1 Traffic Manager TMX Modifications to Support NextGen Studies at NASALangley Research Center Kurt W Neitzke NASA Langley Research Center Innovations in NASWide Simulation George Mason University VA ID: 713334
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Traffic Manager (TMX) Modifications to Support NextGen Studies at NASA-Langley Research Center
Kurt W. Neitzke
NASA Langley Research Center
Innovations in NAS-Wide Simulation
George Mason University, VA
27-28 January 2010Slide2
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Outline
TMX Background & Overview
Development History
Architecture
Supported Research Studies
Current enhancements
Remaining GapsSlide3
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Background **
TMX development began in 1996 by National Aerospace Laboratory of the Netherlands (NLR) to study
“Free Flight”
, where:
Properly equipped aircraft allowed to choose own flight path
While maintaining separation from all other aircraft (airborne separation assistance system (ASAS))
Originally designed to support human in the loop (HITL) studies related to Free Flight to develop and compare different conflict resolution algorithms
TMX updated periodically to date, by NASA Langley and NLR to support specific research studies primarily related to airborne separation assistance
Evolved capabilities now include:
Stand alone Fast-time or Batch simulator
Links readily to other air traffic simulations (e.g. Airspace and Traffic Operations Simulation (ATOS) at NASA-LaRC)
**
Source:
Traffic Manager: A Flexible Desktop simulation Tool Enabling Future ATM Research; Bussink, F.J.L., et. al., 2005 IEEESlide4
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TMX Overview
TMX features Include:Operates on single computer platform, Windows OS
Capable of
~
2000 aircraft simultaneously aloft (typ. supporting regional, not NAS-wide studies)
BADA performance models (200 aircraft reference fleet)
Autopilot model (with basic altitude, speed and heading modes as well as the FMS coupled LNAV (lateral) and VNAV (vertical & speed) modes
Conflict detection & resolution (CD&R) system selectable from up to 10 variants or none, including state, and intent based
Conflict Prevention System (P-ASAS) – “Go – No-Go” bands on cockpit display to prevent pilot maneuvering into short-term (
<
5 min. typically) conflicts
A 4D-FMS with route following, & Required Time of Arrival (RTA) meeting (closed loop) capability
Pilot model with parameters for reaction time, scheduling effects and recovery manoeuvresADS-B models
Separate transmit & receive models
Includes range limits & signal drop-out (simple)
Winds (truth & forecast)
Surveillance view or pilot viewpoint GUI (can disable for batch sim’s)
Source:
Traffic Manager User’s Manual, Version 5.31;
Hoekstra, J.
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TMX Architecture
Source:
Traffic Manager: A Flexible Desktop simulation Tool Enabling Future ATM Research; Bussink, F.J.L., et. al., 2005 IEEESlide6
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TMX Surveillance View
Source:
Traffic Manager User’s Manual, Version 5.31;
Hoekstra, J.
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TMX Surveillance View
AFR aircraft (green) and IFR aircraft (blue)Slide8
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Research Studies Supported
2008; A Performance Assessment of a Tactical Airborne Separation Assistance System Using Realistic, Complex Traffic; Smith, J.C. et. al.,, The 26th Congress of International Council of the Aeronautical Sciences (ICAS)
2004; Fast-time study of Airborne Merging and Spacing for Terminal Arrivals (AMSTAR)
2004; HITL experiment supporting integrated air/ground operations feasibility under the
En Route Free Maneuvering
component of Distributed Air/Ground - Traffic Management (DAG-TM) Concept
2004;
In-Flight
Traffic Simulation for Self-Separation and Sequencing (SSS) Flight Experiment conducted by NASA LaRC as part of the Small Aircraft Transportation System (SATS) project
traffic generation, conflict detection and prevention, visual and audio alerts and was used as a decision support tool in support of self-separation operations
Integral part of Air Traffic Operations Laboratory (ATOL) at NASA-LaRCInteractive background air trafficSlide9
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TMX Enhancements
Integration of Airborne Coordinated Conflict Resolution and Detection (ACCoRD) based CP-Bands
Integration of Strategic, Intent-based CD&R capability
StratWay
(Strategic Waypoint adjustment program)
Integration of NASA TFM functionality
Outline approach for future integration of weather data into TMX
Create a distributed architecture version of TMX to enable NAS-Wide simulation and higher traffic volumes (compared with stand-alone TMX)Slide10
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TMX Conflict Prevention SystemSlide11
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Integration of Airborne Coordinated Conflict Resolution and Detection (ACCoRD) based CP-Bands
Conflict Prevention System displays “bands” to pilot to indicate trajectory changes that will cause a short-term conflict (yellow ~ 3-5 minutes; red~ <3 min.)
