Aerodynamics and Flight Mechanicssis.ae.illinois.edu/Present/NASARvw00/2_FlightMechanics.pdf ·...

Smart Icing Systems NASA Review, June 13-14, 2000

2-1

Aerodynamics and Flight Mechanics

Principal Investigators: Mike Bragg

Eric Loth

Graduate Students: Holly Gurbacki (CRI support)

Tim Hutchison

Devesh Pokhariyal (CRI support)

Ryan Oltman (Frasca)

Jason Merrett

Satish Kumar (FAA)

Jianping Pan


2-2

SMART ICING SYSTEMSResearch 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


2-3

Aerodynamics and Flight Mechanics

Goal: Improve the safety of aircraft in icing conditions.

Objective: 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.

Approach: First use Twin Otter and tunnel data to develop a linear clean and iced model. Then develop a nonlinear model with tunnel and CFD data. Use the models to develop characterization and envelope protection.


2-4

THE AERODYNAMICS ANDFLIGHT MECHANICS GROUP

Wind TunnelData

IcedAerodynamics

Model

ComputationalFluid

Dynamics

Iced AircraftModel

Clean AircraftModel

Aircraft - FlightMechanicsAnalysis

Steady StateCharacterization

Flight Mechanics Model

Devesh Pokhariyal

Tim Hutchison

Jason Merret, Ryan Oltman

Satish Kumar, Jianping Pan

Characterization

Flight Simulation

Envelope Protection

Smart Icing System Research

Holly GurbackiAerodynamic

Sensors


2-5

Outline

• Development of the Iced Aircraft Model

• Steady State Characterization

• Hinge-Moment Aerodynamic Sensor

• CFD Analysis

• Atmospheric Disturbances

• Conclusions and Future Plans


2-6


Wind TunnelData

IcedAerodynamics

Model

ComputationalFluid

Dynamics

Iced AircraftModel

Clean AircraftModel




Devesh Pokhariyal

Tim Hutchison



Characterization

Flight Simulation

Envelope Protection



Sensors


2-7

Outline

• Icing effects model

• ηice and η formulations

• η variations with environmental variables

• Neural network approach

• Performance of neural network predictions

• Conclusions and future work


2-8

Icing Effects Model

To devise a simple, but physically representative, model of the effect of ice on aircraft flight mechanics for use in the characterization and simulation required for the Smart Icing System development research.

Objective:


2-9

Icing Effects Model

• = arbitrary stability and control derivative

(CLα, Cmδe, etc.)

• = icing severity parameter

• = coefficient icing factor

)A(C

iceη

ACk′

)A(Ciceiced)A( C)k1(CA

′η+=

)conditionsicing.,configandgeometryaircraft,IPS(fk )A(CA=′


2-10

ηηice Formulation

• ∆Cd fit as a function of n and AcE − ∆Cd data obtained from NASA TMs 83556 and 105374,

and NACA TNs 4151 and 4155

− n = freezing fraction

− Ac = accumulation parameter

− E = collection efficiency

• ∆Cdref calculated from ∆Cd equation using continuous maximum conditions

( )( )min10t,conditions.max.cont,dataairfoilIRTC

dataairfoilIRTC

refd

dice =∆

∆=η


2-11

ηηice Reference Value

• To nondimensionalize the ∆Cd equation, a reference condition was chosen based on FAA Appendix C Maximum Continuous conditions.

• NACA 0012 c = 3 ft.

MVD = 20 µm V∞ = 175 knots LWC = 0.65 g/m3 t = 10 min

T0 = 25 °F

• These conditions yielded a ∆Cd = 0.0239 at ηice=1


2-12

ηηice Equation (v3.1)

( ) )n(gEAZ c1ice ⋅⋅=ηFor t ≤600s:

For t >600s: ( ) )s600(e1Z icetZ

2ice3 η+−⋅=η

g(n) is a function of n that varies between 0 and 1, and has its maximum at n=0.2

Z2 = f(maximum ηice, ηice at 600s)

Z3 = f(Z2, slope of ηice at 600s)

