We carried out high-fidelity large-eddy simulations to investigate the effects of uniform blowing and uniform suction on the aerodynamic efficiency of a NACA4412 airfoil at the moderate Reynolds number based on chord length and incoming velocity of $$Re_c=200{,}000$$R e c = 200 , 000 . We found that uniform blowing applied at the suction side reduces the aerodynamics efficiency, while uniform suction increases it. This result is due to the combined impact of blowing and suction on skin friction, pressure drag and lift. When applied to the pressure side, uniform blowing improves aerodynamic efficiency. The Reynolds-number dependence of the relative contributions of pressure and friction to the total drag for the reference case is analysed via Reynolds-averaged Navier–Stokes simulations up to $$Re_c=10{,}000{,}000$$R e c = 10 , 000 , 000 . The results suggest that our conclusions on the control effect can tentatively be extended to a broader range of Reynolds numbers.

A detailed analysis of the effects of uniform blowing, uniform suction, and body-force damping on the turbulent boundary layer developing around a NACA4412 airfoil at moderate Reynolds number is presented. The flow over the suction and the pressure sides of the airfoil is subjected to a nonuniform adverse pressure gradient and a moderate favorable pressure gradient, respectively. We find that the changes in total skin friction due to blowing and suction are not very sensitive to different pressure-gradient conditions or the Reynolds number. However, when blowing and suction are applied to an adverse-pressure-gradient (APG) boundary layer, their impact on properties such as the boundary-layer thickness, the intensity of the wall-normal convection, and turbulent fluctuations are more pronounced. We employ the Fukagata-Iwamoto-Kasagi decomposition [K. Fukagata et al., Phys. Fluids 14, 73 (2002)] and spectral analysis to study the interaction between intense adverse pressure gradient and these control strategies. We find that the control modifies skin-friction contributions differently in adverse-pressure-gradient and zero-pressure-gradient boundary layers. In particular, the control strategies modify considerably both the streamwise-development and the pressure-gradient contributions, which have high magnitude when a strong adverse pressure gradient is present. Blowing and suction also impact the convection of structures in the wall-normal direction. Overall, our results suggest that it is not possible to simply separate pressure-gradient and control effects, a fact to take into account in future studies on control design in practical applications.

The application of drag-control strategies on canonical wall-bounded turbulence, such as periodic channel and zero- or adverse-pressure-gradient boundary layers, raises the question on how to distinguish consistently the origin of control effects under different reference conditions. We employ the RD identity (Renard & Deck, J. Fluid Mech., vol. 790, 2016, pp. 339–367) to decompose the mean friction drag and investigate the control effects of uniform blowing and suction applied to an NACA4412 airfoil at chord Reynolds numbers $Re_c=200\,000$ and $400\,000$ . The connection of the drag reduction/increase by using blowing/suction with the turbulence statistics (including viscous dissipation, turbulence kinetic energy production and spatial growth of the flow) across the boundary layer, subjected to adverse or favourable pressure gradients, is examined. We found that the inner and outer peaks of the contributions associated with the friction-drag generation show good scaling with either inner or outer units, respectively. They are also independent of the Reynolds number, control scheme and intensity of the blowing/suction. The small- and large-scale structures are separated with an adaptive scale-decomposition method, namely the empirical mode decomposition (EMD), which aims to analyse the scale-specific contribution of turbulent motions to friction-drag generation. Results unveil that blowing on the suction side of the airfoil is able to enhance the contribution of large-scale motions and to suppress that of small scales; however, suction behaves contrarily. The contributions related to cross-scale interactions remain almost unchanged with different control strategies.

In situ visualization on high-performance computing systems allows us to analyze simulation results that would otherwise be impossible, given the size of the simulation data sets and offline post-processing execution time. We develop an in situ adaptor for Paraview Catalyst and Nek5000, a massively parallel Fortran and C code for computational fluid dynamics. We perform a strong scalability test up to 2048 cores on KTH’s Beskow Cray XC40 supercomputer and assess in situ visualization’s impact on the Nek5000 performance. In our study case, a high-fidelity simulation of turbulent flow, we observe that in situ operations significantly limit the strong scalability of the code, reducing the relative parallel efficiency to only $$\approx 21\%$$≈ 21 % on 2048 cores (the relative efficiency of Nek5000 without in situ operations is $$\approx 99\%$$≈ 99 % ). Through profiling with Arm MAP, we identified a bottleneck in the image composition step (that uses the Radix-kr algorithm) where a majority of the time is spent on MPI communication. We also identified an imbalance of in situ processing time between rank 0 and all other ranks. In our case, better scaling and load-balancing in the parallel image composition would considerably improve the performance of Nek5000 with in situ capabilities. In general, the result of this study highlights the technical challenges posed by the integration of high-performance simulation codes and data-analysis libraries and their practical use in complex cases, even when efficient algorithms already exist for a certain application scenario.

High-fidelity large-eddy simulations of the flow around two rectangular obstacles are carried out at a Reynolds number of $10000$ based on the freestream velocity and the obstacle height. The incoming flow is a developed turbulent boundary layer. Mean-velocity components, turbulence fluctuations, and the terms of the turbulent-kinetic-energy budget are analyzed for three flow regimes: skimming flow, wake interference, and isolated roughness. Three regions are identified where the flow undergoes the most significant changes: the first obstacle's wake, the region in front of the second obstacle, and the region around the second obstacle. In the skimming-flow case, turbulence activity in the cavity between the obstacles is limited and mainly occurs in a small region in front of the second obstacle. In the wake-interference case, there is a strong interaction between the freestream flow that penetrates the cavity and the wake of the first obstacle. This interaction results in more intense turbulent fluctuations between the obstacles. In the isolated-roughness case, the wake of the first obstacle is in good agreement with that of an isolated obstacle. Separation bubbles with strong turbulent fluctuations appear around the second obstacle.