Ryan D. May

Vantage Partners, LLC, Brook Park, Ohio

Donald L. Simon and Ten-Huei Guo

Glenn Research Center, Cleveland, Ohio

Modeling and Detection of Ice Particle Accretion

in Aircraft Engine Compression Systems

NASA/TM—2012-217742

October 2012

AIAA–2012–4649
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Ryan D. May

Vantage Partners, LLC, Brook Park, Ohio

Donald L. Simon and Ten-Huei Guo

Glenn Research Center, Cleveland, Ohio

Modeling and Detection of Ice Particle Accretion

in Aircraft Engine Compression Systems

NASA/TM—2012-217742

October 2012

AIAA–2012–4649

National Aeronautics and

Space Administration

Glenn Research Center

Cleveland, Ohio 44135

Prepared for the

Atmospheric Flight Mechanics Conference

sponsored by the American Institute of Aeronautics and Astronautics

Minneapolis, Minnesota, August 13–16, 2012
Acknowledgments

The authors would like to thank Joe Veres of NASA Glenn Research Center for generating the data used to create the Low Pressure

Compressor (LPC) iced compressor maps. Additional thanks go to Sanjay Garg and James Heidmann of the NASA Glenn Research 

Center for their support, and the Aviation Safety Program’s Vehicle Systems Safety Technologies program for funding this research.

Available from

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7115 Standard Drive

Hanover, MD 21076–1320

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

Level of Review: This material has been technically reviewed by technical management. 
NASA/TM—2012-217742 1 

Modeling and Detection of Ice Particle Accretion in 

Aircraft Engine Compression Systems 

Ryan D. May 

Vantage Partners, LLC 

Brook Park, Ohio 44142 

Donald L. Simon and Ten-Huei Guo 

National Aeronautics and Space Administration 

Glenn Research Center 

Cleveland, Ohio 44135 

Abstract

The accretion of ice particles in the core of commercial aircraft engines has been an ongoing aviation 

safety challenge. While no accidents have resulted from this phenomenon to date, numerous engine power 

loss events ranging from uneventful recoveries to forced landings have been recorded. As a first step to 

enabling mitigation strategies during ice accretion, a detection scheme must be developed that is capable 

of being implemented on board modern engines. In this paper, a simple detection scheme is developed 

and tested using a realistic engine simulation with approximate ice accretion models based on data from a 

compressor design tool. These accretion models are implemented as modified Low Pressure Compressor 

maps and have the capability to shift engine performance based on a specified level of ice blockage. 

Based on results from this model, it is possible to detect the accretion of ice in the engine core by 

observing shifts in the typical sensed engine outputs. Results are presented in which, for a 0.1 percent 

false positive rate, a true positive detection rate of 98 percent is achieved. 

Nomenclature

C-MAPSS40k the Commercial Modular Aero-Propulsion System Simulation 40k 

EPR Engine Pressure Ratio (P50/P2)

HIWC High Ice Water Content 

HPC High Pressure Compressor 

HPT High Pressure Turbine 

ISA International Standard Atmosphere 

LPC Low Pressure Compressor 

LPT Low Pressure Turbine 

Nc Core shaft speed (rpm) 

Nf Fan shaft speed (rpm) 

PLA Power Lever Angle (deg) 

Px Pressure at station X (psig) 

Psx Static pressure at station X (psig) 

Tx Temperature at station X (deg Rankine) 

Wf fuel mass-flow rate (lbm/s)

Xc2 Sensor value X corrected to inlet conditions 
NASA/TM—2012-217742 2 

I. Introduction 

Over the past twenty years, there have been over 200 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 ice-water content (HIWC) conditions can accrete 

on compressor stator blades, understanding the environmental conditions in which accretion can occur, 

and related regulatory guidance. While avoidance of HIWC conditions and compressor redesigns are the 

ideal long-term solutions, it is important to provide some mechanism for existing aircraft to safely operate 

in these conditions if they are encountered. 

Unfortunately, existing airframe sensors and manual monitoring approaches are not capable of 

detecting these HIWC conditions: typical weather RADAR sees the condition as clear air, and there is no 

visible indication of ice growth on the cockpit windows (Ref. 2). The total air temperature probe anomaly 

(Ref. 3) has been found to be correlated with the HIWC conditions, but it is not a reliable indicator as 

only some of the probes on planes that have experienced power loss showed the anomaly (Ref. 2). While 

new sensors are in development and show much promise (Ref. 4), previous work has shown that it may 

be possible to detect icing by monitoring the performance of the engines (Ref. 5).

