1 st Annual Workshop NAS-Wide Simulation in Support of NextGen, 12/10/08 Probabilistic NAS Platform...

1st A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

Probabilistic NAS Platform

George Hunter, Fred WielandBen Boisvert, Krishnakumar Ramamoorthy

Sensis Corporation

December 10, 2008

2

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

Outline

• What is PNP? • Team and development history• Example uses of the model • Software processes and testing• Validation

3

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

Outline


4

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

What is PNP?

• An fast-time and flexible NAS-wide simulation tool– Real-time or fast-time modes

• Half-hour runtime on a laptop, to simulate a day in the NAS– Physics-based: trajectories computed through integrating aerodynamic energy

balance equations by varying the time-step size– System uncertainties (weather, security, operations …)– Plug-and-play architecture

• Dynamic clients (TFM, DAC, AOC, …)– An ATC community resource– Formal software development processes in place– Adaptable to current system or NextGen future concepts

• Uses– Environment in which to design, build and test decision support tools

• TFM, DAC, AOC, …• Fast-time, real-time, shadow-mode

– Potential NAS tool• Service provider, operator, collaborative uses

– Benefits assessment tool• Fast-time tool to evaluate improved

infrastructure, technology, procedures …• Evaluates historic and future traffic

scenarios in weather

5

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

PNP Architecture


(PNP)Weather DataWeather Data

ReportsReports

MATLAB®

ScriptingInterface

MATLAB®

ScriptingInterface

NASDatabase

NA

S S

imu

latio

n

Performance DataPerformance Data

Flight DataFlight Data

Graphical User InterfacePlan View Display


A fast-time physics-based (trajectory-based) NAS-wide

modeling tool

6

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

PNP Architecture


(PNP)Weather DataWeather Data

ReportsReports

SimObjects

MATLAB®

ScriptingInterface

MATLAB®

ScriptingInterface

NASDatabase

MATLAB® ClientMATLAB® Client

External Client(Any Language)External Client(Any Language)

ClientAs Middleware

ClientAs Middleware

Java ClientJava Client

Decis

ion

makin

gN

AS

Sim

ula

tion

Performance DataPerformance Data

Flight DataFlight Data



Prob-TFMProb-TFM


modeling tool

7

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

PNP Client Development

• TFM client development– ProbTFM (Sensis internal development)

• TFM client integration– C2 (algorithms from and used with permission of Bob Hoffman,

Metron)– Constrained LP (algorithms from and used with permission of

NASA, Joey Rios) in progress

• DAC client integration– MxDAC (algorithms from and used with permission of Min Xue,

NASA/UARC)

• AOC client development– Gaming behaviors (collaboration with GMU/Lance Sherry) in

progress

8

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

Capabilities Summary• Real-time• Fast-time• Airport weather impact models• Airspace weather impact models• Weather-integrated decision making• Probabilistic modeling / decision making• Traffic flow management• Dynamic airspace configuration• Surface traffic modeling• Terminal area modeling• Super density operations• Fuel burn modeling• Emissions modeling• Trajectory-based operations• Separation assurance• Plug-n-play• Fast run-time

