Showing papers in "Physical review fluids in 2022"

Wall turbulence at high friction Reynolds numbers

Abstract: An instantaneous streamwise velocity perturbation of a new simulation of a turbulent channel flow at an unprecedented friction Reynolds number of 10000 is conducted. One point statistics and turbulent budgets are discussed, solving some questions and raising new ones.

Everlasting bubbles and liquid films resisting drainage, evaporation, and nuclei-induced bursting

Abstract: Soap bubbles are by essence fragile and ephemeral. Depending on their composition and environment, bubble bursting can be triggered by gravity-induced drainage and/or the evaporation of the liquid and/or the presence of nuclei. In this paper, we design bubbles made of a composite liquid film able to neutralize all these effects and keep their integrity for more than one year in a standard atmosphere.

Connecting wall modes and boundary zonal flows in rotating Rayleigh-Bénard convection

Abstract: Rotating thermal convection captures key properties of geophysical and astrophysical systems. By performing high-resolution direct numerical simulations we strongly connect the physics of wall-localized states of rotating convection with observations of a boundary zonal flow where turbulent convection has a strong boundary localized structure with similar properties. We show that as buoyancy increases at fixed rotation rate, spatial, temporal, and heat-transport properties of the localized state change smoothly and monotonically. Our results connect the physics of rotating convection in finite containers with similar geophysical/astrophysical phenomena at much larger spatial scales.

Direct numerical simulation of turbulence and microphysics in the Pi Chamber

Abstract: Direct numerical simulation is applied to the Pi Chamber experimental facility to understand droplet growth and activation in the context of moist Rayleigh-B\'enard turbulence. While many bulk features of the experimental observations are represented well, the Lagrangian approach to droplet microphysics is used to gain insight where experiments cannot. Common assumptions regarding the activation-deactivation cycle and droplet lifetime are shown to be likely erroneous.

Stochastic model for the hydrodynamic force in Euler–Lagrange simulations of particle-laden flows

Abstract: Drag force models generally used in Eulerian-Lagrangian (EL) methods only represent the mean hydrodynamic force acting upon a suspension; higher-order drag force statistics, arising from neighbor-induced flow perturbations, are neglected, affecting particle velocity variance and dispersion predictions. We develop a force Langevin (FL) model that treats neighbor-induced drag fluctuations as a stochastic process. The stochastic EL framework specifies unresolved drag force statistics, leading to the correct evolution of particle velocity variance over a wide range of Reynolds numbers and solids volume fractions, when compared to particle-resolved DNS of freely evolving homogeneous suspensions.

Reinforcement learning for pursuit and evasion of microswimmers at low Reynolds number

Abstract: Using reinforcement learning, we study the coevolution of pursuing-evasion policies of two microswimmers that can sense each other only through hydrodynamic signals, which provide ambiguous information. We show that both agents find effective ways to overcome the difficulties set by partial information, and we explain the main discovered strategies. The setting here developed may offer a framework to study prey-predator interactions in more complex situations.

Impact regimes of nanodroplets impacting nanopillared surfaces

Abstract: The impact of nanodroplets has received increasing attention due to their wide applications in nanotechnologies. A phase diagram containing four impact regimes is constructed for the impact of nanodroplets on nanopillared surfaces. Several significant differences in the impact regimes are distinguished between the nanoscale and macroscale. The wetting transition at the nanoscale does not follow the macroscale mechanisms and, hence, a new model is proposed to understand the wetting transition mechanism at the nanoscale.

Diffusiophoresis in a Taylor-dispersing solute

Abstract: The motion of particles in a channel containing solute is, in part, a result of diffusiophoresis. Previous studies of diffusiophoresis in narrow channels have focused on quasi-one-dimensional dynamics. We consider the two-dimensional dynamics in an early-time regime. In this regime, spanwise variations in solute concentration have not yet diminished, and cross-channel particle migration may be significant.

