At airports, taxiway maintenance is a complex task affecting airport ground operations, specifically airport surface movement operations managed by ground controllers. It is expected that taxiway maintenance also affects the task load experienced by ground controllers. However, to our best knowledge, no studies were performed so far to quantify and assess this influence. To fill this gap, this research studies the impact of taxiway maintenance on the task load of ground controllers. It is essential to develop this understanding to ensure that taxiway maintenance is planned safely, efficiently, and robustly. This research uses Schiphol Airport as a use case, for which an existing airport surface movement simulator was used to model the ground traffic with a single-agent trajectory planning algorithm. To realistically represent the traffic patterns at Amsterdam Schiphol Airport, the simulated model was calibrated using real trajectory data and expert opinion.
To quantify the task load of ground controllers, the well-known Dynamic Density model and a novel measure, based on controller's mental re-plannings, were used and compared to each other. Based on a maintenance scenario, simulations were performed for two runway configurations, with the aim to study the differences in task load between the nominal and maintenance scenario for different flight schedules. The main outcome of this analysis is that conflicting flows, resulting from taxiway maintenance, have a significant contribution to the task load of ground controllers. It was further found that the effects of these conflicts on the task load are localized and do not propagate further to other areas, and other ground controllers who do not have responsibility over the specific area in question do not experience this impact in task load.

The use of UAVs for aircraft maintenance inspections has emerged as a promising solution to reduce maintenance costs and increase inspection quality by providing efficient and accurate data collection. In this paper a novel path planning approach is proposed for autonomous aircraft maintenance inspections using UAV swarms, leveraging a frontier-following algorithm. The algorithm guides a drone's trajectory to cover an aircraft's surface while maintaining a constant distance tangent to the surface. Designed to operate with delayed depth sensing methods, where scenes are reconstructed using structure from motion principles, this approach can be integrated with online reconstruction techniques. The frontier-following methodology results in a characteristic spiraling pattern behavior and facilitates complex surface tracking. The primary environment sensing detector, an RGB camera, is angled towards a target on the frontier. This allows for preliminary mapping of a route prior to traversal, while a collision avoidance strategy ensures collision-free flight by tracking an obstacle-free tunnel around the drone. Furthermore, this method can be scaled up for application to swarming agents, accelerating surface coverage. Simulation results demonstrate the algorithm's efficiency in achieving comprehensive surface coverage. Frontier-following path planning outperforms the 2D coverage path planning algorithm Random Walk, while performing lower than Spiral Spanning Tree Coverage - a result consistent with the latter's optimality for 2D surfaces. The proposed method's ability to successfully track complex 3D surfaces, a capability lacking in traditional 2D coverage path planning algorithms, renders it a promising solution for autonomous inspection systems.

To accommodate the rising demand for airport resources and airspace capacity, a more efficient and robust orchestration of the components that facilitate air travel is required than is the case today. Over recent years, camera systems for automatic identification of ground-handling events were developed, providing the opportunity to automatically detect ground-handling delays as they manifest. Detecting ground-handling delays early allows the Air Navigation Service Providers (ANSPs), ground handlers and aircraft operators to reallocate their resources, facilitating more stable planning and robustness of the air traffic network. This study proposes a novel interpretable machine learning approach to predict initial ground-handling end time adherence. This approach is based on a discrete Bayesian network composed of a central network structure and modular sub-networks, representing distinct turnaround processes often found on the critical path. The Bayesian network structure and discretization were found to be adequate to model most ground-handling operations for narrow-body aircraft. The model proved to be more accurate than the ground handler in predicting initial Target Off-Block Time (TOBT) adherence at all prediction moments. At the initial TOBT-0 [minutes], a maximum in model accuracy was obtained amounting to 65.76%. In addition, the model demonstrated the capability to detect ground-handling delays earlier. At the initial TOBT-20 [minutes], an average recall of 50.4% was obtained for delays within 5 and 15 minutes. In contrast, the ground handler identified only 4.3% of small ground-handling delays at this prediction moment. The discrete Bayesian network approach is also inherently interpretable, providing transparency into the decision-making process of the model.

