The ever-increasing need for improving the energy yield in wind farms while minimising fatigue loads has created the need for exploring new concepts in the wind sector. One is the notion of wind farms with turbines of varying hub heights, often called vertically- staggered (VS) configurations. Multiple studies have demonstrated the potential benefits of such arrangements, yet the effect of erecting larger-scale wind turbines into an already existing control wind farm has not been explored. This concept has gained remarkable industrial attention, particularly in the German onshore market, as it simplifies the grid connection and land acquisition, among other benefits. Understanding the implications of vertical staggering on turbine performance is therefore essential. In addition, fast and accurate modelling of the inflow conditions modulated by the atmospheric boundary layer (ABL), is crucial in predicting the flow properties and energy yield in wind farms. A novel steady-state, RANS-based inflow model has recently been proposed, yet it lacks detailed validation. The present research aims to assess the reliability of that model in large wind farm flow simulations and subsequently apply it to identify flow patterns and power production characteristics in VS configurations representative of the German onshore wind standards through numerical simulations. RANS modelling combined with the actuator disk method in the PyWakeEllipSys software was employed to demonstrate that despite the inability of that inflow model to yield accurate prediction of the turbulence intensity, the computed velocity field is in fair agreement with LES results in conventionally neutral boundary layer conditions. This is evident, especially for deeper ABL cases. This study argues for the complexity of the flow caused by the collocation of different turbine scales in the investigated VS configurations. It is also highlighted that rotor overlap negatively impacts the power production of both turbine types in VS wind farms. Hence, it is suggested that optimal performance in VS setups can be achieved by minimising the rotor vertical overlap through appropriate adjustments of the hub height.

The mimetic spectral element method is a relatively young method in numerical solutions of partial differential equations and actions that describe physical systems. Its advantage is that it takes the geometrical structure of the problem into account which guarantees consistency of the numerical scheme and the conservation of relevant quantities. This prohibits the solution to diverge into non-physical states since the discretization mimics the physical behaviour of the problem. With relation to physics the mimetic method makes use of the stationary action principle which contains all information about the system and reduces the continuous variables to discrete ones that become ready for computation. The stationary action principle requires a formulation of the Lagrangian such that it implies Newton’s laws of motion or equivalent physical laws. However these Lagrangians are only easily formulated for monogenic systems. This work investigates the class of (discrete) non-monogenic physical systems. The goal is to present a general method to numerically mimic non-conservative systems such that energy (loss) is exact. This is done by modifying a newly introduced systematic method that transforms the non-conservative problem to a system with a doubled degrees of freedom that makes it a conservative or monogenic one. In this modification process new degrees of freedom are introduced which are allowed to have arbitrary variations at the initial and final states of the new action. The system can than be mimicked with the spectral element method where algebraic dual polynomials are used as both approximating polynomials and as test functions that serve as arbitrary variations. Furthermore a gauge transformation is introduced by the modified action such that it contains the initial and final states of the system to make the solution unique.
The introduced method is generally applicable to Lagrangian mechanics of discrete systems with holonomic constraints. The method is applied to the damped harmonic oscillator (DHO) as a test case. The computational results are clearly accurate and converging. However they deviate with one order from the h-refinement spectral element theory of Gauss-Lobatto-Legendre interpolation basis polynomials. Theoretical analysis of the equations of the mimicked (single and doubled) DHO show that energy (loss) is exact. Nevertheless, the computations show an oscillation in energy that remains closely around the conserved value. This oscillation in energy in time was observed in earlier works that applied the mimetic spectral element method to conservative systems. The reason behind this behavior of energy remains a topic for further research. Furthermore, in a follow up research the method can be extended to the Lagrangian field theory and tested with simplified non-conservative fluid problems to verify it for fields.

Wind farm interactions with the surrounding airflow leads to a reduction in velocity greater than the linear sum of single turbine inductions and is known as global or upstream blockage. The mechanisms and magnitude of global blockage effect are not yet fully understood. Models to simulate upstream blockage to improve efficiency estimates and better understand global blockage have thus far not been refined. The aim of this research is to investigate the sensitivity of upstream blockage to the numerical configuration of CFD simulations to improve model accuracy and understanding of global blockage.

RANS simulations are executed under steady state conditions with a k − ϵ turbulence model. A neutral stability, pressure-driven atmospheric boundary layer is modelled with fully developed uni-directional flow. Wind farms are modelled as actuator discs with 5 rows of turbines on flat terrain. Streamwise and spanwise spacing is set to 7 turbine diameters (D) and 5D respectively. Global blockage is measured against the induction of a single turbine at 2.5D upstream. Variables investigated include the domain height, lateral extent, inlet and outlet distances from the wind farm.