ηice(600s) = value of ηice at 600s

Z1=183.339


2-13

0.00

0.13

0.25

0.38

0.50

0.63

0.75

0.88

1.00

0 0.2 0.4 0.6 0.8 1

n

ice

AcE=.002

AcE=.003

AcE=.004

AcE=.005

Cont. Max. Case

Variation with n and AcE

Glaze Ice Rime Ice


2-14

ηηice Variation with AcE and n

0.00

0.20

0.40

0.60

0.80

1.00

0.0000 0.0010 0.0020 0.0030 0.0040 0.0050

AcE

ηηic

e

n=0.1

n=0.2n=0.4

n=0.6n=1


2-15

ηη 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

)A(Ciceiced)A( C)k1(CA

′η+=

AA Cice

C kkηη

=′


2-16

Differences Between ηη and ηηice

ηice ηChord 3 ft. ActualAirfoil NACA 0012 Actual

Velocity 175 knots ActualAngle of Attack 0° Actual

MVD Actual ActualLWC Actual Actual

T∞ Actual ActualTime of encounter Actual Actual


2-17

Effect of LWC and T on ηη

Twin Otter

V≈130 kts

MVD=20 µm

h=9000 ft

Time=600 s

0

0.1

0.2

0.3

0.4

0.5

0.6

0.00 0.50 1.00 1.50 2.00

LWC (g/m3)

T=0°F

T=10°F

T=20°F

T=25°F

T=29°F


2-18

Effect of T and LWC on ηη

Twin Otter

V≈130 kts

MVD=20 µm

h=9000 ft

Time=600 s

0

0.05

0.1

0.15

0.2

0.25

0.3

-10.0 -5.0 0.0 5.0 10.0 15.0 20.0 25.0 30.0

T (°F)

LWC=0.2

LWC=0.65

LWC=1.0


2-19

Effect of MVD and T on ηη

Twin Otter

V≈130 kts

LWC=0.65 g/m3

h=9000 ft

Time=600 s0

0.1

0.2

0.3

0.4

0.5

0.6

0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0 45.0

MVD (microns)

T=0°F

T=10°F

T=20°FT=25°F

T=29°F


2-20

kCA Definitions

• Equations: (using performance data from Twin Otter flight tests)

)k1()C()C( iceCAA Acleaniceη+= 1

)C(

)C(k

clean

ice

A

A

AiceC −=η 2

L0DD KCCC +=


2-21

Neural Network Approach

Proposed Neural Network approach to icing characterization:

Environmental Variables: T, LWC, MVD, etc

Ice Shape Neural Net

Ice Shape: horn height, horn location, etc

2-D Aerodynamic Performance

3-D Aerodynamic Performance, Stability and Control

2-D Aerodynamic Neural Net

3-D Aerodynamic Neural Net


2-22

Neural Networks

• Based on the structure of the human brain, with multiple neurons and synapses

• Each neuron multiplies its inputs by “synaptic weights” to achieve an output

• Very good at handling and fitting data that have complex, nonlinear correlations

• Must be trained with a set of known data

• For the SIS Project, the Matlab Neural Net Toolbox has been used


2-23

Neural Network Architecture

Angle of attack

Horn location

• Sample neuron: Y= f(Σ Wi xi) with xi = inputs to neuron • Wi are trained with known data (f refers to a sigmoidal

function)• Y = output of a neuron

OutputLayer

HiddenLayer 2

HiddenLayer 1

InputLayer

Horn height

Horn LE radius

Cl

Cd

Ch

Cm

Simple neural net example (actual neural net uses 5 layers of 8 nodes each)


2-24

Training Data

• This neural net was trained using data collected by Kim and Bragg, which is presented in AIAA 99-3150

• This data examines three ice horn heights and three leading edge radii at six different locations

• The collected data includes Cl, Cd, Cm and Ch for the NLF(1)-0414 airfoil


2-25

Training Data (cont.)

3.4%

1.7%

0.85%

0.0%

-0.75%

-1.5%

s/c locations


2-26

0.000

0.050

0.100

0.150

0.200

0.250

0.300

0.350

-20.0 -15.0 -10.0 -5.0 0.0 5.0 10.0 15.0 20.0Angle of Attack [°]

Cd

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.)

Prediction of Cd

8 nodes

5 layers

Re=1.8x106

s/c=3.4%

r/w=0.0


2-27

-1.0

-0.5

0.0

0.5

1.0

1.5

-20.0 -15.0 -10.0 -5.0 0.0 5.0 10.0 15.0 20.0

Angle of Attack [°]

Cl

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.)

Prediction of Cl

8 nodes

5 layers

Re=1.8x106

s/c=3.4%

r/w=0.0


2-28

-0.12

-0.1

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

-20.0 -15.0 -10.0 -5.0 0.0 5.0 10.0 15.0 20.0


Cm

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.)

Prediction of Cm

8 nodes

5 layers

Re=1.8x106

s/c=3.4%

r/w=0.0


2-29

-0.30

-0.25

-0.20

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

-20.0 -15.0 -10.0 -5.0 0.0 5.0 10.0 15.0 20.0


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.)

