1. The information in this document is proprietary to, and the property of Sensis Corporation. It may not be duplicated, used, or disclosed in whole or in part for any purpose without express written consent. Probabilistic NAS Platform George Hunter, Diego Escala Sensis Corporation January 27, 2010

2. 2 Sensis Corporation Proprietary Data – See title page Outline PNP design overview and philosophy Probabilistic modeling (George) PNP software architecture (Diego)

3. 3 Sensis Corporation Proprietary Data – See title page Outline PNP design overview and philosophy Probabilistic modeling (George) PNP software architecture (Diego)

4. 4 Sensis Corporation Proprietary Data – See title page PNP Design Overview and Philosophy Requirements NextGen performance and benefits assessment  Incorporate key aspects of NAS from the ground up –Uncertainty, weather Design environment, including real-time evaluation 1 hour run time (nominally), easy to use Design Don’t try to solve every problem  NAS has significant dynamic range Select modeling fidelity: 15 minute/sector discretization Spatially: ~10s nmi Temporally: 15 min Tactical: CDR, etc. Strategic: TFM, DAC, FP, etc. Implicit / nodal modeling Explicit modeling

5. 5 Sensis Corporation Proprietary Data – See title page PNP Design Overview and Philosophy Decouple simulation and decision making Build a little – test a little Continuous improvement Expandable architecture Emphasis on testing, validation and process

6. 6 Sensis Corporation Proprietary Data – See title page Real-time Fast-time Airport weather impact models Airspace weather impact models Weather-integrated decision making Probabilistic modeling / decision making Traffic flow management Dynamic airspace configuration Surface traffic modeling Terminal area modeling Super density operations Fuel burn modeling Emissions modeling Trajectory-based operations Separation assurance Plug-n-play Fast run-time Capabilities Summary Existing Can Support √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √

7. 7 Sensis Corporation Proprietary Data – See title page Outline PNP design overview and philosophy Probabilistic modeling (George) PNP software architecture (Diego)

8. 8 Sensis Corporation Proprietary Data – See title page Probabilistic Modeling Example uncertainties System capacity and loading forecasts Airports and sectors

9. 9 Sensis Corporation Proprietary Data – See title page Forecasted Airport Capacity Illustration of our approach Step 1 TAF Step 2 Mean and Sigma for Ceiling, Visibility and Wind Speed Step 3 Mean and sigma: Ceiling Visibility Wind Speed Forecasts: Ceiling Visibility Wind Speed Forecasts for Ceiling, Visibility and Wind Speed Distributions for Ceiling, Visibility and Wind Speed

10. 10 Sensis Corporation Proprietary Data – See title page Forecasted Airport Capacity Illustration of our approach Step 1 TAF Step 2 Mean and Sigma for Ceiling, Visibility and Wind Speed Step 3 Mean and sigma: Ceiling Visibility Wind Speed Forecasts: Ceiling Visibility Wind Speed Forecasts for Ceiling, Visibility and Wind Speed Distributions for Ceiling, Visibility and Wind Speed

11. 11 Sensis Corporation Proprietary Data – See title page Forecasted Airport Capacity Step 4 Model Step 5 Step 6 Arrivals / hour Departures / hour VFR IFR VFR Cap Dist IFR Cap Dist

12. 12 Sensis Corporation Proprietary Data – See title page Forecasted Airport Capacity Airport capacity distribution that takes into account ceiling, visibility and wind speed forecasts Step 7

13. 13 Sensis Corporation Proprietary Data – See title page Outline PNP design overview and philosophy Probabilistic modeling (George) PNP software architecture (Diego)

14. 14 Sensis Corporation Proprietary Data – See title page PNP Software Overview Client-server architecture Predictive modeling Two runtime modes Fast-time (simulation) Real-time (live)

15. 15 Sensis Corporation Proprietary Data – See title page Fast-time Mode Archived Wx/Tx Data PNP Client 1 Client 2 Client n Local Network Static NAS Data

16. 16 Sensis Corporation Proprietary Data – See title page Live (Real-time) Mode Client 1 Client 2 Client n Local NetworkInternet Wx/Tx Data Server Wx/Tx Data Server PNP Static NAS Data

17. 17 Sensis Corporation Proprietary Data – See title page Probabilistic NAS Platform (PNP) Weather Data Reports Network MATLAB ® Scripting Interface NAS Database NAS Database MATLAB ® Client MATLAB ® Client External Client (Any Language) Client As Middleware Client As Middleware Java Client Decision making NAS Simulation Performance Data Flight Data Graphical User Interface Plan View Display PNP Architecture