If a system is capable of detecting the presence of ice blockage, it may be possible to change the 

engine’s operating point to actively mitigate the impact of that ice. Earlier detection of the presence of ice 

blockage leads to a larger number of feasible mitigation strategies. For example, if a thin layer of accreted 

ice is detected it may be possible to increase the compressor’s speed to shed the ice. However, it is likely 

that ice shedding from the compressor through the rest of the engine core can cause compressor surge, 

flameout, and engine damage (Ref. 6). It is reasonable to assume that the larger the ice blockage is when 

it is shed, the more serious the consequences. Additional possible mitigation techniques include 

modifying engine bleeds and variable stator vanes to increase the flow through the core, or increasing the 

compressor temperature, reducing the chance of accretion. 

From a propulsion system perspective, accreted ice restricts compressor flow capacity and reduces 

compressor efficiency (Ref. 7). These changes can be modeled as a shift in the compressor map. A 

compressor map (shown in Fig. 1) captures the operational behavior of a compression system by relating 

the mass flow rate through the compressor to the pressure ratio across the compressor at given rotational 

speeds. In addition to these speed lines, a compressor map contains a surge line. This surge line represents 

the collection of points beyond which it is not possible for the compressor to operate. If one attempts to 

operate the compressor beyond the surge line, flow reversal will typically occur resulting in loss of thrust 

and potential component damage. 

Previous simulations conducted using icing code (LEWICE) and steady-state compressor design code 

(COMDES) determined that for a generic 40,000 lbf thrust class engine, it is possible for ice to accrete in 

the second stator row of the Low Pressure Compressor (LPC) (Ref. 7). Based on this information, new 

compressor maps were developed that capture the effect of fixed levels of ice blockage (10, 20, 27, 30, 

35, and 40 percent). All of the maps are shown in Figure 1. It is clear that for each speed line there is a 

decrease in mass flow rate and pressure ratio as the size of ice blockage increases. This is particularly true 

at higher rotational speeds. In addition, the surge line moves to lower pressure ratios as the ice blockage 

increases. This means that the compressor is likely operating very close to surge during icing conditions. 

The work described in this paper seeks to develop a detection algorithm using the existing sensor 

suite that can be realistically implemented in modern commercial aircraft gas turbine engines. To develop 

this algorithm, engine icing models will be implemented in a high-fidelity engine simulation through the 

discussed “iced” LPC compressor maps. This will enable a study of the impact of ice accretion on the 

output of the available sensors. Once the algorithm is developed, it will be tested in both iced and noniced conditions and various operating points to determine the detection accuracy and latency. 
NASA/TM—2012-217742 3 

Figure 1.—Low pressure compressor maps for various levels of ice blockage 

(nominal (0 percent), 10, 20, 27, 30, 35, and 40 percent) in the second LPC 

stator row. Data to construct these maps were taken from Reference 5. 

The engine simulation environment and means of simulating the effect of ice accretion in the LPC are 

discussed in Section II. Section III contains an analysis of the change in engine sensor outputs as the ice 

blockage grows larger. Section IV presents a practical detection algorithm with the results shown in 

Section V. Some conclusions and future work are discussed in Section VI. 

II. Modeling Engine Icing 

In order to simulate the effect of ice accretion on engine operation, LPC maps with various levels of 

ice blockage are integrated into a high-fidelity engine simulation. The aircraft engine simulation used in 

this work is the NASA Commercial Modular Aero-Propulsion System Simulation 40k (C-MAPSS40k). 

This simulation models a commercial, high-bypass, turbofan engine in the 40,000 lbf thrust class 

(Ref. 8). The model is a 0-dimensional, physics-based simulation written in MATLAB/Simulink (The 

Mathworks, Inc.), and is capable of full envelope operation. Further, the simulation includes a controller 

with an “industry standard” architecture (Refs. 9 and 10) that can be used as a baseline against which to 

compare the performance of new control laws. The presence of a realistic controller is crucial since the 

engine controller will naturally respond to the presence of the ice blockage, thus changing the operating 

point. In fact, it has been shown that this action may lead to the engine rollback phenomenon observed in 

some of the icing induced power-loss events (Ref. 5). Another key component of C-MAPSS40k is the 

ability to enable realistic sensor noise. This noise is not a Gaussian noise summed to the sensor output; 

rather it is based on work introduced in Reference 11, and is present on all sensors including those used 

for control. The resulting noise appears similar to that of a random walk process. 

Once the compressor maps are encoded in the proper format, they are integrated into C-MAPSS40k. 

