Optimizing Conflict Resolution Techniques to Reduce Aircraft Fuel Consumption and Emissions

This project has received funding from the Scientific and Technological Research Council of Turkey (TUBITAK) under grant number 122M451. Optimizing Conflict Resolution Techniques to Reduce Aircraft Fuel Consumption and Emissions TACTICIAN Project Mustafa Özdemir*, Öznur Usanmaz Department of Air Traffic Control Eskisehir Technical University Eskisehir, Turkey *[email protected] Enis Turhan Turgut Aircraft Airframe and Powerplant Department Eskisehir Technical University Eskisehir, Turkey Müjgan Sağır Department of Industrial Engineering Eskisehir Osmangazi University Eskisehir, Turkey Abstract—This project aims to minimize the effect of conflict resolution strategies on aircraft emissions using a mathematical model and a genetic algorithm with a realistic approach. For this purpose, we take into account speed, heading, and altitude maneuvers during the cruise phase of flight. In addition, changes in fuel and emissions during decent, climb, speed increase, speed decrease and heading maneuvers are also considered. Keywordsconflict resolution; aircraft emissions; mathematical modeling; simulations I. INTRODUCTION This project considers aircraft emissions that occur during the cruising phase of flight and that have a serious impact on climate, air quality, and human health [1]. During the cruise phase, usually, there are many aircraft sharing the same airspace. Therefore, a certain safe separation distance must be maintained horizontally and vertically to avoid conflicts between aircraft. One or more of the conflict resolution methods including speed, level, and heading change are applied by air traffic controllers to prevent the conflict. The chosen resolution method is important for efficient air traffic management. However, it may not always be possible to choose the most appropriate method and to determine optimal parameters (i.e., amount of speed change, degree of heading angle change, and number of flight level change) for the selected method. Furthermore, since air traffic controllers often have to monitor and control many aircraft, they have to endeavor to determine the best conflict resolution strategy in a very limited time. To solve these problems from an environmental point of view and reduce emissions, this study aims to develop a mathematical model to be used in conflict resolutions of aircraft in the cruise phase which minimizes total aircraft emissions. A limited number of studies have been conducted to investigate aircraft emissions considering conflict resolution techniques. In these studies, mostly, CO2 and contrail were examined including only one or two of the conflict resolution methods [2], [3]. However, all the conflict resolution methods can be used in real-world flight operations. In this respect, it would be a more realistic and holistic approach to consider all the conflict resolution methods. There are very few studies optimizing fuel and emissions of aircraft considering speed, heading and altitude maneuvers together. These studies usually tend to handle the problem using heuristic/metaheuristic algorithms (only for contrail or CO2), ignoring how well these algorithms perform in terms of generating results close to the optimum [4], [5]. In this project, our research questions are as follows: ▪ Are the strategies the same for reducing fuel and emissions in conflict resolution? ▪ What is the best conflict resolution strategy to reduce NOx, CO2, CO, and HC separately? How does this strategy differ for each of them? ▪ What are the differences between the results obtained with real-time simulations and those obtained from fast-time simulations, mathematical models and metaheuristic algorithms? ▪ What kind of problems can be encountered in the realtime simulations of the obtained conflict resolution strategies? II. METHODS In this project, we propose a mathematical model and genetic algorithm aiming to determine the most appropriate conflict resolution methods to reduce emissions. We will follow the methodology described in [6] to construct the mathematical model, then, we will improve the model by combining three resolution maneuvers. Aircraft emissions will be calculated using flight data records (FDR), which include many parameters such as time, speed, direction, altitude, fuel consumption, and descent/climb rate. Based on the fuel calculations obtained from the FDR, the emissions at cruise altitudes will be calculated considering aircraft/engine type, altitude, aircraft weight, and speed using the ICAO Emission Database and Boeing Fuel Flow Method (BFFM2). The calculated emission values will be included in the mathematical model as parameters. Moreover, other parameters such as lower and upper limits for speed change, climb and descent rate, and aircraft mass will also be obtained from FDR data. We believe that using FDR data can provide a more accurate emission calculation and lead to more realistic results. 2 After establishing the mathematical model, a genetic algorithm will be developed to solve large-scale problems. Fasttime simulation studies will be carried out through RAMS to test the consistency and validity of the results of the mathematical model and algorithms. Then, real-time simulation studies will be carried out through the EscapeL simulator developed by Eurocontrol to evaluate the applicability of the model in realworld situations and to investigate the differences caused by human-machine interaction. Figure 1. Conceptual flow of the methodology. In the real-time simulation stage, we plan to conduct two different experiments: a) Case 1: We will use the same strategy revealed from the mathematical model or genetic algorithm for each conflict situation. In this case, probably it will not be enough to use the same strategy to prevent a conflict situation since some factors are not considered in the model and algorithm such as controller and pilot reaction time, and time spent performing a conflict resolution maneuver. In this case, we will be able to measure these factors and conduct the necessary adjustments to reach a conflict-free state. b) Case 2: Controllers are free to implement any conflict resolution strategy they prefer. In this case, we hope to see the similarities and differences between the strategies applied by controllers and by the algorithm. To test the model, we will use a circle-shaped sector where aircraft fly to the center at the same speed as shown in Fig. 2. In this way, we will guarantee to have a conflict between all aircraft. After this test, we will use a more realistic sector to anticipate the performance of the model including more aircraft. Figure 2. Conflict situation for three aircraft III. CONCLUSIONS The proposed model can help controllers choose the most appropriate resolution method in terms of emission minimization. Moreover, controllers can be supported to make the right decision under factors such as high workload, time constraints, fatigue, stress, and distractibility. Reducing emissions will reduce their negative effects on the environment and human health. This model can save airline companies from emission-related costs due to international regulations by helping them keep aircraft emissions to a minimum target level. REFERENCES [1] G. J. J. Ruijgrok and D. M. van Paassen, Elements of aircraft pollution. The Netherlands: Delft University Press, 2005. [2] B. Zou, G. S. Buxi, and M. Hansen, “Optimal 4-D Aircraft Trajectories in a Contrail-sensitive Environment,” Networks Spat. Econ., vol. 16, no. 1, pp. 415–446, 2016, doi: 10.1007/s11067-0139210-x. [3] R. Amin and S. Alam, “A Heuristic Search Approach to Find Contrail Avoidance Flight Routes,” in 28th Australasian Joint Conference on Artificial Intelligence, 2015, vol. 9457, pp. 14–20, doi: 10.1007/9783-319-26350-2. [4] Y. Tian, L. Wan, and B. Ye, “Multi-objective optimization of high altitude sector operation based on environmental protection,” Cluster Comput., vol. 22, pp. 15429–15437, 2019, doi: 10.1007/s10586-0182615-z. [5] J.-P. Clarke, K. Feigh, A. Dutta, B. Lee, S. Milway, and C. Tino, “Final Findings on the Development and Evaluation of an En-Route Fuel Optimal Conflict Resolution Algorithm to Support Strategic DecisionMaking Final Findings on the Development and Evaluation of an En-Route Fuel Optimal Conflict Resolution Algorithm to,” no. January. 2012. [6] L. Pallottino, E. M. Feron, and A. Bicchi, “Conflict resolution problems for air traffic management systems solved with mixed integer programming,” IEEE Trans. Intell. Transp. Syst., vol. 3, no. 1, 2002. Fast-time simulations Real-time simulations Genetic algorithm Mathematical model FDR data BFFM2 + ICAO emission data bank Estimation of aircraft emissions Conflict resolution strategies to minimize emissions Geometric data (Aircraft positions) Aircraft mass, flight speed, limits for speed change, etc.