The aviation industry's shift towards sustainable propulsion, notably hydrogen-electric propulsion (HEPS), underscores the need to grasp its effects on flight procedures and pilot duties. This research aims to develop a HEPS engine powertrain model for evaluating its influence on flight procedures, especially during critical phases like go-around for regional turboprop aircraft. The HEPS engine powertrain model was constructed by selecting powertrain components important for modelling based on their relevance to important HEPS system outputs like the thrust generation, battery discharge, and fuel left in the tank. Subsystems such as the fuel cell system, battery, motor, propeller etc. were modelled using MATLAB/Simulink and integrated to form a holistic representation of the HEPS engine. The model underwent testing through a simulated go-around mission, followed by an analysis of its operational envelope, which unveiled significant parameters influencing system behaviour during such manoeuvres. Among the critical parameters identified were the initial battery state of charge (SoC), nominal battery capacity, and propeller efficiency. Thrust generation limitations were identified for heavier aircraft configurations, as well as the relatively low impact of go-around speed on the system's power path. For a HEPS-based Dash 8 Q-300 aircraft with a mass of 38,000 pounds and a go-around speed of 96 knots, the State of Charge (SoC) of a 200Ah battery should be at least 23% for a successful go-around manoeuvre. Using appropriate parameters, the presented model framework assists in identifying such constraints and delineating flight procedural changes for future regional aircraft. The study highlights areas for model enhancement, including better thermal management system models for the fuel cell stack and battery, and improved motor-propeller models. This research contributes to sustainable aviation, guiding the integration of HEPS into flight operations for enhanced efficiency and eco-friendly air travel.

Haptic shared controllers (HSCs) are a promising solution to prevent human over-reliance on automation during tasks such as car driving. However, research has shown that if the HSC is tuned incorrectly, then there is a risk of haptic conflicts between the human and HSC. To address this challenge, this paper presents the design and implementation of a novel adaptive HSC that continuously adjusts its look-ahead time. By estimating the time shift between the reference state of the HSC and the actual state, the HSC adapts to the look-ahead time of the human it is interacting with. Results from a human-in- the-loop experiment show that the novel HSC achieves similar subjective ratings as a fixed preview HSC, as well as a significant improvement over a fixed pursuit HSC. Going from pursuit to preview, objective experiment data shows that as the adaptive HSC adjusts its look-ahead time, haptic conflicts are reduced and tracking performance is increased. The presented findings are a step forward in designing haptic support systems with high chances of user acceptance. The proposed adaptive look-ahead algorithm provides a new method for online estimation of human look-ahead time, with or without a HSC in-the-loop.

This paper explores the impact of Automatic Dependent Surveillance - Contract (ADS-C) on air traffic control (ATC) procedures, focusing on operational efficiency and safety margins in lower airspace. Through 64 simulation configurations, the study evaluates how varying airspace density, separation buffer size, and vertical error in ADS-C data influence operational metrics, such as fuel burn, track miles, and flight time. The simulations utilize synthetic ADS-C data with a 100% equipage rate, providing insights into how ADS-C can be applied to manage intersecting flight trajectories. Results indicate that separation buffer size is the most influential factor. Smaller buffers lead to significant reductions in fuel burn, track miles, and flight time compared to the baseline, though this comes at the expense of increased conflict risks. Airspace density demonstrated trends where higher densities showed the greatest fuel savings but more conflicts, highlighting a trade-off between operational efficiency and safety. These findings support the role of ADS-C in increasing predictability and improving trajectory management, both of which are key to Trajectory-Based Operations (TBO). By improving the accuracy of aircraft intent and trajectory data, ADS-C can optimize flight paths and enable more efficient air traffic management. However, carefully considering separation buffers and airspace density is essential to balance efficiency with safety.

