Ryan D. May

Vantage Partners, LLC, Cleveland, Ohio

Ten-Huei Guo and Donald L. Simon

Glenn Research Center, Cleveland, Ohio

An Approach to Detect and Mitigate Ice Particle

Accretion in Aircraft Engine Compression Systems

NASA/TM—2013-216525

June 2013

GT2013–95049
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Ryan D. May

Vantage Partners, LLC, Cleveland, Ohio

Ten-Huei Guo and Donald L. Simon

Glenn Research Center, Cleveland, Ohio

An Approach to Detect and Mitigate Ice Particle

Accretion in Aircraft Engine Compression Systems

NASA/TM—2013-216525

June 2013

GT2013–95049

National Aeronautics and

Space Administration

Glenn Research Center

Cleveland, Ohio 44135

Prepared for the

Turbo Expo 2013

sponsored by the American Society of Mechanical Engineers (ASME)

San Antonio, Texas, June 3–7, 2013
Acknowledgments

The authors would like to thank the NASA Aviation Safety Program’s Vehicle Systems Safety Technologies Project for funding 

this work.

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Level of Review: This material has been technically reviewed by technical management. 
NASA/TM—2013-216525 1 

An Approach to Detect and Mitigate Ice Particle Accretion in 

Aircraft Engine Compression Systems 

Ryan D. May 

Vantage Partners, LLC 

Cleveland, Ohio 44135 

Ten-Huei Guo and Donald L. Simon 

National Aeronautics and Space Administration 

Glenn Research Center 

Cleveland, Ohio 44135 

Abstract 

The accretion of ice in the compression system of 

commercial gas turbine engines operating in high ice water 

content conditions is a safety issue being studied by the 

aviation sector. While most of the research focuses on the 

underlying physics of ice accretion and the meteorological 

conditions in which accretion can occur, a systems-level 

perspective on the topic lends itself to potential near-term 

operational improvements. This work focuses on developing 

an accurate and reliable algorithm for detecting the accretion 

of ice in the low pressure compressor of a generic 40,000 lbf 

thrust class engine. The algorithm uses only the two shaft 

speed sensors and works regardless of engine age, operating 

condition, and power level. In a 10,000-case Monte Carlo 

simulation, the detection approach was found to have excellent 

capability at determining ice accretion from sensor noise with 

detection occurring when ice blocks an average of 6.8 percent 

of the low pressure compressor area. Finally, an initial study 

highlights a potential mitigation strategy that uses the existing 

engine actuators to raise the temperature in the low pressure 

compressor in an effort to reduce the rate at which ice 

accretes. 

Introduction 

Over the past twenty years, there have been approximately 

150 reported cases of aircraft engine power loss due to the 

accretion of ice crystal particles in the compression system of 

commercial turbofan engines (Ref. 1). The majority of the work 

in response to this aviation safety concern has focused on 

understanding the mechanism by which particles in high icewater content (HIWC) conditions can accrete on compressor 

stator blades, understanding the environmental conditions in 

which accretion can occur, and related regulatory questions 

(Ref. 2). While redesigning the compressor to prevent ice 

accretion is the ideal long-term solution, a systems-level 

analysis highlights some near-term capabilities. 

Previous work has developed a means to simulate the impact 

of ice accretion in the low pressure compressor (LPC) using a 

series of stacked compressor maps (Ref. 3). When these maps 

are integrated into a high-fidelity engine simulation, the overall 

system response to the presence of ice can be simulated 

(Ref. 4). Using this approach, a method to detect the accretion 

of ice using only the existing engine sensors is proposed. 

For this work, the engine simulation known as C-MAPSS40k 

(the Commercial Modular Aero-Propulsion System Simulation 

40,000 lbf) is used (Ref. 5). This simulation is written in 

MATLAB/Simulink and is a generic, modular, physics-based 

simulation of a 40,000 lbf thrust-class engine. This simulation is 

used due to the fact that it is based on dynamic flight test data, 

includes a realistic engine controller (Ref. 6), and realistic 

sensor noise models (Ref. 7). 

