Airspace Concept Evaluation System- State of Development Gano B. Chatterji

1. Airspace Concept Evaluation System- State of Development Gano B. Chatterji gano.b.chatterji@nasa.gov 28 January 2010

2. ACES Development Guided by Research Needs Oceanic In-Trail Procedures Traffic Flow Management Multi-Sector Planner Dynamic Airspace Configuration Integrated Weather Information Separation Assurance With Arrival Merging and Separation Super Density Operations Trajectory Prediction Synthesis & Uncertainty CDAs & Tailored Arrivals Metroplex Operations Merging and Spacing Closely Spaced Parallel Runways Arrivals/Departures Management 2 Enhanced Surface Operations 2

3. Main Points ACES development driven by research needs; Ideas from research being folded into ACES. Validation based on data and not just software; emphasis on plotting, visualization, analysis with large datasets. Results produced by ACES are reasonable. ACES is faster and more stable. ACES has higher fidelity models (surface, terminal area trajectory, separation-assurance). 3

4. Outline • ACES Development: • Separation-Assurance • Traffic Flow Management • Dynamic Airspace Configuration • Weather Data Handling • Trajectory Generators • Weight Estimation • ACES Analyst and Viewer • User Support Helpdesk • Research Examples Using ACES: • Surface Operations • Separation-Assurance • Dynamic Airspace Configuration • Dynamic Airspace Configuration and Traffic Flow Management Integration • System-Wide Study

5. Final Trajectory Weather Polygon Original Trajectory Actual Predicted Separation-Assurance - New Capabilities • Weather: • Weather polygons used for defining weather avoidance areas. • Trajectory Prediction Uncertainties: • Can perturb the predicted trajectories to understand the effects of uncertainty. • Multiple Centers: • Can operate independent Separation Assurance agents in multiple geographic areas to study coordination issues.

6. Traffic Flow Management Support • Objective: • Flexible structure • Disable TFM for open-loop simulations. • Enable/disable TFM in airport, TRACON, center domains. • Support for alternative algorithms • Distributed TFM • Centralized TFM • Linear-Programming based Optimal TFM • Causality and delay attribution • Who caused it and where was it realized. • Approach: • Support services for demand and capacity prediction. • Improved plug-in architecture. • Messaging interface. • Simple GUI based configuration prior to simulation.

7. Dynamic Airspace Configuration Support • Objective: • Implement Dynamic Airspace Configuration algorithms in ACES. • Support for capacity (including workload) metrics. • Approach: • Data interface for ACES traffic and geometry outputs in Enhanced Traffic Management System (ETMS) format. • Communications service for data exchange with DAC algorithms running on other computers. • ACES modified to read back subsector data (sector building blocks).

8. Weather Service Provider Support • Objective: • Support for dynamic convective weather products. • Support for forecast weather products. • Support for grid-based and contour-based weather data. • Approach: • Unified service interface for querying weather data. • Error models for weather forecast from nowcast data when forecast data are unavailable. • Time-shift error • Position error • Severity and coverage error

9. Trajectory Generators • Objective: • Airport to airport trajectory generation. • Surface • Terminal area • Enroute • Choice of trajectory generators. • Approach: • Swappable trajectory generator interface. • Kinematic trajectory generator uses BADA performance tables. • Kinetic trajectory generator uses BADA aircraft performance data and atmosphere data. • Key Finding: • New trajectory generators being tested. • Performance data updated based on BADA 3.7. • Will improve ACES runtime performance.

10. Take-Off Weight Estimation • Objective: • Determine takeoff weight for planned flight using aircraft performance model, and reserve and maneuvering fuel requirement. • Approach: • Iterative procedure to determine fuel and payload. • A closed-form solution based on constant altitude cruise, and climb and descent fuel increment factors. • Key Finding: • Payload-range curves compare well with aircraft manufacturer published data. • Computationally efficient.

11. ACES Analyst Tool Enhancements • ACES Grid Creator • Generation of sector grid maps from ETMS sector files. • ACES Disambiguation Tool • Bug fixes and compatibility enhancements for use with the ACES Grid Creator. • ACES Analyst • Flight data set from ETMS data. • Multiple data converters to support scenario generation. • Analyst reports. • ACES Report Generator • Enhances to generate .csv file versions of the ACES National Metrics. • Analyst reports. • ACES Viewer • Replaces the current ACES VST during runtime. • ACES-SA Web Application • Viewing and analyzing conflict resolution. • SurfTools • Airport surface design tool (STLE) – STLE is part of ACES. • TASSE • ACES runtime configuration management system and surface and terminal area airspace design tool.

