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A fully connected neural network (NN) is used to develop a subgrid-scale (SGS) model mapping the relation between the SGS stresses and filtered flow variables in a turbulent channel flow at $Re_\\tau = 178$. A priori and a posteriori tests are performed to investigate its prediction performance. In a priori test, an NN-based SGS model with the input filtered strain rate or velocity gradient tensor at multiple points provides highest correlation coefficients between the predicted and true SGS stresses, and reasonably predicts the backscatter. However, this model provides unstable solution in a posteriori test, unless a special treatment such as backscatter clipping is used. On the other hand, an NN-based SGS model with the input filtered strain rate tensor at single point shows an excellent prediction capability for the mean velocity and Reynolds shear stress in a posteriori test, although it gives low correlation coefficients between the true and predicted SGS stresses in a priori test. This NN-based SGS model trained at $Re_\\tau = 178$ is applied to a turbulent channel flow at $Re_\\tau = 723$ using the same grid resolution in wall units, providing fairly good agreements of the solutions with the filtered direct numerical simulation (DNS) data. When the grid resolution in wall units is different from that of trained data, this NN-based SGS model does not perform well. This is overcome by training an NN with the datasets having two filters whose sizes are bigger and smaller than the grid size in large eddy simulation (LES). Finally, the limitations of NN-based LES to complex flow are discussed.

We develop a phase-field multiphase lattice Boltzmann model to systematically investigate the dynamic behaviour of a droplet passing through a microfluidic T-junction, especially focusing on the non-breakup of the droplet. Detailed information on the breakup and non-breakup is presented, together with the quantitative evolutions of driving and resistance forces as well as the droplet deformation characteristics involved. Through comparisons between cases of non-breakup and breakup, we find that the appearance of tunnels (the lubricating film between droplet and channel walls) provides a precondition for the final non-breakup of droplets, which slows down the droplet deformation rate and even induces non-breakup. The vortex flow formed inside droplets plays an important role in determining whether they break up or not. In particular, when the strength of vortex flow exceeds a critical value, a droplet can no longer break up. Additionally, more effort has been devoted to investigating the effects of viscosity ratio between disperse and continuous phases and width ratio between branch and main channels on droplet dynamic behaviours. It is found that a large droplet viscosity results in a small velocity gradient in a droplet, which restricts vortex generation and thus produces lower deformation resistance. Consequently, it is easier to break up a droplet with larger viscosity. Our work also reveals that a droplet in small branch channels tends to obstruct the channels and have small vortex flows, which induces easier breakup too. Eventually, several phase diagrams for droplet flow patterns are provided, and the corresponding power-law correlations ( $l_{0}/w=\\unicode[STIX]{x1D6FD}Ca^{b}$ , where $l_{0}/w$ is dimensionless initial droplet length and $Ca$ is capillary number) are fitted to describe the boundaries between different flow patterns.

It has been hypothesized that submesoscale flows play an important role in the vertical transport of climatically important tracers, due to their strong associated vertical velocities. However, the multi-scale, non-linear, and Lagrangian nature of transport makes it challenging to attribute proportions of the tracer fluxes to certain processes, scales, regions, or features. Here we show that criteria based on the surface vorticity and strain joint probability distribution function (JPDF) effectively decomposes the surface velocity field into distinguishable flow regions, and different flow features, like fronts or eddies, are contained in different flow regions. The JPDF has a distinct shape and approximately parses the flow into different scales, as stronger velocity gradients are usually associated with smaller scales. Conditioning the vertical tracer transport on the vorticity-strain JPDF can therefore help to attribute the transport to different types of flows and scales. Applied to a set of idealized Antarctic Circumpolar Current simulations that vary only in horizontal resolution, this diagnostic approach demonstrates that small-scale strain dominated regions that are generally associated with submesoscale fronts, despite their minuscule spatial footprint, play an outsized role in exchanging tracers across the mixed layer base and are an important contributor to the large-scale tracer budgets. Resolving these flows not only adds extra flux at the small scales, but also enhances the flux due to the larger-scale flows.