The stack of “iced” LPC maps is integrated into 

C-MAPSS40k and the user is given an input through which the 

ice blockage level of the LPC can be specified (between 0 and 

27 percent). The LPC operating point is then linearly 

interpolated to lie between the operating points of the two 

nearest maps. In this manner, the user can change the blockage 

level as a function of time to simulate the accretion of ice in the 

LPC second stator row. 

A previously developed engine ice particle accretion 

detection technique (Ref. 8) relied on observing shifts in the 

engine sensor outputs directly. While this approach was 

reasonably effective (97.94 percent true positive rate, 

0.10 percent false positive rate, and mean detection occurs at 

5.519 percent blockage), the false positive rate (false alarm rate) 

was much too high for use in service. Thus, a detection 

technique aimed at directly detecting the change in LPC 

performance is developed. To determine the performance of this 

new algorithm, it is applied to the C-MAPSS40k engine 

simulation and a Monte Carlo study is conducted and evaluated 

against three detection metrics (true positive rate, false positive 

rate, and mean blockage at detection). 

This work then begins to investigate potential strategies to 

mitigate the accretion of ice. It is believed that there is a small 

range of temperatures at which the ice particles can accrete in 

the compressor (Ref. 9) and by changing the engine operating 

point away from this temperature, the ice growth can be 

mitigated or prevented. Various strategies to change the LPC 

temperature are investigated using existing engine actuators in 

both typical and novel ways. 

The next section will describe the detection algorithm in 

general followed by the specifics of the algorithm when applied 

to C-MAPSS40k. The detection work is concluded with the 

results of the Monte Carlo analysis and a discussion of the 
NASA/TM—2013-216525 2 

performance of the detection algorithm. A brief section on 

potential means to mitigate the accretion of ice is then given 

followed by some conclusions. 

Nomenclature 

C-MAPSS40k Commercial Modular Aero-Propulsion 

 System Simulation 40,000 lbf

EPR Engine pressure ratio 

Fnet Net engine thrust 

H Fault influence coefficient matrix 

HIWC High Ice Water Content 

HPC High Pressure Compressor 

LPC Low Pressure Compressor 

LPCeff LPC efficiency parameter 

LPCflow LPC flow capacity parameter 

Ncc2 Core shaft speed corrected to inlet 

conditions 

Nfc2 Fan shaft speed corrected to inlet conditions 

R Sensor noise covariance matrix 

ROC Receiver Operating Characteristic 

T25 LPC outlet gas temperature 

VBV Variable Bleed Valve 

VSV Variable Stator Vane 

w Sensor noise vector 

x Vector of LPC health parameters 

xˆ Estimated vector of LPC health parameters 

y Vector of sensor residuals 

Detection Algorithm 

Based on the simulation results given in Reference 3 and 

summarized in the LPC maps shown in Figure 1, ice accretion 

results in a change in compressor efficiency and a loss of flow 

capacity. The loss of flow capacity can be observed directly 

from the maps by noting that the speed lines shift to the left 

(lower corrected flow rate) as the size of the ice blockage 

increases. Typically there is a loss of compression, which can 

be seen as a downwards shift in the speed lines. However, for 

operating points near the surge line, there are cases where the 

efficiency does not change or even increases. In order to 

determine when ice accretion is occurring, the engine sensors 

can be used to estimate these shifts. In order to apply this 

detection algorithm as widely as possible, only the two shaft 

speed sensors are used. 

In order to develop an algorithm that could feasibly operate 

on an existing commercial aircraft engine, the algorithm is 

designed to be computationally simple and have a minimal 

Figure 1.—LPC maps used to simulate the impact of ice 

blockage in the second row stators. Reproduced from 

(Ref. 8) based on data from (Ref. 3). Surge lines 

(dashed) and speed lines (solid) are shown. 

memory footprint. This rules out many of the common 

approaches in on-board diagnostics such as model-based 

designs and Kalman-filter based algorithms (Ref. 7). Rather, a 

simple linear estimator is used to estimate the LPC 

performance characteristics. 

A critical assumption of this approach is that the engine 

inlet conditions (altitude and Mach number) and the throttle 

must be quasi-steady-state. This assumption means that any 

change in the sensor outputs can be attributed to a change in 

engine operation (due to ice accretion or some other fault). In 

order to determine when the conditions are appropriate to use 

this ice detection approach, there are steady-state detection 

algorithms (Ref. 10) that can be used to trigger the ice 

detection routine. 

