Advanced Subsonic Tech--May 97

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AEROSPACE OPERATIONS SYSTEMS PROGRAM
Dr. J. Victor Lebacqz Director, Aviation System
Capacity Aerospace Operations Systems
Programs NASA 14 December 1999
www.aos.nasa.gov/aosbase www.aos.nasa.gov
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NASA Strategic Enterprises
NASA Enterprises Primary Customers
Ultimate Beneficiary
Ultimate Resource Provider
Space Science Science and Education
Communities Technology Innovators Mission to
Planet Earth Science, Commercial, and Education
Communities Policy Makers Human Exploration and
Development of Space Science and Education
Communities Commercial Sectors Aero- Space
Technology Aerospace and Nonaerospace
Industries Other U.S. Government Agencies
The Public

The Public
Administration and Congress
Decision Makers
Crosscutting Processes Manage Strategically Provid
e Aerospace Products and Capabilities Generate
Knowledge Communicate Knowledge
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OAT Enterprise 3 Pillars

• Global Civil Aviation
• Five stretch goals
• Revolutionary Technology Leaps
• Three stretch goals
• Access to Space
• Two stretch goals

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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

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Growth in Operations, Safety Rate, and Frequency
of Accidents (1980-2015)
Courtesy Boeing
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Goal 1 Aviation Safety
Benefits Safer air transportation worldwide
Dramatic reduction in aviation fatalities
Eliminate safety as an inhibitor to a potential
tripling of the aviation market
Reduce the aircraft accident rate by a factor of
five within 10 years, and by a factor of 10
within 25 years
CHALLENGES
OUTCOMES
2025
2020
2015
2010
2005
2000
1997
2007
2022
System Monitoring Modeling
Monitor for Safety
FAA NAS Architecture
Phase I
Phase II
Phase III
World-wide aviation monitoring allowing
continuous insight and assessment of system
health and operations
CAPACITY Adv. Air Traffic Technologies
Integration of Intelligent Aviation Systems
Real-Time Monitoring of Aviation Systems
Aviation Safety Program
Phase II
Phase I
Information Technology Aerospace Operations
Systems
Accident Prevention
Equip for Safety
Space-Based Aviation Safety System Technologies
(Code S)
AGATE Flight Systems
Elimination of recurring accident causes and
early detection and prevention of new accident
categories
HSR Flight Deck
Aviation Safety Program
Phase I
Phase II
Ultra-Safe Airborne Technology Integration
Aerospace Operations Systems, Rotorcraft,
Propulsion, Flight Research
Accident Mitigation
Design for Safety
AGATE Crashworthiness
Increased survivability of the rare accidents and
incidents that do occur
Safety-Configured X-Plane Design and Demonstration
Aviation Safety Program
Phase II
Phase I
Base RT Program Other Agencies Systems Tech.
Program Planned and Funded Systems Tech.
Program, Required but Unfunded
Airframe Systems Rotorcraft
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OAT Aeronautics Programs Structure
Center Mission COE Facility Group Lead
LaRC Airframe Sys Atmos Science Structures
Materials WTs Aero, Aerothermo Facilities /
Struct Test Facilities
ARC Aviation Ops Systems Astrobiology Info
Tech Simulators Scientific Engineering Computa
tional Facilities
DFRC Flt Rsrch Atmos Flt Ops Aircraft Flight
Facilities
LeRC Aeropropulsion Turbomachinery Propulsion F
acilities
Programs/ Lead Centers
ISE / LaRC
Human Factors
Exp Aircraft Flight Research
Turbomachinery Combustion
Airborne Systems
Safety / LaRC
HPCC / ARC
Inlets, Nozzles Mechanical Engine Components
Air Traffic Management
Test Bed A/C Research Ops
Structures Materials
Competency Group Areas
Capacity / ARC
RPV Research Ops
Propulsion Mats Structs
Aerodynamics
Aero Veh Sys/LaRC
Flight Test Tech Instrument
Hybrid Propulsion
Rotorcraft VSTOL Techs
Mission / Sys Analysis
Prop Sys/LeRC
Flt Rsrch/DFRC
Propulsion Support Tech
Crew Station Design Integ
Av Ops Sys/ARC
Info Tech/ARC
Icing Technologies
Hypersonic Technologies
Rotorcraft/ARC
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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

