Smart Icing Systems NASA Review, May 18-19, 1999
3-1
Aerodynamics and Flight Mechanics
Principal Investigators: Mike BraggEric Loth
Graduate Students: Holly Gurbacki (CRI support)
Tim Hutchison Devesh Pokhariyal (CRI support)
Ryan Oltman
Smart Icing Systems NASA Review, May 18-19, 1999
3-2
SMART ICING SYSTEMS Research Organization
Core Technologies
Flight SimulationDemonstration
FlightMechanics
Controls and Sensor
Integration
HumanFactors
AircraftIcing
Technology
Operate andMonitor IPS
EnvelopeProtection
AdaptiveControl
CharacterizeIcing Effects
IMS Functions
Safety and EconomicsTrade Study
Aerodynamicsand
Propulsion
Smart Icing Systems NASA Review, May 18-19, 1999
3-3
Aerodynamics and Flight Mechanics
Goal: Improve the safety of aircraft in icing conditions.
Objective: 1) Develop a nonlinear iced aircraft model. 2) Develop steady state icing characterization
methods and identify aerodynamic sensors. 3) Identify envelope protection needs and
methods.
Approach: First use Twin Otter and tunnel data to developa linear clean and iced model. Then develop anonlinear model with tunnel and CFD data. Usethe models to develop characterization and envelope protection.
Smart Icing Systems NASA Review, May 18-19, 1999
3-4
THE AERODYNAMICS ANDFLIGHT MECHANICS GROUP
Wind TunnelData
IcedAerodynamics
Model
ComputationalFluid
Dynamics
Iced AircraftModel
Clean AircraftModel
Aircraft - FlightMechanicsAnalysis
Steady StateCharacterization
Flight Mechanics Model
Devesh Pokhariyal, Dr. Bragg
Tim Hutchison, Dr. Bragg
Ryan Oltman, Dr. Bragg
Dr. Loth
Characterization
Flight Simulation
Envelope Protection
Smart Icing System Research
Holly Gurbacki, Dr. Bragg AerodynamicSensors
Smart Icing Systems NASA Review, May 18-19, 1999
3-5
Outline
• Flight Mechanics Model• Development of Clean Aircraft Model• Development of Iced Aircraft Model• Flight Mechanics Analysis of Clean and Iced Aircraft• Steady State Characterization• Hinge-Moment Aerodynamic Sensor• Summary and Conclusions• Future Plans/CFD Analysis
Smart Icing Systems NASA Review, May 18-19, 1999
3-6
Equations of Motion
• 6 DoF equations of motion :
xx TA FFsinmg)WQVRU(m ++−=+− θθ&
yy TA FFcossinmg)WPURV(m ++=−+ θθφφ&
zz TAz FFcoscosmg)VPUQW(m ++=+− θθφφ&
TAyyzzxzxzxx LLRQ)II(PQIRIPI +=−+−− &&
TA22
xzzzxxyy MM)RP(IPR)II(QI +=−+−−&
TAxzxxyyxzzz NNQRIPQ)II(PIRI +=+−+− &&
Smart Icing Systems NASA Review, May 18-19, 1999
3-7
Equations of Motion (cont.)
