NASAJ. V. Lebacqz

AEROSPACE OPERATIONS SYSTEMS PROGRAM

Dr. J. Victor LebacqzDirector, Aviation System Capacity &

Aerospace Operations Systems Programs

NASA

14 December 1999

www.aos.nasa.gov/aosbasewww.aos.nasa.gov

NASAJ. V. Lebacqz

NASA Strategic Enterprises

NASA EnterprisesPrimary Customers

Decision Makers

UltimateBeneficiary

The Public

Administrationand

Congress

UltimateResource Provider

The Public

Space ScienceScience and Education Communities

Technology Innovators

Mission to Planet EarthScience, Commercial, and Education Communities

Policy Makers

Human Exploration and Development of Space

Science and Education CommunitiesCommercial Sectors

Aero- Space TechnologyAerospace and Nonaerospace Industries

Other U.S. Government Agencies

Crosscutting ProcessesManage Strategically

Provide Aerospace Products and CapabilitiesGenerate Knowledge

Communicate Knowledge

NASAJ. V. Lebacqz

OAT Enterprise “3 Pillars”

• Global Civil AviationGlobal Civil Aviation– Five stretch goals

• Revolutionary Technology Leaps

– Three stretch goals

• Access to Space– Two stretch goals

NASAJ. V. Lebacqz

Five Goals for Global Civil Aviation

Reduce the aircraft accident rate by a factor of five within

10 years, and by a factor of 10 within 20 years.

While maintaining safety, triple the aviation system

throughput, in all weather conditions, within 10 years

Reduce the perceived noise levels of future aircraft by a

factor of 2 within 10 years, and by 4 within 20 years

Reduce emissions of future aircraft by a factor of 3 within

10 years, and by 5 within 20 years

Reduce the cost of air travel by 25% within 10 years, and

by 50% within 20 years

NASAJ. V. Lebacqz

1965 1975 1985 1995 2005 2015Year

50

45

40

35

30

25

20

15

10

5

0

Improvement areas:

• Lessons learned• Regulations

• Airplanes

• Flight operations

• Maintenance• Air traffic management

• Infrastructure

Hull loss accidents

per year

Millions of departures

Hull loss accident rate

Airplanes in service

11,060

23,100

1996 2015

Courtesy Boeing

Growth in Operations, Safety Rate, and Frequency of Accidents (1980-2015)

2007

2025202020152010200520001997

World-wide aviation monitoring allowing continuous insight and assessment of system health and operations

Elimination of recurring accident causes and early detection and prevention of new accident categories

2022System Monitoring & Modeling

Accident Prevention

Accident Mitigation

Increased survivability of the rare accidents and incidents that do occur

Phase I Phase II Phase IIIFAA NAS Architecture

CAPACITY — Adv. Air Traffic Technologies

Aviation Safety Program

Phase IIPhase I

AGATE Flight Systems

HSR Flight Deck

Aviation Safety Program

Phase I

AGATE Crashworthiness

Aviation Safety Program

Phase IIPhase I

Airframe Systems & Rotorcraft

Monitor for Safety

Design for Safety

Integration of IntelligentAviation Systems

Real-Time Monitoringof Aviation Systems

Space-Based Aviation Safety SystemTechnologies (Code S)

Phase IIUltra-Safe AirborneTechnology Integration

Safety-Configured X-PlaneDesign and Demonstration

Information Technology & Aerospace Operations Systems

Aerospace Operations Systems, Rotorcraft, Propulsion, & Flight Research

Equip for Safety

Base R&T Program

Other Agencies

Systems Tech. Program; Planned and Funded

Systems Tech. Program, Required but Unfunded

CHALLENGES OUTCOMES

Goal 1: Aviation SafetyReduce the aircraft accident rate by a factor of five within 10 years, and by a factor of 10 within 25 years

Benefits:• Safer air transportation worldwide• Dramatic reduction in aviation fatalities• Eliminate safety as an inhibitor to a potential tripling of the aviation market

NASAJ. V. Lebacqz

ARC

Aviation Ops SystemsAstrobiology

Info Tech

Simulators Scientific & EngineeringComputational Facilities

OAT Aeronautics Programs Structure

Center:

