geometry with 80-percent k/c = 0.0009 –0.020 (–3.0 percent) 0.00 9.4
NASA/TP—2011-216929 14

5.3.3 ED0730 Streamwise Ice

Figure 11 summarizes the aerodynamic effect of the 

ED0730 streamwise ice simulations on the NACA 23012 

airfoil. The three-dimensional casting simulation reduced αstall

to 10.3° from the clean value of 14.4° and reduced Cl,max to 

1.02 from the clean value of 1.48. The three-dimensional

casting simulation increased Cm for α > 4° up to stall and 

increased the minimum Cd to 0.01204 from the clean minimum of 0.00696. Figure 11 also shows the resulting performance with the lower fidelity streamwise ice simulations

installed on the NACA 23012 airfoil (refer to Refs. 38 and 45

for geometry information). For both the two-dimensional 

smooth and simple-geometry simulations, αstall was 11.3°, 1°

higher than for the casting simulation. For the twodimensional smooth simulation, Cl,max was 1.15, 13.5 percent

higher than for the three-dimensional casting simulation. The 

simple-geometry simulation yielded a Cl,max of 1.10, 

7.8 percent higher than for the three-dimensional casting 

simulation. The roughness added to the lower fidelity 

simulations caused a reduction in maximum lift and stalling 

angle and subsequently better agreement with the threedimensional casting configuration. The pitching-moment data 

showed no major differences for the various ice-shape 

configurations except that they reflect the differences in 

stalling angle. The drag data reveal that the simple-geometry 

simulation had a minimum Cd that was much closer to the 

clean configuration than for the airfoil with the threedimensional casting, and it therefore had a very high ΔCd,rms of 

nearly 31 percent computed on the interval –4° ≤ α ≤ 8°. The 

comparison in drag coefficient for the two-dimensional

smooth simulation was much more favorable with 

ΔCd,rms = 9.4 percent. As was the case for Cl and Cm, the 

addition of roughness to both the two-dimensional smooth and 

simple-geometry simulations helped improve the agreement 

with the three-dimensional casting benchmark.

The flowfield features discussed in Phase I are a key to 

understanding these differences in aerodynamic performance.

Broeren et al. (Ref. 45) and Busch (Ref. 38) describe surfaceoil flow visualization data for several of the simulation 

configurations. An example is depicted in Figure 12 for the 

three-dimensional casting configuration at α = 10°. The flow 

is from left to right, and the casting is visible on the left side of 

the image. The markings at the top and bottom indicate 

chordwise position, x/c in percent. The markings along the 

right side of the image indicate spanwise position in inches 

measured from the pressure tap row. The oil streaks are 

aligned with the chordwise flow direction and give way to a 

more speckled pattern between x/c = 0.50 and 0.60, indicating 

boundary-layer separation at this location. Similar flow 

visualizations performed at α = 6° and 8° clearly show that the 

aerodynamics leading to stall were driven by boundary-layer 

separation on the upper surface moving forward from the 

.

Figure 11.—Aerodynamic performance comparison for various 

simulations of the ED0730 streamwise ice shape at a 

Reynolds number of 1.8×106 and a Mach number of 0.18; 

k/c, roughness height per chord length.
NASA/TP—2011-216929 15

Figure 12.—Surface-oil flow visualization image for the 

ED0730 streamwise ice shape three-dimensional

casting simulation at an airfoil angle of attack of 10°, 

a Reynolds number of 1.8×106

, and a Mach number 

of 0.18.

trailing edge with increasing angle of attack. Therefore, the 

airfoil with the ED0730 three-dimensional casting simulation

had a trailing-edge stall. A comparison of flow-visualization 

and surface-pressure-distribution data for the two-dimensional

smooth and simple-geometry configurations showed less

boundary-layer separation (in comparison to the threedimensional casting configuration) at identical angles of 

attack. Therefore, the roughness present on the threedimensional casting was likely responsible for the larger 

extent of trailing-edge separation at higher angles of attack 

leading to stall. Thus the grit roughness that was added to the 

two-dimensional smooth and simple-geometry shapes helped

to improve the comparison with the three-dimensional casting 

data. This effect of roughness increasing the extent of 

boundary-layer separation explains the lower lift coefficient 

and higher drag coefficient observed for α > 6° for the threedimensional casting and roughened configurations.

Differences in drag coefficient at lower angles of attack 

are more difficult to explain. For example, on the interval 

–6° ≤ α ≤ 6°, the drag coefficient of the two-dimensional

smooth configuration follows that of the casting much more 

closely than does the drag coefficient of the simple-geometry 

simulation. This is discussed in detail in Broeren et al. (Ref. 45),

and the following discussion is adapted from that paper.

There are a number of mechanisms that may be responsible 

for the difference in drag coefficient for these configurations.

First, the difference in the size and type of roughness may 

have influenced the size of any local separation zone at the 

ice-shape/airfoil junction. Second, the size and type of 

roughness may have affected the amount of boundary-layer 

momentum extracted by the roughness elements themselves.

Often this is described as roughness element drag. Finally, the 

size and type of roughness may affect the boundary-layer 

transition process, thus affecting the contribution to drag due 

to skin friction in the downstream boundary layer. Determining the complicated balance of these and other factors was 

beyond the scope of this work. However, it was clear that 

there were obvious differences in the surface roughness 

characteristics of the simulations. The three-dimensional

casting simulation had the rime feather features associated 

with this type of ice accretion. Aerodynamically, this could 

be described as three-dimensional roughness. The twodimensional smooth simulation had spanwise running 

“grooves” that resulted from the pencil tracing of the rime 

feathers on the casting. The surface of the simple-geometry 

simulation was completely smooth. A simple experiment was 

performed where the spanwise grooves in the two-dimensional

simulation were smoothed with a thin-film surface covering.

The resulting measurements yielded drag coefficients that 

were much closer to that of the simple-geometry simulation.

These results, combined with the flow-visualization results at 

higher angle of attack indicated that the roughness characteristics of streamwise ice are important for accurate aerodynamic 

simulation. This idea was developed by Bragg et al. (Refs. 22

and 23) at the outset of this research program, but it could not 

be confirmed because of the lack of available data at that time.

This notion led to a detailed study of roughness size and 

concentration for the streamwise ice accretion. Broeren et al. 

(Ref. 45) and Busch (Ref. 38) describe these results in detail,

and they are summarized here. The effect of roughness size 

applied to the two-dimensional smooth and simple-geometry

shapes was investigated using the baseline concentrations of 

80 percent for the k/c = 0.0009 microbeads and 50 percent for 

the k/c = 0.0026 and 0.0033 carborundum grains. Over this 

range, Cl,max decreased nearly linearly with increasing size of 

added roughness. This behavior is interesting from a simulation 

perspective because it is different from the effect of the same 

roughnesses applied directly to the airfoil. In that case, as 

discussed earlier for the ED0762 roughness shape, Cl,max was 

practically independent of roughness size over the range tested.

The effects of roughness concentration were investigated for 

k/c = 0.0009 and 0.0026 applied to two-dimensional smooth,

streamwise ice simulation. There was an effective range of 

concentration where Cl,max decreased with increasing concentration. After a certain concentration was applied, 30 percent for 

k/c = 0.0009 and 20 percent for k/c = 0.0026, no significant 

decline in Cl,max was observed for higher concentrations.
NASA/TP—2011-216929 16

TABLE 5.—SUMMARY OF SIMULATION EFFECTIVENESS FOR 

SUBSCALE MODEL ED0730 STREAMWISE SHAPE

[Cl,max, maximum lift coefficient; αstall, stalling angle; ΔCd,rms, root-mean-square percent difference 

in drag coefficient; α, angle of attack; k/c, ice roughness height per chord length.]

Simulation Simulation Cl,max –

casting Cl,max

Simulation αstall –

casting αstall, 

deg

ΔCd,rms,

percent

(–4° ≤ α ≤ 8°) 

Two-dimensional smooth 0.137 (13.5 percent) 1.03 9.4

Simple geometry 0.087 (8.6 percent) 1.02 30.8

Two-dimensional smooth with 80-percent k/c = 0.0009 0.027 (2.7 percent) 0.01 2.1

Simple geometry with 80-percent k/c = 0.0009 0.013 (1.3 percent) –0.02 4.8

As described by Bragg et al. (Refs. 22 and 23) in their 

review, this effect of roughness concentration has been 

observed in other studies for roughness applied directly to the 

airfoil surface. This was also investigated in Phase VI of this 

program. Broeren et al. (Ref. 45) also present and discuss the 

results for drag coefficient, which were much more complicated 

and therefore difficult to summarize succinctly. The results do 

show that roughness size and concentration can be equally 

important. From a practical perspective, it is very difficult to 

quantify the size and concentration of the rime feather roughness of streamwise ice. Therefore, it is equally difficult to 

determine what roughness size and concentration best simulates 

the rime feather roughness. This is clearly a significant

uncertainty in the aerodynamic simulation process for 

streamwise ice.

The aerodynamic effectiveness of the lower fidelity simulations and added roughness is further quantified in Table 5 for 

Cl,max, αstall, and ΔCd,rms. These data clearly show that the most 

accurate simulation required the addition of roughness. Note, in 

particular, the large improvement in drag performance with the 

roughness added to the simple-geometry simulation.

5.3.4 ED0760 Spanwise-Ridge Ice

Figure 13 summarizes the effect of the ED0760 spanwiseridge ice on the NACA 23012 airfoil performance for various 

simulation fidelities (refer to Refs. 38 and 45 for geometry 

information). Inspection of the iced-airfoil lift curves shows 

that identifying the maximum lift coefficient is difficult and 

perhaps more subjective than for the previous ice shapes. This 

parameter was determined primarily by analyzing the surface 

pressure distributions for which a large chordwise extent of 

separated flow could be identified. For the three-dimensional 

casting configuration, this led to Cl,max = 0.97 at αstall = 14.2°. 

Although the stalling angle was similar to that of the clean 

airfoil, the lift coefficient was substantially degraded. For the 

two-dimensional smooth configuration, Cl,max was 0.95, which 

agreed well with the three-dimensional casting configuration, 

except that the stall occurred at 12.2°, 2° lower. The simplegeometry simulation resulted in more severe degradation, with 

Cl,max determined to be 0.78 at αstall = 11.1°. This difference in 

performance is further illustrated in the pitching moment 

variation with angle of attack being much more negative (nose 

down) than for the other simulations for α > 7°.

The effect of the spanwise-ridge shape on drag coefficient 

was similar in magnitude to that for the horn shape (see 

Figure 9). The minimum Cd for the airfoil with the threedimensional casting spanwise ridge was 0.045 in comparison 

to 0.036 for the three-dimensional casting horn shape. A major 

contribution in drag to the former configuration was due to the 

lower surface ridge. As depicted in Figure 5, this ridge was 

large and nearly perpendicular to the airfoil surface, thus 

amounting to a lower surface spoiler. Other research has 

shown that lower surface ice can have a large effect on drag 

but tends to have only a minimal effect on lift at higher 

(positive) angles of attack (Refs. 46 and 47). The drag data in 

Figure 13 show that the lower fidelity simulations were not 

very effective in reproducing the effect of the threedimensional casting, particularly outside of the range 

0° ≤ α ≤ 5°. The addition of roughness to the lower fidelity 

simulations did have a measurable effect, but it did not 

significantly improve the aerodynamic comparison with the 

three-dimensional casting configuration.

As discussed for the previous ice shapes, an extensive 

analysis of the iced-airfoil flowfield was conducted to better 

understand the variations in the aerodynamic performance 

data. These results are described in detail by Broeren et al. 

(Ref. 45) and Busch (Ref. 38) and are summarized here. 

Surface-oil flow visualization performed for various spanwiseridge simulations indicated that the time-averaged surface 

flow was characterized by three-dimensional features. At 

α = 8°, there was some evidence of a small separation bubble 

associated with the upper surface ridge at certain spanwise 

stations. This was most pronounced for the simple-geometry 

simulation and was consistent with the larger degradations in

aerodynamic performance associated with this configuration.
NASA/TP—2011-216929 17

Figure 13.—Aerodynamic performance comparison for various 

simulations of the ED0760 spanwise-ridge ice shape at a 

Reynolds number of 1.8×106 and a Mach number of 0.18; 

k/c, roughness height per chord length.

The flowfield of all three configurations had evidence of 

trailing-edge separation for this angle of attack as well. Both 

of these features persisted at higher angles of attack leading to 

stall. As a result, the iced-airfoil stall type was classified as a 

combination of thin-airfoil and trailing-edge stall. In this case, 

the combination of flowfield features proved to be difficult to 

simulate accurately with the lower fidelity methods.

The surface pressure distribution comparison of Figure 14

offers a further comparison of the simulation methods. It is 

important to note that, although the surface flow visualization 

indicated spanwise variation, the pressure data were only 

measured at a single spanwise location. This location, 

however, was identical for all configurations. For all of the 

iced-airfoil configurations, there were characteristic discontinuities in Cp due to both the upper and lower surface ridges.

All three cases had a similar pressure signature on the lower 

surface from x/c ≈ 0.15 to ≈ 0.35, which indicated that the 

separation bubble was associated with the lower surface ridge.

Since this ridge was large and relatively two-dimensional, all 

three simulation methods generated a similar pressure 

signature and likely had a similar lower surface flowfield.

The more interesting results are, of course, for the upper 

surface. For the three-dimensional casting and twodimensional smooth cases, the flow accelerated to a Cp of –2.2 

to –2.4, then decelerated in front of the ridge. The change in 

Cp across the ridge (i.e., x/c = 0.10 to 0.14) indicated flow 

acceleration over the ridge. This was followed by a steep 

pressure recovery with Cp that began to match the clean values 

at x/c = 0.50. There was a small divergence of the trailingedge pressure for the three-dimensional casting and twodimensional smooth cases from the clean configuration that 

was consistent with the trailing-edge separation observed in 

the flow visualization. For the simple-geometry simulation, 

the region of nearly constant pressure (from x/c = 0.14 to 0.24)

indicated that a larger separation bubble formed aft of the 

ridge, consistent with the flow visualization. The data show 

that the flowfields for the three-dimensional casting and twodimensional smooth configurations matched fairly closely, 

both of which differed from the simple-geometry configuration. The chief difference in the upper-surface ridge geometry 

between the two-dimensional smooth and the simple-geometry 

simulation was that, in the former case, the leading and trailing faces of the ridge were rounded to match the traced cross 

section shown in Figure 5. In contrast, the simple-geometry 

simulation was a simple rectangle whose leading and trailing 

faces were essentially perpendicular to the airfoil surface.

The importance of such a seemingly minor geometric detail 

to accurate aerodynamic simulation illustrates the potential 

difficulties for spanwise-ridge shapes. Bragg et al. (Refs. 22 and 

23) discuss some aerodynamic features that can potentially
NASA/TP—2011-216929 18

Figure 14.—Comparison of chordwise pressure distribution for the various simulations of the ED0760 spanwise-ridge ice shape at an airfoil attack of attack of

8.2°, a Reynolds number of 1.8×106

, and a Mach number of 0.18.

contribute to this difficulty. The fact that the ridge itself was 

located downstream of the leading edge has several implications. A boundary layer develops on the smooth leading edge 

upstream of the ridge. The characteristics of this boundary 

layer depend on many factors including the airfoil geometry, 

pressure distribution, and Reynolds number. For forwardloaded airfoils like the NACA 23012, an ice ridge farther 

downstream would be located in a region where there would 

be a significant adverse pressure gradient on the clean airfoil.

These factors add complexity to the flowfield via boundarylayer interactions with the ice ridge and adverse pressure 

gradient. Contrast this with the situation for ice located on the 

airfoil leading edge: the stagnation point is located on the 

accretion, and the pressure gradient is normally favorable.

Aerodynamic simulation of spanwise ridges of similar size 

and type to the ED0760 case used here was addressed in later 

studies (Refs. 32 and 33) and led to further development of the 

spanwise-ridge ice classification described in Phase I.

More evidence of the complicated nature of the spanwiseridge simulation is shown in Figure 13 for the cases where 

roughness was added to the lower fidelity simulations. A 

number of roughness cases were attempted and these results 

are presented and discussed by Broeren et al. (Ref. 45) and 

Busch (Ref. 38). As can be seen in Figure 13, there is no clear 

trend in the effect of added roughness. For the twodimensional smooth simulation, the added roughness compromised the agreement with the three-dimensional casting 

data. The k/c = 0.0033 data had the least detrimental effect, 

but the other roughness configurations (not shown) resulted in 

poorer agreement. The maximum lift coefficient was reduced 

to 0.87 in comparison to 0.95 for the two-dimensional smooth 

configuration without roughness and to 0.97 for the airfoil 

with the three-dimensional casting simulation. Relative to 

the two-dimensional smooth simulation results, αstall was 

unchanged when the roughness was added.

Comparisons in drag coefficient were complicated by the

spanwise variations observed in the surface-flow visualization 

results. Since the wake data were acquired at a single spanwise 

station, it was unclear if the data were representative of the 

drag of the iced airfoil. Therefore, the root-mean-square
NASA/TP—2011-216929 19

differences in drag coefficient were calculated using the force 

balance data for the ED0760 ice-shape simulations. Typically, 

drag coefficients obtained from the force balance are not 

reported because of inaccuracies due to model end effects and 

sidewall interactions. Since what was calculated here amounts 

to a difference in Cd values over a given α range, these 

differences were determined to be representative, whereas the 

absolute values would suffer from the described deficiencies.

Therefore, the comparisons of the root-mean-square percent 

difference in drag coefficient from the force balance between 

the lower fidelity simulation and the three-dimensional casting 

simulation over a given angle-of-attack range, ΔCd,rms,bal,

represent a valid analysis on a relative basis. In this case, the

α range was –4° to 8°. For the two-dimensional smooth 

simulation relative to the three-dimensional casting, ΔCd,rms,bal

was 5.7 percent. The addition of k/c = 0.0033 roughness 

caused ΔCd,rms,bal to increase to 28.6 percent. Different effects 

were noted for the k/c = 0.0009 roughness added to the 

simple-geometry simulation. Slightly better agreement was 

obtained in maximum lift coefficient, but the effect of the 

roughness was that this occurred 1° lower in α. The comparison in drag coefficient also improved slightly, with the 

addition of roughness resulting in a ΔCd,rms,bal of 20.9 percent

compared with 26.2 percent without added roughness. The 

contrasting nature of these results further illustrates the 

complexity of the aerodynamic simulation of the ED0760 

spanwise-ridge shape.

Table 6 summarizes the aerodynamic data for the ED0760 

spanwise-ridge ice simulations in terms of Cl,max, αstall, and 

ΔCd,rms. The data show that the most accurate lower fidelity 

simulation was the two-dimensional smooth configuration. The 

other configurations could not reproduce the complicated 

flowfield features that were associated with this ice shape.

