Hybrid RANS-LES methods have become a popular numerical approach for a wide variety of flows. This is due to dissatisfaction with the RANS modelling paradigm in separated flows along with the prohibitive computational cost of pure LES, especially in wall-bounded flows at high Reynolds numbers. However, these methods are susceptible to the grey area problem, where the modeling approach is neither RANS nor LES: rather it is a region with an ambiguous modeling approach. In zonal approaches that function as embedded wall-modeled LES (WMLES), the transition from RANS to LES can be accelerated by improving the synthetic turbulence and its injection into the flow. In this work, a systemic assessment of the two aspects of zonal grey area mitigation methods was carried out. The synthetic turbulence was generated by the synthetic turbulence generator (STG) and injected into the flow using two different forcing terms. To ensure accurate second-order statistics of synthetic turbulence, a priori estimations of the bias error associated with a specific realization of a random number set were implemented and used. This resulted in smaller deviations between the statistics of the synthetic turbulence and the target Reynolds stresses. Furthermore, a modified synthetic turbulence forcing that ensures more accurate estimation of the total shear stress in close proximity to the RANS-LES interface was proposed. Moreover, a dynamic forcing that selectively enhances the production of underestimated Reynolds stresses was implemented and evaluated. These aspects resulted in a faster transition from RANS to LES in terms of both skin friction coefficients and Reynolds stresses. In addition, the WMLES capabilities of the subgrid length scale Δ˜𝜔 together with the subgrid-scale 𝜎-model were explored. This work revealed that this combination is troublesome when used as embedded WMLES with synthetic turbulence, especially in stable flows. This is due to excessively decreased levels of eddy viscosity in the near-wall RANS region.

When placing a Head-Mounted Display (HMD) of a Virtual Reality (VR) or Augmented Reality (AR) system in the cabin of a moving base simulator to provide a larger field-of-regard, the HMD's inertial sensors will interpret the motion of the moving base simulator as if provided by the user, thus distorting the HMD's cabin-fixed pose estimation.
In order to estimate a proper cabin-fixed HMD pose, this thesis proposes an Unscented Kalman Filter (UKF) that fuses the information from the simulator sensors, as well as the sensors of the VR/AR system, including its visual position sensor and IMU. For this, three sensor configurations are proposed and tested offline deterministically based on real data. Although an unexpected latency in the HMD's visual pose sensor prevented a full validation of the algorithms, both the configuration with an IMU attached to the motion base, and the configuration without an IMU but with second-order additive noise variables added to the UKF, resulted in a proper, tight innovation sequence, indicating accurate system state estimation. The third configuration, one using the novel approach of using setpoints, provided a tight, but out-of-bounds, innovation sequence indicative of high accuracy, but also diminished tuning efficacy. It is also sensitive to stability issues when estimating the motion system's dynamics when no movement is present on the moving base. The unaltered setpoint method is not recommended as a solution. However, a hybrid solution of the setpoint and second-order additive noise variables methods should be investigated in a follow-up study to combine stability with high accuracy. Another recommendation is to test all configurations interactively, i.e. online.

Stratospheric Aerosol Injection (SAI) is a geoengineering method to mitigate the effects of increased greenhouse gas concentrations in the Earth’s atmosphere, and to prevent further global warming. SAI does not reverse climate change, it merely counteracts its symptoms by offsetting the radiative forcing of greenhouse gases, and it should be accompanied by aggressive programmes of Carbon Dioxide Removal to cool down the climate. Operational studies suggest that the injection of condensable vapor from specialized high-altitude aircraft would be a reasonable option to form the desired aerosol particles. Yet, remarkably little is known about the growth evolution of aerosol inside a jet engine wake, especially in light of SAI where high initial concentrations of condensable vapor are injected into the exhaust stream. The lack of resolution in the flow field obscures a lot of the intricacies of the aerosol formation process, and raises serious doubts on the steerability of SAI, which is the capacity to spatially and temporally control its effects both in nature and scale. Without reassurance that the steerability requirement can be satisfied, the potential risks to the global ecosystem would be too high. In this respect, the study investigates the mechanisms within the near-field of a jet engine wake that lead to the creation of aerosols and subsequent growth, when accounting for local variations in temperature and relative humidity. This is done through a decoupled plume dispersion model that includes a flow solver which resolves the average velocity and turbulent intensity of the flow, a sectional chemistry module which includes all the relevant microphysical processes that affect aerosol growth, and a diffusion-advection model which calculates the displacement of aerosol, vapor, and chemiions in the flow field. In addition, several modeling errors are identified in classical thermodynamic approaches to high-density aerosol formation. Results demonstrate that the onset of particle formation in the plume is complex, and is heavily dependent on the injection concentration of sulfuric acid vapor and the mixing rate of the plume. Different aerosol particle sizes are formed depending on the location in the plume, leading to a nonuniform distribution of the volume-mean radius across the plume’s cross-sectional area. There is also a
strong indication that core particles experience preferential growth, which implies that the final particle distribution at the end of the plume’s lifetime might not be as uni-modal as self-limited theory predicts. The study concludes with a list of recommendations to solve the challenges that remain in order to truly
understand the early growth evolution of sulfate aerosol in a jet engine wake.