Mission:

COE:

FacilityGroup Lead:

CompetencyGroup Areas:

DFRC

Flt Rsrch

Atmos Flt Ops

Aircraft &Flight Facilities

LaRC

Airframe SysAtmos Science

Structures &Materials

WTs & Aero,Aerothermo Facilities /

Struct Test Facilities

LeRC

Aeropropulsion

Turbomachinery

PropulsionFacilities Programs/

Lead Centers

ISE / LaRC

HPCC / ARC

Capacity / ARC

Aero Veh Sys/LaRC

Prop Sys/LeRC

Av Ops Sys/ARC

Flt Rsrch/DFRC

Info Tech/ARC

Rotorcraft/ARC

HumanFactors

Air TrafficManagement

Rotorcraft &VSTOL Techs

Turbomachinery& Combustion

Inlets, Nozzles &Mechanical Engine

Components

PropulsionMats & Structs

PropulsionSupport Tech

Exp Aircraft Flight Research

Test Bed A/CResearch & Ops

Flight Test Tech& Instrument

AirborneSystems

Structures &Materials

Aerodynamics

Mission / SysAnalysis

Crew StationDesign & Integ

RPVResearch & Ops

HybridPropulsion

HypersonicTechnologies

InformationSystem Techs

Safety / LaRC

Icing Technologies

NASAJ. V. Lebacqz

Aerospace Operations Systems Program

Pioneer advanced research and technology to enable revolutionary advances in Aerospace Operations Systems to support NASA Goals:

Reduce the aircraft accident rate by a factor of 5 within 10 years, and a factor of 10 within 25 years

While maintaining safety, triple the aviation system throughput, in all weather conditions, within 10 years

Safety

Capacity

Aerospace Operations Systems are ground, satellite, and vehicle systems, and human operators, that determine the operational safety, efficiency and capacity of vehicles operating in the airspace, including:

– communication, navigation and surveillance (CNS) systems;– air traffic management systems, interfaces and procedures;– relevant cockpit systems, interfaces and procedures;– operational human factors, their impact on aviation operations, and error mitigation;– weather and hazardous environment characterization, detection and avoidance systems

NASAJ. V. Lebacqz

Weak collaboration among designers and human factors expertsFailure to identify or mitigate risk factors during design phaseMode confusion in use of automated systems

Weak collaboration among designers and human factors expertsFailure to identify or mitigate risk factors during design phaseMode confusion in use of automated systems

Current AOS Program Focus Areas

HumanFactors in Systems

Weather FactorsPrediction & Mitigation

HumanPerformance

Technology Gap Areas System Problems

Human error still cited as a factor in majority of accidents Lack of understanding of cognitive and decision processes Inadequate attention to human limitations such as fatigue

Human error still cited as a factor in majority of accidents Lack of understanding of cognitive and decision processes Inadequate attention to human limitations such as fatigue

Inadequate understanding of icing conditions and effectsExpensive processes to test for certificationLack of shared information regarding weather conditions

Inadequate understanding of icing conditions and effectsExpensive processes to test for certificationLack of shared information regarding weather conditions

NASAJ. V. Lebacqz

Human Factors in Systems Examples

NASAJ. V. Lebacqz

Comparison of Flight Mode Annunciators

ΩΩ300 NAV1 CLB INT LEVEL 23ooo

300 THRUST NAV1 CLB THRUST 23ooo

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1.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

16

Alternative InterpretationsFig

ure

of

Meri

t

Correct Interpretation

Experimental FMA

Control FMA

The aircraft is level at 23,000 ft, the clearance altitude, in VNAV. The crew is waiting for a clearance to 33,000 ft, their cruse altitude.

Observe that there are twoalternative interpretations ofthe Control FMA that are very similar to the correct interpretation.

PI: Ev Palmer, NASA Ames Research Center

NASAJ. V. Lebacqz

Human Memory Constraints in Procedure Execution: Predicting Error Vulnerability

1. FMS transitions out of VNAV when altitude capture achieved.

DFW Approach Scenario

APEX Human Operator Model

Flight Control Automation2. Speed

controlled via MCP.

