Much of the planned installation of wind turbines in Sweden will be located in the northern region, characterized by a lower population density so that problems related to sound pollution and visual acceptance are of lower concern. This area is generally distinguished by complex topography and the presence of forest, that significantly affects the wind characteristics, complicating their modelling and simulation. There are concerns about how good an industrial code can simulate a forest, a question of paramount importance in the planning of new onshore farms. As a first step, a sensitivity analysis was initially carried out to investigate the impact on the ow of different boundary conditions and cell discretization inside the forest for a 2D domain with a homogeneous forest. Subsequently, a comparative analysis between the industrial code WindSim and Large Eddy Simulation (LES) data from Segalini. et al. (2016) was performed with the same domain. Lastly, simulations for a real Swedish forest, Ryningsnäs, was conducted to compare a roughness map approach versus modelling the forest as a momentum sink and a turbulence source. All simulations were conducted for neutral stability conditions with the same domain size and refinement. The main conclusions from each part can be summarized as follows. (i) The results from the sensitivity analysis showed that discretization of cells in the vertical direction inside the forest displayed a correlation between an increasing number of cells and a decreased streamwise wind speed above the canopy. (ii) The validation with the LES data displayed good agreement in terms of both horizontal mean wind speed and turbulence intensity. (iii) In terms of horizontal wind speed for Ryningsnäs, forest modelling was prevailing for all wind directions, where the most accurate simulation was found by employing a constant forest force resistive constant (C2) equal to 0.05. All forest models overestimated the turbulence intensity, whereas the roughness map approaches underestimated it. Based solely on the simulations for Ryningsnäas, a correlation between lower streamwise wind speed and higher turbulence intensity can be deduced.

This thesis aims to help sailboat owners to decide a preferable NACA profile. A CFD comparison in terms of drag and lift coefficients between two NACA profiles have been applied on a typical fin keel. Each profile has been computed with different angles of attack to investigate the impact of small direction changes. ANSYS Fluent 13.0 is used to model the flow according to RANS k-epsilon model. The conclusion is that NACA65 series gives lower drag while NACA64 series gives higher lift. / Syftet med det här examensarbetet är att undersöka skillnaderna för olika NACA-kölprofiler med avseende på tryckkoefficienter Arbetet strävar även efter att ge båtägare en tydligare bild av en fördelaktig NACA-profil. Varje kölprofil har beräknats med olika anfallsvinklar för att undersöka effekten av små vinkeländringar. ANSYS Fluent 13.0 har använts för att modellera flödet enligt k-epsilon-modellen. Slutsatsen är NACA65-serien ger en lägre motståndskoefficient medan NACA64-serien ger en högre lyftkoefficient.

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Simulations were performed to investigate flow separation of an asymmetricdiffuser - like geometry. The geometry used for the simulations was modeledafter an experimental setup with recorded flow data, which was compared tothe simulated data. For all simulations, steady state flow at the inlet was usedwith the assumption of a 2D flow.A grid convergence study consisting of three different grids was performed.From this study no apparent change in simulation results were observed forfiner grids. This is caused by the fact that the coarse grid had a high enoughresolution to fully capture the flow, meaning that the higher resolution gridsyielded small improvements.Additionally, two different turbulence models RN G k − ε and SST k − ωwere used for evaluating which model was best suited to model flow separation.The simulations showed that the RN G k − ε model could not capture the flowseparation and had a poor accuracy when predicting the turbulent kinetic energy(TKE). Simulation results from SST k − ω gave good results in capturing flowseparation and predicting both the velocity and TKE when compared to theexperimental data.Finally, a turbulence intensity study was made for the mid grid with theSST k − ω model. The turbulent intensity was set to 5%, 10%, 15% and 20%at the inlet. This resulted in the point of separation moving further down thegeometry to x/H ≈ [17.68, 18.71, 19.58, 20.72] for respective intensity. The pointof reattachment also moves to x/H ≈ [44.85, 43.60, 42.67, 41.67] for respectiveintensity.In summary for simulating flow separation in turbulent flows the SST k − ωmodel is optimal and an increase in turbulent intensity reduces the recirculationzone.