Prediction of Ch

8 nodes

5 layers

Re=1.8x106

s/c=3.4%

r/w=0.0


2-30

Conclusions

• Linear icing effects model, ηice, is almost finalized

• Initial results from neural net analysis for prediction of 2-D flight performance parameters are promising


2-31

Future Research

• Develop neural nets for prediction of 2-D aerodynamic coefficients based on a larger data set

• Continue exploration of neural nets for prediction of ice shape characteristics

• Develop improved methods for converting 2-D to 3-D aircraft derivatives


2-32


Wind TunnelData

IcedAerodynamics

Model

ComputationalFluid

Dynamics

Iced AircraftModel

Clean AircraftModel




Devesh Pokhariyal

Tim Hutchison



Characterization

Flight Simulation

Envelope Protection



Sensors


2-33

Outline

• NSU2D predictions with upper-surface ice -shapes (to establish the effects of location, size, flap deflection, Re, and airfoil shape)

• WIND predictions for leading-edge ice-shapes (for similar goals to allow 3-D effects)

• Detached Eddy Simulation development with WIND to increase separated flow predictive performance for CL,max and allow unsteady hinge moment prediction


2-34

NSU2D

• Mavriplis D. (ICASE Jan ’91)• Spalart-Allmaras 1-Eq.

Turbulence Model with transition specification

• Unstructured Solution-Adaptive Triangular Element Grid

• Steady state convergence accelerated by employing local-time stepping, residual smoothing, and algebraic multigrid algorithm (AMG)

Original mesh

Adapted mesh


2-35

Velocity Contours

• NACA 23012m

• 0.15” Quarter Round at x/c=0.10

• B.L. tripped

• Re=1.8x106

α=

+2º

α=

-2º

α=

+6º


2-36

Effect of SLD Ice-Shape Location

NACA 23012m, Ice-shape size of k/c=0.0083,B.L. Tripped, Re=1.8x106

HINGE MOMENTLIFT


2-37

Cp

• NSU2D • NACA

23012m• Ice-shape

size of k/c=0.0083

• α=3°• B.L. Tripped• Re=1.8x106


2-38

Cl for Fully Separated Flow

Ice-shape size of k/c=0.0083,B.L. Tripped; Re=1.8x106

Expt. Clean C l,max

NSU2D Clean Fully separated Cl

Expt. Ice-Shape C l,max

NSU2D Ice-Shape Fully separated Cl

NACA 23012m NLF 0414


2-39

Leading Edge Ice Shapes

• WIND – RANS (Ver. 3.0) – NPARC Alliance (AIAA paper 98-0935)

– Same turbulence model used as NSU2D

– To be compatible with NASA GLENN

– To Allow Efficient 3-D Simulations

– To Allow DES

• GRIDGEN (Ver. 13.4)– Pointwise Inc.


2-40

Iced NLF-0414 Airfoil

s

k

c

2.22%3.4%6

4.44%3.4%5

6.67%3.4%4

2.22%03

4.44%02

6.67%01

k/cs/cCase


2-41

Iced NLF-0414 Airfoil : Cl, Cd

s/c=0%, k/c=6.67%Re=1.8 X 106, Ma=0.185


2-42


s/c=0%, k/c=4.44%Re=1.8 X 106, Ma=0.185


2-43

-0.75

-0.5

-0.25

0

0.25

0.5

0.75

1

1.25

-20 -15 -10 -5 0 5 10 15 20

Cl

k/c=6.67% (Exp.) k/c=6.67% (N.N. trained on CFD)



0

0.05

0.1

0.15

0.2

0.25

0.3

-20 -15 -10 -5 0 5 10 15 20

Cd

Neural Net Simulations

s/c=0%, Re=1.8 X 106, Ma=0.185

α

α


2-44


s/c=3.4%, k/c=6.67%Re=1.8 X 106, Ma=0.185


2-45

Iced NLF-0414 airfoil : Cl,max

Re=1.8 X 106, Ma=0.185


2-46

Need for DES

• RANS does not give robust solution to massively separated flow and this may prevent accurate prediction of Cl,max (especially for iced airfoils)

• Solution: Employ eddy-capturing scheme to handle large-separation regions

• Possible Choices:– LES (Large Eddy Simulation) uses subgrid filter to

capture large scales– DNS (Direct Numerical Simulation) resolves all scales– DES (Detached Eddy Simulation) allows LES in free

shear regions and RANS in attached flow


2-47

Approximate CPU time (cycles x N)

1.E+09

1.E+12

1.E+15

1.E+18

1.E+04 1.E+05 1.E+06 1.E+07 1.E+08

ReL

3D-RANS

LES

DNS

DES

CP

U


2-48

Detached Eddy Simulation

• Allows RANS description in the boundary layers and LES description for massively separated regions

• Can be formulated on S-A model with d(distance from the wall) replaced by

• Has only one adjustable constant CDES

˜ d

˜ d ≡ min( d,CDES∆)∆ ≡ max(∆x, ∆y,∆z)

(Spalart et. al. 1999)


2-49

Geometry of the Grid for the Backstep

Viscous Wall B.C.

