Drag-reduction strategies in wall-bounded turbulent flows using deep reinforcement learning

In this work we compare different drag-reduction strategies that compute their actuation based on the fluctuations at a given wall-normal location in turbulent open channel flow. In order to perform this study, we implement and describe in detail the reinforcement-learning interface to a computationally-efficient, parallelized, high-fidelity solver for fluid-flow simulations. We consider opposition control (Choi, Moin, and Kim, Journal of Fluid Mechanics 262, 1994) and the policies learnt using deep reinforcement learning (DRL) based on the state of the flow at two inner-scaled locations ($y^+ = 10$ and $y^+ = 15$). By using deep deterministic policy gradient (DDPG) algorithm, we are able to discover control strategies that outperform existing control methods. This represents a first step in the exploration of the capability of DRL algorithm to discover effective drag-reduction policies using information from different locations in the flow.Comment: 6 pages, 5 figure

A single-crystal neutron diffraction study of wardite, NaAl3(PO4)2(OH)4·2H2O

The crystal structure and crystal chemistry of wardite, ideally NaAl3(PO4)2(OH)4\ub72H2O, was investigated by single-crystal neutron diffraction (data collected at 20 K) and electron microprobe analysis in wavelength-dispersive mode. The empirical formula of the sample used in this study is: (Na0.91Ca0.01)\u3a3 = 0.92(Al2.97Fe3+0.05Ti0.01)\u3a3 = 3.03(P2.10O8)(OH)4\ub71.74H2O. The neutron diffraction data confirm that the crystal structure of wardite can be described with a tetragonal symmetry (space group P41212, a = b = 7.0577(5) and c = 19.0559(5) \uc5 at 20 K) and consists of sheets made of edge-sharing Na-polyhedra and Al-octahedra along with vertex-sharing Al-octahedra, parallel to (001), connected by P-tetrahedra and H bonds to form a (001) layer-type structure, which well explains the pronounced {001} cleavage of the wardite crystals. The present data show that four crystallographically independent H sites occur in the structure of wardite, two belonging to a H2O molecule (i.e., H1\u2013O6\u2013H2) and two forming hydroxyl groups (i.e., O5\u2013H3 and O7\u2013H4). The location of the hydrogen atoms allows us to define the extensive network of H bonds: the H atoms belonging to the H2O molecule form strong H bonds, whereas both the H atoms belonging to the two independent hydroxyl groups form weak interactions with bifurcated bonding schemes. As shown by the root-mean-square components of the displacement ellipsoids, oxygen and hydrogen atoms have slightly larger anisotropic displacement parameters compared to the other sites (populated by P, Al and Na). The maximum ratio of the max and min root-mean-square components of the displacement ellipsoids is observed for the protons of the hydroxyl groups, which experience bifurcated H-bonding schemes. A comparative analysis of the crystal structure of wardite and fluorowardite is also provided