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Cavitation is characterized by the formation of vapor bubbles when the pressure in a working fluid drops sharply below the vapor pressure. These bubbles, upon exiting the low-pressure region burst emanating tremendous amounts of energy. Unsteady cavitating flows have been influential in several aspects from being responsible for erosion damage and vibrations in hydraulic engineering devices to being used for non-invasive medical surgeries and drilling for geothermal energy. While the phenomenon has been investigated using both experimental and numerical methods, it continues to pose a challenge for numerical modelling techniques due to its flow unsteadiness and the cavitation-turbulence interaction. One of the principal aspects to modelling cavitation requires the coupling of a cavitation and a turbulence model. While, scale-resolving turbulence modelling techniques like Direct Numerical Simulations (DNS) and Large Eddy Simulations (LES) upto a certain extent may seem an intuitive solution, the physical complexities involved with cavitation result in extremely high computational costs. Thus, Unsteady Reynolds-Averaged Navier-Stokes (URANS) models have been widely utilized as a workhorse for cavitating simulations. However, URANS models are unable to reproduce the periodic vapor shedding observed in experiments and thus, are often corrected by empirical correction. Recently, some models termed as hybrid RANS-LES models that behave as RANS or LES depending on location of flow have been introduced and employed to model cavitating flows. In addition, there has also been a rise in defining some frameworks that use data from high-fidelity simulations or experiments to drive numerical algorithms and aid standard turbulence modelling procedures for accurately simulating turbulent flows. This dissertation is aimed at (1) evaluating the abilities of these corrections, traditional URANS and hybrid RANS-LES models to model cavitation and (2) optimizing the URANS modelling strategy by designing a methodology driven by experimental data to augment the turbulence modelling to simulate cavitating flow in a converging-diverging nozzle. / Doctor of Philosophy / The famous painting Arion on the Dolphin by the French artist François Boucher shows a dolphin rescuing the poet Arion from the choppy seas after being thrown overboard. Today, seeing silhouettes of dolphins swimming near the shore as the Sun sets is a calming sight. However, as these creatures splash their fins in the water, these fins create a drastic pressure difference resulting in the formation of ribbons of vapor bubbles. As the bubbles exit the low-pressure zones, they collapse and release tremendous amounts of energy. This energy manifests in the form of shockwaves rendering this pleasant sight to the human eye, extremely painful for dolphins. These shocks also impact the metal blades in hydraulic machinery like pumps and ship propellers. This dissertation aims to investigate the physics driving this phenomenon using accurate numerical simulations. We first conduct two-dimensional simulations and observe that standard numerical techniques to model the turbulence are unable to simulate cavitation accurately. The investigation is then extended to three-dimensional simulations using hybrid RANS-LES models that aim to strike a delicate balance between accuracy and efficiency. It is observed that these models are able to reproduce the flow dynamics as observed in experiments but are extremely expensive in terms of computational costs due to the three-dimensional nature of the calculations. The investigation then switches to a data-driven approach where a machine learning algorithm driven by experimental data informs the standard turbulence models and is able to simulate cavitating flows accurately and efficiently.

The primary purpose of this thesis is to quantify the effects of RANS turbulence modeling on the resolution of free shear vortical flows. The simulation of aerodynamic wing-tip vortices is used as a test bed. The primary configuration is flow over an isolated finite wing with aspect ratio, , and Reynolds number, . Tip-vortex velocity profiles, vortex core and wake turbulence levels, and Reynolds stresses are compared with wind tunnel measurements. Three turbulence models for RANS closure are tested: the Lumley, Reece, and Rodi full Reynolds stress transport model and the Sparlart-Allmaras model with and without a proposed modification. The main finding is that simulations with the full Reynolds stress transport model show remarkable mean flow agreement in the vortex and wake due to the proper prediction of a laminar vortex core. Simulations with the Spalart-Allmaras model did not indicate a laminar core and predicted over-diffusion of the tip-vortex. Secondary investigations in this work include the study of wall boundary layer treatment and simulating the wake-age of an isolated rotorcraft in hover using a steady-state RANS solver. By comparing skin friction plots over the NACA 0012 airfoil, it is shown that wall functions are most effective in the trailing edge half of the airfoil, while high velocity gradient and curvature of the leading edge make them more vulnerable to discrepancies. The rotorcraft simulation uses the modified Spalart-Allmaras turbulence model and shows proper, qualitative, resolution of the interaction between the vortex sheet and the tip vortex. / Master of Science

