Robert Narducci and Stanley Orr

The Boeing Company, Philadelphia, Pennsylvania

Richard E. Kreeger

Glenn Research Center, Cleveland, Ohio

Application of a High-Fidelity Icing Analysis 

Method to a Model-Scale Rotor in Forward Flight

NASA/TM—2012-217122

January 2012
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Robert Narducci and Stanley Orr

The Boeing Company, Philadelphia, Pennsylvania

Richard E. Kreeger

Glenn Research Center, Cleveland, Ohio

Application of a High-Fidelity Icing Analysis 

Method to a Model-Scale Rotor in Forward Flight

NASA/TM—2012-217122

January 2012

National Aeronautics and

Space Administration

Glenn Research Center

Cleveland, Ohio 44135

Prepared for the

67th Annual Forum and Technology Display (Forum 67)

sponsored by the American Helicopter Society (AHS)

Virginia Beach, Virginia, May 3–5, 2011
Acknowledgments

This work represents a team effort at The Boeing Company and the authors wish to thank Peter Hartman and Andy Peterson for their 

contributions. The authors would also like to thank Paul Tsao, Colin Bidwell, Mark Potapczuk, Tonja Reinert, Robert Flemming, 

and Roger Aubert for their support and many fruitful discussions. This projected was funded by NASA Contract NNC08CA88C 

through the NASA Glenn Research Center. 

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

Level of Review: This material has been technically reviewed by technical management. 
NASA/TM—2012-217122 1

Application of a High-Fidelity Icing Analysis Method to a 

Model-Scale Rotor in Forward Flight

Robert Narducci and Stanley Orr

The Boeing Company 

Philadelphia, Pennsylvania 19078

Richard E. Kreeger

National Aeronautics and Space Administration

Glenn Research Center

Cleveland, Ohio 44135

Abstract

An icing analysis process involving the loose coupling of 

OVERFLOW-RCAS for rotor performance prediction and with 

LEWICE3D for thermal analysis and ice accretion is applied to 

a model-scale rotor for validation. The process offers highfidelity rotor analysis for the non-iced and iced rotor 

performance evaluation that accounts for the interaction of 

nonlinear aerodynamics with blade elastic deformations. Ice 

accumulation prediction also involves loosely coupled data 

exchanges between OVERFLOW and LEWICE3D to produce 

accurate ice shapes. Validation of the process uses data 

collected in the 1993 icing test involving Sikorsky’s Powered 

Force Model. Non-iced and iced rotor performance predictions 

are compared to experimental measurements as are predicted 

ice shapes.

Introduction

Many helicopters in the world today are restricted from 

flying in any condition that causes the rotors to be susceptible 

to ice accumulation. The result of ice on rotor blades is a loss 

in lift that can cause a critical safety hazard within minutes of 

encountering ice. Ice on the rotor is also accompanied by an 

increase in required power and an increase in vibration. The 

high centrifugal force field on the rotor can cause ice to shed 

once the accumulation becomes large causing dangerous 

projectiles in the surrounding areas.

A reliable analysis system would be useful in understanding 

the limitations of helicopters once icing conditions are 

encountered so that the operating envelope can be safely 

expanded for flight through light or moderate icing conditions. 

Until recently the development of ice analysis tools have been 

focused on the fixed-wing icing problem. Much can be 

leveraged for the rotorcraft problem; however rotor icing 

poses additional significant challenges. The rotor blade chord 

and airfoil thickness are smaller than fixed wing aircraft 

causing ice growth to be larger relative to the chord. Larger 

shapes are harder to predict and their impact to aerodynamic 

performance is more critical. Rotors also experience a diverse 

and changing aerodynamic environment within a single rotor 

revolution. In forward flight, one side of the rotor—the 

advancing side—experiences higher airspeeds and lower 

angles of attack than the opposite side. Likewise, near the hub, 

the relative airspeed is much lower than at the tip where Mach 

numbers approach one. Many of the fixed-wing ice accretion 

codes are not calibrated for such diverse operating conditions. 

These diverse conditions also cause the rotor to accrete both 

rime and glaze ice for the same atmospheric conditions.

The approach taken to develop an icing analysis system for 

rotors leverages methods developed for fixed-wing aircraft. 

