FAA CENTER OF EXCELLENCE FOR ALTERNATIVE JET FUELS & ENVIRONMENT

Project manager: Joe DiPardo, FAA

Lead investigator: Michelle Kirby, Georgia Institute of Technology

September 27-28 2016Alexandria, VA

Rapid Fleet-wide Environmental Assessment Capability

Project 11B

Opinions, findings, conclusions and recommendations expressed in this material are those of the author(s)and do not necessarily reflect the views of ASCENT sponsor organizations.

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Motivation for ASCENT Project 11B

• To complement AEDT with a lower fidelity screeningtool capability that allows for consideration of a large number of technology scenarios that could be quickly analyzed and reduced to a manageable set of scenarios for more focused, high fidelity analysis in the environmental tools suite– Provide quick means of quantifying impact of new

technologies applied at the aircraft level to assess fleet-wide interdependencies on fuel burn, emissions, and noise

• Requires linking/leveraging several necessary components from previous PARTNER efforts– PARTNER P-14:

• Global Regional Environmental Aviation Tradeoff (GREAT) tool• Airport Noise Grid Integration Method (ANGIM)• Generic airport models• Generic vehicle models

– PARTNER P-36• CLEEN technology dashboard

• These tools require further development and connectivity to provide rapid scenario analysis capabilities to complement AEDT

ANGIM

EDS Generic Fleet

Generic Airports

Tech1

TechNTech2

CLEEN Tech Dashboard

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Objectives

• Near Term: Validate ANGIM against AEDT using 2015 “GATBA-like” noise analysis– Compare contour areas across three scenarios (2012, 2030

Evolutionary, and 2030 Aggressive) for a subset of airports– Validate Census data based population exposure method in

ANGIM for each of these airports for each scenario

• Long Term– Develop an interactive environmental decision making tool to

complement AEDT with a screening capability for quicker analysis of a large number of policy scenarios

– Improve link between Global and Regional Environmental Aviation Tradeoff (GREAT) Tool and the Airport Noise Grid Integration Method (ANGIM)

– Enhance user experience

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Outcomes and Practical Applications

• Outcomes– Monthly progress reports– GREAT enhancement list includes but not limited to:

• Customizable retirement curves• Options to run noise with actual or generic airports• Comparison tab for overlaying results from different scenarios• Inclusion of out-of-production vehicles in noise analysis• Normative forecasting techniques for top-down assessment

– Updated GREAT and ANGIM user’s manual– Validation of ANGIM against AEDT technology study

• Practical applications– Screening-level analysis of:

• Technology insertion (e.g. CLEEN)• Goal setting • Trends scenarios for AEE

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Approach: GREAT/ANGIM

• Methods developed to enable rapid analysis of fleet-level environmental impacts– Global and Regional Environmental Aviation Tradeoff

(GREAT)• Metrics: Fuel-Burn, NOx

– Airport Noise Grid Interpolation Method (ANGIM)• Metrics: Grids of DNL values, DNL contours (measures areas &

shape metrics), and population exposure

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Motivation of “GATBA” Study

• Fuel burn and NOx had undergone an extensive peer reviewed V&V exercise and results were deemed acceptable

• ANGIM went through a V&V effort with respect to INM (Bernardo and Levine PhD thesis)

• FAA desired an exercise of a prior Volpe study to test the predictive capability of ANGIM with respect to AEDT2b for a given set of operations and assumptions

• Note: the GT study utilized the operations schedule as a basis for the comparison, but actual results will not compare to the published GATBA study– Rationale will be discussed

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Approach: “GATBA” Analysis Work Flow

• Step 1: Extract Schedule from AEDT– SQL Query– AEDT GUI

• Step 2: Synchronize the assumptions between AEDT and GREAT– Match ANP Airplane ID– Match runway end ID

• Step 3: Run ANGIM and AEDT2b

• Step 4: Post Processing and compare– Results for each airport

• Contour Area• Contour Shape

– Investigate and identify potential causes for differences

Repeat to reduce

the gap

Expand for other

airports and out-

years

Study DB

SQL Scripts

Schedule Data

Runway Data

ANGIM

Match

Runway

End ID

Noise

Results

Noise

Results

Run AEDT

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Schedule and Status

• GREAT forecast/assumptions/options update: Dec. 2014 (Complete)

