Trajectory-Based Operations (TBO) are intended to improve Air Traffic Management (ATM) by enabling earlier planning, more consistent trajectory prediction, and reduced tactical conflict management. During the transition towards advanced Automatic Dependent Surveillance–Contract (ADSC) and Controller–Pilot Data Link Communications (CPDLC) services, aircraft will provide different levels of downlinked intent and Flight Management System (FMS)-integrated trajectory update capability. This paper evaluates how this mixed datalink equipage affects a TBO concept for Amsterdam Area Control Centre (ACC) airspace. A strategic trajectory management model is developed for mixed inbound and outbound traffic between FL110 and FL260, combining fixed Flight Path Angle (FPA) descents with probabilistic Conflict Detection & Resolution (CD&R). Equipage-dependent uncertainty is represented through descent angle, target descent speed, and wind, while traffic density and fleet composition are varied in a Monte Carlo experiment. The results show that increasing equipage capability reduces trajectory adjustments and residual losses of separation, with the clearest benefits in higher density scenarios. Flown track distance and modelled work also decrease as more advanced equipage becomes available, although work reductions are small and occur mainly outside the Amsterdam ACC conflict management volume. Expected Approach Time (EAT) adherence remains broadly comparable across equipage compositions. ... Read more

Battery-electric aviation represents a promising pathway to substantially reduce the overall climate impact of short-haul air transport. This includes complete elimination of both in-flight CO₂ and non-CO₂ effects. Nevertheless, considerable debate and uncertainty remain regarding whether the technology can achieve meaningful scale and deliver a significant impact at continental level. Previous studies, however, have typically examined technical, economic, or environmental feasibility aspects in relative isolation, often focusing on specific routes or individual aircraft types. This thesis addresses these gaps through an integrated framework that couples machine-learning-based demand forecasting, algorithmic network design, uncertainty modelling, and sector-wide GHG mitigation assessment under realistic gravimetric battery energy density constraints. A custom Python-based software tool with an interactive Streamlit dashboard enables evaluation of any combination of European airports and announced electric aircraft types, as well as a fully customisable aircraft model for extensive sensitivity and scenario analysis. Key results show that the XGBoost model predicts baseline demand on unserved routes with high accuracy (R² = 0.79, SMAPE = 18.7%). Electric aircraft could induce approximately 25.1% additional demand through reduced operating costs and higher willingness-to-pay for zero-emission flights. Under projected near- to mid-term solid-state battery pack energy densities (356–480 Wh/kg), 13.8–22.3% of European aviation sector CO₂ emissions could be avoided. Network design identifies an intra-European early-phase hub-and-spoke configuration requiring charging at only 15 strategic hubs plus two connectors, while maximising electrifiable passenger demand. The study shows that battery-electric aviation outperforms competing technologies on short-haul routes in terms of cost, emissions, and energy efficiency. However, achieving the full 22.3% decarbonisation potential requires electrifying more than 50% of all European flights, which translates into challenges, as the R&O analysis of this study shows. Collaborative action as well as incentives and targeted policy support will be essential and will require a shift from Europe’s rather isolated focus on SAF. The network configurations presented in this study provide a strong starting point for collaborative development, while recommendations for future research include realistic decarbonisation pathways integrating the phase-out of conventional aircraft and the study of high-impact policy measures. ... Read more

This thesis presents a trim optimization methodology for aircraft featuring distributed electric propulsion (DEP) systems and horizontal thrust units (HTU). The Unifier C7A-HARW, a 19-passenger hybrid-electric commuter aircraft with 12 wing-mounted propellers and a tail-mounted HTU, serves as the reference configuration. Multiple trim solutions with different types of propulsion systems usage and performance indicators, such as maximum range, endurance or lift-to-drag ratio, are explored through optimization, revealing complex relationships between angle of attack, airspeed, flap deflection, ruddervator deflection, required aerodynamic power and electric power consumed in steady level flight. Some unexpected results demonstrate that maintaining a constant low angle of attack and gradually reducing flap deflection as airspeed increases is desirable for lowering aerodynamic power requirements in trim conditions and that the wing tip propeller has an important role under certain circumstances. Empirical correlations between power requirements, angle of attack, airspeed, flap and ruddervator deflections are established, providing insights into the performance characteristics of DEP aircraft configurations and enabling efficient trim performance predictions useful for conceptual design. ... Read more