This study presents a new approach for integrated tail assignment and maintenance task scheduling decision support. Wasted maintenance resources, restricted fleet availability for schedule flexibility, inconsistent planning, and no emphasis on schedule resilience are all inefficiencies that now arise from airlines' separate and manual tail assignment and maintenance task scheduling. The framework addresses these issues, something not yet discussed in the literature. The framework's objective is to deliver feasible plans that increase schedule efficiency (no cancellations, high fleet availability, high fleet health, and optimal use of maintenance resources) and schedule stability (limited number of late arrival disruptions during operations) the day before operation. At its core, a mixed integer linear programming optimization model abides by airline-specific requirements and priorities. The schedule is modeled using an innovative two-space time-space network (TSN), with one space dedicated to maintenance and the other network activities. Stability is introduced with additional ground arcs positioned after flights that, if assigned, allow the creation of flight-specific buffers based on flights' historical arrival delay distributions. Task scheduling is done in registration specific slots, based on aircraft's individual maintenance needs, to improve schedule efficiency. The performance of the framework is tested using a case study provided by a major European single hub-to-spoke airline, with a heterogeneous fleet of over 50 wide-body aircraft. Comparison against the airline's plans over seven days shows that the proposed approach can provide real-time decision assistance and produce more efficient and stable plans. A 17% reduction in maintenance time was achieved, consequently resulting in a 10% increase in fleet availability on the day of operations. This was possible through higher labor and task interval utilization, suggesting that the framework is more efficient at scheduling maintenance tasks. Lastly, the framework's plans were more resilient to arrival delays, reducing the number of disruptions and propagated delay on the day of operations by over 40%.

Aircraft emissions can pose a health threat by degrading air quality. The actual pollution footprint of aircraft is however not well understood. This research therefore explores the potential of performing near-runway measurements with low-cost sensor nodes to analyse the plume composition and pollutant contribution of individual aircraft. The results showed significant differences in pollutant concentrations between departing- and landing aircraft and among species. The majority of CO plumes of landing aircraft and most NO and NO2 plumes of departures could be analysed while SO2 did not yield meaningful signals. Analysis revealed the cumulative NOx plume concentration of departures to be 3.5 times higher for large aircraft than for medium-sized ones, which could not be observed in terms of CO for landing aircraft. Between sensor nodes the cumulative plume concentrations differed ~8 - 37%. The results of this work demonstrate the use of low-cost sensors for better understanding the emissions footprint of individual aircraft.

Increasing levels of automation in the ATM system are required to allow for higher traffic densities without exceeding the ATCo’s mental capacity. Maastricht Upper Area Control Centre envisions to first automate the control of, so called, ``basic’’ traffic. For this strategy to work, an allocation model is required to determine the complexity of each individual flight entering the airspace, based on objective and predictable flight characteristics, which can allocate a flight to human or automation control. Five metrics are proposed related for the individual flight complexity, and their predictive ability is tested with a human-in-the-loop experiment, where air traffic controllers were tasked with assessing the complexity of an individual flight in a series of static scenarios and indicate which other flights in the scenario affected the complexity score.
Results show that the complexity is depends mostly on flights that near each other within 10 NM in the horizontal plane and have overlapping flight levels. Furthermore, flights requiring a flight level change are considered more complex as the number of possible interactions increases due to the vertical change and the additional dimension increases the uncertainty of the trajectory. The performance of the metrics indicated a dependency on different traffic patterns, leading to the conclusion that the use of only one metric is too limited to describe the complexity, and future research is needed to better understand the interdependence between the metrics.

Aircraft maintenance is critical to an airline's operations to ensure the reliability, availability, and safety of their assets. Recently, the approach of using component prognostics in aircraft maintenance has received increasing attention in academic- and industrial research. Predictive maintenance has demonstrated promising results in using sensor-based prognostics for maintenance decisions. In this paper, we propose a novel predictive maintenance framework that is capable of mapping the individual component degradation levels to an optimal maintenance decision. The independent component degradation levels are computed by a supervised learning model, called "Long Short-Term Memory Networks". Subsequently, the computed degradation levels are utilized in a multi-component maintenance decision framework, by using a model-free reinforcement learning technique named "Deep Q-Learning". The predictive maintenance framework aims to minimize a cost objective based on the type and frequency of a maintenance action. In addition, we analyzed several key performance indicators, such as the number of components used, the component utilization level, as well as the wasted component lifetime. The predictive maintenance framework was evaluated using NASA's turbofan degradation dataset. Ultimately, the results of the numerical experiments showed that the proposed predictive maintenance framework resulted in lower costs than when using a time-based and corrective maintenance policy and competitive costs compared to an ideal maintenance policy. The proposed predictive maintenance framework opens new directions for multi-component sensor-based maintenance decisions. The results found form the basis for application suggestions and future research directions in practice.

