Aerodynamics and Flight Mechanics

Transcription

1 Aerodynamics and Flight Mechanics Principal Investigator: Post Doc s: Graduate Students: Undergraduate Students: Mike Bragg Eric Loth Andy Broeren Sam Lee Jason Merret Kishwar Hossain Edward Whalen Chris Lamarre Leia Blumenthal

2 Core Technologies SMART ICING SYSTEMS Research Organization Aerodynamics and Propulsion Flight Mechanics Control and Sensor Integration Human Factors Aircraft Icing Technology IMS Functions Characterize Icing Effects Operate and Monitor IPS Envelope Protection Adaptive Control System Integration Flight Simulation Flight Test

3 Aerodynamics and Flight Mechanics Goal: Improve the safety of aircraft in icing conditions. Objective: Approach: 1) Develop steady state icing characterization methods and identify aerodynamic sensors. 2) Develop linear and nonlinear iced aircraft models. 3) Identify envelope protection needs and methods. First use Twin Otter and tunnel data to develop a linear clean and iced model. Use the models to develop characterization and envelope protection. Flight Test Data will then be used to develop and validate the characterization,envelope protection and aircraft models.

4 Smart Icing System Research Kishwar Hossain Edward Whalen Sam Lee Jason Merret Flight Test Aerodynamic Sensors Steady State Characterization Characterization Aircraft Models Flight Mechanics Model Envelope Protection Wind Tunnel Data Flight Mechanics Analysis and Envelope Prediction Flight Simulation THE AERODYNAMICS AND FLIGHT MECHANICS GROUP

5 Outline Introduction Aero. And Flight Mechanics Review Assessing Atmospheric Effects on Icing Characterization Envelope Protection Conclusions and Future Plans Bragg Merret Merret Hossain Bragg

6 Assessing Atmospheric Effects on Icing Characterization Jason M. Merret

7 Smart Icing System Research Kishwar Hossain Edward Whalen Sam Lee Jason Merret Flight Test Aerodynamic Sensors Steady State Characterization Characterization Aircraft Models Flight Mechanics Model Envelope Protection Wind Tunnel Data Flight Mechanics Analysis and Envelope Prediction Flight Simulation THE AERODYNAMICS AND FLIGHT MECHANICS GROUP

8 Aero. and Flight Mech. Review Developed clean and iced aircraft model Next generation aircraft based on NASA Twin Otter Flight Dynamics Linear stability and control model using published data General icing effects model developed Input to Flight Simulation Group

9 Aero. and Flight Mech. Review A linear icing effects model was developed that modified the different stability and control derivatives for various levels of icing C = + η ( 1 k ) C ( A) iced ice C ( A) C (A) = arbitrary stability and control derivative η ice = icing severity parameter k C A = coefficient icing factor A

10 Aero. and Flight Mech. Review Developed flight mechanics simulation capability using FDC 1.3 model development Steady state characterization Data generation for N. N. training and testing Flight mechanics analysis

11 Aero. and Flight Mech. Review Flight Dynamics Code 1.3 FDC 1.3 is a free source code developed by Marc Rauw Developed using Matlab and Simulink 6 DoF equations, 12 nonlinear ODEs Autopilot/open loop simulations Atmospheric turbulence model (Dryden spectral model) Code modifications Nonlinear derivatives using AOA look up tables Changes in derivatives due to ice accretion simulated as a function of time Incorporated sensor noise Included hinge moment models Simulated gravity waves and microbursts Pitch rate due to wind gusts (q g )

12 Flight Mechanics Analysis Example Aircraft Conditions Altitude of 7550 ft Velocity of 155 kts η(t = 0 s) = 0.0 η(t = 600 s) = 0.10 Turbulence: z-acceleration RMS = 0.15 g Referenced From Devesh Pokhariyal s Thesis AIAA AIAA

13 Flight Mechanics Analysis Example η ice = Wing Iced Tail Ice d Velocity (knots) ηice = 0.0 ηice = 1.1 Wing Iced Tail Iced Angle of Attack (deg.) η ice = Time (sec.) Time (sec.) Eleva tor Deflection (deg.) η ice = 0.0 Wing Iced Tail Iced η ice = 1.1 Thrust (lbf) Wing Iced Tail Iced η = 0.0 ιχε η = 1.1 ιχε Tim e (sec.) Tim e (sec.)