Existing Can Support

√√√√√√√√√√√√√√√√

√√

√√

√√√√

√√√√

√√√√√√

9

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

Outline



modeling tool

10

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

Team and Development History

People

Env’nment

Data

Funct’ality

Projects

Users

Matlab Java/real-time

WSI collaboration for real-time weather feed

Dynamicclients

NAS-wide,probabilistic

Wx modelingand routing

Clientarchitecture

Internal NWA GMU

KrishnakumarRamamoorthy

BenBoisvert

DiegoEscala

TaeLee

MichelleLu

HuinaGao

GeorgeHunter

2004 2005 2006 2007 20082003

JPDO FAA NASPACNASA NRAs

Web 2.0

Project SystemSystem lead SystemSoftware lead Software Software

11

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

Outline



modeling tool

12

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

Example Project Uses

• JPDO Modeling and Analysis– NextGen performance evaluation with weather

• FAA NASPAC Weather Modeling– Convection impact modeling for NASPAC

• NASA Gaming NRA– Evaluation of NextGen gaming with AOC clients

• NASA MetaSimulation NRA– Investigation of TFM + DAC interactions

• NASA SLDAST RFA– Evaluation of NextGen TFM concepts and models

• NASA Market-Based TFM NRA– Evaluation of NextGen market-based TFM concepts

13

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

NextGen Sensitivity StudiesNextGen Performance Sensitivity Analysis

Benefit of Improved Wx Forecasts

Benefit of Using Clear Weather Forecasts

Persistence forecast11/16/06

Case 2: No distinction between clear and heavy weather forecast accuracy

Case 1: Take advantage of improved forecast accuracy in clear weather

NAS Performance Sensitivity

Kris Ramamoorthy, George Hunter, "Evaluation of National Airspace System Performance Improvement With Four Dimensional Trajectories," AIAA Digital Avionics Systems Conference (DASC), Dallas, TX, October, 2007

George Hunter, Fred Wieland " Sensitivity of the National Airspace System Performance to Weather Forecast Accuracy," Integrated Communications, Navigation and Surveillance Conference (ICNS), Herndon, VA, May, 2008

14

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

Market-Based TFM Studies

UAL233 Delay Cost

NAS Access Valuation Models

SCC

Delay

George Hunter, et. al., "Toward an Economic Model to Incentivize Voluntary Optimization of NAS Traffic Flow," AIAA ATIO Conference, Anchorage, AK, September, 2008.

15

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

Dynamic Airspace Configuration

Nov 12, 2006, LAT=2, #Gen=40

ZFW FAA sectors

George Hunter, "Preliminary Assessment of Interactions Between Traffic Flow Management and Dynamic Airspace Configuration Capabilities," AIAA Digital Avionics Systems Conference (DASC), St. Paul, MN, October, 2008.

16

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

AOC Dispatch Use Case

Reroute with low probability of delay

17

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

Outline



modeling tool

18

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

Unit and System Testing

Regression Testing

Trunk Configuration Management

Branch Configuration Management

Project Monitoring & Control

Development Tracking

Quantitative Project Management

Processes and Testing Cycle

19

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

Project Monitoring and Control

• JIRA is used to track issues– Project Manager and Lead Software Engineer assign task priorities, due dates,

and personnel.

• Weekly telecoms keep distributed team apprised of PNP and communications open

• Project Manager maintains a master schedule in MS-Project

20

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08


• Software engineers use JIRA to track and status development efforts.

21

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08


• Software Engineers are responsible for creating branches from the trunk to develop fixes/enhancements.

• The Configuration Management of the software is accomplished with Subversion– Subversion is an open source version control system

(http://subversion.tigris.org/)

22

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08


• Software Engineers are responsible for creating unit tests to verify the correctness of their code. The JIRA issue number is to be used throughout the code and unit tests for tracking purposes.

• Software Engineers are responsible for running their own system/function tests to verify their software.

• Once testing is validated, code is merged back on to the trunk.

23

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08


• Once all validated JIRA issues are merged unto the trunk, regression testing is performed.

24

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

Regression Testing

• Regression testing• Aggregate results

– Total delay

– Total congestion

– Traffic volume

– #TFM initiatives

– Runtime

• Different scenarios– Truncated demand set

– Full demand set

– Weather

• Automated– Weekly or as required

• Archived• Graphical quick-look

25

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08


• Regression testing validation is performed and the release letter is updated.

• Release is tagged in Subversion.• JIRA issues are closed.• Documentation is updated to reflect changes in

software.