• The longitudinal equations of motion areconsidered initially the forces along theaircraft body axes are resolved into lift anddrag forces to yield:
TcosT)cosDsinL(sinmg)WQVRU(m φφααααθθ +−+−=+−•
TsinT)sinDcosL(coscosmg)VPUQW(m φφααααθθφφ +−−+=+−•
TA22
xzzzxxyy MM)RP(IPR)II(QI +=−+−−•
Smart Icing Systems NASA Review, May 18-19, 1999
3-8
THE AERODYNAMICS ANDFLIGHT MECHANICS GROUP
Wind TunnelData
IcedAerodynamics
Model
ComputationalFluid
Dynamics
Iced AircraftModel
Clean AircraftModel
Aircraft - FlightMechanicsAnalysis
Steady StateCharacterization
Flight Mechanics Model
Devesh Pokhariyal, Dr. Bragg
Tim Hutchison, Dr. Bragg
Ryan Oltman, Dr. Bragg
Dr. Loth
Characterization
Flight Simulation
Envelope Protection
Smart Icing System Research
Holly Gurbacki, Dr. Bragg AerodynamicSensors
Smart Icing Systems NASA Review, May 18-19, 1999
3-9
Twin Otter Clean Model (v1.0)
• Longitudinal model is derived from flightdynamics data in AIAA report 86-9758, AIAAreport 89-0754 and AIAA 93-0754
• Dimensional parameters:Parameter Value UnitsWing Area 39.02 m2Wing Span 19.81 mAspect Ratio 10Mean Aerodynamic Chord 1.981 mMass 4150 kgAirspeed 61.73 m/sAltitude 1524 mMoments of Inertia: Ixx, Iyy, Izz, Ixz 21279, 30000,44986, 1432 kg.m2Flap Deflection 0 deg
Smart Icing Systems NASA Review, May 18-19, 1999
3-10
Clean Dimensional Derivatives (v1.0)
Nondimensional Clean Dimensional CleanParameter Value Parameter Value UnitsCL 0.5 Mδ -10.45 /s2
CLo 0.2 Mu 0 /ft-s
CLq 19.97 Mq -3.06 /s
CLα 5.66 Mαdot -0.804 /s
CLαdot 2.5 MTu 0 /ft-s
CLδE 0.608 Mα -7.87 /s2
CD0 0.0414 Zδ -40.33 ft/s2
K 0.0518 Zα -379.03 ft/s2
Cm 0 Zu -0.31 /sCmo 0.15 Zαdot -2.446 ft/s
Cmq -34.2 Zq -19.7 ft/s
Cmα -1.31 Xα 13.72 ft/s2
Cmαdot -9 XTu 0.0149 /s
CmδE -1.74 Xu -0.033 /sXδ 0 ft/s2
Smart Icing Systems NASA Review, May 18-19, 1999
3-11
Empirical Clean Model Method
• The clean aircraft model is developed using methodsoutlined in NASA TN D-6800 for twin-engine,propeller-driven airplanes.
• Lift, pitching moment, drag and horizontal tail hingemoments are modeled.
• Method is based on theoretical and empiricalmethods (USAF Datcom handbook and NACA/NASAreports)
• Model requires mainly aircraft geometry and thus canbe applied to different aircraft at minimal cost.
• Waiting for Twin Otter data
Smart Icing Systems NASA Review, May 18-19, 1999
3-12
Empirical Clean Model (NASA TN D-6800)
• Representative equations:
powere)hf(hwfn)C()C(CCC LLLLL ∆+∆++=
δδ
powertabe)hf(hwfn)C()C()C(CCC MMMMMM ∆+∆+∆++=
δδ
powersystemcoolingnifihiwi0)C()C()C()C()C()C(CC DDDDDDDD ∆++++++=
tab
h
tab
h
0tab)f(h)f(h)C(
c
)xx()C(
c
)xx()C(C M
h
4/chingeL
h
achingeLh δδδδδδ
′∆+−
∆+−
==
Smart Icing Systems NASA Review, May 18-19, 1999
3-13
THE AERODYNAMICS ANDFLIGHT MECHANICS GROUP
Wind TunnelData
IcedAerodynamics
Model
ComputationalFluid
Dynamics
Iced AircraftModel
Clean AircraftModel
Aircraft - FlightMechanicsAnalysis
Steady StateCharacterization
Flight Mechanics Model
Devesh Pokhariyal, Dr. Bragg
Tim Hutchison, Dr. Bragg
Ryan Oltman, Dr. Bragg
Dr. Loth
Characterization
Flight Simulation
Envelope Protection
Smart Icing System Research
Holly Gurbacki, Dr. Bragg AerodynamicSensors
Smart Icing Systems NASA Review, May 18-19, 1999
3-14
Icing Effects Model
To devise a simple, but physically representative, modelof the effect of ice on aircraft flight mechanics for use inthe characterization and simulation required for theSmart Icing System development research.
Objective:
Smart Icing Systems NASA Review, May 18-19, 1999
3-15
Linear Icing Effects Model
• = Arbitrary coefficient (CLα, Cmδe, etc.)