We experimentally investigate the breakup mechanisms and probability of Hinze-scale bubbles in turbulence. The Hinze scale is defined as the critical bubble size based on the critical mean Weber number, across which the bubble breakup probability was believed to have an abrupt transition from being dominated by turbulence stresses to being suppressed completely by the surface tension. In this work, to quantify the breakup probability of bubbles with sizes close to the Hinze scale and to examine different breakup mechanisms, both bubbles and their surrounding tracer particles were simultaneously tracked. From the experimental results, two Weber numbers, one calculated from the slip velocity between the two phases and the other acquired from local velocity gradients, are separated and fitted with models that can be linked back to turbulence characteristics. Moreover, we also provide an empirical model to link bubble deformation to the two Weber numbers by extending the relationship obtained from potential flow theory. The proposed relationship between bubble aspect ratio and the Weber numbers seems to work consistently well for a range of bubble sizes. Furthermore, the time traces of bubble aspect ratio and the two Weber numbers are connected using the linear forced oscillator model. Finally, having access to the distributions of these two Weber numbers provides a unique way to extract the breakup probability of bubbles with sizes close to the Hinze scale.

The tendency of turbulent flows to produce fine-scale motions from large-scale energy injection is often viewed as a scale-wise cascade of kinetic energy driven by vorticity stretching. This has been recently evaluated by an exact, spatially local relationship (Johnson, P.L. Phys. Rev. Lett., vol. 124, 2020, p. 104501), which also highlights the contribution of strain self-amplification. In this paper, the role of these two mechanisms is explored in more detail. Vorticity stretching and strain amplification interactions between velocity gradients filtered at the same scale account for approximately half of the energy cascade rate, directly connecting the restricted Euler dynamics to the energy cascade. Multiscale strain amplification and vorticity stretching are equally important, however, and more closely resemble eddy viscosity physics. Moreover, ensuing evidence of a power-law decay of energy transfer contributions from disparate scales supports the notion of an energy cascade, albeit a ‘leaky’ one. Besides vorticity stretching and strain self-amplification, a third mechanism of energy transfer is introduced and related to the vortex thinning mechanism important for the inverse cascade in two dimensions. Simulation results indicate this mechanism also provides a net source of backscatter in three-dimensional turbulence, in the range of scales associated with the bottleneck effect. Taken together, these results provide a rich set of implications for large-eddy simulation modelling.

In a seminal article, Maxey (J. Fluid Mech., vol. 174, 1987, pp. 441–465) presented a theoretical analysis showing that enhanced particle settling speeds in turbulence occur through the preferential sweeping mechanism, which depends on the preferential sampling of the fluid velocity gradient field by the inertial particles. However, recent direct numerical simulation (DNS) results in Ireland et al. (J. Fluid Mech., vol. 796, 2016b, pp. 659–711) show that even in a portion of the parameter space where this preferential sampling is absent, the particles nevertheless exhibit enhanced settling velocities. Further, there are several outstanding questions concerning the role of different turbulent flow scales on the enhanced settling, and the role of the Taylor Reynolds number $R_{\\unicode[STIX]{x1D706}}$ . The analysis of Maxey does not explain these issues, partly since it was restricted to particle Stokes numbers $St\\ll 1$ . To address these issues, we have developed a new theoretical result, valid for arbitrary $St$ , that reveals the multiscale nature of the mechanism generating the enhanced settling speeds. In particular, it shows how the range of scales at which the preferential sweeping mechanism operates depends on $St$ . This analysis is complemented by results from DNS where we examine the role of different flow scales on the particle settling speeds by coarse graining the underlying flow. The results show how the flow scales that contribute to the enhanced settling depend on $St$ , and that contrary to previous claims, there can be no single turbulent velocity scale that characterizes the enhanced settling speed. The results explain the dependence of the particle settling speeds on $R_{\\unicode[STIX]{x1D706}}$ , and show how the saturation of this dependence at sufficiently large $R_{\\unicode[STIX]{x1D706}}$ depends upon $St$ . The results also show that as the Stokes settling velocity of the particles is increased, the flow scales of the turbulence responsible for enhancing the particle settling speed become larger. Finally, we explored the multiscale nature of the preferential sweeping mechanism by considering how particles preferentially sample the fluid velocity gradients coarse grained at various scales. The results show that while rapidly settling particles do not preferentially sample the fluid velocity gradients, they do preferentially sample the fluid velocity gradients coarse grained at scales outside of the dissipation range. This explains the findings of Ireland et al., and further illustrates the truly multiscale nature of the mechanism generating enhanced particle settling speeds in turbulence.

