Dawei Xing

With the rapid development of the air transport industry, the problem of airspace congestion and flight delay in the terminal area (TMA) becomes more and more serious. In order to improve the efficiency of flight operations in TMA, point merge procedure had been devised. This paper takes the approach routes in TMA as the research object, taking into account such conditions as obstacle clearance, flight interval, and procedure area. Based on the flight time, fuel consumption, pollutant emission, and noise impact, an optimization model of point merge procedure is constructed. Genetic algorithm is used to optimize the structure of procedure. The Shanghai Hongqiao International Airport is selected for simulation verification, and the actual flow distribution of the airport is analyzed as an example. The results show that the average flight time was reduced by 0.26 min, the average fuel consumption was reduced by 1,240.64 kg, the average NO_x emissions were reduced by 1.09 kg, and the noise impact range was contracted by 55 km² after optimization. The point merge procedure optimization method can be expected to reduce the flight time, fuel consumption, and environmental impact of flights in TMA, so as to optimize the aircraft approach trajectory.

This paper first calculates the departure delay and arrival delay of each flight by mining historical flight data. Then, a new method based on density clustering for identification and visualization of restricted airspace units that considers this activity is proposed. The main objective is to identify the restricted airspace units by calculating the average delay time according to the accumulative delay time of airspace units and the accumulative delay flight. Therefore, the density-based spatial clustering of applications with noise (DBSCAN) clustering method is utilized to match the latitude and longitude coordinates of each spatial domain unit with its delay time to construct a feature matrix, and then clustering analysis is conducted according to the time period. The method aims at identifying the most severe restricted units in each period. The reliability and applicability of the proposed method are validated through a real case study with flight information from Beijing Capital International Airport, Hongqiao International Airport, and Baiyun International Airport during a typical day. The investigation shows that the DBSCAN clustering method can identify the restricted spatial units intuitively on the six flight paths between Beijing Capital International Airport, Hongqiao International Airport, and Baiyun International Airport.

In order to reduce the environmental impact of aircraft operation in the terminal area, this paper researched the route optimization method. Firstly, this paper constructed the air pollutant emission and noise assessment model, and the flight performance model. Secondly, aiming at reducing air emissions and noise level, the multi-objective terminal area route optimization model is established based on the principles of flight safety and flight procedure construction. Then this paper puts forward the path optimization method of emission and noise reduction of terminal area route network, through the research on the priority setting method of terminal area approach and departure route planning. The route segmentation method and NSGA-II algorithm are employed to solve the problem. Finally, a numerical case study is carried out for the Shanghai terminal area, and yields the following results: (1) Compared with the original route network, the optimized route network in the terminal area can significantly reduce emission and noise by reducing pollutant emission by 51.4% and noise influence by 21.5%; (2) The method can also reduce fuel consumption by 60.5% and the total route length by 21.1%.

To cope with the environmental impact of aviation and pollution problems in the future, airlines need to assess environmental impacts and offer countermeasures in advance. In order to measure the influence of environment on the airlines’ operational costs, this paper establishes an aircraft green direct operating cost (GDOC) model to quantify adverse environmental effects, such as air pollution and greenhouse effects, into the direct operating cost (DOC). Furthermore, fuel consumption, flight time, and distance in the cruising stage account for about 80% of the entire flight mission, and optimizing cruise flight performance can contribute greatly to reduce GDOC. Therefore, this paper sets up an optimal control model to minimize GDOC, establishes a discrete time dynamic system for optimizing the cruise altitude and speed profiles, and searches the optimal results by using dynamic programming. Besides, as meteorological conditions affect aircraft aerodynamics, fuel flow rate, contrail formation, and so on, this paper analyzes meteorological uncertainty by using historic meteorological data. Finally, a route is selected as an example, and the rationality of the optimal results is proven by comparing GDOC with DOC. The results and discussion of the numerical test also show that environmental effects on aircraft operation can be reduced significantly by adopting GDOC as the optimization objective, especially the contrail cost, and the step-climb cruise mode can further reduce GDOC effectively.