14 Flight Mechanics Analysis Example Wing Ice Only Tail Ice Only RMS Hinge Moment, Chrms Elevator Aileron Elevato r Clean Aile ro n Clean RM S Hinge M om e nt, Chr m s Elevator Aileron Elevator Clean A ile ron Cle a n Angle of Attack, deg Angle of Attack, deg

15 Introduction Concerns about false alarms in the Smart Icing System were raised at Reno 2000 Since the effects of windshear and other atmospheric disturbances may be similar to icing, false alarms in the Smart Icing System could possibly occur The SIS should be able to distinguish quickly between the icing and atmospheric disturbances

16 Objectives Objective: To devise a simple, but physically representative, model of the effect of microbursts, gravity waves, and other atmospheric disturbances on aircraft flight mechanics. Then use this model to evaluate the effects on the SIS system.

17 Microburst Taken From Mulgund and Stengel, Journal of Aircraft, 1993

18 Microburst Model Microburst model is from Oseguera and Bowles, NASA TM Microbust Parameters are U max : Maximum outflow (ft/s) Z max : Height of maximum outflow (ft) R : Radius of the microburst (ft)

19 Microburst Model NASA TM R = 2500 ft Umax = 63 ft/s Zmax = 119 ft 2-19

20 Wind Model Implementation into FDC F F F x y z = = = X Y Z aerodynamic aerodynamic aerodynamic + X + Y + Z propulsion propulsion propulsion + X + Y + Z gravity gravity gravity + Y + X wind + Z wind wind Wind force components X Y Z wind wind wind = m u = m v (! + qw rv ) (! pw + ru ) w = m w w (! + ) w w pv w w w w pu w

21 Pitch Rate Analysis Pitch rate term on the scale of the aircraft due to turbulence/winds implemented in FDC Required Taylor s Hypothesis to convert time derivative to spatial derivative ww qg = x ww 1 w = x V t q = q q new g w

22 Wind Model Validation A FDC trajectory was compared to results from, Target Pitch Angle for the Microburst Escape Maneuver by S. Mulgund and R. Stengel, JoA, 1993 Simulated microburst parameters R = 3000 ft U max = 80 ft/s 47.4 kts Z max = 150 ft Initial conditions Altitude = 1400 ft Trim condition

23 Wind Model Validation Cessna 402B Model taken from NASA CR Virtually identical Target Pitch Angle (TPA) maneuvers were performed in FDC and the JOA as above FDC was not able to simulate the rate limiters and throttle delay

24 Wind Model Validation 11.0 FDC (Cessna 402B) Journal of Aircraft (Cessna 402B) Angle of Attack (deg) Time (s)

25 Wind Model Validation Angle of Attack profiles very similar Although there are slight differences due to the rate limiters and throttle delay the wind model appears to be functioning correctly Initial decrease in AOA is due to autopilot settings-journal of Aircraft had glideslope tracking while FDC has altitude hold

26 Microburst Analysis 11 different microbursts were simulated in FDC varying radius, and U max Simulation conditions V = 136 kts Initial altitude varied from 1312 ft to 2625 ft Altitude hold autopilot setting No recovery maneuver

27 Microburst Analysis Microburst Number Microburst Paramaters Severity R (ft) Umax (ft/s) Zmax (ft) Umax/R (1/s)

28 Wind Velocities for Microburst # U w, Wind X-Component W w, Wind Z-Component V w, Wind V-Component Wind Velocity (kts) Time (sec)

