The urgent need to further reduce fuel burn and climate impact for next-generation aircraft drives wings to become lighter and with higher aspect ratios to reduce induced drag. The resulting increase in wing flexibility presents both an opportunity and numerous challenges, as such wings experience increased structural loads at the wing root and are more prone to aeroelastic instabilities such as flutter. Passive and active flutter suppression and load alleviation techniques therefore provide a promising solution to enable these wings without the subsequent weight increase. For active flutter suppression and load alleviation, it is particularly important to express the free-flying aeroservoelastic model as an interpretable state-space model with manageable order.

In this regard, the aircraft loads model in the DLR Loads Kernel is formally derived and refined to make it suitable for free-flying aeroservoelasticity, constituting several improvements over the original model. The model is strictly expressed as a closed-form, symbolic, monolithic state-space representation, rigorously derived in this work. Besides improved interpretability and extensibility, this also allows for easily porting the nonlinear state-space model from Python to MATLAB/Simulink, and therefore effectively bridges NASTRAN and Simulink, the industry standards for finite-element modeling and control design respectively. To further improve suitability for free-flying aeroservoelasticity, the spiral nature of the Sears function is approximated using cascaded gust zones using a Padé approximation, significantly reducing the number of disturbance inputs. Physical RFA is employed, with the resulting aerodynamic lag dynamics projected to lag force and moment dynamics, achieving a substantial reduction in lag states. Additionally, the model is augmented with actuator dynamics and an accelerometer sensor model.

As the state-space model requires a representative aircraft, the Embraer Benchmark Wing, used for research on aeroelastic tailoring, is systematically transformed into a free-flying finite-element model, enabling the generation of free-flying aeroservoelastic state-space models with different wing mass and stiffness distributions at various mass cases and in varying flight conditions. Using this aircraft and in the considered simulations, the derived model achieves a 100–400x improvement in simulation time and a 458x reduction in input file size, and thus eliminates the need to save model output. Gust inputs are reduced from n to 2, and lag states from n x p to (6 + m + s) x p, with n aerodynamic panels, p RFA poles, m flexible modes, and s monitoring forces and/or moments. At the same time, these refinements are shown to not compromise model behaviour, as results show excellent agreement. These improvements, in conjunction with the added actuator dynamics and sensor model, make the state-space model well-suited for future work in free-flying active flutter suppression, active load alleviation, and on the longer-term, control co-design.

Contrails contribute substantially to the overall climate impact of aviation. Mitigation strategies include technological measures, such as reducing soot with sustainable fuels, and operational measures, where flights are rerouted vertically to avoid ice-supersaturated regions (ISSRs). Operational avoidance is limited by uncertainty in weather data, contrail lifetime, and resolution of satellite imagery. The aim of this thesis is twofold: to develop a contrail-toflight attribution algorithm to determine contrail avoidance regions and to evaluate it on a controlled dataset with known ground truth. The method enforces local physical consistency constraints based on two hypotheses: nearby contrails share correlated wind errors and originate from flights at similar altitudes due to the limited vertical extent of ISSRs. Overall, the findings support the use of physical-consistency constraints in contrail-to-flight attribution as a useful complement to, and standalone alternative for, geometric matching.

The original Flying V design requires a main landing gear strut height of 6.1m (worlds tallest landing gear) to accommodate the high pitch angle during take-off, introducing weight, drag, operational constraints, and development risks. This research quantifies Flying V’s take-off distance compared to the reference aircraft and develops two alternative configurations aimed at reducing landing gear height and enhancing take-off performance. A parametric aircraft model, including internal components, and a comprehensive flight mechanics toolbox with aerodynamics, propulsion, landing gear, inertia, and pilot models were implemented. The base configuration (FV-0) achieved a 23% gear height reduction with respect to the original configuration (FV-TUD) and a 25% take-off distance decrease compared to the reference aircraft. FV-1 incorporated a 5° static nose-up pitch, reducing take-off distance by 9.2%. FV-2 introduced root flaps, decreasing take-off distance by 10.5% and pitch angle by 0.4°, indicating higher potential for landing gear height reduction. Increasing the Flying V’s take-off distance to match that of the reference aircraft enabled a further 45% reduction in landing gear height relative to FV-0. Landing performance analysis remains necessary for final assessment.

This thesis presents a renewed feasibility analysis of an aircraft employing a Propulsive Empennage (PE), focusing on the Delft University Unconventional Configuration (DUUC). Existing aerodynamic and flight mechanics models from earlier DUUC studies are improved and extended to more accurately assess the concept's viability. Two alternative PE positions - one at the tail and one near the front of the fuselage - are analyzed and benchmarked against the ATR72-600. Enhancements include an updated thrust model, revised mass and center of gravity estimations and an improved control vane model. Despite the potential benefits of ducted fans and clean wing configuration, results indicate that increased mass, center of gravity excursion and increased trim drag outweigh the propulsion gains. Ultimately, both DUUC configurations underperform relative to the benchmark, and while the PE concept introduces intriguing design possibilities, it falls short of outperforming conventional turboprop configurations in cruise condition.

