Operational Data Yashovardhan S. Chati, Hamsa Balakrishnan 6 th - - PowerPoint PPT Presentation

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6th International Conference on Research in Air Transportation, Istanbul Technical University, 2014

Analysis of Aircraft Fuel Burn and Emissions in the Landing and Take Off Cycle using Operational Data

Yashovardhan S. Chati, Hamsa Balakrishnan

International Center for Air Transportation

Department of Aeronautics and Astronautics Massachusetts Institute of Technology

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Motivation

• Aircraft emissions – significant source of air

pollution, take place at a range of altitudes

• Landing and Take Off cycle emissions – local air

quality, impact on health of people in airport vicinity

• Total aviation traffic in 2050: 6.5 – 15.5 times that

in 1990; total fuel burn: 1.5 - 9.5 times; CO2 emissions 1.6 – 10 times (IPCC 1999)

• Emissions depend on engine characteristics (like

fuel flow rate), operational procedures – important to assess their effects to come up with accurate emission inventories

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Current Methods

• ICAO Engine Exhaust Emissions Databank (ICAO)
• Fixed values of thrust settings and times in mode for certification
• Fuel burn, emission indices for each mode
• System for assessing Aviation’s Global Emissions (SAGE)

(FAA 2005)

• Based on publicly available databases
• Doesn’t use operational data for model building
• Comparison with US airline’s FDR data: fuel burn overpredicted

by 5% and 8% in takeoff and descent segments, respectively

(Joosung 2005)

• Want to calculate fuel burn and emissions from
• perational data and compare with current models - use

the Flight Data Recorder

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Flight Data Recorder (FDR)

• “Black box”
• Logs important aircraft parameters in flight
• Most accurate source of operational data
• Can account for effects not explained by physics-based models (like

pilot behavior and operational procedures)

• Can account for variations in performance of the same

aircraft/engine type (due to airline operating and maintenance procedures, specific trajectory flown, weather, ageing, etc.)

mikenv.hubpages.com/hub/Flight-Data-Recorders

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Related Work: Estimation of Fuel Burn and Emissions

• Collins 1982
• Fuel consumption estimated from path profile data
• Energy balance method and empirical relations
• Trani et al. 2004
• Fuel burn studied as a function of altitude, temperature, Mach

number, aircraft mass

• Data from aircraft flight manual charts
• Neural network method
• Allaire 2006
• Combustor model to estimate NOx and CO emissions
• Physics-based model

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Related Work: Use of Operational Data

• Patterson et al. 2009
• Landing and Take Off (LTO) cycle
• Fuel flow rates and times in mode calculated from FDR data
• Khadilkar et al. 2012
• Models taxi fuel burn as a function of taxi time, number of stops,

number of turns, acceleration events

• Standard least squares regression on FDR data

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Related Work: Use of Operational Data

• Ryerson et al. 2012
• Aircraft performance models validated with operational airline data
• Models overestimate operational fuel burn
• Statistical models developed to improve performance models – 10%

improvement in fuel burn estimation

• Chati et al. 2013
• A330-223 FDR data used to highlight important trends in engine

parameters (fuel flow rate, thrust) for different flight phases

• Reduced order thrust calculation model developed

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Details of the FDR Dataset: Aircraft Types Studied

Sr. No. Aircraft Type Engine

• No. of

Engines

• No. of

Flights Considered

1. Airbus A319 - 112 CFMI CFM56-5B6 2 130 2. Airbus A320 - 214 CFMI CFM56-5B4 2 169 3. Airbus A321 - 111 CFMI CFM56-5B1 2 117 4. Airbus A330 - 202 GE CF6-80E1A4/PW 4168 2 84 5. Airbus A330 - 223 PW 4168A 2 179 6. Airbus A330 - 243 RR Trent 772B - 60 2 100 7. Airbus A340 - 541 RR Trent 553 - 61 4 52 8. Airbus A340 - 313 CFMI CFM56-5C4 4 76 9. Boeing B757 - 200 RR RB211 - 535E4 2 150 10. Boeing B767 - 300 GE CF6 – 80C2B6, PW 4060 2 135 11. Boeing B777 - 3FXER GE 90 - 115B 2 131 12. Avro RJ85 / 100 Honeywell LF507 - 1F 4 153

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Details of the FDR Dataset: Different Airports in Study

• 88 airports in total
• AMSL elevation from -11’ (AMS) to 5558’ (JNB)

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Details of the FDR Dataset: Parameters

• 103 parameters
• Engine parameters
• Spool speeds, fuel flow rate, burner pressure, Exhaust Gas

Temperature, Engine Pressure Ratio, net thrust

• Aircraft parameters
• Ambient total and static pressure and temperature,

pressure altitude, latitude, longitude, time instant, gross mass, speeds, Mach number, heading, aircraft accelerations, wind velocities, positions of flaps, slats, spoilers, control surfaces, landing gear, thrust reversers

• Focus in this study on the engine fuel flow rates

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FDR Flight Phase Identification

On the basis of solely the flight trajectory information in the FDR, the trajectory for each flight split into different phases:

 Departure taxi  Takeoff roll and

wheels off

 Ascent/Climb  Cruise  Descent  Touchdown  Arrival landing

roll and taxi

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Landing and Take Off (LTO) Cycle

• Four phases:
• Takeoff roll
• Climbout (upto 3000’ Above Ground Level

(AGL))

• Approach (from 3000’ AGL)
• Taxi/ground idle

Operating Mode Thrust Setting (%

• f Full Thrust)

Time in Operating Mode (s)