26

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

Outline



modeling tool

27

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

System-Level Engineering Validation

• ASPM / ETMS verification tests– Compare ASPM/ETMS data with simulation data

• Calibrate concept to match aggregate field observations

– Models• Trajectory data• Airport capacities (VMC / IMC)• Sector capacities in weather

– Aggregate performance• Mean flight delay• Sector and airport overloadings

– Detailed performance• Flight delay by airport and time of day• Overloading and delay patterns (Spatial and temporal)

Delays by airport and time of day Sector and airport loading by time of day Spatial loading patterns

– Light and heavy weather days

√

28

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

System-Level Software Verification

• Cross check sums– Flights = Operations at all airports

– Flight time = Minutes from sector loads

– Sector load by sector = Sector load by time

– Airport ops = Flights using the airport in demand set

– Delays by flight = Delays by time; and reroutes

• Weather data checks– Compare PNP/Metar airport capacity with ASPM AAR/ADR

– Compare PNP/Metar airport capacity with ASPM IFR periods

– Ensure En route convection versus time of day is smooth

– Ensure WxMAP ≤ MAP for all sector time bins

• Graphical– Ensure reroutes overlaid on weather make sense

• TFM Performance– Number of delays per flight, min and max flight delay

– Maximum airport and sector overloading (ensure are reasonable)

29

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

System-Level Engineering Validation



– Models• Trajectory data• Airport capacities (VMC / IMC)• Sector capacities in weather

– Aggregate performance• Mean flight delay• Sector and airport overloadings




√

30

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

Trajectory Model Validation

• Compared to ETMS flight data (May 2008)

N: 316Mean: 0.321 minStd dev: 11.95 min

Mean: 0.80 minStd dev: 6.51 minR2: 0.012

Detrended for Range

George Hunter, Ben Boisvert, Kris Ramamoorthy, "Advanced Traffic Flow Management Experiments for National Airspace Performance Improvement," 2007 Winter Simulation Conference, Washington, DC, December, 2007

31

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

ProbTFM Performance



– Models• Trajectory data• Airport capacities (VMC / IMC), actual and forecasted• Sector capacities in weather, actual and forecasted

– Aggregate performance• Mean flight delay• Sector and airport loadings




√

32

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

Compare With Field Observations

0

500

1000

1500

2000

2500

3000

0 5 10 15 20 25

Average Delay (minutes per aircraft)

Sec

tor

Co

ng

esti

on

January 7, 2007(Similar resultswith other days)

LAT = 0

LAT = 60 minutes

LAT = 30 mins

(14.5,1657)

• Compare to ETMS/ASPM– Forecast accuracies, Decision making horizon, Delay distribution

33

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

Verification of Results








√

34

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

System Loading Patterns

ProbTFM predicted, 14:45 GMTETMS Actual, 14:45 GMT

ETMS

ProbTFM

ETMSUnderloading Overloading

ProbTFM loading

35

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

Verification of Results







– Light and heavy weather days, control days√

36

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

Conclusion

• The development of PNP has benefited from lessons learned over past two decades in NAS system wide modeling– Plug and play simulation architecture– Supports both analytical and HITL studies– Adaptable to simulate current system as well as NextGen future

concepts– Fast-time, physics-based– Formal software development processes in place– Probabilistic decision making and extensive weather modeling

explicitly incorporated in tool

37

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

Publications1. George Hunter, "Preliminary Assessment of Interactions Between Traffic Flow Management and Dynamic Airspace

Configuration Capabilities," AIAA Digital Avionics Systems Conference (DASC), St. Paul, MN, October, 2008.2. George Hunter, et. al., "Toward an Economic Model to Incentivize Voluntary Optimization of NAS Traffic Flow," AIAA ATIO

Conference, Anchorage, AK, September, 2008.3. George Hunter, Fred Wieland " Sensitivity of the National Airspace System Performance to Weather Forecast Accuracy,"

Integrated Communications, Navigation and Surveillance Conference (ICNS), Herndon, VA, May, 2008.4. George Hunter, Kris Ramamoorthy, "Integration of terminal area probabilistic meteorological forecasts in NAS-wide traffic

flow management decision making," 13th Conference on Aviation, Range and Aerospace Meteorology, New Orleans, LA, January, 2008.

5. Kris Ramamoorthy, George Hunter, "The Integration of Meteorological Data in Air Traffic Management: Requirements and Sensitivities," 46th AIAA Aerospace Sciences Meeting and Exhibit, Reno, NV, January, 2008.

6. George Hunter, Ben Boisvert, Kris Ramamoorthy, "Advanced Traffic Flow Management Experiments for National Airspace Performance Improvement," 2007 Winter Simulation Conference, Washington, DC, December, 2007.