• = icing severity factor
• = coefficient icing factor
)A(Ciceiced)A( C)k1(CA
ηη+=
( )AC
iceηη
ACk
Smart Icing Systems NASA Review, May 18-19, 1999
3-16
Icing Factors
• is the icing severity factor
where: = freezing fraction
= accumulation parameter
= collection efficiency
• is the coefficient icing factor
( )EA,nf cice =ηη
)conditionsicing.,configandgeometryaircraft,IPS(fk )A(CA=
iceηηn
cAE
ACk
Smart Icing Systems NASA Review, May 18-19, 1999
3-17
ηηice Formulation
• ∆Cd fit as a function of n and AcE− ∆Cd data obtained from NACA TM 83556
• ∆Cdref calculated from ∆Cd equation usingcontinuous maximum conditions
• ηice formulated such that ηice=0 at n=0 or AcE=0
( )( )min45t,conditions.max.cont,0012NACAC
dataIRT,'3c,0012NACAC
refd
dice =∆
=∆=ηη
Smart Icing Systems NASA Review, May 18-19, 1999
3-18
∆∆Cd Curve Fit of NASA TM 83556 Data
0
0.02
0.04
0.06
0.08
0.1
0.12
0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045
AcE
∆∆C
d
n=.33 Data
n=.33 Curve Fit
n=1 Data
n=1 Curve Fit
Smart Icing Systems NASA Review, May 18-19, 1999
3-19
ηηice Reference Value
• To nondimensionalize the ∆Cd equation, a referencecondition was chosen based on FAA Appendix CMaximum Continuous conditions.
• NACA 0012 c = 3 ft.
MVD = 20 µm V∞ = 175 knotsLWC = 0.65 g/m3 t = 45 minT0 = 25 °F
• These conditions yielded a ∆Cd = 0.1259 at ηice=1
Smart Icing Systems NASA Review, May 18-19, 1999
3-20
Final ηηice Equation (v1.0)
( ) ( )2c2c1ice EAZEAZ +=ηη
n176.3Z1 = 432
432
2 hngnfnen1dncnbnan
Z++++
+++=
26.179810d
58.248690c
52.54370b
33.4547a
−==
−==
697.36h
595.250g
295.33f
322.4e
−==
−=−=
Smart Icing Systems NASA Review, May 18-19, 1999
3-21
Variation with n and AcE
0.00
0.20
0.40
0.60
0.80
1.00
0 0.2 0.4 0.6 0.8 1n
η ice
AcE=.01
AcE=.02
AcE=.03
AcE=.04
Cont. Max. Case
Glaze Ice Rime Ice
Smart Icing Systems NASA Review, May 18-19, 1999
3-22
ηηice Variation with AcE and n
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1.60
0.000 0.010 0.020 0.030 0.040 0.050
AcE
ηηic
e
n=0.1
n=0.2
n=0.4
n=0.6
n=1
Smart Icing Systems NASA Review, May 18-19, 1999
3-23
Iced Model (v1.0)
• Equations:
)k1()C()C( iceCAA Acleaniceηη+= 1
)C(
)C(k
clean
ice
A
A
AiceC −=ηη 2
L0DD KCCC +=
CL CLo CLq CLα CLαdot CLδE CD0 Kclean 0.5 0.2 19.97 5.66 2.5 0.608 0.041 0.052iced 0.5 0.2 19.7 5.094 2.5 0.55 0.062 0.057kCAηice 0 0 -0.014 -0.1 0 -0.095 0.5 0.1
Cm Cmo Cmq Cmα Cmαdot CmδE
clean 0 0.15 -34.2 -1.31 -9 -1.74iced 0 0.15 -33 -1.18 -9 -1.566kCAηice 0 0 -0.035 -0.099 0 -0.1
Smart Icing Systems NASA Review, May 18-19, 1999
3-24
Comparison of Clean and Iced Model (v1.0)