These data clearly show the large negative impact of added 

roughness. It is important to note that the flowfield characteristics of the ED0760 spanwise-ridge shape were likely some 

combination of the tall and short ridge shapes described in 

Phase I (also see Refs. 32 and 33). The facts that a large 

separation bubble emanating from the upper surface ridge was 

not observed and that added roughness had a large impact on 

the performance suggest that the ridge was not tall. Conversely, 

the key characteristic of short ridges—a small, stable separation 

bubble—also was not observed. Therefore, simulation results 

for this case are not necessarily indicative of large spanwiseridge ice shapes.

5.4 Summary of Simulation Effectiveness for 

Phase III

The results presented here for the Phase III research effort 

were selected to be a representative sample of the total data set. 

Much was learned about the effectiveness of the various 

simulation methods for each of type of ice shape. In all cases the 

performance of the NACA 23012 airfoil with the threedimensional casting simulation was taken as the benchmark, 

and all other results are relative to it. The aerodynamic data are 

summarized in Table 3 to Table 6 for each of the ice shapes 

tested in Phase III. For each of the four ice shapes, the most 

accurate lower fidelity simulation was selected and included in 

Table 7. Selecting the most accurate was somewhat subjective 

because there was not a single simulation of each casting that 

had the lowest values of each of the three parameters (i.e., Cl,max, 

αstall, and ΔCd,rms). More weight was given to Cl,max and αstall

because these values had less uncertainty than ΔCd,rms. This was 

due mainly to the potential spanwise variation in drag that was 

not quantified for all the cases tested. Furthermore, the lower 

fidelity simulations with the best accuracy in terms of Cl,max, and 

αstall also had fairly low values of ΔCd,rms. For example, the most 

accurate simulation of the ED0730 streamwise shape was the 

simple-geometry simulation with k/c = 0.0009 roughness 

because it had the best accuracy in terms of Cl,max and had an 

αstall value that was very close to the two-dimensional smooth 

simulation with k/c = 0.0009 roughness (see Table 5). The 

ΔCd,rms of 4.8 percent for the former simulation was still 

considered to be fairly low, implying good overall comparison 

in Cd between the lower fidelity simulation and the threedimensional casting.

TABLE 6.—SUMMARY OF SIMULATION EFFECTIVENESS FOR SUBSCALE MODEL ED0760 

SPANWISE-RIDGE SHAPE

[Cl,max, maximum lift coefficient; αstall, stalling angle; ΔCd,rms bal, root-mean-square percent difference 

in drag coefficient (balance); α, angle of attack; k/c, ice roughness height per chord length.]

Simulation Simulation Cl,max –

casting Cl,max

Simulation αstall –

casting αstall,

deg

ΔCd,rms,bal,

percent

(–4° ≤ α ≤ 8°)

Two-dimensional smooth –0.023 (–2.4 percent) –1.98 5.7

Simple geometry –0.193 (–20.0 percent) –3.09 26.2

Two-dimensional smooth with 50-percent k/c = 0.0033 –0.100 (–10.3 percent) –2.04 28.6

Simple geometry with 80-percent k/c = 0.0009 –0.149 (–15.4 percent) –4.05 20.9
NASA/TP—2011-216929 20

TABLE 7.—SUMMARY OF SIMULATION EFFECTIVENESS FOR THE MOST ACCURATE 

SIMULATIONS FOR EACH OF THE SUBSCALE MODEL ICE SHAPES

[Cl,max, maximum lift coefficient; αstall, stalling angle; ΔCd,rms, root-mean-square percent difference

in drag coefficient; α, angle of attack; k/c, ice roughness height per chord length.]

Ice shape Simulation Simulation Cl,max –

casting Cl,max

Simulation αstall 

–

casting αstall,

deg

ΔCd,rms,

percent

(linear α

range)

Classification Configuration

Roughness ED0762 50-percent k/c = 0.0026 roughness –0.036 (–3.7 percent) –1.02 15.5

Horn ED0735 Two-dimensional smooth 0.001 (0.2 percent) 0.00 10.7

Streamwise ED0730 Simple geometry with 80-percent k/c = 0.0009

 roughness

0.013 (1.3 percent) –0.02 4.8

Spanwise ridge ED0760 Two-dimensional smooth –0.023 (–2.4 percent) –1.98 5.7

The performance data acquired in Phase III and summarized in Table 7 provide a measure of the aerodynamic

uncertainty associated with quantifying the ice-accretion 

geometry and subsequent iced-airfoil performance. Since it is 

assumed that the three-dimensional casting configurations 

have no uncertainty in terms of ice-accretion geometry, the 

primary source of uncertainty in these data was the aerodynamic performance measurement. The data for the lower

fidelity simulations also contain uncertainties because the 

accretion geometry was represented with less fidelity and 

there were uncertainties in the aerodynamic performance 

measurement. The data in Table 7 for the Cl,max comparison 

indicate that these values are an order of magnitude larger than 

the measurement uncertainty given in Table 2. In the case of 

stalling angle, there were significant effects of ice-accretion 

geometry uncertainty for roughness and spanwise-ridge ice, 

whereas for horn and streamwise ice, the simulation accuracy 

was similar to the measurement uncertainty. Visual inspection 

of the Cd versus α plots in Figure 8, Figure 9, Figure 11, and 

Figure 13 shows that the lower fidelity simulations that had 

good agreement with the casting had a corresponding ΔCd,rms

value that was about 10 percent or less. This was the case for 

all but the ED0762 roughness simulation in Table 7, which

had a value of 15.5 percent. However, it was noted in the 

discussion of this case that better drag simulation could likely 

be achieved using different sizes of roughness on the upper 

and lower surfaces.

The combined results of the subscale-model aerodynamic 

simulation studies indicated that different simulation methods 

should be used to best represent the aerodynamics of the iced

airfoil according to the different types of ice accretions based 

on the governing flow physics. This confirmed the ideas that 

were developed in Phase I.

• For ice roughness, good aerodynamic simulation was 

achieved using distributed grit roughness, but a combination of sizes may be needed to accurately simulate drag 

coefficient, in addition to maximum lift coefficient and 

stalling angle.

• For horn ice, the dominance of the separation bubble flowfield made the two-dimensional smooth and simplegeometry simulations nearly equal in effectiveness. The 

addition of roughness had little effect on the simulation 

fidelity. Better aerodynamic fidelity was achieved with 

three-dimensional simulations (see Busch et al. (Refs. 27, 

28, and 38)), but these are complicated to build and 

require detailed ice measurements.

• For streamwise ice, roughness had to be added to twodimensional simulations to obtain the best agreement in 

performance with the casting configuration. In this case, 

the size and concentration had significant effects such 

that these parameters should be taken into account. This 

was consistent with the effect of the roughness on the 

trailing-edge stall characteristics of the airfoil with the 

streamwise ice configurations.

• For spanwise-ridge ice, the performance effects were

best reproduced by the two-dimensional smooth simulation. Attempts to improve the simulation fidelity were 

largely unsuccessful because of the inability to reproduce 

the three-dimensional flowfield features. In this case, the 

size and location of the upper surface ridge on the 

NACA 23012 model did not result in a large, closed, 

two-dimensional separation bubble. There was also evidence of trailing-edge separation. The combination of 

these effects complicated the ability to simulate the performance of the casting with lower fidelity geometries.

The aerodynamic data for the various levels of ice-shape 

simulation fidelity gathered in Phase III aided in confirming 

and further refining the ice-shape classifications described in 

Phase I. The data illustrate the range in performance data for

typical simulation methods as applied to a subscale model.

Establishing the relationship between simulation fidelity and 

the resulting aerodynamic performance was a key goal of this 

program. Accomplishing this goal for the subscale model led 

to increased understanding of the effects, which was applied 

directly to the full-scale model research phases. This is 

described in Phases IV and V.
NASA/TP—2011-216929 21

6.0 Phase IV: Full-Scale-Model

Ice-Accretion Testing

The objective of this phase was to obtain molds of ice accretions in each of the four ice classifications on a full-scale airfoil.

In a scaling methods development effort such as this, it is 

important to have a set of benchmark data and firm knowledge 

of the full-scale phenomena. This information provides 

validation data for both the subscale testing and scaling methods 

developed. In order to gain full-scale aerodynamic effects 

information for the four types of ice, accurate representations of 

the ice needed to be obtained. Therefore, a full-scale ice 

accretion model was built and tested in the IRT. This effort 

provided the full-scale, reference ice shapes for the program.

The model built for this phase was again a two-dimensional, 

NACA 23012 that spanned the IRT test section from floor to 

ceiling. The model was machined from solid aluminum and had 

a removable leading edge section for making molds of the ice 

accretions. It had a chord length of 72 in. (1.83 m)—4 times that 

of the subscale model. Figure 15 shows a photograph of the 

model mounted vertically in the IRT. It was instrumented with 

pressure taps for alignment purposes and with thermocouples to 

monitor model temperature.

Test conditions for the full-scale icing tests were selected as 

indicated in the Phase II discussion: that is, icing conditions 

that a commuter aircraft might encounter in flight, as defined 

by Appendix C icing conditions, and that would yield ice 

accretions falling within the four ice classifications described 

in Phase I. Some adjustments in the test conditions were made 

because of limitations in the IRT operating envelope. To 

generate the spanwise-ridge ice accretion, an electric foil 

heater was attached to the removable leading edge, similar to 

the subscale-model testing (Phase II). Icing conditions and 

heater settings were adjusted such that water flowed downstream past the heated surface and then froze as a spanwise 

ridge on both the upper and lower surfaces of the model.

The full-scale ice accretions produced in the IRT were, as 

in the subscale testing, recorded using photographs, ice 

tracings, and ice-depth measurements. When a particular ice 

accretion was selected for the Phase V testing, the accretion 

run was repeated, and the removable leading edge and 

resulting ice accretion were detached from the model. The 

leading edge and ice were installed in a mold box, and the 

mold was poured. Molds were made of 10 ice accretions.

There were three each of the horn and streamwise accretions 

and two each of the roughness and spanwise-ridge accretions.

Castings were subsequently made from each of these molds.

The castings were then prioritized according to which were the 

best representatives of the four ice classifications and then 

according to which were of most interest for forming a fullscale, high-fidelity, ice-contaminated aerodynamic validation 

Figure 15.—Full-scale NACA 23012 ice-accretion model 

installed in NASA Icing Research Tunnel.

database. A total of six ice shapes were selected for aerodynamic testing in Phase V, and the IRT test conditions are 

provided in Table 8. There were two each of the roughness 

and streamwise shapes and one each of the horn and spanwiseridge shapes. These ice shapes and the resulting aerodynamic 

effects are described in the following section.

7.0 Phase V: Full-Scale-Model

Aerodynamic Testing

In Phase V of the program, aerodynamic testing was conducted to establish a set of high-fidelity benchmark data useful 

for the development of simulation methods on the subscale 

model. The ice molds acquired in Phase IV were used to 

develop high-fidelity, ice-casting simulations for testing on a 

72-in.- (1.83-m-) chord NACA 23012 model. This model was 

considered to be full-scale or near full-scale for the purposes 

of this research program. The aerodynamic performance 
NASA/TP—2011-216929 22

TABLE 8.—ICING CONDITIONS FOR FULL-SCALE ICING TESTS—ICE ACCRETIONS 

SELECTED FOR AERODYNAMIC TESTING IN PHASE V

[IRT, Icing Research Tunnel; α, angle of attack; MVD, median volume diameter; LWC, liquid water content.]

Ice-shape

classification

IRT run Airspeed,

kn

α,

deg

MVD,

μm

LWC,

g/m3

Total temperature Static temperature Spray time,

oF oC oF oC min

Horn EG1164 175 5.0 20 0.85 28.0 –2.2 20.8 –6.2 11.3

Streamwise 1 EG1162 150 2.0 30 0.55 –8.0 –22.2 –13.5 –25.3 10.0

Roughness 1 EG1126 200 2.0 20 0.50 28.0 –2.2 18.6 –7.4 2.0

Spanwise ridge EG1159 150 1.5 20 0.81 20.0 –6.7 14.8 –9.6 15.0

Streamwise 2 EG1125 200 2.0 15 0.30 4.0 –15.6 –5.3 –20.7 20.0

Roughness 2 EG1134 200 2.0 40 0.55 4.0 –15.6 –5.3 –20.7 2.0

testing was carried out at the Office National d’Etudes et 

Recherches Aérospatiales (ONERA) F1 pressurized wind 

tunnel to achieve near-flight Reynolds number. Only threedimensional casting simulations were tested on the full-scale 

model in order to maximize the number of different ice 

accretions that could be investigated. The pressurized wind 

tunnel was also used to vary Re from 4.5×106 to 16.0×106 and 

M from 0.10 to 0.28 in order to determine the sensitivity of the 

aerodynamic performance to these parameters. The set of 

benchmark data acquired in this phase was then used extensively in Phase VI to complete the research program.

7.1 Ice Accretions Tested

The ice accretions that were acquired in Phase IV and 

selected for full-scale model aerodynamic testing are shown in 

Figure 16 to Figure 21. Six ice accretions were tested: one 

horn shape, one spanwise-ridge shape, two streamwise shapes,

and two roughness shapes. Two each of the streamwise and 

roughness shapes were selected for full-scale testing because 

these were thought to be more challenging for aerodynamic 

subscale simulation. Therefore, it was decided that variations 

within these categories (i.e., streamwise ice and roughness) 

would be useful in quantifying the uncertainties associated 

with subscale simulations of these accretions. 

The horn shape in Figure 16 shows the classic upper surface 

horn typical of this glaze-type accretion. The streamwise 

shape in Figure 17 is more conformal to the leading-edge 

radius, with a smooth zone on the nose followed by downstream rime feather roughness. The glaze roughness in 

Figure 18 had a smooth zone in the stagnation region followed 

by large roughness downstream. The spanwise-ridge shape in 

Figure 19 was formed by applying a heater mat to the model 

leading edge. The heat input and icing conditions were 

adjusted to accrete the upper and lower surface ridges shown 

in Figure 19. The streamwise shape in Figure 20 was selected 

for aerodynamic testing because it had a more “pointed” 

geometry at the leading edge and was less conformal to the 

surface than the other streamwise shape (Figure 17). The 

remaining roughness case in Figure 21 was formed in cold 

conditions, resulting in very fine rime feathers downstream of 

the smooth stagnation region that were very different in size 

and distribution from the glaze roughness case in Figure 18.

Casting simulations were manufactured for aerodynamic 

testing from the molds of these accretions acquired in 

Phase IV of the program.

Figure 16.—Tracing and photograph of EG1164 horn ice 

shape. Tracing was taken from ice casting used for 

aerodynamic testing; pressure orifice locations are 

indicated by open circles.
NASA/TP—2011-216929 23

Figure 17.—Tracing and photograph of EG1162 streamwise ice shape 1. Tracing was taken from ice casting 

used for aerodynamic testing; pressure orifice locations are indicated by open circles.

Figure 18.—Tracing and photograph of EG1126 roughness ice shape 1. Tracing was taken from ice casting 

used for aerodynamic testing; pressure orifice locations are indicated by open circles.
NASA/TP—2011-216929 24

Figure 19.—Tracing and photograph of EG1159 spanwiseridge ice shape. Tracing was taken from ice casting used for 

aerodynamic testing; pressure orifice locations are indicated 

by open circles.

Figure 20.—Tracing and photograph of EG1125 streamwise ice shape 2. Tracing was taken from ice casting 

used for aerodynamic testing; pressure orifice locations 

are indicated by open circles.
NASA/TP—2011-216929 25

Figure 21.—Tracing and photograph of EG1134 roughness 

ice shape 2. Tracing was taken from ice casting used 

for aerodynamic testing; pressure orifice locations are 

indicated by open circles.

7.2 Experimental Methods

All of the aerodynamic testing in Phase V was performed at 

the ONERA F1 full-scale, pressurized wind-tunnel facility

(Ref. 48). The closed-return wind tunnel has a test-section 

measuring 138 in. (3.5 m) high by 177 in. (4.5 m) wide by 

433 in. (11.0 m) long. The maximum test section Mach number 

is 0.36, and the maximum stagnation pressure is 57 psia 

(3.85 bar). The unit Reynolds number can be varied to a 

maximum of Re = 6.0×106

/ft at M = 0.23. Total temperature is 

maintained via a heat exchanger located in the second diffuser 

downstream of the fan. The fan operates at constant speed while 

the test section Mach number is controlled by adjusting the 

pitch of the blades. The test-section inlet flow is conditioned 

through a 7.18-to-1 contraction containing a honeycomb flow 

straightener and three turbulence-reduction screens.

The 72-in.- (1.83-m-) chord NACA 23012 airfoil model was 

mounted vertically in the test section as shown in Figure 22. 

The model had a span of 137.5 in. (3.49 m) and was mounted in 

the floor force balance. Small gaps between the bottom of

Figure 22.—Photograph of the full-scale NACA 23012 airfoil 

model installed in Office National d’Etudes et Recherches 

Aérospatiales (ONERA) F1 wind-tunnel test section and 

model pressure tap layout.

the model and the test-section floor as well as the top of the 

model and the test-section ceiling were maintained so as not to 

cause mechanical hysteresis in the force-balance measurements. The model had a main chordwise row of 72 pressure 

taps located at 43-percent span measured from the test-section 

floor. In addition, there was a row of 20 taps oriented spanwise at x/c = 0.70 on the upper surface. The model was 

designed and built with full-span removable, interchangeable 

leading-edge sections. The baseline leading edge had the clean 

NACA 23012 profile, whereas the alternate leading edge had a 

truncated nose geometry. The latter design facilitated mounting of the various ice-shape casting simulations. Accommodations were also made in the pressure tapping to allow for rapid 

connection of pressure instrumentation in the cast ice shapes.

Also shown in Figure 22 is the wake rake located one chord 

length downstream of the model trailing edge. The wake rake 

had 100 stagnation pressure probes spaced 0.79 in. (20 mm) 

apart and was located at a fixed spanwise station at 57-percent

span above the test-section floor.

Data acquisition runs were performed in angle-of-attack 

sweeps for increasing and then decreasing angle of attack at a 

constant sweep rate of 0.1°/sec. In addition, data were 

acquired at a fixed angle of attack for selected angles over the 

range of the sweep, and repeat runs were performed to ensure 

data precision. The data shown in this report are for increasing 

angle-of-attack sweeps that were averaged to the nearest 0.5°
NASA/TP—2011-216929 26

in postprocessing. The averaging method divided the data into 

0.5° bins and averaged the results. For example, data from 

α = 2.75° to 3.25° were averaged to create a data point close 

to α = 3.0°. The actual angle of attack was the arithmetic mean 

of the angles of attack collected in that bin. During the sweeps, 

data were acquired from analog transducers for the force 

balance and tunnel conditions. The model-surface, test-section 

sidewall, and wake-rake pressures were acquired using an 

electronically scanned pressure system. The acquisition of 

these data was synchronized in time corresponding to the 

angle-of-attack sweep rate.

