Wake vortex encounters (WVE) can pose significant hazard for en-route aircraft. We studied the sensitivity of wake vortex (WV) circulation and decay to aircraft mass, altitude, velocity, density, time of catastrophic wake demise event, eddy dissipation rate, wing span, span-wise load factor, and WV core radius. Then, a tool was developed to compute circulations of WV generated/encountered by aircraft en-route, while disregarding unrealistic operational conditions. A comprehensive study is presented for most aircraft in the Base of Aircraft Data version 4.1 for different masses, altitudes, speeds, and separation values between generator and follower aircraft. The maximum WV circulation corresponds to A380-861 as generator: 864 and 840 m²/s at horizontal separation of 3 and 5 NM, respectively. In cruise environment, these WV may descend 1000 ft in 2.6 min and 2000 ft in 6.2 min, while retaining 74% and 49% of their initial strength, respectively. The maximum circulation of WV encountered by aircraft at horizontal separation of 3 NM from an A380-861 is 593, 726, and 745 m²/s, at FL200, FL300, and FL395, respectively. At 5 NM, the circulations decrease down to 578, 708, and 726 m²/s. Our results allow reducing WVE simulations only to critical scenarios, and thus perform more efficient test programs for computing aircraft upsets en-route.

Cities are increasingly addressing mobility challenges by restricting road traffic, particularly traditional road vehicles that generate greenhouse gas emissions. As an alternative, unmanned aircraft systems (UASs) are emerging as a promising solution for future mobility, offering fast, quiet, cost-effective and environmentally friendly operations. For last-mile delivery, small drones operating at low altitudes are considered especially promising and are already being deployed in some urban areas. According to the EU Drone Strategy 2.0, drone services could generate a market of 14.5 billion and create 145,000 jobs in Europe by 2030. As this sector is still in its early stages, there is a significant uncertainty about the optimal organisation of urban air traffic. In this paper we present a realistic prognosis of how unmanned traffic over a city will utilise the urban very-low-level airspace and assess it using two concepts of operations, with non-structured or structured airspace, both aiming to facilitate the coexistence of competitors sharing the same urban low level airspace. The two concepts are evaluated with delivery operations at scale for a large and densely populated European city. Results for a normalised scenario of 3500 daily operations show that structured airspace produces more conflicts than non-structured airspace (e.g., 316 vs. 54, respectively), and that only for the non-structured airspace can all conflicts be solved with a simple strategic altitude reassignment (vs. 10% of unresolved conflicts for structured airspace). The artificial organisation of slim urban airspace may limit the scalability of business delivery while not reducing the conflict rate.

Complex microscopic simulation models of strategic Air Traffic Management (ATM) performance assessment and decision-making are hindered by several factors. One of the most important is the existence of hidden parameters—such as aircraft take-off weight (TOW) and the selected cost index (CI)—which, if known, would allow for more effective performance modeling methodologies for assessing Key Performance Indicators (KPIs) at various levels of abstraction/detail, e.g., system-wide, or at the level of individual flights. This research proposes a data-driven methodology for the estimation of flights’ hidden parameters combining mechanistic and advanced Artificial Intelligence/Machine Learning (AI/ML) models. Aiming at microsimulation models, our goal is to study the effect of these estimations on the prediction of flights’ KPIs. In so doing, we propose a novel methodology according to which data-driven methods are trained given optimal trajectories (produced by mechanistic models) corresponding to known hidden parameter values, with the aim of predicting hidden parameters’ values of unseen trajectories. The results show that estimations of hidden parameters support the accurate prediction of KPIs regarding the efficiency of flights: fuel consumption, gate-to-gate time and distance flown.