Manned-Unmanned teaming with Loyal Wingmen is part of every current future fighter concept. However, while much effort is put into design and research, it remains to be seen whether the inclusion of Wingmen yields a better mission outcome. Additionally, there is much uncertainty about the ideal design and the Concept of Operations. This work aims to change that by addressing all three elements. Using Knowledge-Based Engineering, a set of Wingmen is designed for the Suppression and Destruction of Enemy Air Defenses. In the frame of a parameter study, physics-based combat simulations are conducted using Agent-Based Modeling. The results are analyzed via the derived Measures of Effectiveness of Mission Effectiveness, Survivability, Cost, and Lethality. The performance of formations including Wingmen is compared to the performance of modern tactics. The results show significant improvements in mission outcomes when Wingmen are deployed. Optimum results are achieved with Wingmen featuring slightly reduced Radar Cross-Section and balanced Specific Excess Power. The Concept of Operations has an effect, too. Fewer Wingmen carrying a larger number of weapons yield higher overall survivability than more but smaller Wingmen. The inverse holds for overall lethality. The results provide valuable insights into the optimal design and function of the Loyal Wingman concept.

The Dutch healthcare sector wrestles with rising costs, staff shortages, and increased demand due to healthcare centralization. This, coupled with worsening traffic congestion, underscores the need for efficient solutions like drone-based medical transport. This paper addresses the need for effective fleet management strategies tailored to the unique demands of the healthcare environment. Specifically, it seeks to develop an adaptive strategy that accommodates the network's expansion and the inherently stochastic and urgent nature of pickup and delivery orders associated with medical transport. Utilizing an agent-based model formulation, the paper introduces a novel approach combining adapted temporal sequential single-item auctions for allocation and scheduling with reinforcement learning for drone repositioning to optimize fleet management. Our findings highlight the strategy's consistent efficiency across various demand scenarios, maintaining performance within predefined limits. Notably, the repositioning module significantly enhances the fleet utility and the fraction of served orders, albeit at the expense of increased cost per delivery. Conversely, the reallocation module causes minimal performance improvement. Under heightened stochasticity introduced by urgent orders, the strategy maintains stable costs per delivery while fleet utility and order fulfillment rates decline. Additionally, our investigation underscores the increasing benefits of repositioning in more stochastic scenarios. Moreover, exploring hybrid fleets reveals that while short-range high-payload drones can reduce cost per delivery, they compromise overall fleet utility and order fulfillment rates. Furthermore, we identify the under-utilization of payload capacity in scenarios with orders weighing up to 2 kilograms for a drone with a payload of 10 kilograms.

In response to the growing demand for air travel, major airports are approaching critical thresholds in their infrastructure capacity. As the transportation sector continues to expand, it is increasingly important to address environmental concerns that arise from aspects, such as noise annoyance and fuel consumption. This paper aims to enhance the existing Flexible Runway Scheduling Model (FRSM) by integrating a tabu search algorithm with Receding Horizon Control (RHC), introducing non-linear noise restrictions, and implementing more sophisticated fuel burn modeling. The main goal is to evaluate how certain improvements affect the FRSM. To achieve this, a methodology has been developed that uses a multi-objective tabu search algorithm to minimize both fuel consumption and noise annoyance while assigning flights to runways. This study provides a comprehensive analysis of Amsterdam Airport Schiphol (AAS) across different scenarios, ranging from a 1.5-hour flight schedule to a full-day simulation, revealing significant findings. For the 1.5-hour and six-hour scenarios, the tabu search algorithm achieves a 55% and 87.3% reduction in computational time with marginal losses of 0.73% and 0.19% in solution accuracy for fuel burn optimization. Throughout all scenarios, the tabu search algorithm consistently results in a reduction of highly annoyed individuals ranging from 2.14% up to 62.5% compared to the existing FRSM, demonstrating its effectiveness. Moreover, the algorithm minimizes the impact on the flight schedule in terms of delay. Notably, as the flight schedule length increases, the performance of the tabu search algorithm improves compared to the existing FRSM. A sensitivity analysis optimization horizon indicates a positive effect on results, albeit with an associated computational cost. In conclusion, this study showcases the positive impacts of the remodeled FRSM, enabling a faster and more accurate trade-off. The research findings provide valuable insights for optimizing runway scheduling at major airports while balancing efficiency gains with environmental considerations.