The code that models the LPC component is passed a variable containing the ice blockage percentage 

(between 0 and 40 percent). In order to simulate ice blockage at levels between the fixed points, the 

operating point is computed on each iced map and then linear interpolation is used to compute the actual 

operating point. 

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%

10%

20%

27%

30%

35%

40%
NASA/TM—2012-217742 4 

Throughout the paper, ice blockages greater than 27 percent will not be studied for the following 

reasons. If the detection scheme has not identified the icing prior to this point it will likely be too late to 

implement most mitigation strategies. Furthermore, the impact of icing on the engine becomes highly 

non-linear at this point and the LPC will be choked for nearly all operating points. 

III. Impact of Icing on Sensed Outputs 

The purpose of the engine control law is to take the throttle angle (PLA) commanded by the pilot and 

regulate the fuel flow rate to the engine to maintain a desired level of thrust in a safe manner. Since thrust 

is not a measurable quantity, either fan speed (Nf) or engine pressure ratio (EPR, which is the ratio of LPT 

exit pressure to inlet pressure) is regulated, both of which are proportional to thrust. While engine control 

is more complex than this simple regulation scheme (see Refs. 9 and 10 for more details), it is sufficient 

for this work to understand that the response of the engine to icing will be different depending on which 

variable is regulated. 

The typical commercial aircraft engine contains between seven and ten sensors used for control and 

monitoring purposes. Most engines contain sensors consisting of inlet pressure and temperature (P2 and

T2), fuel flow (Wf), fan and core speed (Nf and Nc), burner pressure (Ps3) and exhaust gas temperature 

(T50). Engines that are EPR controlled have a LPT exit pressure (P50) sensor. Additionally, many engines 

are equipped with inter-compressor pressure and temperature (P25 and T25) sensors for gas path 

monitoring. The engine schematic shown in Figure 2 highlights the location of these sensors. 

In the following example, the outputs of the ten engine sensors included in the C-MAPSS40k engine 

simulation are shown for a condition in which there is initially no ice in the LPC, but at 10 s ice begins to 

accrete until 20 s, at which point there is 27 percent ice blockage. The simulation is performed for both 

EPR and fan speed controlled engines to generate the plots shown in Figure 3. One can see that the inlet 

conditions (P2 and T2) are identical since both tests were conducted at the same flight condition. As 

expected, Nf is nearly constant for the fan speed controlled engine as is P50 for the EPR controlled engine. 

The six remaining sensors all exhibit common behavior during ice accretion. Further, the magnitude of 

the change is nearly the same in all but Wf and Ps3 where the change is much smaller for fan speed 

controlled and EPR controlled engines, respectively. Based on this information, any detection algorithm 

that seeks to operate under both EPR and fan speed control should focus on Nc, T25, T50, and P25.

P25

T25

P2

T2

Ps3

P50

T50

Wf

HPC Nc HPT

LPC LPT

Nf

Figure 2.—Schematic of a typical twin-spool, high-bypass commercial aircraft engine. 

Reproduced from Reference 8. 
NASA/TM—2012-217742 5 

Figure 3.—Sensor outputs for EPR controlled and fan speed controlled engines at 25,000 ft, 

Mach 0.6, ISA+10, PLA of 68q. Ice accretion begins at 10 s and ends at 27 percent 

blockage at 20 s. 

However, as stated earlier, the station 25 sensors are not present in all engines. Thus it makes sense to 

consider EPR and fan speed controlled engines separately. When EPR controlled, Nf and Wf are useful, 

and when fan speed controlled, P50 and Ps3 are useful. The simulations throughout the remainder of this 

paper will utilize an EPR setpoint controller. 

There are many different types of detection schemes ranging from simple threshold techniques to 

sophisticated adaptive, model-based approaches. However, more complex designs present increased 

certification challenges and also require more processing resources. Thus, the authors seek to develop as 

simple an approach as possible to mitigate some of these implementation challenges and enable earlier 

acceptance and adoption of this approach. 

The original intent was to develop a detection scheme where expected sensor measurement values as 

a function of flight condition are stored in a lookup table or database. The difference between the actual 

sensor measurements and the typical values is calculated. The resulting vector of data, known as a 

residual vector, describes how far each sensor’s output is from the expected. This vector can then be 

operated on to generate a scalar “distance” performance metric. When the distance exceeds a specified 

threshold, the fault is detected. 