3. Crew fails to recall B757 transition behavior. Results in “Habit Capture”, reversion to B737FMS procedure.

4. Aircraft fails to meet speed target for crossing restriction.

Apex Crew SimulationApex Crew Simulation• Flight / Cockpit procedures• Human Performance Model

• Memory Errors• Decision Errors

PI: Roger Remington, NASA Ames Research Center

NASAJ. V. Lebacqz

Design of Displays and Procedures

Offset poles and flags placed at a fixed distance beyond turn improves taxi centerline tracking. Pilots can use symbology’s relative distance cues to mitigate field-of-view (FOV) HUD limitations.

Completed part-task simulator study on Scene-Linked HUD Symbology for taxi turns.

PI: Dave Foyle, NASA Ames Research Center

NASAJ. V. Lebacqz

Initial NAOMS StudiesDevelop a 1st generation, system-wide monitoring capability to measure and communicate the health and status of operational safety performance

National Aviation Operational Monitoring Service (NAOMS): Completed study of the demographics of the NAS Conducted initial studies in support of the NAOMSDeveloped survey instrument to tap on-going activities and special interestsPilot Study - Survey to randomly-selected sample of commercial pilots

A I R C A R R I E R

P I L O T S

G E N E R A L

A V I A T I O N P I L O T S

T E C H N I C I A N S

C O N T R O L L E R S

O T H E R S

F L I G H T

A T T E N D A N T S

N A S A / N A O M S

M I L I T A R Y

P I L O T S

D E I D E N T I F I E D

S U R V E Y D A T A

R E S E A R C H P R O D U C T S

S U R V E Y F O R M , P H O N E C A L L , O R F A C E - T O - F A C E I N T E R V I E W Q U E S T I O N S

D E V E L O P E D B Y N A S A I N C O N S U L T A T I O N W I T H A V I A T I O N C O M M U N I T Y

The Concept of NA OMSThe Concept of NA OMS

(NAS Operational Monitoring Service)(NAS Operational Monitoring Service)

POC Mary Connors, NASA Ames Research Center

NASAJ. V. Lebacqz

Aviation Performance Measurement System

GOAL: To develop data analysis capabilities to facilitate identifying causal factors, accident precursors, and unexpected features in data collected pertaining to the health, performance and safety of the National Airspace System.

• Continuing evolution and evaluation in collaboration with Alaska and United Airlines

• APMS routinely monitors hundreds of parameters for total system performance

• Customizable toolkit converts data into usable information

DATABASELINK AGE

AIRLINEPROPRIETARY

DATABASE

FLIGHT DATAINPUTS

1995 1996 1997 1998 1999 2000

90

10

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S T A T I S T I C A LA N A L Y S I S

S C R E E N I N G F O RS P E C I A L E V E N T S

D A T A B A S EE X P L O R A T I O N

F L I G H TA N I M A T I O N R I S K

A N A L Y S I S

PI: Irv Statler, NASA Ames Research Center

NASAJ. V. Lebacqz

Human Performance Examples

NASAJ. V. Lebacqz

Aviation Fatigue Countermeasures

GOAL: To develop interventions to reduce the effects of fatigue, sleep loss, and circadian disruption on flight crews and ATM personnel.

• Original Education and Training Module for Part 121 operations published as NASA/FAA Technical Memorandum: Crew Factors in Flight Operations X: Alertness Management in Flight Operations.

• Initiated piloted simulation to study effectiveness of online, fatigue-dependent feedback to flight crews.

• Completed 747-400 simulator study on effectiveness of in-flight activity breaks on flight crew alertness.

Hourly in-flight activity breaks showed significant decrease in measured sleepiness and increase in reported alertness

PI: Dave Neri, NASA Ames Research Center

NASAJ. V. Lebacqz

Icing Training Video

Icing Video (Level 3 Milestone 4Q ‘98); Activities in support of concurrent task management (Level 2 Milestone, 4th Q ‘01).

