This study addresses the operational challenges of sustainable aircraft towing at hub airports, partic- ularly focusing on taxi delays. Using a queue-based discrete event simulation, airport taxi operations are modeled to evaluate the impact of tow-trucks on taxi delays and runway throughput. Key parameters in- clude service times for coupling and decoupling, tow-truck availability, and parallel service capacity. The findings reveal that taxi delays and runway throughput are significantly affected by the mean and variability of service times. Allowing multiple parallel service stations in front of the runway can reduce delays but may require infrastructure modifications. The study concludes that while operational towing can reduce emissions, its successful implementation depends on careful management and optimized traffic strategies to avoid compromising airport efficiency.

This thesis presents the development process of an aircraft control law. The control law is
designed using a two-degree-of-freedom (2DoF) structured H∞ loop-shaping approach. This
method allows the reuse of controller structures required by certification procedures while
directly including handling qualities and robust stability requirements in the optimization
process. This strategy is employed to develop a Rate Command and Attitude Hold (RCAH)
demand system aimed at satisfying longitudinal handling qualities. First, the stability of the
open-loop model and its compliance with the handling qualities guidelines are evaluated. Then, the control law is designed. In this step, a detailed description of the design specifications and how to specify them in the context of H∞ control is given. Subsequently, the controller parameters are optimized to satisfy the design specifications and a closed-loop analysis is performed. Finally, a simulator flight testing campaign is conducted to experimentally validate the designed control law. It is shown that the aircraft equipped with the RCAH system achieves better handling quality ratings (HQRs) and more favorable pilot feedback, providing a substantial improvement over the bare airframe.

The relationship between energy consumption and baggage handling systems (BHSs) has not been widely studied. In this study, this relationship is explored by considering BHS configurations - the designed arrangement of devices. The BHS consists of seven main processes: drop-off, transportation from drop-off to screening, hold baggage screening (HBS), transportation from screening to sortation, sortation, early baggage storage (EBS), and make-up. Transportation appears to cause a high share of the system's energy consumption, as it is a part of and connects several processes. An equation-based model is developed to estimate the energy consumption of BHSs with varying configurations. The model only contains parameters available at the BHS's conceptual design phase and is based on the BHS of a midsize airport in Scandinavia. It includes a series of formulas for each process, which depend on the structure of the process elements. The study proved a twofold effect of an airport’s BHS configuration on the overall energy consumption. Firstly, more process elements structured in series result in bags travelling a longer distance on the conveyor within the BHS, thereby increasing energy usage. Secondly, the parameter values for transportation impact energy consumption notably. The research suggested that more process elements structured in series decrease the overlap of device usage, which in turn reduces energy consumption.

The usage of drones in urban environments is expected to grow rapidly in the coming decades. To ensure the safe operations of drones, conflict detection and resolution are vital. Currently, a lot of research has gone into state-based CD&R, which has proven effective in unconstrained airspace but suffers from a large number of false positive conflicts in constrained airspace. The use of intent in constrained CD&R has the potential to reduce the number of false positive conflicts and improve the safety of drone operations significantly. In this paper, an intent-based detection and resolution method for orthogonal constrained very low-level urban airspace is presented and evaluated against a state-based method. The intent-based method calculates the future position along the trajectory at a time interval of 3 seconds for each aircraft, and conflicts are then detected by comparing these positions. The conflicts are solved utilizing a rule-based algorithm. The results show that the intent-based method has a much lower false positive rate for all traffic densities, as well as a higher average detection time before conflict for larger look-ahead times compared to the state-based method. The resolution of the state-based method, however, shows better performance with fewer losses of separation occurrences. With improvements, the intent-based method's low false positive rate, combined with the use of a larger look-ahead time, allows conflicts to be detected more reliably and earlier than the state-based method, thereby facilitating earlier conflict resolution and enhancing safety.