18. 18 Sensis Corporation Proprietary Data – See title page Probabilistic NAS Platform (PNP) Weather Data Reports Network MATLAB ® Scripting Interface NAS Database NAS Database Decision making NAS Simulation Performance Data Flight Data Graphical User Interface Plan View Display PNP Architecture

19. 19 Sensis Corporation Proprietary Data – See title page PNP Communications Basics Subscription model Clients can specify which messages they need, and at what interval they need each Serialized Java objects sent over TCP/IP Compression supported

20. 20 Sensis Corporation Proprietary Data – See title page Advance simulation time Send messages for interval Send heartbeat to clients Wait for client responses PNP Communications Cycle

21. 21 Sensis Corporation Proprietary Data – See title page Step 1: Register with PNP Step 2: Request Data Updates Step 3: Handle Data Updates Client-Server Communications

22. 22 Sensis Corporation Proprietary Data – See title page Registering with PNP public MyPnpClient() { // Connect to the PNP server on the local computer. // buildReceiveRequests() specifies message subscriptions m_Client = new ObjectClientMessageManager(“localhost”, buildReceiveRequests()); } public MyPnpClient() { // Connect to the PNP server on the local computer. // buildReceiveRequests() specifies message subscriptions m_Client = new ObjectClientMessageManager(“localhost”, buildReceiveRequests()); }

23. 23 Sensis Corporation Proprietary Data – See title page Subscribing to Messages private ArrayList buildReceiveRequests() { ArrayList requests = new ArrayList (); // Add a request to receive PnpFlightDetails every 15 minutes requests.add(new MessageReceiveRequest(PnpFlightDetails.class, 15)); // Add a request to receive AirportLoading every 15 minutes requests.add(new MessageReceiveRequest(AirportConditions.class, 15)); // Add a request to receive SectorLoading every 15 minutes requests.add(new MessageReceiveRequest(TerminalConditions.class, 15)); return requests; } private ArrayList buildReceiveRequests() { ArrayList requests = new ArrayList (); // Add a request to receive PnpFlightDetails every 15 minutes requests.add(new MessageReceiveRequest(PnpFlightDetails.class, 15)); // Add a request to receive AirportLoading every 15 minutes requests.add(new MessageReceiveRequest(AirportConditions.class, 15)); // Add a request to receive SectorLoading every 15 minutes requests.add(new MessageReceiveRequest(TerminalConditions.class, 15)); return requests; }

24. 24 Sensis Corporation Proprietary Data – See title page Responding to PNP BatchResponse response = new BatchResponse(1, aoc.getDelayMap(), aoc.getRerouteMap(), aoc.getInflightRerouteMap()); m_Client.send(response); BatchResponse response = new BatchResponse(1, aoc.getDelayMap(), aoc.getRerouteMap(), aoc.getInflightRerouteMap()); m_Client.send(response); [AOC Example]

25. 25 Sensis Corporation Proprietary Data – See title page Data Translation NAS Data Wx radar Winds/temps METAR/TAF Turbulence Icing Flight plans Flight positions Sector def’ns Airport capacities Client-usable data Wx-degraded sector capacities Sector capacity forecasts Sector loading Sector loading forecasts Airport capacity distribution based on wx Airport conditions Flight trajectories

26. 26 Sensis Corporation Proprietary Data – See title page Data Translation NAS Data Wx radar Winds/temps METAR/TAF Turbulence Icing Flight plans Flight positions Sector def’ns Airport capacities Client-usable data Wx-degraded sector capacities Sector capacity forecasts Sector loading Sector loading forecasts Airport capacity distribution based on wx Airport conditions Flight trajectories

27. 27 Sensis Corporation Proprietary Data – See title page Data Available from PNP Server Flight Data Flight Trajectory (1- min) Current Position Sector Schedule NAS Data Airport Congestion Airport Loading Operations per airport Airspace Data Sector Boundaries Sector Congestion Weather Data Forecasts Weather-related Congestion

28. 28 Sensis Corporation Proprietary Data – See title page Programmable Client Functionality Flight Plan Amendments Delay flights Reroute flights  While at gate or in-flight Airspace Modification / Definition Number of sectors Sector boundaries Sector capacities Airport capacities

29. 29 Sensis Corporation Proprietary Data – See title page Uncertainty Modeling Process in ProbTFM PNP Implement reroutes and delays ProbTFM Compute sector capacity distributions Compute congestion costs for current departures based on sector loading, capacity Compute delays and reroutes for flights that exceed congestion threshold Send delays to PNP PNP Send sector loading for a/c enroute Send expected sector loading for a/c at gate Send wx-degraded sector capacities Send airport capacity distributions Send departures for current interval