Lift and pitching-moment coefficients were calculated from 

the force balance and from the integration of the surfacepressure measurements. Good agreement between the 

integrated-pressure data and the force-balance data was

obtained. In this report, the lift and pitching moment data 

reported for the clean configuration were obtained from the 

surface pressures, whereas the data reported for the iced 

configurations were obtained from the force balance. The 

force-balance data are reported because, in many cases, the 

stall of the iced-airfoil configurations was characterized by 

unsteady flow. The available signal conditioning for the forcebalance data allowed for more effective filtering of these 

unsteady effects. The drag coefficient was calculated from the 

wake pressures using standard momentum-deficit methods,

and these values are reported in this paper for all configurations. The performance coefficients provided in this report 

were corrected for wind-tunnel wall effects using the methods 

of Allen and Vincenti (Ref. 42). Moëns (Ref. 49) performed 

more detailed analysis of the tunnel wall effects. The angle-ofattack sweeps were performed for a large range of Reynolds 

numbers and Mach numbers as shown in Table 9. The matrix 

was designed to isolate the independent effects of these 

parameters. Therefore, Reynolds number variations were 

performed at constant M = 0.10 and 0.20, and a Mach number 

variation was performed at a constant nominal Re = 12×106

.

Because of operational constraints, the Reynolds number was 

not maintained exactly constant for each of these conditions, 

and therefore Table 9 indicates the variation over the course of 

the entire test campaign.

TABLE 9.—MATRIX OF REYNOLDS AND 

MACH NUMBER CONDITIONS FOR FULLSCALE AERODYNAMIC TESTING

Reynolds 

number range

Mach number

0.10 0.20 0.28

4.5×106 to 4.6×106 X

8.0×106 to 9.1×106 X X

12.0×106 to 12.3×106 X X X

15.8×106 to 16.0×106 X

The experimental uncertainty in the performance 

coefficients was estimated using the methods of Kline and 

McClintock (Ref. 50) and of Coleman and Steele (Ref. 51) for 

20:1 odds. Table 10 lists these uncertainties for both integrated-pressure and force-balance measurements. The values 

were calculated for the clean model configuration at 

Re = 9.1×106 and M = 0.20. These values were analyzed 

before the wall corrections were applied. The absolute uncertainties in Table 10 are inversely proportional to the dynamic 

pressure (except angle of attack). This condition was selected 

because it corresponds to the average dynamic pressure over 

the range of conditions (Table 9). Therefore, Reynolds and 

Mach number combinations having lower dynamic pressure 

would have slightly larger uncertainties, whereas combinations with higher dynamic pressure would have slightly lower 

uncertainties. All of these uncertainties were acceptable for the 

purposes of this investigation. The relative uncertainties in

pitching moment (both pressure and balance) seem large for 

this example because of the small reference value. For cases 

where the pitching moments were larger—for example, in the 

iced-airfoil case—the absolute uncertainty would be similar, 

therefore resulting in a lower relative uncertainty. This was 

also the case for the uncertainty in drag coefficient. Several 

repeat runs were performed for both clean and iced configurations, and these run-to-run variations in the coefficients were 

much smaller than the uncertainties listed in Table 9. The 

good agreement between the integrated-pressure and forcebalance data provided further assurances of data quality.

TABLE 10.—ESTIMATED EXPERIMENTAL UNCERTAINTIES FOR AERODYNAMIC 

DATA FROM FULL-SCALE MODEL TESTING IN ONERA F1 WIND TUNNEL

Aerodynamic quantity Reference 

value

Absolute 

uncertainty

Relative 

uncertainty,

percent

Angle of attack, α 8.01° ±0.02° ±0.25

Lift coefficient, Cl balance 1.095 ±0.010 ±0.93

Pitching moment coefficient, Cm balance –0.0144 ±0.00071 ±4.9

Pressure coefficient, Cp –1.057 ±0.015 ±1.4

Lift coefficient, Cl pressure 1.096 ±0.0070 ±0.64

Pitching moment coefficient, Cm pressure –0.0148 ±0.0024 ±16.5

Drag coefficient, Cd wake 0.0086 ±0.00048 ±5.5
NASA/TP—2011-216929 27

Flow visualization also was performed during the test 

campaign. Fluorescent minitufts made from 5-lb test nylon

monofilament having a diameter of 0.006 in. (0.15 mm) were 

applied to the top portion of the model span on both the upper 

and lower surfaces. The tufts were illuminated with ultraviolet

black light, and the motion was video recorded during all of 

the data-acquisition runs. Surface-oil flow visualization was 

also performed for selected cases. These runs were performed 

at atmospheric pressure and M = 0.20, and the corresponding 

nominal Re was 8×106

. CassouDeSalle and Gilliot (Ref. 52) 

and CassouDeSalle et al. (Ref. 53) provide more information 

about the experimental arrangement and procedures.

For each ice accretion, a set of casting simulations was

fabricated for aerodynamic testing. The castings were made 

from a hard, two-part polyurethane-type material of the very 

low viscosity needed for it to flow into the intricate details of 

the ice molds. The moldings of the ice accretions were limited 

to a span of 15 in. (0.38 m) at the center of the IRT model.

This was of sufficient length to reproduce the spanwise 

variation in all of the ice-accretion geometries. Therefore, 

multiple sections of casting were fabricated for each ice shape 

to cover the 137.48-in. (3.49-m) span of the NACA 23012 

model. A total of 10 sections were used, each having a 

finished length of 13.75 in. (0.349 m). The sections were 

bolted onto the alternate leading edge of the model, and the 

interfaces were sealed with silicone. Figure 23 shows a

completed installation. One casting section was instrumented 

with surface pressure taps. This was located at the same

Figure 23.—Completed installation of EG1164

horn ice-shape casting sections on the leading edge of the NACA 23012 airfoil model 

(upper surface view).

spanwise station as the main chordwise row on the model 

(43-percent span measured from the test-section floor). The 

orifices were drilled directly into the casting and were located 

in areas where previous research had indicated they would be 

conducive to good surface pressure measurements (Refs. 36

and 41). The number of pressure taps on the entire removable 

section ranged from 29 to 36 with some ice shapes requiring 

higher resolution. The tracings of Figure 16 to Figure 21

indicate the locations of the pressure orifices. These tracings 

were obtained from the casting of the ice shape instead of 

from the tunnel ice accretion.

7.3 Results and Discussion

7.3.1 Clean- Versus Iced-Airfoil Aerodynamics

The NACA 23012 airfoil model was tested in the baseline 

configuration to establish the clean performance over the given 

range of Reynolds and Mach numbers. Extensive validation of 

the clean airfoil performance was performed against archival 

and computational data. Broeren et al. (Refs. 54 and 55) provide 

detailed discussion of the clean model validation that yielded a 

high degree of confidence in these measurements. Of primary 

interest in this program was the effect of the ice shapes on the 

airfoil performance. This is also covered in detail in

Broeren et al. (Refs. 54 and 55), from which the following 

discussion was taken.

The ice-casting simulations applied to the NACA 23012 

airfoil caused a large range of detrimental performance effects 

because of the differences in the ice-accretion geometries and 

roughness levels. Figure 24 illustrates the general range in 

performance degradation for four of the six artificial ice shapes 

at Re = 15.9×106 and M = 0.20. For this Reynolds and Mach 

number condition, the clean airfoil Cl,max was 1.85 and αstall was 

18.1°. The EG1162 streamwise and EG1126 roughness shapes 

had a very similar effect on lift, drag, and pitching moment. The 

effect of the EG1162 ice simulation was to reduce Cl,max to 1.16 

and αstall to 11.9°. The EG1126 roughness shape caused a 

slightly larger lift penalty, yielding Cl,max = 1.09 at αstall = 11.4°.

Both of these ice shapes caused a significant redistribution of 

surface pressure resulting in an increase in the pitching-moment 

slope. The stall characteristics and surface pressure distribution 

indicated that there was likely some boundary-layer separation 

on the aft portions of the airfoil. The plot of drag coefficient 

shows that the EG1162 and EG1126 shapes also had a similar 

effect, with the former having a slightly higher Cd up to 

α ≈ 8.5°, where there was a crossover. At higher angles of 

attack, the EG1162 streamwise ice shape caused a lower drag 

coefficient in comparison to the EG1126 shape, which was 

consistent with the higher lift coefficients.

The performance results with the streamwise (EG1162) and 

roughness (EG1126) shapes sharply contrast the effect of the 

horn (EG1164) and spanwise-ridge (EG1159) shapes. The size 

and location of the latter two ice shapes on the airfoil caused 

large upper surface separation bubbles that significantly altered
NASA/TP—2011-216929 28

Figure 24.—Comparison of performance effects of simulated 

ice configurations on the NACA 23012 airfoil at a Reynolds 

number of 15.9×106 and a Mach number of 0.20.

the flowfield and the subsequent performance. The resulting 

Cl,max for the EG1164 horn shape was 0.86 at αstall = 8.8°, which 

amounted to a 54-percent reduction in Cl,max from the clean 

airfoil at this Reynolds and Mach number condition. The effect 

of the EG1159 spanwise-ridge shape was even more severe with 

Cl,max = 0.52 at αstall = 5.6°. The plot of drag coefficient in 

Figure 24 further illustrates the significant impact of the 

separation bubble for the EG1164 and EG1159 ice-shape 

simulations. It is interesting to note that Cd for the EG1159 

spanwise ridge is lower than Cd for the EG1164 horn shape for 

α < ~–2.5°. This most likely occurred because, at low angles of

attack, drag coefficient is affected primarily by ice accretion on 

the airfoil lower surface (Refs. 46 and 47). A comparison of 

Figure 16 and Figure 19 clearly shows that, for the horn shape, 

the ice accretion on the lower surface was higher and extended 

farther, albeit it was closer to the leading edge. This effect of 

lower and upper surface ice accretion on drag is described by 

Bragg et al. (Ref. 23) and in much more detail by Kim

(Ref. 46). For α > –2.5°, Cd for the spanwise-ridge shape was 

much larger, which was consistent with the lower Cl,max. The 

fact that the spanwise-ridge ice simulation resulted in the largest 

performance degradation was likely due to the location of the 

upper surface ridge. As depicted in Figure 16 and Figure 19, the 

horn height was larger than the upper surface ridge, but the

ridge was located at x/c ≈ 0.06. Lee and Bragg (Ref. 31)

showed that, for the NACA 23012 airfoil, the most sensitive 

location for ice accretion (of this size) in terms of loss in 

maximum lift was downstream of the leading edge and 

upstream of the maximum adverse pressure gradient for the 

clean airfoil. This is also illustrated in Figure 24 for the 

streamwise shape that formed on the airfoil leading edge, 

since the performance penalty was not nearly as severe as for 

the spanwise ridge.

Figure 25 further illustrates the effect of leading-edge ice 

accretion on the NACA 23012 airfoil performance. These data 

are for the EG1125 and EG1134 streamwise and roughness ice 

shapes compared with the streamwise and roughness shapes 

plotted in Figure 24. Of these, it is interesting to note that the 

lowest iced-airfoil Cl,max occurred with the EG1126 roughness 

shape. The drag penalty was also largest for this shape for 

α > 9°. Note that there is a scale change between Figure 24 and 

Figure 25 in the Cd versus α plot to facilitate the comparisons. 

The fact that the two streamwise ice simulations (EG1162 and 

EG1125) had a nearly identical effect on Cl,max and Cd (for 

α > 9°) as did the EG1126 roughness shape reveals an important characteristic about leading-edge ice accretion on the 

NACA 23012 section. A comparison of the ice-accretion 

characteristics in Figure 17, Figure 18, and Figure 20 and icing 

environment in Table 8, shows a wide range of icing conditions 

and resulting sizes and shapes. The obvious implication is that 

the basic ice height or thickness for these types of accretions is 

not a strong driver of the stall performance behavior. A similar 

effect has been observed in previous testing of a subscale
NASA/TP—2011-216929 29

Figure 25.—Comparison of performance effects of roughness 

and streamwise ice simulations on the NACA 23012 airfoil at 

a Reynolds number of 15.9×106 and a Mach number of 0.20.

NACA 23012 airfoil with larger geometric quarter-round ice 

simulations located at the leading edge (i.e., x/c = y/c = 0). Lee 

and Bragg (Ref. 31) showed that the iced-airfoil Cl,max only 

varied from about 1.01 to 0.97 as the ice height (or thickness) 

varied from k/c = 0.006 to 0.014. A similar Cl,max range was 

observed in Figure 25: 1.09 for the EG1126 simulation to 1.16 

for the EG1162 simulation. Despite the similarity in the 

maximum lift coefficients, there is a difference in the stalling 

characteristics, with the EG1162 and EG1126 simulations 

having a sharper stall and the EG1125 simulation resulting in a 

more gradual stall. This implies that significant flowfield 

differences remain among the various artificial ice shapes 

despite the alignment of the maximum lift coefficient. These 

effects are addressed further later in this section.

The data for the EG1134 simulation illustrate the smallest 

maximum lift penalty measured for the given set of artificial 

ice shapes. The Cl,max of 1.28 was about 10 percent higher than 

for the other three ice simulations in Figure 25. The drag 

coefficient on the interval –1.5° < α < 4.7° was slightly higher 

than, but most similar to, the drag coefficient for the other 

roughness simulation, EG1126. It is interesting that the lift 

performance of the NACA 23012 section with the EG1134 

simulation was very similar to that of the 80-grit sandpaper 

(k/c = 0.00023) reported by Broeren et al. (Ref. 10) on the 

36-in. chord model of the same airfoil. For that case, Cl,max was 

about 1.30 at Re = 7.5×106 and M = 0.21. The minimum Cd

for the airfoil with the 80-grit sandpaper was 0.010, about 

10 percent higher than for the EG1134 roughness simulation 

in the present data. The comparison of the EG1134 performance effects to the 80-grit sandpaper on the 36-in.-chord 

model is useful because the latter is roughly equivalent to 

40-grit sandpaper on a full-scale wing, which is often used in 

aircraft certification flight testing applications (Ref. 35).

The trends in the drag coefficient variation with angle of 

attack for the iced-airfoil configurations in Figure 25 may be 

interpreted in terms of the icing environment from which these 

simulations were obtained. For example, in the case of the two 

streamwise ice simulations, Cd for the EG1162 configuration 

was higher than that for the EG1125 configuration in the range 

–4° ≤ α ≤ 8°. This is somewhat surprising given that the 

EG1162 ice shape was more conformal to the airfoil leading 

edge than the EG1125 ice shape was. However, the EG1162 

ice shape was accreted with a larger MVD drop size and, 

therefore, the downstream roughness covered a larger surface 

extent. This may be at least partially responsible for the larger 

drag over this range. It was noted in the preceding paragraph 

that the drag coefficient for the EG1134 roughness configuration was slightly higher than for the EG1126 roughness 

configuration over the interval –1.5° < α < 4.7°. This may 

have occurred because the rime roughness EG1134 was 

accreted with a larger MVD drop size resulting in the ice 

roughness covering a larger surface extent. 

For α > 4.7°, it is likely that a larger extent of trailing-edge 

separation developed for the EG1126 configuration, thus the 
NASA/TP—2011-216929 30

drag coefficient grew larger than for the EG1134 configuration corresponding to the lower stalling angle.

Since an accurate subscale ice-accretion simulation relies on 

appropriately representing the key flowfield features contributing to the aerodynamics, it was important to understand these 

features and the stalling characteristics, in particular. Because

detailed flowfield measurements were not performed, this 

analysis relies primarily on interpretation of surface pressure 

distributions, flow visualizations and past research. 

Figure 26 compares the surface pressure distributions at a 

matched angle of attack for each of the configurations shown 

in Figure 24. These are divided into two plots for clarity. In 

the upper plot, it is noteworthy that the iced-airfoil pressure 

distributions for the streamwise and roughness configurations

do not differ significantly from the clean data. Consistent with 

the definitions provided in Phase I, the performance effects are 

chiefly manifest through the boundary layer with minimal 

flow separation. This explains, in part, the small increase in 

drag from the clean configuration for this angle of attack. This 

situation is clearly in contrast to the lower plot, which 

compares the horn and spanwise-ridge shapes.

For these cases, there was a large deviation from the clean 

data owing to the large extent of separated flow. For the 

EG1164 configuration, the flow separated near the tip of the 

horn, resulting in a region of nearly constant pressure from 

x/c = –0.02 to 0.03 on the upper surface. There was significant 

pressure recovery downstream of x/c = 0.03, but the pressure 

coefficient did not approach that of the clean airfoil until 

x/c = 0.20. For the EG1159 configuration, the separated flow 

region was much larger, as indicated by the region of nearly 

constant pressure from x/c = 0.04 to 0.30 on the upper surface.

This region was followed by a very gradual pressure recovery.

The pressure data for this case also reveal the effect of the lower

surface ridge, which also had a small separation bubble 

associated with it. The large extent of separated flow for the 

EG1159 configuration is consistent with the large degradation 

in the performance coefficients in Figure 24. The surface-oil 

flow visualization performed at this angle of attack indicated a 

time-averaged separation-bubble reattachment zone from 

x/c = 0.64 to 0.68. As discussed in Phase I, the separation 

bubble associated with horn and spanwise-ridge shapes is 

known to have large-scale unsteady characteristics. Timeaveraged flow information can be very useful, but it must be 

interpreted carefully.

The stalling characteristics of the iced-airfoil configurations 

tested in this study are of key interest because in a subscale 

simulation it is important to maintain the stalling characteristics 

in addition to any one parameter such as the maximum lift 

coefficient or stalling angle. The development of airfoil stall 

classifications relates back to the early research of B. Melvill 

Jones (Ref. 56), among others. McCullough and Gault (Ref. 43)

built upon the foundations of this early work and conducted a 

systematic series of airfoil section tests to establish the commonly accepted stall-type definitions in use today.

Trailing-edge stall is preceded by movement of the turbulent 

boundary-layer separation point forward from the trailing edge 

with increasing angle of attack.

Leading-edge stall has abrupt flow separation near the 

leading edge generally without subsequent reattachment. The 

abrupt separation usually results from the bursting of a small 

laminar separation bubble that results in a sharp decrease in lift.

