Aerodynamics and Flight Mechanics Principal Investigator: Mike Bragg Eric Loth Post Doc s: Graduate Students: Undergraduate Students: Sam Lee Jason Merret Kishwar Hossain Edward Whalen Chris Lamarre Leia Blumenthal 2-1
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 2-2
Aerodynamics and Flight Mechanics Goal: Improve the safety of aircraft in icing conditions Objective: Approach: 1) Develop linear and nonlinear iced aircraft models 2) Develop steady state icing characterization methods and identify aerodynamic sensors 3) Identify envelope protection needs and methods 4) Support neural network training, flight simulator development and flight test 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. 2-3
Smart Icing System Research K. Hossain, H. Gurbacki E. Whalen, D. Pokhariyal Sam Lee Aerodynamic Sensors J. Merret, R. Oltman, T. Hutchison Flight Test 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 2-4
Outline Introduction Iced aircraft models Flight mechanics analysis tools Steady icing effects characterization Assessing atmospheric effects on icing characterization Envelope protection Flight test 2-5
Iced Aircraft Models Developed clean and iced aircraft model Next generation aircraft based on NASA Twin Otter Flight Dynamics Linear stability and control model based published data Used to develop characterization methods, flight simulator, etc. Nonlinear model based on B.A.R. tunnel data to be discussed by K. Hossain 2-6
Linear Aerodynamics Model Development Clean stability and control derivative model from published NASA Twin Otter data Iced model development: Based on NASA Twin Otter data Models for completely iced aircraft and tail-only iced aircraft developed from composite of various sources Iced models originally for a single icing encounter η ice model developed to interpolate/extrapolate to other conditions 2-7
h ice Model A linear icing effects model was developed that modified the different stability and control derivatives for various levels of icing C = ( 1+ η 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 2-8
h ice Formulation η ice = C d ref Cd ( IRT airfoil data) ( IRT airfoil data, cont. max.conditions, t = 10min) C d fit as a function of n and A c E C d data obtained from NASA TMs 83556 and 105374, and NACA TNs 4151 and 4155 n = freezing fraction A c = accumulation parameter E = collection efficiency C dref calculated from C d equation using continuous maximum conditions 2-9
h Formulation To capture effects of aircraft geometry, the aircraft specific icing severity factor, η, was developed The aircraft specific icing severity factor incorporates the aircraft specific airfoil, chord, and angle of attack C = + η (1 k ) C ( A)iced ice C (A) k C = k η η A C A ice A 2-10
Differences Between h and h ice η ice Chord 3 ft. Actual Airfoil NACA 0012 Actual Velocity 175 knots Actual Angle of Attack 0 Actual MVD Actual Actual LWC Actual Actual T Actual Actual Time of encounter Actual Actual η 2-11
Effect of T and LWC on h 0.3 0.25 0.2 0.15 0.1 LWC=0.2 LWC=0.65 LWC=1.0 Twin Otter V 130 kts MVD=20 µm h=9000 ft Time=600 s 0.05 0-10.0-5.0 0.0 5.0 10.0 15.0 20.0 25.0 30.0 T ( F) 2-12
Flight Mechanics Computational Tool Need to develop a flight mechanics simulation capability: model development Steady state characterization Data generation for neural net training and testing Flight mechanics analysis 2-13
Flight Dynamics Code, FDC 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 aerodynamic model capability 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 ) 2-14
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 2000-0360 AIAA 2001-0541 2-15