30. 30 Sensis Corporation Proprietary Data – See title page 1.George Hunter, "Meta Simulation Results for Simultaneous Dynamic Resectorization and Traffic Flow Management," AIAA Digital Avionics Systems Conference (DASC), Orlando, FL, October, 2009. 2.George Hunter, Robert A. Vivona, Carlos Garcia-Avello, "Preliminary Evaluation of Trajectory Prediction Impact on Decision Support Automation," AIAA Digital Avionics Systems Conference (DASC), Orlando, FL, October, 2009. 3.Huina Gao, George Hunter, "Future NAS-Wide User Gaming Preliminary Investigation," AIAA Digital Avionics Systems Conference (DASC), Orlando, FL, October, 2009. 4.George Hunter, "Testing and Validation of NextGen Simulators," AIAA Modeling and Simulation Conference, Chicago, IL, August 2009. 5.George Hunter, "Preliminary Assessment of Interactions Between Traffic Flow Management and Dynamic Airspace Configuration Capabilities," AIAA Digital Avionics Systems Conference (DASC), St. Paul, MN, October, 2008. 6.George Hunter, et. al., "Toward an Economic Model to Incentivize Voluntary Optimization of NAS Traffic Flow," AIAA ATIO Conference, Anchorage, AK, September, 2008. 7.George Hunter, "Sensitivity of the National Airspace System Performance to Weather Forecast Accuracy," Integrated Communications, Navigation and Surveillance Conference (ICNS), Herndon, VA, May, 2008. 8.George Hunter, Kris Ramamoorthy, "Integration of terminal area probabilistic meteorological forecasts in NAS-wide traffic flow management decision making," 13th Conference on Aviation, Range and Aerospace Meteorology, New Orleans, LA, January, 2008. 9.Kris Ramamoorthy, George Hunter, "The Integration of Meteorological Data in Air Traffic Management: Requirements and Sensitivities," 46th AIAA Aerospace Sciences Meeting and Exhibit, Reno, NV, January, 2008. 10.George Hunter, Ben Boisvert, Kris Ramamoorthy, "Advanced Traffic Flow Management Experiments for National Airspace Performance Improvement," 2007 Winter Simulation Conference, Washington, DC, December, 2007. 11.Kris Ramamoorthy, George Hunter, "Evaluation of National Airspace System Performance Improvement With Four Dimensional Trajectories," AIAA Digital Avionics Systems Conference (DASC), Dallas, TX, October, 2007. 12.Kris Ramamoorthy, Ben Boisvert, George Hunter, "Sensitivity of Advanced Traffic Flow Management to Different Weather Scenarios," Integrated Communications, Navigation and Surveillance Conference (ICNS), Herndon, VA, May, 2007. 13.George Hunter, Ben Boisvert, Kris Ramamoorthy, "Use of automated aviation weather forecasts in future NAS," The 87th American Meteorological Society Annual Meeting, San Antonio, TX, January, 2007. 14.Kris Ramamoorthy, George Hunter, "Probabilistic Traffic Flow Management in the Presence of Inclement Weather and Other System Uncertainties," INFORMS Annual Meeting, Pittsburgh, PA, November, 2006. 15.Kris Ramamoorthy, Ben Boisvert, George Hunter, "A Real-Time Probabilistic TFM Evaluation Tool," AIAA Digital Avionics Systems Conference (DASC), Portland, OR, October, 2006. 16.George Hunter, Kris Ramamoorthy, Alexander Klein "Modeling and Performance of NAS in Inclement Weather," AIAA Aviation Technology, Integration and Operations (ATIO) Forum, Wichita, KS, September 2006. 17.Kris Ramamoorthy, George Hunter, "A Trajectory-Based Probabilistic TFM Evaluation Tool and Experiment," Integrated Communications, Navigation and Surveillance Conference (ICNS), Baltimore, MD, May, 2006. 18.Kris Ramamoorthy, George Hunter, "Avionics and National Airspace Architecture Strategies for Future Demand Scenarios in Inclement Weather," AIAA Digital Avionics Systems Conference (DASC), Crystal City, VA, October, 2005. 19.George Hunter, Kris Ramamoorthy, Joe Post, "Evaluation of the Future National Airspace System in Heavy Weather," AIAA Aviation Technology, Integration and Operations (ATIO) Forum, Arlington, VA, September 2005. 20.James D. Phillips, “An Accurate and Flexible Trajectory Analysis,” World Aviation Congress (SAE Paper 975599), Anaheim, CA, October 13-16, 1997. Publications

31. 31 Sensis Corporation Proprietary Data – See title page Questions? Thank You George Hunter Diego Escala