An experimental characterization of the turbulent boundary layers developing around a NACA 4412 wing profile is carried out in the Minimum Turbulence Level (MTL) wind tunnel located at KTH Royal Institute of Technology. The campaign included collecting wall-pressure data via built-in pressure taps, capturing velocity signals in the turbulent boundary layers (TBLs) using hot-wire anemometry (HWA), and conducting direct skin-friction measurements with oil-film interferometry (OFI). The research spanned two chord-based Reynolds numbers (Rec=4×105 and 106) and four angles of attack (5°, 8°, 11° and 14°), encompassing a broad spectrum of flow conditions, from mild to strong adverse-pressure gradients (APGs), including scenarios where the TBL detaches from the wing surface. This dataset offers crucial insights into TBL behavior under varied flow conditions, particularly in the context of APGs. Key features include the quasi-independence of the pressure coefficient distributions from Reynolds number, which aids in distinguishing Reynolds-number effects from those due to APG strengths. The study also reveals changes in TBL dynamics as separation approaches, with energy shifting from the inner to the outer region and the eventual transition to a free-shear flow state post-separation. Additionally, the diagnostic scaling in the outer region under spatial-resolution effects is considered, showing further evidence for its applicability for small L+, however with inconsistent results for larger L+. The findings and database resulting from this campaign may be of special relevance for the development and validation of turbulence models, especially in the context of aeronautical applications.

Active flow-control techniques have shown promise for achieving high levels of drag reduction. However, these techniques are often complex and involve multiple tunable parameters, making it challenging to optimize their efficiency. Here, we present a Bayesian optimization (BO) approach based on Gaussian process regression to optimize a wall-normal blowing and suction control scheme for a NACA 4412 wing profile at two angles of attack: 5 and 11∘, corresponding to cruise and high-lift scenarios, respectively. An automated framework is developed by linking the BO code to the CFD solver OpenFOAM. RANS simulations (validated against high-fidelity LES and experimental data) are used in order to evaluate the different flow cases. BO is shown to provide rapid convergence towards a global maximum, even when the complexity of the response function is increased by introducing a model for the cost of the flow control actuation. The importance of considering the actuation cost is highlighted: while some cases yield a net drag reduction (NDR), they may result in an overall power increase. Furthermore, optimizing for NDR or net power reduction (NPR) can lead to significantly different actuation strategies. Finally, by considering losses and efficiencies representative of real-world applications, still a significant NPR is achieved in the 11∘ case, while net power reduction is only marginally positive in the 5∘ case.

This study uses high-resolution large-eddy simulations (LES) to investigate the turbulent flow around a NACA 4412 wing profile at multiple Reynolds numbers based on chord length and free-stream velocity (Rec=2×105, 4×105 and 106) and angles of attack (AoA=5∘, 8°, 11° and 14°). The introduction of adaptive mesh refinement (AMR) and non-conformal meshing into the spectral-element-method code Nek5000 enabled the simulations at higher AoAs exhibiting flow separation by enabling the use of wider domains, allowing to capture the largest turbulent scales associated with flow separation. The results provide a detailed database – including integral quantities, velocity statistics and spectra – which may be used for the evaluation of lower-fidelity turbulence models. Furthermore, closer inspection of specific turbulent-boundary-layer (TBL) profiles allows us to discern between pressure-gradient (PG) and Reynolds-numbers effects on TBLs, showing that Re balances the PG, by reducing the impact of PG on the flow. Lastly, we assess the influence of flow history on TBLs, showing that a consistent flow history over an extended length is needed for TBLs to exhibit comparable profiles and characteristics.

Wall-bounded turbulent flows as those occurring in transportation (e.g. aviation) or industrial applications (e.g turbomachinery), are usually subjected to pressure gradients (PGs). The presence of such PGs affects greatly the development and physics of the turbulent boundary layer (TBL), making it an open research area. An important phenomena associated with the presence of strong adverse PGs (APGs) as appearing in wings, is the separation of the boundary layer, which can lead to stall.

