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ASCENT Project 46Surface Analysis to Support AEDT Aircraft Performance Model (APM) Development Massachusetts Institute of Technology

PI: Hamsa Balakrishnan and Tom Reynolds

PM: Joseph Dipardo and Mohammed Majeed

Cost Share Partner: MIT

This research was funded by the U.S. Federal Aviation Administration Office of Environment and Energy through ASCENT, the FAA Center of Excellence for Alternative Jet Fuels and the Environment, project 46 through FAA Award Number 13-C-AJFE-MIT, Amendment Nos. 21, 35, 44, 47, & 63 under the supervision of J. Dipardo & M. Majeed. Any opinions, findings, conclusions or recommendations expressed in this this material are those of the authors and do not necessarily reflect the views of the FAA.

Objective:Identify and evaluate methods for improving taxi performance modeling in the Aviation Environmental Design Tool (AEDT) in order to better reflect actual operations

Project Benefits:Improved taxi performance modeling in AEDT

Need accurate surface fuel burn prediction to support range of stakeholder analysis needs

Improved surface models could make AEDT outputs even more useful for different stakeholder needs

Research Approach: Major Accomplishments (to date):Recommendations for AEDT 3e: • Updating of baseline fuel flow rates, airport taxi

times, and pre-taxi fuel burn • Queuing model of airport surface operations to

support dispersion analysis and analyses of future demand scenarios

Machine Learning models of spatial dispersion of emissions

Future Work / Schedule:Investigate thrust variations for noise modelingIdentify functionality corresponding to different user classes

1. AEDT APM surface modeling

needs assessment

2. Aircraft surface performance

modeling enhancements

Flight Data Recorder data

ASDE-X data

3. Aircraft surface performance

model validation

Stakeholder input,

supporting documents

& prior research

4. AEDT APM enhancement

recommendations

Model development dataValidation data

2

Inventory Models

• Determine total fuel burn or emissions over some period of time for current or potential future scenarios

LTO Cycle*, User-specified or Out-dated Taxi times

Enhanced Total Surface Fuel Burn

Enhanced Total Taxi Times

Total Pre-taxi Fuel Burn

Enhanced Taxi Fuel Burn Rate

By a/c type[from FDR data]

By airport[from ASPM data]

By a/c type[from FDR & ACRP data]

Enhanced Total Surface Emissions

Emissions Indices

By a/c type [from ICAO engine certification data]

Estimated Taxi Fuel Burn Rate

(7% thrust) Total Surface Fuel Burn

Total Surface Emissions

Emissions Indices

BASE

LINE

IN

VENT

ORY

MOD

ELIN

G

ENHA

NCED

IN

VENT

ORY

MOD

ELIN

G

Multiplier

Adder

LTO: Landing and Take OffFDR: Flight Data Recorder

ASPM: Aviation System Performance MetricsACRP: Airport Cooperative Research Program

3

Dispersion Models

• Determine where on the airport surface fuel burn or emissions occur, and their impact on locations in the vicinity of the airport

4

Queuing Models to Support Dispersion Modeling

• Identify and model major queues on airport surface

Longitude (degs)

Latit

ude

(deg

s)

Nor

mal

ized

Dat

a D

ensi

ty

6 8 10 12 14 16 18 20 22Local time (hr)

0

2

4

6

8

10

Que

ue le

ngth

(24L

)

DataModel

6 8 10 12 14 16 18 20 22Local time (hr)

5

10

15

20

25

Aver

age

taxi

-out

tim

e (m

in)

DataModel

5

Validation using Ultra Fine Particle (UFP) Measurements

• The queuing models can predict the temporal variation and spatial dispersion of emissions measured by the UFP air quality monitors

0 5 10 15 20Local time (hrs)

0

0.01

0.02

0.03

0.04

0.05

Nor

mal

ized

cou

nts 10 nm UFP concentrations

aircraft movements

Temporal model

Spatial model

6

Generalization of Approach

• Clustering to categorize airports by similarity of operational characteristics

Validated queuing models for airport

Highly skewed delays

Highly skewed delaysLarge %age of ops in VMC

Large %age of ops in VMC