Takeoff roll 100 42 Climbout 85 132 Approach 30 240 Taxi/ground idle 7 1560

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TIMES IN MODE AND FUEL BURN

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Methodology

• FDR reported values for fuel flow rates converted to

equivalent values at Sea Level Static-International Standard Atmosphere (SLS–ISA) conditions for an uninstalled engine (Boeing Fuel Flow Method 2 (BFFM2))

• For each aircraft/engine type, operational values of times

in mode, fuel flow rates and fuel burn calculated for different LTO phases – averaged over different flights, 95% confidence intervals

• FDR derived mean values statistically compared with

ICAO databank values

• Two-sided Wilcoxon signed rank test
• Level of significance (α) = 5%

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Operational and ICAO Values: Times in Mode (s)

Statistically significant differences between operational and ICAO values in most cases (ICAO overestimates in most cases) Takeoff roll < climbout < approach < taxi/ground idle

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Fuel Flow Rate (per engine)

Statistically significant differences between operational and ICAO values in most cases Taxi, approach: ICAO overestimates Climbout, takeoff: ICAO underestimates Taxi < Approach < Climbout, Takeoff (opposite to TIM)

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Fuel Mass Consumed (all engines)

ICAO overestimates in most cases Fuel burn = TIM x Fuel flow rate Ground phases: TIM dominates Air phases: fuel flow rate dominates

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Total LTO Cycle Fuel Mass Consumed (all engines)

ICAO overestimates in ALL cases (as large as 47%) Total fuel burn scales linearly with MTOW (0.6 – 0.8%)

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NOx EMISSIONS

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NOx Emissions: Methodology

• Emission Index (EI): mass of emissions produced per unit

mass of fuel burnt

• EI for NOx: function of fuel flow rate (BFFM2)
• FDR values in place of BADA values of fuel flow rates
• Mass of NOx produced = Fuel mass burnt x NOx EI
• Values referenced to SLS-ISA, uninstalled engine

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NOx Emission Index

Taxi, approach, takeoff: ICAO overestimates Climbout: ICAO underestimates Taxi < Approach < Climbout, Takeoff Higher thrust => higher fuel flow rate => higher combustor temp. => higher EI

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NOx Mass Produced (all engines)

ICAO overestimates in most cases (as much as 83%) Climbout: high fuel burn and EI => highest NOx emissions

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Conclusions

• FDR values qualitatively similar in behavior to the

ICAO databank values

• In most cases, ICAO values statistically

significantly different from FDR values and ICAO

• verestimates – differences not attributable to

ambient conditions or engine installation effects

• Confidence intervals: measure of variability

(maintenance, ageing, traffic congestion, operating procedures, pilot behavior, weather, etc.) among the same aircraft/engine type

• Differences can lead to overestimation of global

aircraft fuel burn and emission inventories

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Ongoing and Future Work

• Regression based models for predicting engine

fuel flow rate and emissions from a handful of easily available trajectory variables (like time, altitude, speed) in all the different phases of flight (including cruise) and for different aircraft types

• Model results to be compared with currently used

performance packages

• Sensitivity of the results to the different parameters

in the FDR to be analyzed

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Acknowledgments

• This research was supported in part by the

National Science Foundation under CPS: Large: ActionWebs (award no. 0931843)

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Yashovardhan S. Chati yschati@mit.edu Website: web.mit.edu/aeroastro/labs/icat

International Center for Air Transportation

Massachusetts Institute of Technology

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References

[1] Intergovernmental Panel on Climate Change (IPCC), “IPCC Special

Report – Aviation and the Global Atmosphere - Summary for Policymakers”, 1999.

[2] International Civil Aviation Organization (ICAO), “ICAO Aircraft Engine

Emissions Databank”, [online database], URL: http://easa.europa.eu/environment/edb/aircraft-engine- emissions.php [last accessed on 12 February 2014].

[3] Federal Aviation Administration (FAA), “SAGE: System for assessing

Aviation’s Global Emissions Technical Manual”, Version 1.5, 2005.

[4] Joosung, J.L., “Modeling Aviation’s Global Emissions, Uncertainty

Analysis, and Applications to Policy”, PhD Thesis, Department of Aeronautics and Astronautics, MIT, 2005

[5] Collins, B.P., “Estimation of Aircraft Fuel Consumption", AIAA Journal

• f Aircraft, Vol.19, No. 11, 1982, pp. 969 – 975.

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References (continued…)

[6] Trani, A.A., Wing - Ho, F.C., Schilling, G., Baik, H., Seshadri, A., “A

Neural Network Model to Estimate Aircraft Fuel Consumption", AIAA 4th Aviation Technology, Integration and Operations (ATIO) Forum, Chicago, 2004.

[7] Allaire, D.L., “A Physics – Based Emissions Model for Aircraft Gas

Turbine Combustors”, MS Thesis, Department of Aeronautics and Astronautics, MIT, 2006.

[8] Patterson, J., Noel, G.J., Senzig, D.A., Roof, C.J., Fleming, G.G.,

“Analysis of Departure and Arrival Profiles Using Real-Time Aircraft Data", AIAA Journal of Aircraft, Vol. 46, No. 4, 2009, pp. 1094 – 1103.

[9] Khadilkar, H., Balakrishnan, H., “Estimation of Aircraft Taxi Fuel Burn

Using Flight Data Recorder Archives", Transportation Research Part D,

• Vol. 17, No. 7, 2012, pp. 532 – 537.

[10] Ryerson, M. S., Hansen M., Bonn J., “Validating Aircraft Performance

Models with Airline Data,” International Conference on Research in Air Transportation, University of California, Berkeley, 2012.

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References (continued…)

[11] Chati, Y.S., Balakrishnan, H., “Aircraft Engine Performance Study

Using Flight Data Recorder Archives”, AIAA Aviation Conference, Los Angeles, California, 2013.