Bidwell (Ref. 1) coupled ice accretion analysis with 

computational fluid dynamics (CFD) to evaluate ice on a highlift wing configuration. He used LEWICE3D (Ref. 2) to 

evaluate droplet trajectories, heat transfer, and ice growth 

while relying on OVERFLOW or CFD++ for aerodynamic 

analysis. OVERFLOW (Ref. 3) is well suited for aerodynamic 

assessments of rotorcraft and therefore Bidwell’s approach is a 

natural point of departure for an icing analysis system for 

rotors.

Accurate rotor performance must account for the complex 

interactions between aerodynamics and elastic deformations. 

Additionally, trimmed rotor solutions where thrust targets are 

met and moments are balanced across the rotor disk are 

required to accurately model the environment for the icing 

analysis. Therefore, the pure CFD solutions used in Bidwell’s 

approach are replaced with solutions generated by coupling 

CFD solvers with rotorcraft comprehensive analysis codes. To 

address the rapidly changing aerodynamic environment, ice is 

accumulated with multiple data exchanges between 

aerodynamic solutions and LEWICE3D. This approach was 

introduced by Narducci (Ref. 4) to address ice accretion on 

rotors in hover however rotor trim and elastic coupling were 

de-emphasized. These elements cannot be ignored for forward 

flight rotor analysis.

Details of the analysis method are presented in this paper. 

The process is applied to a model-scale rotor that was studied 

for ice accumulation and performance degradation in the 

NASA Glenn Icing Research Tunnel (IRT). A description of 

the test and conditions for validation follow the explanation of 

the analysis method. Results of the application to the modelscale problem are presented next with comparisons to test data 

for non-iced and iced rotor performance and the accumulation 

of ice. Important conclusions, known limitations, and future 

work are discussed in the final section of this paper.
NASA/TM—2012-217122 2

Analysis Approach

The high fidelity icing analysis approach developed for 

rotor systems follows three basic steps:

• Establish rotor trim and non-iced rotor performance and 

the initial flow field environment using CFD or coupled 

CFD-rotorcraft comprehensive analysis as appropriate;

• Extract representative two-dimensional airfoil 

conditions for blade sections at radial and azimuthal 

locations and predict ice buildup accounting for the 

diverse operating environment of the rotor;

• Reestablish rotor performance for the iced blades. 

The approach is appropriate to address ice accumulation on 

rotors for flight regimes from hover to high-speed forward 

flight. The process is shown pictorially in Figure 1.

The first step involves establishing the non-iced (hereafter 

referred to as “clean”) analysis of the rotor in forward flight. 

This utilizes a coupled process between CFD and a rotor 

comprehensive analysis code. Atmospheric conditions of the 

icing event are modeled with the exception of water drops 

which are assumed to have little impact to the aerodynamic 

solution. The rotor performance for clean blades serves as the 

baseline with which to compare iced rotor performance. The 

rotor solution also provides details including pressure 

distributions and flow conditions local to the blades that are 

useful for the ice accretion analysis.

There are many CFD and rotorcraft comprehensive codes 

that can be used to produce the rotor solutions needed for the 

icing analysis. OVERFLOW for CFD and RCAS (Ref. 5) for the 

rotorcraft comprehensive analysis are commonly used in the 

rotorcraft community and have been adopted here. 

OVERFLOW, developed and maintained at NASA, is a 

Reynolds Averaged Navier Stokes finite volume CFD solver 

that uses an overset system of grids to produce flow solutions 

for complex rotorcraft geometry and conditions. Though mass, 

momentum, and energy are not strictly conserved in the grid 

overlap region, the overset system of grids is ideal for the 

rotorcraft problem because it readily allows motion of one 

component relative to another.

Coupling CFD with a rotor comprehensive code means that 

the blade response to air loads (pitching, flapping, and lead-lag 

motions, and aeroelastic bending and twisting) is represented 

in the solution and that control settings are trimmed to provide 

a balanced rotor. The coupling process is well-established and 

has been validated against several rotor data sets including the 

UH-60A (Refs. 6 to 9). Essentially the process uses a 

combination of quick-running lifting-line air loads and air 

loads from CFD solutions to efficiently produce a high-fidelity 

solution. The method uses lifting-line air loads during trim, 

while successively using CFD-produced air loads for blade 

deflections. This loose-coupling method is outlined in

Figure 2. At iteration n=0, the comprehensive code trims and 

converges with only lifting-line air loads. Blade motions for 

Figure 1.—High-fidelity ice accretion and performance 

degradation methodology.