Continuous Descent Operations (CDOs) can reduce fuel consumption and CO2 emissions. However, their implementation in constrained airspace is often limited by operational procedures and altitude restrictions. Previous studies have evaluated CDO performance under idealised conditions, resulting in insufficient quantification of the effects of real operational constraints. This study investigates how operational constraints on arrival routes influence aircraft vertical descent profiles and the resulting fuel consumption. A framework is introduced that is capable of quantifying fuel consumption across different descent trajectories. The simulation-based framework enables the analysis of incremental modifications to operational restrictions, including changes to level-off altitude and duration. The methodology is applied to arrival traffic at Schiphol Airport, using a dataset of over 8,000 recorded arrivals from the Aircraft Condition Monitoring System (ACMS) and one month of Eurocontrol Demand Data Repository (DDR) traffic data. Level-off segments between Top of Descent (TOD) and the Initial Approach Fix (IAF) are identified and linked to waypoint-based restrictions specified in Letters of Agreement (LoAs) and the Route Availability Document (RAD). The BlueSky air traffic simulator and the Base of Aircraft Data (BADA) 3.16 performance model are used to quantify the fuel impact of modified descent scenarios. The results show a clear relationship between level-off duration, altitude constraints, and fuel consumption. Higher level-off altitudes and shorter durations consistently reduce fuel burn. Regression analysis of recorded flights confirms these trends. A Key Performance Indicator (KPI) based route assessment identifies arrival routes with the greatest potential to reduce fuel consumption, while taking into account operational complexity. The findings show that measurable fuel savings can be achieved through targeted adjustments in airspace restrictions without requiring a complete redesign of the airspace.
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Liquid Hydrogen aircraft will be key in ensuring air travel can remain both accessible and environmentally responsible, but early design tools still use fuel-system performance estimates that are largely insensitive to varying aircraft and fuel system configurations. This thesis develops a workflow coupling the MATLAB Initiator to a ParaPy KBE model to automatically generate LH2 fuel-supply architectures, position components, route cryogenic fuel lines, and size fuel lines, insulation and pumps via constrained optimization with multi-phase flow analysis. Demonstrated on the Initiator-derived A320neo and ATR72 hydrogen concepts. For the A320neo, optimized total fuel-system masses are about 20–26% lower than the Initiator estimates. For the ATR72, masses are around 30–50% higher, potentially driving infeasibility and motivating further research into design of the remaining fuel subsystems, while showing mass does not necessarily scale down with tank volume as assumed by the Initiator, highlighting the importance of physics-based estimation in early design.
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Liquid hydrogen-powered aircraft are a promising candidate for reducing aviation’s climate impact, but safe integration requires crashworthiness considerations already at conceptual design. This work presents a parametric, knowledge-based methodology to design and analyse a fuselage-located cryogenic tank supported by a belly crash structure under a vertical drop test. The methodology is implemented in a parametric application that automatically generates geometry from readily adjustable inputs, enabling quick generation of a wide variety of configurations. A baseline configuration, consisting of 12 sub-tank X-beams per bay configured in six X-crosses, a stiff crossbeam design, and distributed Kevlar tank suspension ropes, exhibited favourable deformation kinematics. A design-of-experiments study was conducted and combined with a surrogate-model-based sensitivity analysis to identify the dominant design parameters governing the crash response. Results show that the coupled frame and crossbeam thickness, together with the crash coefficient, are the most influential. Including a crash structure substantially increases the required tank and fuselage length, and the associated mass penalty is dominated by the added tank and fuselage structure rather than by the crash structure mass. The developed model provides a basis for further research on structural mass optimisation and for providing data for a surrogate integration into a conceptual aircraft design tool. ... Read more