The present doctoral thesis investigates the turbulent flow developing around wing sections, focusing on the impact of adverse-pressure-gradient (APG) conditions on turbulent boundary layers (TBLs) and the physics of flow separation. Both experimental and numerical methods are employed to generate high-fidelity data sets and provide an in-depth analysis of the flow.

The first objective of this thesis is the development of a comprehensive database for the flow around a NACA 4412 wing profile. For this purpose, adaptive mesh refinement (AMR) is used together with the spectral-element method code Nek5000. With AMR, high-resolution Large Eddy Simulations (LES) are conducted at various Reynolds numbers (Re_c = 2×10⁵, 4×10⁵ and 1×10⁶) and angles of attack (AoA=5°, 8°, 11°, 14°), which were previously unattainable. The effect that strong APGs have on TBLs developing around a wing section is assessed through the collection of statistics and time series. The results demonstrate the influence of APG conditions on both the mean and variance profiles of velocity, and on the distribution and production of turbulence energy within the TBL. Additionally, the connection of APG TBLs with flow separation is explored through the development of an in-situ identification and tracking algorithm, tightly integrated into Nek5000. Our findings show that, in contrast to canonical flows, backflow events in TBLs under strong APGs extensively merge to form larger structures that grow exponentially in size, eventually leading to significant flow separation near the wing’s trailing edge.

Furthermore, a wind-tunnel experimental campaign is conducted to validate and extend the numerical results. Pressure, wall-shear stress and velocity measurements were carried out in the MTL wind tunnel at KTH Royal Institute of Technology. The study also scrutinizes measurement methodologies for APG TBLs, examining uncertainties in skin-friction determination and the impact of hot-wire probe lengths on velocity variance profiles.

Finally, a study based on Reynolds-averaged Navier–Stokes (RANS) simulations, utilizing high-fidelity data for validation, is performed to assess the optimization of flow-control schemes based on blowing and suction. This study, later extended to a transonic airfoil, showcases Bayesian optimization (BO) as an efficient method for computational fluid dynamics (CFD)-based optimization problems.

Denna doktorsavhandling undersöker det turbulenta flödet runt vingsektioner, med fokus på effekten av negativa tryckgradienter (APG) på turbulenta gränsskikt (TBL) och fysiken bakom avlösning. Både experimentella och numeriska metoder används för att generera data och genomföra en noggrant analys av flödet.

Det första målet med avhandlingen är att utveckla en omfattande databas för flödet runt en NACA 4412 vingprofil. För detta ändamål används adaptiv nätförfining (AMR) i det spektralelementbaserade programmet Nek5000. Med AMR genomförs väggupplösta large-eddy simuleringar (LES) vid olika Reynoldstal (Re_c = 2×10⁵, 4×10⁵ och 1×10⁶) och anfallsvinklar (AoA=5°, 8°, 11°, 14°), vilka tidigare varit ouppnåeliga. Effekten av den starka negativa tryckgradienten på TBL som utvecklas runt vingsektionen bedöms genom insamling av statistik och tidsserier. Resultaten visar på APG:s påverkan på både medel- och variansprofiler för hastighet, samt på fördelning och produktion av turbulenta energien inom TBL. Dessutom utforskas sambandet mellan APG TBL och avlösning genom utveckling av en in-situ identifierings- och spårningsalgoritm, integrerad i Nek5000. Våra resultat visar att negativa hastigheter händelser i TBL under starka APG interagerar betydligt med varandra, sammanflätar och bildar större strukturer som ökar exponentiellt i storlek, och så småningom leder till betydande avlösning nära vingens bakkant. 

Dessutom genomförs en experimentkampanj i vindtunnel för att validera och utöka de numeriska resultaten. Mätningar av tryck, väggskjuvspänning och hastighet utförs i MTL vindtunneln vid KTH Kungliga Tekniska Högskolan. Studien granskar också mätmetoder för APG TBL, undersöker osäkerheter i bestämningen av väggskjuvspänning samt effekterna av längden på varmtrådprober på mätprofiler för hastighetsvarians.