Figure 2.—The CFD rotor comprehensive 

analysis coupling process.

one rotor period are then passed to the CFD solver. These 

motions contain gross pitching motions from controls as well 

as the elastic response. It is not necessary to achieve a perfect 

periodically-converged solution from CFD at this point; 

successive iterations between the comprehensive code and the 

CFD code will move the solution towards convergence. At 

subsequent iterations, CFD-based air loads are passed back to 

RCAS OVERFLOW

Blade 

Properties

Flight & Icing 

Conditions

Clean Rotor 

Solution

• Establishes clean rotor performance

• Establishes conditions for 2D bladeelement ice analysis

Ice Shapes

Comparison Utilities

Degraded Rotor 

Performance

• Re-established rotor performance 

accounting for ice shape and added 

mass on the blade

Lewice3D OVERFLOW

RCAS OVERFLOW

Iced Rotor 

Solution

iceMaker

Converged?

Done

CFD

yes

no

Comprehensive Rotor Analysis

n = n + 1

Air Loads

Blade Motions

( ) if ( 1)

if ( 0)

= + 1 − 1 ≥

= =

F F F − F − n

F F n

LL

n

CFD

n

LL

n n

LL

n n
NASA/TM—2012-217122 3

the comprehensive code and used with lifting-line air loads 

from the previous solution. The comprehensive code continues 

to compute lifting-line air loads during trim while adding the 

previous iteration CFD and lifting-line “delta” for the total air 

load to drive the aeromechanical solution towards 

convergence. These iterations continue until there is no change 

in trim controls or air loads, at which point the lifting-line air 

loads from the previous iteration cancel the lifting-line air 

loads from the current iteration leaving only the CFD as the 

total air load. This process is fairly efficient and usually 

requires between two and five rotor revolutions as trim and 

periodic convergence move together.

The solution is time-dependent, but periodic as the blades 

respond to local aerodynamic conditions around the azimuth. 

Like the blade response, ice accretion is also influenced by 

local aerodynamic conditions that change over the course of a 

rotor revolution. Ice accumulation is predicted in an 

engineering approach that divides the blade into radial 

elements and accounts for the changing environment that each 

two-dimensional section experiences as it rotates around the 

azimuth. This two-dimensional strip approach is possible 

provided representative blade-element conditions can be 

extracted from the three-dimensional rotor solution. Specific 

required radial data include local angle-of-attack and relative 

velocity as a function of azimuthal position.

The required data for the ice accretion analysis is not readily 

available from the CFD rotor solution as local angle of attack 

and velocity are somewhat nebulous quantities in this setting. 

Regardless of how this information is approximated, the twodimensional blade-fixed flow solution that is used to predict 

ice should closely represent conditions on the rotor blade. One 

way to do this is to closely match the pressure distribution that 

results from a two-dimensional analysis with the instantaneous 

pressure distribution from the three-dimensional rotor 

solution. This can be accomplished by following a recipe.

Extracted pressure from the rotor solution is typically 

presented in coefficient form, using the hover tip speed, Vtip:

2 2

1

*

Vtip

P P Cp

ρ

− = ∞ (1)

Assuming mostly two-dimensional flow along the blade and 

that the flow stagnates on the airfoil section, the pressure 

coefficient can be renormalized by some reference velocity, 

Vref, such that Cpmax is equal to 2 1 1− Mref . The reference 

velocity can be determined by solving:

2

max

2 2

1 max

1

1

ref Vtip M

P P Cp − = 















ρ

− = ∞ (2)

The solution is

[ ] * 2 max *2 4 max * 2 max

2 2 4

2 ref Cp Mtip Cp Mtip Cp Mtip

a V = + − (3)

where a is the speed of sound and Mtip is the hover tip Mach 

number.

A representative angle of attack can be determined a 

number of ways, for example, by utilizing an airfoil database 

of Mach number, force coefficients, and angle of attack. An 

alternative is to use an airfoil analysis tool to find the angle of 

attack that best matches the extracted pressure distribution at 

the reference velocity determined from (3).

The extracted aerodynamic conditions along with the 

conditions of the icing environment allow for an ice accretion 

analysis. The process begins by considering one radial 

location at a time. At a given radial location ice accumulation 

is influenced by local angle of attack and velocity which vary 

along the azimuth as idealized in Figure 3(a). With the 

assumption that the ice shape is not influenced by the 

frequency, the pitch and velocity variation can be 

characterized by a very slow oscillation, for example as 

illustrated in Figure 3(b). Furthermore if we assume the ice 

shape can be approximated by only considering the mean 

angle of attack and the extreme excursions from the mean, the 

blade motion can be represented as a series of quasi-static 

events, as shown in Figure 3(c). This approach segments the 

icing event into discrete time-steps with each time segment 

characterized by conditions at a different azimuthal location. 