12. ACES Viewer • Flexible tool for visualization • Airport, airspace and weather • Trajectories • Conflict scenarios • Trial-plan trajectories • As flown trajectories

13. User Support Helpdesk • Purpose of the ACES Helpdesk : • A single point of contact for answering ACES questions. • Helpdesk Queries: • Users send email queries to aces.helpdesk@aerospacecomputing.com. • Each query assigned a unique tracking number. • Communication via email, using the tracking number, until query resolved. • Common queries during the first two months: • Locating ACES documentation. • ACES setup questions.

14. Research Examples

15. Safe and Efficient Surface Operations (SESO) • Objective: • Improve airport surface capacity and efficiency. • SESO concepts: • Trajectory based surface operations. • Optimized taxi scheduling. • ACES Modeling Capabilities: • Node-link based airport representation. • Time based taxi routes. • Integrated airport/TRACON simulation environment.

16. Separation Assurance • Objective: • Maintain required separation between aircraft. • Meter aircraft at points in space. • Avoid weather hazards. • Approach: • Solve all problems in an integrated fashion for coordination and efficiency. • Key Finding: • Can resolve over 99% of all conflicts for 2X traffic with weather.

17. Dynamic Airspace Configuration • Objective: • Create sectors such that traffic is at or below capacity. • Approach: • Use Genetic Algorithm to select Voronoi polygon generating points. • Iterative partitioning. • Maximize transit-time and minimize boundary crossings. • Key Finding: • Capacity thresholds are not exceeded by traffic. • Delays are reduced.

18. Dynamic Airspace Units • Objective: • Capacity re-allocation by changing sector boundary. • Approach: • Exchanges ‘slices’ between sectors to address over-utilization. • Merges under-utilized sectors. • Key Finding: • Minor adjustments rather than a complete boundary change. ZOB66A workload higher ZOB66B and ZOB66C units are assigned to sector ZOB67 (left).

19. Dynamic Airspace Configuration and Traffic Flow Management Integration • Objective: • Study interaction between airspace configuration and traffic flow management. • Approach: • Integration using data and ACES simulations. • Key Finding: • TFM delay can be determined as a function of number of sectors. • Sectors can be designed to reduce delays due to mismatch between demand and capacity.

20. System-Wide Weather Effect Study • Objective: • Establish weather affected baseline data for common scenario days. • Determine yearly weather delays for current day operations. • Assess the ability of Separation-Assurance, Traffic Flow Management and Dynamic Airspace Configuration to reduce delay in the presence of weather. • Approach: • 17 days of traffic, wind, weather, AAR/ADR, FAA data from 2006 collected. • Traffic volume: low and high • Weather: light, moderate and severe • Average arrival delay with 2006, 2018 and 2025 assumed traffic and capacities computed.

21. Common Scenario Generation • Current Day (2006) • Cluster Analysis • NAS Data Gathering • Database Generation • NAS state data • NAS weather data • NAS wind data • Airport Capacity and State • VAMS • ASPM • JPDO-SMAD + ASPM + VAMS • Airport Taxi Times • VAMS • ASPM • JPDO-SMAD + ASPM + VAMS • Expanded ACES Airport database • Most frequently used terminal area configurations • Runway modeled airports (FAA Metro 7 airports) • Added aircraft types • Terminal Area Transit Times • Data Gathering • Updated ACES transit times • Sector Enhancements for use with ACES • 2006 and 2007 Sector models • Correction of “Gaps and Overlaps” laterally and vertically • Alignment of sector boundaries • Oceanic coverage • Demand Generation (TAF 2008) • 1.0x, 1.1x (NGIP (2018)), 1.2x (NextGen (2025)), 1.5x, 2.0x, 2.5x, and 3.0x. • Unconstrained version of demand • Constrained (time shifted) version of demand • NGIP (2018) configuration • NextGen (2025) configuration

22. Parting Thoughts ACES development driven by research needs; Ideas from research being folded into ACES. Validation based on data and not just software; emphasis on plotting, visualization, analysis with large datasets. Results produced by ACES are reasonable. ACES is faster and more stable. ACES has higher fidelity models (surface, terminal area trajectory, separation assurance).