Slutligen utförs en RANS-studie, där högupplösta data används för validering, för att bedöma optimering av flödeskontrollmetoder baserade på blåsning och sugning. Denna studie, som senare utvidgas till ett transoniskt vingprofil, visar på Bayesiansk optimering som en effektiv metod för CFD (computational fluid dynamics)-baserade optimeringsproblem.

For adverse-pressure-gradient turbulent boundary layers, the study of integral skin-friction contributions still poses significant challenges. Beyond questions related to the integration boundaries and the derivation procedure, which have been thoroughly investigated in the literature, an important issue is how different terms should be aggregated. The nature of these flows, which exhibit significant in-homogeneity in the streamwise direction, usually results in cancellation between several contributions with high absolute values. We propose a formulation of the identity derived by Fukagata et al. (2002), which we obtained from the convective form of the governing equations. A new skin-friction contribution is defined, considering wall-tangential convection and pressure gradient together. This contribution is related to the evolution of the dynamic pressure in the mean flow. The results of the decomposition are examined for a broad range of pressure-gradient conditions and different flow-control strategies. We found that the new formulation of the identity allows to readily identify the different regimes of near-equilibrium conditions and approaching separation. It also provides a more effective description of control effects. A similar aggregation between convection and pressure-gradient terms is also possible for any other decomposition where in-homogeneity contributions are considered explicitly.

Wall-normal blowing and suction has shown to be a promising active control method for friction drag reduction. In this work, we exploit a Bayesian optimization framework based on Gaussian process regression to find a configuration of non-homogeneous wall-normal blowing and suction capable of improving the aerodynamic efficiency of an RAE2822 airfoil in transonic conditions. The RANS simulations are carried out with the open-source solver SU2. During the optimization process, three different scenarios are considered: only the drag is minimized, the drag and the power needed to drive the control system are included, and the actuation power with a specified compressor efficiency are used for the calculation of the efficiency increase. Even in the most realistic case considering the actuation power and efficiencies an increase in the overall efficiency of 1.15% is reached.

Understanding the progress of the Sustainable Development Goals (SDGs) proposed by the United Nations (UN) is important, but difficult. In particular, policymakers would need to understand the sentiment within the public regarding challenges associated with climate change. With this in mind and the rise of social media, this work focuses on the task of uncovering the sentiment of Twitter users concerning climate-related issues. This is done by applying modern natural-language-processing (NLP) methods, i.e. VADER, TextBlob, and BERT, to estimate the sentiment of a gathered dataset based on climate-change keywords. A transfer-learning-based model applied to a pre-trained BERT model for embedding and tokenizing with logistic regression for sentiment classification outperformed the rule-based methods VADER and TextBlob; based on our analysis, the proposed approach led to the highest accuracy: 69%. The collected data contained significant noise, especially from the keyword 'energy'. Consequently, using more specific keywords would improve the results. The use of other methods, like BERTweet, would also increase the accuracy of the model. The overall sentiment in the analyzed data was positive. The distribution of the positive, neutral, and negative sentiments was very similar in the different SDGs.

In this chapter we extend earlier work (Vinuesa et al., Nat Commun 11, 2020) on the potential of artificial intelligence (AI) to achieve the 17 Sustainable Development Goals (SDGs) proposed by the United Nations (UN) for the 2030 Agenda. The present contribution focuses on three SDGs related to healthy and sustainable societies, i.e., SDG 3 (on good health), SDG 11 (on sustainable cities), and SDG 13 (on climate action). This chapter extends the previous study within those three goals and goes beyond the 2030 targets. These SDGs are selected because they are closely related to the coronavirus disease 19 (COVID-19) pandemic and also to crises like climate change, which constitute important challenges to our society.