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Probabilistic NAS Platform

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  1. Probabilistic NAS Platform George Hunter, Fred WielandBen Boisvert, Krishnakumar Ramamoorthy Sensis Corporation December 10, 2008

  2. Outline • What is PNP? • Team and development history • Example uses of the model • Software processes and testing • Validation

  3. Outline • What is PNP? • Team and development history • Example uses of the model • Software processes and testing • Validation

  4. What is PNP? • An fast-time and flexible NAS-wide simulation tool • Real-time or fast-time modes • Half-hour runtime on a laptop, to simulate a day in the NAS • Physics-based: trajectories computed through integrating aerodynamic energy balance equations by varying the time-step size • System uncertainties (weather, security, operations …) • Plug-and-play architecture • Dynamic clients (TFM, DAC, AOC, …) • An ATC community resource • Formal software development processes in place • Adaptable to current system or NextGen future concepts • Uses • Environment in which to design, build and test decision support tools • TFM, DAC, AOC, … • Fast-time, real-time, shadow-mode • Potential NAS tool • Service provider, operator, collaborative uses • Benefits assessment tool • Fast-time tool to evaluate improved infrastructure, technology, procedures … • Evaluates historic and future traffic scenarios in weather

  5. PNP Architecture Graphical User Interface Plan View Display NAS Database Reports Flight Data NAS Simulation Probabilistic NAS Platform (PNP) MATLAB® Scripting Interface Weather Data Performance Data A fast-time physics-based (trajectory-based) NAS-wide modeling tool

  6. PNP Architecture Graphical User Interface Plan View Display NAS Database Reports Flight Data NAS Simulation Probabilistic NAS Platform (PNP) MATLAB® Scripting Interface Weather Data Performance Data SimObjects MATLAB®Client Java Client Client As Middleware Decision making Prob-TFM A fast-time physics-based (trajectory-based) NAS-wide modeling tool External Client (Any Language)

  7. PNP Client Development • TFM client development • ProbTFM (Sensis internal development) • TFM client integration • C2 (algorithms from and used with permission of Bob Hoffman, Metron) • Constrained LP (algorithms from and used with permission of NASA, Joey Rios) in progress • DAC client integration • MxDAC (algorithms from and used with permission of Min Xue, NASA/UARC) • AOC client development • Gaming behaviors (collaboration with GMU/Lance Sherry) in progress

  8. Existing Can Support Capabilities Summary √ • 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 √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √

  9. Outline • What is PNP? • Team and development history • Example uses of the model • Software processes and testing • Validation A fast-time physics-based (trajectory-based) NAS-wide modeling tool

  10. 2003 2004 2005 2006 2007 2008 Huina Gao Krishnakumar Ramamoorthy Ben Boisvert Diego Escala Tae Lee Michelle Lu George Hunter WSI collaboration for real-time weather feed NAS-wide,probabilistic Wx modelingand routing Clientarchitecture Dynamic clients JPDO FAA NASPAC NASA NRAs Internal NWA GMU Team and Development History People Env’nment Data Funct’ality Projects Users Project System lead Software lead Software System Software System Java/real-time Web 2.0 Matlab

  11. Outline • What is PNP? • Team and development history • Example uses of the model • Software processes and testing • Validation A fast-time physics-based (trajectory-based) NAS-wide modeling tool

  12. Example Project Uses • JPDO Modeling and Analysis • NextGen performance evaluation with weather • FAA NASPAC Weather Modeling • Convection impact modeling for NASPAC • NASA Gaming NRA • Evaluation of NextGen gaming with AOC clients • NASA MetaSimulation NRA • Investigation of TFM + DAC interactions • NASA SLDAST RFA • Evaluation of NextGen TFM concepts and models • NASA Market-Based TFM NRA • Evaluation of NextGen market-based TFM concepts

  13. NextGen Performance Sensitivity Analysis Benefit of Improved Wx Forecasts Case 2: No distinction between clear and heavy weather forecast accuracy Persistence forecast11/16/06 Case 1: Take advantage of improved forecast accuracy in clear weather Benefit of Using Clear Weather Forecasts NAS Performance Sensitivity NextGen Sensitivity Studies George Hunter, Fred Wieland " Sensitivity of the National Airspace System Performance to Weather Forecast Accuracy," Integrated Communications, Navigation and Surveillance Conference (ICNS), Herndon, VA, May, 2008 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

  14. Delay UAL233 Delay Cost SCC NAS Access Valuation Models Market-Based TFM Studies George Hunter, et. al., "Toward an Economic Model to Incentivize Voluntary Optimization of NAS Traffic Flow," AIAA ATIO Conference, Anchorage, AK, September, 2008.