Inviscid wall

Outflow

H

9H

105 H 30 H

Inflow


2-50

Backstep – Instantaneous Vorticity

RANS LES

CDES=1.0, ∆t=0.00125 H/Uinf, After 8000 cycles

WIND-DES


2-51

Grid Resolution Study: Sample plot

∆XR/XR=0.07, ∆Xo/δ = 0.05

WIND-DES


2-52

Conclusions

• NSU2D reasonably predicts the trends of all the aerodynamic forces and moments for upper surface icing on NACA 23012m and NLF 0414 (but C l,max not robust)

• WIND-RANS predictions agree reasonably well with experimental results for leading-edge icing for NLF 0414

• WIND-DES has been developed and captures the coherent structures in the free shear layer for backstep flow


2-53

Future Work

• Apply WIND-RANS to 2D ice-shapes on other airfoils (in particular the Twin Otter wing and tailplane) for the 2D aerodynamic net database

• Extend WIND-RANS to 3D iced wings for the 3D aerodynamic net database

• Apply WIND-DES to iced airfoils and wings to allow improved C l,max and predict unsteady hinge moments

• (Far term) Apply WIND-DES to simultaneously model wing and tail with unsteady ice accretion


2-54


Wind TunnelData

IcedAerodynamics

Model

ComputationalFluid

Dynamics

Iced AircraftModel

Clean AircraftModel




Devesh Pokhariyal

Tim Hutchison



Characterization

Flight Simulation

Envelope Protection



Sensors


2-55

Outline

• Introduction• Flight Mechanics Models and Icing Effects• FDC code and modifications to the code• Effect of Ice on Aircraft Flight Mechanics

– Cruise and hold in constant power flight– Turbulence, sensor noise and filters– Icing on selected aircraft components

• Neural Network training data• Conclusions


2-56

Twin Otter Model

• Twin Otter Aircraft Characteristics

Parameter Value UnitsWing Area 39.02 m2Wing Span 19.81 mAspect Ratio 10Mean Aerodynamic Chord 1.981 mMass 4150 kgMoments of Inertia: Ixx, Iyy, Izz, Ixz 21279, 30000,44986, 1432 kg.m2Flap Deflection 0 deg


2-57

Stability and Control Model

• Model is derived primarily from flight dynamics data in AIAA report 86-9758, AIAA report 89-0754 and AIAA 93-0754

• Icing scaled using η parameter (η = 0.0675, η/ηice= 0.79)

• Non-dimensional derivatives:

CYββ CYp CYr CYδδr Clββ Clp Clr Clδδa Clδδr Cn ββ Cnp Cnr Cnδδr Cnδδa

clean -0.6 -0.2 0.4 0.15 -0.08 -0.5 0.06 -0.15 0.015 0.1 -0.06 -0.18 -0.12 -0.001iced -0.48 -0.2 0.4 0.138 -0.072 -0.45 0.06 -0.135 0.0138 0.08 -0.06 -0.169 -0.11 -0.001

CZ0 CZα CZq CZδe CX0 K Cm0 Cmα Cmq Cmδe

clean 0.360 -5.660 -19.970 -0.608 0.041 0.052 0.400 -1.310 -34.200 -1.740wing_ice 0.360 -5.342 -19.700 -0.594 0.050 0.053 0.400 -1.285 -33.000 -1.709tail_ice 0.360 -5.520 -19.700 -0.565 0.046 0.053 0.400 -1.263 -33.000 -1.593all iced 0.360 -5.094 -19.700 -0.550 0.062 0.057 0.400 -1.180 -33.000 -1.566


2-58

• Flight Dynamics Code 1.3– FDC 1.3 is a free source code by Marc Rauw (based in the

Netherlands: http://home-2.worldonline.nl/~rauw/)

– Code developed using MATLAB and SIMULINK

– 6 DoF equations, 12 nonlinear ODEs

– Autopilot/open loop simulations

– Atmospheric turbulence model

– Code modifications:• Nonlinear derivatives represented 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

Flight Dynamics and Control Toolbox


2-59

Open Loop Analysis Tool for Nonlinear Twin Otter Model


2-60

Closed Loop Analysis Tool for Nonlinear Twin Otter Model

outputs


2-61

FDC Validation

• The FDC Code is validated by comparing the response of a doublet to published NASA data (AIAA 99-0636) for the Twin Otter aircraft

• The validation results are published in AIAA 2000-0360

• The response of other Twin Otter models to the elevator doublet are also observed