Thin-airfoil stall is preceded by flow separation at the 

leading edge with reattachment (a laminar separation bubble) at 

a point that moves progressively downstream with increasing 

angle of attack.

Stall type is a function of several variables such as Reynolds 

number, surface roughness, or free-stream turbulence. Therefore, any particular airfoil may exhibit a combination of stall 

types, or its stall type may change over various flow regimes or 

conditions.

Over the range of Reynolds and Mach numbers in this study,

the clean NACA 23012 airfoil clearly exhibited the leadingedge stall type. The abrupt flow separation at stall was evident 

from the flow visualization and pressure distributions and was 

manifested in the sharp drop in lift coefficient. Both Haines

(Ref. 57) and Tani (Ref. 58) conducted extensive reviews of 

airfoil stall research and noted that the abrupt flow separation 

associated with leading-edge stall can result from either the 

bursting of a small laminar separation bubble (as reported by 

McCullough and Gault (Ref. 43)) or from reseparation of the 

turbulent boundary layer downstream of the bubble reattachment location. Haines (Ref. 57) makes the case that the latter 

separation mechanism is probably more likely for moderate- to 

high-Reynolds-number flows. So the bursting of the laminar 

separation bubble is not a necessary prerequisite for leadingedge stall. In either case, there is no question about the basic 

stall type of the clean NACA 23012 section.

The application of the simulated ice shapes to the NACA 

23012 airfoil section alters the stall type in some cases. It is 

clear that the main stall mechanism for the EG1164 horn and 

EG1159 spanwise-ridge shapes was the growth (with angle of 

attack) of a large upper surface separation bubble. Thus, an 

airfoil with these ice shapes would be classified as having the 

thin-airfoil stall type. This conclusion is based on lift performance data in Figure 24 and is corroborated by the corresponding pressure distributions and flow visualization. These 

flowfield physics are fairly well documented (Refs. 22 and 23). 

However, the stalling characteristics of the streamwise and 

roughness ice simulations on the NACA 23012 airfoil are not as 

well understood and were investigated further. 

What is of primary interest here is that for three of the four 

roughness and streamwise ice simulations tested on the NACA 

23012 airfoil, the presence of the simulated ice did not appear to 

significantly alter the leading-edge stall type. This observation 
NASA/TP—2011-216929 31

Figure 26.—Comparison of clean and iced NACA 23012 airfoil pressure distribution 

for various high-fidelity, ice-casting simulations at an angle of attack of 4.2°, a 

Reynolds number of 15.9×106

, and a Mach number of 0.20.
NASA/TP—2011-216929 32

is based on the abrupt loss in lift at stall for the EG1162, 

EG1126, and EG1134 configurations (see Figure 25). Only the 

EG1125 configuration resulted in a more gradual loss of lift 

poststall. Analysis of the pressure distributions and flow 

visualizations by Broeren et al. (Refs. 54 and 55) for the EG1125 

configuration showed that there was likely a small leading-edge 

separation bubble that altered the pressure distribution from the 

clean case such that the tendency to leading-edge stall was 

altered. In fact, the EG1125 configuration could be classified as 

having trailing-edge stall, since lift was reduced because the 

turbulent boundary-layer separation moved forward as angle of 

attack increased. Although a small separation bubble was present 

because of the ice shape, this bubble did not grow significantly 

in chordwise extent leading up to the stall. This partly explains 

the classification of this ice shape as streamwise ice, according to 

the descriptions in Phase I. This behavior is contrasted with the 

EG1164 and EG1159 configurations that had the thin-airfoil stall 

type. The stall mechanism for the EG1125 configuration was 

very different from that for the EG1164 and EG1159 configurations even though they shared a more gradual decrease in lift at 

stall. This is consistent with the characteristics of thin-airfoil and 

trailing-edge stall as defined by McCullough and Gault 

(Ref. 43). The fact that the leading-edge stall characteristics of 

the NACA 23012 airfoil were not significantly altered by the 

presence of one streamwise and two roughness ice shapes is 

important for subscale simulations and computational modeling.

As mentioned in this discussion, leading-edge stall is defined as 

abrupt flow separation near the leading edge of the airfoil 

without subsequent reattachment. With the ice shapes in 

question, there were likely small areas of boundary-layer 

separation present on the scale of the ice roughness. Research 

reported by Kerho and Bragg (Ref. 59) showed that leading-edge 

roughness can lead to a “transitional” boundary layer instead of 

acting as a “trip” with energetic transition to turbulence. The 

transition process due to ice-type roughness develops over a 

large part of the airfoil chord, up to 30 to 50 percent in some 

cases. The present data indicate that the transitional nature of the 

boundary layer was still susceptible to abrupt separation for the 

three ice simulations. In contrast, the larger size of the other ice 

shapes caused larger scale separation and alteration of the 

transition process as well as of the pressure distribution, thus 

leading to an alteration of the stall type.

7.3.2 Reynolds and Mach Number Effects

The pressurization capability of the F1 facility was fully 

exploited for the independent exploration of Reynolds and 

Mach number effects on the airfoil performance. The performance results for the clean NACA 23012 followed classic 

airfoil behavior. This is illustrated in Figure 27, which shows 

the effect on performance of increasing Re from 4.6×106 to 

12.3×106 at constant M = 0.10. For these conditions, the lift 

data show that Cl,max increased from 1.76 to 1.88 with 

commensurate increases in αstall. The variation of Cm with α

Figure 27.—Effect of Reynolds number at constant Mach 0.10 

on the performance of the clean NACA 23012 airfoil.
NASA/TP—2011-216929 33

also decreased slightly with increasing Re. The variation in 

drag coefficient with Reynolds number for α < 3° and α > 10°

also followed historical trends for the NACA 23012 airfoil, 

with Cd decreasing with increasing Re (Ref. 60). The data for 

3° ≤ α ≤ 10° are more difficult to interpret. The uncertainty in 

Cd for these cases was higher than that reported in Table 10

because the dynamic pressure was lower for the data at 

M = 0.10. This illustrates a significant challenge with 

performing drag measurements in a pressurized wind tunnel 

because of the large range in dynamic pressure. However, 

highly accurate measurement of drag coefficient on the clean 

airfoil at low dynamic pressure was not a chief objective of 

this work. Reynolds number effects were also investigated 

over the range of Re = 9.1×106 to 15.9×106 at constant 

M = 0.20. These data (Figure 28) show that there was very 

little variation in the airfoil performance over this Reynolds 

number range. Here the drag coefficient data are more well 

behaved thanks, in part, to the larger dynamic pressures 

associated with the conditions in Figure 28.

The effects of compressibility were also documented for the 

clean NACA 23012 airfoil section by varying M from 0.10 to 

0.28 at a constant Re = 12.1×106

, and these data are given in 

Figure 29. Classic airfoil behavior was observed in the 

characteristic increases in the lift-curve slope and pitchingmoment slope just before stall for increasing Mach number.

Here Cl,max and αstall both decreased with increasing Mach 

number as expected: Cl,max was 1.88 at M = 0.10 versus 1.78 at 

M = 0.28, and αstall was reduced from 19.1° to 17.2°. Comparisons in drag coefficient were again more challenging to 

discern; however, there was the expected increase in Cd with 

increasing M for α > 10°. These data are analyzed in more 

detail by Broeren et al. (Refs. 54 and 55), and the overall 

trends compare favorably with Haines’s (Ref. 57) review 

of scale and Reynolds number effects on airfoil stalling 

characteristics.

Similar comparison plots were generated for each of the 

ice-shape configurations. The effect of Reynolds number

variation at constant M = 0.10 is shown in Figure 30 for the 

EG1162 streamwise shape. In contrast to the clean airfoil, the 

lift and pitching moment data remained virtually unchanged 

despite a nearly three-fold increase in Re from 4.5×106 to 

12.1×106

. Note that the scales on the plots were changed to 

better illustrate any Reynolds number effects. Over this range 

of Reynolds number, Cl,max and αstall were unchanged. The 

drag data show slightly more dependence, with the drag 

coefficient generally decreasing with increasing Reynolds 

number. The reason for this trend was not investigated in 

detail, but this effect on drag has been observed in other icedairfoil studies (Refs. 10 and 11). Here, the absolute uncertainty 

in Cd was larger than that given in Table 10 as described in 

Section 7.2 for M = 0.10, but since the overall values of Cd

were increased from the clean data, the relative uncertainties 

would be lower. Since there was very little or no variation in 

Figure 28.—Effect of Reynolds number at constant Mach 0.20 

on the performance of the clean NACA 23012 airfoil.
NASA/TP—2011-216929 34

Figure 29.—Effect of Mach number at an approximately constant Reynolds number of 12.1×106 on the performance of 

the clean NACA 23012 airfoil.

the airfoil performance with the EG1162 ice shape from 

Re = 4.5×106 to 12.1×106

, it was expected that further 

increases in Reynolds number would continue to show no 

dependence on that parameter. This was, in fact, true as shown 

in Figure 31 for Re = 9.0×106 to 15.9×106 at M = 0.20. As for 

the clean data, drag coefficient was well behaved mostly 

because of the larger dynamic pressures associated with the 

conditions in Figure 31. These data also indicate that the 

Reynolds number dependence of iced-airfoil drag coefficient 

decreased significantly for Re > 9.0×106

. 

Figure 32 shows the effect of compressibility on iced-airfoil 

performance for the EG1162 configuration with 0.10 ≤ M ≤ 

0.28 at Re = 12.1×106

. The overall effects were similar to that 

described for the clean NACA 23012 airfoil, but the magnitudes 

of the effects were reduced. The effect of Mach number on 

maximum lift was similar among all of the iced-airfoil configurations and is described in more detail later in this section.

The data shown here for the EG1162 configuration are

representative of all of the ice simulations tested in this study.

This is particularly true for the lift and pitching moment 

variations with Reynolds number, even for the small roughness shapes. Changes in Reynolds number over this range did 

not substantially change the character of the stall either. That 

is, iced configurations with an abrupt, leading-edge stall 

character maintained this over the Reynolds number range.

The effect on maximum lift coefficient for all of the simulations tested is summarized in Figure 33. Here, the clean 

NACA 23012 airfoil Cl,max showed more variation with 

Reynolds number than did the iced-airfoil configurations as 

expected from the previous discussion. Broeren et al. (Ref. 54) 

provide further comparison of NACA 23012 Cl,max variation 

along with data from other airfoils down to Re = 2.0×106

. That 

data showed that airfoil Cl,max was much more dependent on 

Re < 4.5×106

, the lower limit in this study, with Reynolds 

number independence being achieved for most (clean) airfoils 

at approximately Re = 6.0×106 (Refs. 19 and 57). For the icedairfoil configurations in this study, the noneffect of Reynolds 

number was consistent with the previous data compiled by 

Broeren et al. (Ref. 54). However, that data showed some 

Reynolds number dependence from Re = 2.0×106 to 3.5×106

for certain ice shapes. It is also known from other work (e.g., 

Lee et al. (Ref. 61)) that drag coefficient (in the iced configuration) can depend on Reynolds number much more strongly 

for Re < 2.0×106

. Bragg et al. (Ref. 23) argue that the presence 

of ice contamination on the airfoil governs the boundary-layer 

behavior such that the influence of Reynolds number is 

reduced to lower order effects unlike the clean case. The 

present data continue to support this conclusion.

Mach number performance sweeps conducted for the other 

iced-airfoil configurations yielded similar results to those for 

the EG1162 configuration shown in Figure 32. The effect of 

Mach number on maximum lift coefficient is summarized 

in Figure 34 for all of the ice shapes tested in this study. 
NASA/TP—2011-216929 35

Figure 30.—Effect of Reynolds number at constant Mach 0.10 on the performance of the NACA 23012 airfoil with the EG1162 streamwise ice shape.
NASA/TP—2011-216929 36

Figure 31.—Effect of Reynolds number at constant Mach 0.20 on the 

performance of the NACA 23012 airfoil with the EG1162 streamwise 

ice shape.
NASA/TP—2011-216929 37

Figure 32.—Effect of Mach number at constant Reynolds number of 12.1×106

on the performance of the NACA 23012 airfoil with the EG1162 streamwise 

ice shape.
NASA/TP—2011-216929 38

Figure 33.—Effect of Reynolds number on maximum 

lift coefficient for the clean NACA 23012 airfoil and 

for an airfoil with simulated ice shapes.

Figure 34.—Effect of Mach number on maximum 

lift coefficient for the clean NACA 23012 airfoil 

and for an airfoil with simulated ice shapes.
NASA/TP—2011-216929 39

For the clean NACA 23012 airfoil, Cl,max showed the most 

significant dependence on Mach number over the range tested.

For the iced-airfoil configurations, the horn shape (EG1164) 

and the spanwise-ridge shape (EG1159) exhibited the most 

significant decrease in Cl,max over this range, with the smaller 

ice shapes having much less dependence of Cl,max on Mach 

number. This trend involving smaller ice simulations and 

Mach number effects on maximum lift coefficient has been 

observed in other studies. For example, Broeren et al. 

(Ref. 10) report data for two sandpaper roughness configurations and four intercycle ice configurations on the NACA 

23012 section. For the two sandpaper roughnesses, there was a 

very slight decrease in Cl,max for one case and an increase in 

Cl,max for the other case over 0.12 ≤ M ≤ 0.28. In contrast, the 

much larger, ridge-type intercycle ice configurations resulted 

in Cl,max variations with Mach number similar to that shown in 

Figure 34 for the EG1164 and EG1159 simulations. Addy and 

Chung (Ref. 15) performed Mach number sensitivity testing 

for three-dimensional cast-ice simulations on an NLF–0414 

airfoil section. The Cl,max variation for the smaller, 6-minute 

ice-accretion simulation showed significantly less sensitivity 

to Mach number for 0.12 ≤ M ≤ 0.29 than did the larger, 22.5-

minute ice-accretion simulation. The decrease in Cl,max with 

increasing Mach number for larger ice shapes appears to be 

related to an increase in the size of the separated flow region 

and is described in more detail by Bragg et al. (Ref. 23) and 

Broeren et al. (Ref. 30). Broeren et al. (Ref. 54) provide more 

detailed comparisons to other iced-airfoil data.

8.0 Phase VI: Simulation Validation

Testing

The full-scale model aerodynamic testing described in 

Phase V provided a substantial validation data set for the final 

phase of this program. In Phase VI, the three-dimensional ice 

castings used in the full-scale test were represented with 

scaled, lower fidelity simulations for testing on the subscale 

model using the University of Illinois low-speed facility 

described in Phase III. These results provide full-scale to 

subscale comparisons of iced-airfoil aerodynamics under 

tightly controlled conditions and effectively “close the loop” 

by quantifying the accuracy of two-dimensional and simplegeometry simulations for subscale iced-airfoil aerodynamic 

testing at low Reynolds numbers. The comparisons of the 

subscale results to the full-scale validation database show the 

accuracy to which the iced-airfoil aerodynamics can be 

simulated on a subscale model at a low Reynolds number.

This takes into account the previously described uncertainties 

associated with the ice-accretion geometry and Reynolds and 

Mach number effects. The comprehensive Phase VI results are 

reported in two papers by Busch et al. (Refs. 20 and 44) and in 

the dissertation by Busch (Ref. 62). The key results are 

summarized here.

8.1 Experimental and Ice-Shape Simulation 

Methods

The experimental facility and NACA 23012 model described

in Phase III were utilized again in Phase VI. All aspects relative 

to the experimental methodology remained the same, except 

that that the ice-shape simulations were based on the threedimensional castings described in Phase IV and V. Busch et al. 

(Refs. 20, 44, and 62) used two-dimensional smooth and 

simple-geometry shapes with and without added roughness to 

simulate the three-dimensional casting geometry on the subscale 

model. The two-dimensional smooth simulations were based on 

a pencil tracing of the three-dimensional castings with no 

further smoothing. This cross-sectional geometry was extruded 

across the span of the subscale model. Stereolithography was 

used to fabricate shapes that included pressure taps. The simplegeometry simulations were fabricated with off-the-shelf 

materials that were attached to the clean, baseline airfoil 

geometry. The grit roughness used to simulate the ice roughness 

consisted of various sizes of silicon carbide and aluminum 

oxide grains and glass microbeads. Depending on the ice shape 

being simulated, double-sided adhesive vinyl film was used to 

apply grains or microbeads to the two-dimensional smooth, 

simple-geometry shapes or to the airfoil surface. Details of the 

lower fidelity simulations for each of the iced-airfoil configurations are given in the following section.

8.2 Results and Discussion

8.2.1 Clean Model Comparison

In Phase V, the effect of Reynolds number on the clean 

NACA 23012 airfoil performance was investigated from

Re = 4.6×106 to 15.9×106

. Although the airfoil performance 

changes were significant over this range, a greater effect was 

observed in extending the lower limit of Re to 1.8×106 for the 

subscale model in the University of Illinois wind tunnel.

Figure 35 summarizes the effect of Reynolds number on the 

NACA 23012 airfoil performance for the entire Reynolds 

number range of interest in this program, 1.8×106 ≤ Re ≤

15.9×106

, at approximately matched Mach numbers, M= 0.18 

and 0.20, respectively. At Re = 15.9×106

, the lift-curve slope

in the linear range was very close to the value of 2π rad–1

derived from thin-airfoil theory (Ref. 60). At Re = 1.8×106

, 

the lift-curve slope was reduced to approximately 1.87π rad–1

in the linear range. In addition, Cl,max was reduced by about 

20 percent, from 1.85 to 1.48, with a corresponding reduction 

in αstall, from 18.1° to 14.4°. At the lower Reynolds number 

condition, the pitching moment coefficient exhibited a slightly 

stronger angle-of-attack dependence. In terms of drag 
NASA/TP—2011-216929 40

coefficient, the values for each Reynolds number were similar 

in the low-drag “bucket” evident at Re = 1.8×106 for 

0° ≤ α ≤ 6°. Outside of this range, the drag coefficient was 

significantly lower at the higher Reynolds number. These 

differences in drag were not explored in detail, but they are 

consistent with previously measured results for the NACA 

23012 airfoil section (Ref. 60). The significant effects of 

Reynolds number make the subscale model a poor simulation of 

the full-scale model performance in the clean configuration. The 

opposite is largely true for the iced-airfoil configurations 

because of the aforementioned lack of significant Reynolds 

number effects. This is illustrated in the Sections 8.2.2 to 8.2.5

for the different ice accretion shapes.

Figure 35.—Comparison of performance full-scale and 

subscale models of the clean NACA 23012 airfoil.

8.2.2 Horn Ice Simulation

The EG1164 horn shape was simulated with a twodimensional smooth extrusion and with simple geometry as 

illustrated in Figure 36. In the latter case, the upper and lower

surface horns were simulated with rectangular “spoilers” 

attached to the clean model surface. The two-dimensional

smooth simulation was based on the pencil tracing of the 

EG1164 casting shown in Figure 16. These simulations were 

geometrically scaled according to a factor of 4 reduction in 

chord length between the full-scale and subscale models.