The prediction of ice accumulation for the simplified 

characterization follows the methodology represented by the 

flow diagram in Figure 4. It is a loosely coupled approach 

involving data exchanges between CFD and LEWICE3D at 

each time increment.

The analysis begins by gridding the non-iced airfoil for a 

given radial station. Any grid generator can be used, but the 

Boeing-developed grid generator, MADCAP (Ref. 10), is an 

Figure 3.—Characterization of angle of attack 

and velocity as a function of time or azimuthal 

position. Angle of Attack / Velocity Angle of Attack / Velocity Angle of Attack / Velocity

(a)

(b)

(c)

Time / Azimuthal Position
NASA/TM—2012-217122 4

attractive choice because it is scriptable for batch running and 

has many powerful features to smooth the grid. MADCAP can 

be used, hands free, for many complex ice shapes. This will be 

useful for subsequent time steps which will have ice shapes. 

The next step involves generating an aerodynamic solution. 

LEWICE3D will use the flow solution for its thermodynamic 

calculation and is open to accept solutions from a wide variety 

of flow solvers. The requirement is a robust solver that is 

capable of generating high-quality simulations for complex ice 

shapes. OVERFLOW is a logical choice since it is used in the 

three-dimensional rotor solution and is a standard in the 

rotorcraft community. OVERFLOW can generate time-accurate 

solutions which is often necessary to determine the 

aerodynamic impact of the ice shape. However, in the process 

of predicting ice it is sufficient to produce steady solutions 

which require less computational resources.

Figure 4.—Rotorcraft ice buildup prediction process.

Figure 5.—Photo of the Sikorsky Powered Force Model 

in the NASA IRT.

The flow solution is passed to LEWICE3D for the first ice 

accretion on the clean airfoil. References 11 and 12 explain 

the analysis process of LEWICE3D. The ice is grown for only a 

fraction of the total icing event time. The ice shape is 

extracted from the LEWICE3D output files and fed back into 

the grid generator for analysis of subsequent time steps. The 

process continues with updated flow conditions representing a 

different azimuthal location. For each step, ice is accreted 

directly on the shape from the previous step. In this way 

shadowing effects are taken into account. Shadowing is the 

result of the interaction of the developing ice shape on the 

local aerodynamics and the deposit of droplets on the airfoil. It 

is postulated that the majority of the effect can be captured by 

considering only the minimum and maximum angles of 

oscillation. The process continues until the entire icing event 

time has elapsed. Conditions at each selected azimuthal 

location can be used more than once.

This process is repeated for multiple radial locations along the 

blade. The final three-dimensional ice shape is a composite of 

the two-dimensional shapes computed in the strip approach. 

Once the ice has been established on the blade, the threedimensional rotor analysis is repeated for the iced rotor. In 

addition to accounting for ice in the CFD rotor grid, the input 

files to the comprehensive rotor analysis are also modified to 

reflect the additional mass and change to the blade center of 

gravity. It is assumed that the effect on blade stiffness is small. 

Rotor performance degradation is obtained by comparing the 

forward flight performance characteristics of the iced rotor to 

the clean rotor.

One element of ice prediction on rotors is ice shedding. 

Rotor centrifugal force and aerodynamic loads can cause 

segments of ice to leave the rotor, increasing vibration levels. 

The prediction of ice shedding events, though important, is 

currently not addressed in this process. Furthermore, each 

blade is assumed to have identical ice growth.

Forward Flight Test Case

NASA and the Sikorsky Aircraft Company conducted an 

experimental program to investigate ice accumulation and 

performance degradation for a model-scale rotor. The test was 

conducted at the NASA Glenn IRT in the spring of 1993. The 

model was the Powered Force Model (PFM), pictured in

Figure 5, and featured a four-bladed rotor with rectangular 

blade tips. The test conditions included a range of liquid water 

content, median volume drop diameters, and temperatures. 

Flight conditions also included a range of tunnel velocity, 

rotor tip speed, rotor lift, and propulsive force. A large 

quantity of data was collected including ice shapes, rotor lift 

degradation, power increase, and ice shedding events. The test 

is documented in References 13 and 14. The controlled testing 

environment and detailed documentation make this test ideal 

for validating the high fidelity icing analysis system.

