Aircraft Characterization in Icing Using Flight Test Data Ed Whalen University of Illinois Urbana...

Aircraft Characterization in Icing Using Flight Test Data

Ed Whalen

University of Illinois Urbana Champaign

42nd Annual Aerospace Sciences Conference

Reno, NV January 5-8, 2004

42nd Aerospace Sciences Conference Reno, NV January 5-8, 2004

Aerospace Engineering University of Illinois Urbana Champaign

Research Goals

• Establish a baseline, clean aircraft from flight test data in clear air.

• Identify the changes in trim, stability and control and performance as a result of the onset of icing IPS activation, selective deicing, etc

• Identify which of these parameters are the best indicators of icing

• Investigate the correlation between icing severity, as measured by , and the magnitude of the changes in both trim and stability and control derivatives.

• Aid in the development and evaluation of real-time identification methods for use with the SIS system.



Program Summary

• Two flight test periods – February and March in 2001 and 2002

• 2001 – Collected data across test matrix in both clear air and icing conditions and established a baseline aircraft– 4 icing flights and 5 clear air flights

• 2002 – Focused on elevator doublet data collection in icing conditions– 11 icing flights and 4 clear air flights



The Twin Otter

• deHavilland DHC-6• High-wing, twin engine

commuter class aircraft• Max Gross Weight:

11,000 pounds• Cruise Speed: 130 KIAS• Fully instrumented to

collect aerodynamic, performance, icing and atmospheric data.



Flight Test Cases

Case Icing Flight Doublet Mag. Test Information1.1 Clear Air 0.25g Baseline1.2 Clear Air 0.10 to 0.50 Vary doublet magnitude1.3 Clear Air None Standard maneuvers1.4 Clear Air None Clear air turbulence2.1 Icing 0.25g Doublets during ice accretion2.2 Icing 0.25g Doublets with selective deicing2.3 Icing 0.25g Doublets with intercycle icing2.4 Icing None Intercycle icing2.5 Icing None Standard maneuvers



Data Reduction

• Data filtered using a 10Hz low-pass filter (post-processing in 2001 and in flight in 2002).

• Data corrected for instrument offsets and angular rate contributions to airspeed measurements.

• Aerodynamic parameters recalculated from data.• Filtered data passed to Systems IDentification

Programs for Aircraft (SIDPAC).– Data compatibility used to calibrate instrumentation. – Stepwise regression algorithm used to identify

stability and control derivatives.• Trim data was extracted immediately before each

maneuver by time averaging the data.



Typical Icing Flight

0.00E+00

2.00E-02

4.00E-02

6.00E-02

8.00E-02

1.00E-01

1.20E-01

13:12:00 13:26:24 13:40:48 13:55:12 14:09:36 14:24:00 14:38:24 14:52:48 15:07:12 15:21:36Time

CLa (

1/de

g) Clean Doublet Iced Doublet

Deice

Deice

Deice Wings

Deice Hor. Tail

Deice Vert. Tail and Struts



Atmospheric Turbulence

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

-0.75 -0.55 -0.35 -0.15 0.05 0.25

a Scaling Factor

CLa

(1/

deg

)

Clean Flight Data

Iced Flight Data



Parasite Drag

R2 = 0.88

-0.005

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0 0.01 0.02 0.03 0.04 0.05

D C

D0



Trim Values

-7.0

-6.0

-5.0

-4.0

-3.0

-2.0

-1.0

0.0

0.04 0.05 0.06 0.07 0.08 0.09 0.1 0.11

CD

d e (

deg)

010223f2 (Clean)

020221f1 (Glaze)

010302f1 (Glaze)



Trim Values

-7.0

-6.0

-5.0

-4.0

-3.0

-2.0

-1.0

0.0

-1 0 1 2 3 4 5 6

a (deg)

d e (

deg)

010223f2 (Clean)

020221f1 (Glaze)

010302f1 (Glaze)



IPS Activation: Automatic Cycle

0.090

0.095

0.100

0.105

0.110

14:55:41 15:02:53 15:10:05Time

CLa

(1/d

eg)

Boot Activation

0.000

0.005

0.010

0.015

0.020

14:55:41 15:02:53 15:10:05

Time

D C

D0



IPS Activation: Selective Deicing

0.06

0.07

0.08

0.09

0.10

0.11

13:47:17 13:54:29 14:01:41 14:08:53Time

CLa

(1

/deg

)