• GREAT-ANGIM Connectivity: Feb. 2015 (Complete)

• Beta version of GREAT-ANGIM environment: June 2015 (Complete)

• GREAT-ANGIM Connection for Actual Airports: July 2015 (Complete)

• Scenario Comparison Tab: August 2015 (Complete)

• GREAT comparison against AEDT using “GATBA”: Late Spring 2016 (Complete)

• Inclusion of out-of-production vehicles for noise analysis: October 2016 (60%)

• Incorporation of Normative Forecasting: December 2016 (15%)

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“GATBA” Analysis Status

• GT received GATBA AEDT data from Volpe and restored the DBs in the server in March 2016

• Efforts focused on syncing the assumptions to actually run the analysis Matched AEDT and GREAT aircraft and airports

• Filtered GATBA results to exclude military, BJ, and GA operations for apples to apples comparisons

• Included noise contour comparisons of 3 different atmospheric absorption models (SAE-AIR-1845, SAE-ARP-866A, SAE-ARP-5534) for 5 of the 38 airports

• Completed noise and population exposure comparisons for 38 Shell 1 airports for 3 different scenarios (2012 Baseline, 2030 Evolutionary, and 2030 Aggressive)

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Atmospheric Absorption Model Comparison

• For all the cases, the 866A atmosphere resulted in greater areas than 1845

• For all the cases, the 5534 atmosphere resulted in greater areas than the 866A

• NOTE: Since ANGIM utilizes 1845, AEDT was run with the same atmosphere

model. GT is seeking guidance on which model should be used moving forward

2012 Base

Year subset

of airports

SAE-AIR-1845

Area (sq miles) Area (sq miles) Diff Area (sq miles) Diff

55 19.06 21.97 15.3% 22.98 20.6%

60 7.35 8.38 14.0% 8.74 18.9%

65 2.81 3.16 12.4% 3.26 16.0%

55 68.65 79.35 15.6% 82.82 20.6%

60 29.07 33.43 15.0% 35.13 20.8%

65 12.39 13.89 12.1% 14.44 16.5%

55 57.46 74.11 29.0% 80.58 40.2%

60 21.91 26.47 20.8% 28.35 29.3%

65 9.54 10.70 12.2% 11.14 16.7%

55 25.00 31.42 25.7% 34.24 36.9%

60 7.98 9.38 17.5% 10.01 25.3%

65 2.81 3.10 10.4% 3.20 13.8%

55 46.62 48.22 3.4% 49.39 6.0%

60 20.38 21.41 5.0% 21.96 7.8%

65 8.27 8.70 5.1% 8.90 7.6%

KSAN

KATL

KJFK

KMDW

KMIA

AEDT

Airport ContourSAE-ARP-866A SAE-ARP-5534

Busy

Single

Runway

Large

Hub 1

Large

Hub 2

Large

Hub 3

Medium

Int’l

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-50%

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%

-50% -40% -30% -20% -10% 0% 10% 20% 30% 40% 50%GR

EA

T

AEDT

Changes in Contour Area from 2012 to 2030

2012 vs 2030 Evo

2012 vs 2030 Agg

Recent Accomplishments: AEDT vs ANGIM 2012 to 2030 Contour Area Changes

• Histograms are % error of ANGIM to AEDT2b

• The majority of the airports fall within +/-2% with respect to AEDT for both scenarios

• A few outliers exist, but overall, error is very reasonable

• Plot below shows the difference between the two scenarios and 2012 scenario for ANGIM and AEDT, and it can be seen that the results generated by ANGIM and AEDT have a good agreement

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Population Exposure Comparisons between AEDT and ANGIM

• Conducted comparison for all 3 scenarios (only 2012 results shown here)

• ANGIM effectively mimics AEDT’s contour overlay method for population exposure– Population data storage significantly reduced– Method doesn’t impact ANGIM runtime

• Straight-track assumption is reasonable for approximating population exposure at most airports