Parameter Clean Iced Units Mδ -10.44 -9.4 /s2
Mu 0 0 /ft-s
Mq -3.055 -2.948 /s
Mαdot -0.804 -0.804 /s
MTu 0 0 /ft-s
Mα -7.863 -7.083 /s2
Zδ -40.29 -36.45 ft/s2
Zα -378.72 -342.67 ft/s2
Zu -0.31 -0.31 /sZαdot -2.446 -2.466 ft/s
Zq -19.697 -19.431 ft/s
Xα 13.706 13.898 ft/s2
XTu 0.0149 0.0266 /s
Xu -0.033 -0.0463 /sXδ 0 0 ft/s2
Smart Icing Systems NASA Review, May 18-19, 1999
3-25
THE AERODYNAMICS ANDFLIGHT MECHANICS GROUP
Wind TunnelData
IcedAerodynamics
Model
ComputationalFluid
Dynamics
Iced AircraftModel
Clean AircraftModel
Aircraft - FlightMechanicsAnalysis
Steady StateCharacterization
Flight Mechanics Model
Devesh Pokhariyal, Dr. Bragg
Tim Hutchison, Dr. Bragg
Ryan Oltman, Dr. Bragg
Dr. Loth
Characterization
Flight Simulation
Envelope Protection
Smart Icing System Research
Holly Gurbacki, Dr. Bragg AerodynamicSensors
Smart Icing Systems NASA Review, May 18-19, 1999
3-26
Flight Dynamics and Control Toolbox
• Flight Dynamics Code 1.3– FDC 1.3 is a free source code written by Marc
Rauw (based in the Netherlands)– Code developed using MATLAB and SIMULINK– 6 DoF equations:
• Assumptions– The body is assumed to be rigid during motions– The mass of the aircraft is assumed to be constant during
the time-interval in which its motions are studied– Earth is assumed to be fixed in space & the curvature is
neglected
• 12 - Nonlinear differential equations used to describe themotion
Smart Icing Systems NASA Review, May 18-19, 1999
3-27
Velocity
Alpha
Beta
Forces & Moments
FDC Equations
( ) ββααββββααββααββββαα cossinwqupvsinvrupwcoscosurvqwsinsinFsinFcoscosFm1
V wwwwwwwwwzyx
+−−
+−+
+−−++=
••••
],,,,,,,,,r,q,p,,,,,,,0[CScVM
],,,,,,,,,r,q,p,,,,,,,0[CSVF2
earfrafe3232
N,M,L2
21
N,M,L
2earfrafe
3232z,y,x
221
z,y,x
ββββδδαδαδαδαδαδαδδδδδδδδδββββββααααααρρ
ββββδδαδαδαδαδαδαδδδδδδδδδββββββααααααρρ&
&
⋅⋅⋅⋅⋅=
⋅⋅⋅⋅=
( ) ( ) ββααααααααααααββ
αα tansinrcospqsinurvqwcoswqupvcosFsinFm1
cosV1
wwwwwwzx +−+
+−+
+−−+−=
•••
( ) ααααββααββββααββααββββααββ cosrsinpsinsinwqupvcosvrupwsincosurvqwsinsinFcosFsincosFm1
V1
wwwwwwwwwzyx −+
+−+
+−+
+−+−+−=
••••
Smart Icing Systems NASA Review, May 18-19, 1999
3-28
Pitch, Roll & Yaw Rates
FDC Equations (cont.)
( )( )( )
( )( )( ) ( ) ( )
( ) ( )
( )( ) ( )( )( ) ( ) ( ) N
JxzIzzIxxIxx
LJxzIzzIxx
Jxzqr
JxzIzzIxxJxzIzzIyyIxx
pJxzIyyIxxIxxr
IyyM
rpIyyIxz
rpIyy
IxxIzzq
NJxzIzzIxx
JxzL
JxzIzzIxxIzz
qpJxzIzzIxx
JxzIzzIyyIxxr
JxzIzzIxxJxzIzzIzzIyy
p
2222
22
2222
2
⋅−⋅
+⋅−⋅
+⋅
⋅
−⋅⋅+−
−⋅+−⋅=
+−⋅−⋅⋅−
=
⋅−⋅
+⋅−⋅
+⋅
⋅
−⋅⋅+−
+⋅−⋅
−⋅−=
&
&
&
Smart Icing Systems NASA Review, May 18-19, 1999
3-29
Euler Angles
Position
FDC Equations (cont.)
