University of Illinois at Urbana-Champaign May 18 …sis.ae.illinois.edu/Present/NASArvw98.pdfGene...

Smart Icing SystemsNASA Review

University of Illinois at Urbana-Champaign

May 18-19, 1999

SMART ICING SYSTEMS -- NASA REVIEW

Meeting Schedule – May 18-20, 1999

Tuesday, May 18

11:00 am – 12 noon Meet at 306F Talbot Lab, Walk to 3510 Beckman

12 noon – 1:00 pm Lunch Cafeteria Beckman

1:00 pm – 1:15 pm Welcome B02 CSRL

1:15 pm – 1:45 pm Smart Icing Systems Overview B02 CSRLBragg

1:45 pm – 2:45 pm Safety and Economics Trade Study B02 CSRLSivier/Bradley

2:45 pm – 3:15 pm Break B02 CSRL

3:15 pm – 4:15 pm Aerodynamics and Flight Mechanics B02 CSRLBragg/Loth

4:15 pm – 5:00 pm Discussion B02 CSRL

Wednesday, May 19

8:30 am – 10:00 am Flight Controls and Sensors B02 CSRLPerkins/Melody

10:00 am- 10:30 am Break B02 CSRL

10:30 am – 11:30 am Human Factors B02 CSRLSarter

11:30 am – 12 noon Flight Simulation B02 CSRLSelig

12 noon – 1:00 pm Lunch (boxed lunches) 469A CSRL (Board Room)

1:00 pm – 4:00 pm Discussion and Future Plans 469A CSRL (Board Room)

Thursday, May 20

8:30 am – 11:30 am Review of FAA/UIUC Icing Research 319N TalbotFAA, NASA, Bragg, Loth, Students

11:30 am – 1:00 pm Lunch Illini Union BallroomIcing Center of ExcellenceNASA, FAA, Bragg, Solomon

SMART ICING SYSTEMS -- NASA REVIEW

May 18-20, 1999

List of Participants

University of Illinois at Urbana-Champaign

ProfessorsTamer Basar Elec/Comp Engr [email protected] 217-333-3607Mike Bragg Aero/Astro Engr [email protected] 217-244-5551Eric Loth Aero/Astro Engr [email protected] 217-244-5581Bill Perkins Elec/Comp Engr [email protected] 217-333-0207Nadine Sarter Aviation Res Lab [email protected] 217-244-8657Michael Selig Aero/Astro Engr [email protected] 217-244-5757Ken Sivier Aero/Astro Engr [email protected] 217-333-3364Wayne Solomon Aero/Astro Engr [email protected] 217-244-7646Petros Voulgaris Aero/Astro Engr [email protected] 217-244-0961Chris Wickens Aviation Res Lab [email protected] 217-244-8617

Graduate StudentsJennnifer Bradley Aero/Astro Engr [email protected] 217-244-6224Holly Gurbacki Aero/Astro Engr [email protected] 217-333-2651Tim Hutchison Aero/Astro Engr [email protected] 217-244-0684Beth Kelly Psychology [email protected] 217-244-8718Wen Li Aero/Astro Engr [email protected] 217-333-3202Scott McCray Psychology [email protected] Melody Elec/Comp Engr [email protected] 217-244-9414Ryan Oltman Aero/Astro Engr [email protected] 217-244-0684Devesh Pokhariyal Aero/Astro Engr [email protected] 217-244-3128Eric Schuchard Elec/Comp Engr [email protected] 217-384-0048Jeff Scott Aero/Astro Engr [email protected] 217-244-0684

Undergraduate StudentsThomas Hillbrand Elec/Comp Engr [email protected] 217-373-5187Eric Keller Elec/Comp Engr [email protected] 217-359-2956Eduardo Salvador Elec/Comp Engr [email protected] 217-333-2511

NASA Glenn

Tom Bond Icing Branch [email protected] 216-433-3900Mark Potapczuk Icing Branch [email protected] 216-433-3919

FAA

Gene Hill Seattle [email protected] 425-227-1293Jim Riley Atlantic City [email protected] 609-485-4144

Smart Icing Systems NASA Review, May 18-19, 1999

1-1

Smart Icing Systems: Introduction

Mike BraggUniversity of Illinois at Urbana-Champaign

Smart Icing SystemsNASA Review

May 18 - 19, 1999


1-2

Outline

• Background: icing safety and Roselawn

• Objective

• Smart Icing System solution

• Funding and research review

• Schedule of the presentations


1-3

Date Operator Location Aircraft Type Fatalities Cause

1/13/82 Air Florida Washington, DC Boeing 737 70 Engine/airframe ice/snowon takeoff

2/4/85 North Pacific Soldotna, AK Beech 65-A80 9 Inadequate de-ice system;pilot error

12/12/85 Arrow Airways Gander,Newfoundland

Douglas DC-8 256 Suspected wing icing

11/15/87 Continental Denver, CO Douglas DC-9 28 Ice/frost removal fromaircraft not performed

12/26/89 United Express Pasco, WA BAC BA-Jetstm-3101

6 Stall and loss of controlfrom airframe ice

3/22/92 US Air Flushing, NY Fokker F-28 27 Failure to de-ice prior totakeoff

10/31/94 American Eagle Roselawn, IN ATR-72 68 Ice accretion beyond de-iceboots resulting in aileronhinge-momemt reversal

1/9/97 COMAIR Monroe, MI EMB-120 29 Ice accretion on wingresulting in loss of lateralcontrol. IPS off

Fatal Icing Accidents Involving Passengers:U.S. Airline Operations Since 1982


1-4

Icing Incident & Accidents 1

Type TimePeriod

Number ofIncidents

TotalAccidents

FatalAccidents

TotalFatalities

TotalInjuries

GA 2 1982-93 364 637 172 325 3 unknown

Commuter 4 1983-96 104 80 26 159 121

Jet 1982-96 21 4 3 354 59

1) Source: NTSB Accident Data and FAA Incident Data2) Source: AOPA3) Est, based on 1.89 Total Fatalities per FatalAccidents4) Part 121 & 135 Turbo Prop Operations


1-5

Aircraft Icing Accidents

Accidents

• Air Florida - takeoff accident - performance and S&C.• United Express Jetstream - tail stall - longitudinal S&C.• Roselawn ATR - roll upset - lateral S&C.

Common Features

• Ice accretion.• Aerodynamic effect leads to degradation in

performance and handling qualities.• Pilot is unaware of the full effect of ice on aircraft.• Accident occurs.


1-6

ATR 72 Roselawn Accident


1-7

Icing Research

Old Paradigm

• Accidents are caused by ice accretion and pilot error.• Improve subsystem performance.• Investigate accidents and develop fixes.

New Paradigm

• Accidents are caused by loss of aircraft performanceand control.

• The entire system must be addressed.• Use aircraft state information to develop smart icing

systems with pilot-automation coordination.• Focus research on flight safety - prevent accidents.


1-8

Pilot’s Perspective

Steve Green (ALPA) UIUC April 29, 1999

• When operating in icing conditions the flight crew should:– Monitor airspeed, rate of climb, fuel flow, SAT/TAT,

cloud formation– Monitor aerodynamic surfaces, or representative

surfaces for ice accretion– Allow some specified quantity of ice to build prior to

operating the ice protection system– Determine whether the ice protection system is

adequately clearing ice– Develop and update an opinion as to whether the icing

conditions may adversely affect the safety of flight


1-9

Pilot’s Perspective (cont)

• Safe Operation in Icing

• Provide pilot with aerodynamic monitoring(proximity to CL divergences)

• The pilot must have reliably correlated data,measured in real time, with which to infer theproximity of divergences in CL or Ch in a timelymanner.

Steve Green (ALPA) UIUC April 29, 1999


1-10

Smart Icing Systems

Objective• To develop a human-centered automated system, to

characterize icing effects, operate the IPS, provideenvelope protection and control adaptation.

Approach• An interdisciplinary, systems approach is used to

conduct the research in aerodynamics, flightmechanics, controls and human factors. Flightsimulation is used to demonstrate the concept andvalidate the methods.

Goal• To improve the safety of aircraft operating in icing

conditions.


1-11

New Aircraft Icing Encounter Model

IceAccretion

Ice AccretionSensors

Ice ProtectionSystem(IPS)

Pilot /Automation

AircraftDynamics

Advisory

IceEffects

Ice ManagementSystem(IMS)

Information

Pilot Input

EnvelopeProtection

Control Adaptation

Primary IPS Operation


1-12

Smart Icing Systems / IMS Functions

1.Sense the presence of ice accretion including itseffect on measured aircraft performance, stabilityand control.

2.Automatically activate and manage the ice protectionsystems, and provide the pilot with feedback.

3. If the performance degradation becomes significantmodify the aircraft flight envelope and inform pilot ofthe intentions, actions and limitations of the system.

4.Adapt the flight controls to provide the pilotsadequate handling qualities.


1-13

IPSOperation

andMonitoring

EnvelopeProtection

ControlAdaptation

IcingEncounter

Icing EffectsCharacter-

ization

Pilot /Flight DeckAutomation

IMSIMS

AircraftDynamics

IMS Functional Model


1-14

Defenses in Depth


1-15

Smart Icing Systems Technology

• Control in adverse conditions• Aircraft health monitoring

Related AvSP Research

Builds on these research programs• Reconfigurable/adaptive control research for military

aircraft• Experimental and computational Iced aircraft

aerodynamics and handling qualities research• Human factors and UAV flight simulation researchImplementation• Smart Icing Systems will be part of a larger aircraft

system to monitor and maintain critical aircraftfunctions.