A flow chart of the detection algorithm is shown in Figure 2. 

The remainder of this section will walk through each step of the 

process. 

Once the engine inlet conditions and throttle are determined 

to be quasi-steady, the fan and core shaft speed sensor outputs 

are corrected then processed into two different signals: a longwindow time-averaged signal and a current value. The 

difference between these two signals is called a sensor residual 

and contains information related to short time-scale changes in 

engine performance. This approach allows the algorithm to 

“ignore” the effects of engine aging, engine-to-engine 

variation, and other slight differences in the turbomachinery 

that must be addressed in model-based designs. 

These sensor residuals can be expressed as a function of 

LPC parameters as shown in Equation (1). The variable y is a 

vector of sensor residuals. The vector of LPC parameters 

(efficiency and flow capacity) is x, and the matrix H is called 

the fault-influence coefficient matrix. The input w is zeromean sensor noise with covariance matrix R. 

y  Hx  w (1) 

50 100 150 200

1.2

1.4

1.6

1.8

2

2.2

2.4

2.6

2.8

Mass Flow Rate [lbm/s]

Pressure Ratio

0% Blockage

10%

20%

27%
NASA/TM—2013-216525 3 

Figure 2.—Flow of the ice accretion detection 

algorithm used in this paper. This process is 

conducted every controller time step. 

The LPC parameters used here are typically referred to as 

health parameters (LPCeff and LPCflow) and are commonly 

used in health detection, monitoring, and estimation tasks. For 

example, if the LPCeff has a value of 0.01, this means that 

there is a 1 percent increase in the efficiency of the LPC. A 

value of –0.02 for LPCflow means that there is a 2 percent 

decrease in LPC flow capacity. 

The detection algorithm seeks to determine when LPC 

performance changes are occurring by monitoring for changes 

in the LPC health parameters. Although not directly measurable, 

an estimate of the LPC health parameter vector, xˆ , can be 

obtained by applying a weighted least squares estimation 

technique (Ref. 11) given as: 

x H R H H R y

T 1 1 T 1 ˆ ( )     (2) 

This requires that values of R and H are known. The sensor 

noise covariance matrix can be computed analytically if the 

noise is easily quantifiable or numerically based on historical 

engine data. Because the sensor noise changes based on 

operating condition, R will change as the operating point 

changes. More importantly, since the engine is highly 

nonlinear as a function of flight condition and operating point, 

the fault influence coefficient matrix, H, will be different at 

different conditions. 

The fault influence coefficient matrix is essentially a linear 

relationship between the LPC health parameters and the 

corrected sensor residuals. H can also be thought of as the 

Jacobian of the nonlinear equation: 

y  f (x)  w (3) 

There are numerous techniques available to determine a value 

of H that is appropriate to the current operating point. If a high 

fidelity, non-linear, model is available, the value of H for a 

given flight condition, engine age, or deterioration level can be 

computed and stored on-board. However, with the motivation 

of applying this algorithm to existing hardware, a less memory 

intensive mechanism is proposed. 

Given, a high-fidelity, off-line model, each of the LPC 

health parameters is perturbed by a small amount (1.0 percent) 

and the resulting steady-state changes in the corrected sensor 

residuals are measured. Since, in this particular case, there are 

two health parameters and two sensor residuals, H is a 22 

matrix. For example, the (1,1) element of H is the linear 

impact of a change in LPC efficiency on the corrected fan 

speed residual, computed as the change in corrected fan speed 

divided by the perturbation in LPC efficiency. 