Oceanic surface submesoscale currents are characterized by anisotropic fronts and filaments with widths from 100 m to a few kilometers; an O (1) Rossby number; and large magnitudes of lateral buoyancy and velocity gradients, cyclonic vorticity, and convergence. We derive an asymptotic model of submeoscale frontogenesis—the rate of sharpening of submesoscale gradients—and show that in contrast with “classical” deformation frontogenesis, the near-surface convergent motions, which are associated with the ageostrophic secondary circulation, determine the gradient sharpening rates. Analytical solutions for the inviscid Lagrangian evolution of the gradient fields in the proposed asymptotic regime are provided, and emphasize the importance of ageostrophic motions in governing frontal evolution. These analytical solutions are further used to derive a scaling relation for the vertical buoyancy fluxes that accompany the gradient sharpening process. Realistic numerical simulations and drifter observations in the northern Gulf of Mexico during winter confirm the applicability of the asymptotic model to strong frontogenesis. Careful analysis of the numerical simulations and field measurements demonstrates that a subtle balance between boundary layer turbulence, pressure, and Coriolis effects (e.g., turbulent thermal wind; Gula et al. 2014) leads to the generation of the surface convergent motions that drive frontogenesis in this region. Because the asymptotic model makes no assumptions about the physical mechanisms that initiate the convergent frontogenetic motions, it is generic for submesoscale frontogenesis of O (1) Rossby number flows.

The asthenosphere plays a fundamental role in present-day plate tectonics as its low viscosity controls how convection in the mantle below it is expressed at the Earth’s surface above. The origin of the asthenosphere, including the role of partial melting in reducing its viscosity and facilitating deformation, remains unclear. Here we analysed receiver-function data from globally distributed seismic stations to image the lower reaches of the asthenospheric low-seismic-velocity zone. We present globally widespread evidence for a positive seismic-velocity gradient at depths of ~150 km, which represents the base of a particularly low-velocity zone within the asthenosphere. This boundary is most commonly detected in regions with elevated upper-mantle temperatures and is best modelled as the base of a partially molten layer. The presence of the boundary showed no correlation with radial seismic anisotropy, which represents accumulated mantle strain, indicating that the inferred partial melt has no substantial effect on the large-scale viscosity of the asthenosphere. These results imply the presence of a globally extensive, partially molten zone embedded within the asthenosphere, but that low asthenospheric viscosity is controlled primarily by gradual pressure and temperature variations with depth.A partially molten low-seismic-velocity zone in the asthenosphere is globally prevalent, but partial melting is not the primary control of low asthenospheric viscosity, according to analysis of seismic waves travelling through the mantle.

Nonlinear machine learning for turbulent flows can exhibit robust performance even outside the range of training data. This is achieved when machine-learning models can accommodate scale-invariant characteristics of turbulent flow structures. This study presents a data-driven approach to reveal scale-invariant vortical structures across Reynolds numbers that provide insights for supporting nonlinear machine-learning-based studies of turbulent flows. To uncover conditions for which nonlinear models are likely to perform well, we use a Buckingham-Pi-based sparse nonlinear scaling to find the influence of the Pi groups on the turbulent flow data. We consider nonlinear scalings of the invariants of the velocity gradient tensor for an example of three-dimensional decaying isotropic turbulence. The present scaling not only enables the identification of vortical structures that are interpolatory and extrapolatory for the given flow field data but also captures non-equilibrium effects of the energy cascade. As a demonstration, the present findings are applied to machine-learning-based super-resolution analysis of three-dimensional isotropic turbulence. We show that machine-learning models reconstruct vortical structures well in the interpolatory space with reduced performance in the extrapolatory space revealed by the nonlinearly scaled invariants. The present approach enables us to depart from labelling turbulent flow data with a single parameter of Reynolds number and comprehensively examine the flow field to support training and testing of nonlinear machine-learning techniques.

How non-spherical particles orient as they settle in a flow has important practical implications in a number of scientific and engineering problems. In a quiescent fluid, a slowly settling particle orients so that it settles with its broad side first. This is an effect of the torque due to convective inertia of the fluid that is set in motion by the settling particle, which maximises the drag experienced by the particle. Turbulent fluid-velocity gradients, on the other hand, tend to randomise the particle orientation. Recently the settling of non-spherical particles in turbulence was analysed neglecting the effect of convective fluid inertia, but taking into account the effect of the turbulent fluid-velocity gradients on the particle orientation. These studies reached the opposite conclusion, namely that the particle tends to settle with its narrow edge first, therefore minimising the drag on the particle. Here, we consider both effects, the convective inertial torque as well as the torque due to fluctuating fluid-velocity gradients. We ask under which circumstances either one or the other dominates. To this end we estimate the ratio of the magnitudes of the two torques. Our estimates suggest that the fluid-inertia torque prevails in high-Reynolds-number flows. In this case non-spherical particles tend to settle with orientations maximising drag. But when the Reynolds number is small, then the torque due to fluid-velocity gradients may dominate, causing the particle to settle with its narrow edge first, minimising the drag.