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Authors
Z. Jane Wang
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Unlike a helicopter, an insect can, in theory, use both lift and drag to stay aloft. Here we show that a dragonfly uses mostly drag to hover by employing asymmetric up and down strokes. Computations of a family of strokes further show that using drag can be as efficient as using lift at the low Reynolds number regime appropriate for insects.
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In this study we experimentally investigate bubbly drag reduction in a highly turbulent flow of water with dispersed air at $5.0 times 10^{5} leq text{Re} leq 1.7 times 10^{6}$ over a non-wetting surface containing micro-scale roughness. To do so, the Taylor-Couette geometry is used, allowing for both accurate global drag and local flow measurements. The inner cylinder - coated with a rough, hydrophobic material - is rotating, whereas the smooth outer cylinder is kept stationary. The crucial control parameter is the air volume fraction $alpha$ present in the working fluid. For small volume fractions ($alpha < {4},%$), we observe that the surface roughness from the coating increases the drag. For large volume fractions of air ($alpha geq 4,%$), the drag decreases compared to the case with both the inner and outer cylinders uncoated, i.e. smooth and hydrophilic, using the same volume fraction of air. This suggests that two competing mechanisms are at place: on the one hand the roughness invokes an extension of the log-layer - resulting in an increase in drag - and on the other hand there is a drag-reducing mechanism of the hydrophobic surface interacting with the bubbly liquid. The balance between these two effects determines whether there is overall drag reduction or drag enhancement. For further increased bubble concentration $alpha = {6},%$ we find a saturation of the drag reduction effect. Our study gives guidelines for industrial applications of bubbly drag reduction in hydrophobic wall-bounded turbulent flows.
Large eddy simulations (LES) are employed to investigate the role of time-varying currents on the form drag and vortex dynamics of submerged 3D topography in a stratified rotating environment. The current is of the form $U_c+U_t sin(2pi f_t t)$, where $U_c$ is the mean, $U_t$ is the tidal component and $f_t$ is its frequency. A conical obstacle is considered in the regime of low Froude number. When tides are absent, eddies are shed at the natural shedding frequency $f_{s,c}$. The relative frequency $f^*=f_{s,c}/f_t$ is varied in a parametric study which reveals states of high time-averaged form drag coefficient. There is a two-fold amplification of the form drag coefficient relative to the no-tide ($U_t=0$) case when $f^*$ lies between 0.5 and 1. The spatial organization of the near-wake vortices in the high drag states is different from a Karman vortex street. For instance, the vortex shedding from the obstacle is symmetric when $f^*=5/12$ and strongly asymmetric when $f^*=5/6$. The increase in form drag with increasing $f^*$ stems from bottom intensification of the pressure in the obstacle lee which is linked to changes in flow separation and near-wake vortices.
Efficiently predicting the flowfield and load in aerodynamic shape optimisation remains a highly challenging and relevant task. Deep learning methods have been of particular interest for such problems, due to their success for solving inverse problems in other fields. In the present study, U-net based deep neural network (DNN) models are trained with high-fidelity datasets to infer flow fields, and then employed as surrogate models to carry out the shape optimisation problem, i.e. to find a drag minimal profile with a fixed cross-section area subjected to a two-dimensional steady laminar flow. A level-set method as well as Bezier-curve method are used to parameterise the shape, while trained neural networks in conjunction with automatic differentiation are utilized to calculate the gradient flow in the optimisation framework. The optimised shapes and drag force values calculated from the flowfields predicted by DNN models agree well with reference data obtained via a Navier-Stokes solver and from the literature, which demonstrates that the DNN models are capable of predicting not only flowfield but also yield satisfactory aerodynamic forces. This is particularly promising as the DNNs were not specifically trained to infer aerodynamic forces. In conjunction with the fast runtime, the DNN-based optimisation framework shows promise for general aerodynamic design problems.
In the maritime industry, the injection of air bubbles into the turbulent boundary layer under the ship hull is seen as one of the most promising techniques to reduce the overall fuel consumption. However, the exact mechanism behind bubble drag reduction is unknown. Here we show that bubble drag reduction in turbulent flow dramatically depends on the bubble size. By adding minute concentrations (6 ppm) of the surfactant Triton X-100 into otherwise completely unchanged strongly turbulent Taylor-Couette flow containing bubbles, we dramatically reduce the drag reduction from more than 40% to about 4%, corresponding to the trivial effect of the bubbles on the density and viscosity of the liquid. The reason for this striking behavior is that the addition of surfactants prevents bubble coalescence, leading to much smaller bubbles. Our result demonstrates that bubble deformability is crucial for bubble drag reduction in turbulent flow and opens the door for an optimization of the process.
The nonlinear dynamics of the free surface of an ideal dielectric liquid in a strong electric field is studied. The equation for the evolution of surface electrohydrodynamic waves is derived in the approximation of small surface-slope angles. It is established that the equation can be solved for liquids with sufficiently high values of the permittivity. This makes it possible to describe the interaction of the counter-propagating waves.
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