7. Kris Ramamoorthy, George Hunter, "Evaluation of National Airspace System Performance Improvement With Four Dimensional Trajectories," AIAA Digital Avionics Systems Conference (DASC), Dallas, TX, October, 2007.

8. Kris Ramamoorthy, Ben Boisvert, George Hunter, "Sensitivity of Advanced Traffic Flow Management to Different Weather Scenarios," Integrated Communications, Navigation and Surveillance Conference (ICNS), Herndon, VA, May, 2007.

9. George Hunter, Ben Boisvert, Kris Ramamoorthy, "Use of automated aviation weather forecasts in future NAS," The 87th American Meteorological Society Annual Meeting, San Antonio, TX, January, 2007.

10. Kris Ramamoorthy, George Hunter, "Probabilistic Traffic Flow Management in the Presence of Inclement Weather and Other System Uncertainties," INFORMS Annual Meeting, Pittsburgh, PA, November, 2006.

11. Kris Ramamoorthy, Ben Boisvert, George Hunter, "A Real-Time Probabilistic TFM Evaluation Tool," AIAA Digital Avionics Systems Conference (DASC), Portland, OR, October, 2006.

12. George Hunter, Kris Ramamoorthy, Alexander Klein "Modeling and Performance of NAS in Inclement Weather," AIAA Aviation Technology, Integration and Operations (ATIO) Forum, Wichita, KS, September 2006.

13. Kris Ramamoorthy, George Hunter, "A Trajectory-Based Probabilistic TFM Evaluation Tool and Experiment," Integrated Communications, Navigation and Surveillance Conference (ICNS), Baltimore, MD, May, 2006.

14. Kris Ramamoorthy, George Hunter, "Avionics and National Airspace Architecture Strategies for Future Demand Scenarios in Inclement Weather," AIAA Digital Avionics Systems Conference (DASC), Crystal City, VA, October, 2005.

15. George Hunter, Kris Ramamoorthy, Joe Post, "Evaluation of the Future National Airspace System in Heavy Weather," AIAA Aviation Technology, Integration and Operations (ATIO) Forum, Arlington, VA, September 2005.

16. James D. Phillips, “An Accurate and Flexible Trajectory Analysis,” World Aviation Congress (SAE Paper 975599), Anaheim, CA, October 13-16, 1997.

38

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

Questions?

39

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

Backup

40

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

PNP Systems Requirements

• System requirements– PNP is a Java application– Hardware

• Memory: minimum 1GB, preferred 2GB

• CPU: Pentium (4) 3.2 GHz or better

• Video card: 128MB memory, preferred 256MB

– Software• Java JDK 6 http://java.sun.com/javase/downloads/index.jsp

• MySQL Server 5.0 http://dev.mysql.com

– Third party licenses• Eurocontrol BADA usage license

41

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

Weather Days

• Ten weather days, two control days

42

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

Weather Days

• Weather days– Spectrum of weather days

• Variation in weather type and intensity• Variation in season

– Support real-world comparison• Support same sector data• Variation in traffic demand volume and structure

Different days of week, holidays

• Control days

43

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

44

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

NextGen PerformanceSensitivity Analysis

45

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

En Route and Terminal Area Combined Sensitivities - 2025

46

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

47

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

Benefit of ImprovedConvection Forecasts

48

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

Investment Analysis

49

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

50

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

Benefit of Using Clear Weather Forecasts

51

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

Benefit Evaluation

Persistence forecast, 11/16/06

Case 2: No distinction between clear and heavy weather forecast accuracy

Case 1: Take advantage of improved forecast accuracy in clear weather

52

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

53

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

Market-Based TFM:Valuation of NAS Access

54

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

Congestion-Delay Relationship

• Unconstrained sector congestion cost (SCC) for zero lookahead time (blue) and PNP-ProbTFM simulated delay (black) time histories for all en route NAS sectors and flights, respectively.