The subscale model was outfitted with these lower fidelity 

simulations for aerodynamic testing, and the results are shown 

in Figure 37. The full-scale clean and EG1164 threedimensional casting data were acquired at Re = 15.9×106 and 

M = 0.20. Also shown are data for the two-dimensional

smooth simulation and simple-geometry simulation on the 

subscale model at Re = 1.8×106 and M = 0.18. The scales on 

the plots were adjusted to highlight the comparisons in the 

iced-airfoil data. The data for the full-scale, three-dimensional

casting configuration are considered to give the “true” icedairfoil aerodynamics. The results show that both of the 

subscale simulations effectively reproduced the lift and 

pitching-moment characteristics of the full-scale threedimensional casting configuration. The two-dimensional

smooth configuration yielded a slightly higher maximum lift 

coefficient and stalling angle relative to the three-dimensional

Figure 36.—Geometry comparison of two-dimensional

smooth and simple-geometry simulations for the 

EG1164 horn ice.
NASA/TP—2011-216929 41

Figure 37.—Aerodynamic performance comparison of the EG1164 horn

ice simulations. Full-scale data were acquired at a Reynolds number of 

15.9×106 and a Mach number of 0.20; subscale data were acquired at a 

Reynolds number of 1.8×106 and a Mach number of 0.18.
NASA/TP—2011-216929 42

casting configuration. In contrast, the simple-geometry 

simulation resulted in more conservative estimates. Busch 

et al. (Refs. 44 and 62) note that there was some small

uncertainty in the surface location of the simple-geometry 

simulation, which may have contributed to the differences in 

the stall performance. Other research (Refs. 20, 23, and 62) 

has shown that maximum lift coefficient decreases with 

increasing horn ice-shape distance from the leading edge.

Comparison of the drag coefficient shows that the simplegeometry simulation resulted in higher drag relative to the 

three-dimensional casting over the angle-of-attack range. This 

is also consistent with the lower maximum lift coefficient and 

the associated uncertainty of the upper surface spoiler

location. The agreement in drag coefficient for the twodimensional smooth simulation was significantly better—

keeping in mind that Busch et al. (Refs. 20, 27, and 62)

document significant spanwise variation in drag coefficient for 

horn ice shapes. Such variations could significantly affect the 

drag coefficient comparisons. However, extensive spanwise 

drag measurements were beyond the scope of this project.

Figure 38 further compares the simulation effectiveness in 

terms of surface pressure distribution at a matched α = 6.2°. The 

full-scale clean and EG1164 configuration data were at 

Re = 15.9×106 and M = 0.20, whereas the subscale data were at 

Re = 1.8×106 and M = 0.18. The iced-airfoil upper surface 

pressure signatures were consistent with the presence of a 

separation bubble downstream of the horn. There was fairly 

good agreement between the full-scale three-dimensional

casting data and that of the two-dimensional smooth simulation.

For the simple-geometry case, the pressure recovery region was 

noticeably shifted downstream relative to the other two. This is 

again consistent with possible misalignment of the upper

surface spoiler in the downstream direction and the resulting

lower maximum lift coefficient and higher drag coefficient

relative to the three-dimensional casting configuration.

Figure 38.—Surface pressure distribution comparison of the EG1164 horn ice simulations at an angle of attack of 6.2°. Full-scale data were acquired at a Reynolds 

number of 15.9×106 and a Mach number of 0.20; subscale data were acquired at 

a Reynolds number of 1.8×106 and a Mach number of 0.18.
NASA/TP—2011-216929 43

TABLE 11.—EFFECTIVENESS OF EG1164 HORN SHAPE 

SIMULATIONS ON THE SUBSCALE MODEL

[Cl,max, maximum lift coefficient; αstall, stalling angle; ΔCd,rms, root-mean-square 

percent difference in drag coefficient; α, angle of attack.]

Simulation Simulation Cl,max –

casting Cl,max

Simulation αstall –

casting αstall,

deg

ΔCd,rms,

percent

(–4° ≤ α ≤ 8°)

Two-dimensional smooth 0.03 (2.9 percent) 1.0 10.3

Simple geometry –0.05 (–6.0 percent) –0.1 35.7

Table 11 summarizes the effectiveness of the EG1164 

subscale simulations. As in Phase III, the performance 

comparisons were summarized in terms of three parameters:

Cl,max, αstall, and ΔCd,rms. Note that some of these values are 

slightly different from the values reported previously by 

Busch et al. (Refs. 44 and 62). The main reason for this is that 

Busch et al. (Refs. 44 and 62) used Phase V data at 

Re = 12.2×106 and M = 0.20 as the basis of comparison 

instead of data at Re = 15.9×106 and M = 0.20, as used here.

In other instances, the authors’ interpretation of αstall and Cl,max

may have been slightly different. The data in Table 11

represent the range of uncertainty in aerodynamic simulation 

of a horn ice shape on a subscale model.

As noted earlier in this section, the aerodynamics of the 

two-dimensional smooth simulation matched the full-scale 

iced aerodynamics very closely in terms of maximum lift and 

drag coefficient (ΔCd,rms). For the simple-geometry simulation, 

αstall was matched to within 0.1°, whereas there were larger 

differences in Cl,max and ΔCd,rms. These results confirm that a 

successful subscale horn ice simulation ultimately depends on 

the ability to represent the proper size, angle, and location of 

the horn(s), with the detailed surface geometry and roughness 

being less important.

8.2.3 Ice Roughness Simulation

In the case of the ice-roughness shapes, EG1126 and 

EG1134, only simple-geometry simulations were developed 

because of the lack of a gross ice shape. That is, a twodimensional smooth simulation (on the subscale model) would 

essentially be just the clean airfoil. The simple-geometry 

simulations were constructed by applying various sizes of grit 

roughness to a clean airfoil surface via strips of double-sided 

adhesive vinyl film. Busch et al. (Refs. 20, 44, and 62) 

estimated the roughness size from the three-dimensional 

castings used in Phase V. For the EG1126 shape, the upper 

surface roughness extended from x/c = 0.000, with height 

k/c = 0.0016, to x/c = 0.026, with zero height. The lower 

surface roughness extended from x/c = 0.004, with height 

k/c = 0.0011, to x/c = 0.041, with zero height. The roughness 

height and concentration gradually tapered off in the downstream direction. 

Busch et al. (Refs. 20, 44, and 62) developed a geometrically scaled simulation (summarized in Table 12) that utilized

TABLE 12.—ROUGHNESS SIZE DISTRIBUTION USED 

TO REPRESENT THE GEOMETRICALLY SCALED 

EG1126 THREE-DIMENSIONAL CASTING 

ON THE SUBSCALE MODEL

Roughness height,a

k/c

Concentration Surface location,b

s/c

Upper surface

0.0005 Reduced 0.031 to 0.048

0.0009 Reduced 0.017 to 0.031

0.0006 Full 0.012 to 0.017

0.0013 Full 0.007 to 0.012

Leading edge—smooth, no roughness –0.007 to 0.007

Lower surface

0.0009 Full –0.007 to –0.012

0.0006 Full –0.012 to –0.017

0.0009 Reduced –0.017 to –0.031

0.0003 Reduced –0.031 to –0.048 a

Ice roughness height per chord.

b

Surface distance per chord.

several different roughness sizes. The roughness locations are 

reported as surface distances s/c, which is a better parameter 

for describing locations near the airfoil leading edge. Because 

of the definition of the NACA 23012 airfoil coordinates, there 

are negative x/c values for positive ordinates (Ref. 60). This 

means that there are two points at x/c = 0, one corresponding 

to y/c = 0 and the other to y/c = 0.009. Using surface arclength with s/c = 0 at x/c = y/c = 0 eliminates any ambiguity 

in this regard. Furthermore, a negative s/c corresponds to the 

lower surface, whereas a positive s/c corresponds to the upper 

surface. Table 12 indicates that no roughness was applied in 

the range ‒0.007 ≤ s/c ≤ 0.007, which represents the smooth 

stagnation zone depicted in Figure 18. There are two roughness concentrations listed in the table. In this research, 

roughness concentration is defined as the ratio of the total 

planform area of the roughness elements to the total surface 

area on which the roughness is applied. For full concentration, 

roughness grains saturated the entire surface of the adhesive 

strip. For reduced concentration, an attempt was made to 

match the concentration of roughness on the three-dimensional 

casting. However, roughness concentration on the casting was 

very difficult to measure, so the simulation concentration was 

only qualitatively matched.
NASA/TP—2011-216929 44

Figure 39 shows aerodynamic data for various EG1126 iceroughness simulations on the subscale model. As in Figure 37, 

the full-scale data for the clean and EG1126 three-dimensional 

casting configurations are plotted for Re = 15.9×106 and 

M = 0.20. The first subscale simulation is that described 

earlier in this section. It is an approximately geometrically 

scaled simulation of the three-dimensional casting. This 

combination of roughness sizes, concentration, and surface 

extent (see Table 12) represents a typical approach to subscale 

simulation without any a priori knowledge of the full-scale 

aerodynamics. The lift and pitching-moment data show 

reasonable agreement with the three-dimensional casting data 

up to stall, where the subscale simulation had a lower αstall and 

Cl,max. The subscale simulations do result in an abrupt stall—

indicative of the leading-edge stall type observed with the 

three-dimensional casting configuration. In some circumstances, this could be of equal or more importance than 

simulating the exact values of maximum lift or stalling angle. 

The drag coefficient for this simulation was significantly 

higher than that for the three-dimensional casting, except in 

the range 6° ≤ α ≤ 10°.

Figure 39 shows test results for another subscale simulation 

that modeled the initial roughness of the EG1126 iceroughness case. This simulation, summarized in Table 13, 

consisted of a narrow grit strip with upper and lower surface 

roughness heights equal to that of the largest and most 

TABLE 13.—ROUGHNESS SIZE DISTRIBUTION USED TO 

REPRESENT THE GEOMETRICALLY SCALED INITIAL 

ROUGHNESS OF EG1126 THREE-DIMENSIONAL 

CASTING ON THE SUBSCALE MODEL

Roughness height,a

k/c

Concentration Surface location,b

s/c

Upper surface

0.0013 Full 0.007 to 0.013

Leading edge—smooth, no roughness –0.007 to 0.007

Lower surface

0.0009 Full –0.007 to –0.013 a

Ice roughness height per chord.

b

Surface distance per chord.

upstream roughness of the casting. In terms of lift and pitching 

moment, this simulation had results nearly identical to that of 

the approximately geometrically scaled roughness simulation.

This suggests that the roughness downstream of the initial

roughness does not have a large effect on these parameters.

The same cannot be said of the drag coefficient, which was 

significantly closer to the full-scale, three-dimensional casting 

results. Conversely, this suggests that the chordwise distribution of ice roughness is important for accurate drag simulation.

Finally, a third simulation was constructed with the specific 

intention of matching the maximum lift coefficient and 

stalling angle of the three-dimensional casting. For this case, 

k/c = 0.0003 roughness was applied to the upper and lower 

surfaces over the same chordwise extents as the ice roughness

on the three-dimensional casting. As shown in Figure 39, this 

did have the effect of matching Cl,max and αstall almost exactly 

to that of the casting. However, no improvement was seen in 

the drag coefficient comparison relative to the geometrically 

scaled initial roughness simulation. For these latter two 

simulations, there is a distinct tradeoff in drag penalty between 

the height of the initial roughness and the larger chordwise 

extent of the smaller roughness. The drag coefficient values 

for each simulation were very similar up to about 2° prior to 

stall. These data illustrate the importance of size, concentration, and surface extent and/or location in ice-roughness 

simulation.

These results are summarized in Table 14 for the three 

performance parameters, quantifying the aforementioned 

observations. The k/c = 0.0003 roughness simulation applied 

over the casting extents (chordwise extent of roughness over 

the casting) resulted in the best match of Cl,max and αstall, as it 

was designed to do. But it was the geometrically scaled initial 

roughness simulation that best matched the drag performance 

with the lowest ΔCd,rms. These results indicate that it may be 

very difficult to find one roughness size and extent that 

performs the best across all three parameters. The data further 

suggest that simple geometric scaling may not be adequate for 

ice roughness over this range of model scale and Reynolds 

number. The geometrically scaled simulations are conservative relative to the full-scale results, and this may be advantageous for safety-related testing.

TABLE 14.—EFFECTIVENESS OF EG1126 ROUGHNESS SIMULATIONS ON THE SUBSCALE MODEL

[Cl,max, maximum lift coefficient; αstall, stalling angle; ΔCd,rms, root-mean-square percent difference 

in drag coefficient; α, angle of attack; k/c, ice roughness height per chord length.]

Simulation Simulation Cl,max –

casting Cl,max

Simulation αstall –

casting αstall,

deg

ΔCd,rms,

percent

(–4° ≤ α ≤ 9°)

Approximately geometrically scaled (see Table 12) –0.04 (–3.5 percent) –1.0 28.6

Geometrically scaled initial roughness (see Table 13) –0.03 (–3.0 percent) –1.0 13.1

k/c = 0.0003 roughness with casting extentsa –0.01 (–0.6 percent) –0.1 19.4 a

Chordwise extent of roughness over the casting.
NASA/TP—2011-216929 45

Figure 39.—Aerodynamic performance comparison of the EG1126 iceroughness simulations. Full-scale data were acquired at a Reynolds 

number of 15.9×106 and a Mach number of 0.20; subscale data were 

acquired at a Reynolds number of 1.8×106 and a Mach number of 0.18; 

k/c, roughness height per chord length.
NASA/TP—2011-216929 46

Alternative scaling methods, such as boundary-layer-based 

scaling has been proposed in other work (Ref. 63). Boundarylayer-based scaling methods match the roughness size k based 

on the boundary-layer thickness δ. So the ratio k/δ would be 

equal between the full and subscale roughness. Boundarylayer thickness decreases as Reynolds number increases, 

which means that the boundary-layer thickness is larger on the 

subscale model. This leads to roughness simulations based on 

k/δ that are larger than roughness simulations based on k/c. On 

the basis of the data shown here, larger roughness on the 

subscale model would lead to larger differences in performance between the full-scale model and a geometrically

scaled simulation.

In the aggregate, the data for these three roughness cases 

broadly illustrate the nature of roughness effects on aerodynamics. As expected from Phase I and Phase III, the size, 

concentration, and chordwise location and/or extent all play a 

role in determining the aerodynamic effect. This prompted 

Busch et al. (Refs. 44 and 62) to conduct a detailed study of 

these parameters on the subscale NACA 23012 airfoil, using 

the EG1126 three-dimensional casting as a baseline. The 

authors found that for a fixed roughness size of k/c = 0.0018, 

Cl,max decreased with increasing concentration up to a critical 

value of about 20 percent, above which Cl,max did not decrease 

significantly. These results are consistent with the roughness 

applied to streamwise ice simulations discussed in Phase III

and in an earlier airfoil study by Jackson (Ref. 64). For drag 

coefficient, the authors found no such critical value. The 

ΔCd,rms between the roughness simulation and the threedimensional casting tended only to increase as concentration

was increased. Busch et al. (Refs. 44 and 62) also studied the 

effect of roughness size on the NACA 23012 airfoil. These 

cases were applied at full concentration to minimize any 

effects of concentration. They found that Cl,max decreased 

significantly from 1.48 on the clean model to 1.09 for 

k/c = 0.0003. For larger roughness sizes, the reduction in 

Cl,max was more modest, with Cl,max = 0.90 for k/c = 0.0037.

The reduced sensitivity of Cl,max for k/c ≥ 0.0003 is consistent 

with the Phase III ED0762 ice-roughness results, as well as for 

larger ice shapes located on the leading edge as discussed in 

Phase V. The drag results, however, showed that Cd (or at 

least ΔCd,rms relative to the three-dimensional casting) tended 

to increase approximately proportionally with increases in 

roughness size.

These results confirm the finding of the Phase I study that 

understanding the aerodynamic effects of roughness size and 

concentration is important to accurate subscale simulation.

Geometric scaling of ice roughness was found to produce 

conservative aerodynamic performance penalties for the range 

of model scale and Reynolds number used in this research.

Because of the tradeoffs in aerodynamic effect among size, 

concentration, and surface extent and/or location, more 

research is needed to understand how to design more accurate 

subscale-model ice-roughness simulations.

These ice-roughness effects were investigated further for 

the EG1134 shape, which was also tested in Phase V. This 

configuration had a much larger chordwise extent of roughness than did the previous EG1126 configuration, from 

x/c = –0.002 to 0.080 on the upper surface and from approximately x/c = 0.000 to 0.200 on the lower surface. The 

maximum height was about k/c = 0.0003 on each surface, 

occurring nearest the leading edge and gradually tapering off 

downstream. The approximately geometrically scaled simulation for this case was identical to the k/c = 0.0003 roughness 

case used for the EG1126 experiments described earlier and in 

Table 15. This simulation matched the height of the initial 

roughness on the EG1134 three-dimensional casting, but not 

the entire surface extent. The aerodynamic results are compared in Figure 40. This roughness simulation on the subscale 

model resulted in significantly lower Cl,max and αstall and 

higher Cd for α < ~4°. On the basis of the research conducted 

for the EG1126 configuration, increasing the chordwise extent 

of the roughness on the subscale simulation to better match the 

EG1134 ice-roughness geometry would likely not have a 

measurable impact on the stalling characteristics while most 

likely increasing drag. Since this would not improve the 

comparison with the three-dimensional casting aerodynamics, 

higher fidelity, geometrically scaled roughness simulations

were not considered. However, it is reasonable to consider the 

k/c = 0.0003 roughness to be an approximately geometrically

scaled simulation since the height of the initial roughness was 

geometrically scaled from the full-scale, EG1134 threedimensional casting. These results are consistent with the 

EG1126 simulation results in that geometrically scaled 

roughness simulations yield conservative estimates of the fullscale iced-airfoil performance. Such a subscale configuration 

is often selected without any knowledge of the full-scale 

aerodynamics.