Unsteady numerical simulation has been proven to be an essential tool for research. The quality of the results can be improved by using mesh adaptation. Mesh adaptation uses error indicators to refine the mesh in regions with high errors. The error indicators used are output errors with the most accurate output error estimation method being adjoint-based error estimation. However, for this method, the primal solution needs to be stored, which is storage intensive, especially for large unsteady simulations. The method proposed in this thesis uses a neural network autoencoder to compress and reconstruct the primal solution. This solution is compared to a reconstructed solution using Proper Orthogonal Decomposition (POD).

The one-dimensional unsteady Burgers equation is used as validation for the methods using a manufactured solution while the lid-driven cavity flow is investigated using the proposed method. The manufactured solution of the one-dimensional Burgers case could be exactly reconstructed using two POD modes. For the autoencoder a small latent space was used. For low resolutions, the small latent space did not prove to be a problem as the primal and residual could be captured accurately. However, for higher resolutions, the reconstruction error of the autoencoder became dominant for the residuals and resulted in erroneous adjoint-based error estimates while the primal remained qualitatively similar.

For the lid-driven cavity flow, the POD was still able to capture the solution using a low number of modes due to the smoothness of the solution. This resulted in an unfair comparison between the POD and autoencoder reconstructed solutions. The reconstructed autoencoder error estimates for lower resolutions were more accurate due to the latent space being large enough to capture the residual of the discrete primal accurately enough. When moving to higher resolutions, the autoencoder was not able to reconstruct the residual accurately enough leading to erroneous error estimates. Therefore, the latent space of autoencoders should be sufficiently large in order to gain an accurate reconstruction of the residual. If the latent space is large enough, the error estimate is accurate and the local error estimates can be used as a first iteration error indicator for mesh refinement.

Structure-preserving or mimetic discretisations are a class of advanced discretisation techniques derived by employing concepts from differential geometry. Such techniques can attain specific conservation properties at the discrete level such as conservation of mass, kinetic energy, etc when applied to conservation laws. However, like traditional techniques, they are not entirely robust in specific multiscale cases such as under-resolved simulations which are often encountered in industrial applications. This ties into the concept of Large Eddy Simulations (LES) of which an enticing approach is the Variational Multiscale (VMS) method. Both Mimetic methods and VMS approaches have been extensively studied independently, however, their combination offers the potential of achieving a favorable robust solver capable of handling complicated industrial problems. Moreover, the extension of the Hybridised variant of the Mimetic methods towards advection-dominated problems is an interesting avenue yet to be explored. Therefore, this thesis focuses on the extension of the Hybridised Mimetic method and its combination with the VMS theory. The said combination is tested on simple linear equations, namely the advection-diffusion equation in both steady and unsteady cases. Additionally, the Hybridised Mimetic method is studied in more complex test cases such as the Burgers' equation and the incompressible Euler/Navier-Stokes equations.

We present a reduced order model (ROM) for a one-dimensional nonlinear gas dynamics problem: the isentropic piston. The main body of the PDE, the geometrical definition of the mesh nodes, and the boundary conditions are parametrized. The full order model is obtained with a Galerkin finite element discretization, under the Arbitrary Lagrangian Eulerian formulation (ALE). To stabilize the system, an artificial viscosity term is included. The nonlinear convective term is linearized with a second-order extrapolation. The reduced basis to express the solution is obtained with the POD technique. To overcome the explicit use of the Jacobian transformation, typical in the context of moving meshes, a system approximation technique is introduced. The (Matrix) Discrete Empirical Interpolation Method, (M)DEIM, allows us to work with a weak form defined in the physical domain (and hence the physical weak formulation) whilst maintaining an efficient assembly of the algebraic operators, despite their change with every time step. Two alternative methods are presented to collect and compress the snapshots for the discretized solution-dependent convective term. Each method leads to a different offline stage. All in all, our approach is purely algebraic and the reduced model makes no use of full order structures, thus achieving a perfect offline-online split. A concise description of the reduction procedure is provided. The reduced model is certified with a posteriori error estimations obtained via mode truncation.