Hydroisomerization of alkane isomers is an important step in the manufacture of current kerosene and sustainable aviation fuels. Zeolites are used as acid catalysts in the process. It is therefore important to have predictions of the maximum loading of hydrocarbons in zeolites. Here, a cascade model using machine learning models is used to predict the maximum loading of alkane isomers in zeolites. The cascade is composed of a gradient-boosted tree classifier stage that predicts whether adsorption occurs or not, and a regressor predicting the value of the maximum loading. The final dataset consists of 45 different molecules (both linear and branched alkanes up to C16) and 97 different zeolites structures, resulting in 4365 datapoints. Descriptors include information on the geometry and topology of zeolite channels, as well as shape and size of molecules. Extra composite descriptors are also present to provide the models a physical basis for predictions. Multiple regressors of different nature are considered: Support Vector Regressors, Gradient-Boosted Trees, extreme Gradient-Boosted Trees, and the TabPFN pretrained model. Out of all the models, TabPFN yields the highest generalization performance and lowest error. An interpretability analysis is conducted to assess whether the decisions abide by the governing physics of adposition. It is confirmed that the top descriptor choices abided by the necessary physical constraints, but also that secondary properties such as shape-based selectivity are also accounted for. It is shown that despite both classifier and regressor being insensitive to random splits in data, the regressor is prone to overfitting at low fractions of data withheld for testing. The cascade model is compared with an Artificial Neural Network for training and deployability. Despite training taking more resources for the neural network, the latter is lighter both in memory and storage when compared to the cascade. This work builds on previous research in predicting the Henry coefficient at zero loading. Using this previous model and the findings of this work, one can draw the full adsorption isotherm for any alkane, thus enabling the analysis of adsorption behaviour of alkane mixtures using IAST. ... Read more

Liquid hydrogen (LH2) is a promising option for deep aviation decarbonisation and could be deployed at airports within the next decade. Compared with Jet-A1, LH2 refuelling may require larger safety and exclusion zones that reduce stand availability and constrain apron access. Operational studies often assume these zones, while safety studies rarely turn consequence-driven distances into enforceable apron constraints and quantify delay impacts. This work provides an integrated safety-to-operations assessment for LH2 refuelling at compact airports. The objective is to identify the main safety distance drivers for open- apron LH2 releases during refuelling through a qualitative safety assessment and quantify how zone size and enforcement, refuelling logistics, and resource capacity affect departure delays as LH2 penetration increases.
A qualitative hazard and accident-pathway assessment (HAZID/HAZOP supported by bow-tie logic) was conducted to identify the main LH2 refuelling hazards and accident path- ways, and to determine how these hazards drive conservative safety-distance requirements. The outcomes were then used to select representative scenarios and define zoning modes for the operational analysis. These modes are translated into operational rules (e.g., stand closures and route restrictions) and implemented in a stochastic discrete-event simulation of a regional airport apron with LH2-specific turnaround processes. Experiments vary penetra- tion rate, stand layouts, refuelling logistics and transfer duration (S1–S5), and LH2/Jet-A1 truck-fleet sizing. Performance is assessed via statistical comparison of departure-delay met- rics.
The safety assessment identifies dispersion-and-ignition outcomes (flash fire and jet fire) as the dominant safety distance drivers for open-apron LH2 releases and motivates practical distance scales that define the zoning modes. In the operational simulation, zoning has lit- tle effect at low LH2 adoption, but delays increase sharply once conservative zones remove neighbouring stand capacity and push the apron into a capacity-limited regime. For smaller zones, performance is driven mainly by refuelling logistics and refueller availability rather than by routing restrictions. Undersized service fleets primarily worsen the tail of very late departures, with the binding constraint shifting between the LH2 and Jet-A1 fleets depend- ing on demand. Overall, safe scale-up requires joint design of zoning rules and refuelling resources to prevent structural apron bottlenecks. ... Read more