1-16

SMART ICING SYSTEMS Research Organization

Core Technologies

Flight SimulationDemonstration

Aerodynamics and

Propulsion

FlightMechanics

Control and Sensor

Integration

HumanFactors

AircraftIcing

Technology

Operate andMonitor IPS

EnvelopeProtection

AdaptiveControl

CharacterizeIcing Effects

IMS Functions

Safety and EconomicsTrade Study


1-17

Faculty and Students

• Safety and Economics Systems Study– Prof. Ken Sivier (AAE)– Jennifer Bradley (AAE)

• Aerodynamics, Propulsion and Flight Mechanics– Profs. Mike Bragg (AAE) and Eric Loth (AAE)– Devesh Pokhariyal, Ryan Oltman, Tim Hutchison, Holly Gurbacki (AAE)

• Control and Sensor Integration– Profs. Tamer Basar (ECE/CSL), Bill Perkins (ECE/CSL),

Petros Voulgaris (AAE/CSL)– James Melody (ECE/CSL), Wen Li (AAE), Eric Schuchard (ECE/CSL)– Eric Keller (ECE/CSL), Thomas Hillbrand (ECE/CSL),

Eduardo Salvador (ECE/CSL)• Human Factors

– Profs. Nadine Sarter (ARL), Chris Wickens (ARL)– Beth Kelly(Psych), Scott McCray (Psych)

• Simulation– Prof. Michael Selig (AAE) – Jeff Scott (AAE)


1-18

SIS Schedule

99 00 01 03

Federal Fiscal Year98

Concept Development CRI Funding

SIS Core Technologies / IMS research

Systems Study

Icing Encounter Flight Simulator

Preliminary IMS Methods

IMS Flight Simulator Demo

02

May 1999


1-19

SIS Funding

• UIUC Critical Research Initiative Funding− Aircraft Icing Research Center− July 1997- July 1999 $200K

• $200K in F98 NASA Funds to initiate SIS Research

• Jan. 1999 - Dec. 2002 $1.7 million NASA Glenn Grant


1-20

SIS Homepage

http://www2.aae.uiuc.edu/~sis/


1-21

Meeting Schedule

Tuesday May 18:1 - 1:15 Welcome1:15 - 1:45 Smart Icing Systems Introduction (Bragg)1:45 - 2:45 Safety and Economics Trade Study (Sivier/Bradley)2:45 - 3:15 Break3:15 - 4:15 Aerodynamics and Flight Mechanics (Bragg/Loth)4:15 - 5: 00 Discussion

Wednesday May 19:8:30 - 10:00 Flight Control and Sensors (Perkins/Melody)10:00 - 10:30 Break10:30 - 11:30 Human Factors (Sarter)11:30 - 12:00 Icing Encounter Flight Simulator (Selig)12:00 - 1:00 Lunch1:00 - 4:00 Discussion and Future Plans

1


2-1

Safety & Economics Trade Study

Principal Investigator: Prof. Ken Sivier

GraduateResearch Assistant: Jennifer Bradley

2


2-2


Core Technologies

Icing-EncounterFlight Simulator

Aerodynamics and

Propulsion

FlightMechanics

Control and Sensor

Integration

HumanFactors

AircraftIcing

Technology


EnvelopeProtection

AdaptiveControl


IMS Functions


3


2-3


Goal: Establish, through the use of safety and tradestudies, the impact of new IPSs, especially the SIS,on the safety and costs of operation of TBPcommuter aircraft.

4


2-4


Objective: 1) Develop methodologies for evaluatingthe impact of IPSs on the safety and costsof operation of TBP commuter aircraft

2) Establish a baseline for existing IPSs3) Evaluate the impact of new IPSs on safety

and costs4) Evaluate the impact potential of the SIS on

safety and costs

5


2-5


Approach: 1) Examine ATS safety history and study theapplication of the SIS within thatframework

2) Perform economics trade studies focusedon existing and new IPSs, including theSIS

6


2-6

Safety & Economics Waterfall Chart

99 00 01 02

AccidentAnalysis

Regional Jet Study

Federal Fiscal Years

98

Ice ProtectionTrade Study

Effects on DomesticAir Transportation

Safety History

Baseline TBP Study

7


2-7

SAFETY AND ECONOMICS TRADE STUDY

SIS Safety Impact

Smart Icing System Research

Statistical Analysis

of Icing Events Safety History

SIS Application toAccident History

Safety Study

8


2-8

Safety Study

• Aircraft Icing Events– Accidents– Incidents– Mishaps

• Engine Type

• Primary Factors

• Flight Phase

9


2-9

Safety Study (cont.)

• Roselawn, IN Accident Analysis– Accident History– SIS Application

• Conclusions• Recommendations

10


2-10

Types of Events

• Accidents– Person suffers serious injury or death– Aircraft receives substantial damage

• Incidents– Not an accident– An occurrence that could affect the safety of

operations

• Mishaps– An icing encounter that did not warrant an accident

or incident report

11


2-11

Databases

• NTSB Accident/Incident Database– Aircraft Accidents and Incidents - 1983 to present

• FAA Incident Data System

– Aircraft Incidents - 1978 to present

• NASA Aviation Safety Reporting System

– Aircraft Mishaps

– Voluntary Aircraft Reports -1988 to present

12


2-12

Aircraft Icing Events

10390

98

6883

155163 160

177172

133 134

184

115

0

20

40

60

80

100

120

140

160

180

200

Nu

mb

er o

f Ic

ing

Eve

nts

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

Year

MishapIncidentAccident

13


2-13

Icing Accident Fatalities

52

33

46

24

61

22

4145 48

67

27

88

3931

0

10

20

30

40

50

60

70

80

90

100

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

Year

Nu

mb

er o

f F

atal

itie

s

14


2-14

Engine Type

• Reciprocating (carburetor)• Reciprocating (fuel injection)• Turboprop• Turbojet• Turbofan• Turboshaft (helicopters)

15


2-15

Aircraft Icing AccidentsEngine Type

Recip (carb), 73.1%

Recip (fuel inj), 22.5%

Turboprop, 3.2%

Turbofan, 0.4%

Turboshaft, 0.4%

Turbojet, 0.4%

16


2-16

Primary Factors

• Flightcrew• Aircraft• Maintenance• Weather• Airport/ATC• Miscellaneous/Other• Unknown

17


2-17

Aircraft Icing AccidentsPrimary Factors

Flightcrew, 46.7%

Airframe-manufacturer,

0.4%

AircraftSystems, 1.4%

Weather, 36.7%

Airport/ATC, 0.6%

Maintenance, 3.3%

Other, 1.0% Unknown, 10.0%

18


2-18

Flight Phases

Preflight/Taxi

Takeoff/Climb

Cruise

Descent

Approach

Landing

Maneuver (not shown)

19


2-19

Aircraft Icing Accidents Flight Phases

Unknown, 1.0%

Preflight/Taxi, 0.2%

Takeoff/Climb, 20.5%

Cruise, 39.2%Descent, 9.0%

Approach, 17.8%

Landing, 1.8%

Maneuvering, 10.5%

20


2-20

American Eagle, Flt. 4184

• October 31, 1994

• Flight from Indianapolis, IN to Chicago, IL

• Avions de Transport Régional, model 72-212 (ATR-72)

• Roselawn, Indiana

• Total aboard: 68

21


2-21

ATR-72 Ice Protection

• Level I– all probe & windshield heating systems

• Level II– activates pneumatic engine intake boots;

electric prop heaters; elevator, rudder, & aileronhorn heat; electric side window heaters

• Level III– activates wing, horizontal & vertical stabilizer

leading edge boots; routinely prop to 86%

22


2-22

ATR-72 Ice Protection

PneumaticAnti/Deicing

Wing LeadingEdges

Horizontal Tail PlaneLeading Edges

Engine AirIntakes

Gas PathDeicer

Ice EvidenceProbe

Electronic IceDetector

Windshields Probes Propellers HornsElectrical Anti-

icing

23


2-23

ATR Time History - 1

Time (CST) Event1455:20 -- Cleared for takeoff

Normal climb to 16,000 ft

1513 -- (Began descent from 16,000 ft to 10,000 ft)-- (FDR – Level III activation)

(FDR – Prop RPM = 86%)1524:39 -- Entered hold at 10,000 ft

-- (FDR – deice off)Flt attendant & Capt conversing – both flight &non-flight related subjects

1533:13 -- Capt – "high deck angle"-- (FDR – AOA = 5 deg)

1533:26 -- Flaps moved to 15 deg-- (FDR – AOA â to 0 deg)

1541:07 -- Caution alert-- (FDR – Level III activation)-- (FDR – Prop increase)

24


2-24

ATR Time History - 2

Time (CST) Event1542:38 -- (Third circuit of hold)1548:34 -- "showing ice"1549:44 -- Capt leaves cockpit1554:13 -- Capt returns1555:42 -- FO – "we still got ice"1556:51 -- (FDR – descent to 8000 ft; autopilot on)1557:22 -- Flap overspeed warning1557:28 -- Flaps going to 0 deg (AOA & pitch á)1557:33 -- (Descent thru 9130 ft)

-- (AOA á thru 5 deg; Ailerons – RWD)1557:34 -- (Ailerons – 13.45 deg RWD)

-- (Autopilot disconnect)-- Autopilot disconnect warning-- Rolls right (AOA & pitch â)

1557:57 -- (Descent thru 1700 ft)-- End of Recording

25


2-25

ATR 72-212 DFDR DataA

merican

Eag

le Flig

ht 4184*

*Freezing D

rizzle: Tow

ards A B

etter K

nowledge and a B

etter Protection, Issue 1,

AT

R, F

rance, 11/05/95

26


2-26

Hinge Moments vs. Angle of Attack

AIAA 99-0092 “Effects of Simulated-Spanwise Ice Shapes on Airfoils:Experimental Investigation” by S. Lee & M.B. Bragg

27


2-27

SIS Modified - 1

Time (CST) Event SIS Action1455:20 -- Cleared for takeoff

Normal climb to 16,000 ft1513 -- (Began descent from 16,000 ft

to 10,000 ft)-- (FDR – Level III activation) ßdetection of ice

ßanti-ice/deice on(FDR – Prop RPM = 86%)ßnotice to pilot

1524:39 -- Entered hold at 10,000 ft-- (FDR – deice off)

ßnotice to pilot á AOAßflap hinge moment ∆ßflt envelope change

Flt attendant & Captconversing – both flight & non-flight related subjects ßnotice to pilot of flt

envelope change

28


2-28

SIS Application - 2

Time (CST) Event SIS Action1533:13 -- Capt – "high deck angle"

-- (FDR – AOA = 5 deg)1533:26 -- Flaps moved to 15 deg

-- (FDR – AOA â to 0 deg)1541:07 -- Caution alert ßanti-ice/deice still on

-- (FDR – Level III activation)-- (FDR – Prop increase)

1542:38 -- (Third circuit of hold)1548:34 -- "showing ice" ßanti-ice/deice still on1549:44 -- Capt leaves cockpit1554:42 -- Capt returns1555:42 -- FO- "we still got ice" ßanti-ice/deice still on1556:51 -- (FDR – descent to 8000 ft)

-- (FDR – autopilot on)1557:22 -- Flap overspeed warning

29


2-29

SIS Application - 3

Time (CST) Event SIS Action1557:28 -- Flaps going to 0 deg ßnotice to pilot á AOA

-- (AOA & pitch á) ßflt envelope changesßautopilot disconnect

warning1557:33 -- (Descent thru 9130 ft)

-- (AOA á 5 deg) ßnotice to pilot á AOA-- (Ailerons – RWD)

1557:34 -- (Ailerons – 13.45 deg RWD)-- Autopilot disconnect ßnotice to pilot á AOA-- Autopilot disconnect warning-- Rolls right

(AOA & pitch â)1557:57 -- (Descent thru 1700 ft)

-- End of Recording

30


2-30

Safety StudySummary and Conclusions

• An average of at least 131 icing encountersa year (1983-1996).