This research investigates the role of time prediction accuracy in optimizing Continuous Descent Operations (CDO) within the aviation sector, with a specific focus on assessing the additional benefits brought forth by the integration of Air-Ground Datalink technologies. Continuous Descent Operations, characterized by uninterrupted and efficient descent profiles, hold promise for reducing fuel consumption, emissions, noise, and overall operational costs. However, the extent to which accurate time predictions contribute to the success of CDO remains a critical yet understudied aspect.

As aviation recovers to pre-pandemic levels large amounts of Air Traffic Flow Management delay also return, as the traffic at airports and in airspaces reaches their old levels. These delays have a yearly cost of around 500 million euros in the European Civil Aviation Conference area and are detrimental to the experience of passengers. This paper proposes a change in the interaction between the A-CDM process at Schiphol and EUROCONTROL's Network Manager, varying at what point the slot improvement process for regulated flights is stopped; providing more transparency in the network with this change. The resulting change in ATFM delay is investigated by modelling these two systems and freezing the slots at a set time, using historical input data to accurately model the turnaround process. The results generated using the described methods are inconclusive. More data and an additional implementation of using the CTOTs calculated by the model in the simulated turnaround are needed in order to make statistically sound conclusions on whether changing the T-DPI-s horizon has any effect. Nevertheless, this paper still presents insights and a novel framework for continued research into the subject.

The International Air Transport Association (IATA) and the European Commission (EC) have stated their desire for the aviation industry to reach net zero carbon emissions by 2050 and in addition these emissions must be reduced by 45% in 2030. In order to reach these goals, all possible contributions to a reduction in carbon emissions are considered. One of these is the Air Traffic Management (ATM) procedure of Continues Descent Operations (CDO). These operations describe a flight path for the approach where there are minimum level flight segments. This paper investigates the maximum possible impact of this procedure for maximum capacity operations. Four different days have been analysed where the individual components of the procedure are investigated; vertical optimisation, lateral optimisation and the combined case. Using ADS-B data from busy days in august of 2019, these procedures are described and simulated using fast-time ATM simulation software Bluesky using Base of Aircraft Data (BADA) performance data and total-energy model. Compared to current operations, vertical optimisation could possibly safe 20% of fuel, lateral optimisation could theoretically safe 16% fuel and the combined case has the potential to safe up to 27% fuel without negatively impacting capacity.

In this paper, the emissions mitigation potential of implementing operational towing at major European airports is evaluated. Using mixed-integer linear programming, a scenario simulating mixed taxiing operations is used to find the relationship between fixed vehicle costs and target emission savings at an airport and European level. Using formulae derived from the ICAO Advanced Emissions Model, fuel burn and emissions are calculated for conventional taxiing, single-engine taxiing (SET), and hybrid and electric operational towing. An assignment optimization model is used to assign vehicles in a vehicle fleet to flights; yielding the minimum fuel to cover a flight schedule with mixed taxiing operations. As the number of vehicles in the fleet increases, the saving potential for each additional vehicle is calculated, linking fuel and emission savings with vehicle costs. Two scenarios are compared; Scenario 1 with hybrid towing vehicles and Scenario 2 with electric towing vehicles. Applying this method to 30 case-study airports, a maximum jet fuel reduction of 66% for hybrid towing and 57% for electric towing is calculated; outperforming SET at 29 of the 30 airports. The average taxi time and aircraft compatibility are the driving factors that influence the maximum fuel savings potential. The total average taxi minutes of compatible flights is thus the best metric to predict an airport's potential, with a coefficient of determination equal to 0.96 for hybrid towing and 0.97 for electric towing. Because of the shorter refuelling downtime, hybrid vehicles can service more jobs per day than electric vehicles. This means a smaller hybrid fleet size can achieve the same CO2 and jet fuel savings on a European level. As the fleet size increases, the marginal savings per hybrid vehicle decrease at a higher rate than electric vehicles. This means that the maximum emissions savings potential can be a misleading metric for comparing strategies. By using this approach to link vehicle fleet size and potential savings, a more extensive trade-off of emission mitigation strategies is possible, allowing stakeholders to find the best strategy to fit a budget or emission savings target.

