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.