11 satellites in each of 6 polar orbits Aireon ADS-B...

Reduced Separation in US Oceanic AirspaceBenef i ts Analys is through Fast -Time Model ing

Presented by: Dan Howell, Rob Dean, and Joseph Post

Date: 6/20/2019

This work was supported by FAA Surveillance and Broadcast Services through contract 693KA9-18-D-00010

ASEPS Project Overview

2Reduced Separation in US Oceanic Airspace

• The Surveillance & Broadcast Services Advanced Surveillance Enhanced Procedural Separation (SBS ASEPS) Project has been investigating the use of reduced oceanic separation to enhance operations in US oceanic airspace

➢ Space-based ADS-B (SBA)

➢ Automatic Dependent Surveillance – Contract (ADS-C, more frequent update)

• Goal: Increase the efficiency and capacity of operations in Oceanic Flight Information Regions (FIRs) through surveillance enhancements for reduced oceanic separation standards

• FAA has been conducting a cost-benefit analysis of increased ADS-C reporting and SBA

Iridium NEXT Satellites

3

• 66 cross-linked satellites in Low Earth Orbit (LEO) 11 satellites in each of 6 polar orbits

• Aireon ADS-B receiver hosted payload

SpaceX Falcon 9

Oceanic Communications and Surveillance Operational View

4

GNSS

Inmarsat/Iridium

ATC Facility Service

Provider

ADS-C

Iridium NEXT

Service

Provider

ADS-B

HF Voice

CPDLC

Comm Nav Surveillance

US Oceanic Airspace


Benefits of Improved Oceanic Surveillance

• Improved Accommodation of altitude requests

• Improved arrival/departure services at non-radar airports

• Reduced Vertical Collision risk

• Reduced convective weather impact

• Improved accommodation of descents, routing, and speed requests

• Improved controller situational awareness

• Accurate and timely information for search and rescue


Improved Accommodation of Altitude Requests

8

Oceanic Entry

Altitude (FL310)

Filed Cruise

Altitude

(FL350)Intermediate Step

Altitude (FL330)

New York Oceanic Boundary

Step climb

blocked (conflict)

Successful

Climb Initiated

Altitude

Profile

• Oceanic flights often long

• Strong desire to minimize fuel

• Higher altitudes generally result in higher fuel efficiency

• Aircraft frequently take off very heavy and cannot reach optimal altitude until some fuel is burned off

• Once aircraft are light enough, they often want to climb to reach a more fuel-efficient altitude

• Competition for optimal altitudes frequently occurs

Reduced Separation in US Oceanic Airspace

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,0001

18

35

52

69

86

10

3

12

0

13

7

15

4

17

1

18

8

20

5

22

2

23

9

25

6

27

3

29

0

30

7

32

4

34

1

35

8

37

5

39

2

40

9

42

6

44

3

46

0

47

7

49

4

51

1

52

8

54

5

56

2

57

9

59

6

Altitud

e (ft

)

Position Report Number

My Flight to Europe

Step Climbs and

Surveillance Gap

My Flight (PHL-LHR June 15)

9

*www.flightaware.com

Simulation Model & Methodology

• Global Oceanic Model (GOM)

➢ Fast-time simulation tool developed in MATLAB for examining fuel burn and flight time in oceanic airspace

➢ Time-based model that simulates individual aircraft from origin to destination along specified paths

➢ Randomness built into model through aircraft takeoff mass and equipage levels

➢ Produced under partnership between FAA and Virginia Tech

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•Representative Days

•Growth Assumptions for future demandFlight Schedules

•ADS-B Out

•FANS (ADS-C + CPDLC)Aircraft Avionics

•Historical Flight Plans

•Existing Route StructureRoutes

•Historical Altitude Request Information

•Magnitude of altitude changesAltitude Request

Information

•BADA Performance Model

•Takeoff weight estimatesAircraft Performance

•Carrier Fleet Forecasts

•Future aircraft typesFleet Evolution

•Current and Future Separation Standards

•Types of resolutions used in oceanSeparation And Conflict

Management Data

Global

Oceanic

Model

Modeled Trajectories

Summary Metrics


Current Oceanic Environment

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• Advanced Technologies and Oceanic Procedures (ATOP) System

➢ Designed to reduce manual processes controllers have historically used to ensure separation

➢ Processes oceanic aircraft and weather data and calculates separation criteria near instantaneously

➢ Provides efficient track and altitude alternatives through Conflict Prediction and Reporting (CPAR)

• Mixed equipage leads to varying separation minima

➢ Current standards lead to high likelihood of preferred routings and altitudes, but expected increases in traffic in future will impact this

• Procedural Separation and Control

➢ “Control By Exception”: Procedural separation whereby controller notified of possible separation situation when there is an actual or predicted violation. Controller does not continuously monitor aircraft positions and altitudes as with tactical separation in a radar environment