Despite widespread research into the possibilities of improving aerodynamic efficiency, a plateau seems to have been reached for the conventional configuration. Hence, the potential of unconventional configurations are being investigated in recent years. Flying V is one such configuration that promises a lift to drag ratio about 24 in cruise conditions, an improvement of 25% with respect to the NASA Common Research Model that was used as conventional configuration benchmark. In addition, the flight dynamic characteristics of such an aircraft must be investigated to ensure flight safety. Sub-scale flight testing (SSFT) allows the characterization of flight dynamics using sub-scaled models. In order to mitigate the risk of loss of control situations, the static stability and control characteristics of the model must be investigated. This research work aims to support SSFT by designing a sub-scale model and assessing its aerodynamic characteristics by wind tunnel testing. The sub-scaled design is representative of a 4.6% geometrically scaled model of full-scale Flying V design based on Froude scaling laws.

To test the aerodynamics of the future flying model, wind tunnel testing have been conducted. Balance measurements of 4.6% scaled half-model have been collected in an open jet wind-tunnel. A total of three control surfaces are installed on the model and the two inboard have been prescribed, during the design phase, to provide pitching moment control authority. The shift in aerodynamic center at higher angles of attack has been registered and large deflections of the control surfaces have been noticed to influence the shift of the aerodynamic center up to 6% of the mean aerodynamic chord.

Aside of the experimental investigations, RANS simulations have been performed using the Spalart-Allmaras one equation turbulence model. Although discrepancies have been identified between the wind tunnel and numerical results, especially in terms of drag and pitching moment coefficient, the CFD results are used to get a better understanding of the influence of vortical flows on the genesis of lift and drag over the scaled model. Based on the performed CFD simulations, it can be concluded that the performed CFD simulations are insufficient to reproduce the pitching moment behavior recorded during wind tunnel testing.

The designed sub-scaled model proved to be able to substain flight loads up to more than 2.5g at MTOM conditions. Based on the performed analyses, the center of gravity is suggested to be located between 1.33 and 1.39 meters behind the nose of the configuration. The deployed control surfaces can trim the aircraft up maximum lift coefficients between 0.6 and 0.7, depending on the location of the center of gravity, with an ultimate static stability margin equal to -4.4%. The results highlight that a reduction in pitching moment control authority would cause a reductions up to 20 % on the maximum lift coefficient achievable in trimmed conditions due to lack of control authority for forward location of the center of gravity. The designed model can therefore be used for future SSFT activities and landing speeds are estimated to be lower than 20 m/s for the proposed range of center of gravity locations at MTOM conditions.

Margins for cargo airlines that were already thin have been put further under pressure, which stresses the importance of operating a profitable schedule. Research on modelling passenger operations has proven that integrating different steps in the schedule planning process can yield a significant increase in profit. For cargo operations no integrated model exists that can provide an integrated schedule planning from scratch within reasonable computation time.
The model proposed in this work aims to integrate schedule design, fleet assignment, aircraft routing and cargo routing for both express freight- and general freight airlines. A dynamic programming optimisation framework is introduced that decomposes the schedule planning problem for a week of operations into sub-problems, that each aim to optimise the rotation of an individual aircraft. The model takes important operational constraints and airline requirements into account, such as maintenance and a minimum service frequency per flight leg, while optimising for minimum cost, maximum profit, or connectivity. Tests were conducted on three real-life case studies, that reflect the applicability of the methodology developed. The results show that the generated schedules meet imposed requirements and reflect real airline operations, according to industry experts. Furthermore, the model provides results in reasonable computation time, which for a small airline is under 10 minutes.