In Haptic Shared Control (HSC), human-like reference generators and adaptive strategies have shown promising potential for minimizing human-machine conflicts, though these advances have thus far been limited to simple control tasks. This paper extends the application of low-conflict HSC to a more realistic driving task. An Adaptive HSC (A-HSC) design for constant-velocity steering under changing visibility is proposed, where the A-HSC dynamically adjusts its steering support to align with the human driver's behaviour. In a human-in-the-loop simulator experiment with 16 subjects, the proposed A-HSC adapted successfully to the drivers' steering behaviour, converging to an average look-ahead time of 0.46s in low visibility and 1.01s in high visibility. When visibility decreased during the task, the driver's trust and control authority influenced the adaptation, with half the drivers complying with the A-HSC support allowing it to retain high-visibility settings. The A-HSC outperformed the fixed low-visibility HSC system in reducing driver control effort, minimizing conflicts and improving subjective acceptance. However, it ranked lower than the fixed high-visibility HSC overall. To further enhance A-HSC adaptability and driver acceptance, future research should investigate how varying the authority balance between the HSC and the driver affects adaptation, and explore more intuitive HSC structures, potentially based on the driver's visual aim point.

Increasing runway capacity is a key objective for many airports worldwide, including Schiphol Airport. One of the restricting factors that has become more prevalent throughout the years lies in the task load of the Air Traffic Ground Controllers. To overcome this limiting factor, it becomes essential to mitigate this high task load. This study proposes a methodology for estimating ground controller task load at Schiphol Airport, which is used for analyzing the effectiveness of a mitigation method employed at Schiphol Airport, namely the splitting of the combined North-Centre sector. To achieve this, the research adopts the concept of task load estimation via dynamic density modelling, which has primarily been explored when applying it to the airborne segments of Air Traffic Management. The task load estimation models for the different ground control sectors are developed through a multi-phase approach encompassing data collection, candidate independent variable selection, a double correlation analysis, and a sequential search regression for model building. The research successfully constructed task load estimation models for four out of the five analysed sectors. Furthermore, two of these models are employed in a case study to evaluate the task load reduction strategy. The findings highlight that splitting the combined North-Centre sector to alleviate the task load experienced by the North sector ground controller can be an effective mitigation strategy, particularly when the Centre sector experiences a significant level of traffic load.

One of the current trends in the aviation world is to work towards an increasingly more computer-aided approach to flying. Despite the improvements, limitations still inevitably exist in terms of power and storage capabilities in the aircraft avionics. To overcome this problem, different solutions have been proposed. A data-driven approach is implemented in this work on a practical application of aircraft performance function. Within the function, the aerodynamics and propulsion submodels are the target of the reduction activity. Neural networks and other surrogate model structures are tested and evaluated on the use case. Notably, several different network architectures are implemented in order to investigate a set of trade-offs between approximation accuracy and model complexity. An analysis of the error introduced by the model approximation is carried out to evaluate the impact at global functional level.

It has been shown that expert gaze behavior can be used to enhance training for novices, among others in the field of laparoscopic surgery. The research in this paper focused on the effect of changing pilot gaze behavior on flight performance and detection task performance. Two groups of novices were shown a recording of expert behavior, including an indicator of the expert's gaze. The actively trained group was additionally shown a gaze director during training, to suppress the field of view outside of a set gaze point. Said gaze point moved around in a basic scanning sequence, encouraging this scanning pattern in the participants. Both groups were then evaluated on flight and detection performance in a segment of straight level flight, during which objects around the aircraft had to be detected. The \ga showed a larger change in gaze behavior between the before and after training tests, accompanied by a reduction in flight performance. Detection task performance was comparable for both groups. The gaze director thus seemed to distract participants from the tasks. One suggestion for future improvement is to focus on tuning the gaze director's timing using questionnaires. Alternatively, further research could focus more on the passive exposure of novices to expert gaze behavior, and compare this to a control group.

The haptic feedback signal in haptic interfaces is usually in the form of a force on the control device. In contrast, the “active side stick”, investigated in the 80s and 90s, uses an admittance display, whereby the position of the device is linked to the feedback signal, and force applied on the device is used as the control signal. These devices are usually tuned in a serendipitous manner. To better understand the potential and tuning rules for these devices, tuning is investigated with a model-based approach and verified in pilot-in-the loop experiments using various aircraft dynamics. It was found that certain gain settings offered considerable benefits in terms of tracking performance as well as the control effort exerted by the pilot, while taking into account the system's stability margins. Based on these findings, a comprehensive tuning procedure is proposed for control systems involving an active manipulator.