➢ ATOP uses 2 hour lookahead for conflicts and controllers are expected to resolve conflicts that will occur 30 minutes or less in the future

FANS Equipage• Future Air Navigation System (FANS)

interfaces with ATOP and includes

avionics to support Performance-Based

Navigation, Data Link, and ADS-C position

reporting

• FANS equipage is primary constraint of

SBA-usage since ADS-B Out (required)

will soon be mandated in US airspace

• Current FANS equipage levels (by aircraft

type) were obtained from an analysis of

flight plans provided by MITRE

• Logistics curve fitted to the percentage of

Datalink equipped aircraft time trend in

each ocean to specify overall equipage

levels for future years

• FANS equipage randomly assigned to

unequipped flights until overall target level

was achieved

• Remaining unequipped flights passing

through Gander, Shanwick, and Santa

Maria were equipped to reflect upcoming

mandate


Separation Standards and Schedules

13

Aircraft Equipage Legacy CaseMore Frequent

ADS-CSBA

FANS via SATCOM

with RNP30/30 NM 23/23 NM 15/15 NM

No FANS 50/80 NM 50/80 NM 50/80 NM

•Separation Standards for US Oceanic Airspace


•Schedules And Traffic Growth

➢20 representative days from FY16 used

in model

➢2016, 2020, 2025, 2030 and 2035 were modeled

➢Results linearly interpolated for intervening years

➢Using current FAA policy on traffic growth for investment decisions, growth was capped in 2028 based on a 10 year sliding window.

Aircraft Altitude Requests• In controlled airspace, aircraft must

request an ATC clearance to deviate from their assigned altitude

• Historical altitude requests and requests cleared were analyzed in ZAN/ZOA and ZNY

• A regression was used to determine whether pilot requests increased as a result of separation changes in ZNY and ZAN

➢ Two independent variables: monthly trend variable and separation change variable

➢ ZNY: Change was significant, indicated a 15% increase in requests handled over the previous average

➢ ZOA/ZAN: 11% over the previous average

➢ Monthly trend variable was not significant in Atlantic, negative in Pacific

• A similar increase in altitude requests was applied in GOM to approximate possible effect of reduced separation on pilot behavior


Other Model Considerations

• Aircraft Performance

➢ Base of Aircraft Data (BADA) model used

➢ Assumes nominal take-off weight related to aircraft type and distance to be flown

➢ No consideration of actual flight planning process

• Fleet Evolution

➢ SWAC model incorporates a fleet evolution algorithm that replaces old aircraft types with newer ones as specified by FAA’s Carrier Fleet Forecast for 2016-2037

➢ Some types are not supported in GOM. In these cases, we assumed an increase in fuel efficiency of 40% compared to previous type of the same size/range

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Other Model Considerations• Interaction with neighboring FIRs

➢ Agreements with surrounding international Air Navigation Service Providers (ANSPs) are a major factor in how the US controls flights in oceanic airspace

➢ Example: if neighboring ANSP requires higher longitudinal separation than U.S., U.S. controllers must plan and respond appropriately

➢ FAA must consider this when deciding if and when to approve reduced separation standards

• Model Validation

➢ Number of Altitude Change Requests

➢ Percentage of altitude change requests granted

➢ Distribution of flight times and distances traveled across oceanic regions

➢ Distribution of cruise altitudes upon entry to U.S. oceanic airspace

• Limitations

➢ Only conflicts in oceanic airspace considered

➢ No adjustments of wheels-off times for foreign departures

➢ GOM does not model convective weather or turbulence

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Modeled Airspace and Trajectory Data


Results• 480 simulations conducted to get a full set of results across years,

representative days, oceans, and alternatives

➢ 4 years

➢ 20 unique days

➢ 2 oceans

➢ 3 alternatives

• Annual Direct Fuel Savings (kg)

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SBA Enhanced ADS-C

FY Atlantic Pacific Atlantic Pacific

2020 16,265,582 18,547,428 13,030,382 16,086,360

2025 21,524,161 24,156,380 16,342,274 18,475,954

2030 25,429,404 25,586,382 18,357,171 19,258,831

2035 25,926,102 24,885,958 18,722,780 18,753,088


Additional Cost-To-Carry Benefit• Oceanic Flights long in duration, ranging up to 14 hours in the Pacific

and 10 hours in the Atlantic

• To account for reduced fuel loading and a consequent additional reduction in fuel burn, we adjusted the results using a method to quantify the incremental fuel impact of reducing takeoff mass[1]

➢Incremental fuel Savings = Weight Savings × flight hrs × 0.005

• Annual Indirect (Cost-to-Carry) Fuel Savings (kg)

19

SBA Enhanced ADS-C

FY Atlantic Pacific Atlantic Pacific

2020 4,259,587 6,013,700 3,441,648 5,226,565

2025 5,575,130 7,865,542 4,302,496 6,000,308

2030 6,587,617 8,312,610 4,847,205 6,281,038

2035 6,695,970 8,070,657 4,927,289 6,106,602

[1] FAA Office of Policy and Plans, “Economic Values for FAA Investment

and Regulatory Decisions, A Guide,” September 2015.