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Current AOS Program Focus Areas
Technology Gap Areas
System Problems
Weak collaboration among designers and human
factors experts Failure to identify or mitigate
risk factors during design phase Mode confusion
in use of automated systems
Human Factors in Systems
Human Performance
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
Weather Factors Prediction Mitigation
Inadequate understanding of icing conditions and
effects Expensive processes to test for
certification Lack of shared information
regarding weather conditions
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Human Factors in Systems Examples
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Comparison of Flight Mode Annunciators
Correct Interpretation
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.
Figure of Merit
Experimental FMA
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Alternative Interpretations
Observe that there are twoalternative
interpretations ofthe Control FMA that are very
similar to the correct interpretation.
Control FMA
PI Ev Palmer, NASA Ames Research Center
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Human Memory Constraints in Procedure Execution
Predicting Error Vulnerability
Flight Control Automation
2. Speed controlled via MCP.
DFW Approach Scenario
4. Aircraft fails to meet speed target for
crossing restriction.
1. FMS transitions out of VNAV when altitude
capture achieved.
APEX Human Operator Model
3. Crew fails to recall B757 transition behavior.
Results in Habit Capture, reversion to
B737 FMS procedure.
Apex Crew Simulation Flight / Cockpit
procedures Human Performance Model Memory
Errors Decision Errors
PI Roger Remington, NASA Ames Research Center
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Design of Displays and Procedures
Completed part-task simulator study on
Scene-Linked HUD Symbology for taxi turns.
Offset poles and flags placed at a fixed distance
beyond turn improves taxi centerline tracking.
Pilots can use symbologys relative distance cues
to mitigate field-of-view (FOV) HUD limitations.

PI Dave Foyle, NASA Ames Research Center
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Initial NAOMS Studies
Develop 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 NAOMS Developed survey instrument to tap
on-going activities and special interests Pilot
Study - Survey to randomly-selected sample of
commercial pilots
POC Mary Connors, NASA Ames Research Center
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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.

• APMS routinely monitors hundreds of parameters
  for total system performance

• Customizable toolkit converts data into usable
  information

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

PI Irv Statler, NASA Ames Research Center
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Human Performance Examples
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Aviation Fatigue Countermeasures

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

• 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

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

• 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.

PI Dave Neri, NASA Ames Research Center
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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
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Perceptual Models Metrics

• 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

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

Sample stimuli
Contrast Threshold (dB)
Gain (dB)
Spatial Standard Observer
Derived Contrast Sensitivity Function
ModelFest Data
PI Beau Watson, NASA Ames Research Center
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Analysis Tool for Human Depth Cue Integration
Model
Experiment
PI Barbara Sweet, NASA Ames Research Center
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Weather Factors Prediction and Mitigation Examples
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Icing Characterization
Goal

• 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

Objectives

• 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

PI Dean Miller, NASA Glenn Research Center
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Icing Computational Modelling
Ice Shape Tracing Providing Validation Data
Ice Shape Comparison Results Computational vs.
Experimental
PI Mark Potapczuk, NASA Glenn Research Center
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Breakout Sessions

• 4 Next Generation Capacity Technologies
• Dr. Tom Edwards Moderator
• Dr. Heinz Erzberger Direct-To Tool
• Tom Davis Multi-Center Traffic Management
  Advisor Tool
• Dr. Len Tobias Collaborative Arrival Planner
  Tool
• 5 Capacity Distributed Air Ground Traffic
  Management
• Steve Green Moderator
• Steve Green Distributed Air-Ground Traffic
  Management
• Dr. Ev Palmer Linking Cockpit and Air Traffic
  Control Automation
• Sandy Lozito Shared Air-Ground Separation
  Responsibilities
• 6 Improved Capacity Through Vertical Flight
• Ed Aiken Moderator
• Sandy Hart Improving Rotorcraft Safety
• Mark Betzina Tiltrotor Noise Abatement (Wind
  Tunnel Tests)
• Bill Decker Tiltrotor Noise Abatement
  (Simulation Flight Tests)
• Dr. John Zuk Runway-Independent Aircraft
  Operations

• 1 Next Generation Capacity Technologies
• Dr. Tom Edwards Moderator
• Dr. Heinz Erzberger Direct-To Tool
• Tom Davis Multi-Center Traffic Management
  Advisor Tool
• Dr. Len Tobias Collaborative Arrival Planner
  Tool
• 2 Aviation Human Factors
• Dr. 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