• Primary factors are flightcrew and weather,making up a total of 83% of the accidents.

• Performed accident analyses:– October 31, 1994 -- Roselawn, IN– January 9, 1997 -- Monroe, MI– December 26, 1989 -- Pasco, WA

31


2-31

Safety Study Future Research

• Obtain a better description of SIS.• Analyze accidents using improved SIS model.• Further categorization of accidents by

aircraft type. • Analyze accidents to find the specific action

or lack of action that initiated the event.

32


2-32

SAFETY AND ECONOMICS TRADE STUDY

SISCost Impact


ACSYNTAnalysis Tool

Baseline Studies &TOC Sensitivity

IPS EconomicsTrade Study

SIS

Projection

IPS Data

TBP Aircraft &

Mission Models

Economics Trade Study

33


2-33

Economics Trade Study

• Analysis Tool• Baseline Aircraft• Mission Profiles• Sensitivity Studies

– Weight Sensitivity– Altitude Sensitivity

• Ice Protection Trade Study• Conclusions• Recommendations

34


2-34

ACSYNT

• AirCraft SYNThesis

• NASA Ames Research Center

• Phoenix Integration, Inc.

• Design Capabilities

• Performance Analysis

• Economic Analysis

35


2-35

Baseline Aircraft

35-passenger 70-passengerAircraft Model Fairchild F-27

(F-27)Scaled Fairchild F-27(F27-70)

Gross Takeoff Weight 34,750 lb 57,840 lbWing Span 95 ft 104 ftAircraft Length 76 ft 112 ftEngine Rolls-Royce Pratt & Whitney - Model Dart 7 Mark 528 PW150A - SFC 0.71 0.43

36


2-36

Mission Profiles

Takeoff Landing

Cruise @ 13,000 ft

Total Range = 107.5 nmi

Loiter @ 13,000 ftfor 10 min

CMI MDW

Mission A

Takeoff Landing

Cruise @ 13,000 ft

Total Range = 277 nmi

CMI DET

Mission B

Mission C

Takeoff Landing

Cruise @ 13,000 ft

Total Range = 277 nmi

CMI DET

Cruise @9,000 ft

37


2-37

Economic Analysis Input

• Annual Aircraft Utilization = 2925 block hr• Aircraft Economic Life = 20 yr• Stage Length = 319 sm• Load Factor = 75%• Fuel Cost = $2.01 per gal

• Crew Cost ≈ $42,000 yr

38


2-38

F-27 Weight Sensitivity

1.575

1.580

1.585

1.590

1.595

1.600

1.605

1.610

1.615

1.620

31,500 32,250 33,000 33,750 34,500 35,250 36,000

Gross Takeoff Weight (lb)

TO

C (

$/A

SM

)

1105

1115

1125

1135

1145

1155

1165

1175

1185

To

tal F

uel

(lb

)

Total FuelTOC

Mission BCruise Altitude = 13,000 ft

Cruise Mach = 0.39

TOC Sensitivity = 0.1 ¢/ASM/100 lb

Fuel Sensitivity = 1.6 lb/100 lb

39


2-39

F27-70 Weight Sensitivity

0.732

0.734

0.736

0.738

0.740

0.742

0.744

0.746

0.748

55,500 56,250 57,000 57,750 58,500 59,250 60,000

Gross Takeoff Weight (lb)

TO

C (

$/A

SM

)

1168

1170

1172

1174

1176

1178

1180

1182

1184

1186

1188

1190

To

tal F

uel

(lb

)

Total FuelTOC

Mission BCruise Altitude = 13,000 ft

Cruise Mach = 0.54

TOC Sensitivity = 0.04 ¢/ASM/100 lb

Fuel Sensitivity = 0.5 lb/100 lb

40


2-40

F-27 Altitude Sensitivity

1.54

1.56

1.58

1.60

1.62

1.64

1.66

1.68

0 10,000 20,000 30,000 40,000

Cruise Altitude (ft)

TO

C (

$/A

SM

)

1080

1100

1120

1140

1160

1180

1200

1220

1240

1260

1280

To

tal F

uel

(lb

)

TOCTotal Fuel

Mission BAverage WG = 34,750 lb

Cruise Mach = 0.39

TOC Sensitivity = 0.8 ¢/ASM/1000 ft

Fuel Sensitivity = 12.1 lb/1000 ft

41


2-41

F-27 Altitude Sensitivity

1.52

1.54

1.56

1.58

1.60

1.62

1.64

1.66

1.68

0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000


TO

C (

$/A

SM

)

1.50

1.55

1.60

1.65

1.70

1.75

Blo

ck T

ime

(Blo

ck h

r)

TOCBlock Hour


Cruise Mach = 0.39

Time Sensitivity = 0.01 block hr/1000 ft


42


2-42

F27-70 Altitude Sensitivity

0.72

0.73

0.74

0.75

0.76

0.77

0.78

0 10,000 20,000 30,000 40,000


TO

C (

$/A

SM

)

900

950

1000

1050

1100

1150

1200

1250

1300

1350

1400

To

tal F

uel

(lb

)

TOC

Total Fuel


Cruise Mach = 0.54


Fuel Sensitivity = 19.6 lb/1000 ft

43


2-43

F27-70 Altitude Sensitivity

0.70

0.71

0.72

0.73

0.74

0.75

0.76

0.77

0.78

0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000


TO

C (

$/A

SM

)

1.18

1.20

1.22

1.24

1.26

1.28

1.30

1.32

1.34

1.36

1.38

Blo

ck T

ime

(Blo

ck h

r)TOCBlock Hour


Cruise Mach = 0.54


Time Sensitivity = 0.008 block hr/1000 ft

44


2-44

Ice Protection EconomicsTrade Study

Includes• Acquisition estimates• Costs due to Weight• Costs due to Drag• Technology Factors• Complexity Factors

Does Not IncludeCost due to:

• Delays• Airport Diversions• Cancellations• Accidents• Litigation• IPS Energy Usage

45


2-45

Ice Protection Systems & Components

• Magnetostrictive Ice Detector• Heat of Transformation (HOT) Ice Detector• Standard Pneumatic Deicer• Silver Estane Pneumatic Deicer• Small Tube Pneumatic (STP) Deicer• Pneumatic Impulse Ice Protection (PIIP™)• Electrothermal• Electro-mechanical• Smart Icing System (SIS)

46


2-46

SIS Cost Analysis

• Ice Protection System– Standard Pneumatic Deicer

• Avionics• Increased Technology & Complexity

Factors– Deicing System– Avionics

47


2-47

Economic Analysis Input

Tec

hnol

ogy

Fac

tor

Com

pone

ntW

eigh

t (lb

)

Ani

t/Dei

cing

Equ

ipm

ent

Com

plex

ityF

acto

r

Avi

onic

sC

ompl

exity

Fac

tor

Dra

gP

enal

ty( ∆

CD

min)

Control Group 1.0 0 1.0 1.0 -Magnetostrictive IceDetector (2)

1.2 2 1.2 1.2 -

HOT Ice Detector (2) " 4 1.3 " -Standard PneumaticDeicer*

1.3 98 " 1.3 0.0011

Silver EstanePneumatic Deicer*

1.4 98 " " 0.0011

STP Deicer* 1.45 98 " " 0.0011PIIP* " 98 1.4 " -Electro-MechanicalDeicer*

" 331 " " -

Electrothermal* " 59 " " -SIS* 1.6 98 1.5 1.6 0.0011* Protection for wing, horizontal and vertical stabilizers

48


2-48

1.49

1.49

2.31

2.90

3.19

3.19

3.28

3.16

4.94

0.00

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5

No Ice Protection

Magnetostrictive Sensor

HOT Sensor

Standard Pneumatic

Silver Estane

STP

PIIP

Electro-mechanical

Electrothermal

SIS

∆∆TOC ($100,000/Yr)

F-27 Ice Protection Trade Study

Mission BBaseline case = $24.8 million/yr

PreliminaryEstimates

49


2-49

2.31

4.77

4.86

2.11

0 1 2 3 4 5

SIS Breakdown

SIS Total

Std Pneum.Breakdown

Std Pneum. Total

∆∆TOC ($100,000/yr)

Technology FactorComponent WeightIP ComplexityAvionics ComplexityAvionics Development

Breakdown of Change in TOC


50


2-50

1.67

1.70

2.72

3.36

3.69

3.60

3.71

3.60

5.53

0.0 1.0 2.0 3.0 4.0 5.0 6.0

No Ice Protection

Magnetostrictive Sensor

HOT Sensor

Standard Pneumatic

Silver Estane

STP

PIIP

Electro-mechanical

Electrothermal

SIS

∆∆TOC ($100,000/Yr)

0.00

F27-70 Ice Protection Trade Study

Mission BBaseline case = $27.7 million/yr


51


2-51

Economics Trade StudySummary and Conclusions

• Selected the software tool, ACSYNT, for makingIPS economics trade studies

• Developed two TBP commuter aircraft models for the studies

• Established TBP commuter mission models• Evaluated the fuel required and TOC

sensitivities to cruise altitude and TOGW• Found TOC impact of several existing and new

IPSs including the projected SIS

52


2-52

• Calibrate the ACSYNT economics study results using:– Actual aircraft operational costs.– Better acquisition and operational costs for

ice protection systems and components. • Obtain data and include additional icing related

items in total operating costs– Number of delays & cancellations a year– Costs of delays or cancellations– Costs of accidents & incidents

(aircraft, liability, & negative publicity effects)• Perform ice protection trade study for a

regional jet aircraft.