Results (Graphical)


Model Variability• Additional runs performed to

examine the variability of the results

➢ Random seed affecting take-off weights built into the model

➢ Manually randomized which flights for a particular aircraft type were equipped when equipage was not 100%

• Developed distribution of ratios between variability and default simulations

➢ Considerable variability (up to 25% in inner-quartile range)

➢ Default results near middle of range


Conclusions• Results from this analysis suggest that reduced separation in oceanic

airspace produces significant benefits through improved accommodation of altitude requests

• Results vary greatly by airspace volume. Factors include:

➢ Route length

➢ Traffic density

➢ Equipage

• Results sensitive to modeling assumptions

➢ Aircraft gross weight

➢ Avionics capability

➢ Climb Request Behavior

• Continuing to explore the potential for SBA to save fuel and provide other benefits to airspace users and the FAA


ASEPS Going Forward

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Miami Center

Miami Oceanic

Havana

Port au Prince Santo

Domingo

San Juan

63

62

5860

40

New York Oceanic

ZAN

Anchorage

Oakland

New York

ZAN

ZOAZNY

43

LONG-TERM (5+ years)

MID-TERM (3-5 years)NEAR-TERM (1-3 years)


Acknowledgements


THANK YOU


References▪ ICAO FIR viewer available at website http://gis.icao.int/flexviewer

▪ Lockheed Martin, “Advanced Technologies and Oceanic Procedures (ATOP) Operational Concept,” FAA-ATOP-2004-0395, November 2004.

▪ SBS Program Office, “Concept of Operations Document for Advanced Surveillance - Enhanced Procedural Separation (ASEPS),” SBS-071, Rev. 02, September 2016.

▪ Aireon LLC, Global Air Traffic Surveillance, information available at website: https://aireon.com/services/global-air-traffic-surveillance/, viewed January 2019.

▪ FAA, “14 CFR Part 91 Automatic Dependent Surveillance-Broadcast (ADS-B) Out Performance Requirements To Support Air Traffic Control (ATC) Service; Final Rule,” May 28 2010.

▪ FAA, Air Traffic Organization 2012 Safety Report, 2015.

▪ FAA, ATO Top 5 Fact Sheet, 2015.

▪ Skybrary, Use of Selected Altitude by ATC, 2011.

▪ Aireon LLC, ALERT, information available at website: https://aireon.com/services/aireonalert/ viewed January 2019.

▪ Trani, A., Li, T., Spencer, T., Tsikas, N., Hinze, N., and Gunnam, A., “Global Oceanic Model Development,” presentation to IATA, January 2016.

▪ Cheng, F., Gulding, J., Baszczewski, B., Galaviz, R., Chow, A., “Modeling the effect of weather conditions in sample day selection using an optimization method,” 2012 Integrated Communications Navigation and Surveillance conference, April 2012.

▪ FAA Office of Performance Analysis, “FY2016 Sample Day Selection,” February 10 2017.

▪ FAA Office of Policy and Plans, FAA Aerospace Forecast 2016-2036, website: https://www.faa.gov/data_research/aviation/aerospace_forecasts/.

▪ Post, J., Gulding, J., Noonan, K., Murphy, D., Bonn, J. and Graham, M, “The Modernized National Airspace System Performance Analysis Capability,” 8th AIAA Aviation, Technology, Integration, and Operations (ATIO) Conference, Anchorage, Alaska, September 2008.

▪ Post, J., “FAA Employs New Model in Benefits Calculation,” Journal of Air Traffic Control, Air Traffic Control Association, Washington D.C., Summer 2013.

▪ Eurocontrol, Base of Aircraft Data (BADA) website, available at http://www.eurocontrol.int/eec/public/standard_page/proj_BADA.html

▪ US DOT Bureau of Transportation Statistics, Air Carrier Statistics Database, available at website https://www.transtats.bts.gov/Fields.asp?Table_ID=293

▪ Antonio Trani, “BADA Model,” presentation to FAA, April 15 2016.

▪ FAA Office of Policy and Plans, Fleet Forecast, September 2015.

▪ International Air Transport Association, “IATA Technology Roadmap 4th Edition,” June 2013.

▪ FAA Office of Policy and Plans, “Economic Values for FAA Investment and Regulatory Decisions, A Guide,” September 2015.

▪ FAA, “Air Traffic Control,” FAA Order 7110.65, Washington, D.C., December 2015.


http://gis.icao.int/flexviewer

https://aireon.com/services/global-air-traffic-surveillance/

https://aireon.com/services/aireonalert/

https://www.faa.gov/data_research/aviation/aerospace_forecasts/

http://www.eurocontrol.int/eec/public/standard_page/proj_BADA.html

https://www.transtats.bts.gov/Fields.asp?Table_ID=293

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