The rotor for the PFM used SC2110 airfoils from root to tip. 

The chord was 4.222 in. and the diameter was 6.093 ft

Icing & Flight

Conditions

Build Airfoil Grid

(MADCAP)

Generate Aero Soln

(OVEFLOW)

Accrete Ice

(LEWICE3D)

t = tfinal

?

t = t + ∆t

Stop

no yes
NASA/TM—2012-217122 5

producing a solidity of 0.147. The rotor was modeled in the 

analysis with a 21.5 percent cutout and hinges for flap and lag 

at 8.2 percent. The rotor had a linear twist of 11.5°. Though 

tested with a scaled Blackhawk fuselage, the analysis featured 

only the rotor and an idealized centerbody.

While the 1993 PFM test featured a wide variety of flow 

and icing conditions, Condition “17” is ideal for code 

validation since ice shapes and rotor performance degradation 

data were recorded. The condition also did not experience any 

ice shedding events which would be unaccounted in the 

analysis. Data for Condition 17 was gathered on several runs, 

particularly 8, 47, 63, and 64. The details of those runs are 

shown in Table 1. The rotor was flown in dry air to a specific 

shaft angle, lift and condition of zero flapping. During ice 

accumulation, runs 8, 47, and 63 operated at zero flapping and 

constant collective; Run 64 operated at zero flapping and 

constant lift. Both modes of operation were analyzed.

TABLE 1.—VALIDATION CONDITIONS (CONDITION “17”)

Run 8, 47, 63 64

Tip speed (ft/s) 675 675

Ts (°C) –15.1 –15.0

Density (sl/ft3) 0.00254 0.002559

LWC (g/m3) 0.5 0.5

Drop diameter (μm) 15 15

Time (sec) 93 91

Advance ratio 0.198 0.200

Trim variables Collective Lift

Results

The initial calculation with OVERFLOW coupled to RCAS 

simulated Condition 17 of the 1993 powered force model test 

in the IRT. The simulation provides the baseline for clean 

rotor performance. The calculation estimated 18.2 hp and a 

propulsive force coefficient of 0.089 for the targeted lift

condition of CL/σ = 0.064. The propulsive force coefficient, 

shown as the solid triangle in Figure 6, was over-predicted 

compared to experimental data. However for this condition, 

being relatively benign, the propulsive force prediction 

produced exclusively with RCAS using lifting-line 

aerodynamics agrees very well with the coupled OVERFLOWRCAS solution. Rotor solutions with lifting-line 

aerodynamics, being an order of magnitude faster to run, were 

produced for a sweep of shaft angles and advance ratios. 

Agreement with experimental measurements is very good at 

advance ratios of 0.3 and 0.4; unfortunately, the largest 

discrepancy occurs at the validation condition. It is speculated 

that at the lower advance ratio, interaction of the blades with 

the rotor wake is more important and ignoring the tunnel 

walls, floor and model fuselage could have affected the wake 

position to an extent greater than expected.

The OVERFLOW-RCAS solution also serves as the basis to 

extract local conditions acting on the blade. Solving Equation 

(3) along the blade from root to tip at azimuth positions of 0°

(behind the pilot), 90° (advancing side), 180° (directly in front 

of the pilot), and 270° (retreating side) produced the curves 

shown in Figure 7. These represent the free stream velocity 

experienced by blade elements in the airfoil fixed reference 

frame. The local angle of attack distribution, shown in Figure 

8, is determined by using these velocity values and matching 

the aerodynamic load acting on the blade. The flow field for 

the 50 percent radial location on the retreating side produced 

the pressure distribution and velocity field shown in Figure 9. 

The two-dimensional pressure distribution has slightly more 

suction on the leading edge but otherwise represents the threedimensional slice well.

Figure 6.—Propulsive force for CL/σ = 0.064 for the 

clean PFM rotor.

Figure 7.—Distribution of local velocity at azimuthal 

positions of 0°, 90°, 180°, and 270°.

-10.0 -5.0 0.0 5.0

X Bar

Shaft Angle

IRT

IRT Fit

RCAS

CFD-CSD

μ = 0.2

μ = 0.3

μ = 0.4

0.0

50.0

100.0

150.0

200.0

250.0

300.0

0 10 20 30 40

Velocity (m/s)

Radius (in)

Azimuth 0

Azimuth 90

Azimuth 180

Azimuth 270
NASA/TM—2012-217122 6

Figure 8.—Distribution of local angle of attack at 

azimuthal positions of 0°, 90°, 180°, and 270°.