Economics Trade StudyFuture Research


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


3-2


Core Technologies


FlightMechanics

Controls and Sensor

Integration

HumanFactors

AircraftIcing

Technology


EnvelopeProtection

AdaptiveControl


IMS Functions


Aerodynamicsand

Propulsion


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.


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


Holly Gurbacki, Dr. Bragg AerodynamicSensors


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


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 +=+−+− &&


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 +=−+−−•


3-8


Wind TunnelData

IcedAerodynamics

Model

ComputationalFluid

Dynamics

Iced AircraftModel

Clean AircraftModel







Dr. Loth

Characterization

Flight Simulation

Envelope Protection




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


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


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


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

′∆+−

∆+−

==


3-13


Wind TunnelData

IcedAerodynamics

Model

ComputationalFluid

Dynamics

Iced AircraftModel

Clean AircraftModel







Dr. Loth

Characterization

Flight Simulation

Envelope Protection




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:


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


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


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


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


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


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

−==

−=−=


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


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


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


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


3-25


Wind TunnelData

IcedAerodynamics

Model

ComputationalFluid

Dynamics

Iced AircraftModel

Clean AircraftModel







Dr. Loth

Characterization

Flight Simulation

Envelope Protection




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


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 −+

+−+

+−+

+−+−+−=

••••


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

⋅−⋅

+⋅−⋅

+⋅

⋅

−⋅⋅+−

−⋅+−⋅=

+−⋅−⋅⋅−

=

⋅−⋅

+⋅−⋅

+⋅

⋅

−⋅⋅+−

+⋅−⋅

−⋅−=

&

&

&


3-29

Euler Angles

Position


( ) θθψψθθϕϕϕϕϕϕϕϕϕϕθθ

θθϕϕϕϕ

ψψ

sinptancosrsinqp

sinrcosqcos

cosrsinq

&&

&

&

+=++=−=

+=

( ){ } ( )( ){ } ( )( ) θθϕϕϕϕθθ

ψψϕϕϕϕψψθθϕϕϕϕθθ


coscoswsinvsinuz

cossinwcosvsinsincoswsinvcosuy

sinsinwcosvcossincoswsinvcosux

eeee

eeeeee

eeeeee

++−=

−−++=

−−++=

&

&

&


3-30


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


coscoswsinvsinuz

sinsinwcosvcossincoswsinvcosux

eeee

eeeeee

++−=

−−++=&

&


3-31

Open Loop Analysis Tool for Nonlinear Twin Otter Model


3-32

Closed Loop Analysis Tool for Nonlinear Twin Otter Model


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


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


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


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


3-37

Validation of FDC 1.3 (cont.)

• Validation of the FDC (TIP flight p4601, iced)


3-38


Wind TunnelData

IcedAerodynamics

Model

ComputationalFluid

Dynamics

Iced AircraftModel

Clean AircraftModel







Dr. Loth

Characterization

Flight Simulation

Envelope Protection




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


3-40

Dynamic Analysis, Clean & Iced


3-41

Dynamic Analysis, Clean & Iced (cont.)


3-42


Wind TunnelData

IcedAerodynamics

Model

ComputationalFluid

Dynamics

Iced AircraftModel

Clean AircraftModel







Dr. Loth

Characterization

Flight Simulation

Envelope Protection




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.


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.


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.


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.


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.


3-48

Comparison for αα and δδE (No turbulence)

0ice =ηη

83.0ice =ηη

0ice =ηη

83.0ice =ηη


3-49

Comparison for CD and V (No turbulence)

83.0ice =ηη

0ice =ηη

0ice =ηη 83.0ice =ηη


3-50

Comparison for αα and δδE (turbulence)

0ice =ηη

83.0ice =ηη

0ice =ηη

83.0ice =ηη


3-51

Comparison for CD and V (turbulence)

0ice =ηη

83.0ice =ηη0ice =ηη

83.0ice =ηη


3-52


Wind TunnelData

IcedAerodynamics

Model

ComputationalFluid

Dynamics

Iced AircraftModel

Clean AircraftModel







Dr. Loth

Characterization

Flight Simulation

Envelope Protection




3-53

Wind Tunnel Testing


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


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


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


3-57

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.


3-58

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)


3-59


Wind TunnelData

IcedAerodynamics

Model

ComputationalFluid

Dynamics

Iced AircraftModel

Clean AircraftModel







to be decided, Dr. Loth

Characterization

Flight Simulation

Envelope Protection

Fully Non-Linear APC Overview


AE


3-60

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


3-61

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


3-62

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


3-63

Sample CFD

• NSU2D LE ice-shape predictions


3-64

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


3-65

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


3-66

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


3-67

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)


3-68

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

Smart Icing SystemsFlight Controls and Sensors

NASA Review May 18-19, 1999

Principal Investigators: Tamer Ba�sar (CSL/ECE)

William Perkins� (CSL/ECE)

Petros Voulgaris (CSL/AAE)

Graduate Students: Wen Li (NASA Support)

James Melody� (CRI Support)

Eric Schuchard (Fellowship)

Undergrad Students: Eric Keller (NSF Support)

Thomas Hillbrand (Fellowship)

Eduardo Salvador (NSF Support)

* presenting

4-1

Smart Icing Systems

Smart Icing Systems Research Organization


Core Technologies

Aerodynamcs

and

Propulsion

Flight

Mechanics

Control and

Sensor

Integration

Human

Factors

Aircraft

Icing

Technology

IMS Functions

Characterize

Icing E�ects

Operate and

Monitor IPS

Envelope

Protection

Adaptive

Control

Flight Simulation

Demonstration

Safety and Economics

Trade Study

4-2

Smart Icing SystemsFlight Controls and Sensors


Goal: Improve the safety of aircraft in icing conditions.

Develop smart systems to improve ice tolerance.

Objectives:

1) Develop fast and reliable methods and algorithms for in ight

identi�cation of aircraft ight dynamics.

2) Develop robust ice detection and classi�cation methods and al-

gorithms that incorporate identi�ed parameters and other avail-

able sensor information.

3) Investigate utility of control recon�guration to maintain ight

characteristics in the presence of icing.

Approach: Apply existing parameter identi�cation techniques to

parameter identi�cation of ight dynamics. Investigate detection

methods based on the identi�ed parameters. Evaluate performance

according to timeliness and accuracy of icing characterization.

4-3

Smart Icing Systems

Smart Icing Systems Research


THE CONTROL AND SENSOR

INTEGRATION GROUP

LTI - Linear, Time-Invariant

LTV - Linear, Time-VaryingEric Keller, Eduardo Salvador, Prof. Ba�sar

Thomas Hillbrand, Prof. Ba�sar

Wen Li, Prof. Voulgaris

Jim Melody, Prof. Ba�sar

Eric Schuchard, Prof. Perkins

Characterizaton

Flight

Simulation

Adaptation

LTI Longitudinal

Dynamics ID

LTI Longitudinal

Dynamics Detection

LTV Longitudinal

Dynamics ID

LTV Longitudinal

Dynamics Detection

Nonlinear 6-axis

Dynamics ID

Nonlinear 6-axis

Dynamics Detection

Adaptation/Handling

Event Recovery

Nonlinear Dynamics

Development

Sensor and Drag

Charact. Integration

4-4

Smart Icing Systems

Flight Controls and Sensors Outline


� Ice detection & characterization overview

� Identi�cation during maneuver

{ Batch Algorithm

{ Recursive Algorithm: H1

{ Recursive Algorithm: EKF

� Neural network detection & classi�cation during maneuver

� Identi�cation during steady level ight

� Summary & Conclusions

� Future Plans

4-5

Smart Icing Systems

Ice Characterization Block Diagram


ID

Algorithm(s)

Ice Detection

& Sensor Fusion

Envelope Prot

& IPS I/F

Flight

Dynamics

(depend on �)

Flight

Controller

+

other sensors� parameters

^� parameter estimates

output

input

^�

Pilot

Pilot

IPS

IMS

4-6

Smart Icing Systems

Icing Characterization Philosophy


� Icing matters to the extent that it a�ects the ight dynamics.

� E�ect of icing on ight dynamics is captured by parameter �.

� By observing behavior of dynamics, can infer the value of �

) Parameter Identi�cation (ID)

� From estimated parameter ^�(t), detect and classify icing e�ects.

� Icing detection will also incorporate

{ aerodynamic sensors

{ steady-state characterization

{ hinge moment sensing

{ external environmental sensors

) Sensor Integration

� Inform pilot of icing directly and via envelope protection

4-7

Smart Icing Systems

Longitudinal Flight Dynamics Model


Linearized model of longitudinal ight dynamics

_u = �g cos(�) + (Xu +XTu)u+X��+XÆEÆE

_w � Uq = �g� sin(�)+ Zuu+ Z��+ Z _� _�+ Zqq+ ZÆEÆE

_q = Muu+MTuu+M��+MT��+M _� _�+Mqq+MÆEÆE

where u forward velocity w downward velocity

� angle of attack q pitch rate

� pitch angle ÆE elevator angle

U , � trim conditions (i.e., linearization point)

and fM�, Z�, X�g are stability and control (S/C) derivatives.

Model v1.0 clean and iced (�ice = 1) S/C derivatives:

M� MÆE Mq Z� ZÆE Zq X� Xu +XTu

Clean -7.86 -10.44 -3.055 -378.7 -40.30 -19.70 13.71 -0.018

Iced -7.08 -9.40 -2.948 -342.7 -36.45 -19.43 13.90 -0.020

other derivatives are invariant to icing. Extensive simulation for this model has

shown that only M�, MÆE, and possibly Mq are useful for icing characterization.4-8

Smart Icing SystemsParameter ID Framework


� Let � := [M�; MÆE; Mq; Z�; ZÆE ; Zq; X�; (Xu+XTu)]T

be parameters to identify and convert ight dynamics to

_x = A(x; v)�+ b(x; v) + w

z = x+ n

where x = [q � � u]T state

v = ÆE input

z measured output

w state disturbance (a.k.a., process noise)

n measurement noise

� n(t) represents inaccuracies in the measurement,

e.g., instrument accuracy limitations

� w(t) represents unknown excitation of the ight dynamics,

e.g., turbulence, modeling error

� system excitation is necessary for identi�cation

� unknown exogenous signals n(t) and w(t) limit accurately of

estimated parameter

4-9

Smart Icing Systems

Parameter ID Algorithm Categorization


Objective: Given some information, , (e.g., input, output, and

state measurements) identify � accurately in the presence of w and n.