2-62

Elevator Doublet Input

-8

-6

-4

-2

0

2

4

0 2 4 6 8 10 12 14

TIP Flight p5220Current Steady-State

Miller and Ribbens1 8

DATCOM

δe (

de

g)

Time (s)

Twin Otter

V0=111 kts

h=5620 ft


2-63

Twin Otter Response to Elevator Doublet

5610

5620

5630

5640

5650

5660

0 2 4 6 8 10 12 14

TIP F l igh t p5220

Current Steady-State

Mi l le r and Ribbens1 8

D A T C O M

Alt

itu

de

(ft

)

T ime ( s )

Twin Otter

V0=111 kts

h=5620 ft


2-64

Atmospheric Turbulence and Noise

• The turbulence model used in the FDC 1.3 steady state analysis is based on the Dryden spectral density distribution

• Turbulence intensity can be varied and are characterized by the effect on the aircraft z-acceleration

• Sensor noise magnitudes are twice the uncertainty values given in AIAA 93-0398 and are modeled as band limited white Gaussian distributions

• The effects of turbulence provide an overlap between the quasi-steady characterization and the dynamic characterization


2-65

Effects of Filtering

• Filters are applied to data obtained from the constant power cruise flight conditions, in the clean and iced configurations. The initial trim values for the Twin Otter are:

• V = 160 kts

• h = 9000 ft

• A/C RMS z-acceleration = 0.15g

• The data, sampled at variable time steps, is post-filtered using low-pass Butterworth filters


2-66

120

130

140

150

160

170

0 50 100 150 200 250 300 350

Turbulence and Sensor Noise

Filtered Turbulence and Sensor Noise

Vel

ocity

(kt

s)

Time (s)

Effects of Filtering, V

Twin Otter

Const. Alt.

Const. Power

V0= 160 kts

h=9000 ft

ηice= 0 to 0.5

Filter:

2nd order low pass Butterworth

fcutoff=0.17 Hz


2-67

Performance in Holding Turn and Cruise

• A holding pattern is represented by a standard-rate turn and cruise flight at constant power.

• Initial trim conditions for the standard-rate turn and cruise case in clean and iced configuration:

• V = 136 kts

• h = 6560 ft


Standard 2 minute turn rate.


2-68

Performance in Holding Turn and Cruise, V

Twin Otter

Const. Power

Const. Alt.

V0=136 kts

h=6560 ft

η = 0 to 0.5

Holding turn:

3°/s turn

120

125

130

135

140

0 50 100 150 200 250 300 350

Cruise, No Ice

Cruise, Ice

Turn, No Ice

Turn, Ice

V (

kts)

Time (s)


2-69

Holding Turn and Cruise, αα

Twin Otter

Const. Power

Const. Alt.

V0=136 kts

h=6560 ft

η = 0 to 0.5

Holding turn:

3°/s turn

1

1.5

2

2.5

3

3.5

0 50 100 150 200 250 300 350

Cruise, No Ice

Cruise, Ice

Turn, No Ice

Turn, Ice

α (

deg)

Time (s)


2-70

Holding Turn and Cruise, δδe

Twin Otter

Const. Power

Const. Alt.

V0=136 kts

h=6560 ft

η = 0 to 0.5

Holding turn:

3°/s turn-3

-2.5

-2

-1.5

-1

-0.5

0 50 100 150 200 250 300 350

Cruise, No Ice

Cruise, Ice

Turn, No Ice

Turn, Ice

δ e (d

eg)

Time (s)


2-71

Holding Turn and Cruise, δδa

Twin Otter

Const. Power

Const. Alt.

V0=136 kts

h=6560 ft

η = 0 to 0.5

Holding turn:

3°/s turn-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0 50 100 150 200 250 300 350

Cruise, No Ice

Cruise, Ice

Turn, No Ice

Turn, Ice

δ a(d

eg)

Time (s)


2-72

Holding Turn and Cruise, δδr

Twin Otter

Const. Power

Const. Alt.

V0=136 kts

h=6560 ft

η = 0 to 0.5

Holding turn:

3°/s turn

-0.8

-0.7

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

0 50 100 150 200 250 300 350

Cruise, No Ice

Cruise, Ice

Turn, No Ice

Turn, Ice

δ r(deg

)

Time (s)


2-73

Hinge Moment Models

• Models are used in simulations to study the potential use of hinge moment sensors as aerodynamic performance monitors

• Ch and Ch_rms capture the effects of icing on the flow field over the airfoil surface.

• Ch_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)


2-74

Hinge Moment Models

• Models are based on limited experimental hinge moment data.

