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.

It is the aim of this research to assess the mission performance of a boxwing aircraft by developing a configurationagnostic, multifidelity optimal control toolbox for performance and mission analysis. The boxwing aircraft, sometimes named a PrandtlPlane (PrP), is an unconventional aircraft. An instance with redundant controls is designed within the PARSIFAL project. This specific aircraft is designed for commercial transport in the shortrange segment (a 4000 km design range) and for a high passenger capacity (up to 308 passengers). Because of the beneficial induced drag characteristics inherent to the boxwing configuration, the PrP represents a possible solution towards the sustainable future of aviation. To investigate the potential of the PrP as an alternative to conventional commercial aircraft, the mission performance assessment of the aircraft has been split into two components. The first part of the assessment covers the comparison between the performance of the PARSIFALdesigned PrP and that of a competitor aircraft with a similar design range, the A320, while allowing only nonredundant controls. The second part of the assessment involves the quantification of the PrP’s performance when allowing redundant controls in the form of Direct Lift Control (DLC), enabling the aircraft to increase its net lift without a change in pitching moment. Analyses of the PrP and its competitor aircraft for various ranges have shown that the PrP outperforms its competitor in terms of relative fuel consumption. When flying its minimumfuel mission, the PrP’s competitor consumes less fuel in absolute terms. Nonetheless, because the PrP carries more than twice as many passengers, it consumes up to 14.5 % less fuel per passenger per kilometre. In other respects the PrP’s performance is inferior to that of its competitor. The 5400 km maximum range of the PrP is considerably lower than its competitor’s maximum range of 6200 km. Moreover, at a fueloptimal Mach number of approximately 0.7 the PrP cruises appreciably slower than the cruise Mach number for which it was designed, unlike its competitor. In general, the PrP flies its trajectories much slower than its competitor at an approximately 10 % lower average velocity in the minimumfuel missions. If both time and fuel are considered equally in the cruise altitude optimisation, the design altitude of 11 km is deemed appropriate. If only fuel consumption is considered, the PrP would benefit in fuel economy from lowering the initial cruise altitude at the cost of increased mission time. At an optimal altitude of 9.3 km, the PrP would consume 2.2 % less fuel than at its design altitude of 11 km at the cost of even slower flight. The sensitivities of the PrP’s mission time and fuel performance to changes in its design Zerofuel Mass (ZFM) have been investigated. Keeping the Maximum Takeoff Mass (MTOM) constant while varying the ZFM, design mission simulations were run for the PrP for several objective functions. It was found that when flying for minimum fuel, a 1 % increase in ZFM incurs a fuel consumption penalty of over 1 % through a nearlinear, direct proportionality. Likewise, the mission time varies nearly linearly with the ZFM; a 1 % increase results in an approximate mission time increase of nearly 0.5 %. The incremental aerodynamic lift and drag due to control surface deflections for DLC were modelled using a flatplate approximation. With this approach, the projected missionlevel benefits of using DLC are marginal. On the design mission, the results indicate an increase in fuel economy of 0.6 % on the minimumfuel mission and negligible temporal gains on the minimumtime mission. It is however emphasised that numerical uncertainties due to the discretisation of the problem pollute all obtained solutions to some degree, such that appropriate caution should be exercised when interpreting these results in an absolute sense. In future research, a grid refinement study would be a valuable addition to quantify and bound these uncertainties. It is deemed equally important to look into a more sophisticated way to model the control surface aerodynamics necessary for assessing the benefits of DLC. A broader recommendation pertains to future research on boxwing aircraft aerodynamic design. The current research has indicated that the optimal trajectories for the PrP result in very distinct flight profiles when optimising for different objectives. Therefore, it would be interesting to see how the aerodynamic design could evolve, such that flying for fuel economy wouldn’t require such a compromise in temporal performance and vice versa.