Examining Figure 1 the relationship between the corrected 

fan speed and the change in LPC performance as ice accretes 

is observed. To determine how much H changes based on 

operating point a series of simulations is conducted at various 

 
NASA/TM—2013-216525 4 

Figure 3.—The values of the fault influence coefficient matrix, 

H, for the C-MAPSS40k application as a function of altitude, 

Mach number, and engine power. The black line represents 

the piecewise-linear curve fit to the data. Blue points are at 

Mach 0.4, red at Mach 0.6, and green at Mach 0.8. 

altitudes, Mach numbers, and power levels. The resulting 

values of each of the four elements of H for the C-MAPSS40k 

application are plotted in Figure 3. Each color is a different 

Mach number (blue: Mach 0.4, red: Mach 0.6, and green: 

Mach 0.8). Altitudes from 10,000 to 30,000 ft are shown as 

are the complete range of engine power levels. Note that the 

x-axis of the plots is the corrected fan speed. When plotted in 

this manner there is a visible pattern to the data. For an initial 

implementation, a simple, one-dimensional interpolation 

(using fewer than six data points each) is used for each of the 

four elements of H. The black line in each of the plots shows 

the “fitted” line used for the interpolation. While there are 

many points far from the “fitted” line, this rough 

approximation is adequate, as will be seen later on, for the 

purposes of ice accretion detection at this preliminary stage. 

With values of y, R, and H, it is now possible to compute 

the estimated health parameter shifts using Equation (2). With 

this estimate, detection logic must be applied. Once the logic 

output exceeds a threshold (thresh) for a predefined number of 

controller time steps, the “ice accretion” fault will be 

triggered. While there are many possible detection logics, two 

are chosen and examined in this paper: 

flow 1 LPC  thresh (4) 

2

2

eff

2 LPCflow  LPC  thresh (5) 

The first is simply the change in LPC flow capacity while 

the second uses the sum of the square of each of the estimated 

terms. Equation (4) is used based on the observation from the 

compressor map shown in Figure 1 that icing is associated 

with a decrease in LPC flow capacity. The sum of squares 

logic used in Equation (5) is a traditional detection logic that 

will track changes in the two parameters from zero. 

Example Application 

To determine the performance of the detection algorithm, it 

is applied to the C-MAPSS40k engine simulation. Based on 

the current value of the corrected fan speed, the appropriate 

value of H will be determined via interpolation in accordance 

with the curve fit information shown in Figure 3. The sensor 

noise covariance matrix is assumed to be constant and is 

numerically computed for an operating point in the middle of 

the anticipated engine icing envelope. The detection logic is 

then applied to the estimated health parameters. If the logic 

returns a “true” then a counter is incremented. Once the “true” 

condition persists for a predetermined number of time steps, 

the fault condition is annunciated. The duration chosen is 

influenced by the frequency of sensor disturbances (sensor 

noise, turbulence, etc.). In this work, where the detection logic 

sample rate was synchronized with the C-MAPP40k controller 

time step of 0.015 sec, 67 consecutive time steps 

(approximately 1 sec) were required. When the engine inputs 

are no longer in steady state, the long window time averaged 

values are reset and the computation is restarted once steady 

state is reached. 

In order to evaluate the effectiveness of the detection 

strategy, a 10,000 run Monte Carlo simulation is conducted 

using C-MAPSS40k. Each of the simulations is 4 min long. 

Half have no engine icing, and half experience a 2 min ice 

accretion from 0 percent blockage to 27 percent blockage. 

Each of the simulations is conducted at a steady, but random 

altitude, Mach number, ambient temperature, and power level. 

The flight conditions are randomly distributed across the 

envelope at which it is believed ice accretion can occur. 

Further each simulation uses an engine that is a different “age” 

(level of normal engine deterioration between new and end-oflife) and also includes typical engine-to-engine variation. This 

variation means that each engine has slightly different 

performance of each of the turbomachinery components. 

These random variations are typical of engines found in large 

commercial fleets (Ref. 12). 

To determine the performance of this detection algorithm, 

these 10,000 Monte Carlo tests are run through the algorithm. 

Since the detection performance is directly related to the 

detection threshold, the threshold is varied. For each detection 

threshold, the true positive and false positive rates are 

computed and plotted in Figure 4. Each threshold tested 

produces a single point (marked with an x) and the lines are 

generated by varying the threshold from near zero to a large 

value. Due to the similarity of the two curves, a “zoomed-in” 

inset is shown to highlight the difference. 

This type of plot is known as a Receiver Operating 

Characteristic (ROC) curve (Ref. 13). The ideal detection 

algorithm will enclose an area under the curve of one (capable 

of achieving a true positive rate of 100 percent while a 

0 percent false positive rate). The detection logic of 

Equation (4) achieves this goal. Specifically, a threshold of 

–0.6 percent using the logic of Equation (4) has a true positive 

rate of 100 percent and a false positive rate of 0.0 percent. 