0 10 20 30

3580

3600

3620

Nf [rpm]

0 10 20 30

1.1

1.105

1.11

x 10

4

Nc [rpm]

0 10 20 30

488.5

489

489.5

T2 [qR]

0 10 20 30

632

634

636

T25 [qR]

0 10 20 30

1160

1180

1200

T50 [qR]

0 10 20 30

7.916

7.918

7.92

P2 [psi]

0 10 20 30

17

17.5

18

P25 [psi]

0 10 20 30

175

180

185

Ps3 [psi]

time [s]

0 10 20 30

9.7

9.8

9.9

P50 [psi]

0 10 20 30

1.3

1.32

Wf [lb

m/s]

time [s]

EPR Controlled

Nf Controlled
NASA/TM—2012-217742 6 

To study the validity of this approach, nominal sensor outputs are recorded at a flight condition of 

25,000 ft, Mach 0.6, ISA, and a PLA of 68q, for a mid-life (50 percent deteriorated) engine. Three 

variations were then evaluated: 

1. Ice accretion from 0 to 27 percent occurs between 10 and 20 s at the trim flight condition in a mid-life 

engine 

2. No ice accretion at the trim flight condition but with an end-of-life (100 percent deteriorated) engine 

3. No ice accretion at a flight condition of 25,100 ft, Mach 0.62, ISA+10q, PLA of 68q with an end-oflife engine. 

The residuals for each case are computed and plotted in Figure 4. The flight condition for Case 3 was 

chosen to be slightly shifted from the trim point in order to evaluate the impact of changes in operating 

point on the ability to detect the presence of icing. Ideally for detection, one would find that the residuals 

from the “icing” event are larger than those corresponding to other variations (such as engine 

deterioration or flight condition). Unfortunately, that is not the case here. 

Figure 4.—Engine sensor residuals for three flight cases, corrected to inlet conditions (subscript c2). Ice 

accretion occurs in Case 1 from 0 percent at 10 s to 27 percent blockage at 20 s. 

0 10 20 30

0

2

4

6

'

Ps3c2 [psi]

time [s]

0 10 20 30

0

0.05

0.1

'

Wfc2 [lb

m/s]

time [s]

0 10 20 30

0

10

20

'

Nfc2 [rpm]

0 10 20 30

0

50

100

'

Ncc2 [rpm]

0 10 20 30

0

20

40

60

'

T50c2 [qR]

0 10 20 30

0

0.5

1

1.5

'

P25c2 [psi]

0 10 20 30

0

1

2

3

'

T25c2 [qR]

Case 1 (trim condition, mid-life, with icing)

Case 2 (trim condition, end-of-life, no icing)

Case 3 (off-trim, end-of-life, no icing)
NASA/TM—2012-217742 7 

The magnitude of the residual for Case 1 at 27 percent blockage is less than or approximately equal to 

the residuals of the other cases for four of the seven sensors. The only sensors that allow for the icing to 

be distinguished from the other non-iced cases are the fan speed, core speed, and station 25 pressure 

sensor. Further complicating matters is the fact that the two shaft speed residuals from Case 1 do not 

exceed the residuals of Case 3 until approximately 19 s (equivalent to 24 percent blockage). Any 

additional deviation from the trim point condition would likely further reduce the usefulness of these two 

sensors for ice accretion detection. This leaves us with only the P25 sensor to utilize, which again, is not 

included in every engine. Further, any detection scheme based solely on a single sensor is not robust to 

sensor failure. Thus another scheme must be developed. 

IV. A Practical Detection Scheme 

To overcome the challenges of the residual based approach while still achieving the goals of simple 

design and low computational overhead, a heuristic-based detection scheme is developed. One of the 

results of the data plotted in Figure 3 is that there is a clear signature associated with the accretion of ice 

in the LPC. Regardless of the control scheme, there is a clear rise in the core shaft speed (Nc) and the 

exhaust gas temperature (T50). When EPR is controlled, there is an additional increase in the fuel flow rate 

(Wf) and fan speed (Nf). When under fan speed control, there is a slight decrease in the core pressures (Ps3

and P50). If the station 25 sensors are present then there is an additional indication as the value of both 

pressure and temperature at station 25 decreases (directly due to the change in the LPC map). The case 

where the station 25 sensors are not present poses a particular challenge due to that fact that the icing 

signature is not unique. In fact, any increase in fuel flow rate will result in an increase in core shaft speed, 

fan shaft speed (when EPR controlled), and exhaust gas temperature. However, if the inputs to the engine 

are relatively constant then we should still be able to detect this increase in engine operating point. 

These observations constitute the fault signature and can be used to develop a detection heuristic. 

Note that this detection approach only works when the engine input conditions are near steady-state 

(relatively constant P2, T2, and PLA). This limitation should not be overly burdensome as nearly all cases 

of expected engine icing have occurred during cruise or descent where the flight condition is very slowly 

changing and the engine throttle is constant. 