Figure 9.—Comparison of local flow conditions; the left computed from twodimensional analysis, the right extracted from the three-dimensional solution 

on the retreating blade at 50 percent r/R.

The ice accretion process uses the results in Figure 7 and 

Figure 8. For the OVERFLOW-LEWICE3D coupling, OVERFLOW

solutions are generated on very fine airfoil grids with 699 

chordwise points and 121 points normal to the surface. The 

grid features clustering near the leading edge to resolve flow 

features that arise from the ice shape. In addition to clustering 

normal to the airfoil surface to resolve the boundary layer, 

cells remain small in the vicinity of the airfoil to resolve 

separation wakes which tend to be prevalent with ice.

For this approximation of ice growth, the event time is 

segmented in four steps and considers local conditions at 

azimuthal positions of 0°, 90°, 180°, and 270°. The sequence 

of ice growth for radial location r/R = 90 percent is shown in 

Figure 10 as an example. Ice is grown on a clean airfoil 

section when the blade is directly in front of the rotor hub. The 

next sequence of ice growth is accumulated on top of the 

previous ice shape using conditions on the retreating side. The 

next step predicts the further accumulation of ice using 

conditions directly behind the pilot. The final accumulation 

uses conditions from the advancing side.

One advantage of this process is that it takes into account 

the varying conditions on the rotor. However, it can introduce 

-4.0

0.0

4.0

8.0

12.0

16.0

0 10 20 30 40

Angle of Attack (deg)

Radius (in)

Azimuth 0

Azimuth 90

Azimuth 180

Azimuth 270

-3.0

-2.0

-1.0

0.0

1.0

2.0

-0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2

(P - P∞) / qlocal

X/C

-3.0

-2.0

-1.0

0.0

1.0

2.0

-0.2 0 0.2 0.4 0.6 0.8 1 1.2

(P - P∞) / qlocal

X/C

2D Analysis 3D Analysis
NASA/TM—2012-217122 7

a bias in the resulting shape depending on which azimuthal 

location is used to initiate the ice accretion. Using conditions 

around the rotor several times mitigates the bias; however this 

may not be possible for short icing events. In this validation 

case, ice is accreted using conditions at each of the four 

azimuthal conditions only once. The resulting shapes at 40, 

50, 70, and 90 percent agree well with recorded ice tracings 

from the experiment as shown in Figure 11.

The reasonable correlation of the ice shapes shown in 

Figure 11 allows the process to be confidently applied in 1-in.

increments along the blade. Ram heating typically prevents ice 

from forming at the tip, however fairly large ice masses exist 

on the tip at these conditions. The ice shapes are stacked to 

create a three dimensional geometry of the iced blades. All 

four blades are considered identical. Ice feathers and the 

increase in surface roughness are not captured in the CFD 

model. In addition to the CFD grid, the RCAS model is also 

updated to account for the additional ice mass on the leading 

edge. The NASA invented tool, THICK, computes the mass of 

ice and was used to generate the results shown in Figure 12

and which are reflected in the RCAS model. Updates include 

mass, CG location, and moments of inertia. The iced rotor 

performance was quantified in two separate OVERFLOWRCAS simulations. The first targeted the same lift condition 

and zero-flapping; the performance quantities of interest 

include the increase in required power and loss of propulsive 

force. The second analysis maintained constant collective, but 

adjusted cyclic control to zero the flapping. In addition to the 

increase in power and decrease in propulsive force, this 

scenario also experiences a loss in lift. Not all performance 

increments were experimentally reported, but where possible 

the comparisons are made in Table 2 and Table 3. The 

changes in performance are generally over-predicted but are 

reasonable. Figure 13 shows the experimental trends in 

horsepower increase as a function of time. Also shown in the 

figure with solid symbols are the results of the analysis 

representing the conclusion of the icing event.

Figure 10.—Sequential build up of ice at the 90 percent radial 

station using the flow conditions at 180°, 270°, 0°, and 90°

azimuthal positions. Figure 11.—Correlation of predicted and measured ice shapes.

-0.10

-0.05

0.00

0.05

0.10

-0.10 -0.05 0.00 0.05 0.10 0.15 0.20

Y/C

X/C

SC2110

Experiment

Predicted
NASA/TM—2012-217122 8

Figure 12.—Mass of ice growth on the leading edge of the 

PFM for Condition 17.