TABLE 15.—ROUGHNESS SIZE DISTRIBUTION USED TO 

REPRESENT THE GEOMETRICALLY SCALED EG1134 THREEDIMENSIONAL CASTING ON THE SUBSCALE MODEL

Roughness height,a

k/c

Concentration Surface location,b

s/c

Upper surface

0.0003 Full 0.007 to 0.048

Leading edge—smooth, no roughness –0.007 to 0.007

Lower surface

0.0003 Full –0.007 to –0.048 a

Ice roughness height per chord.

b

Surface distance per chord.
NASA/TP—2011-216929 47

Figure 40.—Aerodynamic performance comparison of the EG1134 iceroughness simulations. Full-scale data were acquired at a Reynolds 

number of 15.9×106 and a Mach number of 0.20; subscale data were 

acquired at a Reynolds number of 1.8×106 and a Mach number of 0.18; 

k/c, roughness height per chord length.
NASA/TP—2011-216929 48

TABLE 16.—EFFECTIVENESS OF EG1134 ROUGHNESS SIMULATIONS ON THE SUBSCALE MODEL

[k/c, ice roughness height per chord length; Cl,max, maximum lift coefficient; αstall, stalling angle; 

ΔCd,rms, root-mean-square percent difference in drag coefficient; α, angle of attack.]

Simulation

k/c roughness

Simulation Cl,max –

casting Cl,max

Simulation αstall –

casting αstall,

deg

ΔCd,rms,

percent

(–4° ≤ α ≤ 10°)

0.0003 with EG1126 extents –0.19 (–15.0 percent) –1.7 16.4

0.00016 –0.05 (–3.8 percent) –0.6 13.5

0.00007 0.02 (1.5 percent) –0.1 7.8

Two other roughness sizes were applied to the subscale 

model for comparison to the EG1134 three-dimensional casting 

configuration. The roughness sizes tested were approximately 

one-half and one-quarter of the height of the approximately 

geometrically scaled roughness. These aerodynamic results are 

compared in Figure 40, and data show that the k/c = 0.00007 

roughness best matched the performance of the full-scale, threedimensional casting simulation. The simulation effectiveness is 

summarized in Table 16. The significant improvement in Cl,max

between roughnesses of k/c = 0.0003 and 0.00007 further 

illustrates the sensitivity to roughness height in this range, 

which was discussed earlier in this section. Contrast this effect 

to roughness sizes larger than k/c = 0.0003, which had reduced 

Cl,max sensitivity (see Figure 39 and Table 14). Significant 

improvements were also observed in αstall and ΔCd,rms comparisons with the three-dimensional casting. The results for the 

k/c = 0.00007 roughness simulation of the EG1134 casting were 

only possible because the full-scale aerodynamics were known.

An appropriate scaling length or method that would lead to this 

result without any knowledge of the full-scale aerodynamics is 

unknown.

8.2.4 Streamwise Ice Simulation

There were two full-scale streamwise ice shapes tested in 

Phase V: EG1162 and EG1125. The latter, depicted in 

Figure 20, was characterized by a gross ice shape that 

protruded from the airfoil leading edge, with significant 

geometric slope discontinuities at the ice-to-airfoil junction 

and large roughness. The EG1162 shape, depicted in 

Figure 17, was more conformal to the leading edge of the 

NACA 23012 airfoil. This ice shape also had large roughness 

immediately downstream of the smooth stagnation point 

region. Busch et al. (Refs. 44 and 62) report that the upper 

surface roughness extended from x/c = ‒0.008 to 0.065 with a 

maximum height of k/c = 0.0014. The lower surface roughness 

extended from x/c = ‒0.005 to 0.060 with a maximum height 

of k/c = 0.0011. Both of these streamwise ice shapes were 

geometrically scaled and simulated on the subscale model by 

using two-dimensional smooth and simple-geometry shapes 

with and without added roughness. 

The cross-section geometries for the lower fidelity simulations of the EG1162 shape are shown in Figure 41. The twodimensional smooth simulation was based on a pencil tracing 

of the full-scale three-dimensional casting with no further 

smoothing. It was fabricated using stereolithography. The 

simple-geometry simulation was built up on the clean leading 

edge of the subscale model. Spanwise-running spacers were 

used to replicate the “high points” of the ice cross section and 

a thin-film covering was applied over the top of the spacers 

(Ref. 62). This resulted in a smoothly wrapped surface. Both 

of these artificial ice shapes were geometrically scaled 

according to a factor of 4 reduction in chord length between 

the full-scale and subscale models. Various sizes of grit 

roughness were also applied to the two-dimensional smooth 

and simple-geometry configurations at chordwise locations 

and extents similar to the casting. Roughness was applied in a 

fully saturated concentration to minimize possible variations 

due to differences in concentration.

Figure 41.—Geometry comparison of two-dimensional

smooth and simple-geometry simulations for streamwise ice shape EG1162. 
NASA/TP—2011-216929 49

Figure 42 illustrates the effectiveness of the subscale, lower

fidelity simulations of the EG1162 streamwise shape. As 

before, the full-scale data were acquired at Re = 15.9×106 and 

M = 0.20, and the subscale data were acquired at Re = 1.8×106

and M = 0.18. The data for the three-dimensional casting 

simulation are considered to give the “true” iced-airfoil 

aerodynamic performance. For lift and pitching moment, the 

subscale simulations resulted in reasonable agreement with the 

three-dimensional casting data up to stall. The subscale 

simulations had an abrupt, leading-edge stall that accurately 

represented the full-scale behavior. However, both subscale 

simulations yielded a Cl,max that was too high, whereas αstall

for the two-dimensional smooth configuration was lower 

than that of the three-dimensional casting, and αstall for the 

simple-geometry configuration was higher than that of the 

three-dimensional casting. In terms of drag, both subscale 

simulations matched the full-scale three-dimensional casting 

data very well up to about α = 4°, where the data started to 

diverge. It is interesting to note the striking similarities in 

these results to the Phase III experiments with the ED0730 

streamwise shape. The aerodynamic performance data for the 

two-dimensional smooth and simple-geometry configurations 

relative to the three-dimensional casting shown in Figure 11

were nearly identical to those observed here. More accurate 

simulation thus required representation of the ice roughness in 

addition to the gross ice geometry.

Numerous trials were conducted with grit roughness added to 

the two-dimensional smooth and simple-geometry representations of the EG1162 streamwise shape. The roughness was 

applied to the subscale simulations at the appropriate locations 

according to where it was found on the three-dimensional

casting. Figure 43 shows an example of the aerodynamic results 

for roughness added to the two-dimensional smooth simulation.

Two roughness sizes are shown, k/c = 0.0013 and 0.0003. The 

former corresponds to approximately geometrically scaled 

roughness, whereas the latter was determined empirically to 

give the best agreement with the three-dimensional casting data.

Therefore, the two-dimensional smooth simulation with the 

k/c = 0.0013 roughness could be considered to be the highest

fidelity subscale simulation without a priori knowledge of the 

three-dimensional casting configuration aerodynamics. The 

performance data plotted in Figure 43 indicate that this subscale 

configuration results in very conservative performance penalties 

relative to the full-scale three-dimensional casting configuration. In contrast, k/c = 0.0003 roughness improves the simulation fidelity of the two-dimensional smooth simulation. This

conundrum is identical to that faced in the ice-roughness 

simulations discussed in the previous section. It is not clear 

which length scale would lead to the selection of k/c = 0.0003 

roughness without advanced knowledge of the full-scale, icedairfoil aerodynamics.

The aerodynamic comparisons of the subscale simulation 

effectiveness of the full-scale EG1162 streamwise shape are 

summarized in Table 17 in terms of Cl,max, αstall, and ΔCd,rms. 

The first three simulations that were geometrically scaled 

required no a priori knowledge of the full-scale iced-airfoil 

aerodynamics. An interesting result is that, overall, more 

accurate simulation was achieved by neglecting to add 

roughness. However, the uncertainty is biased toward the 

nonconservative side, with overestimates of Cl,max and αstall

and an underestimate of drag relative to the three-dimensional

casting data. Adding geometrically scaled roughness to either 

subscale simulation produced much more conservative results 

as indicated in the table for the two-dimensional smooth case.

The addition of k/c = 0.0003 roughness to the two-dimensional

smooth simulation did not change the resulting Cl,max, but it 

did improve the αstall comparison. Although ΔCd,rms is very 

similar for the two cases, inspection of Figure 43 shows that 

the addition of roughness resulted in slightly higher drag than 

for the three-dimensional casting configuration, whereas for 

the two-dimensional smooth configuration the drag coefficient 

was lower. It is also important to note that the leading-edge 

stall character of the full-scale iced airfoil was adequately 

represented with each of the subscale simulations.

The other streamwise ice shape tested in Phase V, EG1125, 

was less conformal to the airfoil leading edge with large rimefeather roughness as shown in Figure 20. Busch et al. 

(Refs. 44 and 62) report that the upper surface roughness 

extended from x/c = –0.008 to 0.019 and had a maximum 

height of k/c = 0.0018. On the lower surface, roughness 

extended from x/c = ‒0.006 to 0.029 and had a maximum 

height of k/c = 0.0012. Geometrically scaled, lower fidelity 

simulations were fabricated using methods similar to the 

EG1162 configuration described earlier in this section, 

including the addition of grit roughness. Figure 44 depicts the 

cross-sectional geometries of the two-dimensional smooth and 

simple-geometry simulations. Busch et al. (Refs. 44 and 62) 

note that there was a key difference in these two simulations 

for the EG1125 shape. Cross sections of upper and lower 

surface rime feathers recorded during the tracing process 

resulted in small, spanwise-running ridges in the twodimensional smooth extrusion located near the ice/airfoil 

junction as shown in Figure 44. Since these rime feathers were 

fundamentally three-dimensional features of the ice accretion, 

the resulting spanwise-running ridges on the two-dimensional 

smooth simulation were not the best representation of the ice 

accretion. These ridges were faired into the main ice shape by 

covering the entire simulation with an adhesive film. This is 

similar to the method used to fabricate the simple-geometry 

simulation, except that, in this case, the two-dimensional 

smooth geometry was used as the substrate instead of the 

spanwise-running spacers.
NASA/TP—2011-216929 50

Figure 42.—Aerodynamic performance comparison of the EG1162 streamwise ice simulations. Full-scale data were acquired at a Reynolds 

number of 15.9×106 and a Mach number of 0.20; subscale data were 

acquired at a Reynolds number of 1.8×106 and a Mach number of 0.18.
NASA/TP—2011-216929 51

Figure 43.—Aerodynamic performance comparison of the EG1162 streamwise ice simulations with added roughness. Full-scale data were acquired 

at a Reynolds number of 15.9×106 and a Mach number of 0.20; subscale 

data were acquired at a Reynolds number of 1.8×106 and a Mach number 

of 0.18; k/c, roughness height per chord length.
NASA/TP—2011-216929 52

TABLE 17.—EFFECTIVENESS OF EG1162 STREAMWISE ICE SIMULATIONS ON THE SUBSCALE MODEL

[Cl,max, maximum lift coefficient; αstall, stalling angle; ΔCd,rms, root-mean-square percent difference in 

drag coefficient; α, angle of attack; k/c, ice roughness height per chord length.]

Simulation Simulation Cl,max –

casting Cl,max

Simulation αstall 

–

casting αstall,

deg

ΔCd,rms,

percent

(–4° ≤ α ≤

10°)

Two-dimensional smooth 0.03 (2.8 percent) –0.6 11.4

Simple geometry 0.09 (7.7 percent) 0.4 16.1

Two-dimensional smooth with k/c = 0.0013 roughness –0.14 (–11.8 percent) –1.6 28.8

Two-dimensional smooth with k/c = 0.0003 roughness 0.03 (2.8 percent) 0.1 10.3

Figure 44.—Geometry comparison of two-dimensional

smooth and simple-geometry simulations for EG1125 

streamwise ice shape.

The aerodynamic effect of these subscale, lower fidelity 

simulations for the full-scale EG1125 shape are illustrated in 

Figure 45, using the same Reynolds and Mach number 

combinations as in the previous plots. The data show that the 

simple-geometry simulation had the best overall agreement with 

the performance results for the three-dimensional casting. For Cl

and Cm in the stall region, the gradual, trailing-edge stall 

characteristics were preserved, but Cl,max and αstall were slightly 

higher for the simple-geometry configuration. The comparison 

in drag coefficient is very good over the entire angle-of-attack 

range shown, resulting a very low value of ΔCd,rms = 7.6 percent.

For the two-dimensional smooth simulation, the comparison 

was not as good, particularly in that the drag coefficient was 

higher than for the three-dimensional casting over the entire 

angle-of-attack range shown in Figure 45. However, the 

performance results clearly show the effect of fairing the feather

ridges that were inadvertently incorporated into the twodimensional smooth simulation. Busch et al. (Refs. 44 and 62)

gauged this effect by fairing the ridges and greatly improved the 

comparison to the three-dimensional casting data over the 

baseline two-dimensional smooth simulation. There was only a 

slight overestimation of Cl,max and αstall, and Cd was only slightly 

underestimated for α > ~6°. These trends relative to the threedimensional casting data are very similar to those observed for 

the two-dimensional smooth simulation of the EG1162 shape 

(see Figure 42).
NASA/TP—2011-216929 53

Figure 45.—Aerodynamic performance comparison of the EG1125 streamwise ice simulations. Full-scale data were acquired at a Reynolds number 

of 15.9×106 and a Mach number of 0.20; subscale data were acquired at a 

Reynolds number of 1.8×106 and a Mach number of 0.18.
NASA/TP—2011-216929 54

Figure 46 summarizes the effect of roughness added to the 

EG1125 two-dimensional smooth simulation. Since the grit 

roughness was applied using double-sided adhesive films, the 

film substrate had the effect of fairing the feather ridges. 

Therefore, that configuration was an appropriate basis for 

comparison as plotted in the figure. The first roughness 

variation shown was approximately geometrically scaled 

roughness applied to the two-dimensional smooth simulation. 

It consisted of k/c = 0.0015 roughness on the upper surface 

and k/c = 0.0012 and 0.0009 roughness on the lower surface. 

This was the highest fidelity subscale simulation constructed 

without a priori knowledge of the full-scale data. The results 

are very similar to those for the geometrically scaled roughness (k/c = 0.0013) applied to the two-dimensional smooth 

simulation of the EG1162 shape shown in Figure 43. The 

roughness was clearly too large for adequate aerodynamic 

representation of the three-dimensional casting because of the 

lower Cl,max and αstall and higher Cd over the entire angle of 

attack range. Several empirical trials were conducted for 

roughness of various sizes in order to improve the matching 

with the three-dimensional casting aerodynamic performance. 

The best result was for k/c = 0.00007 roughness, as indicated 

in Figure 46. For this configuration, the stalling characteristics 

in terms of Cl,max and αstall were adequately matched to the 

casting, whereas the Cd of the subscale simulation was higher 

up to α = 6°, where there was a crossover. These results are 

very analogous to those observed for the EG1162 case. This 

suggests that the aerodynamic results for subscale simulation 

of the full-scale streamwise ice shapes may be applicable to a 

broader range of streamwise ice accretions as classified by the 

chief aerodynamic characteristics described in Phase I.

Further comparisons between the EG1162 and EG1125 

simulation effectiveness can be drawn in terms of Cl,max, αstall, 

and ΔCd,rms shown in Table 18 for subscale simulations of the 

latter relative to the full-scale, three-dimensional casting 

results. The first three simulations listed were geometrically 

scaled and required no a priori knowledge of the full-scale, 

iced-airfoil aerodynamics. Note that the two-dimensional 

smooth simulation, listed last, was also geometrically scaled. 

However, this was not used for comparison because of the 

inadvertent inclusion of the spanwise-running ridges resulting 

from tracing a fundamentally three-dimensional ice feature. 

As for the EG1162 configuration, more accurate simulation 

was achieved without added roughness, with the caveat that 

the results are biased toward nonconservative performance 

penalties. Even in the case of the k/c = 0.00007 roughness, 

which was specifically selected to improve the comparison, 

only modest improvement is observed in Cl,max and αstall, 

whereas ΔCd,rms increased by nearly a factor of 2 from the twodimensional smooth with faired feathers and simple-geometry 

configurations. The simulations without roughness did have a 

slightly sharper stalling characteristic than did the full-scale 

EG1125 case, which may be important in some cases.

8.2.5 Spanwise-Ridge Ice Simulation

The final type of ice shape investigated in Phase VI was the 

spanwise ridge represented by the EG1159 shape described in 

Phase V. This ice shape was simulated with a two-dimensional

smooth extrusion and simple geometry as illustrated in 

Figure 47. As for the other ice shapes, the two-dimensional

smooth simulation was based on a pencil tracing of the 

EG1159 three-dimensional casting shown in Figure 19 and

was fabricated using stereolithography. The simple-geometry 

simulation was constructed using rectangular spoilers attached 

to the clean model surface at the appropriate locations, as 

indicated in Figure 47. Both of these artificial ice shapes were 

geometrically scaled according to a factor of 4 reduction in 

chord length between the full-scale and subscale models.

The subscale model was outfitted with these lower fidelity

simulations for aerodynamic testing, and the results are

provided in Figure 48. The full-scale clean and EG1159 threedimensional casting data were acquired at Re = 15.9×106 and 

M = 0.20. Also shown are data for the two-dimensional

smooth simulation and the simple-geometry simulation on the 

subscale model at Re = 1.8×106 and M = 0.18. The data for the 

three-dimensional casting configuration are considered to give 

the “true” iced-airfoil aerodynamics. The results show that 

both of the subscale simulations effectively reproduced the lift 

and pitching-moment characteristics of the three-dimensional

casting configuration. For both subscale ice configurations, the

TABLE 18.—EFFECTIVENESS OF EG1125 STREAMWISE ICE SIMULATIONS ON THE SUBSCALE MODEL

[Cl,max, maximum lift coefficient; αstall, stalling angle; ΔCd,rms, root-mean-square percent difference in 

drag coefficient; α, angle of attack; k/c, ice roughness height per chord length.]

Simulation Simulation Cl,max –

casting Cl,max

Simulation αstall –

casting αstall,

deg

ΔCd,rms,

percent

(–4° ≤ α ≤ 9°)

Two-dimensional smooth with faired ridges 0.01 (0.6 percent) –0.3 8.9

Simple geometry 0.04 (3.8 percent) 0.4 7.6

Two-dimensional smooth with geometrically scaled roughness –0.13 (–11.1 percent) –0.6 46.3

Two-dimensional smooth with k/c = 0.00007 roughness 0.0 (–0.2 percent) –0.6 15.9

Two-dimensional smooth –0.06 (–5.1 percent) –0.6 21.9
NASA/TP—2011-216929 55

Figure 46.—Aerodynamic performance comparison of the EG1125

streamwise ice simulations with added roughness. Full-scale data were 

acquired at a Reynolds number of 15.9×106 and a Mach number of 

0.20; subscale data were acquired at a Reynolds number of 1.8×106

and a Mach number of 0.18; k/c, roughness height per chord length.
NASA/TP—2011-216929 56

Figure 47.—Geometry comparison of two-dimensional

smooth and simple-geometry simulations for EG1159 

spanwise-ridge ice shape.

stall characteristics were slightly more benign relative to the 

full-scale, three-dimensional casting, and the former had

slightly higher stalling angles. This is better illustrated by the 

stall break in the pitching-moment coefficient variation with 

angle of attack. The drag data indicated that the subscale 

simulations resulted in higher drag values up until about 

α = 3°. This may have resulted from the fact that the ridge 

geometry of the three-dimensional casting had significant 

spanwise variation, particularly on the lower surface. As the 

angle of attack increased to stall, the lower surface ice 

geometry had less of an impact on the measured drag coefficient. Since the subscale simulations had more benign stall 

characteristics as observed in the lift and pitching moment, the 

relative change in drag coefficient was consistent. Overall, the 

two-dimensional smooth simulation matched more closely the 

drag coefficient of the three-dimensional casting.