Research into constructing reduced-order models (ROM) to reduce computational cost or to interpret complex datasets from numerical or experimental sources has steadily been gaining popularity over the past few years. Many methods currently exist to construct ROMs. The proper orthogonal decomposition (POD) is one of the most widely adopted methods used for these purposes in the field of fluid dynamics. The POD modes favor high-energy structures due to the formulation of the POD however. This may result in an inaccurate ROM, as it might be possible that dynamically relevant, yet low-energy structures are truncated in favor of higher-energy structures. In recent years, an alternative goal-oriented method has been proposed. This goal-oriented method not only has less limits on the goal function that is optimally represented relative to thePOD, the resulting reconstruction of the flow field is model constrained as well. Both these properties are conjectured to lead to more dynamically relevant modes. This goal-oriented, reduced-order modeling (GOROM) technique has been formalized for the advection, Burger’s and incompressible Navier-Stokes equations in 1D and the Stokes equations in 2D, all with homogeneous boundary conditions. This work extends the formulation to the 3D incompressible Navier-Stokes equations with inhomogeneous boundary conditions and provides a parallel implementation. The method is applied to a DNS dataset of a 2D transitional boundary layer with and without a forward-facing step present and compared to the performance of the POD. It is found that the GOROM modes provide a basis which is numerically more accurate than the POD modes for most cases, yet do not provide additional insight into the flow physics. The results presented nevertheless show promise for further applications to construct more accurate and goal-oriented ROMs with the GOROM technique, especially for problems with nonlinear interactions and nonlinear goal functions.

Fault-tolerant flight control faces challenges as developing a model-based controller for each unexpected failure is unrealistic, and online learning methods can handle limited system complexity due to their low sample efficiency. In this research, a model-free coupled-dynamics flight controller for a jet aircraft able to withstand multiple failure types is proposed. An offline-trained cascaded Soft Actor-Critic Deep Reinforcement Learning controller is successful on highly coupled maneuvers, including high-bank coordinated climbing turns. The controller is robust to six unforeseen failure cases, including the rudder jammed at -15°, the aileron effectiveness reduced by 70%, a structural failure, icing and a backward c.g. shift as the response is stable and the climbing turn is completed successfully. Robustness to biased sensor noise, atmospheric disturbances, and to varying initial flight conditions and reference signal shapes is also demonstrated.

Propellers have a high propulsive efficiency and can help in the reduction of fuel consumption of aircraft. For propellers which are mounted in front of the wing, the interaction of the slipstream with the lifting surfaces can alter the lift distribution significantly. In the use of modern optimization tools, fast and accurate analysis of the wing aerodynamics is needed, including the influence of the propeller slipstream. Current potential flow based wing analysis tools, such as the lifting-line and vortex lattice method, highly overestimate the lift increase due to the axial induced velocity component in the slipstream, compared to wind tunnel and CFD data. The shortcomings of the existing wing analysis tools are found in the approach of adding the slipstream velocities to the wing induced circulation. At the wing segments that are submerged in the propeller slipstream, it is assumed that the slipstream is infinity large. To account for the effect of the finite slipstream dimensions on the lift distribution, potential flow methods based on the image vortex technique, are used to correct the existing analysis tools. These tools were validated using CFD simulations using the Euler equations. From the validation process, one method was found to produce a good agreement with the CFD data and this can be used to improve the modeling of propeller-wing interactions.

Very complex flows can be expensive to compute using current CFD techniques. In this thesis, models based on deep learning were used to replace certain parts of the flow domain, with the objective of replacing well-known regions with simplified models to increase efficiency. To keep the error produced by the deep learning model bounded, a traditional CFD model and deep learning model were coupled using a boundary overlap area. In this overlap area, the flow computed by the traditional CFD model was used by the deep learning model as an input. It was demonstrated that since traditional CFD model continuously feeds in reliable information into the deep learning domain, the error remains bounded. Furthermore, it was found that the accuracy of the deep learning models depends significantly on the random initial weights. Therefore, deep learning models trained differently must be carefully compared.

This thesis presents a new control philosophy based on Direct Force Control (DFC) for over-actuated aircraft. It is predicated on simultaneously controlling all six Degrees of Freedom (DoF). The Innovative Control Effectors (ICE) aircraft with its thirteen-part control effector suite is the subject of study. Active-Set (A-S) based Incremental Nonlinear Control Allocation (INCA) studied in previous work is extended and adapted to suit 6DoF incremental control commands. Translational inner loops were designed and connected to various outer loops. DFC provided substantial increases in tracking performance for air-to-air refueling for steady straight and steady turns with varying degrees of turbulence. A hybrid DFC strategy for vertical control paired with lateral control through roll showed stability and controllability improvements for high crosswind landings.