Detection of this icing signature then consists of determining when the appropriate sensor outputs are 

changing. To do this, once the engine input parameters are in quasi-steady-state (Ref. 12) a long horizon 

moving average is computed for each of the variables of interest in addition to a much shorter horizon 

filter (or simply the already available conditioned signal). For sensor measurements that increase during 

icing, we look for when the lightly filtered signal is greater than the heavily filtered signal and the reverse 

for decreasing sensors. When all of the sensors have the proper condition, a counter is incremented for 

each consecutive control time step for which the true condition exists. If any of the conditions become 

false, then the counter is reset. Once this counter exceeds a threshold value, the ice accretion is declared 

as occurring. This procedure is shown as a flow chart in Figure 5 where the input “sensor values” is the 

vector of all the sensors used for detection. 

Figure 5.—Flow chart depicting the ice blockage detection scheme. The counter block will reset to zero when a 

“false” value is received. 
NASA/TM—2012-217742 8 

To determine the value of the threshold, a series of steady-state, non-iced simulations are conducted. 

The inputs to these simulations are chosen randomly around the flight envelope with sensor noise, 

random engine deterioration, and engine-to-engine variation (Ref. 13) providing variability. The output of 

these Monte-Carlo tests is then run through the detection scheme to determine what threshold value will 

result in the desired false positive rate. 

V. Results 

The results presented in this section are for an EPR controlled engine and for linear ice growth from 

0 to 27 percent blockage over 120 s. As more information on the physical ice accretion process becomes 

available this can be modified to generate more realistic results. From a detection standpoint, it would 

require modifying the length of the moving average filters, but not the basic structure. For the purposes of 

this paper, the time horizon is fixed at 1.5 s for the fast filter. The window size of the long moving 

average is tested at 60, 90, and 120 s to determine the impact on detection speed and accuracy. These 

values are relatively arbitrary with the primary consideration being to minimize the additional delay in 

detection time due to the faster filter. The other consideration is that the effects of the ice accretion should 

not significantly impact the long average. It is expected that the long filter’s horizon and the ice accretion 

time (to some fixed “undesirable” value) will be of similar magnitude. 

Four cases are considered: without and with station 25 sensors, and desired false positive rates of 

1 and 0.1 percent. 

A. Without Station 25 Sensors 

When operating an engine without station 25 sensors, the detection algorithm looks for cases where 

the outputs of four sensors (Nfc2, Ncc2, T50c2, and Wfc2) are increasing. Running this algorithm with a 60 s 

window on the 5,000 non-iced simulations results in maximum counter values (cases when all conditions 

are true) as shown in Figure 6. The long tail means that the detection threshold will have to be quite high 

to get the desired false positive rates. In fact, the threshold values are 1387 and 1032 for 0.1 and 1 percent 

false positive rates, respectively. Note that the time step of the simulation is 15 ms, thus these detection 

thresholds are equivalent to 20.81 and 15.48 s, respectively. 

Figure 6.—Histogram of maximum counter values for 5000 un-iced 

simulations using a 60 s moving average window for detection 

without station 25 sensors. Relative frequency is the number of 

occurrences divided by the total number of test cases. 

200 400 600 800 1000 1200 1400 1600 1800

0

0.002

0.004

0.006

0.008

0.01

0.012

0.014

Maximum Counter Value

Relative Frequency
NASA/TM—2012-217742 9 

To evaluate the performance of the detection scheme, 5,000 more simulations (steady-state operation 

with variability due to operating point, sensor noise, engine age, and engine-to-engine variation) are 

conducted all with ice accreting from 0 to 27 percent between time 120 and 240 s. The histogram of when 

detection occurs is shown in Figure 7 for both threshold values. Not surprisingly, the detection typically 

occurs sooner for the case tuned to have a 1.0 percent false positive rate. However, both cases show 

surprisingly good true positive detection rates (97.58 and 98.04 percent for the 1 percent false positive and 

0.1 percent false positive cases, respectively). Also, it is interesting to note that there are rare cases where 

the presence of icing is detected extremely early (e.g., 2.17 s after initiation or 0.488 percent blockage). This 

is simply due to the random nature of the sensor noise meeting the conditions prior to the onset of icing. 

The same process described above is repeated for windows of 90 and 120 s to determine the impact of 

that parameter on detection accuracy and speed. The results (and those for the 60 s window) are summarized 

in Tables 1 and 2. From this we observe that the choice of window length has minimal impact on the 

detection capabilities as long as the window is “long enough.” From the data here, the window could be 

chosen at 50 percent of the time to “unsafe” ice level and still achieve satisfactory results. 

