1. National Aeronautics and Space AdministrationGMU-SIM-JCS, January 20101 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 2010

2. National Aeronautics and Space AdministrationGMU-SIM-JCS, January 20102 Outline I.TMX Background & Overview A.Development History B.Architecture C.Supported Research Studies II.Current enhancements III.Remaining Gaps

3. National Aeronautics and Space AdministrationGMU-SIM-JCS, January 20103 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 IEEE

4. National Aeronautics and Space AdministrationGMU-SIM-JCS, January 20104 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 manoeuvres ADS-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.

5. National Aeronautics and Space AdministrationGMU-SIM-JCS, January 20105 TMX Architecture Source: Traffic Manager: A Flexible Desktop simulation Tool Enabling Future ATM Research; Bussink, F.J.L., et. al., 2005 IEEE

6. National Aeronautics and Space AdministrationGMU-SIM-JCS, January 20106 TMX Surveillance View Source: Traffic Manager User’s Manual, Version 5.31; Hoekstra, J.

7. National Aeronautics and Space AdministrationGMU-SIM-JCS, January 20107 TMX Surveillance View AFR aircraft (green) and IFR aircraft (blue)

8. National Aeronautics and Space AdministrationGMU-SIM-JCS, January 20108 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-LaRC –Interactive background air traffic

9. National Aeronautics and Space AdministrationGMU-SIM-JCS, January 20109 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)

10. National Aeronautics and Space AdministrationGMU-SIM-JCS, January 201010 TMX Conflict Prevention System

11. National Aeronautics and Space AdministrationGMU-SIM-JCS, January 201011GMU-SIM-JCS, January 201011 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: ˉHeading changes ˉVertical speed changes ˉHorizontal 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 NLR

12. National Aeronautics and Space AdministrationGMU-SIM-JCS, January 201012 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 GMU-SIM-JCS, January 201012 Integration of Airborne Coordinated Conflict Resolution and Detection (ACCoRD) based CP-Bands

13. National Aeronautics and Space AdministrationGMU-SIM-JCS, January 201013GMU-SIM-JCS, January 201013 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)

14. National Aeronautics and Space AdministrationGMU-SIM-JCS, January 201014GMU-SIM-JCS, January 201014 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 Exhibit

15. National Aeronautics and Space AdministrationGMU-SIM-JCS, January 201015 GMU-SIM-JCS, January 2010 15 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 Exhibit

16. National Aeronautics and Space AdministrationGMU-SIM-JCS, January 201016GMU-SIM-JCS, January 201016 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 scenarios

17. National Aeronautics and Space Administration Time Sync. Resolutions Traffic data Distributed TMX Architecture TMX Node ADS-B CD&R Scheduling Time Synchronization Output Recording Central Control Node Distributed TMX Nodes

18. National Aeronautics and Space AdministrationGMU-SIM-JCS, January 201018GMU-SIM-JCS, January 201018 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-time

19. National Aeronautics and Space Administration Distributed TMX Status Start/stop TMX nodes Receive TRAFFIC data Traffic range computations ADS-B updates Conflict detection checks Conflict resolution computations Send resolutions to TMX nodes

20. National Aeronautics and Space Administration Distributed TMX Validation Two A/C case TMX vs. 1-node D-TMX 1000 A/C case TMX vs. 1-node D-TMX 1000 A/C case TMX vs. multi-node D-TMX (250 A/C per TMX node) Detailed (1000 A/C) case checking trajectories and resolutions

21. National Aeronautics and Space Administration 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”?