Domain heights ranging from 5D to 25D are investigated for change in magnitude and scale of upstream blockage for a laterally infinite wind farm. A clear trend of increasing blockage with domain height is observed. At domain heights of less than 15D, upstream velocity is increased by a maximum of 0.18% (5D). Larger domain heights produce a maximum velocity reduction of 0.23% (25D). The shape of upstream blockage is independent of domain height.

A finite wind farm of 5 columns and lateral extents ranging from 2.5D to 20D on each side are utilized to investigate the impact on blockage. Wider domains of 5D to 20D display increasing blockage with width, while a domain of 2.5D exhibits behaviour similar to a laterally infinite wind farm. Blockage ranging from 0.13% (10D) to 0.31% (20D) reduction in velocity is shown to be highest at the center column of turbines and decreases toward the outer columns.

Inlet and outlet distances ranging from 15D to 100D are modelled. Upstream blockage for inlet distances of 50D to 100D produce consistent upstream blockage magnitude and extent of 0.22% and 30D respectively. Shorter inlet distances result in decreased upstream blockage with a minimum of 0.12% (15D). The shape of blockage remains consistent through all inlet ranges. Outlet distance have no identifiable impact on upstream blockage magnitude and extent.

Changes to the numerical configuration show a clear correlation of increased blockage with cross sectional area of the domain. Constraining the domain in the vertical and lateral directions constricts flow resulting in reduced blockage. Blockage becomes independent of inlet distances at values of 50D and higher. Outlet distance has no identifiable impact on upstream blockage. Choosing a numerical configuration with adequately sized domain boundaries is pertinent in producing realistic upstream blockage.

The objective of this thesis was to further the validation effort on the SU2 flow solver for non-ideal compressible flows. To do so, an uncertainty quantification infrastructure based on the V&V20 validation standard was developed. To determine whether a model error was present, the different sources of uncertainty were combined and then compared to the error between the SU2 simulations and the experiments. A variety of paradigmatic test cases were proposed which, together, would constitute a robust validation of the SU2 flow solver. Two paradigmatic unit test cases were analyzed; namely, a supersonic inviscid flow and a Mach 2.0 flow over a 2.5 degree wedge. The operating conditions of the experiments were as follows: an inlet total temperature and pressure of T_0=252°C and P_0=18.4 bara, and a back pressure of P_out=2.1 bara, for the first case, and T_0=180°C, P_0=3.5 bara and P_out=1.0 bara, for the second. The solver is able to predict the shock wave angle accurately. This step towards the validation was successful because the calculated total expanded uncertainty of 1.79% is higher the comparison error of 0.57%.In the second part of the thesis, the uncertainty quantification infrastructure was adapted to a test case constituted by more complicated flow features; namely, a supersonic flow through a 5-channel blade row. The computed uncertainties were on the same order of magnitude as in the supersonic inviscid flow test case. The simulations were verified with state-of-the-art CFD software for the quantities pressure and the Mach number. The positive result of the validation represents a step forward in the validation of the flow solver for non-ideal turbomachinery cases. The adaptation of the infrastructure to the cascade blade row paves the way for complex cases and for system response quantities of interest in industrial applications of CFD software, such as efficiency, to be assessed.

In the global search to more sustainable developments, research in the field of propeller propulsion systems is experiencing a renewed interest. The combination of efficiency and noise production is not studied as extensively as propeller efficiency alone in past studies. In this study, the relation between propeller noise and efficiency is investigated. Adjusted parameters are sweep, advance ratio and the collective pitch angle. The performance of randomly generated blades is evaluated using both RANS and BEM methods first, after which the noise is evaluated using Hanson’s frequency formulation. All results are combined in a multi-fidelity kriging surrogate model. The results suggest that maximum efficiency and minimum noise cannot be obtained at the same operating conditions. Adjusting advance ratio and pitch of a given propeller during flight may reduce the noise emissions at the cost of efficiency. Sweep is also shown to have a considerable effect on both efficiency and noise.

