Modeling NextGen Functions in Future ATM Concepts Evaluation Tool (FACET)

1. Modeling NextGen Functions in Future ATM Concepts Evaluation Tool (FACET) Banavar Sridhar NASA Ames Research Center Second Annual Workshop Innovations in NAS-Wide Simulation Washington, DC January 27-28, 2010

2. Outline • NASA ATM Research in Traffic Flow Management • FACET Baseline Capability • New Functionality • Simulation and Optimization • Integration of TFM and TMA Concepts • Collaborative Decision-Making • Environmental Models and Trajectory Optimization • Concluding remarks

3. Integrated TFM Solution User schedules and flight plans Observed traffic National Airspace System Weather Translation Traffic Predictions Metrics TFM Simulation 20 min - 8 hrs Meteorological Data • Aircraft-level (FACET) • Aggregate-level Airspace Adaptation Data TFM Decisions TFM Optimization Collaborative Traffic Flow Management Investigate modeling, simulation and optimization techniques to minimize total system delay (or other performance functions) subject to airspace and airport capacity constraints while accommodating three times traffic in the presence of uncertainty

4. Software Environment • FACET • Software environment for developing and testing Traffic Flow Management and Dynamic Airspace Concepts • AIAA Engineering Software of the Year (2009) • Available to universities world-wide and U.S. companies • Integration with Optimization Methods • Integrated with MATLAB tools • Integrated with Linear Programming (CPLEX) tools • Integration with other NASA ATM Software Systems • Center-TRACON Automation System (CTAS) • ACES (Airspace Concept Evaluation System)

5. Interaction of Simulation and Optimization

6. Coordinated TFM and TMA Operations* * S. Grabbe, B. Sridhar, A. Mukherjee and A. Morando, “Integrated Strategic and Local Arrival Flight Scheduling,” Submitted to the AIAA Guidance, Navigation and Control Conference, 2-5 August 2010, Toronto, Canada

7. Motivation • Traffic flow management currently accomplished through a loosely coordinated set of national and regional level controls • Predicted interactions and integrated impact of these controls are not well understood • Long (sometimes duplicate) pre-departure delays assigned to some flights • and inconsistently control traffic flows • - GDP assigns EDCT to satisfy the airport capacity constraint • TMA delays flight to fit it into the overhead stream • Controls tend to under, over, and inconsistently control traffic flows 3 of 17

8. Objectives • Develop an integrated test-bed to facilitate integrated traffic flow management and metering studies • Explore concepts and models for improving the inter-operability between traffic flow management and metering. 8 of 17

9. GDP scenario at DFW

10. Simulation and analysis of aircraft trajectories with environmental constraints* *N.Chen, B. Sridhar and H.Ng, “Strategies for Reducing Contrail Formations Using Predicted Contrail Frequency Index,” Submitted to the AIAA Guidance, Navigation and Control Conference, 2-5 August 2010, Toronto, Canada. B.Sridhar, N. Chen and H. Ng,“Simulation and optimization methods for assessing the impact of aviation operations on the environment,” 27th Congress of the International Council of the Aeronautical Sciences (ICAS), 19-24 September 2010, Nice, France.

11. Impact of Aviation on Climate Change* • Increased urgency to deal with factors affecting climate change • Climatic changes include • Direct emissions: CO2 , Water vapor and other greenhouse gasses (best understood) • Indirect effects from NOx affecting distributions of Ozone and Methane (Ozone and Methane effects have opposite signs) • Effects associated with contrails and cirrus cloud formation • Aviation responsible for 13% of transportation-related fossil fuel consumption and 2% of all anthropogenic CO2 emissions • Large uncertainty in the understanding of the impact of aviation on climate change *“Workshop on the Impacts of Aviation on Climate Change,” June 7-9, 2006, Boston, MA.