RANS models in CFD are used to predict the liner wall heat transfer characteristics of a gas turbine annular combustor with radial swirlers, over a Reynolds number range from 50,000 to 840,000. A three dimensional hybrid mesh of around twenty five million cells is created for a periodic section of an annular combustor with a single radial swirler. Different turbulence models are tested and it is found that the RNG k-e model with swirl correction gives the best comparisons with experiments. The Swirl number is shown to be an important factor in the behavior of the resulting flow field. The swirl flow entering the combustor expands and impinges on the combustor walls, resulting in a peak in heat transfer coefficient. The peak Nusselt number is found to be quite insensitive to the Reynolds number only increasing from 1850 at Re=50,000 to 2200 at Re=840,000, indicating a strong dependence on the Swirl number which remains constant at 0.8 on entry to the combustor. Thus the peak augmentation ratio calculated with respect to a turbulent pipe flow decreases with Reynolds number. As the Reynolds number increases from 50,000 to 840,000, not only does the peak augmentation ratio decrease but it also diffuses out, such that at Re=840,000, the augmentation profiles at the combustor walls are quite uniform once the swirl flow impinges on the walls. It is surmised with some evidence that as the Reynolds number increases, a high tangential velocity persists in the vicinity of the combustor walls downstream of impingement, maintaining a near constant value of the heat transfer coefficient. The computed and experimental heat transfer augmentation ratios at low Reynolds numbers are within 30-40% of each other. / Master of Science

Reynolds-Averaged Navier-Stokes (RANS) simulations are widely used for engineering design and analysis involving turbulent flows. In RANS simulations, the Reynolds stress needs closure models and the existing models have large model-form uncertainties. Therefore, the RANS simulations are known to be unreliable in many flows of engineering relevance, including flows with three-dimensional structures, swirl, pressure gradients, or curvature. This lack of accuracy in complex flows has diminished the utility of RANS simulations as a predictive tool for engineering design, analysis, optimization, and reliability assessments. Recently, data-driven methods have emerged as a promising alternative to develop the model of Reynolds stress for RANS simulations. In this dissertation I explore two physics-informed, data-driven frameworks to improve RANS modeled Reynolds stresses. First, a Bayesian inference framework is proposed to quantify and reduce the model-form uncertainty of RANS modeled Reynolds stress by leveraging online sparse measurement data with empirical prior knowledge. Second, a machine-learning-assisted framework is proposed to utilize offline high-fidelity simulation databases. Numerical results show that the data-driven RANS models have better prediction of Reynolds stress and other quantities of interest for several canonical flows. Two metrics are also presented for an a priori assessment of the prediction confidence for the machine-learning-assisted RANS model. The proposed data-driven methods are also applicable to the computational study of other physical systems whose governing equations have some unresolved physics to be modeled. / Ph. D. / Reynolds-Averaged Navier–Stokes (RANS) simulations are widely used for engineering design and analysis involving turbulent flows. In RANS simulations, the Reynolds stress needs closure models and the existing models have large model-form uncertainties. Therefore, the RANS simulations are known to be unreliable in many flows of engineering relevance, including flows with three-dimensional structures, swirl, pressure gradients, or curvature. This lack of accuracy in complex flows has diminished the utility of RANS simulations as a predictive tool for engineering design, analysis, optimization, and reliability assessments. Recently, data-driven methods have emerged as a promising alternative to develop the model of Reynolds stress for RANS simulations. In this dissertation I explore two physics-informed, data-driven frameworks to improve RANS modeled Reynolds stresses. First, a Bayesian inference framework is proposed to quantify and reduce the model-form uncertainty of RANS modeled Reynolds stress by leveraging online sparse measurement data with empirical prior knowledge. Second, a machine-learning-assisted framework is proposed to utilize offline high fidelity simulation databases. Numerical results show that the data-driven RANS models have better prediction of Reynolds stress and other quantities of interest for several canonical flows. Two metrics are also presented for an a priori assessment of the prediction confidence for the machine-learning-assisted RANS model. The proposed data-driven methods are also applicable to the computational study of other physical systems whose governing equations have some unresolved physics to be modeled.