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
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
2
Integrated TFM Solution
User
schedules
and flight
plans
Observed
traffic
National Airspace System
Weather Translation
TFM Simulation
20 min - 8 hrs
Meteorological
Data
Metrics
• Aircraft-level (FACET)
• Aggregate-level
TFM Decisions
Airspace
Adaptation
Data
Traffic
Predictions
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
3
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)
5/28/2016
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Interaction of Simulation and Optimization
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
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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) predeparture 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
Objectives
• Develop an integrated test-bed to facilitate integrated traffic
flow management and metering studies
• Explore concepts and models for improving the interoperability between traffic flow management and metering.
8 of 17
GDP scenario at DFW
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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,
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.
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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
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Environmental Considerations*
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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
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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
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Persistent Contrail Formation Model
RHw (% ) at 225 hPa
RHi>100% at 225 hPa
RHW Contours
RHi (% ) at 225 hPa
RHI Contours
Aircraft
Rhi>100%
Persistent Contrail
RHI>100% Contours
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Contrail Frequency Index
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)
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Fuel optimal contrail
avoidance aircraft trajectories
• Fuel
optimal trajectories generated using point
mass aircraft dynamics and trajectory optimization
based on Singular Perturbation Theory
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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
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Key TFM Research Activities
Weather Impacted Airspace/Airport
Capacity Estimation
(AFD/Chan, AFD/Love, TI/Wolfe,
TI/Wang, AFC/Sheth, UARC/Islam,
MIT-LL, NRA/Krozel, NRA/Cook)
National Airspace
System
User
schedules
and flight
plans
TFM Simulation
20 min - 8 hrs
Weather
Data
Airspace
Adaptation
Data
Integration of TFM Capabilities
(AFC/Grabbe, UARC/Mukherjee,
UARC/Morono, UARC/Lock)
Observed
traffic
Weather Translation
TFM Decisions
Aggregate-level Demand
Estimation and Flow
Modeling
(AFC/Bloem, NRA/Bayen,
TI/Timucin)
Metrics for Correlating
the Performance of the
NAS with Weather
(AF/Sridhar,
UARC/Chen, TI/Wang,
TI/Kulkarni,
AFD/Walker)
Traffic
Predictions
• Aircraft-level
• Aggregate-level
TFM
Optimization
Collaborative
Traffic Flow
Management
Weather Rerouting
(AFC/Grabbe,
UARC/Mukherjee,
UARC/Ng
Metrics
Assigning Aircraft-level
Delays to Satisfy
Airport/Airspace
Constraints
(AFC/Rios, AFC/Grabbe,
UARC/Mukherjee,
TI/Agogino, NRA/Ball,
NRA/Clarke)
Influence of User Preferences
on Flight Routing
(AFC/Sheth, AFC/Bilimoria,
TI/Wolfe, TI/Enomoto,
UARC/Jarvis, NRA/Idris)
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FACET Software Architecture
National
Weather
Service
FAA
Traffic Data
Winds
Severe
Weather
FACET CORE FEATURES
Air and Space
Traffic Integration
Route Parser &
Trajectory Predictor
Data
Visualization
Historical
Database
User
Interface
Climb
Aircraft
Descent
Performance
Cruise
Data
Adaptation
Data
Airborne
Self-Separation
Tracks
Flight
Plans
Airspace
Airways
Airports
APPLICATIONS
Traffic & Route
Analyzer
Direct Routing
Analysis
Controller
Workload
System-Level
Optimization
Traffic Flow
Management
Simulation and Optimization
Java Application
Read/Implement
Flight Controls
FACET
NAS Simulation
Generate/Introduce
Simulation
Uncertainties
Run
optimization
model in
CPLEX/AMPL
4D Trajectories
Weather Forecasts
Start
Simulation
Application Program
Create
CPLEX/AMPL
Input File
Interface
Repeat N
time steps
Log
airspace/airport
occupancy/usa
ge statistics
Generate weather
impacted
airspace/airport
capacity
Flight Controls
System Uncertainties
Flight Schedules
Weather Forecasts
Airspace Configuration
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Aircraft Dynamics
• Flat-Earth, inertial reference, point mass aircraft model
xÝ V cos cos   u(x, y)
yÝ V sin  cos   v(x, y)
EÝ (T  D)V /mg
hÝ V sin 
Ý (L cos   mgcos  ) /mV

Ý L sin  /mV cos 

mÝ  (h,V,T)
• Find the optimal trajectory given the arrival and departure
airports, wind conditions subject to environmental conditions

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)
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