Geometric effects induce anomalous size-dependent active transport in structured environments

Abstract: A multiscale investigation of swimming bacteria in a micropillar array permits measurement of cell body sizes and long-term diffusivities of individuals in a structured medium. When pillar spacing is on the order of the bacterium size, diffusivity behaves anomalously. While shorter cells are trapped by individual pillars through hydrodynamic attraction, causing them to circulate around the pillars, longer cells escape more easily due to simultaneous interactions with multiple pillars. Such size-dependent trapping and escaping is well characterized by a geometric model, suggesting the essential role of environmental and bacterial geometry in governing long-range transport.

Canonical orbits for rapidly deforming planar microswimmers in shear flow

Abstract: Classically, the rotation of ellipsoids in shear Stokes flow is captured by Jeffery’s orbits. Here, we demonstrate that Jeffery’s orbits also describe high-frequency shape-deforming swimmers moving in the plane of a shear flow, employing only basic properties of Stokes flow and a multiple-scales asymptotic analysis. In doing so, we support the use of these simple models for capturing shapechanging swimmer dynamics in studies of active matter and highlight the ubiquity of ellipsoid-like dynamics in complex systems. This result is robust to weakly confounding effects, such as distant boundaries, and also applies in the low-frequency limit.

Active gyrotactic stability of microswimmers using hydromechanical signals

Abstract: Using machine learning, we find a simple yet efficient strategy for microswimmers to actively adjust their swimming direction in response to hydromechanical signals, allowing robust vertical migration in turbulence. In contrast, passive bottom-heavy swimmers migrate much slower upwards and settle much slower downwards. Our results may be important to understand daily long-range vertical migration of plankton in the turbulent ocean, or to engineer efficient strategies for fabricated microswimmers.

Non-Boussinesq subgrid-scale model with dynamic tensorial coefficients

Abstract: A major drawback of Boussinesq-type subgrid-scale stress models used in large-eddy simulations is the inherent assumption of alignment between large-scale strain rates and filtered subgrid-stresses. A priori analyses using direct numerical simulation (DNS) data has shown that this assumption is invalid locally as subgrid-scale stresses are poorly correlated with the large-scale strain rates [Bardina et al., AIAA 1980; Meneveau and Liu, Ann. Rev. Fluid Mech. 2002]. In the present work, a new, non-Boussinesq subgrid-scale model is presented where the model coefficients are computed dynamically. Some previous non-Boussinesq models have observed issues in providing adequate dissipation of turbulent kinetic energy [e.g.: Bardina et al., AIAA 1980; Clark et al. J. Fluid Mech., 1979; Stolz and Adams, Phys. of Fluids, 1999]; however, the present model is shown to provide sufficient dissipation using dynamic coefficients. Modeled subgrid-scale Reynolds stresses satisfy the consistency requirements of the governing equations for LES, vanish in laminar flow and at solid boundaries, and have the correct asymptotic behavior in the near-wall region of a turbulent boundary layer. The new model, referred to as the dynamic tensor-coefficient Smagorinsky model (DTCSM), has been tested in simulations of canonical flows: decaying and forced homogeneous isotropic turbulence (HIT), and wall-modeled turbulent channel flow at high Reynolds numbers; the results show favorable agreement with DNS data. In order to assess the performance of DTCSM in more complex flows, wall-modeled simulations of high Reynolds number flow over a Gaussian bump exhibiting smooth-body flow separation are performed. Predictions of surface pressure and skin friction, compared against DNS and experimental data, show improved accuracy from DTCSM in comparison to the existing static coefficient (Vreman) and dynamic Smagorinsky model.

