ames research center 1 facet: future air traffic management concepts evaluation tool banavar sridhar...

Ames Research CenterAmes Research Center

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FACET:Future Air Traffic Management

Concepts Evaluation Tool

Banavar SridharShon Grabbe

First Annual WorkshopNAS-Wide Simulation in Support of NEXTGEN

10 December, 2008


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Outline

FACET Description

FACET uses in NEXGEN analysis– Tube Designs– Optimization– Network Analysis

Issues– Lack of methodology– Simulation tools– Integration of existing tools


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Future ATM Concepts Evaluation Tool (FACET)

Environment for exploring advanced ATM concepts FACET design balances fidelity and flexibility

– Utilizes less complex models of aircraft performance and terminal airspace

– Enables zoom from national to regional to single aircraft level FACET architecture enables modeling of ~15,000 aircraft

trajectories at the national level in a few seconds Runs on a desktop computer (Linux, Solaris, Mac OS X, Win

XP) – Works with existing FAA systems on an enterprise server– Accessible via Web to users of Flight Explorer®, Matlab®, and Jython

3 Operational Modes: Playback, Simulation, Hybrid Used for visualization, off-line analysis and real-time

planning applications


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Animation: A Day in the Life of Air Traffic

• Smithsonian’s National Air & Space Museum is using FACET in “America by Air” exhibit


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FACET Displays

3-DConvective Weather

Traffic Winds


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FACET Software Architecture

NationalWeatherService

Winds

SevereWeather

FAATraffic Data

Tracks

Flight Plans

Aircraft Performance

Data

ClimbDescent

Cruise

AirspaceAirways

Airports

Adaptation Data

HistoricalDatabase

Traffic & Route Analyzer

User Interface

Route Parser &Trajectory Predictor

FACET CORE FEATURESAPPLICATIONS

Air and Space Traffic Integration

AirborneSelf-Separation

DataVisualization

Direct Routing Analysis

Controller Workload

System-Level Optimization

Traffic Flow Management


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Concept of Tube NetworkConcept of Tube Network

Dynamic airspace configuration is a key element ofthe Next Generation Air Transportation System

– Flexible airspace boundaries that are dynamically configured

– New airspace classes such as tube airspace

Tube network connects regions with high traffic volume– Network is dynamic: tailored to demand, winds, and weather

– Tube airspace segregated from other airspace classes

– Tube traffic gets benefits, e.g., better routes and arrival slots

– Control mode inside tube may include self spacing/merging

– Concept of operations is not well defined at present

Initial study to expose key research issues– Develop a common analysis method

– Define and evaluate performance metrics


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Design of Tube Structures

Implemented in Future ATM Concepts Evaluation Tool by simulating traffic above 12,000 ft

Historical air traffic data from Aug. 24, 2005 used in four 6-hour blocks

Five designs based on different methods– Jet routes– Delaunay triangulation (Sridhar, et al.)– Traffic density– Hough transform (Xue, et al.)– Network cost optimized (Gupta, et al.)


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50 great circle tubes Maximize use by ≤ 5%

additional travel distance

Hough Transform

• Cost of each node, link and flight travel time of the network optimized (67 links)

Network Cost Optimization


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Instantaneous occupancy– Utilization and activation/deactivation trigger

Volume occupancy– Capacity and duration

Number of conflicts– Communication and workload

Frequency of tube crossings– Communication and workload

Encounter angles of tube crossings– Communication and workload

Performance Metrics


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Number of Conflicts

Number of conflicts with and without tubes (simulation: 5 nmi, 1000 ft)

Conflictcount

Center

Jet Routes

Delaunay Triangles

Traffic Density

Hough Transform

Cost Optimized

Delaunay Triangles

Cost Optimized

Hough Transform

Traffic Density

Jet Routes

Nominal

Worse

Better


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Optimization-Simulation Environment


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Strategic Departure Control Model

2-hr Planning Horizon:

• 4,500 flights, 949 airports and 987 sectors

• 600,000 variables and 650,000 constraints

Objective function:

Minimize the total system delay

Inputs:

- Scheduled departure times and flight plans

- Sector and airport capacities

Outputs: departure delay assigned to each flight

[Bertsimas and Stock-Patterson, 1998]


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Strategic Weather Translation

• Active area of research

• Four reduced capacity

scenarios considered (0%,

20%, 40%, and 60%) if

Convective Weather

Avoidance Model (CWAM)

60% deviation probability

contours existed


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Tactical Weather Translation

Avoided Convective Weather Avoidance Model (CWAM)

60% deviation contours at FL300


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Rerouting vs Ground Holding Delays

Benefits of departure control model limited without accounting for flow-based weather impacts


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Model Validation


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Additional viewgraphs


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From current flight plan structure of one day, Airport and Airspace Network (AAN) has ~8000 nodes

AAN has 225 nodes with > 250 links (250G) and ten Centers have more than ten 250G nodes each

There are 22 1000G nodes in the system today

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2

3

45

6

7

251

…

250G Node

US Air Traffic Network


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Projected growth of tripling of passengers by 2025 along with increased air taxis and UAVs

Terminal Area Forecast (TAF) generated growth rates used to create 3X current traffic

3X AAN has 1443 250G nodes and all Centers have more than forty 250G nodes

There are 262 1000G nodes in the future system

Future Traffic Scenarios


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Convective weather related delay days of more than 200,000 minutes are increasing

Weather is considered a disturbing agent, either random or selective

Impact of Weather

The density of 250G nodes is seen much higher in some regions

ames research center 1 facet: future air traffic management concepts evaluation tool banavar sridhar...

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