Current estimates show that the presence of unmanned aviation is likely to grow exponentially over the course of the next decades. Even with the more conservative estimates, these expected high traffic densities require a re-evaluation of the airspace structure to ensure safe and efficient operations. One structure that scored high on both the safety and efficiency metrics, as defined by the Metropolis project, is a layered airspace, where aircraft with an intended heading are assigned to a specific altitude layer. However, a problem arises once aircraft start to vertically traverse between these layers, leading to a large number of conflicts and intrusions. One way to potentially reduce the number of intrusions during these operations is by using conventional conflict resolution algorithms. These algorithms however have also been shown to lead to instabilities at higher traffic densities. As recent years have shown tremendous growth in the capabilities of Deep Reinforcement Learning, it is interesting to see how well these methods perform in the field of conflict resolution. This research investigates and compares the performance of multiple Soft Actor Critic models with the Modified Voltage Potential algorithm during vertical manoeuvres in a layered airspace. The final obtained performance of the trained models is comparable to that of the Modified Voltage Potential algorithm and in certain scenarios, the trained models even outperform the MVP algorithm. Overall, the results show that DRL can improve upon the current state of conflict resolution algorithms and provide new insight into the development of safe operations.

Automatic Dependent Broadcast-Surveillance (ADS- B) was introduced to increase the safety of air travel. Since then it has proven to be a useful source of air traffic data for academic research because it is publicly available. Another source of information are the communications between air traffic control (ATC) and aircraft. Methods are (being) developed to enhance the existing radio communications with digital text messages. These are not publicly available while access to these could further the research in this field.
The lack of publicly available machine-readable ATC data hinders research in the field of air traffic management. Therefore the objective of this study is to develop a method that uses open- source data to infer ATC commands. This two-part exploratory study is presented to address this objective.
The first part explains the rule-based analysis used to infer ATC commands. This method uses a set of rules to detect lateral and vertical changes by approaching the ADS-B data as a time series. This approach can detect changes using the turn rate and rate of climb which can correspond to ’heading’, ’direct to’ and ’altitude’ commands. It was not possible to detect changes in speed due to the variability in the wind.
The second part of this paper tests the found method in an air traffic simulator. For 111 flights, ADS-B data was gathered using the Opensky network. The previous method was applied to this data to create a list of possible ATC commands. For every unique flight, two flight plans were made. The first was based on the found list of commands. The second flight plan used the original ADS-B data. These flight plans were simulated and differences between trajectories were used to test the fidelity of the rule-based analysis.
The results showed that the mean absolute horizontal error was below 15km for 75% of the flights and the mean absolute vertical error was below 100m for 75% of the flights. A remark- able correlation was found between the availability of ADS-B data points along the trajectory and the error in the horizontal distance. When more datapoints are available a smaller error in horizontal distance is observed.
It was found that a method could be developed using a rule- based analysis that produces a list of possible ATC commands. The could be done for both lateral and vertical commands, but not speed commands.

Due to the continuous growth of air traffic up to the year 2020, the air transportation network has become more complex, and the airports and airspace busier. However, capacities have not grown at the same rate as air traffic, making Air Traffic Control one of the most encountered primary delays. A data driven approach is taken in order to expose the drivers of the ATC delays for KLM Royal Dutch Airlines flights at Schiphol Airport. The used data consists of public and proprietary data, and contains information related to the weather, KLM flight operations, operational data, and Airport Collaborative Decision Making. To perform the analysis, two causal methods were used, association rule mining as a baseline method and a Bayesian network as the state-of-the-art model. Both methods were able to identify various conditions that trigger and/or prevent ATC delay occurrence, and agreed on the majority of the identified influential factors of the ATC delay. It was found that the main influences of ATC delay are the average startup delay of flights in the 20 minute time interval of the flight's departure, as well as the received pure ATFM delay and the assigned regulation delay key. Additionally, other influential parameters on the ATC delay related both to the amount of traffic volume and congestion at the airport, as well as individual variables of the flight, such as the propagation of arrival delay, the number of updates in the CDM process and the delay in the closure of the doors. The main discrepancies in the results could be attributed to the limitations of both methods. In general, it was found that both methods are suitable to diagnose direct causes or influencing factors on a target variable. The Bayesian network method was found to be more suitable to better understand a system and the dynamics between a large number of variables, as the conditional dependencies can be observed from the learned structure, and are not hidden in a large number of frequent patterns. However, first diagnoses of influential variables can also be done using association rule mining, which could find more indirect effects on the target variable compared to the Bayesian network, in which indirect relations might be lost in the structure learning process.