2000 2500 3000 3500 4000 4500

0

2

4

6

H(1,1) [rpm/%]

2000 2500 3000 3500 4000 4500

-25

-20

-15

-10

-5

0

5

H(1,2) [rpm/%]

2000 2500 3000 3500 4000 4500

-20

-15

-10

-5

0

Nfc2 [rpm]

H(2,1) [rpm/%]

2000 2500 3000 3500 4000 4500

-40

-30

-20

-10

0

10

Nfc2 [rpm]

H(2,2) [rpm/%]
NASA/TM—2013-216525 5 

Figure 4.—Receiver operating characteristic curve for two 

detection logics used to detect the accretion of ice in the 

engine LPC. The inset is a magnified view of the upper left 

corner of the plot. 

Interestingly, the logic of Equation (4) exceeds the 

performance of Equation (5). It was anticipated that the 

second detection logic will be more effective as it makes use 

of all of the available information; however this intuition 

proved to be incorrect. During non-iced operation, the 

distances should always be zero (or near zero) and then 

increase in magnitude as the ice accretes in the LPC and 

causes a shift in the health parameters. 

Note that while the left hand side of the second detection 

logic (Eq. (5)) is always positive, the same is not true for the 

logic given in Equation (4). Based on the data in Figure 1, the 

flow capacity of the LPC always decreases during ice 

accretion (which agrees with common intuition). Thus, this 

threshold will be a negative number. Throughout the rest of 

the paper the magnitude of the threshold is shown in plots to 

allow for more direct comparisons. Including the sign of the 

change in flow capacity improves the specificity of the fault 

detection as a different fault that manifests as an increase in 

flow capacity would not trigger the detection using 

Equation (4), but might when using Equation (5). 

In addition to true and false positive rates, it is important to 

determine when the detection occurs. The mean blockage at 

which detection occurs is plotted for each threshold value in 

Figure 5. As expected, as the magnitude of the threshold 

increases, the detection latency increases. Additionally, for 

any given threshold value, the mean blockage at detection 

using the detection logic of Equation (4) (decrease in flow 

capacity) performs better than almost all threshold values used 

with Equation (5) (sum of squares). Using the threshold of 

–0.6 percent (perfect detection using Equation (4)) results in 

detecting blockages at an average of 6.80 percent blockage, no 

non-zero threshold will produce earlier average detection 

when used with Equation (5). 

Figure 5.—Mean ice blockage level when icing fault is 

annunciated for various detection thresholds using the 

detection logic of both Equations (4) and (5). 

Figure 6.—Histogram of the ice blockage level when 

detection occurs using the detection logic of 

Equation (4) and a threshold of –0.6 percent. Ice 

accretion reaches 27 percent blockage in 2 min. 

In addition to the mean, the complete distribution of ice 

blockage level when the fault is annunciated is important to 

know. This is shown in Figure 6 for the case where 

Equation (4) is used for detection and the threshold value is 

–0.6 percent. Again, the mean blockage when detected is 

6.80 percent with a standard deviation of 2.20 percent and a 

maximum of 25.63 percent. From this data we can compute 

the probability that detection will occur before a specific 

blockage level is reached. For example, in 92.62 percent of the 

cases tested, the blockage is detected prior to reaching 

10 percent. This becomes useful if other studies highlight a 

particular blockage threshold that should not be exceeded 

prior to the initiation of mitigation strategies. For example, if it 

is necessary to begin mitigation efforts prior to 15 percent 

blockage, then approximately 1.5 percent of cases shown in 

Figure 6 would be detected too late. 