29 q g Effect Microburst #5 Microburst #5 with q g Trem 1.1 Angle of Attack (deg) Time (sec)

30 Results for Microbursts and Icing Angle of Attack (deg) Microburst #5 Microburst #9 η ice = 0.50, η/η ice =0.08 η ice = 0.91, η/η ice =0.09 η ice = 1.10, η/η ice = Time (sec) Velocity (kts) Microburst #5 Microburst #9 η ice =0.50,η/η ice =0.08 η ice =0.91,η/η ice =0.09 η ice =1.10,η/η ice = Time (sec)

31 Results for Microbursts and Icing Altitude (ft) Microburst #5 Microburst #9 η ice =0.50,η/η ice =0.08 η ice =0.91,η/η ice =0.09 η ice =1.10,η/η ice = Altitude (ft) Elevator Deflection (deg) Microburst #5 Microburst #9 η ice =0.50,η/η ice =0.08 η ice =0.91,η/η ice =0.09 η ice =1.10,η/η ice = Time (sec) Time (sec)

32 Comparison to an Icing Case Compared the rates of change of alpha, velocity, and altitude Case dα/dt (deg/s) dv/dt (kts/s) dh/dt (ft/min) dδ e /dt (deg/s) Microburst Microburst Microburst Microburst Microburst Microburst η ice = 0.50, η/η ice = η ice = 0.91, η/η ice = η ice = 1.10, η/η ice =

33 Comparison to an Icing Case Microburst rates of change were larger than the worst icing case initially, but after a long icing encounter the rates were comparable These large differences should make it straightforward to distinguish between icing and wind shear encounters Altitude is maintained for the icing case, but not in the microburst case In addition dynamic identification data would be available to help identify windshear as well

34 Gravity Waves Similar to a surface wave on water Result of density variation with height Commonly caused by mountains Propagate vertically Horizontal wavelengths vary from 1 km to 100+ km

35 Gravity Waves In an earth fixed reference frame waves appear to be fixed Velocity amplitudes are small in the troposphere, but can be large in the mesosphere Gravity waves and icing are not exclusive events and frequently occur simultaneously

36 Gravity Waves W. Hocking, 2001

37 Gravity Waves W. Hocking, 2001

38 Gravity Waves W. Hocking, 2001

39 Gravity Wave Model Only the vertical velocity entered into FDC using the same method as the microbursts Wavelengths varied from 1 to 64 km Amplitudes of 0, 0.5,1 and 2 m/s studied All combinations of gravity waves, icing and random atmospheric turbulence studied Icing, η = 0.0, 0.04, and 0.08 z-acceleration RMS = 0.15g

40 Gravity Wave Model Wavelength (km) Approximate Period at 70m/s (s)

41 Amplitude and Period Variations Angle of Attack (Filtered) (deg) Amplitude 0.0 m/s and Period 229 sec Amplitude 0.5 m/s and Period 229 sec Amplitude 1.0 m/s and Period 229 sec Amplitude 2.0 m/s and Period 229 sec Angle of Attack (Flitered) (deg) 3.0 Amplitude 0.5 m/s and Period 14 sec Amplitude 0.5 m/s and Period 29 sec Amplitude 0.5 m/s and Period 114sec 2.5 Amplitude 0.5 m/s and Period 229 sec Amplitude 0.5 m/s and Period 457 sec Amplitude 0.5 m/s and Period 914 sec Time (sec) Time (sec)

42 Amplitude and Period Variations Angle of Attack (Filtered) (deg) 6.0 Amplitude 0.0 m/s and Period 229 sec Amplitude 0.5 m/s and Period 229 sec 5.0 Amplitude 1.0 m/s and Period 229 sec Amplitude 2.0 m/s and Period 229 sec Time (sec) Z Wind Velocity (kts) Amplitude 0.0 m/s and Period 229 sec Amplitude 0.5 m/s and Period 229 sec Amplitude 1.0 m/s and Period 229 sec Amplitude 2.0 m/s and Period 229 sec Time (sec)