CP Bands on:
Heading changes
Vertical speed changes
Horizontal speed changes
Trajectory changes may be due to conflict resolution or part of the flight plan
Can be used by pilot model (in batch study, or as background traffic in HITL experiment) or directly by human pilot in HITL experiment
Includes formal methods proof of “correctness” of the CP-Bands algorithms
Replaces existing CP-Bands developed by NLRSlide12
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Conflict Bands algorithm uses ACCoRD to determine conflict envelope
Supports multiple conflict regions
Deterministic, formal V&V
Heading conflict zone corrected with altitude and time
Resolution with multiple simultaneous conflicts
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Integration of Airborne Coordinated Conflict Resolution and Detection (ACCoRD) based CP-BandsSlide13
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Strategic CD&R algorithm under development is “StratWay” (Strategic Waypoint adjustment program)
Performs piece-wise inspection of planned waypoints
Uses Bands algorithms for conflict detection and resolution options
Moves minimum number of waypoints to de-conflict
Integration of Strategic, Intent-based CD&R capability
StratWay
(Strategic Waypoint adjustment program)Slide14
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Integration of NASA TFM Functionality **
Concept to manage air traffic flow under uncertainty in airspace capacity and demand
Sequential optimization method
Integrates deterministic integer programming model for assigning delays to aircraft under en route capacity constraints
Reactively accounts for system uncertainties
Assigns only departure controls
Two additional elements associated with the ref. TFM Capability related to tactical weather re-routing, and airborne holding will not be integrated into TMX at this time
**
Source:
Sequential Traffic Flow Optimization with Tactical Flight Control Heuristics; Grabbe, Shon, et. al., 2008, AIAA Guidance Navigation and Control Conference and ExhibitSlide15
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Integration of NASA TFM Functionality
Not included in current TMX mod’s
~
Source:
Sequential Traffic Flow Optimization with Tactical Flight Control Heuristics; Grabbe, Shon, et. al., 2008, AIAA Guidance Navigation and Control Conference and ExhibitSlide16
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Define approach for integrating weather data into TMX
Purpose:
allow the evaluation of different strategic weather mitigation approaches using TMX
Current, simple TMX weather avoidance capability uses CP-Bands to tactically avoid 3-D weather poly-spaces
New Weather databases available soon via NRA;
Realistic Weather Data to Support NextGen ATM Concept Simulations
(two NRA awards: Sensis, & Raytheon)
provide recorded real-world and simulated weather data
Provide associated software tools to manage the data and create appropriate scenariosSlide17
Time Sync.
Resolutions
Traffic
dataDistributed TMX Architecture
TMX Node
TMX Node
TMX Node
TMX Node
ADS-B
CD&R
Scheduling
Time Synchronization
Output Recording
Central Control Node
Distributed TMX NodesSlide18
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Distributed TMX Architecture; Development Objectives
Capability to handle NAS-wide simulation
20,000+ aircraft simultaneously aloft
Handle full range of mixed AFR-IFR aircraft
Improve code efficiency
Shorten simulation run-timeSlide19
Distributed TMX Status
Start/stop TMX nodes
Receive TRAFFIC dataTraffic range computationsADS-B updatesConflict detection checksConflict resolution computations
Send resolutions to TMX nodesSlide20
Distributed TMX Validation
Two A/C case TMX vs. 1-node D-TMX
1000 A/C case TMX vs. 1-node D-TMX1000 A/C case TMX vs. multi-node D-TMX (250 A/C per TMX node)Detailed (1000 A/C) case checking trajectories and resolutionsSlide21
Remaining Gaps
(fr. Presenter’s perspective)
NAS-Wide simulation tools have matured greatly over the past five years – however:They span a broad system - The NAS! (can
the World be far behind?)Determining a “prudent mix” of which NAS systems will be explicitly
vs.
implicitly modeled
to deliver the desired information is study-dependent often
Understanding the validity bounds of results is difficult, and typically “in the eye of the beholder”
Don’t know whether current simulation capabilities are sufficient to answer highest priority NextGen research questions right now or not
Need to enter vigorous period of exercising the tools to reveal their capability shortcomings
Use multiple tools to simulate the same scenario and compare results
Synthesize comparison to formulate future tools & methods development plan
How can broader, lower detail simulations (like NAS-wide) be more directly complementary to narrower, more detailed simulations (and vice versa?)
Do NAS-Wide Simulations need to – “do it all”?