0 10000 20000 30000 40000 50000 60000 70000 80000 90000 0 10000 20000 30000 40000 50000 60000 70000 80000 90000 Actual fuel burn (kg) SAGE fuel burn using OAG-based trajectories (kg) LMI 5-year collaborative effort with Volpe National Transportation Systems Center and the Logistics Management Institute (LMI) funded by the FAA Office of Environment and Energy OBJECTIVE “Internationally accepted computer model used for estimating aircraft emissions and evaluating the effects of different policy and technology scenarios on aviation-related emissions, costs, aircraft performance, and industry responses.” • Implementation of new aircraft technology • Improvements to air traffic control/airspace capacity • Enhancements to airport infrastructure • Improvements in aircraft operations • Fastest growing transport mode (~4%/yr doubling in 20 yrs) • Only mode with direct emissions deposition at altitude • Global, regional, and local effects being recognized as important • Currently only local air quality addressed through certification standards, but desire to reduce aviation emissions intensifying • Important to know which options are effective in mitigating environmental impacts while meeting increased air travel demand and providing cost-effective solutions for industry MIT ROLE • Build fuel burn and emissions components • Assess uncertainties associated with data used, and performance, fuel burn and emissions predicted • Use SAGE to analyze one or two important aviation policy questions Joosung Lee, Kelly Klima, Professor Ian Waitz and Professor John-Paul Clarke Department of Aeronautics and Astronautics, MIT AIR TRANSPORT GROWTH AND ENVIRONMENTAL IMPACT SAGE SAGE System for Assessing System for Assessing Aviation’s Global Aviation’s Global Emissions Emissions What are the recent/future trends in fuel consumption and emissions performance of world fleet and what are their technological, operational and economic drivers? What are the environmental benefits of operational initiatives such as de-rated takeoff, Continuous Descent Approach (CDA), and Communication, Navigation, Surveillance/Air Traffic Management (CNS/ATM)? What/how large are uncertainties in complex computer models, and how should they be treated in policy- making processes? Global Aviation Fuel Consumption (SAGE Output) Updated Radiative Forcing of Aircraft in 2003, Prediction for 2050 (IPCC, 1999) SAGE Fuel Burn Results Comparison to Actual Airline Data PROGRESS • Years 2000-02 emissions inventories complete • Aggregate fuel burn results in good agreement (to less than 10% difference) with measured data • Takeoff gross weight, wind, and aerodynamic and engine performance as largest sources of error • Continued model assessment for improvement • Policy scenario analysis on-going OAG (schedule-based dispersion trajectories) mean error = -1.0%, σ = 22.0% Atmospheric Conditions Emissions Fuel Burn Aerodynamic Drag Engine Thrust

SAGE LMI - MIT - Massachusetts Institute of Technologyweb.mit.edu/aeroastro/sites/waitz/publications/SAGE... · 2013-06-24 · “Internationally accepted computer model used for

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Page 1: SAGE LMI - MIT - Massachusetts Institute of Technologyweb.mit.edu/aeroastro/sites/waitz/publications/SAGE... · 2013-06-24 · “Internationally accepted computer model used for

10000

20000

30000

40000

50000

60000

70000

80000

90000

0 10000 20000 30000 40000 50000 60000 70000 80000 90000

Actual fuel burn (kg)

SAG

E fu

el b

urn

usin

g O

AG-b

ased

traj

ecto

ries

(kg)

LMI

5-year collaborative effort with Volpe National Transportation Systems Center and the Logistics Management Institute (LMI) funded by the FAA Office of Environment and Energy OBJECTIVE “Internationally accepted computer model used for estimating aircraft emissions and evaluating the effects of different policy and technology scenarios on aviation-related emissions, costs, aircraft performance, and industry responses.” • Implementation of new aircraft technology • Improvements to air traffic control/airspace capacity • Enhancements to airport infrastructure

Improvements in aircraft operations

• Fastest growing transport mode (~4%/yr doubling in 20 yrs) • Only mode with direct emissions deposition at altitude • Global, regional, and local effects being recognized as important • Currently only local air quality addressed through certification

standards, but desire to reduce aviation emissions intensifying • Important to know which options are effective in mitigating

environmental impacts while meeting increased air travel demand and providing cost-effective solutions for industry

JJoooossuunngg LLeeee,, KKeellllyy KKlliimmaa,, PPrrooffeessssoorr IIaann WWaaiittzz aanndd PPrrooffeessssoorr JJoohhnn--PPaauull CCllaarrkkee DDeeppaarrttmmeenntt ooff AAeerroonnaauuttiiccss aanndd AAssttrroonnaauuttiiccss,, MMIITT

AIR TRANSPORT GROWTH AND ENVIRONMENTAL IMPACT

SAGESAGE System for Assessing System for Assessing Aviation’s Global Aviation’s Global EmissionsEmissions

Global Aviation Fuel Consumption (SAGE Output) Updated Radiative Forcing of Aircraft in 2003, Prediction for 2050 (IPCC, 1999)

SAGE Fuel Burn Results Comparison to Actual Airline Data

OAG (schedule-based dispersion trajectories)mean error = -1.0%, σ = 22.0%

Atmospheric Conditions

Emissions

Fuel Burn

Aerodynamic Drag

Engine Thrust

• MIT ROLE • Build fuel burn and emissions components • Assess uncertainties associated with data used, and

performance, fuel burn and emissions predicted • Use SAGE to analyze one or two important aviation

policy questions

PROGRESS • Years 2000-02 emissions inventories complete • Aggregate fuel burn results in good agreement (to

less than 10% difference) with measured data • Takeoff gross weight, wind, and aerodynamic and

engine performance as largest sources of error • Continued model assessment for improvement • Policy scenario analysis on-going

What are the recent/future trends in fuel consumption and emissions performance of world fleet and what are their technological, operational and economic drivers? What are the environmental benefits of operational initiatives such as de-rated takeoff, Continuous Descent Approach (CDA), and Communication, Navigation, Surveillance/Air Traffic Management (CNS/ATM)? What/how large are uncertainties in complex computer models, and how should they be treated in policy-making processes?