( ) θθψψθθϕϕϕϕϕϕϕϕϕϕθθ
θθϕϕϕϕ
ψψ
sinptancosrsinqp
sinrcosqcos
cosrsinq
&&
&
&
+=++=−=
+=
( ){ } ( )( ){ } ( )( ) θθϕϕϕϕθθ
ψψϕϕϕϕψψθθϕϕϕϕθθ
ψψϕϕϕϕψψθθϕϕϕϕθθ
coscoswsinvsinuz
cossinwcosvsinsincoswsinvcosuy
sinsinwcosvcossincoswsinvcosux
eeee
eeeeee
eeeeee
++−=
−−++=
−−++=
&
&
&
Smart Icing Systems NASA Review, May 18-19, 1999
3-30
FDC Equations (cont.)
The current aircraft model (without turbulence) is usingthe following equations for the longitudinal mode:
( )ββααββαα sinsinFcoscosFm1
V zx +=•
( ) ( ) ββααααααααββ
αα tansinrcospqcosFsinFm1
cosV1
zx +−+
+−=
•
( ) ( )IyyM
rpIyyJxz
rpIyy
IxxIzzq 22 +−⋅−⋅⋅
−=&
ϕϕϕϕθθ sinrcosq −=&
( ){ } ( )( ) θθϕϕϕϕθθ
ψψϕϕϕϕψψθθϕϕϕϕθθ
coscoswsinvsinuz
sinsinwcosvcossincoswsinvcosux
eeee
eeeeee
++−=
−−++=&
&
Smart Icing Systems NASA Review, May 18-19, 1999
3-31
Open Loop Analysis Tool for Nonlinear Twin Otter Model
Smart Icing Systems NASA Review, May 18-19, 1999
3-32
Closed Loop Analysis Tool for Nonlinear Twin Otter Model
Smart Icing Systems NASA Review, May 18-19, 1999
3-33
Validation of the FDC (TIP flight p5220)
• Code is validated by comparing the responseof a doublet to published NASA data (AIAA99-0636) for the Twin Otter aircraft.
• Flight conditions:• V = 187 ft/s
• alt. = 5620 ft
• α = 3.53 deg
• δF = 0 deg
Smart Icing Systems NASA Review, May 18-19, 1999
3-34
Clean and Iced Model (v2.0 TIP)
CX0 CXα CXα2 CXδe
Clean -0.076 0.390 2.910 0.096Iced -0.090 0.714 1.744 0.068ηice.kC(A) 0.182 0.831 -0.401 -0.290
CZ0 CZα CZα2 CZq CZδe
Clean -0.311 -7.019 4.111 -3.182 -0.234Iced -0.318 -7.510 6.573 -7.395 -0.3541ηice.kC(A) 0.020 0.070 0.599 1.322 0.513248
Cm0 Cmα Cmα2 Cmq Cmδe
Clean -0.011 -0.879 -3.852 -19.509 -1.8987Iced -0.026 -0.790 -3.930 -21.480 -1.8629ηice.kC(A) 1.361 -0.101 0.020 0.101 -0.01886
Ref: Robert Miller and W. Ribbens, AIAA 99-0636
Smart Icing Systems NASA Review, May 18-19, 1999
3-35
Validation of FDC 1.3
• Validation of the FDC (TIP flight p5220, no ice)
0
1
2
3
4
5
6
7
8
0 5 10 15Time, sec
Alp
ha, d
eg
Flight DataFDC
-10-8-6-4-202468
10
0 5 10 15Time, sec
q, d
eg/s
ec
Flight DataFDC
Smart Icing Systems NASA Review, May 18-19, 1999
3-36
Validation of FDC 1.3 (cont.)
181182183184185186187188189190
0 5 10 15Time, sec
Vel
oci
ty, f
t/se
c
Flight DataFDC
• Validation of the FDC (TIP flight p5220, no ice)
5610561556205625563056355640564556505655
0 5 10 15Time, sec
Alt
itu
de,
ft
Flight DataFDC
Smart Icing Systems NASA Review, May 18-19, 1999
3-37
Validation of FDC 1.3 (cont.)
