Mach Number Selection for Cruise Phase Using Ant Colony Algorithm with RTA Constrains Alejandro...
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Mach Number Selection for Cruise Phase Using Ant Colony Algorithm
with RTA ConstrainsAlejandro Murrieta-Mendoza, Antoine Hamy, Ruxandra M. BotezUniversité du Québec / ÉTS/ LARCASE
1
Motivation
Airline Expenses: 26% - 40%
Fuel
innovation.columbia.edu
- Profit- Financial Planning
- CO2 - Global Share: 2% - NOx- H20- Hydrocarbons
2
MotivationCanada: The Green Aviation Research & Development Network (GARDN)
Develop technologies to reduce aircraft noise and emissions.
Governmental Funding + Industrial Funding + University Expertise
3
Introduction
How to reduce fuel?
www.turbosquid.com
SA 3.0
Winglets
Engines Improvements
Morphing wings
Trajectories and Airspace
4
Introduction
• Conventional 3D Trajectories– Flight Plan.– Voice communication.
• 4D Time based Trajectories: IBO/TBO– Flight Plan– Advanced Systems– Required Time of Arrival
5
Objectives
• Combination of Mach numbers that fulfill the RTA.
ETA = RTA• Reduce the fuel consumption
– Reduce CO2, NOx, HC, etc…
innovation.columbia.edu
6
Trajectory Studied
• Cruise Phase– Bucharest – Grand Canary
• Constant Altitude
7
Methodology
• Flight Cost
Performance Database
Experimental Flight Data
8
𝐶𝑜𝑠𝑡=𝐹𝑢𝑒𝑙 𝐵𝑢𝑟𝑛𝑒𝑑+𝐶𝑜𝑠𝑡 𝐼𝑛𝑑𝑒𝑥∗ h𝐹𝑙𝑖𝑔 𝑡𝑇𝑖𝑚𝑒
Methodology
𝐺𝑆=𝑇𝐴𝑆±𝑊𝑖𝑛𝑑𝑆𝑝𝑒𝑒𝑑∗ cos(𝑊𝑖𝑛𝑑𝐴𝑛𝑔𝑙𝑒)
h𝐹𝑙𝑖𝑔 𝑡 𝑇𝑖𝑚𝑒=𝐺𝑆
𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒
RTAWaypointTotal FlightTime FlightTime
i
RTAWaypointTotal Fuel Burn Flight Burn
i
𝑅𝑇𝐴• Flight Cost
9
Methodology
• How to manage the Mach Number?– Mach Number as a grid
10
Mach Number Options
Lateral Flight Plan
Methodology
• Ant Algorithm– Ants wander around for food sources.
– Different ants find the food source.
– Over time the shortest path is selected.
– Use pheromone to keep track of the path• The more ants are in a path, the more pheromone there is
11
Methodology
• Ant Algorithm– Probability to select a given Mach Number
– Pheromone Evaporation
12
_ * _
_ * _
Fije ij Conso ij
PijFik
e ik Conso ik
1 1 F t F t N P Cij ij ij ij
e Tt Tr
Results
• 2 Turbo-Fan Aircraft
• Real Flight Plan Waypoints were used– Weather obtained from flight plan
• RTA waypoint just before the ToD.
• Arbitrary imposed RTAs.
13
Results
14
0 200 400 600 800 1000 1200 1400 1600 1800 20000.77
0.78
0.79
0.8
0.81
0.82
0.83
0.84
0.85
Distance (nm)
Mac
h
Mach Progression, RTA: 4h 20min
4h 20min 0sec.
• RTA: 4h20m0s– Tolerance: +/- 30 sec
Tailwind Headwind Hedwind
Tailwind
Results
15
0 200 400 600 800 1000 1200 1400 1600 1800 20000.74
0.75
0.76
0.77
0.78
0.79
0.8
0.81
0.82
0.83
0.84
Distance (nm)
Mac
h
Mach Progression – Different RTA
4h 20min4h 24min4h 30min4h 34min
• RTA1: 4h20m (blue) RTA2: 4h24min (green)• RTA3: 4h30m (red) RTA4: 4h36min
(magneta)
Results
• Flight Cost
16
Flight RTA Flight Time Difference (s) Fuel Burn (T)1 4h20 4h20m00s - 16.0572 4h25 4h24m59s 1 16.8483 4h30 4h30m40s 40 18.6804 4h35 4h34m43s 17 19.431
Flight # Mach Number
Flight Time
Fuel Burn (Ton)
1 0.80 4h29m07s 18.42 0.81 4h25m30s 18.83 0.82 4h22m07s 19.1
NO RTA
RTA
Conclusion
• In almost all cases, the ACO respected the RTAs imposed.
• The algorithm was able to improve fuel burn. – Less fuel burn equals less emissions.
• The algorithm takes local decisions.• A global weather view is required to reduce
the Mach Number variation.
17
Thank YouQ & A
18