0 0.5 1.0 1.5 2.0 2.5 3.0

0

5

10

15

20

25

|threshold| [%]

mean % blockage

when detection occurs

LPCflow< thresh

LPCeff

2 +LPCflow2 > thresh

0 5 10 15 20 25

0

0.002

0.004

0.006

0.008

0.01

0.012

% blockage when detected

Frequency
NASA/TM—2013-216525 6 

Practical Considerations 

The fact that this detection algorithm worked extremely well 

in the Monte Carlo analysis conducted here does not guarantee 

that it will perform well in practice. One of the largest 

drawbacks of this algorithm is the fact that the engine inputs 

(operating point and throttle) must all be constant in order to 

detect the accretion of ice. While we have no data at the 

moment on the environmental disturbances encountered while 

operating in the HIWC conditions typically associated with 

convective storms, the presence of turbulence is nearly certain. 

If the frequency of these disturbances is higher than that of 

the sensor noise, then there should be minimal loss in 

performance as the detection algorithm requires that the 

conditions persist for some predefined period of time (here we 

used 1 sec), essentially serving as a low pass filter. The length 

of this window can be increased if necessary to separate the 

frequency at which the airframe disturbances occur from those 

of the icing detection. For example, when using a window of 

5 sec, 100 percent true positive rate and 0 percent false 

positive rate detection is again achieved at the cost of 

increasing the mean blockage at detection to 8.30 percent. 

Further increasing the length of the window to 10 sec results 

in 0.0 percent false positive rate, a 99.96 percent true positive 

rate, and mean blockage at detection of 9.51 percent. In 

general, these disturbances would delay detection as well as 

increase the rate of false positives. Without further 

information, it is difficult to determine if this decrease in 

performance is acceptable. 

In addition to the environmental disturbances causing 

transients, icing frequently occurs during the descent phase of 

flight (Ref. 1). Generally during descent operation the aircraft 

speed and glide slope are kept relatively constant, typically a 

1,000 to 4,000 ft/min descent. Since ice accretion is on the 

order of minutes, these potentially large changes in altitude 

would violate the steady-state assumption that is critical to this 

detection algorithm. 

Another variable is the ice growth rate. While small scale 

icing studies have been done (Ref. 14), it is unclear how 

quickly ice accretes in the engine compressor. To address this 

area of uncertainty, another Monte Carlo study, comprised of 

1,000 cases, was conducted. In addition to the variability in 

the earlier study, the ice accretion time is random, uniformly 

distributed between 1 and 5 min. Using the detection logic of 

Equation (4) with the threshold of –0.6 percent the true 

positive rate is 100 percent for these additional test cases. The 

distribution of the blockage level at the time of detection is 

shown in Figure 7. This distribution is similar to that of 

Figure 6 except that the mean is slightly higher (8.00 versus 

6.80 percent) and the standard deviation is lower (1.87 versus 

2.20 percent). 

Due to the construction of the linear filter, all changes in 

engine performance are ascribed to changes in the LPC 

characteristics. While it is possible to increase the size of x to 

include the health parameters of other engine components, this 

will increase the size of H as well as the fidelity necessary. 

Figure 7.—Histogram of the ice blockage level when 

detection occurs using the detection logic of Equation (4) 

and a threshold of –0.6 percent. Ice accretion rate is 

uniformly randomly distributed and reaches 27 percent 

blockage between 1 and 5 min. 

In order to maintain the proposed structure, it is possible to 

determine how well the estimation process is working by 

estimating the sensor residuals using the estimated health 

parameters by substituting x  xˆ and w  0 into Equation (1) 

to obtain 

yˆ  Hxˆ (6) 

This estimated vector of sensor residuals then allows for a 

“sanity check” because the difference between the actual and 

the estimated residuals should be small. This check can be 

useful for determining when the detection algorithm should 

not be running due to an unexpected problem. For example, 

when a fault has occurred in an engine component or the H

matrix is not appropriate to the current operating point. While 

this cross check was not implemented in this work (as it has 

no bearing on the performance of the detection algorithm) it is 

expected to be used in any fully-developed detection system. 