• Validation of the FDC (TIP flight p4601, iced)
Smart Icing Systems NASA Review, May 18-19, 1999
3-38
THE AERODYNAMICS ANDFLIGHT MECHANICS GROUP
Wind TunnelData
IcedAerodynamics
Model
ComputationalFluid
Dynamics
Iced AircraftModel
Clean AircraftModel
Aircraft - FlightMechanicsAnalysis
Steady StateCharacterization
Flight Mechanics Model
Devesh Pokhariyal, Dr. Bragg
Tim Hutchison, Dr. Bragg
Ryan Oltman, Dr. Bragg
Dr. Loth
Characterization
Flight Simulation
Envelope Protection
Smart Icing System Research
Holly Gurbacki, Dr. Bragg AerodynamicSensors
Smart Icing Systems NASA Review, May 18-19, 1999
3-39
Clean and Iced Dynamic Comparison
Initial trimmed flight conditions:• Altitude = 5600 ft• Velocity = 187.5 ft/sec• Angle of Attack = 3.52°• Aircraft Model v2.0Clean:
ηice = 0.0Iced:
ηice = .15
Smart Icing Systems NASA Review, May 18-19, 1999
3-40
Dynamic Analysis, Clean & Iced
Smart Icing Systems NASA Review, May 18-19, 1999
3-41
Dynamic Analysis, Clean & Iced (cont.)
Smart Icing Systems NASA Review, May 18-19, 1999
3-42
THE AERODYNAMICS ANDFLIGHT MECHANICS GROUP
Wind TunnelData
IcedAerodynamics
Model
ComputationalFluid
Dynamics
Iced AircraftModel
Clean AircraftModel
Aircraft - FlightMechanicsAnalysis
Steady StateCharacterization
Flight Mechanics Model
Devesh Pokhariyal, Dr. Bragg
Tim Hutchison, Dr. Bragg
Ryan Oltman, Dr. Bragg
Dr. Loth
Characterization
Flight Simulation
Envelope Protection
Smart Icing System Research
Holly Gurbacki, Dr. Bragg AerodynamicSensors
Smart Icing Systems NASA Review, May 18-19, 1999
3-43
Steady State Characterization
Goal:• Analyze quasi-steady data characterized by control
inputs and disturbances insufficient to excite dynamicmodes to characterize the icing encounter.
Objectives:• Determine onset of icing on an aircraft in real-time
during flight.• Estimate the severity of ice accretion in terms of its
effect on performance, stability and control.• Identify location of ice accretion on the A/C and the
potential safety hazards.
Smart Icing Systems NASA Review, May 18-19, 1999
3-44
Steady State Characterization (cont.)
Approach:• Acquire flight data, using onboard sensors.• Process aircraft data to obtain nondimensional
parameters in iced and equivalent clean conditions.• Compare iced and clean models to back out “useful
flight parameters” such as ∆CL, ∆δE, ∆CD, and ∆Ch.• Set threshold values to determine onset of icing.
• Analyze the ∆’s and other sensor information to determine the type and location of ice accretionand the potential safety hazards.
Smart Icing Systems NASA Review, May 18-19, 1999
3-45
S. S. Characterization Results
• Use FDC 1.3 auto-pilot configuration set at:– constant altitude– constant power
• Analyze the effects of icing on:– angle of attack– elevator deflection– velocity and drag characteristics
• The clean and iced configurations have beencompared for conditions with and without turbulence.
• The icing simulations are set at ηice = .15 and thesimulations are run for 22 minutes.
Smart Icing Systems NASA Review, May 18-19, 1999
3-46
Steady State Flight Conditions
The initial conditions for the steady state analysis are:• Altitude = 9000 ft• Velocity = 268 ft/sec• Angle of Attack = 0.5°• Elevator Deflection = -0.6°
ηice(t = 0) = 0ηice(t = 22 min.) = 0.83
Clean and Iced Model v2.1 is used.
Smart Icing Systems NASA Review, May 18-19, 1999
3-47
Turbulence Model
• The turbulence model used in the FDC 1.3 steadystate analysis is based on the Dryden spectraldensity distribution.
• The turbulence intensity produces an aircraft z-acceleration of 0.13g RMS with typical variations of+/- 0.5g encountered over the 22 minute flight.
