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Modeling NextGen Functions in Future ATM Concepts Evaluation Tool (FACET)
Banavar Sridhar NASA Ames Research Center
Second Annual WorkshopInnovations in NAS-Wide Simulation
Washington, DCJanuary 27-28, 2010
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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
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Integrated TFM Solution
TFM Simulation20 min - 8 hrs
National Airspace SystemUser
schedules and flight
plans
Observed traffic
Weather Translation
TFM Optimization
Traffic Predictions
Metrics
Collaborative Traffic Flow Management
TF
M D
ec
isio
ns
• Aircraft-level (FACET)• Aggregate-levelAirspace
Adaptation Data
Meteorological Data
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
04/21/23 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)
Interaction of Simulation and Optimization
Coordinated TFM and TMA Operations*
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* S. Grabbe, B. Sridhar, A. Mukherjee and A. Morando, “Integrated Strategic and LocalArrival Flight Scheduling,” Submitted to the AIAA Guidance, Navigation and Control
Conference, 2-5 August 2010, Toronto, Canada
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
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.
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, Nice, France.
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
Persistent ContrailRhi>100%
Aircraft
RHi>100% at 225 hPa
100
120
140
160
180
200
RHi (%) at 225 hPa
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40
60
80
100
120
140
160RHw (%) at 225 hPa
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30
40
50
60
70
80
RHW Contours RHI Contours
RHI>100% Contours
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
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• Fuel optimal trajectories generated using point mass aircraft dynamics and trajectory optimization based on Singular Perturbation Theory
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)
Aggregate-level Demand Estimation and Flow
Modeling(AFC/Bloem, NRA/Bayen,
TI/Timucin)
Influence of User Preferences on Flight Routing
(AFC/Sheth, AFC/Bilimoria, TI/Wolfe, TI/Enomoto,
UARC/Jarvis, NRA/Idris)
Assigning Aircraft-level Delays to Satisfy Airport/Airspace
Constraints (AFC/Rios, AFC/Grabbe,
UARC/Mukherjee, TI/Agogino, NRA/Ball,
NRA/Clarke)
Metrics for Correlating the Performance of the
NAS with Weather(AF/Sridhar,
UARC/Chen, TI/Wang, TI/Kulkarni,
AFD/Walker)
Weather Rerouting(AFC/Grabbe,
UARC/Mukherjee, UARC/Ng
Integration of TFM Capabilities(AFC/Grabbe, UARC/Mukherjee,
UARC/Morono, UARC/Lock)
TFM Simulation20 min - 8 hrs
National Airspace SystemUser
schedules and flight
plans
Observed traffic
Weather Translation
TFM Optimization
Traffic Predictions
Metrics
Collaborative Traffic Flow
Management
TF
M D
eci
sio
ns • Aircraft-level
• Aggregate-level
Airspace Adaptation
Data
Weather Data
FACET Software Architecture
NationalWeatherService
Winds
SevereWeather
FAATraffic Data
Tracks
Flight Plans
Aircraft Performance
Data
ClimbDescent
Cruise
AirspaceAirways
Airports
Adaptation Data
HistoricalDatabase
Traffic & Route Analyzer
User Interface
Route Parser &Trajectory Predictor
FACET CORE FEATURESAPPLICATIONS
Air and Space Traffic Integration
AirborneSelf-Separation
DataVisualization
Direct Routing Analysis
Controller Workload
System-Level Optimization
Traffic Flow Management
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FACET
NAS Simulation
4D Trajectories
Weather Forecasts
Application Program
Interface
Start Simulation
Log airspace/airport occupancy/usa
ge statistics
Generate weather impacted
airspace/airport capacity
Create CPLEX/AMPL
Input File
Run optimization
model in CPLEX/AMPL
Read/Implement Flight Controls
Generate/Introduce Simulation
Uncertainties
Java Application
Repeat N time steps
Flight SchedulesWeather Forecasts
Airspace Configuration
Flight ControlsSystem Uncertainties
Simulation and Optimization
• Flat-Earth, inertial reference, point mass aircraft model
• Find the optimal trajectory given the arrival and departure airports, wind conditions subject to environmental conditions
Aircraft Dynamics
€
˙ x = V cosϕ cosγ + u(x,y)
˙ y = V sinϕ cosγ + v(x,y)˙ E = (T − D)V /mg˙ h = V sinγ
˙ γ = (Lcosφ − mgcosγ ) /mV
˙ ϕ = Lsinφ /mV cosγ
˙ m = −σ (h,V ,T)
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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