The validation of SU2 for modelling classical non-ideal compressible fluid dynamics will advance the research into efficient ORC turbomachinery design. This study determines the validity of the two-dimensional flow solver for predicting the isentropic expansion of Siloxane MM through a converging-diverging nozzle using compressible Euler equations, adiabatic flow, and the Peng-Robinson equation of state. Two flows with an inlet stagnation temperature of 525K were considered: an expansion from 18.4 bar to 2.1 bar, and an expansion from 11.1 bar to 1.3 bar.  Mach number along the centreline and static pressure along the nozzle surface were used as the direct system response quantities used in the analysis. Experimental data and uncertainty came from the ORCHID, model input uncertainty was quantified using stochastic collocation, and the numerical uncertainty was calculated using the Richardson extrapolation. The conclusions were based on a hybrid of the ASME V&V 20 and Real Space validation metrics, with a novel Engineering Response Quantity analysis based on determining the effects of system uncertainty on performance parameters. The studied SU2 model provide valid predictions for Mach number, and invalid predictions for static pressure. The largest error is in the kernel region, where E_Mach=0.111 and E_Pressure = 112 kPa. Mach number has a maximum simulation uncertainty of 2% at the transition to the reflex region. Pressure has a maximum uncertainty of 3% at the throat. In the context of turbomachinery the simulation uncertainties translate to +/-0.001 and +/-0.02 on a loss coefficient calculated across a theoretical normal shock, for Mach and pressure respectively. Considering +/-0.01 as significant for a loss coefficient the Mach uncertainty is negligible. Input uncertainty is the largest component of the pressure uncertainty, while experimental uncertainty is dominant for Mach. The input parameters which provide the highest contribution to the uncertainty are critical pressure and temperature. The developed infrastructure can be used for expanding the validation of SU2 to different flow cases.

Prior to the detailed design of turbines, turbomachinery engineers must rely on mean-line and throughflow models to come to a preliminary design. These models are based on empirical loss correlations and are often derived from cascade experiments and numerical analyses that are confined to the subsonic and transonic regime. Axial turbines for rocket propulsion applications are characterised by a near zero degree of reaction and supersonic stator vanes that yield a complex flow field, making the prediction of losses challenging with existing correlations. The goal of this study is to investigate the variation of loss generation in supersonic axial turbine stator vanes with the isentropic exit Mach number. The profile losses will be split into components that can be attributed to different loss generation mechanisms whose relative magnitude may point to where performance improvement can be made. The investigation is performed on stator vanes that are used in the first turbine stage of a 1MN-class gas generator cycle type rocket engine. The stator vanes will be optimised for the profile losses by exploiting a novel adjoint optimisation framework for turbomachinery and the effect on the exit flow field will be investigated. The computational risk will be mitigated to ensure that the feasibility of the research is not jeopardised. The successful outcome of this research will lead to supersonic loss characteristics of axial turbine stator vanes, reduced development costs, increased efficiency levels and pave the way for future work on optimisation methods for turbomachinery applications.

This research aims to develop a method to create a wind farm layout that is robust against the uncertainty source, the inter-year variation of Weibull parameters and wind direction sector probabilities. A wind farm layout optimisation problem under uncertainty corresponds to optimisation under uncertainty (OUU), which is computationally expensive. It is, therefore, proposed to solve this issue by applying surrogate modelling. The thesis considers and compares two different surrogate models, polynomial chaos expansion (PCE) and Kriging, on two different applications. First, the surrogate model is built for the wind farm power model to emulate the medium-fidelity model that requires to evaluate the wake effect in every wind speed and wind direction. Another application of the surrogate model is for the uncertainty quantification (UQ). As the computational expensive Monte Carlo method is traditionally used to propagate the uncertain variables, the use of surrogate models can further reduce the number of uncertain samples to obtain the statistic output response.
Once the statistic of annual energy production (AEP) of the wind farm can be computed efficiently, the optimisation under uncertainty problem is performed with the genetic algorithms. The computational time of optimisation under uncertainty is reduced by using the surrogate UQ model, and a layout that may be more robust to the inter-year variation is found as indicated by a higher P90 value of AEP.