Smart Icing Systems NASA Review, May 18-19, 1999
3-48
Comparison for αα and δδE (No turbulence)
0ice =ηη
83.0ice =ηη
0ice =ηη
83.0ice =ηη
Smart Icing Systems NASA Review, May 18-19, 1999
3-49
Comparison for CD and V (No turbulence)
83.0ice =ηη
0ice =ηη
0ice =ηη 83.0ice =ηη
Smart Icing Systems NASA Review, May 18-19, 1999
3-50
Comparison for αα and δδE (turbulence)
0ice =ηη
83.0ice =ηη
0ice =ηη
83.0ice =ηη
Smart Icing Systems NASA Review, May 18-19, 1999
3-51
Comparison for CD and V (turbulence)
0ice =ηη
83.0ice =ηη0ice =ηη
83.0ice =ηη
Smart Icing Systems NASA Review, May 18-19, 1999
3-52
THE AERODYNAMICS ANDFLIGHT MECHANICS GROUP
Wind TunnelData
IcedAerodynamics
Model
ComputationalFluid
Dynamics
Iced AircraftModel
Clean AircraftModel
Aircraft - FlightMechanicsAnalysis
Steady StateCharacterization
Flight Mechanics Model
Devesh Pokhariyal, Dr. Bragg
Tim Hutchison, Dr. Bragg
Ryan Oltman, Dr. Bragg
Dr. Loth
Characterization
Flight Simulation
Envelope Protection
Smart Icing System Research
Holly Gurbacki, Dr. Bragg AerodynamicSensors
Smart Icing Systems NASA Review, May 18-19, 1999
3-53
Wind Tunnel Testing
Smart Icing Systems NASA Review, May 18-19, 1999
3-54
Sensing Unsteady Hinge Moments
• Objective: Use unsteady hinge moment to sense potentialcontrol problems and nonlinearities due to ice-induced flowseparation
• Approach:– NACA 23012 airfoil model with simple flap and forward-facing
quarter round simulated ice
– Steady-state Cl, Cd and Cm from balance and pressures andCh from hinge-moment balance and pressures
– Time series and frequency spectra of unsteady Ch from hinge-moment balance
– Time series and frequency spectra of flow unsteadiness fromhot-wire anemometer placed within and aft of separationbubble, in shear-layer, and over flap
Smart Icing Systems NASA Review, May 18-19, 1999
3-55
Unsteady Ch RMS
0
0.01
0.02
0.03
0.04
0.05
-10 -5 0 5 10 15 20
AOA (degrees)
Uns
tead
y C
h R
MS
Clean x/c = .02 x/c = .10 x/c = .20
Cl , max
Cl , max
Cl , max
NACA 23012, Re = 1.8 million, δδ f = 0
Smart Icing Systems NASA Review, May 18-19, 1999
3-56
99 00 01 03
Federal Fiscal Year98 02
Aerodynamics and Flight Mechanics Waterfall Chart
Linear Iced Aircraft Model
CFD
Wind Tunnel Testing
Nonlinear Iced Aircraft Model
Determine Need for and SelectAerodynamics Sensors
Steady State Characterization of Icing Effects
S. S. Char. Method
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Summary and Conclusions
• Clean twin otter linear models developed.• Method for including icing effects in linear
model developed.• Flight mechanics computational method
available.• Iced twin otter models for TIP studies
available and performs well.• Steady state characterization formulated and
initial results promising.
