Transcript AvMet Overview

Using Simulation in
NextGen Benefits
Quantification
Alexander Klein
July 22, 2014
AvMet Applications, Inc.
1800 Alexander Bell Dr., Ste. 130
1
Reston,
VA 20191
Simulation Model Spectrum
Analytical models (e.g. Excel based)
DART
Queuing/network models
Superfast-time simulation models
Medium-to-high detail
Entire NAS
“Weather-aware”
“NextGen-aware”
Highly detailed
Day-in-the NAS in 2 min
High-fidelity fast-time simulation models
Airport surface / TRACON
Group of sectors to Center
Real-time Human-in-the Loop simulators
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NAS, 07/08/11, 00Z, 3,800 flights in the air
Airspace, weather and flows in the Northeast
Individual reroute
ALPHA
LGA/EWR/JFK Flows
BRAVO
N38 10’55’’
W78O14’19’’
O
N37O42’34’’
W78O36’28’’
CHRLY
ATL departure and arrival flows
DART:
Weather-Aware,
Runway-to Runway,
Superfast-Time
NAS/ATM Simulation
Model
Rerouted
flight plan
Original
flight plan
Nonpermeable Wx
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Permeable Wx
Examples of DART Supported NextGen Benefit
Analysis Studies
• NextGen technology elements
• DataComm, ELVO, NVS, RNP-E
• Technology Portfolios
• Equipage and traffic growth scenarios, e.g. through 2030
• NextGen weather products and tools
• CSS-WX, NWP
• New procedures (Wx related), technology driven benefits
• CATMT, EDR
• New procedures, safety concerns – dis-benefits
• CRO, Winter weather
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DART Next-Generation ATM Study Example
Tentative NextGen Portfolio Deployment Schedule;
DART Model Estimated Annual Savings vs. Same Year's Do-Nothing Baseline;
Each Portfolio Element Assessed Separately; ASQP Carriers, 2011 $$
$1,000,000,000
Added Gain if Technologies
1,2 and 3 Were Deployed
Together
$900,000,000
$800,000,000
Added Gain if Technologies
1 and 2 Were Deployed
Together
$700,000,000
$600,000,000
Technology 3
$500,000,000
$400,000,000
Technology 2
$300,000,000
$200,000,000
Technology 1
$100,000,000
$2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025
A sample DART NextGen portfolio-of-technologies study output (including DataComm). This batch
required 16,000 simulations, each representing a full day in the US NAS with 40-60,000 flights,
weather, forecasts, and NextGen technology effects simulation, and took 5 days to complete.
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Improved Convective Forecast Accuracy –
Benefit Analysis Using DART
• A range of forecast product features evaluated in DART using an entire
convective season (instead of a handful of weather situations)
• Simulated operational benefits (reduced excess operating costs) of:
• Improved forecast accuracy (from ‘current’ to ‘more accurate’ to ‘perfect’)
• Use of convective echo tops forecast information
• Operational impact of using finer weather grid resolution
• More effective use of TMIs, more streamlined reroutes
90.0
160.0
80.0
140.0
70.0
120.0
60.0
100.0
50.0
80.0
40.0
Avg Cnx
60.0
Avg Dvrt
30.0
40.0
20.0
GndDly/1000
AirDly/1000
10.0
20.0
0.0
0.0
Baseline


 Improved forecast
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CoSPA with Echo Tops, 3D View
FL
NC
Wx at FL400 shown
Flight N255QS
cruising alt = FL430
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Back-up Slides
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DART for Assessing “Value” of Alternative ATM
Strategies/Decisions (Realized or Needed)
An Example
J29
J29
VUZ
Optimized solution: Airway J29 open to relieve traffic
on VUZ playbook reroute; reduced MIT, less delay
VUZ
Non-optimal solution: VUZ playbook reroute traffic uses
standard route; J29 closed; heavier MIT, longer delays
Only the traffic using select NAS Playbook reroutes is shown;
Color-coding by delay: 0-15, 15-20, 30-60, 60-120, >120 min
arrival delay
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Validation Using a
Multi-Day Period
ZDC distribution
of flight
altitudes
NAS metrics obtained from DART
over a multi-day period (e.g. an
entire convective season) are
compared with historical data from
FAA statistics
NAS arrival delays – daily totals
Delays - DART Simulation (Actual Traffic Demand) w. LAMP En-route Rechecks vs. ASQP Arrival Delay - Summer 2011
320000
280000
Delay minutes - daily total
240000
200000
160000
DART
ASQP Relevant
120000
80000
40000
0
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Date
Validation Using a
Multi-Day Period
Normalized RMSE
Normalized RMSE is a measure
of DART-vs-ASQP variance error
over the entire convective season
Arr Delay
Cnx
Diversions
13%
16%
15%
NAS cancellations – daily
totals
Cancellations - DART Simulation (Actual Traffic Demand) w. LAMP En-route
Rechecks vs. ASQP - ASPM77 Airports - Summer 2011
700
NAS arrival delays – daily totals
600
Cancellations - daily total
500
DART
400
ASQP
300
200
100
0
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Date
Selected DART Output Metrics
• Delays/Cancellations/Diversions/Reroutes Statistics
•
Delays by type (ground, airborne, holding), cause (e.g. airport capacity, runway,
en-route weather, GDPs, AFPs, etc.), and stage (departure, en-route, approach)
•
Scope: by individual air carrier, by airport, and the NAS summary for the day
•
Excess operating costs can be computed from these outputs
• Hourly movements and delays for major airports
• Traffic demand, directional capacity and occupancy for all Sectors/Centers
•
Original demand, demand adjusted by DART, capacity degradation due to
diagnostic and forecast weather, maximum and average occupancy every 15 min
• “Denied sector entry requests” as a measure of airspace availability
• Sector events
•
Entry/exit, altitude changes, vectoring, airway transitions, potential conflicts, etc.
• Airway weather impact statistics
• Individual flight statistics (Dep/Arr times, route length, delays, Wx impact)
• Flight plans and 1-min trajectories exported in flat-file TFMS/ASDI format
• WITI metrics (en-route, TRACON, terminal, Centers, Flows)
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