In our day to day lives, there are many different things that are taken for granted. This is the case of exotic fruits, fish, meat, exclusive drinks in the supermarket, flowers. In addition, traveling is made fast for various purposes, such as to cross the globe in order to meet colleagues in conferences, visit loved ones, take vacations, or knowing that an organ can help our lives in less than a day of travel in case of an emergency.
There are goods that require extreme care in order to arrive quickly and safely to our cities. These goods are diverse, such as specialized machinery, high sensitive microchips, chemicals oils, among many others. These goods allow economic growth and development (ATAG, 2016) (Boeing, 2016b). All their transportation is possible due to aviation.
In the early 1900s, the traveling from Europe to America would take around 5 days. Nowadays cruises slowed it down a little bit to allow passengers to enjoy their trip for 7 days (New York, USA to Southampton, UK). However, for a business trip, for someone that is time constrained, or for perishing/urgent goods, this is a long time considering that a flight from New York to London takes roughly 7 hours. The same time consideration is true for a continental flight within the US and Canada, driving a car from Montreal to Vancouver would take a non-stop trip of 46 hours, while taking a flight would take around 5h30m. Even in the presence and acceptance of strict regulations, aircraft remain the safest way of traveling.
As aviation is the most convenient way of traveling, it has allowed connecting the world in ways that no one in history could imagine. This way has brought as a consequence the exchange of ideas, better understanding of other cultures, human development, commercial exchange, and economy growth. Lives have been saved due to rescue missions, it sets the basis of space conquest, and it is fundamental for national defense systems (ATAG, 2005).
However, as all in life, it comes with a price: huge quantities of fossil fuel are required to power a flight, thus it pollutes.
Table des matières
INTRODUCTION
0.1 Fuel Burn Reduction Efforts Before Airborne
0.2 Satement of the problem
0.3 Research Objetives
0.4 Methodology
0.4.1 Aircraft Numerical Performance Model
0.4.2 Weather Forecast Model
0.4.3 Beam Search
0.4.4 Search Space Reduction
0.4.5 Artificial Bee’s Colony
0.4.6 Golden Section Search
0.4.7 Ant Colony Optimization Algorithm
0.5 Literature Contribution
CHAPTER 1 LITERATURE REVIEW
1.1 Fuel Burn Reduction Efforts Before Airborne
1.2 Fuel Burn Reduction Efforts During Airborne
CHAPTER 2 RESEARCH APPROACH AND THESIS ORGANIZATION
2.1 Thesis Organization
2.1.1 First Journal Paper
2.1.2 Second Journal Paper
2.1.3 Third Journal Paper
2.1.4 Fourth Journal Paper
2.1.5 Fifth Journal Paper
CHAPTER 3 NEW METHODOLOGY FOR NAV FLIGHT TRAJECTORY COST
CALCULATION USING A FMS PERFROMANCE DATABASE
3.1 Introduction
3.2 Methodology
3.2.1 The Conventional Flight
3.2.2 The Performance Database (PDB)
3.2.3 Flight Cost
3.2.4 Trajectory Calculation Method
3.3 Results
3.3.1 Flight Comparison
3.3.2 Cost Index Effect
3.3.3 Computation Time For Different Trajectories
3.3.4 Cruise Aircraft Distance Between Weight Update Points
3.4 Conclusion
CHAPTER 4 NEW REFERNCE TRAJECTORY OPTIMIZATION ALGORITHM
FOR A FLIGHT MANAGEMENT SYSTEM INSIPIRED IN BEAM