SCC

Delay

55

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

Aggregate Delay Model

• Hypothesize a first-order lag transfer function

K

1

1

sSCC(s) Delay(s)

Simulated delay

Modeled delay

56

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

• Hypothesize a second-order transfer function

Simulated delay

Modeled delay

Aggregate Delay Model

57

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

Transfer Functions Summary

58

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

Explicit Cost Model

• Evaluate cost of NAS access by removing the flight• Remove one flight

– 11/16/06, UAL233, A320– Morning departure from Bradley International (KBDL) to Chicago

O’Hare airport (KORD)– Relatively high cost flight

• 90.02 SCC

59

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

Remove UAL233

• Delay reduction by time bin in simulation run– Delay reduction of 8141 minutes

60

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

61

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

NAS Performance Sensitivity Studies• Performance sensitivity to:

• Delay distribution policy (most important factor)

• TFM system agility

• System forecasts (least important factor)

Nov 12, 2006

ETMS/ASPM

Minimum Delay

Non AgileMinimum Delay

Delay Distribution

Non AgileDelay Distribution

62

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

63

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

Dynamic Airspace Configuration

64

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08 NAS Sectorization

• Nov 12, 2006

65

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08 MxDAC Afternoon Sectorization

• Nov 12, 2006, LAT=6, #Gen=20

66

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08 MxDAC Midday Sectorization

• Nov 12, 2006, LAT=2, #Gen=40

Coeff_peak_ac_var=0.0Coeff_avg_ac_var=0.0Coeff_crossings=0.0Coeff_transition_time=0.0Coeff_residual_capacity=1.0

67

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08 Delay-Congestion Performance

MxDAC off

MxDAC on, LAT = 4 hrs

MxDAC on, LAT = 2 hrs

68

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

69

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

Equity Analysis:Cost of Delay Distribution

70

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

Cost of Distributing Delay

• RMS delay can be reduced by spreading delay to more flights– But at the cost of increased total delay

Nov 12, 2006$65/minute

Increased delay distribution

71

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

72

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

AOC Dispatch Use Case

73

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

Dispatcher Successfully Finds a Reroute

Reroute with low probability of delay

74

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

75

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

Project Monitoring and Control

• JIRA is used to track issues– Project Manager and Lead Software Engineer assign task priorities, due dates,

and personnel.

• Weekly telecoms keep distributed team apprised of PNP and communications open

• Project Manager maintains a master schedule in MS-Project

76

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08


• Software engineers use JIRA to track and status development efforts.

77

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08


• Software Engineers are responsible for creating branches from the trunk to develop fixes/enhancements.

• The Configuration Management of the software is accomplished with Subversion– Subversion is an open source version control system

(http://subversion.tigris.org/)

78

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08


• Software Engineers are responsible for creating unit tests to verify the correctness of their code. The JIRA issue number is to be used throughout the code and unit tests for tracking purposes.

• Software Engineers are responsible for running their own system/function tests to verify their software.

• Once testing is validated, code is merged back on to the trunk.

79

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08


• Once all validated JIRA issues are merged unto the trunk, regression testing is performed.

80

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

Regression Testing

• Regression testing• Aggregate results

– Total delay

– Total congestion

– Traffic volume

– #TFM initiatives

– Runtime

• Different scenarios– Truncated demand set

– Full demand set

– Weather

• Automated– Weekly or as required

• Archived• Graphical quick-look

81

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08


• Regression testing validation is performed and the release letter is updated.

• Release is tagged in Subversion.• JIRA issues are closed.• Documentation is updated to reflect changes in

software.

82

1s

t A

nn

ua

l W

ork

sh

op

NA

S-W

ide

Sim

ula

tio

n i

n S

up

po

rt o

f N

ex

tGe

n,

12

/10

/08

Risk Management

• Lessons learned analysis– A wrap up meeting is held to discuss all issues on a project in

which proactive steps can be documented to avoid the same mistakes

1 st Annual Workshop NAS-Wide Simulation in Support of NextGen, 12/10/08 Probabilistic NAS Platform...

Documents

Transcript of 1 st Annual Workshop NAS-Wide Simulation in Support of NextGen, 12/10/08 Probabilistic NAS Platform...