43 Altitude and Velocity Results Altitude (ft) Amplitude 0.0 m/s and Period 229 sec Amplitude 0.5 m/s and Period 229 sec Amplitude 1.0 m/s and Period 229 sec Amplitude 2.0 m/s and Period 229 sec Time (sec) Velocity (Filtered) (deg) Amplitude 0.0 m/s and Period 229 sec Amplitude 0.5 m/s and Period 229 sec Amplitude 1.0 m/s and Period 229 sec Amplitude 2.0 m/s and Period 229 sec Time (sec)

44 Turbulence and Gravity Wave Results Angle of Attack (Filtered) (deg) 6.0 Amplitude 0.0 m/s and Period 229 sec Amplitude 0.5 m/s and Period 229 sec Amplitude 1.0 m/s and Period 229 sec 5.0 Amplitude 2.0 m/s and Period 229 sec Altitude (ft) Amplitude 0.0 m/s and Period 229 sec Amplitude 0.5 m/s and Period 229 sec Amplitude 1.0 m/s and Period 229 sec Amplitude 2.0 m/s and Period 229 sec Time (sec) Time (sec)

45 Turbulence and Gravity Wave Results Velocity (filtered) (kts) 170 Amplitude 0.0 m/s and Period 229 sec Amplitude 0.5 m/s and Period 229 sec Amplitude 1.0 m/s and Period 229 sec 160 Amplitude 2.0 m/s and Period 229 sec Time (sec) Elevator Deflection (filtered) (kts) 2.0 Amplitude 0.0 m/s and Period 229 sec 1.5 Amplitude 0.5 m/s and Period 229 sec Amplitude 1.0 m/s and Period 229 sec 1.0 Amplitude 2.0 m/s and Period 229 sec Time (sec)

46 Icing and Gravity Wave Results Angle of Attack (Filtered) (deg) Amplitude 0.5 m/s, Period 229 sec, and η = 0.00 Amplitude 0.5 m/s, Period 229 sec, and η = 0.08 Amplitude 0.5 m/s, Period 229 sec, and η = 0.00 Amplitude 0.5 m/s, Period 229 sec, and η = 0.08 Icing η =0.08 Altitude (deg) Amplitude 0.5 m/s, Period 229 sec, and η = 0.00 Amplitude 0.5 m/s, Period 229 sec, and η = 0.08 Amplitude 0.5 m/s, Period 229 sec, and η = 0.00 Amplitude 0.5 m/s, Period 229 sec, and η = 0.08 Icing η = Time (sec) Time (sec)

47 Icing and Gravity Wave Results Angle of Attack (Filtered) (deg) 2.0 Amplitude 0.5 m/s, Period 229 sec, and η = 0.00 Amplitude 0.5 m/s, Period 229 sec, and η = 0.08 Amplitude 0.5 m/s, Period 229 sec, and η = 0.00 Amplitude 0.5 m/s, Period 229 sec, and η = 0.08 Icing η = Time (sec)

48 Icing and Gravity Wave Results Velocity (Filtered) (kts) Amplitude 0.5 m/s, Period 229 sec, and η =0.00 Amplitude 0.5 m/s, Period 229 sec, and η =0.08 Amplitude 0.5 m/s, Period 229 sec, and η =0.00 Amplitude 0.5 m/s, Period 229 sec, and η =0.08 Icing η = Time (sec) Elevator Deflection (Filtered) (deg) Amplitude 0.5 m/s, Period 229 sec, and η = 0.00 Amplitude 0.5 m/s, Period 229 sec, and η = 0.08 Amplitude 0.5 m/s, Period 229 sec, and η = 0.00 Amplitude 0.5 m/s, Period 229 sec, and η = 0.08 Icing η = Time (sec)