The aviation sector faces increasing pressure to significantly reduce its climate impact. Particularly in the medium-range narrowbody aircraft segment, which accounts for a large share of global passenger traffic and emissions, large gains can be made. While hydrogen propulsion offers the potential for zero in-flight CO2 emissions, its implementation is challenged by volumetric storage penalties, operational limitations and infrastructure development. A dual-fuel aircraft concept, capable of operating on both liquid hydrogen and kerosene or sustainable aviation fuel (SAF), may provide a transitional solution that balances environmental benefits with operational flexibility. While previous studies into dual-fuel propulsion concepts showed the potential to reduce CO2 emissions, a research gap was identified in the development of a conceptual aircraft design method employing sequential dual-fuel use throughout the mission.

This thesis investigates the impact of implementing a dual-fuel propulsion system using hydrogen and kerosene (or SAF replacement) on the design and performance of a medium-range narrowbody tube-and-wing turbofan aircraft. A parametric conceptual design model is developed using Python and the commercial ParaPy Python package, incorporating preliminary aircraft sizing, hydrogen tank structural and thermal modelling, aerodynamic analysis, engine performance modelling, mission analysis and well-to-wake energy and emission evaluation. Several fuel-use scenarios are evaluated, including full kerosene, full hydrogen, hydrogen-kerosene combinations, and varying fuel splits during cruise, for design ranges of 2500 km and 5000 km.

The results show that introducing dual-fuel capability mainly affects aircraft design through an increase in fuselage length due to hydrogen tank integration, with this effect being more pronounced at 5000 km than at 2500 km range. Across both ranges, increasing hydrogen use reduces total fuel weight, but increases operational empty weight, resulting in only small changes in maximum take-off weight due to these counteracting effects. Dual-fuel operation partially mitigates the fuselage length and passenger capacity penalties observed for full-hydrogen configurations at both ranges. Although tank-to-wake CO2 emissions decrease with increasing hydrogen use, overall equivalent CO2 emissions remain strongly dependent on hydrogen production pathways, with dual-fuel operation offering advantages over full-hydrogen concepts under near-term electricity grid assumptions. Overall, this study demonstrates that dual-fuel aircraft concepts offer a promising intermediate pathway towards aviation decarbonisation, enabling gradual integration of hydrogen while maintaining competitive operational performance. ... Read more

This research tackles optimization measures applied to the European ATM Network. It focuses on the Network Manager’s (NM) ability to become an active decision-making entity, strengthening its current mediation position in the relationship with relevant stakeholders (air navigation service providers - ANSPs, airspace users - AUs) inside the Collaborative Decision-Making (CDM) process. A game theoretic model, with the aim to measure the acceptance of the stakeholders to partially cede decision autonomy to the NM within the aforementioned process with the goal of improving overall ATM Network performance, is investigated and analysed. ... Read more

Battery state of health (SOH) estimation is one of the three main analytical tasks of a battery management system (BMS), when viewed from engineering maintenance and prognostics perspective. With the current global effort towards more suitable and greener processes, lithium-ion batteries have shown to be an important element in facilitating this transition. One industry where this can be noticed in particular is the transportation sector, where a strong shift towards battery electric vehicles (BEV) can be observed. Within the aviation sector, current research efforts include electrical flight. However, numerous challenges remain, that are typically observable within a safety critical domain such as aerospace. One these challenges includes the determination of uncertainty in battery SOH prediction. This would provide improved transparency on the capabilities and limitation of a model, when used as part of a battery system. Within this report we propose the use of a bidirectional gated recurrent unit (Bi-GRU) with learnable soft attention, to predict battery SOH based on charge measurements. Uncertainty analysis is enabled through the use of simultaneous quantile regression (SQR) and orthonormal certificate (OC), to be able to highlight and distinguish the aleatoric and epistemic uncertainty of the proposed model. We afterwards evaluate the model for point prediction accuracy using standard metrics, and evaluate the produced uncertainty using specialised test cases and calibration metrics. We achieved strong results using the proposed framework on a 2-phase fast charging dataset published by Toyota. ... Read more

Developing Knowledge-Based Engineering (KBE) applications remains a significant challenge in high-tech industries like aerospace, where front-loaded product development demands extensive automation. The manual code completion phase of these applications is particularly problematic: it consumes considerable time, requires specialized expertise in proprietary frameworks, and creates steep learning curves that limit broader adoption. While recent advances in Artificial Intelligence (AI) have revolutionized software development assistance, commercial AI systems consistently fail when working with specialized KBE frameworks, like ParaPy. In the absence of sufficient training data on proprietary frameworks, these systems produce code that appears correct but is actually non-functional.