Figure 7.—Histogram of percent blockage when ice accretion is detected using a 60 s 

moving average window when tuned to achieve a 1 percent false positive (FP) rate 

and a 0.1 percent false positive rate, when station 25 sensors are not available. 

TABLE 1.—SUMMARY OF ICE ACCRETION DETECTION RESULTS FOR THREE WINDOW 

LENGTHS TUNED TO ACHIEVE 1.0 PERCENT FALSE POSITIVE RATE, 

NOT USING THE STATION 25 SENSORS 

1.0% False Positive Rate 

Window

length 

Threshold

value

True positive 

rate

Blockage when detected 

Mean Standard 

deviation 

Median 

60 s 1032 97.58% 4.896% 1.166% 4.770% 

90 s 1027 97.96% 4.822% 1.128% 4.703% 

120 s 990 98.14% 4.703% 1.058% 4.561% 

TABLE 2.—SUMMARY OF ICE ACCRETION DETECTION RESULTS FOR THREE WINDOW 

LENGTHS TUNED TO ACHIEVE 0.1 PERCENT FALSE POSITIVE RATE, 

NOT USING THE STATION 25 SENSORS 

0.1% False Positive Rate 

Window

length 

Threshold

value

True positive 

rate

Blockage when detected 

Mean Standard 

deviation 

Median 

60 s 1387 98.04% 6.131% 1.229% 5.981% 

90 s 1363 98.06% 5.976% 1.161% 5.846% 

120 s 1338 98.10% 5.895% 1.087% 5.738% 

0 5 10 15 20 25

0

0.005

0.01

0.015

0.02

1.0% FP Rate

Blockage when Detected [%]

Relative Frequency

0 5 10 15 20 25

0

0.005

0.01

0.015

0.02

Blockage when Detected [%]

0.1% FP Rate
NASA/TM—2012-217742 10 

B. With Station 25 Sensors 

When the station 25 pressure and temperature sensors are available, the ice accretion fault signature 

should be much easier to identify. This is due to the fact than an increase in the fuel flow rate will 

generally cause an increase in both station 25 sensor outputs, however when icing is present we observe 

that both outputs decrease. Thus the detection algorithm looks for a period of time where there is an 

increase in the outputs of the Nfc2, Ncc2, T50c2, and Wfc2 sensors and a simultaneous decrease in the outputs 

of the T25c2 and P25c2 sensors. Again, the long and short window filters are used to process the sensor 

outputs prior to determining if the signature is present. This algorithm is then run on the 5,000 un-iced 

simulations to determine the threshold that will result in the desired false positive rate. 

The histogram of the un-iced results is shown in Figure 8 when a 60 s window is used. Immediately 

we observe that the distribution is much tighter and closer to zero than that shown in Figure 6, even with 

the presence of a very long tail on the right hand side. By analyzing the data we find that to achieve a 

false positive rate of 1 percent, a threshold of 547 iterations must be used, and for 0.1 percent the 

threshold is 741. Again, this shows that the icing signature is much more distinguishable when the station 

25 sensors are available to use. 

When the detection algorithm with a 60 s moving average window is run on the 5,000 Monte Carlo 

cases where ice accretion occurs from 120 to 240 s the detection time histograms shown in Figure 9 are 

achieved. When the threshold is selected to achieve a 1 percent false positive rate, the average time of 

detection is 21.6 s after initiation of ice accretion, which is equivalent to 4.86 percent ice blockage. With 

the 0.1 percent false positive rate, the average time of detection increases slightly to 26.6 s after initiation 

or 5.99 percent ice blockage. Further, the true positive rates are 97.06 and 97.24 percent for 1 and 

0.1 percent false positive rates, respectively. 

As before, the tests were repeated using windows of 90 and 120 s for the long moving average filter. 

The results for both 1.0 percent false positive rate and 0.1 percent false positive rates are shown in 

Tables 3 and 4, respectively. Again, the impact of increasing the window length is relatively small, 

resulting in only minor improvements in true positive rate and percent blockage before ice accretion 

detection.

Figure 8.—Histogram of maximum counter values for 5000 un-iced 

simulations using a 60 s moving average window for detection 

with station 25 sensors. 

0 200 400 600 800 1000

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

Maximum Counter Value

Relative Frequency
NASA/TM—2012-217742 11 

Figure 9.—Histogram of percent blockage when ice accretion is detected using a 60 s moving 

average window when tuned to achieve a 1 percent false positive (FP) rate and a 0.1 percent 

false positive rate, when station 25 sensors are used. 