12. Why another simulation? • Focus on impacts of subsonic aviation emissions at cruise altitudes in the upper troposphere and lower stratosphere (~14Km and above) • Emissions at cruise altitudes have a larger impact than emissions on the surface • Need a air traffic simulation and optimization tool-box with fuel, emission and contrails models

13. Environmental Considerations*

14. Environment Simulation Modules • Aircraft Dynamics • 3-DOF equations • Wind models • RUC 40/20 KM grid • Contrails • Computed using RUC data • Fuel burn • Leverage FAA SAGE models • Emission • Leverage FAA SAGE models • Trajectory optimization

15. Contrails • Occur if ambient temperature along the aircraft trajectory is colder and moister than a threshold defined by thermodynamic parameters • Contrails persist under certain conditions (Relative humidity with respect to ice >100%) • Effect different during night and day

16. Persistent Contrail Formation Model RHW Contours RHI Contours Aircraft Rhi>100% Persistent Contrail 16 RHI>100% Contours

17. Contrail Frequency Index

18. Trade-offs Amongst Aviation Emissions Impacting Climate • Flight altitude effects on ozone, contrail formation and other effects • Differential impact of night and day operations • Routings to avoid certain regions with specialized chemistry (e.g. supersaturated air, cirrus, or polar)

19. Fuel optimal contrail avoidance aircraft trajectories • Fuel optimal trajectories generated using point mass aircraft dynamics and trajectory optimization based on Singular Perturbation Theory

20. Concluding Remarks • Presented recent changes to FACET software to enable evaluation of TFM concepts in support of NextGen • Emphasized current research on TFM-TMA interaction and environmental modules

21. Key TFM Research Activities National Airspace System Observed traffic User schedules and flight plans Weather Translation Traffic Predictions Metrics TFM Simulation 20 min - 8 hrs Weather Data • Aircraft-level • Aggregate-level Airspace Adaptation Data TFM Decisions Collaborative Traffic Flow Management TFM Optimization Weather Impacted Airspace/Airport Capacity Estimation (AFD/Chan, AFD/Love, TI/Wolfe, TI/Wang, AFC/Sheth, UARC/Islam, MIT-LL, NRA/Krozel, NRA/Cook) Metrics for Correlating the Performance of the NAS with Weather (AF/Sridhar, UARC/Chen, TI/Wang, TI/Kulkarni, AFD/Walker) Aggregate-level Demand Estimation and Flow Modeling(AFC/Bloem, NRA/Bayen, TI/Timucin) Assigning Aircraft-level Delays to Satisfy Airport/Airspace Constraints (AFC/Rios, AFC/Grabbe, UARC/Mukherjee, TI/Agogino, NRA/Ball, NRA/Clarke) Integration of TFM Capabilities (AFC/Grabbe, UARC/Mukherjee, UARC/Morono, UARC/Lock) Influence of User Preferences on Flight Routing (AFC/Sheth, AFC/Bilimoria, TI/Wolfe, TI/Enomoto, UARC/Jarvis, NRA/Idris) Weather Rerouting(AFC/Grabbe, UARC/Mukherjee, UARC/Ng

22. National Weather Service Winds SevereWeather APPLICATIONS Air and Space Traffic Integration AirborneSelf-Separation DataVisualization Direct Routing Analysis Controller Workload System-Level Optimization Traffic Flow Management FACET CORE FEATURES Tracks Flight Plans FAA Traffic Data Route Parser & Trajectory Predictor User Interface Historical Database Climb Descent Cruise Aircraft Performance Data Traffic & Route Analyzer Airspace Airways Airports Adaptation Data FACET Software Architecture

23. Simulation and Optimization Java Application Read/Implement Flight Controls FACET NAS Simulation 4D Trajectories Weather Forecasts Generate/Introduce Simulation Uncertainties Run optimization model in CPLEX/AMPL Start Simulation Application Program Interface Create CPLEX/AMPL Input File Log airspace/airport occupancy/usage statistics Repeat N time steps Flight Controls System Uncertainties Generate weather impacted airspace/airport capacity Flight Schedules Weather Forecasts Airspace Configuration

24. Aircraft Dynamics • Flat-Earth, inertial reference, point mass aircraft model • Find the optimal trajectory given the arrival and departure • airports, wind conditions subject to environmental conditions

25. Trajectory Optimization Options • No winds • Separate route and altitude profile optimization • Near optimal wind routes • Dynamic programming • Singular perturbation • Linear programming • Heuristics • Inclusion of contrail constraints (state constraints)