The effectiveness of these subscale simulations is summarized in Table 19 in terms of Cl,max, αstall, and ΔCd,rms. The data 

for the two-dimensional smooth and simple-geometry ice 

shapes are very similar to that presented in Table 11 for the horn

ice shape. The two-dimensional smooth simulation provided

better overall matching in terms of the three parameters, but 

with slightly nonconservative results for Cl,max and αstall. The 

simple-geometry case was not as accurate of a simulation, but it 

provided conservative estimates for Cl,max and Cd. Busch et al.

(Refs. 44 and 62) did experiment with applying grit roughness 

to the two-dimensional smooth simulation. The results are 

summarized in Table 19 for approximately geometrically scaled 

roughness applied to both the upper and lower surface ridges.

Similar to what has been already been discussed, this resulted in 

more conservative estimates of Cl,max and Cd.

The minimal effect of the applied roughness was attributable 

to the main flowfield feature described in Phase I: the large 

separation bubble associated with the upper surface ridge at 

angles of attack preceding stall. An important aspect of accurate 

subscale simulation is the representation of such key flowfield 

features in addition to good comparisons in aerodynamic 

performance parameters. The presence of the upper surface 

separation bubble results in a distinct pressure signature as 

described in connection with Figure 10. This is further illustrated in Figure 49 for the full-scale, three-dimensional casting 

compared with the subscale two-dimensional smooth simulation 

at α = 3.1°. The upper surface pressure data for the latter case 

had a higher suction plateau (Cp ≈ –1.5), followed by a more 

rapid pressure recovery relative to the three-dimensional casting 

data. This indicates that the separation bubble generated by the 

upper surface ridge was likely smaller on the subscale model. 

The performance data were consistent with this observation, 

since at α = 3.1° the subscale two-dimensional smooth ice 

shape had a drag coefficient that was lower than for the threedimensional casting, with stall being slightly delayed. The stall 

type in this case was that of thin airfoils, with the separation 

bubble increasing in size with increasing angle of attack. So, a 

smaller separation bubble implies an earlier stage in the stall 

progression. The size comparison of the separation bubble was 

further corroborated with surface-oil flow-visualization results. 

TABLE 19.—EFFECTIVENESS OF EG1159 SPANWISE-RIDGE ICE SIMULATIONS ON THE SUBSCALE MODEL

[Cl,max, maximum lift coefficient; αstall, stalling angle; ΔCd,rms, root-mean-square percent difference in drag coefficient;

α, angle of attack; k/c, ice roughness height per chord length.]

Simulation Simulation Cl,max –

casting Cl,max

Simulation αstall 

–

casting αstall,

deg

ΔCd,rms,

percent

(–4° ≤ α ≤ 4°)

Two-dimensional smooth 0.01 (2.1 percent) 0.5 11.4

Simple geometry –0.02 (–4.6 percent) 0.4 20.0

Two-dimensional smooth with geometrically scaled roughness –0.04 (–7.5 percent) 0.4 12.3
NASA/TP—2011-216929 57

Figure 48.—Aerodynamic performance comparison of the EG1159 

spanwise-ridge ice simulations. Full-scale data were acquired at a 

Reynolds number of 15.9×106 and a Mach number of 0.20; subscale 

data were acquired at a Reynolds number of 1.8×106 and a Mach 

number of 0.18.
NASA/TP—2011-216929 58

Figure 49.—Surface pressure distribution comparison of the EG1159 spanwiseridge ice simulations at an angle of attack of 3.1°. Full-scale data were acquired 

at a Reynolds number of 15.9×106 and a Mach number of 0.20; subscale data 

were acquired at a Reynolds number of 1.8×106 and a Mach number of 0.18.

Busch et al. (Refs. 44 and 62) present and describe a comparison of flow-visualization images, one taken from the full-scale 

model testing in Phase V and one taken from the subscale 

model testing in Phase VI. Figure 50 compares these images, 

which were acquired at α = 3.1°, corresponding to the pressure 

coefficient data in Figure 49. Since the full-scale model flow 

visualization was performed at atmospheric pressure, Re was 

reduced to 7.8×106 at M = 0.20. The effect of Reynolds number 

was so small in the iced-airfoil case, that it is likely that there 

was no significant change in the pressure data shown in 

Figure 49. In Figure 50(a), for the EG1159 three-dimensional

casting on the full-scale model, flow is from left to right, with 

the casting visible on the far left of the image. The estimated 

mean separation bubble reattachment location has been 

highlighted and is positioned at approximately x/c = 0.44.

Downstream of this line, the flow moved toward the airfoil 

trailing edge. Upstream of this line, the flow moved upstream,

indicative of the recirculation region aft of the ridge. In 

Figure 50(b), for the two-dimensional smooth simulation on the 

subscale model, flow is also from left to right, with the upper

surface ridge visible at the far left of the image. The estimated 

mean separation bubble reattachment location, which is 

highlighted in this image, was positioned at approximately 

x/c = 0.33. The subscale flowfield was qualitatively similar to 

the full-scale case with the recirculation region upstream of the 

mean reattachment location. The fact that the separation bubble 

was smaller in the subscale case is also consistent with the 

pressure data described in Figure 49. This provides some 

confidence that the subscale simulation was not only reasonable 

in terms of lift, pitching moment, and drag, but also in terms of 

the proper flowfield characteristics.

Unlike the spanwise-ridge ice shape considered in Phase III

(i.e., ED0760), the present spanwise ridge (EG1159) definitely 

matched the “tall” ridge characteristics described in Phase I.

The large separation bubble dominated the iced-airfoil 

aerodynamics, leading to stall at the lowest Cl,max and αstall of 

all of the Phase V ice shapes. As a result, the subscale

simulation proved to be very effective in reproducing the full-
NASA/TP—2011-216929 59

Figure 50.—Comparison of surface-oil flow visualization 

images of EG1159 spanwise-ridge ice simulations on 

the NACA 23012 airfoil at an angle of attack of 3.1°. 

Flow is from left to right with the approximate mean 

separation bubble reattachment location highlighted in 

each frame. (a) Full-scale, three-dimensional casting 

configuration at a Reynolds number of 7.8×106 and a 

Mach number of 0.20. (b) Subscale, two-dimensional 

smooth configuration at a Reynolds number of 1.8×106

and a Mach number of 0.18. 

scale results. This explains why the simulation effectiveness 

listed in Table 19 is better than that listed in Table 6 for the 

Phase III spanwise ridge. As alluded to earlier, these results 

suggest that a subsclassification of spanwise-ridge ice shapes 

is needed as put forth by Broeren et al. (Refs. 32 and 33).

8.3 Summary of Simulation Effectiveness for 

Phase VI

The ice-accretion simulation methods developed in Phase III

were used here in Phase VI to determine the accuracy to which 

the full-scale, high-Reynolds number iced-airfoil aerodynamics 

could be obtained on the subscale model at low-Reynolds 

number. In all cases the performance of the NACA 23012 

airfoil with the full-scale, three-dimensional casting simulations 

from Phase V was taken as the benchmark, and all other results 

were compared to it. The aerodynamic data were summarized in 

Table 11, Table 14, and Table 16 to Table 19 for each of the ice 

shapes tested in Phase VI. Following the method of Busch et al.

(Refs. 44 and 62), the effectiveness of the geometrically scaled 

simulations of each of the six Phase V ice shapes was compiled

in Table 20. The geometrically scaled simulations were selected 

for comparison because these were designed without any 

a priori knowledge of the full-scale aerodynamics. Thus, these 

results account for uncertainties in geometric simulation 

fidelity, model scale, and Reynolds number.

The combined experimental uncertainty in lift coefficient 

from the subscale and full-scale experiments can be estimated 

from the data provided in Table 2 and Table 10. The uncertainty in the subscale experiments was very small, ±0.00019 

(see Table 2). For the full-scale experiments, the Cl uncertainty was approximately a factor of 2 smaller than the value of 

±0.01 shown in Table 10 because of the increased dynamic 

pressure at Re = 15.9×106 and M = 0.20. Therefore, the 

combined experimental uncertainty in Cl was approximately 

±0.005. According to Table 20, the simulation effectiveness in 

terms of Cl,max was larger than the experimental uncertainty for 

all of the ice shapes. The experimental uncertainty was 

potentially a significant contribution for only the EG1159 ice 

shape. This means that the primary source of simulation 

uncertainty (or simulation ineffectiveness) in Cl,max was likely 

due to the infidelities in the geometric representation and 

Reynolds number effects. Even though Reynolds number 

effects on Cl,max have been shown to be small—the lower limit 

of Reynolds number in the full-scale model tests was 

Re = 4.5×106 to 4.7×106

—it is difficult to estimate the amount 

of simulation uncertainty in Cl,max due to Reynolds number 

variations down to Re = 1.8×106 used in the subscale testing. 
NASA/TP—2011-216929 60

TABLE 20.—SUMMARY OF SIMULATION EFFECTIVENESS FOR GEOMETRICALLY SCALED SIMULATIONS 

FOR EACH OF THE FULL-SCALE MODEL THREE-DIMENSIONAL CASTINGS

[Re, Reynolds number; M, Mach number; Cl,max, maximum lift coefficient; αstall, stalling angle; ΔCd,rms, root-mean-square 

percent difference in drag coefficient; α, angle of attack; k/c, ice roughness height per chord length.]

Ice shape of full-scale, 

three-dimensional casting

(Re = 15.9×106

, M = 0.20)

Geometrically scaled

simulation

(Re = 1.8×106, M = 0.20)

Simulation Cl,max –

casting Cl,max

Simulation αstall –

casting αstall,

deg

ΔCd,rms,

percent

(linear α range)

Classification Configuration

Horn EG1164 Two-dimensional smooth 0.03 (2.9 percent) 1.0 10.3

Roughness 1 EG1126 Approximately geometrically scaled (see

Table 12)

–0.04 (–3.5 percent) –1.0 28.6

Roughness 2 EG1134 k/c = 0.0003 roughness with EG1126 extents –0.19 (–15.0 percent) –1.7 16.4

Streamwise 1 EG1162 Two-dimensional smooth with k/c = 0.0013 

roughness

–0.14 (–11.8 percent) –1.6 28.8

Streamwise 2 EG1125 Two-dimensional smooth with geometrically

scaled roughness

–0.13 (–11.1 percent) –0.6 46.3

Spanwise ridge EG1159 Two-dimensional smooth 0.01 (2.1 percent) 0.5 11.4

The simulation effectiveness in terms of αstall for all of the 

ice shapes was an order of magnitude larger than for the 

combined experimental uncertainty for both the subscale and 

full-scale experiments (see Table 2 and Table 10). However, 

the angle-of-attack resolution at which the data were acquired 

may play a larger role. Generally this was 0.5° for the fullscale data and 1.0° for the subscale data. So α < 1.0° in 

Table 20 could be due to a combination of all effects: angleof-attack resolution, experimental uncertainty, and simulation 

uncertainty. Only the EG1134 and EG1162 configurations had 

absolute αstall > 1.0° in Table 20. Depending on the objective 

of scaled simulation experiments, these kinds of differences in 

stalling angle may be acceptable. Often the character of the 

stall behavior (e.g., abrupt stall vs. gradual stall), may be more 

important than the exact values of stalling angle of attack.

Such metrics are unfortunately difficult to quantify and 

summarize in tables.

For the comparisons of ΔCd,rms in Table 20, determining the 

individual contribution due to experimental uncertainty is 

nontrivial. There is the added uncertainty because potential 

spanwise variations in drag were not quantified. However, 

visual inspection of the drag comparison plots has shown that 

ΔCd,rms ≤ 10 percent represents a reasonable simulation of drag 

performance over the linear range. For the six ice shapes in 

Table 20, only two—EG1162 and EG1159—had ΔCd,rms values 

close to 10 percent. There was a large range among the others.

As described in the previous discussions, drag coefficient is 

affected by the simulation details, such as roughness size, 

concentration, and location. Clearly, high-fidelity simulations 

and more closely matched Reynolds numbers are required for a 

more accurate drag coefficient simulation.

Busch et al. (Refs. 44 and 62) also used as a basis for comparison the subscale simulations that were constructed to best 

match the full-scale aerodynamics. These were labeled “mostaccurate” simulations and are listed in Table 21. For the 

EG1164 and EG1159 shapes, the most-accurate simulation 

was the two-dimensional smooth, geometrically scaled 

simulation. For these ice shapes, the dominate driver of the 

iced-airfoil aerodynamics was the large separation bubble 

emanating from the horn (EG1164) or spanwise ridge 

(EG1159). The geometric details of the ice shape and 

roughness did not play a primary role in the resulting aerodynamic performance. For the other four ice shapes (EG1126, 

EG1134, EG1162, and EG1125), there were significant 

improvements in the simulation effectiveness relative to the 

geometrically scaled simulations summarized in Table 20. In 

terms of Cl,max, the simulation uncertainty (see Table 21) was 

still larger than the combined experimental uncertainty 

(±0.005), indicating that geometry and Reynolds number 

effects played a role. The opposite was true in terms of αstall, 

where the simulation uncertainty (see Table 21) was less than 

the angle-of-attack resolution used in acquiring the data.

Significant improvements in ΔCd,rms were also observed.

These data for the “most-accurate” simulations provide a 

lower limit of the total simulation uncertainty that is realistically achievable over this range of model scale and Reynolds 

number. It is interesting to note that the some of the values in 

Table 21 were larger than the values in Table 7. (There were 

some notable exceptions, particularly in terms of αstall.) These 

differences were at least partly attributable to model scale and 

Reynolds number effects.
NASA/TP—2011-216929 61

TABLE 21.—SUMMARY OF SIMULATION EFFECTIVENESS FOR MOST-ACCURATE SUBSCALE SIMULATIONS 

FOR EACH OF THE FULL-SCALE MODEL THREE-DIMENSIONAL CASTINGS

[Re, Reynolds number; M, Mach number; Cl,max, maximum lift coefficient; αstall, stalling angle; ΔCd,rms, root-mean-square 

percent difference in drag coefficient; α, angle of attack; k/c, ice roughness height per chord length.]

Ice shape of full-scale, 

three-dimensional casting

(Re = 15.9×106

, M = 0.20)

Most accurate subscale simulation

(Re = 1.8×106, M = 0.20)

Simulation Cl,max –

casting Cl,max

Simulation αstall –

casting αstall,

deg

ΔCd,rms,

percent

(linear α range)

Classification Configuration

Horn EG1164 Two-dimensional smooth 0.03 (2.9 percent) 1.0 10.3

Roughness 1 EG1126 k/c = 0.0003 roughness with casting extentsa –0.01 (–0.6 percent) –0.1 19.4

Roughness 2 EG1134 k/c = 0.00007 roughness 0.02 (1.5 percent) –0.1 7.8

Streamwise 1 EG1162 Two-dimensional smooth with k/c = 0.0003 

roughness

0.03 (2.8 percent) 0.1 10.3

Streamwise 2 EG1125 Two-dimensional smooth with faired ridges 0.01 (0.6 percent) –0.3 8.9

Spanwise ridge EG1159 Two-dimensional smooth 0.01 (2.1 percent) 0.5 11.4 a

Chordwise extent of roughness over the casting.

9.0 Subscale Iced-Airfoil Aerodynamic 

Simulation Methodology

The objectives of this study as presented in the Introduction 

were to provide high-fidelity, full-scale, iced-airfoil aerodynamic data and validated subscale simulation methods that 

produce the essential full-scale aerodynamic characteristics.

Busch (Ref. 62) presents the following method where the subscale Re = 1.8×106 and the model scale is one-fourth of the fullscale geometry. This analysis was based on the results of Phase

VI together with the current understanding of iced-airfoil 

flowfield characteristics described in this report and in more 

detail by Busch (Ref. 62). It is anticipated that these methods 

should provide results within the accuracies presented (see

Table 20) for scales and Reynolds numbers at least to these 

levels. The following was adapted from Busch (Ref. 62).

(1) Given the ice accretion to be simulated, classify it as 

ice roughness, streamwise ice, horn ice, or tall or short 

spanwise-ridge ice.

(2) For the appropriate classification, document the iceshape geometric features that have been identified as 

having a significant effect on the iced-airfoil flowfield.

The size, shape, location, and other attributes of these

important features may be quantified from icingtunnel tests, flight-tests, computational methods like 

LEWICE, or other sources.

(3) Depending on the ice-shape type, construct simplegeometry or two-dimensional smooth simulations that

geometrically scale the gross ice geometry.

(4) For shapes in which roughness is important, add geometrically scaled grit roughness of the appropriate 

concentration. This was shown to generally yield conservative performance estimates. To bracket the “true”

iced-airfoil performance, the authors recommend that 

the simulation be tested both with and without the grit 

roughness.

Each type of iced-airfoil flowfield is different and is, thus,

affected by different geometric features. The following 

summaries identify these features on the basis of the aerodynamics for each of the known ice-accretion types. After the

geometric features have been determined, steps (2) to (4) can be 

completed. 

9.1 Ice Roughness

Roughness elements on the airfoil leading edge often cause 

regions of localized separation, increasing skin friction and 

extraction of momentum from the flow, and may cause the 

boundary layer to undergo early transition, causing premature 

trailing-edge stall, reduced maximum lift coefficient, and 

increased drag coefficient. Therefore, it is important for an iceroughness simulation to reproduce these effects at each angle of 

attack in the range of interest. Geometric features that affect the 

iced-airfoil performance are roughness height, concentration, 

location, and chordwise extent. Roughness height affects both 

airfoil maximum lift coefficient and drag coefficient. As 

roughness height increases (for constant roughness concentration, location, and chordwise extent), Cl,max decreases and Cd

increases. The maximum lift coefficient is most sensitive to 

changes in roughness height for very small heights and becomes 

less sensitive at larger heights. Roughness concentration also 

affects airfoil maximum lift coefficient and drag coefficient. As 

roughness concentration increases, Cl,max decreases up to a critical concentration (which depends on roughness height), beyond 

which Cl,max becomes much less sensitive to changes in concentration. As roughness concentration increases, Cd increases, 

even beyond the critical concentration at which Cl,max becomes 

insensitive. Roughness extent affects mainly the drag coefficient, but large changes may affect the maximum lift coefficient. Decreases in chordwise extent of only a few percent 

chord caused measurable reductions in iced-airfoil Cd. Multiple 

combinations of roughness height and concentration may 
NASA/TP—2011-216929 62

provide accurate modeling of iced-airfoil aerodynamics over an 

angle-of-attack range.