The goals of governments and agencies around the world is to minimize fuel emissions for the upcoming decades. The Flying V offers a new concept, which is an aircraft of flying wing configuration, offering higher aerodynamic efficiency than conventional tube and wing configuration. However, such an configuration offers challenges with its stabilty and control characteristics, with difficulties arising in control authority, like for instance, longitudinal trim. This study aims to find the optimal control surface layout for the FV-1000 aircraft.First, the certification requirements relevant to each control surface to be sized in this study (aileron, elevator and rudder) are gathered, primarily based on CS-25 regulations, and they are expressed mathematically. AVL is chosen as the tool to conduct aerodynamic analysis, for sizing purposes. Two coefficients are compared for their viability as objective function for optimizing a control surface,hinge moment coefficient and drag coefficient, hinge moment coefficient turns out to be the better option, it also provides control surface for a given span as small as possible. A consistent optimization framework is applied to both the aileron and elevator, wherein the spanwise domain is discretized and various combinations of span length and hinge line chord percentage are evaluated. The resulting optimal configurations are spatially adjacent, with the sizing of each control surface constrained by the span available due to presence of the other. During validation procedure, the comparisons made between VLM and wind-tunnel when extrapolated to comparison between VLM analysis and full scale flight case, it is observed that aileron is oversized, (for time to bank) while control elevators are undersized (to be precise, for pull-up maneuver). Certain solutions like high-lift devices or other methods of reducing AoA are suggested to make control surface like elevator comply with requirements. Rudders are the final surface to be sized which satisfy requirements of OEI trim at VLM analysis and even for full scale case, but is not able to satisfy the Steady sideslip requirement by significant margin for both cases. However, these results signify need of research on other options to assist these control surfaces, like drag rudders to assist with directional control authority, high lift devices to decrease AoA, assist surfaces like elevator in meeting the certification requirement, which are discussed.

This research proposes a novel reconfigurable and force-balanced aerial manipulator design for fast variable payload tasks. Its force-balancing properties allow for fast end-effector movements while minimizing disturbances introduced to the aerial platform. The manipulator is composed of three pantograph legs connecting the end-effector to the drone base. Each pantograph is equipped with two moving counter-masses that provide the balancing properties to the manipulator. The counter masses are moved by fast linear actuators allowing the manipulator to be force-balanced for different payloads. Extensive testing, performing end-effector trajectory tracking tasks, was performed both on a floating base setup and in flight. The results indicate that the manipulator significantly decreased the reaction forces transmitted to the base. Specifically, it achieved a 45% reduction when comparing the unbalanced and balanced configurations, and a 17% reduction when these configurations included a 53 [g] payload. The drone's position-tracking error during flight also improved, with reductions of 19% and 34% for the same two configurations, respectively.

With the drive to decarbonise the motorsport industry a variety of clean powertrains were developped and raced. While extensive lap time optimisation has been performed on hybrid and battery electric cars, no such optimisation has been performed on hydrogen fuel cell racing cars yet. This work models the Forze 8, a hydrogen fuel cell electric racing car, and compares its optimal lap, found by solving optimal control problems, to racing cars equipped with battery electric and internal combustion engine powertrains, all with the same peak power and weight on two different tracks. In qualifying scenarios, the battery electric racing car is faster than the internal combustion engine car by 1.0 [s] on both tracks, while the fuel cell electric car is slowest, trailing 5.3 and 3.8 [s] behind the battery electric car at Assen and Zandvoort respectively. In racing scenarios, the internal combustion engine car is quickest with the fuel cell electric car behind by 5.6 and 2.9 [s] at Assen and Zandvoort respectively. The battery electric car has the best single-lap performance due to its high continuous power, while the fuel-cell electric car is better suited for endurance scenarios as it carries more energy. This work shows that both battery and fuel cell electric racing cars can be valid alternatives for internal combustion engine racing cars.

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.