The hydrodynamics of a twisting, bending, inextensible fiber in Stokes flow

Abstract: In swimming microorganisms and the cell cytoskeleton, inextensible fibers resist bending and twisting, and interact with the surrounding fluid to cause or resist large-scale fluid motion. In this paper, we develop a novel numerical method for the simulation of cylindrical fibers by extending our previous work on inextensible bending fibers [Maxian et al., Phys. Rev. Fluids 6 (1), 014102] to fibers with twist elasticity. In our “Euler” model, twist is a scalar function that measures the deviation of the fiber cross section relative to a twist-free frame, the fiber exerts only torque parallel to the centerline on the fluid, and the perpendicular components of the rotational fluid velocity are discarded in favor of the translational velocity. In the first part of this paper, we justify this model by comparing it to another commonly-used “Kirchhoff” formulation where the fiber exerts both perpendicular and parallel torque on the fluid, and the perpendicular angular fluid velocity is required to be consistent with the translational fluid velocity. Through asymptotics and numerics, we show that the perpendicular torques in the Kirchhoff model are small, thereby establishing the asymptotic equivalence of the two models for slender fibers. We then develop a spectral numerical method for the hydrodynamics of the Euler model. We define hydrodynamic mobility operators using integrals of the Rotne-Prager-Yamakawa tensor, and evaluate these integrals through a novel slender-body quadrature, which requires on the order of 10 points along a smooth fiber to obtain several digits of accuracy. We demonstrate that this choice of mobility removes the unphysical negative eigenvalues in the translation-translation mobility associated with asymptotic slender body theories, and ensures strong convergence of the fiber velocity and weak convergence of the fiber constraint forces. We pair the spatial discretization with a semi-implicit temporal integrator to confirm the negligible contribution of twist elasticity to the relaxation dynamics of a bent fiber. We also study the instability of a twirling fiber, and demonstrate that rotation-translation and translation-rotation coupling change the critical twirling frequency at which a whirling instability onsets by ∼ 20%, which explains some (but not all) of the deviation from theory observed experimentally. When twirling is unstable, we show that the dynamics transition to a steady overwhirling state, and quantify the amplitude and frequency of the resulting steady crankshafting motion.

Pattern selection for thermocapillary flow in rectangular containers in microgravity

Abstract: The performance of Phase Change Material (PCM) devices in microgravity can be significantly improved by thermocapillary convection. However, the melting process in this case is affected by a series of instabilities and mode transitions due to the evolving size and shape of the liquid domain. We perform a numerical investigation of pattern selection for thermocapillary flow in ideal rectangular containers of liquid n-octadecane in microgravity and show how this can be applied to explain the more complex dynamics of melting PCMs. In particular, the locations of travelling and standing wave instabilities predicted in this way show very good agreement with numerical simulations of PCM melting.

Experimental observations of internal wave turbulence transition in a stratified fluid

Abstract: Recent developments of the weak turbulence theory applied to internal waves exhibit a power-law solution of the kinetic energy equation close to the oceanic Garrett & Munk spectrum, confirming weakly nonlinear wave interactions as a likely explanation of the observed oceanic spectra. However, finite-size effects can hinder wave interactions in bounded domains, and observations often differ from theoretical predictions. This article studies the dynamical regimes experimentally developing in a stratified fluid forced by internal gravity waves in a pentagonal domain. We find that by changing the shape and increasing the dimensions of the domain, finite-size effects diminish and wave turbulence is observed. In this regime, the temporal spectra decay with a slope compatible with the Garrett-Munk spectra. Different regimes appear by changing the forcing conditions, namely discrete wave turbulence, weak wave turbulence, and strongly stratified turbulence. The buoyancy Reynolds number Re b marks well the transitions between the regimes, with weak wave turbulence occurring for 1 (cid:46) Re b (cid:46) 3 . 5 and strongly non-linear stratified turbulence for higher Re b .

Flow and mixing induced by single, colinear, and colliding contractile waves in the intestine

Abstract: The flow of liquid food bolus in different intestinal contraction regimes is studied experimentally, analytically, and numerically. We show that a particle subjected to a peristaltic wave has a nonintuitive propulsion-reflux motion. When multiple waves are generated sequentially, as happens in the gut, reflux is found to be maximized for an inter-wave length corresponding to that observed physiologically in animals, indicating a possible evolutionary bolus absorption optimization. We find that counter-propagating waves generate a high-pressure region from which high-velocity bolus jets emerge. As a result, these waves generate 80 times more mixing than waves going in the same direction.