Another practical implementation issue is the question of 

how accurate the icing models implemented in C-MAPSS40k 

are. An engine altitude test facility at NASA Glenn, known as 

the Propulsion Systems Laboratory, is undergoing modifications to facilitate engine icing tests. Testing in this facility 

should enable ice accretion model verification. Work must 

also be done to characterize the disturbances encountered 

during flight in high ice water content conditions which should 

be provided by an upcoming NASA flight campaign planned 

to occur in Darwin, Australia. Overall, this detection algorithm 

handles the expected in-flight variation including engine 

deterioration, engine-to-engine variation, flight condition, 

power level, and ice accretion rate. The impact of disturbances 

in altitude, speed, and power level on detection performance is 

not able to be determined at this point due to the lack of flight 

data in these HIWC conditions. The detection of ice accretion 

0 5 10 15 20 25

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

x 10-3

% blockage when detected

Frequency
NASA/TM—2013-216525 7 

during descent proves to be a challenge due to the fact that the 

sink rates are high enough to produce large shifts in engine 

performance. 

Mitigation of Icing 

There are two possible engine control system mitigation 

strategies that can be applied once ice accretion has been 

detected in the engine core. The first is to change the engine 

operating point in such a way that the ice can no longer grow 

or possibly even start to melt. Potential means of achieving 

this goal is studied throughout this section. The other goal, 

which is not considered in this work, is to change the engine 

operating point in order to preserve safety margin and (to a 

lesser extent) engine performance. 

At the present time it is unclear what techniques are 

effective at preventing or mitigating ice accretion. However, it 

is believed that the accretion can only occur under a relatively 

narrow range of temperature, ice water content, and liquid 

water content conditions (Ref. 8). Thus, it is anticipated that 

changing the engine operating point to a higher (or lower) 

LPC temperature can move the engine outside of this range. 

The impact of any change to the engine operating point is 

heavily dependent on the nature of the engine control system. 

Due to the lack of a thrust measurement, most engines use either 

fan shaft speed or engine pressure ratio (EPR), turbine outlet 

pressure divided by engine inlet pressure, as the controlled 

variable. The engine controller will regulate the fuel flow rate to 

maintain the desired setpoint (either fan speed or EPR). All of 

the following work uses an EPR controlled engine. 

To change the temperature of the air in the LPC, one must 

increase (or decrease) the amount of work being done on the 

air by the compressor. To increase the temperature, the 

compressor must operate at a higher speed, which also 

produces more thrust. This can be done by increasing the 

throttle or by closing any inter-compressor bleed valves. To 

decrease the temperature, the throttle can be decreased or 

bleed valves opened. 

To evaluate the impact of these changes an engine 

simulation like C-MAPSS40k can be used. Unfortunately, this 

simulation does not model individual compressor stages so 

only LPC inlet and outlet temperatures are available; LPC 

metal temperature is calculated as well but, in steady-state, is 

simply an average of the inlet and outlet gas temperatures. 

During transients, the heat convection is modeled which 

results in slower changes. Additionally, there is no ice 

accretion physics modeled in C-MAPSS40k. Thus, this 

simulation can be used to test potential control law changes, 

but in-depth evaluations require a more detailed simulation. 

The engine modeled in C-MAPSS40k is a typical twin-spool 

turbofan engine as shown in Figure 8. The primary actuator is 

the fuel metering valve and secondary actuators are the station 

25 bleed (also known as the inter-compressor bleed, variable 

bleed valve (VBV)) and the high pressure compressor (HPC) 

variable stator vanes (VSV) or inlet guide vanes. 

Figure 8.—Schematic of the engine modeled in C-MAPSS40k. 

reproduced from Reference 5. 

Figure 9.—Engine response to a 1 throttle transient at 

0 sec. The flight condition is at 20,000 ft, Mach 0.6, 60

throttle. 

By modifying these various actuator positions, we can 

observe the effect on the LPC metal temperature as well as the 

gas temperature. The following simulations are conducted at a 

steady-state condition within the icing envelope (20,000 ft, 

Mach 0.6, and 50 percent throttle). In this first case, shown in 

Figure 9, a 1 throttle step change occurs at 15 sec. The 

resulting changes in LPC outlet temperature (T25), LPC metal 

temperature, and thrust are shown. The change results in a 

2 R change in outlet gas temperature and a 1 R change in 

the LPC metal temperature. As expected, there is a change in 

net engine thrust. At the present time it is unclear if this 

change in temperature is enough to have any impact on the ice 

growth rate.