Today's leading projections of climate change predicate on Atmospheric General Circulation Models (GCMs). Since the atmosphere consists of a staggering range of scales that impact global trends, but computational constraints prevent many of these scales from being directly represented in numerical simulations, GCMs require "parameterisations'' - models for the influence of unresolved processes on the resolved scales. State-of-the-art parameterisations are commonly based on combinations of phenomenological arguments and physics, and are of considerably lower fidelity than the resolved simulation. In particular, the parameterisation of low-altitude stratocumulus clouds that result from small-scale processes in sub-tropical marine boundary layers is widely considered the largest source of uncertainty that remains in contemporary GCMs' prediction of the temperature response to a global increase in CO2. Improvements in the capacity of machine learning algorithms and the increasing availability of high-fidelity datasets from global satellite data and local Large Eddy Simulations (LES) have identified data-driven parameterisations as a high-potential option to break the deadlock. However, early contributions in this field still rely on inconsistent multiscale model formulations and are plagued by instability. To sketch a clearer picture on the sources of the accuracy and instability of data-driven parameterisations, this work proposes a framework in which no assumptions on the model form are made, building on recent work at the TU Delft. It uses Artificial Neural Networks (ANNs) to infer exact projections of the unresolved scales processes on the resolved degrees of freedom. These ``interaction terms'' naturally arise from Variational Multiscale (VMS) model formulations. The resulting VMM-ANN framework limits error to the data-driven interaction term approximations, offering explicit insight into their functioning.
The model is assessed in the context of a statistically stationary convective boundary layer turbulence problem, which is further reduced to a one-dimensional, forced inviscid Burgers' equation. Simple, feedforward ANNs with relatively local input stencils that are trained on error-free data a priori to inserting them in forward simulations (offline) are capable of predicting the interaction terms of this problem much better than traditional, algebraic VMS closures in offline settings at various levels of discretisation; they also generalise well to uncorrelated instances of the turbulence. However, this performance does not translate to simulations of forward problems. It is shown that the model suffers from instability due to i) ill-posed nonlinear solution procedures and ii) self-inflicted error accumulation. These correspond to two dimensions of forward simulations that are not accounted for by offline training on error-free data. The first instability mode can be dealt with by reformulating the VMM-ANN model architecture; the second is conjectured to demand training strategies that account for the self-induced errors. Finally, despite scaling well, the framework is still found to be comparatively computationally expensive. In all, appreciable challenges therefore remain in order to capitalise on the promise offered by ANNs to improve the parameterisation of clouds in GCMs in particular and turbulence in general.

The second-generation of supersonic civil transport has to match ambitious targets in terms of noise reduction and efficiency to become economically and environmentally viable. High-fidelity numerical optimization offers a powerful approach to address the complex trade-offs intrinsic to this novel configuration. Past and current research however, despite proving the potential of such design strategy, lacks in deeper insight on final layouts and optimization workflow challenges. Stemming from the necessity to quantify and exploit the potential of modern design tools applied to supersonic aircraft design, this work partially fills the gap in previous research by investigating RANS-based aerodynamic
optimization for both supersonic, transonic and subsonic conditions. The investigation is carried out with the state-of-the-art, gradient-based MDO framework \textit{MACH}, developed at University of Michigan's MDO Lab - which hosted the author for the 14-month research stint. Details of the tool and a brief overview of supersonic aircraft design and modern aerodynamic optimization strategies are reported in the first part of this manuscript.
After circumscribing the research niche, I perform single and multi-point optimization to minimize the drag over an ideal supersonic aircraft flight envelope and assess the influence of physical and numerical parameters on optimization accuracy and reliability. Leading and trailing edge morphing capabilities are introduced to improve the efficiency at transonic and subsonic flight speed by relaxing the trade-offs on clean shape optimization. Benefits in terms of drag reduction are quantified and benchmarked with fixed-edges results. It is observed how the optimized airfoils outperform baseline reference shapes from a minimum of 4\% up to 86\% for different design cases and flight
conditions. The study is then extended to the optimization of a planar, low-aspect-ratio, and low-sweep wing, using the same schematic approach of 2D analysis. I investigate the influence of wing twist alone and twist and shape on cruise performance, obtaining a drag reduction of 6\% and 25\% respectively as the optimizer copes with both viscosity and compressibility effects over the wing. Results for 3D multi-point optimization suggest that the proposed strategy enables a fast and effective design of highly-efficient wings, with drag reduction ranging from a minimum of 24\% up to 74\% for cruise at different speeds and altitudes, including edge deflection. Ultimately, this work provides an extensive and, to the best of author knowledge, unprecedented insight on the optimal design solutions for this specific aircraft configuration and the challenges of the optimization framework. The benefits of RANS-based aerodynamic shape optimization to capture non-intuitive design trade-offs and offer deeper physical insight are ultimately discussed and quantified. Given the promising results in terms of performance improvements and design efficiency, it is hoped that this work will foster the implementation of this method for more comprehensive full-configuration, multidisciplinary supersonic aircraft optimization studies.