AEDT ANGIM

Contour SAE AIR 1845 SAE AIR 1845

Population Count Population Count Diff Pct Diff

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Airports

55 3249948.988 4060910.091 810961 24.95%

60 837198.3775 1215490.222 378292 45.19%

65 140887.5546 211779.6866 70892 50.32%

AEDT ANGIM

ContourSAE AIR 1845 SAE AIR 1845

Population Count Population Count Diff Pct Diff

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Airports*

55 2405839.694 2517398.412 111559 4.64%

60 679404.4952 737368.5975 57964 8.53%

65 126028.5925 128507.4332 2479 1.97%

* 4 Airports removed due to large ground track divergence

2012 Scenario Comparison Example of divergent tracks

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Interfaces and Communications

• External– NASA/ERA Project– Georgia Tech External Advisory Board

• Within PARTNER– P10, P36, P37, and P45– Volpe and FAA

• Publications– José E. Bernardo, Matthew J. LeVine, Michelle Kirby, and Dimitri Mavris, “Analysis

of Aircraft Vehicle Class Contributions to Airport Noise Exposure”, Journal of Aerospace Operations, Accepted, Awaiting Publication. (doi: not yet available)

– José E. Bernardo, Michelle Kirby, and Dimitri Mavris, “Probabilistic Assessment of Fleet-Level Noise Impacts of Projected Technology Improvements”, Journal of Air Transport Management, Under Review, (doi: not yet available)

– Amelia J. Wilson, Matthew J. LeVine, Jose Enrique Bernardo, Michelle Kirby, and Dimitri N. Mavris. “Development of Generic Ground Tracks of Performance Based Navigation Operations for Fleet-Level Airport Noise Analysis”, 15th AIAA Aviation Technology, Integration, and Operations Conference, AIAA Aviation, (AIAA 2015-3029), doi:10.2514/6.2015-3029

– Matthew J. Levine, “A framework for technology exploration of aviation environmental mitigation strategies,” PhD Thesis, Georgia Institute of Technology, Dec 2015

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Summary

• Working to improve capabilities and user-interface of previously delivered version of GREAT with ANGIM– Expanding tool capabilities derived from PARTNER P-14– Linking capabilities together to provide one-stop source for fleet-level

analysis with interdependencies between metrics

• Next steps include:– Evaluating best approach for incorporating noise signatures of Out-of-

Production vehicles without sacrificing runtime or accuracy– Incorporating normative techniques including equivalencies for noise analysis– Populating a library of technology vehicles to explore a multitude of

technology scenarios– Comparing integrated GREAT/ANGIM capabilities to AEDT scenarios from

GATBA analysis

• Key challenges include:– Adding more modularity without sacrificing computational speed– Debugging multiple integrated codes in different languages (C#, VBA, etc.)– Ensuring a complete understanding of the GATBA analysis so as to identify

differences in results

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References

• Global and Regional Environmental Aviation Tradeoff (GREAT)– “CO2 Emission Metrics for Commercial Aircraft Certification: A National Airspace System Perspective,” A PARTNER

Project 30 Findings Report, NO. PARTNER-COE-2012-002

• Airport Noise Grid Interpolation Method (ANGIM)– Bernardo, Kirby, & Mavris, “Development of a Rapid Fleet-Level Noise Computation Model,” AIAA Journal of Aircraft,

Nov. 2014

• Generic Airports– Bernardo, Kirby, & Mavris, “Development of Generic Airport Categories for Rapid Fleet-Level Noise Modeling,” Journal

of Aerospace Operations, TU Delft, June 2015, DOI: 10.3233/AOP-150045

• Generic Vehicles– LeVine, Kirby, & Mavris, “An Average Generic Vehicle Method for Fleet-Level Analysis of Noise and Emissions

Tradeoffs,” currently under FAA review (preparing for submission to AIAA Journal)

ContributorsStaff:

Prof. Dimitri Mavris (PI), Dr. Michelle R. Kirby (Co-PI), Dr. Holger

Pfaender, Dr. Dongwook Lim, Dr. Yongchang Li, Dr. Matthew LeVine

Graduate Students:

Evanthis Kallou, Junghyun Kim