Interest grows rapidly in electric and hybrid electric aircraft. To determine the optimal performance and energy management required with such novel powertrain configurations, a knowledge-based aircraft and powertrain performance model is developed. The model is then used to set up an optimal control problem, which is transcribed to a non-linear programming problem using global orthogonal Legendre-Gauss-Radau collocation for single phase problems, and Hermite-Simpson local collocation for multiphase problems. The solution to the control problem allows identification of the best control strategies and energy management strategies. A case study is performed on the HY4 hybrid fuel cell aircraft and the hybrid electric Pipistrel Panthera. Solutions show that for best fuel economy, flying at a minimum drag airspeed, and keeping a constant power setting, proved more important than the choice of altitude. This was more noticeable for the HY4, with its relatively low power available and good aerodynamic properties following from its glider-based airframe.
The Fuel-optimal energy management strategies proved identical for both aircraft investigated. Batteries are used to provide a power boost during takeoff, after which batteries are discharged gradually throughout the remainder of the flight to maximize discharge efficiency. The engine or fuel cell are kept at approximately constant cruise power settings throughout the flight. The Panthera showed
consistent flight profiles with increasing range. For the HY4, however, achieved airspeeds reduced with increasing range, and additional measures were required to force a climb to non-zero altitudes due to its under-powered nature. The fuel-optimal trajectories offered an average of 10-15% of possible fuel
savings, depending mostly on the size of the onboard batteries. Fuel savings increased significantly at low ranges300km, where the contributions of the batteries have more impact.
Comparing different transcription methods and problem setups, it was concluded that global orthogonal, or pseudo-spectral, methods like Legendre-gauss-Radau collocation are not only faster, but also more consistent compared to simpler direct collocation methods. However, if the problem complexity increases and the performance limits of the aircraft are pushed, switching to a simpler method like Hermite-Simpson collocation reduced the time required to find a solution, with negligible differences in the resulting trajectories. Opting for a multiphase problem set-up, essentially splitting the problem in a series of individual subproblems, appeared less advantageous. While offering more control over the trajectories, time required to find solutions increased drastically, and offered no additional insight into the best energy management strategies.

The Flying-V is a novel aircraft configuration that has shown a promising fuel saving potential compared to a conventional aircraft. The V-shaped configuration of the Flying-V integrates the passenger cabin and cargo volume into the lifting surface and has fins to provide lateral and directional stability. Several studies have been conducted into various aspects of the Flying-V, including the aerodynamics, structure and handling qualities. The objective of this research is to evaluate the flight performance characteristics of a Flying-V aircraft and compare results to a reference aircraft, by simulating takeoff, landing, climb and cruise using a flight mechanics model. More specifically, the performance characteristics of the Flying-V-1000 (FVK) and Airbus A350-1000 (A35K) are evaluated and compared. The subject of flight performance answers practical questions about what an aircraft is capable of. Using an in-house developed flight mechanics toolbox, various sub-models are integrated to create a flight mechanics model. The aerodynamic model is based on data from a vortex-lattice method developed by Airbus, enhanced with empirical zero-lift drag and wave drag models. Other sub-models that have been integrated include an inertia model, a propulsion model, a pilot model and a landing gear model.
On average, the FVK features a 25% shorter takeoff distance than the A35K. This difference is mainly due to the significantly smaller minimum unstick speed of the FVK, which is a consequence of the FVK’s larger tailstrike attitude. For both aircraft, geometric tailstrike constraints determine the minimum unstick speed, rather than the elevator effectiveness. The shorter takeoff distance of the FVK also leads to an averaged 30% shorter Balanced Field Length (BFL). The relative difference between the BFL of both aircraft is larger than for the takeoff distance, because the FVK is able to brake more effectively than the A35K.
The landing distances and approach speeds of both aircraft are similar, although the total landing distance is distributed differently over the phases of the landing manoeuvre. Due to a combination of a higher touchdown attitude and smaller ground attitude, the derotation distance of the FVK is significantly larger than for the A35K, assuming equal derotation rates for both aircraft. The FVK is able to compensate its longer derotation distance with a shorter braking distance to achieve a similar total landing distance as the A35K. An analysis of the pilot’s vision during the landing flare manoeuvre has shown that the FVK’s obscured segment can be twice as large as for the A35K. For landings performed with poor visibility, this could be problematic for the FVK.
Due to its larger maximum L/D, the FVK has better climb performance characteristics than the A35K. With One Engine Inoperative, the FVK is able to meet the climb gradient requirements of CS25.125 for altitudes up to 8000 ft at Maximum Takeoff Mass. The absolute service ceilings of the FVK and A35K were found to be 13.4 km and 12.0 km, respectively.
Evaluation of the Specific Air Range (SAR) parameter shows that the FVK outperforms the A35K in terms of cruise efficiency. The maximum trimmed aerodynamic efficiency, transonic efficiency and Range Parameter (RP) were found to be respectively 17%, 21% and 21% higher for the FVK. The Mach numbers and lift coefficients where the RP maxima are located suggest an optimal cruise altitude of 12.0 km for the FVK and 11.3 km for the A35K.

Classically, aircraft controls are designed such that every control surface type primarily influences a single degree of freedom by creating a moment. Increased availability of computational resources and novel aircraft configuration allow a deviation from this approach and to utilize individual control surfaces to generate moments around multiple axes. The research investigates the impact of control allocation algorithms on the required control surface span and area for a box-wing configuration aircraft, the PrandtlPlane. The unique geometry of two full wings allows more flexibility in control surface placement. An optimization system for automatic control surface sizing under the constraints of adequate handling qualities has been developed and used to compare mechanical gearing, the constrained pseudo inverse, and the direct allocation algorithms. The results show that the PrandtlPlane configuration can benefit from the use of control allocation, showing a clear advantage of the direct allocation algorithm.