This paper presents a System of Systems Engineering approach to aircraft design. For this purpose, conventional design disciplines are coupled with Agent-Based Modeling and Simulation (ABMS) defining a unique optimization problem. The proposed methodology is applied to design seaplanes for an on-demand transportation system connecting the Greek islands. Within this network, diverse scenarios are analyzed by varying parameters of the model such as fleet size and travel demands at each seaport. The objective is to show the impact of including ABMS in the design workflow on the optimized seaplane design parameters. The optimum designs are evaluated on the basis of a number of classic performance metrics, to assess to what extent they can represent a competitive alternative to existent maritime means of transportation. The results reveal optimal fleet performance for seaplanes characterized by lower cruise speeds and passenger capacities, as compared to those derived from conventional methodologies and to existing designs.

The aerodynamic model of a combat aircraft is essential for its success and competitiveness compared to other combat aircraft. This thesis aims to research the most optimal machine learning model to create an aerodynamic model of a combat aircraft. The very large but still sparse, highly nonlinear dataset forms a challenge for using specific machine learning models. Tree-based models, artificial neural networks (ANNs), and Bayesian neural networks (BNNs) have been identified to be individually capable of modelling the aerodynamics of combat aircraft. For ANNs, additional research was performed into genetic algorithms, as a robust hyperparameter optimisation method. Transfer learning, which reduced the training time by around 14%. Finally, adding gradient information of the training data as an additional input, reduced the mean squared error (MSE) by 7%. Scalable BNNs, which used Bayes-by-backprop, were developed to handle the large dataset. The uncertainties coming from the BNN were overconfident in the results compared to the tolerances of the dataset. The uncertainties showed only a weak correlation with the MSE of a given prediction. Finally, to leverage each of the model's advantages, a stacked model was created which improves the predictive performance on average by 27% compared to the best base model in terms of the MSE on the test dataset. With the stacked model implemented, each of the aerodynamic coefficients could be predicted with over 97% of the predictions of the test data within the tolerance. This is an average increase of 0.5% for each of the aerodynamic coefficients compared to the best base models. Due to the strict regulations related to this aerodynamic model, the machine learning model that is created cannot replace the aerodynamic model at this time. However, the implementation of this machine learning model allows engineers to design the aerodynamic model faster, and with greater precision.

Flying characteristics of a multi-engined light general aviation (GA) aircraft during and after an engine failure is often a major safety consideration when both designing and operating the aircraft. In the meantime, the propellers, being large rotating masses, can exert considerable gyroscopic effect on the aircraft during flight, itself contributing to a coupling between the pitch and yaw axis, thus affecting flight dynamics. This study presents an investigation on the impact of propeller gyroscopic effects on the flying motion of a representative twin-engine GA aircraft. This is done using a modular flight mechanics toolbox that performs analyses in both frequency domain and time domain. A steady-state windtunnel aerodynamic and control surface model with empirically estimated unsteady aerodynamic coefficients, along with a propeller governor engine system simulation, complements the gyroscopic inertia model in the simulation setup. Firstly, a modal analysis showed that all modes aside the spiral mode does not get discernibly affected by the rotating inertia typical to the reference aircraft’s propellers. Then, time-domain simulations of various rapid maneuvers show that gyroscopic effect does cause significant change in the angular response of the coupled axis, e.g. sideslip angle response during a pitch input only maneuver, whilst its impact on long term phugoid motion remained inconclusive due to undesired and uncontrolled roll motion. To compensate for this, maneuvers were performed again with a manually tuned simple wing leveler and results showed that pitch input maneuvers does not show much deviation in phugoid motion, whereas yaw input maneuvers such as sudden left engine failure shows discernible difference in airspeed and altitude responses, though the difference in magnitude is still small. Next, comparisons made with different powertrain responsiveness showed that in a power reducing case such as sudden one engine failure, the effect of the powertrain time delay is independent from the influence of gyroscopic effects, whereas for a power increase case, such as going around, the impact of the two is simultaneous and intertwined. Finally, a sensitivity study on unsteady aerodynamic coefficients showed that their effects on flying motion are generally independent from the gyroscopic effect.

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.

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.