Space has always been fascinating to humans, since the dawn of civilization. From the first astronomers and philosophers of ancient times, who looked at the night sky searching for answers, to the scientists and engineers of modern missions, commanding space probes to the edge of the solar system, space has always been at the epicenter of scientific discovery and human curiosity. From the launch of Sputnik 1 in 1957, to the robotic rovers exploring Mars, space missions have always relied on the latest technological advancements in order to enable physical or remote exploration of celestial bodies. Traditionally, designing a space computer required significant amount of resources, leading to designs with impressive radiation performance records. However, such designs were lacking computational performance, required years of development and as a result increased the total cost of the mission.

In recent years, the advent of CubeSats meant that access to space became available to a wider community of enthusiasts, researchers and private companies who were developing low-mass spacecraft made out of Commercial Off-The-Shelf components (COTS). These components however, were designed with the goal of maximizing performance and power, with little to no flight heritage. Several novel technologies were proposed in the field of error detection and mitigation, in an effort to bridge the gap between COTS processors and their radiation-hardened counterparts. Even though the commercial semiconductor industry has increased the reliability of its products by continuously improving their designs and processes, CubeSats or other low-mass spacecraft that use these components are still less reliable than their larger counterparts.

Given the aforementioned, this thesis aimed at exploring the latest developments in the field of space embedded systems and error detection techniques, in an effort to produce a software flow able to detect faults with increased compatibility across processor models. In order to accomplish this goal, the thesis was carried out at ARM Limited, as part of the Software Test Libraries (STL) team responsible for developing efficient assembly tests for detecting random faults. The Cortex-M55 CPU was chosen as the test-bed for this work, in order to develop STL routines for a reference module. TheMain Interface Unit (MIU) was chosen to act as the proof-of-concept, since it is an important module in every Cortex-M processor, interfacing the core with the main memory.

More specifically, a series of tests were developed for every major module within the MIU. The design started from the largest module first, which yielded a good trade-off between coverage and time. The tests comprised of efficient assembly routines designed to trigger specific memory access patterns, targeting different portions of logic each time. At the same time, a verification software flow was developed in order to test the newly designed routines against a multitude of possible configurations and initialization parameters. This activity was necessary to ensure that the developed software will be able to operate in a variety of end applications, either in the context of a Real-Time Operating System or baremetal application.

The developed STL tests were subjected to a series of fault simulations using a state-of-the-art hardware simulation tool called ZOIX. Permanent faults caused by accumulated damage were modeled as stuck-at-faults, whereas transient soft-errors were modeled as transient toggle faults. Determining an accurate fault injection interval, required knowledge of the radiation environment that a COTS-based mission would encounter. A state-of-the-art space simulation environment called SPENVIS was used in order to acquire metrics on selected reference missions on Low Earth Orbit and Geosynchronous Equatorial Orbit. This helped setting the upper limits on upset rates, which were in turn used during fault simulation to recreate realistic conditions.

The developed software tests exhibited solid performance in detecting permanent faults, while achieving promising results in transient fault simulations, given certain assumptions. A series of recommendations is given for future research work on the current framework, in an effort to learn from the challenges faced and tackle some of the identified limitations. Given certain assumptions, there is evidence to believe that STLs could be indeed used for random error detection in future CubeSat missions, without increasing the total cost disproportionately.

