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ActionWebs : Energy -efficient Air Transportation Systems. Hamsa Balakrishnan Massachusetts Institute of Technology NSF−UC Berkeley−MIT ActionWebs Kickoff Meeting December 17, 2009. The Air Transportation System. Key player in global travel and commerce - PowerPoint PPT Presentation
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Hamsa Balakrishnan Massachusetts Institute of Technology
NSF−UC Berkeley−MIT ActionWebs Kickoff Meeting
December 17, 2009
ActionWebs: Energy-efficient Air Transportation Systems
The Air Transportation System
Key player in global travel and commerce• 2 billion passenger enplanements in 2006 (US: ~700 mil.)• 29 million flights worldwide in 2007 (US: ~30,000 flights
per day)• Number of flights expected to grow ~2-3x by 2025• Jet fuel prices are volatile
Air traffic delays
In 2007, domestic air traffic delays cost the US economy $41 billion• 24% of arrivals at least 15 min late (avg. delay: 46 min)• ~20% of total domestic flight time was delay
• 1,565 flights delayed on the ground (not at gate) for over 3 hours
• 60% rise in rate of passenger complaints (2006-07)• $19 billion in direct operating costs to the airlines• $12 billion cost to passengers
Data sources: IATA, FAA ASPM and OPSNET databases, Bureau of Transportation Statistics, Joint Economic Committee, Airline Quality Rating 2008
Air Traffic Management: current functional architecture
Approved handoffs
Clearances/advisories
[Adapted from A. Haraldsdottir (Boeing), 1998]
Flight Planning
Flight schedules
Weather
National Flow
Planning
Facility Flow
Planning
Filed flight plans
Capacity schedule
Aircraft Guidance
& Navigatio
n
Aircraft state
< 5 min
Pilot
Recorded ETMS data
Hrs-day
Facility Traffic Control
5 min
Facility ATC
Airline Operations Center
Central Flow Mgmt Unit
Traffic Mgmt Unit
Facility Traffic
Planning
Approved flight plans/ planned
flow rates
Traffic situation
5-20 min
Desired traffic loads
Negotiate handoffs
Airlines Facility
Traffic Manager
Efficiency SafetyPlanning Execution
Throughput
Consequence: Delay propagation!
Key research objectives
More decentralized decision-making More flexible and dynamic trajectories Increase efficiency of operations Increase operational robustness in the presence of
weather Decrease fuel burn and environmental impact Multi-objective control techniques for balancing tradeoffs
The use of onboard sensing in ActionWebs, combined with the development of hybrid systems models of aircraft trajectories can help us achieve these objectives
More decentralized decision-making
An Eulerian model for distributed feedback control
s1|s2 s2|s3
s2|s4
s3|s2s2|s1
s4|s2
s3|s5
s5|s3
s3|s6
s6
Sector 1
Sector 2
Sector 3
Sector 4
Sector 5 Network model has two nodes for each sector boundary (one on each sector)
Links correspond to the regions of airspace
Set of queues associated with each node, each containing aircraft currently in that region of the airspace
Aircraft move from one node to another, at rates determined by the flow rates along the links
Le Ny and Balakrishnan, ACC 2009]
An Eulerian model for distributed feedback control
At every node, there is a queue corresponding to each destination
Then, we model the dynamics of queue m at node i by:
is the rate allocation on link (i,j) to flow m (control variable) Putting this together,
airport arrivals/departures inflow outflow
Advantages of this framework
Can apply distributed feedback control policies • For example, MaxWeight
Attractive idea, because sector controllers only need to talk to their neighbors (much like today!)