Greenhouse gas emissions have globally increased temperatures and have led to global warming. A major part of these greenhouse gas emissions is emitted by motor vehicles in Europe. With an aim to limit global temperature increase by targeting emissions from heavy duty vehicles (HDV) which travel long-haul international journeys, this thesis aims to study the aerodynamics of heavy duty vehicles subjected to the inflow of an atmospheric boundary layer approaching the vehicle at a non-zero wind yaw angle

Surrogate modeling is a family of engineering techniques that attracts great interest today and can be applied in many challenging fields. A big advantage of it is that surrogate models (models based on these techniques) offer reliable results by being computationally cheaper than other candidate models. The savings in computational time is usually paramount for problems that involve a lot of variables and parameters and many iterative processes.

In the wind energy industry in particular, the design of the best layout of the wind farm is a problem that has been presented in the literature as an optimization problem; that is, a problem to optimize the wind farm layout in respect to some objective the modeler deems appropriate. More often than not, maximizing the expected power of the layout is mainly considered as this objective. The layout's expected power is -- among other things -- heavily dependent on the layout and the wake interactions between the turbines. The iterative search among many layouts to find the best one can be done with the help of a well-known optimization tool, the binary genetic algorithm. However, this tool cannot work alone, it solely facilitates the search over an adequate number of candidate solutions. To make it work, the modeler should provide it with some model that assesses how good in terms of the objective that has been set.

In this thesis therefore, the theory, the development and the use of two models of interest are investigated: Gaussian Process Regression (a surrogate model) and the Monte Carlo Method (a method based on random sampling). Great care was given to compile the theoretical basis of these models in order to be a good reference point for the non-experienced reader. The nature of these two models differs quite a bit, but they both can be used by the modeler to yield interesting results. These results will be compared to each other and against a third model's results, a specific wake model. This third model is the Original Model which the Gaussian Process Regression model and the Monte Carlo Method model utilize and compare against. The reliability of the results and computational speed will be the measure of success and ranking for these three models.

Finally, the comparison of the three models continues in how potent they are to propose an optimized layout for a wind farm. Each of the three models is coupled with the binary genetic algorithm that is developed specifically to connect with them. Afterwards, the proposed best layouts are discussed. The results show that the Gaussian Process Regression model performs reliably and very fast in comparison to the Original model. On the other hand, the Monte Carlo model, although also fast when it is used to find an optimized layout, could not be verified that it performs reliably and therefore, its results cannot be trusted without going into further investigation. After the comparison, further discussion follows with some recommendations for future research.

The current wind turbine design tools are based on aerodynamic simulations using blade element momentum (BEM) codes to calculate loads and power, see for instance the tools HAWC2, Bladed or FAST. This engineering method is computationally efficient, but theoretically valid only for steady two dimensional flow in non-yawed conditions.

To overcome these limitations engineering add-ons are used based on measurements or advanced methods. However, these include approximations which may result in introduced errors, especially in
extreme operational conditions such as yaw. Moreover, future turbines may utilize flaps or slats and experience tip speeds higher than 110 m/s leading to Mach and Reynolds number effects for which the current tools are not validated yet.

Therefore, the objective of this Master thesis is the development of an aero-servo-elastic simulation method based on an aerodynamic method with increased fidelity compared to BEM such as computational fluid dynamics (CFD). This was achieved by replacing the BEM module of NREL FAST by an OpenFOAM CFD code. Therefore, FAST and OpenFOAM were coupled by utilizing the neutral interface MpCCI.

Finally, it was investigated how such a method can be justified when compared to the state-of-the-art tools, which use BEM. The implemented coupled method was thereby validated against experimental data from the NREL phase VI experiment. The Master thesis project, which was carried out externally at the CFD department of Fraunhofer IWES, showed that there is a need for more accurate methods especially in extreme conditions such as heavy yaw.

For the past fifty years, there has been a constant increase in the pressure ratio of gas turbines motivated by the need to increase the cycle efficiency. This has moved the operating pressure of gas turbine combustor to be above the critical pressure of jet fuels. This increasing trend in pressure of combustor, places the next generation gas turbines in supercritical regime of majority of jet fuels, making Supercritical Combustion a significant topic of research in the field of gas turbine. Understanding the fluid behavior at and above the critical point provides opportunity for better and efficient combustor design.