Spontaneous locomotion of phoretic particles in three dimensions

Abstract: The motion of an autophoretic spherical particle in a simple fluid is analyzed. This motion is powered by a chemical species which is absorbed or emitted by the particle and which diffuses and is advected in the surrounding fluid. The transition from the nonmotile to the motile state occurs if the Péclet number Pe (defined as the ratio of the solute emission rate over the solute diffusion rate) is sufficiently large. We first analyze the axisymmetric case (restricting the particle to a unique direction). In this case, we find that the motion of the particle transits from a motionless to a directed motion at a given critical Pe. Increasing Pe, we find a second critical value where the particle becomes stagnant in a symmetric flow. A further increase of Pe leads to a recovery of motile motion. When Pe is increased even further, the particle shows a periodic motion undergoing a subharmonic cascade before entering chaos. In this regime, the mean-square displacement behaves quadratically with time (a ballistic regime). When the axisymmetry constraint is relaxed, allowing the particle to freely move in three-dimensional space, we find that at a small Pe the particle moves in a straight manner. There exists a critical value where the particle exhibits an oscillatory motion with a meandering trajectory. Increasing Pe further leads to chaotic bursts for some time, before entering fully into chaos via the intermittency scenario at a critical Pe number. In this regime, the particle shows run-and-tumble–like dynamics: The trajectory is then characterized by a ballistic swimming nature at a short time and a diffusive nature at a long time.

Probabilistic forecasts of extreme heatwaves using convolutional neural networks in a regime of lack of data

Abstract: Understanding extreme events and their probability is key for the study of climate change impacts, risk assessment, adaptation, and the protection of living beings. Forecasting the occurrence probability of extreme heatwaves is a primary challenge for risk assessment and attribution, but also for fundamental studies about processes, dataset and model validation, and climate change studies. In this work we develop a methodology to build forecasting models which are based on convolutional neural networks, trained on extremely long climate model outputs. We demonstrate that neural networks have positive predictive skills, with respect to random climatological forecasts, for the occurrence of long-lasting 14-day heatwaves over France, up to 15 days ahead of time for fast dynamical drivers (500 hPa geopotential height fields), and also at much longer lead times for slow physical drivers (soil moisture). This forecast is made seamlessly in time and space, for fast hemispheric and slow local drivers. We find that the neural network selects extreme heatwaves associated with a North-Hemisphere wavenumber-3 pattern. The main scientific message is that most of the time, training neural networks for predicting extreme heatwaves occurs in a regime of lack of data. We suggest that this is likely to be the case for most other applications to large scale atmosphere and climate phenomena. For instance, using one hundred years-long training sets, a regime of drastic lack of data, leads to severely lower predictive skills and general inability to extract useful information available in the 500 hPa geopotential height field at a hemispheric scale in contrast to the dataset of several thousand years long. We discuss perspectives for dealing with the lack of data regime, for instance rare event simulations and how transfer learning may play a role in this latter task.

Gradient-free optimization of chaotic acoustics with reservoir computing

Abstract: The suppression of chaotic acoustic oscillations is a challenging problem in optimization. This is because gradient-based optimization struggles to optimize chaotic systems. In this paper, we develop a Bayesian approach based on reservoir computing to suppress chaotic oscillations without calculating the gradient.

Viscoplastic sessile drop coalescence

Abstract: The evolution of the liquid bridge formed between two coalescing sessile yield-stress drops is studied experimentally. We find that the height of the bridge evolves similar to a viscous Newtonian fluid, h 0 ∼ t , before arresting at long time prior to minimizing its liquid/gas interfacial energy. We numerically solve for the final arrested profile shape and find it depends on the fluid’s yield stress τ y and coalescence angle α , represented by the Bingham number τ y h drop /σ modified by the drop’s height-width aspect ratio. We present a scaling argument for the bridge’s temporal evolution using the length scale found from an analysis of the arrested shape as well as from the similarity solution derived for the bridge’s evolution.

Laminar vortex dynamics around forward-swept wings

Abstract: We numerically study laminar separated flows over forward-swept wings at Reynolds number Re=400. The flows over forward-swept wings generally feature a pair of counter-rotating tip vortices, which shape the wake dynamics significantly. The forward-swept wing also experiences enhanced lift with additional vortex lift under tip-vortex-induced downwash effects. The present results provide a comprehensive understanding of sweep effects on laminar separated flows over finite-aspect-ratio wings and offers coverage of less-explored areas of the low-Re aerodynamic database.