614

616

618

T25 [R]

546.5

547

547.5

548

548.5

LPC Metal 

Temp [R]

-5 0 5 10 15

8800

9000

9200

9400

9600

Fnet [lbf

]

time [s]

Throttle Increase

Throttle Decrease
NASA/TM—2013-216525 8 

Figure 10.—Engine response to a VBV open and VBV 

close transient at 0 sec. The flight condition is 20,000 ft, 

Mach 0.6, 60 throttle. 

When the VBV is commanded to be fully open (or closed), 

there is a change in pressure at the outlet of the LPC. This 

results in changes to the engine setpoint and thus an eventual 

increase or decrease in fuel flow rate. The results are shown 

in Figure 10. There is a significant change in operating point 

when the VBV is fully opened—a roughly –30 R change in 

gas temperature associated with an increase in engine thrust. 

This thrust change is due to the fact that bleeding air off the 

engine core flow decreases EPR, so the control system 

increases the fuel flow rate, speeding up the engine and 

increasing thrust. Surprisingly, there is no change when the 

VBV is closed fully. This turns out to be caused by the fact 

that steady-state VBV position is almost closed (10.8 percent 

open), so closing the valve the rest of the way results in an 

insignificant change in engine performance. This highlights a 

potential problem when using the VBV to mitigate ice 

accretion. There is some nominal VBV position to produce 

safe compressor surge margins. Thus, if the VBV is fully 

opened or closed there may not be much of a change in the 

actual position. In addition, these off-nominal changes result 

in reduced compressor surge margin for either the LPC or 

HPC at a time when the margin may already be reduced due 

to icing effects (as shown in Fig. 1). 

Lastly, when a 5 positive and negative shift is introduced 

into the variable stator vanes, the resulting engine outputs are 

shown in Figure 11. The VSV are used to achieve optimal 

HPC efficiency during all stages of operation; by shifting them 

off-nominally fuel efficiency is lost. Small (1 R) changes in 

Figure 11.—Engine response to a positive and negative 5

VSV transient at 0 sec. The flight condition is 20,000 ft, 

Mach 0.6, 60 throttle. 

outlet gas temperature result along with minimal change in 

engine thrust and compressor surge margin. 

Depending on the size of the change in operating condition 

necessary to prevent ice accretion, a combination of these 

changes may need to be employed. Ideally, there would be no 

thrust change, but the thrust change may be acceptable if it 

mitigates the icing. As every engine is different, alternate 

actuators may be available that are more effective. Each would 

need to be studied on a case-by-case basis. Regardless, even a 

simple change such as modifying the throttle leads to changes 

in LPC temperature and may be useful to mitigate engine ice 

accretion. 

Means to mitigate ice accretion remains a large challenge. 

The proposals put forth in this paper need to be tested in a 

simulation environment capable of simulating the physics of 

ice crystals entering the compressor. As the fundamental 

physics work continues, it is expected that more refined 

models about the conditions necessary for ice accretion will 

become available. This information will then determine the 

direction of mitigation efforts. 

Conclusions 

An algorithm designed to detect the accretion of ice in the 

low pressure compressor (LPC) of an aircraft engine was 

developed. This algorithm was then applied and tested on a 

40,000 lbf thrust class engine simulation. This approach uses a 

low memory footprint linear estimator to estimate the changes 

580

600

620

T25 [R]

530

540

550

LPC Metal 

Temp [R]

-5 0 5 10 15

9000

9500

10000

Fnet [lbf

]

time [s]

VBV Close

VBV Open

615

616

617

T25 [R]

547

548

549

LPC Metal 

Temp [R]

-5 0 5 10 15

9100

9200

9300

Fnet [lbf

]

time [s]

VSV Close 5

VSV Open 5
NASA/TM—2013-216525 9 

in efficiency and flow capacity of the LPC during steady-state 

operation. A number of detection logics were then tested, two 

of which were presented here. An approach relying on 

monitoring for decreases in the estimated flow capacity was 

found to perfectly discriminate between icing and non-icing 

events using a 10,000 case Monte Carlo analysis of engines 

operating across the flight envelope and at different power 

levels, deterioration levels, and accounting for engine-toengine variation. An initial overview of potential mitigation 

capabilities of the existing engine actuators was then 

conducted and found that it is possible to alter the conditions 

inside the LPC by varying the actuators from the nominal 

schedule. 

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