Figure 13.—Horsepower increase.

TABLE 2.—ICED ROTOR PERFORMANCE 

CHARACTERISTICS WITH CONSTANT LIFT

Experiment CFD–CSD

Input

Shaft angle –2.6 –2.6

Vtip 675 fps 675 fps

T∞ –15.1 °C –15.1 °C

Target CL/σ 0.064 0.064

Advance ratio 0.198 0.198

Output

Thrust N/A 332.0 lb

Power N/A 48.5 hp

Drag N/A –6.4 lb

X-bar N/A 0.044

∆Thrust N/A 11.5 lb

∆Power ~19.0 hp 30.3 hp

TABLE 3.—ICED ROTOR PERFORMANCE 

CHARACTERISTICS WITH CONSTANT COLLECTIVE

Experiment CFD–CSD

Input

Shaft angle –2.6 –2.6

Vtip 675 fps 675 fps

T∞ –15.1 °C –15.1 °C

Collective Constant 6.9°

Advance ratio 0.198 0.198

Output

Thrust NA 264.5 lb

Power NA 42.0

Drag NA –0.97 lb

X-bar NA 0.008

∆Thrust ~ –40 lb –56.0 lb

∆Power ~13.5 hp 23.9 hp

Figure 14.—Effect of blade ice on pitch link loads.

Understanding the sources of the discrepancy requires 

additional experimental parameters for Condition 17 which 

unfortunately are not readily available. However, there is hope 

for predicting iced rotor performance decrements having 

achieved good correlation with the ice shape prediction. 

Additional iced rotor testing is planned in 2012 to provide the 

additional insight needed to improve the performance 

decrement prediction.

Aeroelastic analysis offers a direct benefit by providing 

icing analysis with proper trim and blade motions. Coupled 

CFD-rotorcraft comprehensive analysis also provides internal 

loads that may allow designers to accommodate icing when 

sizing parts. The effect on pitch link loads was quite clear in 

both OVERFLOW-RCAS simulations, where blade ice produced 

a 47 percent increase in steady and a 97 percent increase in 

vibratory load (constant lift case). The increase in pitch link 

loads as a function of blade position is shown in Figure 14.

0.00

0.20

0.40

0.60

0.80

1.00

1.20

0.00 0.20 0.40 0.60 0.80 1.00 1.20

Mass of Ice x 1000 (sl/ft)

Radial Location (r/R)

0 40 80 120

Horse Power Increase

Time (sec)

Constant Lift

Constant Collective

-50

-40

-30

-20

-10

0

0 90 180 270 360

Pitch Link Force (lbs)

Azimuthal Location

Constant Lift

Constant 

Collective

Clean
NASA/TM—2012-217122 9

Summary and Conclusions

• An analysis method to evaluate a rotor in icing 

conditions was presented and applied to a model-scale 

rotor in forward flight. The method is a combination of 

two- and three-dimensional analyses, using threedimensional methods for rotor performance and 

degradation and two-dimensional for ice accretion.

• The process to predict rotor forward flight performance 

utilized loosely coupled CFD analyses with rotor 

comprehensive analyses to establish trim and rotor blade 

deformation.

• The clean rotor analysis predicted rotor power to 

approximately 12 percent of experimentally measured 

power for 320 lb of thrust and an advance ratio of 0.2.

• Ice shapes were reasonably well-predicted from root to 

tip using a loosely coupled OVERFLOW-LEWICE3D

process for two-dimensional airfoils. Analysis 

conditions were extracted from the clean threedimensional rotor flow solution.

• Rotor performance degradation was predicted and 

measured for two conditions: 1.) fixed collective, shaft 

angle, and zero-flapping; 2.) fixed lift, shaft angle, and 

zero-flapping. In both cases the power increment was 

over-predicted.

• For fixed collective, analysis predicted a 17 percent loss 

in lift and a 130 percent increase in power; experiment 

showed a 13 percent decrease in lift and a 65 percent

increase in power.

• For fixed lift, analysis predicted a 166 percent

increase in power; the experiment measured 92 percent.

• Pitch link loads were predicted to increase 

significantly—47 percent increase in steady and 

97 percent increase in vibratory for fixed lift.

• The analysis process has provisions to include shedding 

in the future. The experimental test case did not have an 

ice shed event.

References

1. Bidwell, C., “Icing Calculations for a 3D, High-Lift Wing 

Configuration,” AIAA-2005-1244, 43rd AIAA Aerospace 

Sciences Meeting and Exhibit, Reno, NV, Jan. 10–13, 2005.

