Wings, turbulent boundary layers and flow separation

Abstract: 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 (Rec = 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.