Airspace Concept
Evaluation SystemState of Development
Gano B. Chatterji
gano.b.chatterji@nasa.gov
28 January 2010
ACES Development Guided by
Research Needs
Oceanic In-Trail Procedures
Traffic Flow Management
Multi-Sector Planner
Dynamic Airspace Configuration
Separation Assurance
Integrated Weather Information
With Arrival Merging and Separation
Super Density Operations
Trajectory Prediction Synthesis & Uncertainty
CDAs & Tailored Arrivals
Metroplex Operations
Merging and Spacing
Closely Spaced Parallel Runways
Arrivals/Departures Management
Enhanced Surface Operations
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Main Points
• ACES development driven by research needs; Ideas from
research being folded into ACES.
• Validation based on data and not just software; emphasis on
plotting, visualization, analysis with large datasets.
• Results produced by ACES are reasonable.
• ACES is faster and more stable.
• ACES has higher fidelity models (surface, terminal area
trajectory, separation-assurance).
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Outline
• ACES Development:
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Separation-Assurance
Traffic Flow Management
Dynamic Airspace Configuration
Weather Data Handling
Trajectory Generators
Weight Estimation
ACES Analyst and Viewer
User Support Helpdesk
• Research Examples Using ACES:
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Surface Operations
Separation-Assurance
Dynamic Airspace Configuration
Dynamic Airspace Configuration and Traffic Flow Management Integration
System-Wide Study
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Separation-Assurance - New Capabilities
• Weather:
Weather
Polygon
Final
Trajectory
– Weather polygons used for defining
weather avoidance areas.
Original
Trajectory
• Trajectory Prediction Uncertainties:
– Can perturb the predicted trajectories to
understand the effects of uncertainty.
Actual
Predicted
• Multiple Centers:
– Can operate independent Separation
Assurance agents in multiple geographic
areas to study coordination issues.
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Traffic Flow Management Support
• Objective:
– Flexible structure
• Disable TFM for open-loop simulations.
• Enable/disable TFM in airport, TRACON, center domains.
– Support for alternative algorithms
• Distributed TFM
• Centralized TFM
• Linear-Programming based Optimal TFM
– Causality and delay attribution
• Who caused it and where was it realized.
• Approach:
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Support services for demand and capacity prediction.
Improved plug-in architecture.
Messaging interface.
Simple GUI based configuration prior to simulation.
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Dynamic Airspace Configuration Support
• Objective:
– Implement Dynamic Airspace Configuration algorithms in ACES.
– Support for capacity (including workload) metrics.
• Approach:
– Data interface for ACES traffic and geometry outputs in Enhanced
Traffic Management System (ETMS) format.
– Communications service for data exchange with DAC algorithms
running on other computers.
– ACES modified to read back subsector data (sector building blocks).
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Weather Service Provider Support
• Objective:
– Support for dynamic convective weather products.
– Support for forecast weather products.
– Support for grid-based and contour-based weather data.
• Approach:
– Unified service interface for querying weather data.
– Error models for weather forecast from nowcast data when forecast
data are unavailable.
• Time-shift error
• Position error
• Severity and coverage error
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Trajectory Generators
• Objective:
– Airport to airport trajectory generation.
• Surface
• Terminal area
• Enroute
– Choice of trajectory generators.
• Approach:
– Swappable trajectory generator interface.
– Kinematic trajectory generator uses BADA performance tables.
– Kinetic trajectory generator uses BADA aircraft performance data and
atmosphere data.
• Key Finding:
– New trajectory generators being tested.
– Performance data updated based on BADA 3.7.
– Will improve ACES runtime performance.
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Take-Off Weight Estimation
• Objective:
– Determine takeoff weight for planned flight using aircraft performance
model, and reserve and maneuvering fuel requirement.
• Approach:
– Iterative procedure to determine fuel and payload.
– A closed-form solution based on constant altitude cruise, and climb and
descent fuel increment factors.
• Key Finding:
– Payload-range curves compare well with aircraft manufacturer published
data.
– Computationally efficient.
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ACES Analyst Tool Enhancements
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ACES Grid Creator
– Generation of sector grid maps
from ETMS sector files.
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ACES Viewer
– Replaces the current ACES VST
during runtime.
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ACES Disambiguation Tool
– Bug fixes and compatibility
enhancements for use with the
ACES Grid Creator.
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ACES Analyst
– Flight data set from ETMS data.
– Multiple data converters to
support scenario generation.
– Analyst reports.
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ACES-SA Web Application
– Viewing and analyzing conflict
resolution.
•
SurfTools
– Airport surface design tool
(STLE) – STLE is part of ACES.
•
TASSE
– ACES runtime configuration
management system and
surface and terminal area
airspace design tool.
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ACES Report Generator
– Enhances to generate .csv file
versions of the ACES National
Metrics.
– Analyst reports.