TABLE 3.—SUMMARY OF ICE ACCRETION DETECTION RESULTS FOR THREE 

WINDOW LENGTHS TUNED TO ACHIEVE 1.0 PERCENT FALSE POSITIVE 

RATE USING THE STATION 25 SENSORS 

1.0% False Positive Rate 

Window

length 

Threshold

value

True positive 

rate

Blockage when detected 

Mean Standard 

deviation 

Median 

60 s 547 97.06% 4.868% 2.374% 4.408% 

90 s 530 97.64% 4.653% 2.219% 4.217% 

120 s 510 98.46% 4.489% 2.062% 4.095% 

TABLE 4.—SUMMARY OF ICE ACCRETION DETECTION RESULTS FOR THREE 

WINDOW LENGTHS TUNED TO ACHIEVE 0.1 PERCENT FALSE POSITIVE 

RATE USING THE STATION 25 SENSORS 

0.1% False Positive Rate 

Window

length 

Threshold

value

True positive 

rate

Blockage when detected 

Mean Standard 

deviation 

Median 

60 s 741 97.24% 5.981% 2.838% 5.357% 

90 s 698 97.66% 5.589% 2.622% 5.033% 

120 s 694 97.94% 5.519% 2.530% 4.973% 

0 5 10 15 20 25

0

0.005

0.01

0.015

0.02

Blockage when Detected [%]

Relative Frequency

1.0% FP Rate

0 5 10 15 20 25

0

0.005

0.01

0.015

0.02

Blockage when Detected [%]

0.1% FP Rate
NASA/TM—2012-217742 12 

C. Summary 

The true positive rates for all twelve configurations tested are relatively consistent between 97 and 

98 percent. As expected, requiring a lower rate of false positives increases the detection time. Most 

interestingly though, the results of the detection algorithm are quite similar regardless of whether the 

station 25 sensors are used. Ice accretion is detected with slightly lower blockage when the station 25 

sensors are included (as shown in Table 5), but it is not as large as one would expect given the differences 

in the threshold values.

TABLE 5.—MEDIAN ICE BLOCKAGE LEVEL AT DETECTION FOR THE FOUR 

CONFIGURATIONS TESTED USING A WINDOW OF 60 S 

False positive rate Without station 25 sensors With station 25 sensors 

1.0% 4.770% 4.408% 

0.1% 5.981% 5.357% 

While a false positive rate of 1 or even 0.1 percent is not necessarily acceptable for in-flight use, these 

rates could be further reduced by the addition of a HIWC detector device. Since it is currently believed 

that the HIWC condition is necessary for ice particle accretion in the engine core, a “true” output from a 

HIWC detector could be added to the detection scheme that would dramatically reduce the false positive 

rate while not impacting the detection capability of the engine sensor based approach. 

It is important to recognize that these fault signatures are not found only in cases of engine ice 

accretion. Other faults, such as stuck fuel metering valves, can manifest with similar signature. When the 

station 25 sensors are added to the detection scheme it becomes more discriminating, however a variety of 

LPC faults can produce the same trends. Direct estimation of the changes in the LPC map due to ice 

accretion requires a more complicated detection. Again, the addition of an HIWC sensor would improve 

the specificity of the detection algorithm. 

VI. Conclusion 

A simple algorithm for the detection of ice particle accretion in the engine low pressure compressor 

has been developed. This approach uses the typically existing engine sensor suite and is computationally 

simple in order to enable rapid implementation on modern aircraft engines. Two approaches were studied, 

one with and one without the presence of the station 25 pressure and temperature sensors. Both 

approaches had similar results in regard to true positive detection rates, while there is a slight advantage 

in detection time when using the station 25 sensors. Regardless of approach, the detection algorithm 

would benefit greatly from the addition of a high ice water content sensor. This sensor would allow for 

significant improvements in false positive rates while not impairing the detection capability. 

Much work remains to be done in this area. The ice accretion models need to be validated once 

sufficient data are gathered. The overall engine system performance also needs to be validated once 

experimental data are available. Further work on developing more complex detection schemes may also 

be beneficial for improving detection and reducing the detection latency. Finally, work on developing 

mitigation strategies needs to be done to determine what course of action can and should be taken to 

enable the engines to continue to operate and produce thrust safely. 
NASA/TM—2012-217742 13 

References 

1. Mason, J., and Strapp, W., “Current Perspectives on Jet Engine Power Loss in Ice Crystal 

Conditions: Engine Icing,” Presentation at 2008 AIAA Atmospheric and Space Environments, 

June 23rd, 2009. 