• Ch and Ch_rms models are a functions of angle of attack, elevator deflection and icing parameter, η

• Hinge moment models do not include the effect of control surface mass.


2-75

Ch model

-0.4

-0.35

-0.3

-0.25

-0.2

-0.15

-0.1

-0.05

0

0.05

-10 -5 0 5 10 15 20

Angle of attack, deg

Ch η = 0η = 0

η =0.14η =0.14η=0.2η=0.2η=0.1η=0.1


2-76

Ch_rms model

0

0.01

0.02

0.03

0.04

0.05

0.06

-10 -5 0 5 10 15 20 25

Angle of Attack, deg

Ch

rms

η = 0η = 0η=0.07η=0.07η=0.14η=0.14η=0.2η=0.2


2-77

Ice Location Effects

• Ice accretion on different aircraft locations was considered:

• Tail ice

• Wing ice

• A constant power, constant altitude scenario, maintained by the autopilot, was considered

• V = 155 kts

• h = 7550 ft



2-78

Effect of Ice Location on αα

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

0 100 200 300 400 500 600

Time (sec.)

An

gle

of

Att

ack

(deg

.)

Wing Iced

Tail Iced

ηη ice = 0.0

ηη ice = 1.1


2-79

Effect of Ice Location on V

135

140

145

150

155

160

0 100 200 300 400 500 600

Time (sec.)

Vel

oci

ty (

kno

ts)

Wing Iced

Tail Iced

ηη ice = 0.0 ηη ice = 1.1


2-80

Effect of Ice Location on δδE

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

0 100 200 300 400 500 600

Time (sec.)

Ele

vato

r D

efle

ctio

n (

deg

.)

Wing Iced

Tail Iced

ηη ice = 0.0

ηη ice = 1.1


2-81

Effect of Ice Location on Thrust

1320

1340

1360

1380

1400

1420

1440

1460

1480

0 100 200 300 400 500 600

Time (sec.)

Th

rust

(lb

f)

Wing Iced

Tail Icedηηice = 1.1

ηηice = 0.0


2-82

Determining Ice Location

• An analysis of the aerodynamic outputs shows the difficulty in determining the location of ice – the tail ice encounter resembles a less severe all aircraft icing encounter

• The increase in drag due to ice accretion dominate the aerodynamic outputs in both tail and all aircraft iced cases


2-83

Hinge Moment Sensors

• The wing and tail hinge moments are calculated for the aileron and elevator deflections respectively

• The η values used for the tail is based on the Twin Otter horizontal tail chord length of 4.75 ft. A linearized relationship between the wing and tail icing severity parameter is used

• A constant power, constant altitude scenario, maintained by the autopilot, was considered

• V = 155 kts• h = 7550 ft• A/C RMS z-acceleration = 0.15g


2-84

Effect of Ice Location on Wing Ch

0.004

0.006

0.008

0.01

0.012

0.014

0.016

0.018

0 100 200 300 400 500 600

Time (sec.)

Win

g H

ing

e M

om

ent

Co

effi

cien

t

Aileron Iced

Elevator Iced

ηη ice = 1.1

ηη ice = 0.0


2-85

Effect of Ice Location on Tail Ch

0

0.005

0.01

0.015

0.02

0.025

0 100 200 300 400 500 600

Time (sec.)

Tai

l Hin

ge

Mo

men

t C

oef

fici

ent

Aileron Iced

Elevator Iced

ηη ice = 0.0

ηη ice = 1.1


2-86

Effect of Ice Location on Wing Ch_rms

0.003

0.0035

0.004

0.0045

0.005

0.0055

0.006

0.0065

0.007

0 100 200 300 400 500 600

Time (sec.)

RM

S W

ing

Hin

ge

Mo

men

t C

oef

fici

ent

Aileron Iced

Elevator Iced

ηη ice = 0.0

ηη ice = 1.1


2-87

Effect of Ice Location on Tail Ch_rms

0

0.002

0.004

0.006

0.008

0.01

0.012

0.014

0.016

0 100 200 300 400 500 600

Time (sec.)