  15. Nov 12, 2006, LAT=2, #Gen=40 ZFW FAA sectors Dynamic Airspace Configuration 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.

  16. Reroute with low probability of delay AOC Dispatch Use Case

  17. Outline • What is PNP? • Team and development history • Example uses of the model • Software processes and testing • Validation A fast-time physics-based (trajectory-based) NAS-wide modeling tool

  18. Project Monitoring & Control Quantitative Project Management Development Tracking Branch Configuration Management Regression Testing Unit and System Testing Trunk Configuration Management Processes and Testing Cycle

  19. Project Monitoring and Control • JIRA is used to track issues • Project Manager and Lead Software Engineer assign task priorities, due dates, and personnel. • Weekly telecoms keep distributed team apprised of PNP and communications open • Project Manager maintains a master schedule in MS-Project

  20. Development Tracking • Software engineers use JIRA to track and status development efforts.

  21. Branch Configuration Management • Software Engineers are responsible for creating branches from the trunk to develop fixes/enhancements. • The Configuration Management of the software is accomplished with Subversion • Subversion is an open source version control system (http://subversion.tigris.org/)

  22. Unit and System Testing • Software Engineers are responsible for creating unit tests to verify the correctness of their code. The JIRA issue number is to be used throughout the code and unit tests for tracking purposes. • Software Engineers are responsible for running their own system/function tests to verify their software. • Once testing is validated, code is merged back on to the trunk.

  23. Trunk Configuration Management • Once all validated JIRA issues are merged unto the trunk, regression testing is performed.

  24. Regression Testing • Regression testing • Aggregate results • Total delay • Total congestion • Traffic volume • #TFM initiatives • Runtime • Different scenarios • Truncated demand set • Full demand set • Weather • Automated • Weekly or as required • Archived • Graphical quick-look

  25. Quantitative Project Management • Regression testing validation is performed and the release letter is updated. • Release is tagged in Subversion. • JIRA issues are closed. • Documentation is updated to reflect changes in software.

  26. Outline • What is PNP? • Team and development history • Example uses of the model • Software processes and testing • Validation A fast-time physics-based (trajectory-based) NAS-wide modeling tool

  27. System-Level Engineering Validation • ASPM / ETMS verification tests • Compare ASPM/ETMS data with simulation data • Calibrate concept to match aggregate field observations • Models • Trajectory data • Airport capacities (VMC / IMC) • Sector capacities in weather • Aggregate performance • Mean flight delay • Sector and airport overloadings • Detailed performance • Flight delay by airport and time of day • Overloading and delay patterns (Spatial and temporal) • Delays by airport and time of day • Sector and airport loading by time of day • Spatial loading patterns • Light and heavy weather days √

  28. System-Level Software Verification • Cross check sums • SFlights = SOperations at all airports • SFlight time = SMinutes from sector loads • SSector load by sector = SSector load by time • SAirport ops = SFlights using the airport in demand set • SDelays by flight = SDelays by time; and reroutes • Weather data checks • Compare PNP/Metar airport capacity with ASPM AAR/ADR • Compare PNP/Metar airport capacity with ASPM IFR periods • Ensure SEn route convection versus time of day is smooth • Ensure WxMAP ≤ MAP for all sector time bins • Graphical • Ensure reroutes overlaid on weather make sense • TFM Performance • Number of delays per flight, min and max flight delay • Maximum airport and sector overloading (ensure are reasonable)

  29. System-Level Engineering Validation • ASPM / ETMS verification tests • Compare ASPM/ETMS data with simulation data • Calibrate concept to match aggregate field observations • Models • Trajectory data • Airport capacities (VMC / IMC) • Sector capacities in weather • Aggregate performance • Mean flight delay • Sector and airport overloadings • Detailed performance • Flight delay by airport and time of day • Overloading and delay patterns (Spatial and temporal) • Delays by airport and time of day • Sector and airport loading by time of day • Spatial loading patterns • Light and heavy weather days √

  30. N: 316 Mean: 0.321 min Std dev: 11.95 min Detrended for Range Mean: 0.80 min Std dev: 6.51 min R2: 0.012 Trajectory Model Validation • Compared to ETMS flight data (May 2008) George Hunter, Ben Boisvert, Kris Ramamoorthy, "Advanced Traffic Flow Management Experiments for National Airspace Performance Improvement," 2007 Winter Simulation Conference, Washington, DC, December, 2007