2. Mason, J.G., Strapp, J.W., Chow, P., “The Ice Particle Threat to Engines in Flight,” AIAA 2006-

206, 44th AIAA Aerospace Sciences Meeting and Exhibit, Jan 9-12, 2006. 

3. Lawson, R.P., Angus L.J., and Heymsfield A.J., “Cloud Particle Measurements in Thunderstorm 

Anvils and Possible Threat to Aviation,” Journal of Aircraft, Vol. 35, No. 1, 1998, pp. 113-121. 

4. Davison, C., Ratvasky, T., and Lilie, L., “Naturally Aspirating Isokinetic Total Water Content 

Probe: Wind Tunnel Test Results and Design Modifications,” SAE Technical Paper 2011-38-0036, 

2011, doi:10.4271/2011-38-0036. 

5. May, R.D., Guo, T-H., Veres J.P., Jorgenson, P.C.E., “Engine Icing Modeling and Simulation 

(Part 2): Performance Simulation of Engine Rollback Phenomena,” 2011-38-0026, SAE 

International Conference on Aircraft and Engine Icing and Ground Deicing, Chicago, IL, 

Jun 13-17, 2011. doi:10.4271/2011-38-0026 

6. Mason, J., “Engine Power Loss in Ice Crystal Conditions,” AERO Quarterly, Boeing, Quarter 4, 

2007. 

7. Jorgenson, P.C.E., Veres, J.P., May, R.D., Wright, W.B., “Engine Icing Modeling and Simulation 

(Part I): Ice Crystal Accretion on Compression System Components and Modeling its Effects on 

Engine Performance,” 2011-38-0025, SAE International Conference on Aircraft and Engine Icing 

and Ground Deicing, Chicago, IL, Jun 13-17, 2011. doi:10.4271/2011-38-0025. 

8. May, R.D., Csank, J., Lavelle, T.M., Litt, J.S., and Guo, T.H., “A High-Fidelity Simulation of a 

Generic Commercial Aircraft Engine and Controller,” AIAA-2010-6630, 2010 AIAA Joint 

Propulsion Conference, Nashville, TN, July, 2010. 

9. Csank, J., May, R.D., Litt, J.S., Guo, T.H., “Control Design for a Generic Commercial Aircraft 

Engine,” AIAA-2010-6629, 46th AIAA Joint Propulsion Conference & Exhibit, Nashville, TN, July, 

2010. 

10. Jaw, L., and Mattingly, J.D., Aircraft Engine Controls: Design, System Analysis, and Health 

Monitoring, American Institute of Aeronautics and Astronautics, Inc., VA, 2009. 

11. Armstrong, J.B., Simon, D.L., “Implementation of an Integrated On-Board Aircraft Engine 

Diagnostic Architecture,” AIAA–2011–5859, NASA/TM—2012-217279, 47th AIAA Joint 

Propulsion Conference and Exhibit, San Diego, California, July 31–August 3, 2011. 

12. Simon, D.L., Litt, J.S., “A Data Filter for Identifying Steady-State Operating Points in Engine Flight 

Data for Condition Monitoring Applications,” NASA/TM—2010-216359, ASME Journal of 

Engineering for Gas Turbines and Power, July 2011, Volume 133, Issue 7, 071603 (8 pages). 

13. Simon, D.L., “Propulsion Diagnostic Method Evaluation Strategy (ProDiMES) User’s Guide,” 

NASA/TM—2010-215840, January, 2010. 
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Modeling and Detection of Ice Particle Accretion in Aircraft Engine Compression Systems 

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14. ABSTRACT

The accretion of ice particles in the core of commercial aircraft engines has been an ongoing aviation safety challenge. While no accidents 

have resulted from this phenomenon to date, numerous engine power loss events ranging from uneventful recoveries to forced landings have 

been recorded. As a first step to enabling mitigation strategies during ice accretion, a detection scheme must be developed that is capable of 

being implemented on board modern engines. In this paper, a simple detection scheme is developed and tested using a realistic engine 

simulation with approximate ice accretion models based on data from a compressor design tool. These accretion models are implemented as 

modified Low Pressure Compressor maps and have the capability to shift engine performance based on a specified level of ice blockage. 

Based on results from this model, it is possible to detect the accretion of ice in the engine core by observing shifts in the typical sensed 

engine outputs. Results are presented in which, for a 0.1 percent false positive rate, a true positive detection rate of 98 percent is achieved.

15. SUBJECT TERMS

Ice formation; Commercial aircraft; Propulsion system; Engine rollback; Fault detection 

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