RM

S T

ail H

ing

e M

om

ent

Co

effi

cien

t

Aileron Iced

Elevator Iced

ηη ice = 1.1

ηη ice = 0.0


2-88

Hinge Moment for Varying Trimmed V

• Ch and Ch_rms are calculated for the following trim velocities:

• V = 78 kts• V = 97 kts

• V = 117 kts• V = 136 kts• V = 155 kts

• All other conditions are held constant:• H = 6560 ft

• ηice= 0.7112


2-89

Wing Ice Cases

Wing Ice Only

-0.25

-0.2

-0.15

-0.1

-0.05

0

0.05

0 2 4 6 8 10 12 14


Hin

ge

Mo

me

nt,

Ch

Elevator

Aileron

Elevator Clean

Aileron Clean

Wing Ice Only

0

0.005

0.01

0.015

0.02

0.025

0.03

0 2 4 6 8 10 12 14


RM

S H

ing

e M

om

ent,

Ch

rms

Elevator

Aileron

Elevator Clean

Aileron Clean


2-90

Tail Ice Cases

Tail Ice Only

-0.18

-0.16

-0.14

-0.12

-0.1

-0.08

-0.06

-0.04

-0.02

0

0.02

0 2 4 6 8 10 12 14


Hin

ge

Mo

me

nt,

Ch

Elevator

Aileron

Elevator Clean

Aileron Clean

Tail Ice Only

0

0.005

0.01

0.015

0.02

0.025

0 2 4 6 8 10 12 14


RM

S H

ing

e M

om

en

t, C

hrm

s

Elevator

Aileron

Elevator Clean

Aileron Clean


2-91

Icing Characteristic N-Net Input Data

FDCLat/Long.

Trim Characterization

Dynamic Parameter ID

Clean TrimCharacterization

Hinge Moment

Measurements

ηη

Turbulence

Measurement Noise

Chw , Ch

t

Red – Random SignalsBlue – Neural Net Data

Cmδδe, Cmq, Cn*, …

∆α∆α, ∆δ∆δe, ∆∆Cd,∆∆Cd_dot, …

αα, δδe, Cd, …

α, δα, δE

Chw

_RMS , Cht_RMS

Expected CleanDerivatives

IcingCharacterization

Neural Network


2-92

Conclusion

• Use of ηice parameter provides a simple model to determine iced aircraft data

• Effect of ice on V, α, and δe for the constant power case are significant, and could be used to characterize the accretion

• Effects of turbulence and sensor noise can be filtered.

• Flight maneuvers increase the apparent effects of ice and improve the potential for ice detection

• The use of hinge-moment data to distinguish tail from wing ice is encouraging


2-93

Future Research

• Validate a fully nonlinear force and moment model

• Explore constant velocity and other scenarios using FDC

• Obtain hinge moment data for more airfoils and ice-shapes including the Twin Otter airfoil

• Examine envelope protection strategies


2-94


Wind TunnelData

IcedAerodynamics

Model

ComputationalFluid

Dynamics

Iced AircraftModel

Clean AircraftModel




Devesh Pokhariyal

Tim Hutchison



Characterization

Flight Simulation

Envelope Protection



Sensors


2-95

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 so appropriate recovery maneuvers can be executed

• Preliminary analysis of the effects of microbursts will determine if further research is required


2-96

Objectives

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.

To evaluate concerns of possible false alarms of the SIS due to atmospheric disturbances

Objective:

Motivation:


2-97

Microburst

• Taken From Mulgund and Stengel, Journal of Aircraft, 1993


2-98

Microburst Model

• Microburst model is from Oseguera and Bowles, NASA TM 100632

• Microbust Parameters are – Umax : Maximum outflow (ft/s)

– Zmax : Height of maximum outflow (ft)

– R : Radius of the microburst (ft)


2-99

Microburst Model

• NASA TM 100632

• R = 2500 ft

• Umax = 63 ft/s

• Zmax = 119 ft


2-100

Implementation in FDC

• Wind force components

windgravitypropulsioncaerodynamiz

windgravitypropulsioncaerodynamiy

windgravitypropulsioncaerodynamix

ZZZZF

YYYYF

XXXXF

+++=

+++=

+++=

( )( )( )wwww

wwww

wwwx

pupvwmz

rupwvmY

rvqwumX

−+−=+−−=

−+−=

&

&

&


2-101

Comparision

• A FDC trajectory was compared to results from, Target Pitch Angle for the Microburst Escape Maneuver by S. Mulgund and R. Stengel, JoA, 1993

• Due to current limitations in the FDC the exact aircraft maneuver could not be simulated

• Both aircraft were light twin-turboprops• Instead of a Target Pitch Angle (TPA) escape

maneuver a maximum power maneuver was used


2-102

Comparison

• Simulated microburst parameters– R = 3000 ft

– Umax = 80 ft/s ~ 47.4 kts

– Zmax = 150 ft

• Initial conditions– Altitude = 1400 ft

– Trim condition


2-103

Comparison

-2.0

-1.0

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

-10000 -5000 0 5000 10000Position (ft)

Alp

ha (

deg)