This research validates that retrieval-augmented approaches offer a practical alternative to retraining models for specialized domains with limited data, such as ParaPy. In these approaches, AI systems dynamically access domain-specific knowledge during operation rather than relying solely on their training. This has significant implications for industries or institutions using proprietary tools where comprehensive model retraining is economically infeasible.

The research implements this approach by developing and evaluating a dual-agent framework for AI-assisted KBE application development that operates within industrial privacy and security constraints. The framework comprises a Developer Agent optimized for code generation and debugging with the ParaPy SDK, and an Educational Agent focused on ParaPy learning support and documentation. Both agents access a knowledge infrastructure that uses semantic search over indexed ParaPy documentation, curated examples, and technical references. Additionally, the Developer Agent employs verification mechanisms that progressively check code at two core levels: syntax correctness (ensuring the code follows programming language rules) and successful execution (confirming the code runs without errors).

User testing revealed how different skill levels benefit from AI assistance. Intermediate users benefited most, showing dramatic improvements in productivity and performance. Novice users achieved substantial productivity gains and reduced framework-specific (ParaPy) errors significantly, with task completion rates approaching expert baseline performance. Expert users, however, experienced slight performance degradation due to reduced code review under time pressure. The framework successfully reduced knowledge barriers for novice and intermediate users, broadening access to specialized engineering tools.

Despite these successes, the framework has persistent limitations in understanding three-dimensional spatial relationships. This is critical for KBE applications where code must define the precise position, orientation, and assembly of physical components. The framework struggles to correctly place components in space or apply proper rotational transformations. This produces code that may be syntactically correct but results in misaligned parts or incorrectly oriented features. These geometric errors require iterative refinement with human guidance, representing a fundamental limitation of the current approach and language model architectures.

While geometric reasoning limitations suggest fundamental boundaries of current AI capabilities, the demonstrated productivity improvements and reduced knowledge requirements establish a foundation for broader AI adoption in knowledge-intensive engineering domains. The framework contributes a validated operational prototype that addresses critical gaps in AI-assisted KBE development: reducing the manual coding bottleneck, lowering knowledge barriers for new users, and providing privacy-compliant deployment options. The framework functions most effectively as a development accelerator requiring expert oversight, supporting human engineers rather than replacing them.

Keywords: Knowledge-Based Engineering, Large Language Models, Code Generation, ParaPy, Retrieval-Augmented Generation, AI-Assisted Development, Aerospace Engineering, Multi-Agent Systems ... Read more

The FireFly is a multi-role VTOL firefighting aircraft. It was designed with a water tank capacity of 10,000 L and a dash speed of over 400 kph. The aircraft is capable of refilling the water tank with help of a snorkel device connected to the tank whilst flying in hover. ... Read more