Since the first cargo flight in 1910 (Morrell [42]), air cargo has proven to be of great value to society. With over US $6 trillion worth of goods being transported through air cargo, accounting for around 35% of all global trade measured by value (Boeing [15]), it is impossible to deny its significance in today’s world. Air cargo provides us with a fast, reliable and safe method of transporting goods, making it attractive to industries where short shipping times are of the essence. Generally, there are two conventional ways of transporting air cargo – through the use of full-freighter aircraft and through the use of the belly space under the main cabin on passenger aircraft...

One of the challenges in executing an MDO system is the selection of the best performing MDO architecture in terms of computational effort for a given MDO problem. To address this, several comparative studies have been created, which analyze the relative performance of MDO architectures with respect to internal features of MDO problems, such as the number of design variables and nature of interdisciplinary coupling. However, the existing research is limited in applicability. An inclusive prediction model, that can recommend the best MDO architecture for any MDO problem is not found yet in literature. In this thesis, the intention is to conduct a thorough comparison of two commonly used MDO architectures with respect to a database of sequentially generated MDO problems, having a range of internal features. Following this, a prediction model of MDO architecture is to be developed by applying a suitable machine learning algorithm on the generated database.

Cargo airlines' Revenue Management (RM) departments oversee the acceptance/rejection process of incoming bookings. The overarching goal is to accept as many bookings as possible, hence maximizing profit, while avoiding overbooking and offloading of already accepted shipments, to ensure customer satisfaction. This whole process is characterized by great uncertainty, arising from the fact that the exact shipment dimensions and the available aircraft capacity are not always known beforehand. The decision process is further complicated by the fact that loading of shipments is generally performed by experience, resulting to inefficient use of the available space. These factors pose challenges to the acceptance/rejection of an incoming booking request, that can lead to loss of potential revenue for the airline. A novel combined forecasting and stochastic packing model is presented, that tackles the aforementioned issues. The forecasting block of our model, instead of providing point estimates, generates the probability distribution of the predicted capacity and shipment dimensions through the use of the Monte Carlo (MC) dropout technique in Long Short Term Memory (LSTM) and Multi-Layer Perceptron (MLP) network respectively. The stochastic packing block falls into the general category of Knapsack Problems and is solved using the Extreme Point (EP) heuristic within a user-defined confidence level. It uses the generated distributions to select the optimal ULD configuration and palletization strategy, and makes a decision to accept or decline an incoming booking request accordingly. For evaluation purposes, four case studies with real booking data provided by our partner airline are carried out. The results support the validity of the model as well as the efficiency and robustness of the solution method followed.

Recent studies and accident investigations show the detrimental effects of startle and (automation) surprise on flight crew performance in terms of cognitive reasoning and sensemaking. Previous research conducted at Delft University of Technology shows a positive effect of training variability on crew performance in surprising tasks related to training, but in case of novel, unrelated surprising tasks, training methods remain to be investigated. Other recent studies and proposals suggest to use checklist-based training methods to prepare pilots for puzzling and surprising scenarios, but effects on flight crew performance have yet to be published. Therefore a simulator-based, between-subjects experiment was conducted to explore the effects of a four-item checklist-based training on pilot performance. Training elements focused on managing stress and enhancing situation awareness, as well as improving sensemaking and decision-making processes. It was expected that trained pilots would exhibit better overall performance in test scenarios involving off-nominal, surprising situations. Nevertheless, results indicated no significant difference in pilot response to an initial disturbance, but did show significant performance improvement in scenarios involving a second, ensuing upset compared to the baseline group. This suggests that checklist-based training methods offer a structured problem-solving approach, resulting in better response to consecutive disturbances in select scenarios.

Air traffic is growing every year and getting ever more complex. There is however no quick and robust way to analyze a traffic situation in an airspace and compare it to other, similar traffic situations. This research project proposes a new metric to compare air traffic situations in an airspace, based on geometric traffic data. The new metric is called the airspace quality. The project shows to what extend simple properties and metrics are usable and into what area they need to be extended. The resulting metric shows promise but also shows sensitivity issues in certain cases.