Better predictability of future aircraft locations will result in more efficient air-traffic management operations and therefore an increase in airspace capacity. However, flight paths tend to have a stochastic character causing their predictability to decrease with the look-ahead time. In this research a quantitative analysis is made of the statistical properties of flight trajectory deviations and their dependency on the look-ahead time based on historical ADS-B flight segments and corresponding flight plan data. An empirical boundary on the prediction horizon for future aircraft locations in en-route flights is determined based on a deterministic state propagation method. Using these results, an analysis is made of how these uncertainty distributions affect conflict detection performance. A comparison is therefore made between deterministic, probabilistic and intent based conflict detection methods when applied to medium-term conflict scenarios. Results show that for lower look-ahead time windows up to 10 minutes, a deterministic approach proves to be the most reliable. Only beyond this time window, the use of flight plan information proves to have beneficial effects on the detection performance. The outcome of this research can be used as a benchmark for the development of novel conflict detection approaches.

Demand for air traffic is growing worldwide. In order to accommodate this growth, existing infrastructure such as gates should be used more efficiently. One technique to increase gate utilization is by introducing gate pit-stops. Introducing gate pit-stops leads to additional towing movements which can potentially interfere with other traffic on the apron. This in turn will lead to delays, which is undesirable. Currently, gate pit-stops are only performed for flights with a turnaround time of three hours or more. In order to investigate whether pit-stops can be effectively introduced on a broader scale, a model has been created that simulates gate assignments and apron movements. This model provides insight in the effect of gate pit-stops on gate utilization and apron congestion for different airport scenarios. Results show that introducing pit-stops for flights with a turnaround time of less than 180 minutes leads to an increase in flights that can be handled at a gate instead of a remote stand, at a small delay penalty. Airlines and airports can benefit from these findings as they will be able to handle more flights at a gate using existing infrastructure.

Research on reinforcement learning algorithms to play complex video games have brought forth controllers surpassing human performance. This paper explores the possibilities of applying these techniques to the sequencing and spacing of aircraft. Two experiments are performed. First a single aircraft must learn to fly a 4D trajectory using only heading commands. To train an agent Duelling Deep Q-Networks has been applied to learn a successful policy, however, learning is unstable and does not provide a suitable basis for extending this to a multi-agent setting. Second, a multi-agent experiment is performed where aircraft have to sequence and space themselves for landing without a 4D constraint. A Bidirectional Communication Net has been trained using Deep Deterministic Policy Gradients first on a single traffic scenario and then on multiple traffic scenarios. Emerging strategies have been seen in the single scenario training e.g. a holding, but no optimal policy was found. Training on multiple traffic scenarios showed no coordination efforts between the aircraft. Further analysis showed the importance of a proper reward function and exploration strategies which were likely the cause of not finding an optimal policy for a multi-agent setting.

The purpose of this research is to investigate the effects of individually established buffer times, based on historical delay data, on the robustness of an operational stand and gate allocation schedule. Using flight schedules and the structural lay-out of Amsterdam Airport Schiphol, a stand and gate allocation model is created using BEONTRA software. Eight allocation schedules are
specified with each a set of different buffer times, which are established after grouping and analysing the historical delay data. The flight schedule that is used as input is the one for peak day Monday 16 July 2018. The allocation schedules are then tested amongst others on probability of overlap and on delay data as a result of simulation. A by LvNL validated model of Schiphol is used in fast-time simulation programme AirTOp to simulate common delays on the allocation
schedules. The outcome is compared to the outcome of two baseline schedules, after which conclusions are drawn and recommendations are given with regard to the effectivity of buffer time optimisation.

The master thesis is about testing the effectiveness of the Airport demand management strategies. Based on the problem of increasing pressure on airport capacity, due to the increase of the demand on flying. The goal is to find a system which supports planning of air traffic control by generating knowledge about the throughput of arrivals. A Discrete Event Simulation approach is used to simulate the behaviour of arrivals in case of capacity issues. The model uses random generated slot allocated schedules to test the effectiveness of several strategies. Eventually the result of the strategies is presented in an optimum ratio between delays and cancellations of the tested strategies. The main conclusion of the research is that the model is capable of testing the effectiveness of airport demand management strategies. It also provides knowledge on expected bottlenecks of the arrival operations by generating the average delay per hour and queue propagation for each strategy.