This report covers the investigation of impact of uncertainties on the multidisciplinary design optimization of a medium-range single-aisle turbofan aircraft for minimum global warming impact. The employed workflow for the investigation is a five step process, starting with the implementation of the deterministic climate impact model and carrying out of the design optimization for minimal climate impact. The second step involves the characterisation of uncertainties, where the uncertainties within the climate impact model are identified and quantified. The third step involves the uncertainty analysis, where Monte Carlo simulations are performed to estimate the variability in the average temperature reduction potential of the climate-optimized aircraft with respect to the cost-optimized aircraft. In the fourth step, a robust design optimization is carried out using a non-sorted genetic algorithm to minimize both the average temperature response and variability in average temperature response potential. The sensitivity analysis is carried as the last step using the Morris and variance-based Sobol methods, to identify what the key uncertain parameters are towards the uncertainty in climate impact of the aircraft designs. Scientific uncertainty is identified within the linear climate impact model for the carbon impulse response function parameters, species radiative efficiencies, the NOx and contrail altitude forcing factors, methane lifetime, species efficacies, and are all assigned a probabilistic description. Scenario uncertainty is identified in the future average global CO2 atmospheric concentration projection, for which different realistic future scenarios are characterised. Carrying out the uncertainty analysis has shown that the average temperature response reduction potential of the climate-optimized aircraft is highly uncertain, having a 90% likelihood ranging between 17 and 98 % of the average temperature response of the cost optimized aircraft. This is primarily due to large variability in the estimation of contrail average temperature response. Although the robustness-based optimization did not allow to find any significant improvement in robustness for the climate-optimized aircraft, it did allow to identify an array of robust climate-optimized design solutions. From the sensitivity analysis, it was found that the uncertain parameters showing predominant influence on the output variability are the contrail-related radiaitive efficiency and forcing factors. Additionally, a variability of ±50% in average temperature response apportioned to CO2 emissions was identified due to uncertainty related to future average global CO2 concentration projections. ... Read more

Zero-carbon-dioxide-emitting hydrogen-powered aircraft have, in recent decades, come back on the stage as promising protagonists in the fight against global warming. Nevertheless, most recent studies agree that hydrogen aircraft would underperform their kerosene counterparts in terms of operative empty mass and specific energy consumption. The main cause for the drop in performance lays in the fuel storage, as not only the liquid hydrogen has to be kept in cryogenic conditions and pressurised, but for the same energy content, it has four times the volume of kerosene. The inevitable consequences are an increase in fuselage size, which adds mass and drag to the aircraft, and the addition of an heavy fuel storage and distribution system. On the other side, hydrogen has 2.8 times higher specific energy, and the consequent reduction in fuel mass could balance the previously mentioned drawbacks. Literature on the topic shows that the optimal fuel storage solution depends on the aircraft mission, but most studies disagree on what solutions are optimal for each aircraft range category.The objective of this research was to identify and compare possible solutions to the integration of the hydrogen fuel containment system on short, medium and long-range airliners. The capabilities of an automated synthesis program for CS-25 aircraft have been expanded with validated structural and thermodynamic physics-based tank design models, to allow for the design and analysis of liquid hydrogen aircraft. Studies were performed on several design options. The effect of using an integral tank structure was found to be negligible for short-range aircraft, but increasingly more beneficial for medium and long-range aircraft. The effect of increasing the fuselage diameter was found to be favourable, especially when seats abreast could be added without the addition of one aisle. The effect of using a combination of an aft and a forward tank was found to be detrimental in terms of operational empty mass, beneficial in terms of specific energy consumption and negligible in terms of maximum take-off mass. The use of spherical tanks was found to be slightly beneficial, but only when compared to a non-spherical tank version using the same tank layout, non-integral tank structure, and same cabin layout. The study on the venting pressure revealed that with increasing aircraft size the optimal venting pressure in terms of main aircraft performance decreases whereas the sensitivity to those same parameters to the choice of venting pressure increases. The use of direct gas venting as a means to contain the pressure rise did not appear to provide significant performance improvements. The optimal designs, in terms of operational empty mass, maximum take-off mass and specific energy consumption, feature increased fuselage diameters, the use of the aft & forward tank layout, non-spherical tanks and no direct venting. The short-range aircraft uses non-integral tanks and high venting pressure, while the medium and the long-range aircraft benefit from an integral tank structure and a lower venting pressure. Nevertheless, the sensitivity to these design choices is not significant, meaning that with a different set of assumptions and/or requirements different design choices may become optimal. The overall best performing LH2 aircraft for the short, medium and long-range categories were found to have respectively 8%, 24% and 22% higher operative empty mass, -2%, 1% and -5% higher maximum take-off mass and 5%, 13% and 5% higher specific energy consumption than their kerosene versions. ... Read more