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• Flexible tool for
visualization
• Airport, airspace and
weather
• Trajectories
ACES Viewer
– Conflict scenarios
– Trial-plan trajectories
– As flown trajectories
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User Support Helpdesk
• Purpose of the ACES Helpdesk :
– A single point of contact for answering ACES questions.
• Helpdesk Queries:
– Users send email queries to
aces.helpdesk@aerospacecomputing.com.
– Each query assigned a unique tracking number.
– Communication via email, using the tracking number, until
query resolved.
• Common queries during the first two months:
– Locating ACES documentation.
– ACES setup questions.
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Research Examples
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Safe and Efficient Surface Operations (SESO)
• Objective:
– Improve airport surface capacity
and efficiency.
• SESO concepts:
– Trajectory based surface
operations.
– Optimized taxi scheduling.
• ACES Modeling Capabilities:
– Node-link based airport
representation.
– Time based taxi routes.
– Integrated airport/TRACON
simulation environment.
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Separation Assurance
• Objective:
– Maintain required separation
between aircraft.
– Meter aircraft at points in space.
– Avoid weather hazards.
• Approach:
– Solve all problems in an
integrated fashion for
coordination and efficiency.
• Key Finding:
– Can resolve over 99% of all
conflicts for 2X traffic with
weather.
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Dynamic Airspace Configuration
• Objective:
– Create sectors such that
traffic is at or below capacity.
• Approach:
– Use Genetic Algorithm to
select Voronoi polygon
generating points.
– Iterative partitioning.
– Maximize transit-time and
minimize boundary crossings.
• Key Finding:
– Capacity thresholds are not
exceeded by traffic.
– Delays are reduced.
New
Current
Num. of sectors
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Num. of overloaded
sectors
0
1
2,471
2,851
Num. of boundary
crossings
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Dynamic Airspace Units
• Objective:
– Capacity re-allocation by
changing sector boundary.
ZOB67C
ZOB67B
ZOB66
• Key Finding:
– Minor adjustments rather
than a complete boundary
change.
ZOB67A
ZOB67
• Approach:
– Exchanges ‘slices’ between
sectors to address overutilization.
– Merges under-utilized
sectors.
ZOB66A
Sample Dynamic FPAs
Today
ZOB67C
ZOB67B
ZOB67A
ZOB66B
ZOB66C
ZOB66A
ZOB67C
ZOB67B
ZOB67A
ZOB66B
ZOB66C
Sample Dynamic FPAs
ZOB66A
ZOB66B
ZOB66C
Sample Dynamic FPAs
ZOB66A workload higher
ZOB66B and ZOB66C units are
assigned to sector ZOB67 (left).
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Dynamic Airspace Configuration and Traffic
Flow Management Integration
• Objective:
– Study interaction between airspace
configuration and traffic flow
management.
• Approach:
– Integration using data and ACES
simulations.
• Key Finding:
– TFM delay can be determined as a
function of number of sectors.
– Sectors can be designed to reduce
delays due to mismatch between
demand and capacity.
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System-Wide Weather Effect Study
• Objective:
– Establish weather affected baseline data for common scenario days.
– Determine yearly weather delays for current day operations.
– Assess the ability of Separation-Assurance, Traffic Flow Management
and Dynamic Airspace Configuration to reduce delay in the presence
of weather.
• Approach:
– 17 days of traffic, wind, weather, AAR/ADR, FAA data from 2006
collected.
• Traffic volume: low and high
• Weather: light, moderate and severe
– Average arrival delay with 2006, 2018 and 2025 assumed traffic and
capacities computed.
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Common Scenario Generation
 Current Day (2006)
• Cluster Analysis
• NAS Data Gathering
• Database Generation
 NAS state data
 NAS weather data
 NAS wind data
 Airport Capacity and State
• VAMS
• ASPM
• JPDO-SMAD + ASPM + VAMS
 Airport Taxi Times
• VAMS
• ASPM
• JPDO-SMAD + ASPM + VAMS
 Expanded ACES Airport database
• Most frequently used terminal area
configurations
• Runway modeled airports (FAA Metro 7
airports)
• Added aircraft types
 Terminal Area Transit Times
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Data Gathering
Updated ACES transit times
 Sector Enhancements for use with
ACES
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2006 and 2007 Sector models
Correction of “Gaps and Overlaps”
laterally and vertically
Alignment of sector boundaries
Oceanic coverage
 Demand Generation (TAF 2008)
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1.0x, 1.1x (NGIP (2018)), 1.2x
(NextGen (2025)), 1.5x, 2.0x, 2.5x,
and 3.0x.
Unconstrained version of demand
Constrained (time shifted) version of
demand
 NGIP (2018) configuration
 NextGen (2025) configuration
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Parting Thoughts
• ACES development driven by research needs; Ideas from
research being folded into ACES.
• Validation based on data and not just software; emphasis on
plotting, visualization, analysis with large datasets.
• Results produced by ACES are reasonable.
• ACES is faster and more stable.
• ACES has higher fidelity models (surface, terminal area
trajectory, separation assurance).
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