� Static algorithms: parameter estimate at any instant, ^�(tn), is

based solely on measurements at that instant, tn.

{ solve matrix equation

{ no solution when dim(x) � dim(�)

� Batch algorithms: static algorithms that process measurements

in batches: ^�(tm) depends on ftm�k; ::: ;tm�1; tmg

{ noise sensitivity depends on excitation level and batch period

� Recursive algorithms: parameter estimate is based on past and

present measurements: ^�(t) depends on [0; t]

{ characterized by di�erential equations with i.c.'s

{ convergence rate is a function of excitation level

4-10

Smart Icing Systems

Parameter ID Information Structures


ID algorithm depends on type of information available

� Full state derivative information (FSDI): input, state, and state derivative are

available,

t := (x(t); _x(t); u(t))

i.e., n = 0 and ( _q, _�, _u) and (q, �, �, u) are measured

� Full state information (FSI): input and state are available,

t := (x(t); u(t))

i.e., n = 0 and (q, �, �, u) are measured

� Noise perturbed full-state information (NPFSI): input and noisy measurement

of state are available,

t := (z(t); u(t))

i.e., n 6= 0

� Noise perturbed partial-state information (NPPSI): input and noisy

measurement of only part of the state are available

t := (z(t); u(t))

where z = Cx+ n, e.g., � is not measured.

4-11

Smart Icing Systems

Noise CharacterizationNASA Review May 18-19, 1999

Assume: n and w are zero-mean white Gaussian noise, hence

completely characterized by their covariances

Covariance of w:

� Consider turbulence as a vertical velocity perturbation

) only � is directly a�ected.

� Assume noise covariance equal to energy of _� for a 5Æ doublet

� _q �q � _� � _u

0Æ/s2 0Æ/s 0.026Æ/s 0 knot/s

Covariance of n:

� Instrument resolution speci�cations for NASA Twin Otter:

�q �� u

0.0167Æ/s 0.0293Æ 0.003Æ 0.076 knot

4-12

Smart Icing Systems

Identi�cation during Maneuver



INTEGRATION GROUP







Characterizaton

Flight

Simulation

Adaptation

LTI Longitudinal

Dynamics ID

LTI Longitudinal

Dynamics Detection

LTV Longitudinal

Dynamics ID

LTV Longitudinal

Dynamics Detection

Nonlinear 6-axis

Dynamics ID

Nonlinear 6-axis

Dynamics Detection

Adaptation/Handling

Event Recovery

Nonlinear Dynamics

Development

Sensor and Drag


4-13

Smart Icing Systems

Maneuver Icing Scenario


Icing Scenario:

� During a period of steady level ight, ice accretes but lack of

excitation limits parameter ID e�ectiveness.

� Afterwards, a maneuver is performed during which parameter ID

takes place.

Model of Scenario:

� Begin ID simulations at beginning of maneuver

� Parameters assumed constant over the maneuver

� Maneuver is modeled as an elevator doublet

� Use simple threshold (mean of clean and iced parameters) for

quick and dirty evaluation of algorithms

� Must also consider ID of clean aircraft for \false alarms"

Question: Is there a reliable indication of icing in a reasonable

amount of time?

4-14

Smart Icing Systems

Static Least-Squares FSDI ID


� Assume that _x(t), x(t), and u(t) are known, and take w(t) equal

to its mean, i.e., w(t) � 0.

� At each time instant, t, we have the system of n linear equations

in r unknowns, �:

A(xt; vt)� = _xt � b(xt; vt) (1)

where xt 2 IRn and � 2 IRr.

� Solve directly for ^�(t) using matrix least squares:

^� =

hATAi�1

AT ( _xt � b) (2)

� Solution will not exist if the rank of A is less than r, e.g., if r > n.

4-15

Smart Icing Systems

Batch Least-Squares FSDI ID


� Collect several measurements in batch and concatenate equations

A(xt1; vt1)� = _xt1 � b(xt1; vt1)

A(xt2; vt2)� = _xt2 � b(xt2; vt2)

...

A(xtm; vtm)� = _xtm � b(xtm; vtm)9>>=

>>;) Am� = _Xm � Bm

� Excitation ) nondegenerate equations for t1, t2, : : : , tm

) rank of Am is r with suÆcient number of measurements, m.

� Solve directly for ^�(tm) using matrix least squares:

^�(tm) =

�ATmAm��1ATm

�_Xm � Bm�

� By including disturbances, the error in the estimate, ~�(tm), is given by

~�(tm) =

�ATmAm��1ATmWm

with WTm :=

�w(t1)T w(t2)T � � � w(tm)T�

.

� If the system is poorly excited, or if the batch period is small, the error can

be very sensitive to w.

4-16

Smart Icing SystemsBatch Least-Squares FSI ID


� Extend to FSI case via integrating pre�lter.

� With x(t) and v(t) known, integration yields x = �At� + �bt + �wt where

_�At = A(x(t); v(t)), _�bt = b(x(t); v(t)), and _�wt = w(t).

� Pure integrator is not stable. In order to stabilize, include pole at �� < 0

_�At = �� At + A(x(t); v(t)); �AÆ = 0

_�bt = ��bt + b(x(t); v(t)); �bÆ = xÆ

� Apply matrix LS to pre�ltered equation

�At1� = xt1 ��bt1

...

�Atm� = xtm ��btm9=

; ) �A� = X � �B

� Then ~�=

��AT �A��1 �AT W where W is concatenated pre�ltered noise

_�wt = ��wt + w(t); �wÆ = 0

� NPFSI ?

) use measurement z in place of x, but sensitive to measurement noise.4-17

Smart Icing Systems

Batch LS Results: Clean & Iced w/ no

Measurement NoiseNASA Review May 18-19, 1999

Batch LS FSI Algorithm with Tb = 8 s, � = 10, and sampling rate 30 Hz

5Æ doublet maneuver over 10 seconds

with process noise but no measurement noise

Iced Aircraft Clean Aircraft

0 1 2 3 4 5 6 7 8 9 100.8

0.85

0.9

0.95

1

1.05

1.1

1.15

1.2

MÆE

M�

time (s)

Norm

alizedEstim

ates

0 1 2 3 4 5 6 7 8 9 100.8

0.85

0.9

0.95

1

1.05

1.1

1.15

1.2

MÆE

M�

time (s)

Norm

alizedEstim

ates

Notice: A reliable indication of icing is not given for either MÆE or M�.

4-18

Smart Icing Systems







0 1 2 3 4 5 6 7 8 9 100.8

0.85

0.9

0.95

1

1.05

1.1

1.15

1.2

MÆE

M�

time (s)

Norm

alizedEstim

ates

0 1 2 3 4 5 6 7 8 9 100.8

0.85

0.9

0.95

1

1.05

1.1

1.15

1.2

MÆE

M�

time (s)

Norm

alizedEstim

ates

Notice: A reliable indication of icing is not given for either MÆE or M�.

4-19

Smart Icing Systems





with process noise reduced by a factor of 100 in energy and no measurement noise


0 1 2 3 4 5 6 7 8 9 100.8

0.85

0.9

0.95

1

1.05

1.1

1.15

1.2

MÆE

M�

time (s)

Norm

alizedEstim

ates

0 1 2 3 4 5 6 7 8 9 100.8

0.85

0.9

0.95

1

1.05

1.1

1.15

1.2

MÆE

M�

time (s)

Norm

alizedEstim

ates

Notice: A reliable indication of icing for both MÆE and M� is available in 1 s.

4-20

Smart Icing Systems

Recursive Parameter ID Algorithms


� Extended Kalman �lter (EKF):

{ augment the state with the parameters and estimate this augmented state

{ can accommodate both state disturbance and measurement noise

{ estimate may diverge, a.k.a. \lose lock"

{ can be generalized to time-varying parameters

{ very common in practice

� H1 identi�cation:

{ generalization of recursive least-squares (RLS) and least-mean-squares

(LMS)

{ guaranteed disturbance attenuation between disturbances and parameter

estimation error

{ can accommodate both state disturbance and measurement noise

{ persistency of excitation results in asymptotic convergence of estimate

for time-invariant parameters

{ can be generalized to time-varying parameters

4-21

Smart Icing Systems

H

1 FSDI AlgorithmNASA Review May 18-19, 1999

� Guaranteed disturbance attenuation level for any greater than some �

k�� ^�k2Q(x;v)

kwk2+ j�� ^�Æj2QÆ

� 2

where k�kQ is an L2 norm with a chosen weighting function Q(x; v) � 0 and

j � jQÆ

is a weighted Euclidean norm with QÆ > 0.

� x, _x, and v are known. For > � parameter estimate ^� is given by

_^� = ��1A(x; v)T [ _x�A(x; v)^�� b(x; v)] ; ^�(0) = ^�Æ

_� = A(x; v)TA(x; v)� �2Q(x; u); �(0) = QÆ

where �(t) 2 IRr�r.

� Generally, � is unknown and may be in�nite.

� However, Q(x; v) := A(x; v)TA(x; v)) � = 1

= 1) generalized LMS estimator:

_^� = Q�1Æ

A(x; v)T [ _x�A(x; v)^�� b(x; v)]

� If " 1 the limiting �lter is the RLS estimator.

4-22

Smart Icing Systems

H

1 NPFSI AlgorithmNASA Review May 18-19, 1999

� input is known, but only noisy state measurement z = x+ n is available.

� Guaranteed disturbance attenuation level > �,

k�� ^�k2Q(x;v)

kwk2+ knk2+ j�� ^�Æj2QÆ

+ jxÆ � ^xÆj2PÆ

� 2

where xÆ is actual initial state, ^xÆ is initial state estimate, and PÆ > 0.

� Both the state and the parameter must be estimated. For > �:�_^x

_^��

=

�0 A

0 0� �

^x^�

�+�

b0

�+��1�

I0

�(z � ^x) ;

_� = ��

0 A

0 0�

��

0 0

AT 0�

�+�

I 0

0 � �2Q

��

I 0

0 0�

�;

with �(t) 2 IR(n+r)�(n+r) and �(0) = diag(PÆ; QÆ).

� It can be shown that Q := �T2�2 yields � = 1, where �2 2 IRn�r is o�-diagonal

portion of �.