The steady growth of air traffic volumes and the subsequent increase of congestion at major airports require airports to increase their operational efficiency. Inefficient or the absence of gate reassignment models increase the possibility and occurrence of gate blockage and at the same time reduce the comfort of passengers as they often receive the notice that they have to wait for their assigned gate to become available. This thesis describes a model to optimally reallocate flights to available gates on the actual day of operation for a large airport, such as Amsterdam Airport Schiphol (AMS). It takes into account the scheduled arrival time of a flight for the initial assignment, during daily operation the model updates the scheduled arrival time with the estimated arrival time as soon as the flight is enroute. The proposed model allows the user to do a trade-off between delaying, gate changing and remote handling of flights. The sliding time window has shown to reduce the number of gate changes required during daily operation in order to mitigate gate conflicts. Validation of the model with data obtained from AAS showed that the approach is feasible for real world application and that it improves the operational efficiency of the airport.

The most common operational measure in aviation to reduce the effect of environmental noise pollution is noise abatement procedures. Noise abatement procedures are recommended flying techniques based on noise optimal trajectories, which aim at reducing the noise impact on local communities as much as possible. Atmospheric conditions influence sound propagation, however the noise abatement procedures are often fixed and independent of weather conditions.

This research has been set up in order to analyse whether the implementation of weather-adaptive noise abatement procedures result in different noise optimal trajectories in different weather conditions. The research objective is to assess the influence of weather on optimised noise abatement departure procedures for one fly over, by developing an optimisation tool that allows for the synthesis of weather adaptive noise abatement procedures within the terminal manoeuvring area.

To analyse the influence of different atmospheric conditions on the noise optimal trajectories in various weather conditions, a sound propagation model has been developed with the inclusion of variable air temperature and lapse rate, relative humidity, wind profile and ground surface type. Together with an aircraft performance model enhanced with varied atmospheric conditions and a noise impact model, the noise optimal trajectories in different weather conditions are obtained using optimal control theory in GPOPS. The model can be adapted to various requirements and aircraft types.

The resulting trajectories are used to evaluate whether weather-adaptive noise abatement procedures should be implemented or not and to evaluate the performance of optimal control theory in this framework.

With the increase in air traffic volume, aircraft are required to be equipped with a transponder by aviation authorities to increase the surveillance in air traffic control. These transponders transmit periodically Automatic Dependent Surveillance-Broadcast (ADS-B) signals and by interrogation the Mode S signals. These signals can be received by anyone with a simple receiver and are a new source for aviation research. It contains flight trajectories and aircraft states which were previously only available for air traffic control and aircraft operators. This paper uses ADS-B and Mode S data to derive an accurate meteorological model and uses this model to determine aircraft performance parameters, which are usually not publicly available. A Meteo-Particle model is used and validated with the European Centre for Medium-Range Weather Forecasts (ECMWF) model. For wind speed, wind direction and temperature the mean absolute error are 2.73 m/s, 7.73 degrees and 1.32 K respectively. The true airspeed is the actual airspeed that the aircraft experiences. It is required to determine aircraft performances. The true airspeed can be obtained with the wind derived in the Meteo-Particle model and ADS-B ground speed. Using the true airspeed information, aircraft performance parameters for climb, cruise and descend phase are determined for 16 aircraft types. These derived aircraft performance parameters, based on a large quantity of ADS-B and Mode S data, are a good alternative to the close-sourced performance parameters.

The Air Traffic Management research community expects that the responsibility of maintaining a minimum safe separation distance between aircraft will move from the groundbased Air Traffic Control, a centralized system, towards the flight deck, a decentralized system. Previous research on the decentralized, also known as distributed Air Traffic Management systems has shown promising results. However, aspects of coordination when more than two aircraft controlled by human operators are still to be explored. This paper presents an experimental research to study the coordination behavior of multiple actors using an ecological interface, known as the Solution Space Diagram (SSD), for the Conflict Detection & Resolution (CD&R) tasks. To judge the performance of humans, an automated conflict resolution algorithm, known as the Modified Voltage Potential (MVP), was used as the baseline.Several multi-aircraft scenarios were designed and simulated in a human-in-the-loop experiment. Results show that humans using the SSD display for the CD&R tasks operated safely for the designed scenarios. Compared to the resolutions of the MVP algorithm for the same scenarios, the humans depicted a safer and less efficient approach than the automated algorithm. For future research, attention was brought to aspects participant training in multi-actor experiments. The primary recommendation made was to explore the aspects of the efficiency of the human while using the SSD display by providing cues within the SSD.