49 Atmospheric Turbulence FDC turbulence model is based on the Dryden spectral density distribution Turbulence intensity characterized by the effect on the aircraft z-acceleration Previously intensity varied solely using the turbulence scale length (L) This study varies both the scale length and the standard deviation of turbulence velocity (σ) All z-acceleration results are for 70 m/s

50 Velocity Correlations from FDC 0.4 Sclae Length 92 m 0.35 Sclae Length 41 m Sclae Length 23 m Sclae Length 15 m z-acceleration RMS (m/s 2 ) Sclae Length 363 m Sclae Length 500 m Sclae Length 750 m Sclae Length 1000 m Velocity RMS (m/s)

51 Scale Length Correlations From FDC Velocity RMS 1 m/s Velocity RMS 0.75 m/s Velocity RMS 0.5 m/s Velocity RMS 0.25 m/s 0.30 z-acceleration RMS (m/s 2 ) Scale Length (m)

52 NASA Dryden Filter Standards During the turbulence analysis the NASA Dryden Filter Standards were obtained from NASA-HDBK-1001 Scale length only varies with altitude and is constant for a given altitude Trubulence Type Horizontal Ref. Length (m) Vertical Ref. Length (m) Horizontal Vel. RMS (m/s) Vertical Vel. RMS (m/s) z-acceleration RMS (m/s 2 ) Light Moderate Severe

53 Turbulence Results z-acceleration (g) z-accel. RMS = 0.15, L = 23 m z-accel. RMS = 0.15, L = 92 m z-accel. RMS = 0.15, L = 500 m z-accel. RMS = 0.15, L = 1000 m Time (sec)

54 Turbulence Results Angle of Attack (Flitered) (deg) 4 z-accel. RMS = 0.15, L = 23 m z-accel. RMS = 0.15, L = 92 m 3.5 z-accel. RMS = 0.15, L = 500 m z-accel. RMS = 0.15, L = 1000 m Velocity (Flitered) (kts) z-accel. RMS = 0.15, L = 23 m z-accel. RMS = 0.15, L = 92 m z-accel. RMS = 0.15, L = 500 m z-accel. RMS = 0.15, L = 1000 m Time (sec) Time (sec)

55 Turbulence Results Altitude (ft) z-accel. RMS = 0.15, L = 23 m z-accel. RMS = 0.15, L = 92 m z-accel. RMS = 0.15, L = 500 m z-accel. RMS = 0.15, L = 1000 m Time (sec) Elevator Deflection (filtered) (deg) 1 z-accel. RMS = 0.15, L = 23 m z-accel. RMS = 0.15, L = 92 m z-accel. RMS = 0.15, L = 500 m 0.5 z-accel. RMS = 0.15, L = 1000 m Time (sec)

56 Conclusions Microbursts are easy to distinguish because of the rapid rates of change Effects are similar but of different magnitude or occur at different times (late in the encounter) Gravity Waves are more difficult to distinguish Should be distinguishable because of the mechanics of the phenomenon Turbulence modeling is important and varying scale length changes the effects of the turbulence More accurate method of varying turbulence is to vary the intensity Pitch rate effect is minimal and could be neglected to save computation time

57 Future Work Incorporate Microburst and Gravity wave detection into the Smart Icing System Develop Neural Network to distinguish between Icing and atmospheric disturbance Use updated turbulence model in neural network data generation

58 Envelope Protection Kishwar Hossain University of Illinois

59 Smart Icing System Research Kishwar Hossain Edward Whalen Sam Lee Jason Merret Flight Test Aerodynamic Sensors Steady State Characterization Characterization Aircraft Models Flight Mechanics Model Envelope Protection Wind Tunnel Data Flight Mechanics Analysis and Envelope Prediction THE AERODYNAMICS AND FLIGHT MECHANICS GROUP Flight Simulation

60 Objective Safe operation of an aircraft under icing conditions within a reduced envelope. Develop algorithm for estimating the reduced envelope. Provide envelope information for pilot cues and the flight control system.