4-23

Smart Icing Systems

Recursive H1: Iced, No Measurement Noise


H1 FSDI Algorithm with = 3 and QÆ = (1� 10�6)I



0 5 10 15

0.9

0.95

1

1.05

1.1

1.15

1.2

MÆE

M�

time (s)

Norm

alizedEstim

ates

Notice: Using simple threshold, both M� and MÆE

give indication in < 1 s.

4-24

Smart Icing Systems

Recursive H1: Clean, No Measurement Noise


H1 FSDI Algorithm with = 3 and QÆ = (1� 10�6)I



false alarm scenario with various initial parameter estimation errors

M� parameter estimates MÆE parameter estimates

0 5 10 150.93

0.94

0.95

0.96

0.97

0.98

0.99

1

1.01

1.02

1.03

time (s)

Norm

alizedEstim

ate

0 5 10 150.93

0.94

0.95

0.96

0.97

0.98

0.99

1

1.01

1.02

1.03

time (s)

Norm

alizedEstim

ate

Notice: M� and MÆE estimates never yield false

alarms using simple detection threshold.

4-25

Smart Icing Systems

Recursive H1: Iced, w/ Measurement Noise


H1 NPFSI Algorithm with = 3 and QÆ = (1� 10�7)I


with process noise and measurement noise

0 5 10 15

0.9

0.95

1

1.05

1.1

1.15

1.2

1.25

MÆE

M�

time (s)

Norm

alizedEstim

ates


give indication in < 1 s.

4-26

Smart Icing Systems

Recursive H1: Clean, w/ Measurement Noise







0 5 10 150.93

0.94

0.95

0.96

0.97

0.98

0.99

1

1.01

1.02

1.03

time (s)

Norm

alizedEstim

ate

0 5 10 150.93

0.94

0.95

0.96

0.97

0.98

0.99

1

1.01

1.02

1.03

time (s)

Norm

alizedEstim

ate

Notice: M� and MÆE estimates never yield false

alarms using simple detection threshold.

4-27

Smart Icing Systems

Recursive H1: Iced, w/ Measurement Noise





0 5 10 15

0.9

0.95

1

1.05

1.1

1.15

1.2

1.25

MÆE

M�

time (s)

Norm

alizedEstim

ates


again give indication in < 1 s.

4-28

Smart Icing Systems

Recursive H1: Clean, w/ Measurement Noise







0 5 10 150.93

0.94

0.95

0.96

0.97

0.98

0.99

1

1.01

1.02

1.03

time (s)

Norm

alizedEstim

ate

0 5 10 150.93

0.94

0.95

0.96

0.97

0.98

0.99

1

1.01

1.02

1.03

time (s)

Norm

alizedEstim

ate

Notice: M� gives false alarm for large

initial estimation errors.

Notice: MÆE estimates never cross the

threshold.

4-29

Smart Icing SystemsRecursive EKF ID Algorithm


� Kalman �lter provides state estimate. Recast the parameter ID problem into

a state estimation problem:

_x = A�+ b+ w

_� = 0

)y :=

�x

��

)

8>><>>:

_y =

�A(x; v)�+ b(x; v)

0

�+�

w0

�

z = [I 0] y+ n

� In the Kalman �lter framework, the state disturbance, measurement noise,

and initial state, yÆ, are assumed to be Gaussian with:

E fw(t)g � 0

E fn(t)g � 0

E fyÆg = �yÆ

cov fw(t); w(�)g = P(t)Æ(t� �)

cov fn(t); n(�)g = R(t)Æ(t� �)

cov fyÆ; yÆg = QÆ

Furthermore, w(t) and n(t) are assumed to be uncorrelated:

cov fw(t); n(�)g � 0

� For linear systems Kalman �lter provides minimum-variance, unbiased state

estimate.

� However, augmented system is always nonlinear ) extended Kalman �lter.4-30

Smart Icing Systems

Recursive EKF ID Algorithm (cont'd)


� Extended Kalman �lter: linearize the system about an estimated (augmented)

state trajectory.

� The resulting algorithm is:

_^y =

�A(^x; v)^�+ b(^x; v)

0

�+�(t)HT ^R(t)�1 [z �H^y]

_�(t) = D(^y; v)�(t) +�(t)D(^y; v)T + �P(t)��(t)HT ^R(t)�1H�(t)

where, � 2 IR(n+r)�(n+r), H = [I 0],

�P(t) =

�^P(t) 0

0 0�

; and; D(^y; v) =

"@

@^xA(^x; v)^�+ @@^xb(^x; v) 0

A(^x; v)T 0#

� For a linear system, ^P(t) = P(t) and ^R(t) = R(t)

^R(t) = R(t); ^P(t) = P(t); & �(0) = QÆ

are optimal, but not for a nonlinear system. Hence, ^P(t) = ^P(t)T � 0,

^R(t) = ^R(t)T > 0, and QÆ = QTÆ

� 0 are used as algorithm design parameters.

4-31

Smart Icing SystemsRecursive EKF Results: Iced


EKF Algorithm with ^P (t) � 0:1I and ^R(t) � (1� 10�5)I



0 5 10 15

0.9

0.95

1

1.05

1.1

1.15

1.2

1.25

MÆE

M�

time (s)

Norm

alizedEstim

ates

Notice: Using the simple threshold, both

M� and MÆE give an indication in < 2 s.

4-32

Smart Icing Systems

Recursive EKF Results: Clean







0 5 10 150.8

0.85

0.9

0.95

1

1.05

1.1

1.15

1.2

1.25

time (s)

Norm

alizedEstimate

0 5 10 150.88

0.9

0.92

0.94

0.96

0.98

1

1.02

1.04

1.06

time (s)

Norm

alizedEstimate

Notice: Using simple threshold, both M� and MÆE give false alarms for all initial errors.4-33

Smart Icing SystemsRecursive EKF Results: Iced





0 5 10 15

0.9

0.95

1

1.05

1.1

1.15

1.2

1.25

MÆE

M�

time (s)

Norm

alizedEstimates

Notice: Using simple threshold, only MÆE yields a

reliable indication of icing.

4-34

Smart Icing Systems

Recursive EKF Results: Clean







0 5 10 150.8

0.85

0.9

0.95

1

1.05

1.1

1.15

time (s)

Norm

alizedEstimate

0 5 10 150.9

0.95

1

1.05

1.1

1.15

time (s)

Norm

alizedEstimate

Notice: Using simple threshold, both M� and MÆE give false alarms for all initial errors.4-35

Smart Icing Systems

Detection and Classi�cation during Maneuver



INTEGRATION GROUP







Characterizaton

Flight

Simulation

Adaptation

LTI Longitudinal

Dynamics ID

LTI Longitudinal

Dynamics Detection

LTV Longitudinal

Dynamics ID

LTV Longitudinal

Dynamics Detection

Nonlinear 6-axis

Dynamics ID

Nonlinear 6-axis

Dynamics Detection

Adaptation/Handling

Event Recovery

Nonlinear Dynamics

Development

Sensor and Drag


4-36

Smart Icing Systems

Detection and Classi�cation Formulation


Objective: Given parameter estimate, ^�(t), and other sensor information, reliably

detect the presence of icing and classify its severity in a timely manner.

Approach:

� Train neural networks (NN) to recognize correlations between parameter es-

timates, other sensor information, and icing.

� Activate the NN at beginning of maneuver

� Feed batch of sampled parameter estimates to NN.

� NN will take advantage of trends in parameter estimates, improving over

threshold detection.

� Use separate detection and classi�cation networks for eÆciency

Results to date:

� NN have been applied to H1 NPFSI identi�cation.

� Other sensor information has not yet been incorporated.

4-37

Smart Icing Systems

Neural NetworksNASA Review May 18-19, 1999

Neural Network Sigmoidal Activation Function

Input Nodes

Output Nodes

−2 −1.5 −1 −0.5 0 0.5 1 1.5 2−1

−0.8

−0.6

−0.4

−0.2

0

0.2

0.4

0.6

0.8

1

� NN are layered networks of interconnected nodes. Nodes ) activation func-

tions, lines ) weights, multiple lines are summed.

� Weighted sum of inputs to node plus a bias are input to activation function.

Often, sigmoidal activation functions are used.

� Sigmoidal activation functions generalize discrete switching.

� For a given structure (# of layers and nodes) training refers to optimization

of biases and weights based on a suite of test cases.

� NN are general enough to recognize complex nonlinear relationships, such as

between our sensor information and icing.

4-38

Smart Icing Systems

Neural Network vs. Threshold


� For detection based on parameter estimates alone, NN will take advantage

of any consistent temporal patterns in parameter estimates.

� If no consistent trends, NN will not perform better than thresholding at the

�nal estimate sample.

� Evaluate consistency of trends by running same simulations for various noise

realizations:

Recursive H1 NPFSI MÆE estimates Batch LS MÆE parameter estimates

0 5 10 150.96

0.98

1

1.02

1.04

1.06

1.08

1.1

1.12

1.14

1.16

time (s)

Norm

alizedEstimate

0 1 2 3 4 5 6 7 8 9 100.8

0.85

0.9

0.95

1

1.05

1.1

1.15

1.2

time (s)

Norm

alizedEstimate

4-39

Smart Icing SystemsRecursive H1 Detection Network


Detection Network Results

using �ve seconds of MÆE and M� estimates as input

elevator input doublets varying from 1Æ to 10Æ and from 5s to 15s

0 10 20 30 40 50 60 70 80 90 100 110

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

simulation case #

smaller amplitude doublets

ActualIcingLevel�ice

mark indication

clean

� iced

4-40

Smart Icing Systems

Recursive H1 Classi�cation Network


Four-level Classi�cation Network Results

using �ve seconds of MÆE and M� estimates as input

elevator input doublets varying from 1Æ to 10Æ and from 5s to 15s

0 10 20 30 40 50 60 70 80 90 100 110

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

simulation case #

smaller amplitude doublets

ActualIcingLevel�ice

mark �ice class.

0

2 1/3

+ 2/3

� 1

4-41

Smart Icing Systems

Identi�cation during Steady-Level Flight



INTEGRATION GROUP







Characterizaton

Flight

Simulation

Adaptation

LTI Longitudinal

Dynamics ID

LTI Longitudinal

Dynamics Detection

LTV Longitudinal

Dynamics ID

LTV Longitudinal

Dynamics Detection

Nonlinear 6-axis

Dynamics ID

Nonlinear 6-axis

Dynamics Detection

Adaptation/Handling

Event Recovery

Nonlinear Dynamics

Development

Sensor and Drag


4-42

Smart Icing Systems

Steady-Level Flight Icing Scenario


Icing Scenario:

� During steady level ight the clean aircraft passes through a \cloud" of icing

conditions and ice accretes continuously.