2

3

4

5

6

7

8

9

10

11

12

FDC, Present Method

JOA


2-104

Microburst Analysis

• 11 different microbursts were simulated in FDC varying Radius, and Umax

• Simulation conditions– V = 136 kts

– Initial altitude varied from 1312 ft to 2625 ft

– Altitude hold autopilot setting

– No recovery maneuver


2-105

Microburst Analysis

Microburst Number Microburst Paramaters SeverityR (ft) Umax (ft/s) Zmax (ft) Umax/R (1/s)

1 1000 5 150 0.00502 1000 10 150 0.01003 1000 20 150 0.02004 3000 5 150 0.00175 3000 10 150 0.00336 3000 20 150 0.00677 3000 60 150 0.02008 3000 120 150 0.04009 5000 10 150 0.002010 5000 20 150 0.004011 5000 40 150 0.0080


2-106

Wind Velocities for Microburst #5

-2

-1

0

1

2

3

4

5

0 100 200 300 400 500

Time (s)

Vel

oci

ty (

kts)

Uw, Wind X-Component

Vw, Wind Y-Component

Ww, Wind Z-Component


2-107

Results for Microbursts and Icing

0

2

4

6

8

10

12

0 100 200 300 400 500

Time (s)

Alp

ha

(deg

)

Microburst #5

Microburst #9

0.04XXXXXXXXXXXXXX

0.08

0.1

ηice = 0.50, η/ηice = 0.08

ηice = 0.91, η/ηice = 0.09

ηice = 1.10, η/ηice = 0.09


2-108


100

105

110

115

120

125

130

135

140

0 100 200 300 400 500

Time (s)

Vel

oci

ty (

kts)

Microburst #5

Microburst #9

0.04XXXXXXXXXXXXXX

0.08

0.1

ηice = 0.50, η/ηice = 0.08

ηice = 0.91, η/ηice = 0.09

ηice = 1.10, η/ηice = 0.09


2-109


1550

1575

1600

1625

1650

1675

1700

0 100 200 300 400 500

Time (sec)

Alti

tud

e (f

t)

7400

7420

7440

7460

7480

7500

7520

7540

Alti

tud

e (f

t)

Microburst #5

Microburst #9

0.04XXXXXXXXXXXXXX

0.08

0.1

ηice = 0.50, η/ηice = 0.08

ηice = 0.91, η/ηice = 0.09

ηice = 1.10, η/ηice = 0.09


2-110


-10

-8

-6

-4

-2

0

2

0 100 200 300 400 500

Time (s)

δδ e (

deg

)

Microburst #5

Microburst #9

0.04XXXXXXXXXXXXXX

0.08

0.1

ηice = 0.50, η/ηice = 0.08

ηice = 0.91, η/ηice = 0.09

ηice = 1.10, η/ηice = 0.09


2-111

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) dδe/dt (deg/s)

Microburst 1 0.1718 -0.4505 -466.6 -0.0343Microburst 2 0.3830 -1.1468 -990.3 -0.0805Microburst 4 0.0269 -0.2000 -150.0 -0.0143Microburst 5 0.0472 -0.6000 -150.0 -0.0427Microburst 6 0.1345 -1.5000 -266.7 -0.0851Microburst 9 0.0229 -0.2917 -55.5 -0.0203

0.04 0.0040 -0.0323 0.0 0.00390.08 0.0204 -0.7951 0.0 -0.01960.1 0.1030 -0.2537 -5.0 -0.0943


2-112

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 sould 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


2-113

Conclusions

• Initial analysis of the microbursts demonstrated that the encounters would be distinguishable

• Effects are similar but of different magnitude or occur at different times (late in the encounter)

• Different strategies for recovery needed– Very important not to misinterpret the encounter

• Still need to address the gravity waves and other atmospheric disturbances


2-114

Future Work

• Validate the FDC windshear model • Add icing to the windshear encounters

• Possibly develop a windshear neural network to detect windshear

• Analyze the effect other atmospheric encounters, gravity waves, etc– Need a simple model for gravity waves


2-115

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

Char. Method


2-116

Conclusions

• Linear icing effects model, ηice, is almost finalized• Initial results from neural net analysis for prediction of

2-D flight performance parameters are promising• Effect of ice on V, α, and δe for the constant power case

are significant, and could be used to characterize the accretion.

• The use of hinge-moment data to distinguish tail from wing ice is encouraging.

• CFD reasonably predicts important trends. Moving to WIND-DES for better max lift prediction.

• Initial analysis of the microbursts demonstrated that the encounters would be distinguishable.


2-117

Future Research

• Continue exploration of neural nets for prediction of iced aircraft characteristics.

• Validate a fully nonlinear force and moment model.

• Improve the hinge moment models.

• Develop envelope protection strategies.

• Apply WINDS-DES to iced airfoils to improve maximum lift prediction.

• Analyze the effect other atmospheric encounters, gravity waves, etc.

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