Tertiary pegmatite dikes of the Central Alps

The largest field of Alpine Oligocene pegmatite dikes is in the Central Alps within the Southern Steep Belt (SSB) of the Alpine nappes; it extends for about 100 km in an E–W direction and 15 km in a N–S direction north of the Periadriatic Fault, from the Bergell pluton (to the east) to the Ossola valley (to the west). The pegmatite field geographically overlaps (1) the highest temperature domain of the Lepontine Barrovian metamorphic dome and (2) the zone of Alpine migmatization. We have studied pegmatites in two areas: (1) the Codera area on the western border of the Bergell pluton and (2) the Bodengo area between the Mera and the Mesolcina valleys. Most pegmatites show a simple mineral assemblage consisting of K-feldspar, quartz, and muscovite ± biotite, and only a minor percentage of the dikes (< 5%) contains Sn-Nb-Ta-Y-REE-U oxide, Y-REE phosphate, Mn-Fe-phosphate, Ti-Zr-silicate, Be-Y-REE-U-silicate and oxide minerals (beryl, chrysoberyl, bertrandite, bavenite, and milarite), garnet (almandine-spessartine), tourmaline (schorl to rare elbaite), bismuthinite, magnetite, and rarely dumortierite and helvite. The mineral assemblages, geological context, and chemical compositions allow the distinction between LCT (lithium, cesium, tantalum) and mixed LCT-NYF (niobium, yttrium, fluorine) pegmatites (with only one exception of an NYF dike in the Bodengo area). The LCT pegmatites of the Central Alps did not reach a high degree of geochemical evolution. The most fractionated pegmatites are found in the Codera area and contain Mn-rich elbaite, triplite, pink-beryl, and Cs-Rb-rich feldspar. In the Bodengo area pegmatites locally contain miarolitic cavities and the most evolved pegmatites correspond to the beryl-columbite-phosphate type. From a structural point of view two main types of pegmatites can be distinguished: (1) pegmatites that were involved in ductile deformation and (2) pegmatites that postdated the main ductile deformation of the SSB. Many pegmatites of the Codera valley belong to the first structural type: they were emplaced at relatively high ambient temperature (ca. 500 °C) and locally show a pervasive recrystallization of quartz and a mylonitic structure. The Codera dikes trend about 70° and are steeply dipping. In the Bodengo area the main set of pegmatites (trending approximately N–S to NNE–SSW) crosscuts the ductile deformation structures of the SSB, but the area also includes an earlier generation of boudinaged and folded pegmatite dikes. The undeformed pegmatites from this area may contain miarolitic pockets. There is no systematic difference in the mineral assemblage between the two structural types of pegmatites. However, the chemistry of pegmatite minerals, especially of garnet, in addition to field data suggests that the dikes of the Codera and Bodengo areas represent two distinct generations of pegmatites. Structural data and the few existing radiometric ages suggest that pegmatites were emplaced over a time span between 29 and 25 Ma (and possibly as young as 20 Ma), with the youngest dikes postdating the ductile deformations of the Alpine nappes. The present work presents a first comprehensive field description and geochemical – mineralogical characterization of the Alpine pegmatite field of the Central Alps

Predicting the wall-shear stress and wall pressure through convolutional neural networks

The objective of this study is to assess the capability of convolution-based neural networks to predict wall quantities in a turbulent open channel flow. The first tests are performed by training a fully-convolutional network (FCN) to predict the 2D velocity-fluctuation fields at the inner-scaled wall-normal location $y^{+}_{\rm target}$, using the sampled velocity fluctuations in wall-parallel planes located farther from the wall, at $y^{+}_{\rm input}$. The predictions from the FCN are compared against the predictions from a proposed R-Net architecture. Since the R-Net model is found to perform better than the FCN model, the former architecture is optimized to predict the 2D streamwise and spanwise wall-shear-stress components and the wall pressure from the sampled velocity-fluctuation fields farther from the wall. The dataset is obtained from DNS of open channel flow at $Re_{\tau} = 180$ and $550$. The turbulent velocity-fluctuation fields are sampled at various inner-scaled wall-normal locations, along with the wall-shear stress and the wall pressure. At $Re_{\tau}=550$, both FCN and R-Net can take advantage of the self-similarity in the logarithmic region of the flow and predict the velocity-fluctuation fields at $y^{+} = 50$ using the velocity-fluctuation fields at $y^{+} = 100$ as input with about 10% error in prediction of streamwise-fluctuations intensity. Further, the R-Net is also able to predict the wall-shear-stress and wall-pressure fields using the velocity-fluctuation fields at $y^+ = 50$ with around 10% error in the intensity of the corresponding fluctuations at both $Re_{\tau} = 180$ and $550$. These results are an encouraging starting point to develop neural-network-based approaches for modelling turbulence near the wall in large-eddy simulations.Comment: 33 pages, 10 figures. arXiv admin note: substantial text overlap with arXiv:2107.0734