Model of Scenario:

� Use AcE accretion model with freezing fraction n= 0:2.

� Assume that airplane ies through icing \cloud" in time Tc, and that the LWC

along ight path has raised-cosine shape.

� Then AcE as a function of time is the solution of

ddtAcE =

�2[1� cos (2�t=Tc)] ; AcE(0) = 0

where assumed value of �ice(Tc) determines � from

�ice(t) = Z1(n)AcE(t) + Z2(n)[AcE(t)]2

Question: Can parameter ID augment steady-state characterization during

moderate turbulence?

4-43

Smart Icing Systems

Steady-Level Flight Icing Scenario (cont'd)


Assume: freezing fraction n= 0:2

Choose: icing cloud length Tc and �ice(Tc)

LWC AcE(t)

TcTc

Rdt

)

TcTc

ddtAcE � LWC � 1� cos(2�t=Tc) AcE(t) � t� Tc2�sin(2�t=Tc)

�ice(t)

)

Tc

�ice(Tc)

�ice(t) = Z1(n)AcE(t) + Z2(n)[AcE(t)]2

4-44

Smart Icing Systems

Recursive H1 Time-varying Algorithm


� The actual parameters are allowed to vary with time, according to

_� = H�+Kd

where H and K are assumed to be known and d is (unknown) parametric

disturbance.

� For the FSDI case, we have guaranteed disturbance attenuation level > �,

k�(t)� ^�(t)k2Q(x;v)

kwk2 + kdk2 + j�� ^�Æj2QÆ

� 2

� For > � the algorithm is

_^� = H^�+��1AT [ _x�A^�� b] ; ^�(0) = ^�Æ

_� = ��H �HT��KKT�+ATA� �2Q(x; u); �(0) = QÆ

� In this case, H = 0 and K can be calculated from Z1(n), Z2(n), and the S/C

derivative �ice-coeÆcients.

� Note: MÆE is an input coeÆcient and cannot be estimated without input.4-45

Smart Icing Systems

Recursive H1 Results: Moderate Icing


H1 FSDI Algorithm with = 1:0001, Q = ATA, and QÆ = (1� 10�4)I

5 minute icing cloud with �nal icing value of �ice = 1


Actual and Estimated M�

0 1 2 3 4 5 6

0.9

0.92

0.94

0.96

0.98

1

1.02

^M�(t)

M�(t)

time (min)

Norm

alizedM

�Estim

ate | actual M�

| estimated M�

� � � classi�cation levels

| classi�cation delays

�ice Level Delay

0.2 17 s

0.4 24 s

0.6 31 s

0.8 43 s

1.0 > 100 s

4-46

Smart Icing Systems

Recursive H1 Results: Rapid/Severe Icing


H1 FSDI Algorithm with = 1:0001, Q = ATA, and QÆ = (1� 10�4)I

2 minute icing cloud with �nal icing value of �ice = 1:5


Actual and Estimated M�

0 0.5 1 1.5 2 2.5 3

0.84

0.86

0.88

0.9

0.92

0.94

0.96

0.98

1

1.02

^M�(t)

M�(t)time (min)

Norm

alizedM

�Estim

ate | actual M�

| estimated M�

� � � classi�cation levels

| classi�cation delays

�ice Level Delay

0.25 12 s

0.50 17 s

0.75 20 s

1.00 28 s

1.25 64 s

1.50 < 80 s

4-47

Smart Icing Systems

Conclusions


� Recursive H1 and EKF algorithms yield timely estimates during

maneuver with measurement noise.

� Batch estimation performs poorly with and without measure-

ment noise

� NN applied to recursive H1 during maneuver detected tailplane

icing correctly 97% of the time, for doublet inputs greater than 1Æ.

� NN applied to recursive H1 during maneuver classi�ed tailplane

icing correctly 97% of cases, for doublet inputs greater than 1Æ.

� For batch detection and classi�cation, NN will not improve over

threshold applied after some �xed period.

4-48

Smart Icing Systems

Issues/Near Term Plans


� Re�ne turbulence process noise model

� H

1 NPFSI algorithm during steady-level ight

� Batch LS algorithm during steady-level ight

� NN accuracy/excitation/batch time tradeo�

� ID of lateral dynamics

� Sliding or expanding window for NN detection during maneuver

� Detection with parameter ID and steady-state characterization4-49

Smart Icing Systems

Future Plans


� Uni�ed approach to various icing event types: lateral/longitudinal,

handling/performance

� Integrate sensor info into detection and classi�cation

� Extend ID and detection/classi�cation to full nonlinear dynamics

{ Apply linear-based algorithms to nonlinear model at trim point

{ Develop algorithms based on direct parameterization of non-

linear model

� Investigate adaptive control for handling event recovery, not just

prevention

� Support incorporation of algorithms into Icing Encounter Flight

Simulator

4-50

Smart Icing Systems

Flight Controls and Sensors Waterfall Chart



98 99 00 01 02 03

ID of LTI DynamicsID of LTV Dynamics

Ice Detection for LTI Dynamics

Ice Detection for LTV Dynamics

ID of Nonlinear Dynamics

Sensor IntegrationDetection for Nonlinear Dynamics

Adaptation/Event Recovery

Support IE Flight Simulator

4-51


5-1

Human Factors/Cognitive Engineering

Principal Investigators: Nadine B. Sarter Christopher D. Wickens

Graduate Students: Beth Kelly

Scott McCray


5-2


Core Technologies


Aerodynamics and

Propulsion

FlightMechanics

Controls and Sensor

Integration

HumanFactors

AircraftIcing

Technology

EnvelopeProtection

AdaptiveControl


IMS Functions


Human Factors



5-3

Goal: Improve the safety of aircraft in icing conditions. Develop smart systems to improve ice tolerance.

Objective: Design human-centered interfaces that a) inform pilots about presence and performance effects of icing conditions b) communicate IMS status/activities/limitations to

crew in timely and effective manner c) provide pilots with advisories for handling

inflight icing encounters safely

Approach: Identify pilots’ information requirements (survey/focus groups/incident and accident analysis) Design use-centered cockpit interfaces Evaluate effectiveness and robustness of different implementations in simulator



5-4

Ice Management System Functions

- Information Automation (Presentation/management of data concerning onset/development of icing conditions and IMS status/activities)

- Advisory System (e.g., support pilots in responding to different types of handling events in timely and effective manner) - Control Automation (e.g., limit/alter pilot input for envelope protection, flight control adaptation)



5-5

THE HUMAN FACTORS GROUP

Command vs. Status Display Simulation Study

Large-Scale Pilot Survey

Beth Kelly, Scott McCray, and Nadine Sarter

Scott McCray, Nadine Sarter

Beth Kelly, Nadine Sarter

Information Requirements and Representation for

Effective Pilot-Automation Communication and

Coordination


Scott McCray, Beth Kelly, and Nadine Sarter

Pilot Survey and Focus Groups

Incident/AccidentProcess Tracing

Analysis

EnvelopeProtection

AdaptiveControl



Flight Simulation

Demonstration


5-6

Pilots’ Information Requirements

• Survey of Regional Carriers• Focus Groups with Regional Pilots• Analysis of Icing-Related Incident and

Accident Reports

Information Requirements


5-7

Pilot Survey (in collaboration with ALPA)

• Sent to 6,400 pilots from 9 regional carriers• Ratings and explanation of importance of information

on 3 areas: - Characteristics of icing - Aircraft configuration and performance - IMS/IPS status/activities



5-8

3 Critical information - needed at all timesRating scale: 2 Sometimes important (when? why?- please comment)

1 Nice to have0 Not needed

(during suspected or actual icing conditions)

Information Type Rating Your CommentsIce CharacteristicsShape of Ice Accretion

Location of Ice Accretion

Rate of Ice Accretion

Amount of Ice Accretion

Type of Icing (clear vs. rime)



5-9

Open-ended questions about:

- company policies/procedures - operational experiences - training - monitoring behavior - frequency of encounters - suggestions for IMS functions/design



5-10

Pilot Survey:

Considered “critical at all times”:- Location/Rate/Amount of Ice Accretion- OAT- Stall Margin/Angle of Attack- IMS/IPS status- Current IMS actions- Reliability of IMS Sensors



5-11

Underestimate importance of:• Shape of Ice Accretion• Reliability of and Reasoning Behind IMS Recommendations

ð Implications for Trainingð Limited Usefulness of Subjective Data



5-12

Process-Tracing Analysis of Incidents and Accidents

Time Who Cue Approach InterpretationAction

SelectionModality

15:25Both PIlots Buffet Data-driven

Ice buildup on wings

Activate level 3 deice equipment

Haptic

15:20 CaptainIce accretion behind boots

Knowledge-driven

Is this just like Roselawn ?