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Future Research
• Complete and refine linear models, icing effectsmodel and perform icing accident analysis
• Complete steady state characterization method• Study and develop envelop protection schemes• Identify aerodynamic and other sensors• Develop fully nonlinear model
– Experiment & CFD for AE data bases– Neural Networks for AE interrogation
• Assess Feasibility of a VIFT (Year 4)
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THE AERODYNAMICS ANDFLIGHT MECHANICS GROUP
Wind TunnelData
IcedAerodynamics
Model
ComputationalFluid
Dynamics
Iced AircraftModel
Clean AircraftModel
Aircraft - FlightMechanicsAnalysis
Steady StateCharacterization
Flight Mechanics Model
Devesh Pokhariyal, Dr. Bragg
Tim Hutchison, Dr. Bragg
Ryan Oltman, Dr. Bragg
to be decided, Dr. Loth
Characterization
Flight Simulation
Envelope Protection
Fully Non-Linear APC Overview
Holly Gurbacki, Dr. Bragg AerodynamicSensors
AE
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Fully Non-Linear Model
• Objective:For flight mechanics, we would like non-linear Aero-Performance Curves (APC): CL, CD, Cm, Ch as afunction of α , δ , etc. and environmental conditions
• Method:– Use high-quality previous and new data sets
(experimental and computational) to construct All-Encounters (AE) data bases
– Construct neural networks for use in interrogation ofany condition atmospheric/flight condition
– Apply neural networks to flight mechanics
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All Encounters Data Bases
• Method:– Collect data of ice-shape characteristics (x/cice, k/cice) as
a function of flight conditions (α , d, LWC, T0, Re, etc.):• Icing Research Tunnel Shapes
• In-Flight Icing Shapes
• LEWICE shapes
– Collect data of 2-D APC (Cl, Cm,..) as a function of ice-shape characteristics (x/cice, k/cice) and α and δ :
• Icing Research Tunnel Aerodynamic Tests
• Wind Tunnel Tests for various Ice Shapes
• CFD for various Ice Shapes
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Computational Fluid Dynamics
• NSU2D will be the primary code for CFD– unstructured adaptive triangulated grid– Can handle complex shapes & multi-element– Spallart-Almaras turbulence model– Employs en transition model– Unsteady capabilities for flow shedding
• To improve prediction fidelity• To investigate unsteady aerodynamic sensing
– To be parallelized for SG Origin 2000
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Sample CFD
• NSU2D LE ice-shape predictions
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Neural Network Approach
• Construct Separate Neural Networks for:– Ice-shape characteristics as a function of environmental
conditions– 2-D APC as a function of ice-shape characteristics and
flight conditions
• Train Neural Networks with AE databases• Use analytical methods to convert 2-D APC to
CL,CD,Cm, Ch
• Replace Linear Model with Non-Linear NeuralNetworks for Flight Mechanics
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Neural Network for Ice-Shape
α
δ
• Sample neuron: Y= f(Σ Wi xi) with x i = (α , d, LWC, etc.)
• Wi are trained with data (f refers to a sigmoidal function)
• Y refers to output of a neuron; final output: x/cice, k/cice
x/cice, k/cice
OutputLayer
HiddenLayer 2
HiddenLayer 1
InputLayer
d
LWC
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Neural Network Interrogation
• Input are ice-shape characteristics & flight condition• Final output are the 2-D APC: Cl,Cd,Cm, or Ch
Cl ,Cd ,Cm ,Ch
α
δ
x/cice
OutputLayer
HiddenLayer 2
HiddenLayer 1
InputLayer
k/cice
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Feasibility of a Virtual Icing Flight Test
• Objective:Assess Feasibility of a VIFT (for a follow-on study) whichwould simulate a complete icing encounter includingtemporal resolution of ice accretion, pilot input, IMS, etc.
• Goal: Consider requirements for integrating– Ice Accretion (LEWICE)– Aerodynamic Predictions (NSU2D)– A/C Flight Dynamics Model (Selig)– Pilot Input / Flight Simulator Output (Sarter/Selig)– IMS Control (Basar/Perkins)
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Virtual Icing Flight Test Configuration?
a) receive input from pilot, IM S,
and aerodynamic performance
b) computes A /C dynamic state
c) displays cockpit viewincluding IM S warnings/info
" Aero Workstation" for
simulated A/C performance" A/C Workstation" for virtual airplane state & pilot I/O
Computes as function. of time
a) ice shape from LEW ICE
b) instantaneous aero forces &
moments as a function of
angle-of-attack & flap defl.
A tmospheric model: real time conditions: pressure, temperature, humidity, L W C of clouds
aero forces/moments,
virtual ice sensor output
" I M S Workstation" for icing
ID and IMS decision making:
modify display/IPS/contr ol
From A /C state data can:
a) issue warnings to pilot
b) initiate IPS operation
c) modify flight envelope
d) institute control adaptation
Dedicated NC SA
Supercomputer
A /C dynamic state, IPS on?
IPS initiating
A /C state, pilot input
icing sensor data
pilot
yoke
Send appropriate IM S output:
see levels a)-d) listed below
t
H 1H 0
x
L
Dt
t