- Completed beta version of icing educational video for ice contaminated tailplane stall. Video contains information and graphic depiction on weather conditions conducive to icing; reviewed by customer community; 250 copies distributed (150 requested by FAA/Flight Standards) - ‘98

- Cockpit Interruptions and Distractions article - Printed in Directline and reprinted in numerous airline safety magazines - ‘99

POC: Tom Bond, NASA Glenns Research Center

NASAJ. V. Lebacqz

• There is a need for a Spatial Standard Observer (SSO) to provide objective measures of visibility and contrast of spatial imagery (e.g., CIE Photometric and Colorimetric Standards)

• Recent multi-lab collaborative data collection (ModelFest) provides a basis for design of SSO

10 20 30 40Stimulus Number

- 50

- 40

- 30

- 20

- 10

0

dlohserhT

HBdL

abwamnbambrbccccvrcwttc

1 2 5 10 20 50cyclesêdeg

- 30

- 20

- 10

0

10

Bdabwamnbambrbccccvrcwttc

Derived Contrast Sensitivity Function

ModelFest Data

Filter

Channels

Integrate

Contrast

Power

Con

tras

t Thr

esho

ld (

dB)

Gai

n (d

B)

Spatial Standard Observer

Perceptual Models & Metrics

Sample stimuli

• NASA/PPSF-supported SSO design presented at Optical Society of America (9/26/99)

PI: Beau Watson, NASA Ames Research Center

NASAJ. V. Lebacqz

Analysis Tool for Human Depth Cue Integration

Experiment

Model

+

−Integrated

ActualDepthDepth

ControlTask

Display

DesiredDepth

RelativeSize

StereoDisparity

Human OperatorPerception and Action

PI: Barbara Sweet, NASA Ames Research Center

NASAJ. V. Lebacqz

Weather Factors Prediction and Mitigation Examples

NASAJ. V. Lebacqz

Normal Icing

SLD

Icing Characterization

Particle Sizing Probe

• Comprehensive characterization of meteorological parameters and frequency of occurrence for icing conditions which aircraft will encounter

– within current FAA aircraft icing certification envelope– conditions which fall outside envelope (e.g. - SLD)

• Supports NASA goal of enhanced safety and capacity

Goal

• Quantify meteorological parameters associated with icing conditions (water droplet size, concentration of water in icing cloud, temperature, etc)

• Support the development of improved icing cloud instrumentation

Objectives

NASA Twin Otter

PI: Dean Miller, NASA Glenn Research Center

NASAJ. V. Lebacqz

Icing Computational Modelling

Ice Shape Tracing; Providing Validation Data

Ice Shape Comparison Results Computational vs. Experimental

PI: Mark Potapczuk, NASA Glenn Research Center

NASAJ. V. Lebacqz

Breakout Sessions

1: Next Generation Capacity Technologies

Dr. Tom Edwards: ModeratorDr. Heinz Erzberger: Direct-To ToolTom Davis: Multi-Center Traffic

Management Advisor ToolDr. Len Tobias: Collaborative Arrival

Planner Tool

2: Aviation Human FactorsDr. Terry Allard: Moderator

Dr. Dave Neri: Fatigue Countermeasures

Dr. Judith Orasanu: CRM & Training

Drs. Beau Watson and Roger Remington: Vision and Cognition

3: Information Technologies for Aviation

Dave Alfano: Moderator

John Kaneshige: Intelligent Flight Controls

Dr. Dave Korsmeyer: Design Cycle Improvements

Yuri Gawdiak: Data Sharing

4: Next Generation Capacity Technologies

Dr. Tom Edwards: ModeratorDr. Heinz Erzberger: Direct-To ToolTom Davis: Multi-Center Traffic Management

Advisor ToolDr. Len Tobias: Collaborative Arrival Planner Tool

5: Capacity: Distributed Air Ground Traffic Management

Steve Green: ModeratorSteve Green: Distributed Air-Ground Traffic

Management Dr. Ev Palmer: Linking Cockpit and Air Traffic

Control AutomationSandy Lozito: Shared Air-Ground Separation

Responsibilities

6: Improved Capacity Through Vertical Flight

Ed Aiken: ModeratorSandy Hart: Improving Rotorcraft SafetyMark Betzina: Tiltrotor Noise Abatement (Wind

Tunnel Tests)Bill Decker: Tiltrotor Noise Abatement (Simulation

& Flight Tests)Dr. John Zuk: Runway-Independent Aircraft

Operations