SEARCH
4.1 Introduction
4.2 Methodology
4.2.1 Numerical Performance Model
4.2.2 Flight Cost Computation
4.3 The Optimization Algorithm
4.3.1 The Search Space: A Decision Graph
4.3.2 Problem Definition
4.3.3 The Beam Search Algorithm
4.4 Exhaustive Search Algorithm
4.5 Results
4.5.1 The Optimism Coefficient Effect
4.5.2 The Beam Search Algorithm and the Exhaustive Search
4.5.3 The Beam Search Algorithm and Results Obtained from the FMS/PTT
4.5.4 The Beam Search Algorithm Considering Wind Influence
4.6 Conclusion
CHAPTER 5 AIRCRAFT VERTICAL ROUTE OPTIMIZATION BY BEAM
SEARCH AND INITIAL SEARCH SPACE REDUCTION
5.1 Introduction
5.2 Methodology
5.2.1 The Studied Flight
5.2.2 The Numerical Performance Model
5.2.3 Flight Cost: Fuel burn and Flight Time Computations Using
the Numerical Performance Model
5.2.4 Interpolations: Computing the Required Value from
the Numerical Performance Model
5.2.5 The Flight Cost Computation: Fuel Burn and Considerations
5.2.6 Problem Definition: The Vertical Reference Trajectory Optimization
5.3 The Optimization Algorithm
5.3.1 Algorithm’s Input
5.3.2 Search Space Reduction Module (SSRM)
5.3.3 The Vertical Reference Trajectory Search Space:
the Graph Construction
5.3.4 The Vertical Reference Trajectory Search Space:
the Graph Construction
5.3.5 The Bounding Function: Heuristics
5.3.6 Weather presence in the bounding function
5.4 Results
5.4.1 Standalone Algorithms Results Comparison
5.4.2 The reference trajectory flight cost between the trajectory
provided by a FMS and the trajectory of the developed algorithm
5.4.3 The reference trajectory flight of the developed algorithm
compared to a real lateral trajectory.
5.5 Conclusion
CHAPTER 6 4D AIRCRAFT EN-ROUTE OPTIMIZATION ALGORITHM USING
THE ARTIFICIAL BEE COLONY
6.1 Introduction
6.2 Methodology
6.2.1 Flight Cost
6.2.2 The Studied Flight: The Search Space
6.2.3 The Optimization Algorithm
6.2.4 Algorithm Summary
6.3 Results
6.3.1 Number of Iterations’ Influence on the Resulting Trajectory
6.3.2 The ABC´s Robustness
6.3.3 Real Flights Study
6.3.4 Multiple Flights Fuel Reduction
6.4 Conclusion
CHAPTER 7 3D AND 4D AIRCRAFT REFERENCE TRAJECTORY
OPTIMIZATION USING THE ANT COLONY OPTIMIZATION
7.1 Introduction
7.2 Numerical Models and the Search Space
7.2.1 Fuel Consumption: The Numerical Performance Model
7.2.2 Fuel Burn Computation
7.2.3 Flight Cost
7.2.4 Weather Information
7.2.5 The Search Space
7.3 Introduction to the Ant Colony Optimization Algorithm
7.3.1 Bio Mimicry and Metaheuristic algorithms
7.3.2 The Ant Colony In Nature
7.3.3 The ACO algorithm implementation for trajectory optimization
7.4 3D Reference Trajectory Optimization
7.4.1 ACO First Stage: 3D – Module 1 (M1)
7.4.2 ACO First Stage: 3D – Module 2 (M2)
7.4.3 ACO First Stage: 3D – Module 3 (M3)
7.4.4 Functioning
7.5 4D Reference Trajectory Optimization: RTA Fulfillment
7.5.1 RTA (4D) First Module – M1
7.5.2 RTA Second Module – M2:
7.5.3 RTA Third Module – P3:
7.5.4 RTA Functioning
7.6 Results
7.6.1 The ACO algorithm trajectory comparison with the geodesic trajectory 227
7.6.2 The ACO algorithm versus a real as flown flight plan results
7.6.3 The ACO algorithm versus different as flown flights
7.6.4 The Required Time of Arrival
7.7 Conclusion
CHAPTER 8 DISCUSSION OF RESULTS
CONCLUSION
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