Build model from archived ETMS (surveillance) data
Le Ny and Balakrishnan, ACC 2009; Kannan, Harvard BA thesis, 2009]
For example, the airspace surrounding SFO
Numbers denote maximum inter-sector handoff rates in aircraft/min
Scheduling with routing
No routing (predefined routes):
With routing:(better balanced loads in two routes)
Time (in hrs) Time (in hrs)
Sector load (# of aircraft)
Sector load (# of aircraft)
More flexible and dynamic trajectories Increase efficiency of operations Increase operational robustness in the presence of
weather
Routing using convective weather forecasts
Convective weather has a significant impact on NAS operations, especially in summer
As the time horizon increases, forecast errors increase (especially when we are trying to forecast storm intensity in a 1 km x 1 km pixel)
5 min 30 min
Route flexibility improves accuracy
Forecast Actual
B
Decrease fuel burn and environmental impact Multi-objective control techniques for balancing tradeoffs
Environmental impacts of air transportation
Aviation is responsible for 3% of total global carbon emissions• Aircraft contribute about 12% of CO2 emissions from the
transportation sector• According to the European Union, international aviation is
one the largest growing contributors to CO2 emissions, having increased 87% between 1990 and 2004
The aviation sector was responsible for 187.5 million metric tons of CO2 emissions in the US in 2007 (about 3% of total emissions)
[Commission of the European Communities, 2006; EPA 2007]
Environmental impact of air traffic delays
Air traffic delays have an environmental cost, in addition to the inconvenience to passengers, airline costs and impact on the economy
In 2007, domestic air traffic delays• Cost airlines an additional $1.6 billion in fuel costs• Consumed an additional 740 million gallons of jet fuel• Released an additional 7.1 million tons of CO2 into the
atmosphere
Enhancing system capacity and improving efficiency will result in environmental benefits as well
[Joint Economic Committee of the US Senate, 2008]
Surface emissions from taxiing aircraft
In 2007, aircraft in the US spent over 63 million minutes taxiing in to their gates, and over 150 million minutes taxiing out to their runways• An estimated 6 million tons of CO2, 45,000 tons of CO,
8,000 tons of NOx and 4,000 tons of hydrocarbons are emitted annually by aircraft taxiing out for departure
These flights burn fuel and contribute to emissions at low altitudes, and adversely impact local air quality
Taxi-out emissions correspond to about 5% of the fuel burn and emissions from aircraft operations
How do we optimize surface traffic movement to reduce aircraft emissions from taxi processes?
[FAA ASPM database; Balakrishnan et al. 2008]
Aircraft taxi trajectories from surface surveillance data
Aircraft taxi trajectories from surface surveillance data
Effect of stopping and starting while taxiing
Potential fuel burn impact from stopping on the surface
No significant impact
Effect of stopping and starting while taxiing
Impact depends on pilot actionsNo significant change in throttle setting
Using CFDR data to estimate impact of different taxi profiles
ICAO emissions databank assumes that aircraft taxi at a constant throttle setting of 7%
Using CFDR data (from Swiss Air) corresponding to taxi profiles of various aircraft, we • Developed a regression model for fuel burn, that considers
the baseline fuel burn and the impact of stop-start events Stop-start impact: Estimate of the form
“The extra fuel burn from a start-stop event is equivalent to x additional minutes of taxi time”
• Developed a (linear) regression model between fuel burn and throttle settings
• Conducted above analysis for 9 aircraft types
Fuel burn = Baseline fuel burn rate*(taxi time) + (Stop-start impact)*(# of stop-start events)
CFDR vs. ICAO fuel burn estimates
Impact of a stop-start event
Labels indicate throttle settings
Minimizing fuel burn impacts of aircraft trajectories
Multi-objective control of taxi trajectories
Identification of hybrid system model of taxi
trajectory
Surface surveillance
Flight data recorder
Identification of fuel burn
model
Surface surveillance
Flight data recorder
(archival data)
(real-time data)
Multiple real-world objectives
Maximize throughput
Minimize average delay
Minimize maximum delay
Maximize robustness
Minimize fuel consumption
Minimize fuel costs
Minimize operating costs
Minimize passenger delays
Multiple stakeholders with differing objectives, tradeoffs involved
[Balakrishnan and Chandran, Operations Research, to appear; Lee and Balakrishnan, Proceedings of the IEEE, 2008]
Summary
ActionWebs concepts can help achieve• More decentralized decision-making• More flexible and dynamic trajectories• Increased efficiency of operations• Increased operational robustness in the presence of
weather• Decreased fuel burn and environmental impact
The Air Transportation System test bed will be an exciting application of these concepts!