The complexity in numerical modelling of supercritical fluids, due to the dramatic property variations, solubility in gas and difficulty in tracking the vanishing interface, requires alternate modelling techniques that are well suited for treating multi-phase systems efficiently. Lattice Boltzmann Methods(LBM) is one such system gaining popularity in the recent decade due to the inherent capability to model complex multi-phase flows. LBM is a hybrid particle-continuum method based on the kinetic theory, which entails solution of the Boltzmann equation for the distribution function of particles properties on a lattice node.

Despite the advantages of LBM, there is considerable gap in effective application of this method as a solution for engineering problems. This research work is aimed to address this issue by creating a single and multi-component solver using LBM and making quantitative comparison with commercial fluid dynamic flow solver, ANSYS FLUENT. The results form created single-phase LBM solver agrees with the results from FLUENT single-phase solution in addition to showing superiority in terms of computational time and resources. In case of the multi-component model, LBM provided accurate results with coarser mesh refinement. A detailed investigation of LBM application for two simple systems has been studied and reported,bthus laying a foundation for future numerical investigation of supercritical fluid modelling.

Flight testing is an activity that exists since the beginning of flight, and is still seen as an essential part of the aircraft development process. While numerical simulation has been improved significantly over the last decade, flight testing remains attractive in the exploration and evaluation of the flight envelope, especially in the region where numerical simulation lacks prediction accuracy. Free-flying model testing is a potential technique, which is recognized by some of the largest research institutes. Using such relatively cheap scale models aids in the determination of the flight behavior of full-scale aircraft in an early stage in the design process. Furthermore it takes less time to develop a scalemodel and is also safer to fly. A 5.5% scale model of the Boeing 737-700 is created by the TU Delft as a first step towards investigating the potential of dynamically scaled flight testing. This Delft University Unconventional Concept (DUUC) has a conventional tube-wing configuration and features ducted fan engines mounted aft on the fuselage, which replace the conventional empennage. A horizontal and vertical jet vane is placed in the duct, which guide the propeller slipstream and thereby control the attitude of the aircraft. Successful flights have already been performed with this model, which proves the propulsive empennage concept. However, the stability and control behavior has only been assessed at a low level. Flow characteristics entailed by the ducted fan are accounted for by means of approximate functions and empirical relations instead of looking at the physical interaction between the components within the duct. This study focusses on the processing of said interaction by performing a CFD study of which the results are used in the longitudinal equations of motion of the DUUC. This way, certain motion can be simulated from which the effect of the unconventional empennage on the stability and control can be assessed. In order to study the aerodynamics of the propulsive empennage and subsequently its stability, a CADmodel is developed using PARAPY. This is a knowledge based engineering platform written in the Python language and allows for parametrization of the model such that geometric alterations can easily be implemented. Previous studies within the TU Delft have lead to a multi-model generator written in PARAPY. The fuselage and wing generation modules that were developed can readily be applied in the generation of the DUUC model. The propulsive empennage module is created by the author of this thesis, consisting of several sub-parts: (1) outer duct that forms a shielding around the propeller, (2) the pylon that connects the duct to the fuselage, (3) a center body to which the (4) propeller and (5) control vanes are attached. The full aircraft model has been used in a series of CFD simulations with ANSYS FLUENT. Inviscid steady calculations are performed using a pressure-based solver with a SIMPLE pressure-velocity coupling scheme. The propeller is modeled as an actuator disk whereby the thrust is defined by specifying a pressure jump across the thin surface. The thrust produced by the ducted fan is based on the turboprop installed on the ATR72-600 aircraft, which has similar dimensions compared to the Boeing 737-700. Both the full-scaleDUUC as well as the 5.5% scale model are subjected to this aerodynamic analysis. Minor discrepancies were found between the two models, which is a result of the artificial viscosity that is implemented in the Euler scheme to smoothen strong gradients in the solution (numerical dissipation). Due to the location of the engine, the thrust line is located above the center of gravity which contributes to a stabilizing effect. The effect of several parameters on the stability and control has been analysed, such as the center of gravity location, mass and inertia, and geometric scaling factor. The slight difference in aerodynamic performance between the full-scale and subscalemodels, means that the trimmed solution for steady horizontal flight is also slightly different with a maximum error of 2.5 degrees in elevator deflection. More drag is obtained as the aircraft scale goes down, resulting in a higher required thrust to achieve force balance in a trimmed flight condition. Furthermore it is found that by scaling the aircraft down according to Froude scaling theory, the response becomes much faster. This response can in turn be scaled back, which proved to be a very useful estimate of the full-scale simulation.