2. Bidwell, C., “User’s Manual for the NASA Glenn Three 

Dimensional Grid Based Ice Accretion Code (LEWI3DGR),” 

NASA Glenn Research Center, Cleveland, OH, 2003.

3. Buning, P.G., Jespersen, D.C., Pulliam, T.H., Chan, W.M., 

Slotnick, J P., Krist, S.E., Renze, K.J., “OVERFLOW User’s 

Manual, Version 1.8b,” NASA Langley Research Center, 

Hampton VA, 1998.

4. Narducci, R., Kreeger, R.E., “Analysis of a Hovering Rotor in 

Icing Conditions,” American Helicopter Society 66th Annual 

Forum, Phoenix, AZ, May 11–13, 2010.

5. “RCAS User’s Manual, Volume 1, Rotorcraft Comprehensive 

Analysis System Version 2.0,” U.S. Army Aviation and 

Missile Command, 2003.

6. Potsdam, M., Yeo, H., Johnson, W., “Rotor Airloads Prediction 

using Loose Aerodynamic/Structural Coupling,” 60th 

American Helicopter Society Annual Forum, Baltimore, MD, 

June 7–10, 2004.

7. Biedron, R., Lee-Rausch, E., “Rotor Airloads Prediction Using 

Unstructured Meshes and Loose CFD/CSD Coupling,” AIAA2008-7341, 26th AIAA Applied Aerodynamics Conference, 

Honolulu, HI, Aug. 18–21, 2008.

8. Datta, A., Chopra, I., “Prediction of the UH-60A Main Rotor 

Structural Loads Using CFD/Comprehensive Analysis 

Coupling,” Journal of the American Helicopter Society, 

Vol. 53, (4), Oct. 2008, pp. 351–365.

9. Sankaran, V. et al. “Application of the Helios Computational 

Platform to Rotorcraft Flowfields” AIAA–2010–1230, 48th 

AIAA Aerospace Sciences Meeting Including the New 

Horizons Forum and Aerospace Exposition, Orlando, FL, 

Jan. 4–7, 2010.

10. Gopalsamy, S., Michal, T., Shih, A., “Grid Generation in the 

Framework of the Unstructured Grid Consortium API using the 

Geometry and Grid Toolkit GGTK,” AIAA–2006–0528, 44th

AIAA Aerospace Sciences Meeting and Exhibit, Reno, NV, 

Jan. 9–12, 2006.

11. Bidwell, C. Pinella, D. and Garrison, P. “Ice accretion 

calculations for a commercial transport using the LEWICE3D, 

ICEGRID3D and CMARC programs,” AIAA–1999–0250, 

37th Aerospace Sciences Meeting and Exhibit, Reno, NV, Jan. 

11–14, 1999.

12. Wright W., “Validation Results for LEWICE 3.0,” AIAA–

2005–1243, 43rd AIAA Aerospace Sciences Meeting and 

Exhibit, Reno, NV, Jan. 10–13, 2005.

13. Flemming, R.J., Britton, R.K., “Experimental Investigation of 

the Effects of Ice Accretion on a Main Rotor in the NASA 

Glenn Research Icing Tunnel,” RITA TR 02-D-07-03.1-P3 

Vol. 2, Sep. 15, 2003. 

14. Flemming, R.J., “The Past Twenty Years of Icing Research and 

Development at Sikorsky Aircraft,” AIAA–2002–0238, 40th

AIAA Aerospace Sciences Meeting and Exhibit, Reno, NV, 

Jan. 14–17, 2002.
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Application of a High-Fidelity Icing Analysis Method to a Model-Scale Rotor in Forward 

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14. ABSTRACT

An icing analysis process involving the loose coupling of OVERFLOW-RCAS for rotor performance prediction and with LEWICE3D for 

thermal analysis and ice accretion is applied to a model-scale rotor for validation. The process offers high-fidelity rotor analysis for the noniced and iced rotor performance evaluation that accounts for the interaction of nonlinear aerodynamics with blade elastic deformations. Ice 

accumulation prediction also involves loosely coupled data exchanges between OVERFLOW and LEWICE3D to produce accurate ice 

shapes. Validation of the process uses data collected in the 1993 icing test involving Sikorsky’s Powered Force Model. Non-iced and iced 

rotor performance predictions are compared to experimental measurements as are predicted ice shapes. 

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