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Different types of neural networks have been used to solve the flow sensing problem in turbulent flows, namely to estimate velocity in wall-parallel planes from wall measurements. Generative adversarial networks (GANs) are among the most promising methodologies, due to their more accurate estimations and better perceptual quality. This work tackles this flow sensing problem in the vicinity of the wall, addressing for the first time the reconstruction of the entire three-dimensional (3-D) field with a single network, i.e. a 3-D GAN. With this methodology, a single training and prediction process overcomes the limitation presented by the former approaches based on the independent estimation of wall-parallel planes. The network is capable of estimating the 3-D flow field with a level of error at each wall-normal distance comparable to that reported from wall-parallel plane estimations and at a lower training cost in terms of computational resources. The direct full 3-D reconstruction also unveils a direct interpretation in terms of coherent structures. It is shown that the accuracy of the network depends directly on the wall footprint of each individual turbulent structure. It is observed that wall-attached structures are predicted more accurately than wall-detached ones, especially at larger distances from the wall. Among wall-attached structures, smaller sweeps are reconstructed better than small ejections, while large ejections are reconstructed better than large sweeps as a consequence of their more intense footprint. [ABSTRACT FROM AUTHOR]

The aim of this work is to analyse the formation mechanisms of large-scale coherent structures in the flow around a wall-mounted square cylinder, due to their impact on pollutant transport within cities. To this end, we assess causal relations between the modes of a reduced-order model obtained by applying proper orthogonal decomposition to high-fidelity simulation data of the flow case under study. The causal relations are identified using conditional transfer entropy, which is an information-theoretical quantity that estimates the amount of information contained in the past of one variable about another. This allows for an understanding of the origins and evolution of different phenomena in the flow, with the aim of identifying the modes responsible for the formation of the main vortical structures. Our approach unveils that vortex-breaker modes are the most causal modes, in particular, over higher-order modes, and no significant causal relationships were found for vortex-generator modes. We validate this technique by determining the causal relations present in the nine-equation model of near-wall turbulence developed by Moehlis et al. (New J. Phys. , vol. 6, 2004, p. 56), which are in good agreement with literature results for turbulent channel flows. [ABSTRACT FROM AUTHOR]

In this study, a new well-resolved large-eddy simulation of an incompressible near-equilibrium adverse-pressure-gradient (APG) turbulent boundary layer (TBL) over a flat plate is presented. In this simulation, we have established a near-equilibrium APG over a wide Reynolds-number range. In this so-called region of interest, the Rotta-Clauser pressure-gradient parameter β exhibits an approximately constant value of around 1.4, and the Reynolds number based on momentum thickness reaches Reθ = 8700. To the best of the authors' knowledge, this is to date the highest Reθ achieved for a near-equilibrium APG TBL under an approximately constant moderate APG. We evaluated the self-similarity of the outer region using two scalings, namely the Zagarola-Smits and an alternative scaling based on edge velocity and displacement thickness. Our results reveal that outer-layer similarity is achieved, and the viscous scaling collapses the near-wall region of the mean flow in agreement with classical theory. Spectral analysis reveals that the APG displaces some small-scale energy from the near-wall to the outer region, an effect observed for all the components of the Reynolds-stress tensor, which becomes more evident at higher Reynolds numbers. In general, the effects of the APG are more noticeable at lower Reynolds numbers. For instance, the outer peak of turbulent-kinetic-energy (TKE) production is less prominent at higher Re. Although the scale separation increases with Re in zero-pressure-gradient TBLs, this effect becomes accentuated by the APG. Despite the reduction of the outer TKE production at higher Reynolds numbers, the mechanisms of energisation of large scales are still present. [ABSTRACT FROM AUTHOR]

The drag reduction with blowing is mostly attributed to Graph $C {f,G}$, which results from the decreased Graph $C {f,C}$ and Graph $C {f,P}$, which overwhelm the increase in Graph $C {f,D}$. As for the distribution of Graph $C {f,G}$ contributions in figures 5( I c i ) and 5( I f i ), negative contributions are observed in the region Graph $y^+\lesssim 80$ for the uncontrolled reference case at Graph $Re c=200\,000$ (Graph $y^+\lesssim 130$ at Graph $Re c=400\,000$) and positive values are observed beyond this region. The Graph $C {f,G}$ component was further decomposed into three terms related to the mean wall-normal convection (Graph $C {f,C}$), streamwise development (Graph $C {f,D}$) and the pressure gradient (Graph $C {f,P}$). As shown in figures 9( I d i )-9( I f i ), the small-scale spectra of Graph $v'$ are more intense beyond the buffer layer at Graph $y^+\approx 50$, with the spanwise wavelength scale Graph $\lambda z^+$ peaking at approximately Graph $80$ - Graph $100$ for all of the three cases, and the large-scale Graph $v'$ spectra peak at approximately Graph $(\lambda z/\delta {99},y/\delta {99})\approx (0.9,0.35)$. However, the specific quantities of Graph $R G$ and its constituents (Graph $R C$, Graph $R D$ and Graph $R P$) are very different from those on the suction side. [Extracted from the article]