9.2 Streamwise Ice

A short separation bubble often forms because of discontinuities in the ice geometry or at the interface between the ice 

and airfoil. This separation bubble removes momentum from 

the reattached boundary layer, increasing the likelihood of 

early separation. Therefore, it is important for a subscale 

simulation to generate a short separation bubble of similar size 

at a similar location, which requires a modification of the 

airfoil leading-edge geometry. To model the maximum lift 

coefficient, this modification does not have to exactly match 

that of the original ice shape as long as an appropriate 

separation bubble is generated. Further compounding the 

effect of the short separation bubble is the presence of 

roughness on the ice, which acts in a manner similar to that 

described for ice roughness. Accordingly, it is important to 

represent the height, concentration, location, and extent of 

surface roughness on streamwise ice. Streamwise ice may also 

be more three-dimensional than other types of ice shapes 

because of the presence of ice feathers and nodules. If one 

uses a two-dimensional smooth simulation to model the icedairfoil aerodynamics, these features should not be included in 

the geometry used to make the simulation since they would be 

extruded into two-dimensional ridgelike features, causing a 

different effect on the flowfield than did the feathers on the 

original ice shape.

9.3 Horn Ice

The horn ice flowfield is dominated by a long separation 

bubble generated from the tip of the horn. The aerodynamics 

of a horn ice shape can be accurately modeled by reproducing

the size of this separation bubble and the point at which 

separation occurs. Features shown to affect the size of the 

separation bubble and the airfoil maximum lift and drag 

coefficients are horn height, location, angle, and sometimes tip 

radius. With all other parameters equal, for horns located on 

the upper surface of the airfoil, increasing horn height usually 

reduces Cl,max and increases Cd because of the larger separation 

bubble. Horns located farther aft and with greater angles, up to 

90°, also tend to cause larger aerodynamic penalties than those 

located closer to the leading edge and with lower angles.

Surface roughness does not usually have a large effect on the 

separation bubble size, and it does not need to be included in a 

horn ice simulation.

9.4 Tall Spanwise-Ridge Ice

Tall spanwise-ridge ice is similar to horn ice in that the 

flowfield is dominated by a long separation bubble, but it is 

different in that the boundary layer develops upstream of the 

ice shape. As with horn ice, it is important to properly 

represent the long separation bubble generated by the ridge.

However, because the boundary layer has formed on the 

airfoil surface prior to reaching the ridge, surface roughness 

located upstream of and on the ridge is important to represent 

as well. Roughness downstream of the ridge, located in the 

separation bubble, does not have a significant effect on airfoil 

performance. Ridge features affecting the airfoil maximum lift

and drag coefficients are height, location, and surface 

roughness upstream of the separation point. Increasing ridge 

height causes a larger separation bubble, reduces airfoil Cl,max, 

and increases Cd. Ridges on the upper surface between the 

locations of maximum local air velocity (minimum Cp) and 

maximum adverse pressure gradient of the clean airfoil cause 

the largest reductions in Cl,max, and ridges located near the 

clean airfoil maximum local air velocity (minimum Cp) cause 

the largest increases in Cd. The addition of surface roughness 

decreased Cl,max and increased Cd at most angles of attack.

9.5 Short Spanwise-Ridge Ice

Short spanwise-ridge ice shapes generate short separation 

bubbles, rather than the long bubbles generated by tall 

spanwise-ridge ice, and have only a local effect on the airfoil 

Cp distribution. It is important for a subscale simulation to 

appropriately represent this separation bubble, and ridge 

height, location, and geometry have all been shown to be 

important. Airfoil Cl,max decreases and Cd increases with 

increasing ridge height for a given location or for more 

forward locations and a given height. The geometry of the 

ridge also affects airfoil aerodynamics because differentshaped simulations of identical height and location may yield 

different performance penalties. As with tall ridges, the 

boundary layer develops upstream before reaching the short 

ridge, and surface roughness was shown to have a significant 

effect on both Cl,max and Cd. The addition of surface roughness 

caused conservative estimates of aerodynamic performance 

and worsened simulation accuracy. This may have been due, 

in part, to Reynolds number effects because no data exist 

regarding Reynolds number effects in the range 1.8×106 ≤

Re ≤ 4.6×106 and because effects above Re = 4.6×106 have 

been shown to be small. More research is required to further 

develop the short-ridge aerodynamic characteristics.

10.0 Summary, Conclusions, and

Recommendations

10.1 Summary

This report presents the results of a six-phase research 

program designed to study the aerodynamic scaling and 

simulation of ice accretion on airfoils. The overall goal of this 

program was to determine the level of geometric fidelity 

required for artificial ice shapes to yield aerodynamic 

performance results to within a known level of uncertainty,
NASA/TP—2011-216929 63

thus attempting to answer the question, “How good is good 

enough?” The research program was organized into six 

phases, employing both subscale and full-scale model testing.

It was important to have a set of full-scale benchmark data and 

firm knowledge of the full-scale phenomena. This information 

provided validation data for both the subscale testing and 

scaling methods developed.

In Phase I: Ice-Shape Classification, previous research was 

analyzed to classify ice shapes according to the fundamental 

flowfield physics. Four types of ice were defined: roughness, 

horn, streamwise, and spanwise ridge. For horn and spanwiseridge shapes, the flowfield was characterized by a large 

separation bubble that grew in size with increasing angle of 

attack. The separation bubble was the dominant flowfield 

feature contributing to the stall of the iced airfoil. For 

roughness and streamwise ice, the flow was characterized by 

local flow separation on the scale of the roughness, but this 

was not largely affected by angle-of-attack changes. Instead, 

the aerodynamic effect of these shapes was manifested 

through the boundary layer such that there was a greater 

tendency for trailing-edge separation at lower angles of attack 

for the iced airfoil. There were other important differences 

among these four types as described in this report. It was these 

key flowfield features that affected the scaling and simulation

carried out in this research and that guided the experiments.

Since each of the four types has different fundamental aerodynamics, if techniques can be developed to simulate these 

four shapes, then most ice accretions can be simulated.

The objective of Phase II: Subscale-Model Ice-Accretion

Testing was to obtain high-fidelity ice shapes for the subscale 

model having the characteristics developed in Phase I. Iceaccretion testing was conducted in the NASA Icing Research 

Tunnel (IRT) using an 18-in.- (0.46-m-) chord, twodimensional wing section with an NACA 23012 airfoil profile.

Icing conditions were based on the in-flight icing environment 

that a commuter aircraft might encounter. For selected cases, 

molds were acquired of the ice accreted on the model under 

these conditions. Castings were then made from these molds 

and used as the high-fidelity ice shapes for the tests in 

Phase III. The purpose here was not to produce scaled iceaccretion geometry but to generate ice accretions that were 

representative of the four ice classifications to use for 

aerodynamic simulation development in Phase III.

In Phase III: Subscale-Model Aerodynamic Testing, the 

high-fidelity ice shapes obtained in Phase II were used 

to develop aerodynamic simulation methods on the subscale 

model. Aerodynamic testing was performed in the University 

of Illinois wind tunnel using an 18-in.- (0.46-m-) chord twodimensional wing section with NACA 23012 airfoil profile.

Airfoil performance was measured at Reynolds number 

Re = 1.8×106 and Mach number M = 0.18 with artificial ice 

shapes having various levels of simulation fidelity. The threedimensional casting simulation was made from a mold of the 

IRT ice accretion, had the highest geometric fidelity, and was 

considered to result in the “true” iced-airfoil aerodynamics.

Lower fidelity simulations such as two-dimensional smooth 

and simple geometry were developed to quantify the resulting 

aerodynamic effect. The ability of the lower fidelity simulations to reproduce the aerodynamics of the airfoil with the 

three-dimensional casting simulation was quantified in terms 

of lift, drag, and pitching-moment coefficient (Cl, Cd, and Cm) 

versus angle of attack (α). In addition, surface pressure 

distributions and surface-oil flow visualizations were compared. It was important that the lower fidelity simulations 

reproduce the fundamental flowfield features as well as the 

integrated performance coefficients. The effectiveness of the 

most accurate lower fidelity simulation method for each of the 

four ice-shape types was summarized in Table 7 in terms of 

Cl,max, αstall, and root-mean-square difference in drag coefficient between the lower fidelity simulation and the threedimensional casting simulation over a given angle-of-attack 

range ΔCd,rms. The simulation accuracy ranged from 0.2 to 

–3.7 percent in Cl,max, 0.00° to –1.98° in αstall, and 4.8 to 15.5

percent in ΔCd,rms. The Phase III results quantified the simulation effectiveness for various levels of fidelity on the subscale model. These simulation methods were then applied to 

Phase VI of the program.

The objective of Phase IV: Full-Scale-Model Ice-Accretion

Testing was to obtain high-fidelity ice shapes for the full-scale 

model having the characteristics developed in Phase I.

Ice-accretion testing was conducted in the NASA IRT using a 

72-in.- (1.83-m-) chord, two-dimensional wing section with an 

NACA 23012 airfoil profile. Test conditions for the full-scale 

icing tests were selected as indicated in Phase II: that is, icing 

conditions that a commuter aircraft might encounter in flight,

as defined by icing conditions in Appendix C of Code of 

Federal Regulations (CFR) Part 25, and that would yield an 

ice accretion falling within the four ice classifications 

described in Phase I. For selected cases, molds were acquired

of the ice accreted on the model under these conditions.

Castings were then made from these molds and used as the 

high-fidelity ice shapes for the tests in Phase V.

In Phase V: Full-Scale Model Aerodynamic Testing, the ice 

shapes acquired in Phase IV were used for aerodynamic 

testing on the full-scale model to obtain a benchmark data set 

for the validation of subscale simulation methods. Aerodynamic testing was performed in the ONERA F1 wind-tunnel 

using a 72-in.- (1.83-m-) chord, two-dimensional wing section 

with an NACA 23012 airfoil profile. Airfoil performance was 

measured from Re = 4.5×106 to 15.9×106 and M = 0.10 to 0.28 

with high-fidelity, three-dimensional casting simulations 

attached to the model. Six ice-shape simulations were tested:

one horn shape, one spanwise-ridge shape, two streamwise 

shapes, and two roughness shapes. The artificial ice shapes 

had a large detrimental effect on the performance of the 

NACA 23012 airfoil. The spanwise-ridge shape caused the 

largest reduction in maximum lift, with Cl,max = 0.52 compared 

to the clean value of Cl,max = 1.85 at Re = 15.9×106 and 
NASA/TP—2011-216929 64

M = 0.20. The two roughness and streamwise ice simulations 

had a similar range of performance effects on the airfoil. At 

Re = 15.9×106 and M = 0.20, the range of Cl,max was 1.09 to 

1.28 and the range of minimum Cd was 0.0082 to 0.0106. The 

range of performance effects was small relative to the large 

differences in the size and geometry of the roughness and 

streamwise ice shapes tested.

The pressurization capability of the ONERA F1 facility was 

used to document the effects of Reynolds and Mach numbers on 

the clean and iced performance. The clean airfoil Cl,max

increased from 1.76 to 1.88 as Re increased from 4.6×106 to 

12.3×106 at constant M = 0.10. Increasing M from 0.10 to 0.28 

at a constant Re = 12.1×106 reduced Cl,max from 1.88 to 1.78.

The trends in lift and moment slope versus angle of attack and 

drag coefficient were consistent with classic airfoil behavior.

For the airfoil with the ice simulations, there was virtually no 

measurable change in maximum lift coefficient over the entire 

Reynolds number range tested. Changes in Mach number had 

minor effects on maximum lift for the horn and spanwise-ridge 

shapes, but virtually no effects for the others. This lack of 

significant Reynolds and Mach number effects was important to 

Phase VI, the subsequent subscale-model simulation.

In Phase VI: Simulation Validation Testing, the methods 

developed in Phase III were used to scale and simulate the 

full-scale ice shapes for testing on the subscale model at lower 

Reynolds numbers. The objective of this phase was to “close 

the loop” by using the subscale-model data to reproduce the 

aerodynamic effects of the ice shapes tested on the full-scale 

model at high Reynolds numbers. Aerodynamic testing was 

again performed in the University of Illinois wind tunnel using 

the 18-in.-chord NACA 23012 airfoil model at Re = 1.8×106

and M = 0.18. Scaled, lower fidelity simulations of the threedimensional ice casting configurations from Phase V were 

developed and tested on the subscale model. As in Phase III, 

the ability of the lower fidelity simulations to reproduce the 

aerodynamics of the airfoil with the three-dimensional casting

simulation was quantified in terms of lift, drag, and pitchingmoment coefficient versus angle of attack. Surface pressure 

distributions and surface-oil flow visualizations were also 

compared. It was important that the lower fidelity simulations 

reproduced the fundamental flowfield features as well as the 

integrated performance coefficients. 

The effectiveness of the geometrically scaled, lower fidelity 

simulations for each of the six ice shapes was summarized in 

Table 20 in terms of Cl,max, αstall, and ΔCd,rms. The simulation 

accuracy ranged from 2.1 to –15.0 percent in Cl,max, 1.0° to 

–1.7° in αstall, and 10.3 to 46.3 percent in ΔCd,rms. Note that 

this range of simulation accuracy was much larger than what 

was obtained in Phase III. These results account for uncertainties in geometric simulation fidelity, model scale, and 

Reynolds number. The results for the geometrically scaled 

simulations represent typical methods used without any 

knowledge of the full-scale iced-airfoil aerodynamics. Scaled, 

lower fidelity simulations also were constructed to imitate the 

full-scale aerodynamics as closely as possible. The effectiveness of these simulations, which was summarized in Table 21,

ranged from 2.9 to –0.6 percent in Cl,max, 1.0° to –0.3° in αstall, 

and 7.8 to 19.4 percent in ΔCd,rms. These data for the “mostaccurate” simulations provide a lower limit of the total 

simulation uncertainty that is realistically achievable over this 

range of model scale and Reynolds number.

10.2 Conclusions

This program has resulted in a systematic framework for 

conducting the aerodynamic analysis of iced airfoils on 

subscale models at low Reynolds numbers. An ice-accretion 

classification system based on flowfield physics was developed and used to guide the experiments. Methodologies were

then developed for defining various levels of geometric 

fidelity for ice-shape simulations on the basis of the classification. The aerodynamic fidelity of the ice-shape simulations 

was first quantified through subscale-model iced-airfoil 

performance. High-quality aerodynamic data were generated 

for high-fidelity, ice-casting simulations on a full-scale model 

at high Reynolds numbers. This provided a benchmark 

database that is directly applicable to airplanes. The Reynolds 

and Mach number effects on clean- and iced-airfoil performance were also quantified. Thus, the combined results of the 

full-scale and subscale-model experiments quantified the

uncertainty associated with various levels of geometric iceshape fidelity in terms of lift and drag for a factor of 4 in 

model scale and a factor of 8 in Reynolds number. The lower

limit for these results is Re = 1.8×106

, and it is likely that more 

significant aerodynamic effects will occur for even lower 

Reynolds numbers. Geometric scaling of gross ice-shape 

features is appropriate for horn ice, streamwise ice, and 

spanwise-ridge ice. For roughness and streamwise ice, the 

details of roughness are important for accurate aerodynamic 

simulation. For horn ice, any roughness details may be 

important for accurate drag simulation but not for lift. These 

results showed that geometric scaling of roughness will likely 

result in conservative performance penalties and thus more 

accurate roughness simulation requires a different length 

scale. These conclusions can be directly related to the key 

flowfield features that govern the iced-airfoil aerodynamics 

for the four types of ice shapes. This work culminated in the

proposed methodology for subscale iced-airfoil aerodynamic 

simulation described in this report.

10.3 Recommendations

This study addressed a large number of important factors 

regarding subscale simulation of icing aerodynamics effects.

This was accomplished primarily in terms of maximum lift, 

stalling angle, and drag using an NACA 23012 airfoil. More 

detailed study of the simulation methods and accuracy in terms 

of pitching moment and control surface forces and moment is 
NASA/TP—2011-216929 65

recommended since these can also be important in some cases.

It is also known that the stalling characteristics can be equally 

important to maximum lift coefficient and stalling angle.

Subscale simulation should be evaluated in terms of these 

effects as well. The effect of airfoil geometry should be 

considered in future work. Finally, an analogous research 

program should be implemented for three-dimensional swept 

wings to better understand how these two-dimensional airfoil 

data relate to swept-wing aircraft configurations.

Glenn Research Center

National Aeronautics and Space Administration

Cleveland, Ohio, February 17, 2011

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Aerodynamic Simulation of Ice Accretion on Airfoils 

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6. AUTHOR(S)

Broeren, Andy, P.; Addy, Harold, E., Jr.; Bragg, Michael, B.; Busch, Greg, T.; Guffond, 

Didier; Montreuil, Emmanuel 

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14. ABSTRACT

This report describes recent improvements in aerodynamic scaling and simulation of ice accretion on airfoils. Ice accretions were classified 

into four types on the basis of aerodynamic effects: roughness, horn, streamwise, and spanwise ridge. The NASA Icing Research Tunnel 

(IRT) was used to generate ice accretions within these four types using both subscale and full-scale models. Large-scale, pressurized windtunnel testing was performed using a 72-in.- (1.83-m-) chord, NACA 23012 airfoil model with high-fidelity, three-dimensional castings of 

the IRT ice accretions. Performance data were recorded over Reynolds numbers from 4.5×106 to 15.9×106 and Mach numbers from 0.10 to 

0.28. Lower fidelity ice-accretion simulation methods were developed and tested on an 18-in.- (0.46-m-) chord NACA 23012 airfoil model 

in a small-scale wind tunnel at a lower Reynolds number. The aerodynamic accuracy of the lower fidelity, subscale ice simulations was 

validated against the full-scale results for a factor of 4 reduction in model scale and a factor of 8 reduction in Reynolds number. This 

research has defined the level of geometric fidelity required for artificial ice shapes to yield aerodynamic performance results to within a 

known level of uncertainty and has culminated in a proposed methodology for subscale iced-airfoil aerodynamic simulation.

15. SUBJECT TERMS

Aircraft icing; Airfoil; Aerodynamic stalling; Scale models 

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