Accurate measurement of emissions (i.e. volume flow rate multiplied by concentration) is vital to the control and reduction of air pollution. We quantify the uncertainty of flow measurements by S-type pitot tubes in narrow exhaust stacks (diameter < 0.5 m) in support of recent European Union (EU) regulations for emissions from medium-size combustion plants. The contribution of blockage and wall effects to the uncertainty budget is evaluated. The aim of this thesis is to characterize flow uncertainty and to aid the development of more accurate measurement methods for volume flow rates in narrow exhaust stacks. This thesis will provide the scientific basis for implementation of EU regulations concerning emissions from medium-size combustion plants. Characterization of flow fields in narrow exhaust stacks is key to accurate volume flow rate measurements. We use numerical simulations to study the mean velocity profile and turbulence statistics of fully developed turbulent flow. To investigate the impact of wall effects on measurement uncertainty, we simulate the flow eld around an S-type pitot tube in close proximity to the stack wall. In addition, we conduct S-type pitot tube measurements at various wall-distances and bulk velocities. Throughout this thesis, we use several uncertainty quantification techniques to evaluate the uncertainty of numerical simulations and experiments. Studying spatial variation in measurement uncertainty gives insight in the most suitable locations for S-type pitot tube measurements in narrow exhaust stacks. The results of this thesis suggest that wall effects dominate over other sources of measurement uncertainty in the near wall region. Throughout the measurement plane, the combined uncertainty of measurement error sources exceed the impact of blockage. We recommend to determine volume flow rates in narrow exhaust stacks by S-type pitot tube measurements in the stack center. A correction factor can be determined to compute the volume flow rate in a narrow exhaust stack from the maximum flow rate in the stack center.

This Masters thesis is a new perspective on Large Eddy Simulation. The capability of goal oriented model constrained optimization technique to generate stable reduced order models without any additional stabilization term or subgrid scale modelling has been demonstrated. The low dimensional projection modes sought by the optimization program comprise the dissipative scales implicitly, thereby ensuring energy balance and eliminating the need for an SGS model.

A robust and flexible numerical framework was developed for modelling of dielectric-barrier discharge applicable to plasma actuated flow control. In OpenFOAM, a solver was built which solves convection-diffusion equations for ionized species coupled to Gauss’ law for electrostatics and includes a chemical model to account for the various chemical reactions taking place in non-equilibrium cold plasma. The framework relies on a predictor-corrector method and was verified and qualitatively validated in 1D using a volumetric cathode-fall glow discharge case. Quantitative validation in 1D and extension to 2D was recommended for future work as steps towards increased capability of the solver.

The continuous increase in fuel prices and stricter regulations on emissions have pushed the attention of truck designers into new areas to provide more fuel effcient solutions. The flow in the front wheelhouse is a relatively unexplored area, compared to studies on the shapes of the cabin, roof and trailer. However, it is known that the underhood is responsible for almost half of the total aerodynamic drag, with the front wheelhouse being one of the contributors. As a step towards improving the flow in the underhood, this thesis, performed at DAF Trucks N.V., focussed on the identiffication of aerodynamic phenomena in the front wheelhouses of heavy trucks using CFD simulations.

Traditionally, geometry has been represented differently in the field of Computer Aided Design (CAD) and Finite Element Analysis (FEA). This means that the CAD geometry, which can be seen as exact, must be converted to an Analysis Suitable Geometry (ASG) for input in a FEA program. A cumbersome and time consuming process, more commonly known as meshing. Furthermore, most engineering analysis techniques use linear or quadratic approximations of the originally exact geometry. Besides the geometry error, these crude geometry approximations can give rise to numerical errors such as spurious oscillations. In order to avoid these problems an integrated approach is necessary which unifies CAD and FEA.

The results of a preliminary experimental research on a Nanosecond Dielectric Barrier Discharge Plasma Actuator are presented in this document. For carrying out such a research, experiments were performed in the low speed facilities of Delft University of Technology. Four wind tunnels and several data acquisition techniques have been used in order to investigate the physical actuation mechanism that a NS-DBD plasma actuator is able to achieve. Leading Edge Flow Separation Control has been demonstrated at a Reynolds number up to 3.3 millions, and with a Mach number about 0.3. Set-ups of tests like Schlieren or PIV and results of such tests have been shown and discussed in this report. For industrial application the issue of applicability rose up. A scalability study has shown the capability of the NS-DBD Plasma Actuator of working on different scales. From a phenomenological study several actuation principles have been derived. Moreover, from the large data set that was obtained possible physical mechanisms that underlay the NS-DBD plasma actuation have been proposed. The most remarkable results achieved are: maximum lift coefficient increased about 20% with respect to its theoretical maximum, stall angle delayed about 5 degrees, drag due to the wake reduced about 300% in a leading edge flow separated condition. Eventually possible outlook and future research aimed to get the plasma actuator at an “industrial” stage is proposed. This experimental study has been carried out as a joined project between Delft University of Technology and NEQLab Research BV. The first one supported the project with knowledge and facilities, the second one with technology, equipments and manpower.