  31. ProbTFM Performance • ASPM / ETMS verification tests • Compare ASPM/ETMS data with simulation data • Calibrate concept to match aggregate field observations • Models • Trajectory data • Airport capacities (VMC / IMC), actual and forecasted • Sector capacities in weather, actual and forecasted • Aggregate performance • Mean flight delay • Sector and airport loadings • Detailed performance • Flight delay by airport and time of day • Overloading and delay patterns (Spatial and temporal) • Delays by airport and time of day • Sector and airport loading by time of day • Spatial loading patterns • Light and heavy weather days √

  32. Compare to ETMS/ASPM Forecast accuracies, Decision making horizon, Delay distribution January 7, 2007(Similar resultswith other days) LAT = 60 minutes (14.5,1657) LAT = 30 mins LAT = 0 Compare With Field Observations

  33. Verification of Results • ASPM / ETMS verification tests • Compare ASPM/ETMS data with simulation data • Calibrate concept to match aggregate field observations • Models • Trajectory data • Airport capacities (VMC / IMC), actual and forecasted • Sector capacities in weather, actual and forecasted • Aggregate performance • Mean flight delay • Sector and airport loadings • Detailed performance • Flight delay by airport and time of day • Overloading and delay patterns (Spatial and temporal) • Delays by airport and time of day • Sector and airport loading by time of day • Spatial loading patterns • Light and heavy weather days √

  34. ETMS ProbTFM System Loading Patterns ProbTFM predicted, 14:45 GMT ETMS Actual, 14:45 GMT ETMSUnderloading Overloading ProbTFM loading

  35. Verification of Results • ASPM / ETMS verification tests • Compare ASPM/ETMS data with simulation data • Calibrate concept to match aggregate field observations • Models • Trajectory data • Airport capacities (VMC / IMC), actual and forecasted • Sector capacities in weather, actual and forecasted • Aggregate performance • Mean flight delay • Sector and airport loadings • Detailed performance • Flight delay by airport and time of day • Overloading and delay patterns (Spatial and temporal) • Delays by airport and time of day • Sector and airport loading by time of day • Spatial loading patterns • Light and heavy weather days, control days √

  36. Conclusion • The development of PNP has benefited from lessons learned over past two decades in NAS system wide modeling • Plug and play simulation architecture • Supports both analytical and HITL studies • Adaptable to simulate current system as well as NextGen future concepts • Fast-time, physics-based • Formal software development processes in place • Probabilistic decision making and extensive weather modeling explicitly incorporated in tool

  37. Publications • 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. • George Hunter, et. al., "Toward an Economic Model to Incentivize Voluntary Optimization of NAS Traffic Flow," AIAA ATIO Conference, Anchorage, AK, September, 2008. • George Hunter, Fred Wieland " Sensitivity of the National Airspace System Performance to Weather Forecast Accuracy," Integrated Communications, Navigation and Surveillance Conference (ICNS), Herndon, VA, May, 2008. • 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. • 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. • George Hunter, Ben Boisvert, Kris Ramamoorthy, "Advanced Traffic Flow Management Experiments for National Airspace Performance Improvement," 2007 Winter Simulation Conference, Washington, DC, December, 2007. • 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. • 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. • 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. • 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. • Kris Ramamoorthy, Ben Boisvert, George Hunter, "A Real-Time Probabilistic TFM Evaluation Tool," AIAA Digital Avionics Systems Conference (DASC), Portland, OR, October, 2006. • 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. • Kris Ramamoorthy, George Hunter, "A Trajectory-Based Probabilistic TFM Evaluation Tool and Experiment," Integrated Communications, Navigation and Surveillance Conference (ICNS), Baltimore, MD, May, 2006. • 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. • 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. • James D. Phillips, “An Accurate and Flexible Trajectory Analysis,” World Aviation Congress (SAE Paper 975599), Anaheim, CA, October 13-16, 1997.

  38. Questions?

  39. Backup

  40. PNP Systems Requirements • System requirements • PNP is a Java application • Hardware • Memory: minimum 1GB, preferred 2GB • CPU: Pentium (4) 3.2 GHz or better • Video card: 128MB memory, preferred 256MB • Software • Java JDK 6 http://java.sun.com/javase/downloads/index.jsp • MySQL Server 5.0 http://dev.mysql.com • Third party licenses • Eurocontrol BADA usage license

  41. Ten weather days, two control days Weather Days

  42. Weather Days • Weather days • Spectrum of weather days • Variation in weather type and intensity • Variation in season • Support real-world comparison • Support same sector data • Variation in traffic demand volume and structure • Different days of week, holidays • Control days

  43. NextGen PerformanceSensitivity Analysis

  44. En Route and Terminal Area Combined Sensitivities - 2025

  45. Benefit of ImprovedConvection Forecasts

  46. Investment Analysis

  47. Benefit of Using Clear Weather Forecasts