61 Typical Flight Envelope Aerodynamic Limits Thrust/Power Limits Structural Limits The flight envelope is primarily a function of load factor, velocity and altitude. The clean aircraft flight envelope remains constant. Example of a Clean Aircraft Flight Envelope from Ramer 1989.

62 Boeing 777 Fly-by-wire system Feel Actuators Bank angle protection Visual cue past 30 o, aural cue at 35 o At 60 o force required increases by about 50 lb. Stall protection Aural Warning and stick shaker at 148 kt with clean and 118 kt with 20 o flap deflection. Pilot has to overcome 25 lb back-pressure force applied by system at stick shaker. 15 lb / 1 o AOA increase beyond onset of stick shaker.

63 Airbus A320, A330, A340 Fly-by-wire Predetermined envelope Bank angle Side stick neutral till 33 o. With full stick deflection, bank angle is limited to 67 o or 45 o if angle of attack is high. Roll back to 33 o if side stick is released Stall Pitch attitude protection of up to +30 o and 15 o

64 ATR 72 Stall Protection Aural warning and stick shaker at α = 18.1 o. Stick Pusher at an angle closer to stall. If IPS in level II, α for stick shaker is reduced to 11.2 o in cruise. α for stick pusher reduced as well.

65 The Dynamic Envelope Typically there are predetermined limit values. Problem - Limits change with level of ice accretion. Solution - In icing conditions the limits have to be determined and enforced dynamically during flight.

66 Iced Aircraft Envelope Protection Aerodynamically the iced aircraft needs to be protected from: Wing stall Critical during take off and landing. Horizontal tail stall Most likely when increasing flap deflection. Excessive bank angle or roll upset. Loss of longitudinal and lateral control

67 Critical Parameters The critical parameters are thus: α w : Wing angle of attack α t : Tail angle of attack φ : Roll angle Limits need to be defined for these parameters as a function of ice accretion.

68 Limitations of Sensors Typically sensor data are used to cue the pilot or drive the flight control system. Problems: Desired info not obtainable from available sensors, sensor data not effective for rapid changes. Solution: System needed for prediction of future values from available sensor data and control positions.

69 One Approach Calise et al propose: Estimate the control input that results in reaching a limit boundary once the aircraft reaches a maneuvering steady-state condition. Assume that the aircraft is likely to approach the limits during a sustained maneuver. Use estimated limit boundary control inputs to provide envelope protection.

70 Dynamic Trim Dynamic flight condition sustained over several seconds Angular acceleration = 0 β!, α! = 0 Fast states - Parameters that remain constant during dynamic trim. Slow states - Parameters that change throughout the maneuver. Example of Turn Maneuver from Calise et al

71 Envelope Protection Formulation: Clean Aircraft x If is the state vector u And is the control vector x! = g( x, u ) y p is the limit parameter vector. y = h( x, u p )

72 Reaching the Limit Boundary Determine control deflections required to reach limit boundary. fi δ y pi = δ u + y! u fi u y pi y! u pi pi δt t u Control deflection which will cause the aircraft to reach the i th limit boundary. Example for a helicopter from Calise et al

73 Applying this Formulation to Icing Icing parameter vector: State vectors and derivatives: Control vectors: θ ice x slow fast = [ V, γ ] T x = [ q, α, p, r, β, φ] x = g( x, u, θ ice) u = [ δ, δ, δ, δ ] e power a r T T Limit Parameter vectors: y y p p = limit [ α T,...] = h( x, u, θ ice = limit T [ α,...] = H ( θ ice ) )

74 The Output Equations Two limit parameter equations 1. y p = T [ α,...] = h( x, u, θ ice) Used to solve for value of control deflection needed to exceed limit boundary. 2. y p limit = limit T [ α,...] = H ( θ ice ) Estimates the limit boundary from the icing condition.