Deice Wings

Deice Horizontal Stabilizer

Deice Vertical Stabilizer, Struts and Landing Gear

Fully Iced

0.000

0.005

0.010

0.015

0.020

0.025

13:47:17 13:54:29 14:01:41 14:08:53

Time

D C

D0

Deice Wings

Deice Horizontal Stabilizer

Deice Vertical Stabilizer, Struts and Landing Gear

Fully Iced



H∞ Parameter Identification

• H∞ generally refers to an algorithm/controller that achieves guaranteed performance in the presence of unknown harmful input. – “worst-case performance”

• H∞ does not require statistical descriptions of unknown quantities. • Given pilot input, think of ID as a system with turbulence and

measurement noise as an unknown input and the parameter estimate error as the output. We would like to have the estimate error go to zero regardless of input.

• The H∞ ID provides a worst-case gain from unknown input to error.• The H∞ ID algorithm is recursive and hence depends on an initial

estimate.• The H∞ ID algorithm is robust to model uncertainties and can be

used for time-varying and nonlinear systems as well.



Tuning the H∞ Algorithm

• The H-infinity ID algorithm was tuned so that the final estimate of a doublet portion of data is largely insensitive to the initial estimate value hence, estimates are dominated by excitation provided by the doublet input.

• Performed multiple calculations for various initial estimate values.

• Looking for variation in final estimate values to be a small fraction of variation in initial estimate values

• Involves a tradeoff between responsiveness to doublet excitation and sensitivity to measurement noise and turbulence.

• Obtained good convergence for all (CZa, CMa, CMq, and CMde) but CZa for doublets with low trim velocity (approx 47 m/s).



Clean Parameter Estimation

CLa Estimation CMde Estimation



Parameter Identification in Icing

CLa Estimation CMde Estimation



Comparison of H∞ Results with SIDPAC Results (CLa

SIDPAC Values H∞ Values % DifferenceClear Air High Velocity (9)

Mean 0.1081 0.1185 9.70%Range [0.1067,0.1096] [0.1172,0.1196] [8.0,11.5]%Low Velocity (17) N/A N/A N/A

Natural Icing High Velocity (3)Mean 0.1092 0.1201 10.00%Range [0.1080,0.1103] [0.1158,0.1248] [7.2,13.2]%Low Velocity (5) N/A N/A N/A



SIDPAC Values H∞ Values % DifferenceClear Air High Velocity (9)

Mean -0.0314 -0.0346 10.50%Range [-0.0321,-0.0306] [-0.0351,-0.0341] [7.5,13.3]%Low Velocity (17)Mean -0.0334 -0.0351 5.20%Range [-0.0346,-0.0313] [-0.0357,-0.0343] [2.3,10.1]%

Natural Icing High Velocity (3)Mean -0.0314 -0.0328 4.50%Range [-0.0323,-0.0299] [-0.0343,-0.0300] [0.3,7.7]%Low Velocity (5)Mean -0.0309 -0.0333 7.70%Range [-0.0321,-0.0296] [-0.0351,-0.0316] [4.5,10.3]%

Comparison of H∞ Results with SIDPAC Results (CMde)



Other Comparisons to SIDPAC Results

Derivative TM 4099 (deg-1) AIAA 93-0398 (deg-1) Illinois (deg-1)

CLa 0.1003 0.1072CLq 0.3498 0.3517CLde 0.0118 0.0118CMa -0.0229 -0.0258 -0.0266CMde -0.031 -0.0305 -0.0299CMq 0.611 -0.65 -0.5653Cn 0.00136 0.0015Cnr -0.0031 -0.0035Cndr -0.00218 -0.0023



Conclusions

• CLa and CMa indicate the effects of icing on the aircraft, but are significantly affected by atmospheric turbulence.

• Parasite drag is an excellent indicator of the severity of the ice accretion, as seen through its correlation with an icing severity parameter, .

• Trim values, especially a, de and CD, are excellent indicators of icing onset and the effect of icing on control and performance.

• The effect of IPS operation is visible in both the stability parameters and the parasite drag including: selective deicing, standard deicing boot cycles and full deicing.



Conclusions

• Using trim values to characterize the effect of icing shows the most promise, at this time, in terms of accuracy and reliability.

• Further investigation into the effects of atmospheric turbulence is required to improve parameter identification.

• Real-time H∞ PID provides pitching moment derivative estimates that are consistent with SIDPAC estimates

• Real-time H∞ PID provides CLa estimates that are consistent with SIDPAC for higher trim velocities.

Aircraft Characterization in Icing Using Flight Test Data Ed Whalen University of Illinois Urbana...

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