High-tech giants and start-ups are investing in drone technologies to provide urban air delivery service, which is expected to solve the last-mile problem and mitigate road traffic congestion. However, air delivery service will not scale up without proper traffic management for drones in dense urban environment. Currently, a range of Concepts of Operations (ConOps) for unmanned aircraft system traffic management (UTM) are being proposed and evaluated by researchers, operators, and regulators. Among these, the tube-based (or corridor-based) ConOps has emerged in operations in some regions of the world for drone deliveries and is expected to continue serving certain scenarios that with dense and complex airspace and requires centralized control in the future. Towards the tube-based ConOps, we develop a route network planning method to design routes (tubes) in a complex urban environment in this paper. In this method, we propose a priority structure to decouple the network planning problem, which is NP-hard, into single-path planning problems. We also introduce a novel space cost function to enable the design of dense and aligned routes in a network. The proposed method is tested on various scenarios and compared with other state-of-the-art methods. Results show that our method can generate near-optimal route networks with significant computational time-savings.

Safety is a top priority for civil aviation. To help airlines further improve safety, various clustering-based methods were developed to better understand their current flight operations and detect unknown risks from onboard flight data. However, existing methods can only be carried on historical data in batches, resulting in its inability to update and adjust as new data come in. New onboard flight data related to anomaly detection are generated at airlines every day. The addition of new data will inevitably cause changes in the clustering results. Yet it would be computational costly to run clustering on all data as they accumulate. Therefore, anomaly detection methods that allow real-time update of cluster models as new data come in are more practical for airlines. This paper presents a reinforcement learning method to identify common patterns in flight data via cluster analysis and update its clusters as new data come in. This method is based on Gaussian Mixture Model (GMM) and uses online (recursive) expectation-maximization (EM) algorithm to update clustering results over time. An initial result of clusters can be obtained by performing GMM-based clustering on historical flight data. Then, as new data come in, the parameters of GMM are updated via an online EM algorithm. By recording the GMM parameters, the method can also track changes in clusters over time. We demonstrated the proposed method using Flight Data Recorder (FDR) data from real operations of an airline. The evolution of clusters was observed as new batches of flight data are fed into the proposed method.

Aircraft mass is the most important parameter airliners use to determine how much cargo and fuel they should take for the fight to maximize their proft while keep the total weight under the safety limit. For many practical reasons, airliners can not get the accurate aircraft mass data by weighting every cargo and passenger for each fight. Several studies have proposed methods to estimate aircraft mass based on radar data or ADS-B data. But due to the measurement errors in the data, and also due to uncertainties in aircraft confguration, it is hard to calculate accurate aircraft mass data for every fight. In the paper, the quick access recorder data are used for analysis, parameters that are not available in radar data or ADS-B data can now be used to eliminate parameters with large errors in the level fight point-mass dynamics model. Equations are reformulated as a set of overdetermined linear equations with nonlinearly structured errors in system matrix. The set of equations does not depend on inaccurate parameters like thrust and it does not require accurate knowledge of the aircraft like geometry, aerodynamic coefcients, etc. The set of equations is solved by an improved structured nonlinear total least squares method using Monte Carlo method. The method is applied to 120 real fights of Boeing 777300ER aircraft, the result shows a good accuracy for some type of fights.

The structural evolution of Cu₄₅Zr₄₅Ag₁₀ metallic glass was investigated by in situ transmission electron microscopy heating experiments. The relationship between phase separation and crystallization was elucidated. Nucleation and growth-controlled nanoscale phase separation at early stage were seen to impede nanocrystallization, while a coarser phase separation via aggregation of Ag-rich nanospheres was found to promote the precipitation of Cu-rich nanocrystals. Coupling of composition and dynamics heterogeneities was supposed to play a key role during phase separation preceding crystallization.