Flight Mechanics Analysis Example Velocity (knots) 160 155 150 145 140 h ice = 0.0 Wing Iced Tail Iced 135 0 100 200 300 400 500 600 Time (sec.) h ice = 1.1 1.6 hice= 1.1 1.4 Wing Iced 1.2 Tail Iced 1 0.8 0.6 hice= 0.0 0.4 0.2 0 AngleofAtack(deg.) -0.2-0.4-0.6 0 100 200 300 400 500 600 Time (sec.) 1.2 1480 1 1460 0.8 0.6 hice= 1.1 0.4 0.2 0 Wing Iced -0.2 Tail Iced ElevatorDeflection(deg.) -0.4-0.6-0.8 0 100 200 300 400 500 600 Time (sec.) hice= 0.0 Thrust (lbf) 1440 1420 1400 1380 1360 1340 1320 hice= 0.0 η ice =0.0 Wing Iced Tail Iced 0 100 200 300 400 500 600 Time (sec.) hice= 1.1 η =1.1 2-16
Hinge Moment Models Models are used in simulations to study the potential use of hinge moment sensors as aerodynamic performance monitors C h and C h_rms capture the effects of icing on the flow field over the airfoil surface. C h_rms is the RMS of the unsteady hinge moment, which is a measure of flow field separation due to ice accretion Models based on hinge moment measurements taken at UIUC on a NACA 23012 airfoil with quarter round ice-shapes (AIAA 99-3149) 2-17
Flight Mechanics Analysis Example Wing Ice Only 0.03 0.025 Tail IceOnly RMS Hinge Moment, Chrms 0.025 0.02 0.015 0.01 0.005 Elevator Aileron Elevator Clean Aileron Clean 0.02 0.015 0.01 RM 0.005 Elevator Aileron Elevator Clean Aileron Clean 0 0 2 4 6 8 10 12 14 0 0 2 4 6 8 10 12 14 Angle of Attack, deg Angle of Attack, deg Control surface hinge moment can help identify ice location 2-18
Atmospheric Effects on Characterization 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 Study performed to analyze the effects of microbursts, windshear, and icing on aircraft For more information see: Jason Merret M.S. thesis, AIAA 2001-0541, AIAA 2002-0814 2-19
Microburst Taken From Mulgund and Stengel, Journal of Aircraft, 1993 Microburst model used from Oseguera and Bowles, NASA TM 100632 2-20
Wind Model Validation 11.0 FDC (Cessna 402B) Journal of Aircraft (Cessna 402B) 10.0 9.0 Angle of Attack (deg) 8.0 7.0 6.0 5.0 4.0 3.0 0.0 50.0 100.0 Time (s) 2-21
Results for Microbursts and Icing Angle of Attack (deg) 14 12 10 8 6 4 2 0 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 = 0.09 0 100 200 300 400 Time (sec) Velocity (kts) 140 130 120 110 100 90 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 = 0.09 80 0 100 200 300 400 Time (sec) 2-22
Results for Microbursts and Icing 1700 7600 2 Altitude (ft) 1675 1650 1625 1600 1575 1550 1525 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 = 0.09 7500 7400 7300 7200 7100 Altitude (ft) Elevator Deflection (deg) 0-2 -4-6 -8 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 = 0.09 1500 0 100 200 300 400 Time (sec) 7000-10 0 100 200 300 400 Time (sec) 2-23
Gravity Waves Result of density variation with height Commonly caused by mountains Propagate vertically Horizontal wavelengths vary from 1 km to 100+ km 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 2-24
Gravity Wave Model Only the vertical velocity modeled in 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 Several combinations of gravity waves, icing and random atmospheric turbulence studied Icing, η = 0.0, 0.04, and 0.08 z-acceleration RMS = 0.15g 2-25
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 = 0.08 1.8 1.5 1.3 1.0 0 50 100 Time (sec) 2-26
Atmospheric Effects 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 2-27
Iced Aircraft Envelope Protection Aerodynamically the iced aircraft needs to be protected from: Wing stall Horizontal tail stall Excessive bank angle or roll upset. Loss of longitudinal and lateral control K. Hossain will present the open loop method and V. Sharma the closed loop methods under development for SIS 2-28
Flight Test Goal: Objective: Improve the safety of aircraft in icing conditions. 1) Acquire and analyze flight data to assist in the development of icing characterization 2) Evaluate characterization methods in flight Approach: In cooperation with NASA acquire detailed flight dynamics data on the Twin Otter with and without ice. Use data to develop and test ID and characterization methods including the effects of dynamic input, sensor noise, repeatability, uncertainty, IPS detection, etc. Ed Whalen will present flight test research results 2-29