75 Focus on Limit Boundaries For clean aircraft is known. y plimit y plimit for iced aircraft needs to be estimated as a function of the icing vector. Available data are being analyzed for this purpose.

76 Estimating the Limit Parameter Insufficient 3-D aircraft data available for analysis. 3-D aircraft nonlinear models lack fidelity. Use 2-D airfoil data to explore what may be useful. θ ice

77 The Analysis The following relationships are being investigated: C lmax vs. C lα. α stall vs. C lα. C lmax vs. C l at a given α. C lmax vs. α at a given C l. C lmax vs. C d at a given C l. C lmax vs. C h C h,rms

78 C lmax vs. C lα C lmax C l C lα α

79 Sample Result C lmax NLF0414 Leading Edge Ice Shapes Re = 1.8E6 x/c = 3.4% x/c = 1.7% x/c = 0.85% x/c = 0% Open small horns Closed large horns C lα

80 α stall vs. C lα C l C lα α stall α

81 Sample Result α stall NLF0414 Leading Edge Ice Shapes Re = 1.8E6 α = C lα

82 C lmax vs. C l at a given α 1.5 C l C lmax 1 C lmax 0.5 Iced Clean α 1-1 α

83 Sample Result C lmax C lmax C l C l NLF0414 Leading Edge Ice Shapes Re = 1.8E6 α = 1 NLF0414 Leading Edge Ice Shapes Re = 1.8E6 α = 4

84 C lmax vs. α at a given C l 1.5 C l1 1 Clean C l 0.5 α Iced α

85 Sample Results C lmax NLF Leading Edge Ice Shapes Re = 1.8E6 C l1 = α

86 C lmax vs. C d at a given C l Clean Case Iced Case C d C d C l C lmax

87 Sample Result C lmax NLF0414 Leading Edge Ice Shapes Re = 1.8E6 C l1 = C d

88 Hinge Moment Leading edge ice accretion Flow separation on lower surface Flap deflects toward reduced pressure Effect of ice accretion on a control surface from Gurbacki H., (Hinge moment negative as shown)

89 C lmax Vs. C h Linear lift Non-linear lift Separation C lmax Ch Clean, no ice Sim ulated Cleanice located Cleanat: x/c=0.0 2 x/c=0.1 0 x/c= α (degrees) Gurbacki, H. & Bragg M. B., 2000.

90 C lmax Vs. C h,rms Clean, no ice Sim ulated Clean, ice no ice located Clean, at: no ice Linear lift Non-linear lift Ch,RMS x/c=0.0 2 x/c=0.1 0 x/c=0.2 0 Separation C lmax Break in C h Break in C h,rms α (degrees) Gurbacki, H. & Bragg M. B., 2000.

91 AOA Vs. Hinge Moment Test Case α stall C h break C h,rms break Clean 15.4 o 15.4 o 15.4 o x/c = o 7 o 5 o x/c = o 3 o -3 o x/c = o 1 o -4 o Gurbacki, H. & Bragg M. B., 2000.

92 Conclusion Further development required. Hinge moments show best results so far. 3-D data needed for complete formulation. Other sensor data may be useful. Neural Nets could be used for predictive purposes. Additional C h, C h,rms data are being acquired.

93 Aero. and F.M. Conclusions Microbursts should be easy to distinguish because of the rapid rates of change Gravity Waves will be more difficult to distinguish Turbulence modeling is important and varying scale length changes the effect of the turbulence Envelope protection scheme formulated - 3-D data needed to complete Hinge-moment data promising for use in calculating limit parameter

94 Aero. and F.M. Future Work Incorporate Microburst and Gravity wave detection into the Smart Icing System Use updated turbulence model in neural network data generation Formulate initial model of the effect of ice on envelope limit parameters Acquire additional C h and C h,rms data to improve models