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

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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

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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

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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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