The Single European Sky ATM Research (SESAR) project is the technological pillar of the European Commission’s Single European Sky Initiative to modernize air traffic management (ATM). Here, we describe the process of establishing SESAR and the main parts of the project: the research and development (R&D) part, which is led by the SESAR Joint Undertaking; the deployment part, which is managed by the SESAR Deployment Manager; and the European ATM Master Plan, which collects and lays out both the R&D and deployment needs. The latest European ATM Master Plan was adopted just prior to the current pandemic. The huge loss in air traffic due to the pandemic, and the speed of the recovery of the aviation industry will require reprioritization, but the main elements that have been established—particularly those in support of the environment—remain valid.
Owing to its high heat storage capacity and fast heat transfer rate, packed bed latent heat storage (LHS) is considered as a promising method to store thermal energy. In a packed bed, the wall effect can impact the packing arrangement of phase change material (PCM) capsules, inducing radial porosity oscillation. In this study, an actual-arrangement-based three-dimensional packed bed LHS model was built to consider the radial porosity oscillation. Its fluid flow and heat transfer were analyzed. With different cylindrical sub-surfaces intercepted along the radial direction in the packed bed, the corresponding relationships between the arrangement of capsules and porosity oscillation were identified. The oscillating distribution of radial porosity led to a non-uniform distribution of heat transfer fluid (HTF) velocity. As a result, radial temperature distributions and liquid fraction distributions of PCMs were further affected. The effects of different dimensionless parameters (e.g., tube-to-capsule diameter ratio, Reynolds number, and Stefan number) on the radial characteristics of HTF and PCMs were discussed. The results showed that different diameter ratios correspond to different radial porosity distributions. Further, with an increase in diameter ratio, HTF velocity varies significantly in the near wall region while the non-uniformity of HTF velocity in the center region will decrease. The Reynolds and Stefan numbers slightly impact the relative velocity distribution of the HTF—while higher Reynolds numbers can lead to a proportional improvement of velocity, an increase in Stefan number can promote heat storage of the packed bed LHS system.
Intractable delays occur in air traffic due to the imbalance between ever-increasing air traffic demand and limited airspace capacity. As air traffic is associated with complex air transport systems, delays can be magnified and propagated throughout these systems, resulting in the emergent behavior known as delay propagation. An understanding of delay propagation dynamics is pertinent to modern air traffic management. In this work, we present a complex network perspective of delay propagation dynamics. Specifically, we model air traffic scenarios using spatial-temporal networks with airports as the nodes. To establish the dynamic edges between the nodes, we develop a delay propagation method and apply it to a given set of air traffic schedules. Based on the constructed spatial–temporal networks, we suggest three metrics—magnitude, severity, and speed—to gauge delay propagation dynamics. To validate the effectiveness of the proposed method, we carry out case studies on domestic flights in the Southeastern Asia region (SAR) and the United States. Experiments demonstrate that the propagation magnitude in terms of the number of flights affected by delay propagation and the amount of propagated delays for the US traffic are respectively five and ten times those of the SAR. Experiments further reveal that the propagation speed for US traffic is eight times faster than that of the SAR. The delay propagation dynamics reveal that about six hub airports in the SAR have significant propagated delays, while the situation in the United States is considerably worse, with a corresponding number of around 16. This work provides a potent tool for tracing the evolution of air traffic delays.
The normal operation of aircraft and flights can be affected by various unpredictable factors, such as severe weather, airport closure, and corrective maintenance, leading to disruption of the planned schedule. When a disruption occurs, the airline operation control center performs various operations to reassign resources (e.g., flights, aircraft, and crews) and redistribute passengers to restore the schedule while minimizing costs. We introduce different sources of disruption and corresponding operations. Then, basic models and recently proposed extensions for aircraft recovery, crew recovery, and integrated recovery are reviewed, with the aim of providing models and methods for different disruption scenarios in the practical implementation of airlines. In addition, we provide suggestions for future research directions in these topics.