Keeps monitoring

Visual



5-13

© Nikolic and Sarter, 1997[modified from Neisser(1976)]

Expectations/Questions

direct AttentionAllocation

and Sampling of Environment

verify/answer ICING

Data-Driven

Knowled

ge-D

riven



5-14

• Identify What Cues Are (Not) Successful In Capturing Attention

• Identify Problems with Cue/Pattern Interpretation

• Identify Mismatches Between Cue/Diagnosis/Action Selection

….to inform the design of IMS displays that provide information in timely, effective, and meaningful manner

GOAL



5-15

Simulator Study To Examine the Effectiveness of Command vs. Status Displays

- 30 instructor pilots from Institute of Aviation- 3 conditions: - baseline - status display - command display- medium fidelity simulation of twin-engine aircraft- simulates icing cues

Implementation of Advisory Functions


5-16

Wing versus Tail Icing

Symptoms

- increase in descent rate- airframe buffet

- forward pull on yoke- increase in descent rate- yoke buffet

Response

- add power- maintain/extend flaps- reduce pitch

- reduce power- retract flaps- increase pitch



5-17

3 experimental groups: - no aid (except for probe) - status display - command display

Within-subjects variables: - accuracy of system-provided information and recommendation - familiarity of condition (wing vs. tail) - manual vs. autopilot control

“… the crew is often unaware of a developing instability or control degradation until the autopilot gives up and hands the pilot a very serious and rapidly deteriorating problem.” (Green, 1998)



5-18

Dependent variables:

- RT to probe onset - speed/nature of pilots’ response - stall ? - ability to recover - timing of autopilot disconnect - tracking performance - subjective comments on displays



5-19

The Status Display (Wing Icing)



5-20

Command Display

- Provides recommendations for pitch, power, and flap settings

- Bottom of stall speed arc will move to show increase in stall speed due to ice formation

- Information is integrated with existing displays to reduce information access costs



5-21

The Command Displays (Wing Icing)



5-22

- baseline condition reaches stall more frequently but also:

- baseline notices glideslope failure more often

- all groups are considerably affected by inaccurate information from the system (in particular, the status display group)

- autopilot is masking problems and thus creates problems with recovery once disengaged

Preliminary Findings



5-23

23

Human Factors Waterfall Chart


Process-Tracing Analyses

Command vs. Status Displays

Trend Displays/Task+System Integration/Envelope Protection and Flight Control Adaptation

Implementation

Evaluation/Refinement of Integrated IMS Cockpit Interface

99 00 01 03

Federal Fiscal Year

98 02


5-24


- Increased need for human-machine communication and coordination due to increasing levels of system complexity and autonomy

- Need for use-centered interface that takes into consideration human information-processing abilities, strategies, and limitations as well as task context

HUMAN(S)

MACHINE(S) (TASK) CONTEXT

“Cognitive Triad”


5-25

Future Research

i Development of Trend Displays To Support Monitoring, Decision-Making, and Action Selection

i Integration with other Pilot Tasks and Cockpit Systems (e.g., TCAS; high visual demands during approach)

i Implementation of/Interfaces Related To Envelope Protection and Flight Control Adaptation


6-1

Icing-Encounter Flight Simulator

• Principal Investigator: Michael Selig• Graduate Research Assistants:

– Jeff Scott (NASA funds spring 1999, leave of absencestarted May 1999)

– Two new RAs TBD• Additional Support:

– Brian Fuesz, Boeing (CRI funds summer/fall 1998)– Undergraduate Students

• Kamran Islam (CRI support summer 1998)• Jay Thomas (to start summer 1999)• Eunice Lee (to start summer 1999)• Elizabeth Rendon (to start summer 1999)


6-2

Core Technologies

Icing-EncounterFlight Simulator

Aerodynamics and

Propulsion

FlightMechanics

Control and Sensor

Integration

HumanFactors

AircraftIcing

Technology


EnvelopeProtection

AdaptiveControl


IMS Functions


Smart Icing Systems Organization


6-3

Smart Icing Systems Research

THE ICING-ENCOUNTERSFLIGHTSIMULATORGROUP

SimulatorDesign

Icing-EncountersFlight Simulator

Undergraduate Students, Dr. Selig

Brian Fuesz, Dr. Selig

RA, TBD, Dr. Selig

RA, TBD, Dr. Selig

Aerodynamicsand

Propulsion

Flight Mechanics

Control and Sensor

Integration

Human Factors

Aircraft IcingTechnology

IMS


6-4

Icing-Encounter Flight Simulator

• Goal– To improve the safety of aircraft in icing conditions

• Objectives– Function as a systems integrator bring together the

various flight simulator components composed of anaircraft model (TBD), flight mechanics, aerodynamics,propulsion, controls, sensors, the ice protection system,the smart icing system, and human factors.

– Perform "virtual flight tests" to examine the effects oficing on aircraft operations under a variety of conditions

• Approach– Develop an Icing-Encounters Flight Simulator– Apply the simulator to past accident scenarios (future)


6-5

Smart Icing Systems Research (Sim)

EPSim

WinPioneer

IceWin

Navy Effort

NASA SIS Effort

• Outline


6-6

Baseline Simulator History

• Navy Pioneer EPSim (Generation 1)

• Pioneer UAV System– Pioneer UAV

• 460-lb• 14-ft span

– External Pilot

– Ground Control Station• Internal Pilot• Payload Operator

– Reconnaissance

– 1991: Iraqi troops surrender to Pioneer UAV


6-7

Baseline Simulator History (cont.)

• The Problem


6-8


• EPSim– Funding - USA-CERL and Navy (NRaD, San Diego)– Used in Training the External Pilot– Platform: SGI Onyx ($100,000+ workstation)– Language: C++ with very specific SGI-graphics features


6-9



6-10

UIUC WinPioneer

• Generation 2• EPSim

Converted tothe PCWindowsPlatform usingMicrosoftVisual C++

• 3D View


6-11

UIUC WinPioneer (cont.)

• PreliminaryInstrumentView


6-12


• NetworkedTelemetryProgram

• RunsSeparatelyon anotherComputer


6-13


• NetworkedMap-ViewProgram


6-14


• Architecture– Driven by Legacy Code– WinPioneer/EPSim Block Diagram (messy)


6-15


• WinPioneer Demonstration– Pioneer UAV Aircraft Model– Platform: 266 MHz Toshiba Laptop– Simulation Running at 20 Hz Display Frequency– Keyboard Inputs (“joysticks”)– Features Demonstrated

• Throttle up• Phugoid mode and damping• Down elevator trim to level flight• Turning flight and spiral divergence mode

For Software See: http://www.uiuc.edu/ph/www/m-selig


6-16

Icing-Encounter Simulator - IceWin

• Generation 3 Begins• PC Platform. Why?

– Makes better use of resources (people and costs)– Top end graphics is not a requirement– Eases portability– Software available for code development in a

collaborative environment– MS Visual C/C++, Ver 5– MS system specific extensions will be kept to minimum– Uses OpenGL graphics, becoming a standard– Integrable with Matlab Simulink generated source code– Distributed simulation already demonstrated

• Use Some Modules Available on the Web


6-17

IceWin System Requirements

• Crew Controls– Control Inputs for Rudder Pedals, Control Stick/Yoke,

Flaps, Aileron/Elevator/Rudder Trims, and DualThrottles

• Control Surfaces (model asymmetric aerodynamics)– Rudder, Right/Left Ailerons, Right/Left Flaps, Elevator,

and Trims• Aerodynamics Data

– Functions Should Output Coefficients (rather thanstability derivatives)

– Each Control Surface Should Have Its Own Database– Data Must Cover Entire Range of Possible Input Values


6-18

IceWin System Requirements (cont.)

• Data Output– Each Object Should Include Accessor Methods to Allow

Output of Any and All Attributes of the Object• Control Systems and Autopilots

– Must Be Able to Incorporate Control System CodeProduced by Automated Software Generation ToolsSuch as Matlab Simulink

• Flight Recorder– Need a Simulated Flight Data Recorder (including pitot-

static probe, accelerometers, etc.)– Data Provided to Smart Icing System (SIS)


6-19


• Icing Effects– Modify Aerodynamics Data as Ice Accumulates– Allow Incorporation of Different Icing Models

• Icing Systems– Ice Protection System and Ice Management System

• Realtime Processing– Must Be Able to Run at a Variable Frame Rate Set by a

Data File– Goal of Running at 30 Hz on an NT-based PC (300+

MHz)


6-20


• Computing Platform– Win32 (Windows 95/98 and Windows NT)– Design for Portability to Unix Platforms (SGI and Linux)

• Graphics Support– Use the OpenGL API– Instrument View– 3-D View

• Fixed wrt Earth• Fixed wrt Earth with Lag• Moving with A/C• Moving with A/C with Lag• Zoom and Rotate Controls• Terrain Including Slopes


6-21


• Pilot Input Hardware– Serial Joystick (using serial port communications with

ADR2000)– Gameport Joystick (for Win95/98 -- later windows

2000?)– Keyboard

• Atmosphere Model– Standard Atmosphere Model

• Wind– Mean Wind, Gusts, Turbulence, Wind Shear

• Ground Reactions– Model Landing Gear as Springs and Dampers


6-22


• Input Files– Need a Single Startup File– Other Files are Hierarchical

• Fuel Model– No Current Requirement for Fuel Burn and CG Effects


6-23


• Neural Net Implementation• Aircraft IMS State at Each Higher Level

– Sense the ice. Tell the crew.– Automatically activate the IMS. Tell the crew.– Modify the flight envelope. Tell the crew.– Adapt the controls. Tell the crew.

• Icing Encounters to Model– Tailplane stall– Roll upset– Others


6-24

IceWin Simulator Design

• Simulation Executive (Data Flow)

Aircraft

Input ObjectGraphics

Sim Exec


6-25

IceWin Simulator Design (cont.)

• Simulation Executive Design

Aircraft

Input Object

KeyboardJoystick

GameportJoystick

N

N SerialJoystick

N N

N

Graphics

Sim Exec


6-26

IceWin Simulator Design (cont.)

• Top-Level Aircraft Design

Structure(Virtual)

Aircraft

Landing Gear Aerodynamics

Propulsion(Virtual)

Wing

Tail

CaptiveList

Flight ControlSystem (Virtual)

N

Airframe(Virtual)

Vehicle(Virtual)

N

1

Fuselage

ComponentsList

N

N1


6-27

Schedule

99 00 01 03

Federal Fiscal Year98 02

Req’mnts Definition and

Simulator Design

IceWin Development

Input Modules from Other Groups

IceWin DemonstrationSupport of Analysis Activities


6-28

Schedule

99 00 01 02

Req’mnts Definition and

Simulator Design

IceWin Development

Input Modules from Other Groups

IceWin Demonstrationin Support of Analysis Activities


98


6-29


• WinPioneer– 3D view– Instrument view (preliminary version for human factors

work)• Demonstrated distributed simulation via networked

computers– Telemetry program– Map view program

• Downloadable executable Win95/98/NT version availableoff the UIUC icing pages


6-30

Summary and Conclusions (cont.)

• WinPioneer User's Manual and Programmer's Manual onthe Web in Html Format


6-31

Summary and Conclusions (cont.)

• Aircraft ModelSpecificationsDocument

• Moving aheadwith PC-BasedIceWin


6-32

Future Research (year 1 plans)

• Simulator activities in the first year will focus on thefollowing five topics:– Conclude simulator design in C/C++– Re-write the core flight vehicle model part of the C/C++

code so that it can be more easily adapted for use bythe other groups

– Implement icing models into simulator for a nominalaircraft

– Integrate the aerodynamics model of the nominalaircraft into the simulator (to replace the Pioneer)

– Begin to incorporate a flight control system into thesimulator flight vehicle (might be too ambitious)

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