Aerodynamics and Flight Mechanics
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1 Aerodynamics and Flight Mechanics Principal Investigator: Mike Bragg, Eric Loth Post Doc s: Andy Broeren, Sam Lee Graduate Students: Holly Gurbachi(CRI), Tim Hutchison, Devesh Pokhariyal, Ryan Oltman, Jason Merret, Jianping Pan, Kishwar Hossain, Edward Whalen Undergraduate Students: Chris Lamarre, Leia Blumenthal 2-1
2 SMART ICING SYSTEMS Research Organization Core Technologies 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 Research/Integration Flight Simulation Flight Test 2-2
3 Aerodynamics and Flight Mechanics 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
4 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 on published data Used to develop characterization methods, flight simulator, etc. Nonlinear models: CFD research Neural net based method Model based on B.A.R. tunnel data 2-4
5 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 2-5
6 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 2-6
7 h ice Formulation η ice = C d ref C ( IRT airfoil data ) d ( IRT airfoil data, cont. max.conditions, t = 10 min ) C d fit as a function of n and A c E C d data obtained from NASA TMs and , 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 2-7
8 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 = ( A )iced (1 + ηice k C ) C = k C k η η A C A ice A ( A ) 2-8
9 Differences Between h and h ice η ice η 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 2-9
10 Effect of T and LWC on h LWC=0.2 LWC=0.65 LWC=1.0 Twin Otter V 130 kts MVD=20 µm h=9000 ft Time=600 s T ( F) 2-10
11 CFD Efforts To Provide Aero Data NSU2D predictions with upper-surface ice shapes WIND predictions for leading-edge ice shapes Detached Eddy Simulation, DES, development with WIND to increase separated flow predictive performance for C L,max After some exploratory research this method for building Aerodynamic aircraft models was abandoned. Research in icing CFD continues funded through other programs 2-11
12 Velocity Contours NACA 23012m 0.15 Quarter Round at x/c=0.10 B.L. tripped Re=1.8x10 6 α=+6º α=+2º α=-2º 2-12
13 Iced NLF Airfoil: Horn Ice Re=1.8 x 10 6, Ma=0.185 s/c=0% k/c=6.67% s/c=3.4% k/c=6.67% 2-13
14 Neural Network Aero Model Approach Proposed Neural Network approach to icing effects modeling: Environmental Variables: T, LWC, MVD, etc Ice Shape Neural Net Ice Shape: horn height, horn location, etc 2-D Aerodynamic Neural Net 2-D Aerodynamic Performance 3-D Aerodynamic Neural Net 3-D Aerodynamic Performance, Stability and Control 2-14
15 Neural Net Prediction of Airfoil Aero Coefficients Clean (Exp.) Clean (N.N.) Clean (Exp.) k/c=2.22% (Exp.) k/c=2.22% (N.N.) C l Clean (N.N.) k/c=2.22% (Exp.) 1.0 k/c=2.22% (N.N.) k/c=4.44% (Exp.) k/c=4.44% (N.N.) 0.5 k/c=6.67% (Exp.) k/c=6.67% (N.N.) C d k/c=4.44% (Exp.) k/c=6.67% (Exp.) k/c=4.44% (N.N.) k/c=6.67% (N.N.) Angle of Attack [ ] -1.0 Angle of Attack [ ] Clean (Exp.) Clean (N.N.) 0.15 C m 0.04 k/c=2.22% (Exp.) k/c=2.22% (N.N.) k/c=4.44% (Exp.) k/c=4.44% (N.N.) 0.02 k/c=6.67% (Exp.) k/c=6.67% (N.N.) Angle of Attack [ ] Ch Clean (Exp.) Clean (N.N.) k/c=2.22% (Exp.) k/c=2.22% (N.N.) k/c=4.44% (Exp.) k/c=4.44% (N.N.) k/c=6.67% (Exp.) k/c=6.67% (N.N.) Angle of Attack [ ] 2-15
16 Non-linear Aerodynamics Model Model based on B.A.R. rotary balance test results on the Twin Otter model Changes in the forces and moments were modeled as functions of icing = C C + k αη C + k δ η C ( Aiced ) ( A) clean C, wing ( Aclean ) C, e tail ( Aclean ) ( A) ( A) -C A : Force or moment coefficient of interest - η : Icing severity factor -K CA : Scaling factor for a particular force or moment coefficient η wing = 0.00, η tail =0.00 η wing = 0.08, η tail =0.00 η wing = 0.16, η tail = η wing = 0.00, η tail =0.00 η wing = 0.00, η tail =0.12 η wing = 0.00, η tail = C L C m C m η wing = 0.00 η wing η wing = 0.08 = AoA AoA δe 2-16
17 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 ) Envelope Protection 2-17
18 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
19 Flight Mechanics Analysis Example h ice = Wing Iced Tail Iced Velocity (knots) h ice = 0.0 h ice = 1.1 Wing Iced Angle of Attack (deg.) h ice = Tail Iced Time (sec.) Time (sec.) Elevator Deflection (deg.) h ice = 0.0 Wing Iced Tail Iced h ice = 1.1 Thrust (lbf) h = 0.0 η ice ice =0.0 Wing Iced Tail Iced h = 1.1 η ice ice = Time (sec.) Time (sec.) 2-19
20 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 airfoil with quarter round ice-shapes (AIAA ) 2-20
21 Flight Mechanics Analysis Example Wing Ice Only Tail Ice Only RMS Hinge Moment, Chrms Elevator Aileron Elevator Clean Aileron Clean RMS Hinge Moment, Chrms Elevator Aileron Elevator Clean Aileron Clean Angle of Attack, deg Angle of Attack, deg Control surface hinge moment can help identify ice location 2-21
22 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 , AIAA
23 Microburst Taken From Mulgund and Stengel, Journal of Aircraft, 1993 Microburst model used from Oseguera and Bowles, NASA TM
24 Wind Model Validation 11.0 F D C (C e ss na B) 10.0 Jou rna l of A ircra ft (C e ssna B) 9.0 Angle of Attack (deg) Time (s) 2-24
25 Results for Microbursts and Icing A ngle of Attac k (deg) M icro bu rst # 5 M icro bu rst # 9 η ice = 0.5 0, η/η ice = 0.08 η ice = 0.9 1, η/η ice = 0.09 η ice = 1.1 0, η/η ice = T im e (s ec ) Velocity (kts) M icro bu rst #5 M icro bu rst #9 η ice = 0.5 0, η/η ice = η ice = 0.9 1, η/η ice = η ice = 1.1 0, η/η ice = T im e (s ec) 2-25
26 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 2-26
27 Icing and Gravity Wave Results A ngle of A ttack (Filtered) (deg) Am plitude 0.5 m /s, Period 2 29 sec, and η = 0.00 Am plitude 0.5 m /s, Period 2 29 sec, and η = 0.08 Am plitude 0.5 m /s, Period 2 29 sec, and η = 0.00 Am plitude 0.5 m /s, Period 2 29 sec, and η = 0.08 Icing η = Time (sec) 2-27
28 Conclusions Simulation capability developed through modified FDC code Linear and nonlinear aero models developed based on experimental data Microburst characteristics fundamentally different and should be distinguishable Atmospheric effects such as gravity waves may need to be considered in IMS design 2-28
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