ActionWebs

Hamsa Balakrishnan, David Culler, Edward A. Lee, S. Shankar Sastry, Claire Tomlin (PI)

University of California at Berkeley

July 23 2010

Goals of today’s meeting

1. Present and discuss research results and plans2. Discuss how to better work together3. Discuss results and ideas for education and

outreach4. Discuss budget5. Prepare for CPS PI meeting (August 10-12, DC)

Schedule9-9.45: Claire Tomlin, ActionWebs Overview10-10.45: Shankar Sastry, Closing the loop around

sensor networks11-11.45: Hamsa Balakrishnan: CPS and Air

Transportation12-12.30: Lunch12.30-1.15: Edward Lee: Hybrid and embedded systems1.30-2.15: David Culler: CPS and buildings2.30-2.50: Kristen Gates: CPS Education 3.00-4.20: Students and Postdocs (Eleftherios Matsikoudis,

Wei Zhang, Anil Aswani)4.20-5: Discussion and next steps

Energy efficient air transportation systems

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

Energy efficient, high productivity buildings

• 40% total energy consumption, 72% electricity usage• Isolated subsystems, not much modeling• Better operations needed

ActionWebs

Observe and infer with a viewpoint to planning and modifying action:

1. Dealing with uncertainty

2. Tasking sensors3. Programming the

ensemble4. Multiple objectives5. Embedding

humans

Research projects

• Optimization of Traffic Flow in the National Airspace System (Wei Zhang, Maryam Kamgarpour)

• System Identification combining physics-based models and data (Anil Aswani, Jeremy Gillula)

• Localization in buildings (Michael Vitus, Wei Zhang)• Game-theoretic routing of GPS-Assisted Vehicles for

Energy Efficiency (Anil Aswani)• Estimation and control of discrete time stochastic

hybrid systems (Jerry Ding, Alessandro Abate)• Designing automation that works well with humans

(Haomiao Huang)

Goals of today’s meeting

1. Present and discuss research results and plans2. Discuss how to better work together

– ActionWebs Seminar: Tuesdays 4-5pm?

3. Discuss results and ideas for education and outreach– CPS Education Workshop (August 12, DC)

http://cyberphysicalsystems.org/cpsew– Intro textbook for undergrad class

http://LeeSeshia.org– Robotics, undergrads, and high school students

4. Discuss budget5. Prepare for CPS PI meeting (August 10-12, DC)

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] [Balakrishnan]

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

Effect of stopping and starting while taxiing

• Potential fuel burn impact from stopping on the surface

No significant impact

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)

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)

Analyzing Benefits of Continuous Descent Approach (CDA)

Analysis Approach

Take current aircraft arrival trajectories

Move the constant altitude (Level) section to a high altitude

Objective: Study fuel benefits of implementing CDA in the current airspace structure

[Kamgarpour]

Results on Airport Savings

AirportAverage (kg)

Maximum (kg)

Type Annual Savings $$

ATL 33 317B763 1.18E+07DFW 38 721MD11 7.75E+06SFO 88 1623B744 1.39E+07LAX 20 507B741 1.92E+06JFK 40 479B744 7.57E+06

Scope of the Study 5 days of data for ATL, SFO, LAX airports

4 days of data for DFW, 1 day of data for JFK

Energy efficient Buildings

17[Culler]

10-8-2008

Structural: Soda Electrical

12 KV dist.2x Substation

1200

A 2

77/4

80 3

pha

se

2500

A 1

20/2

08 3

pha

se

2x ChillerMCM1

HP1A400

HP1A400

HP2A600

HP3A400

HP4A400

HP5A400

HP6A100

HP7A400

MCM2

Lighting

Pumps

Fans LP2E225

LP2D225

LP2C225

LP1A400

LP2A800

HP3A400

HP4A400

HP5A400

HP6A100

HP7A400

LP1B400

LP2B225

LP2G225

LP2F225

LP2E225

LP2D225

LP2C225

LP3B225

LP2E225

LP2D225

LP2C225

LP4B225

LP2E225LP2C

225LP5B

225

LP2D225

LP2C225

LP2B225

LP2D225

LP2C225

LP2B225

LP2K225LP2J

225LP2I

225LP2H

225LP2G

225LP2F

225

LP2K225LP2J

225LP2I

225LP2H

225LP2G

225LP2F

225

LP2J225

LP2I225

LP2H225

LP2G225

LP2F225

~42 circuits each

Machine roomsClassroomsOffices

Testbeds

1. Future air transportation systems testbed• Connections with NASA Ames (SDO, separation

assurance), NASA Langley (safety)• Simulators (FACET, ACES), actual flight data

(CFDR)2. Mobile sensor net testbed3. Buildings

• Berkeley campus: Soda, Cory, Sutardja Dai, California Halls

• LBL (DOE2)

Education and Outreach

• Cyber-physical systems science (CPSS)• Curriculum development at Berkeley/MIT• Outreach

– Robotics at the RFS– Curriculum development at SJSU– SUPERB-CSS– Research Experience for Teachers (RET)

[Gates]