LES wall modeling for heat transfer at high speeds

Abstract: Temperature transformation does not collapse data, but the resulting wall model accurately predicts temperatures in high-speed boundary layer flows. Here we explain why this is so. The insights gained lead to a new turbulent Prandtl number formulation and more accurate wall models.

Neural network–based pore flow field prediction in porous media using super resolution

Abstract: Predicting the pore flow velocity directly from the sub-sampled pore structure is an ill-conditioned problem. Inspired by multi-grid methods for solving systems of linear equations, we use velocity fields simulated on coarse meshes to remedy such ill-conditioning. This leads to a super-resolution-assisted geometry-to-velocity mapping for porous media.

Nonmodal elastic instability and elastic waves in weakly perturbed channel flow

Abstract: Adding tiny amounts of long polymer chains to a fluid dramatically alters its behavior. In this work, we show that channel flows of such viscoelastic fluids at vanishingly small Reynolds numbers are highly sensitive to weak disturbances, even though they are linearly stable. We thus show experimentally that a weak disturbance elicits strong fluctuations with a continuous spectrum in three different regimes and elastic waves far from the excitation location through a non-modal bifurcation.

Water wave interactions with perforated elastic disks: Quadratic pressure discharge condition

Abstract: A fully coupled numerical model is developed to study wave interactions with perforated elastic disks. The flow past the perforated surface is represented by a quadratic pressure discharge condition with practical validity. The nonlinear nature of the pressure drop condition results in a dependency of hydrodynamic responses on wave steepness.

Three-dimensional direct numerical simulations of vortex-induced vibrations of a circular cylinder in proximity to a stationary wall

Abstract: In this paper three-dimensional direct numerical simulations (3-D DNS) on vortex-induced vibrations of an elastically mounted circular cylinder near a stationary wall at a subcritical Reynolds number of 500 and a gap ratio of 0.8 are conducted. It is found that the three-dimensionality increases linearly with amplitude, leading to substantial variations in the vortex dynamics. The interactions of the vortices with the wall-generated boundary layer play significant roles in altering the cylinder vibration.

Non-Boussinesq subgrid-scale model with dynamic tensorial coefficients

Abstract: A major drawback of Boussinesq-type subgrid-scale stress models used in large-eddy simulations is the inherent assumption of alignment between large-scale strain rates and filtered subgrid-stresses. A priori analyses using direct numerical simulation (DNS) data has shown that this assumption is invalid locally as subgrid-scale stresses are poorly correlated with the large-scale strain rates [Bardina et al., AIAA 1980; Meneveau and Liu, Ann. Rev. Fluid Mech. 2002]. In the present work, a new, non-Boussinesq subgrid-scale model is presented where the model coefficients are computed dynamically. Some previous non-Boussinesq models have observed issues in providing adequate dissipation of turbulent kinetic energy [e.g.: Bardina et al., AIAA 1980; Clark et al. J. Fluid Mech., 1979; Stolz and Adams, Phys. of Fluids, 1999]; however, the present model is shown to provide sufficient dissipation using dynamic coefficients. Modeled subgrid-scale Reynolds stresses satisfy the consistency requirements of the governing equations for LES, vanish in laminar flow and at solid boundaries, and have the correct asymptotic behavior in the near-wall region of a turbulent boundary layer. The new model, referred to as the dynamic tensor-coefficient Smagorinsky model (DTCSM), has been tested in simulations of canonical flows: decaying and forced homogeneous isotropic turbulence (HIT), and wall-modeled turbulent channel flow at high Reynolds numbers; the results show favorable agreement with DNS data. In order to assess the performance of DTCSM in more complex flows, wall-modeled simulations of high Reynolds number flow over a Gaussian bump exhibiting smooth-body flow separation are performed. Predictions of surface pressure and skin friction, compared against DNS and experimental data, show improved accuracy from DTCSM in comparison to the existing static coefficient (Vreman) and dynamic Smagorinsky model.

Suppression of the wake steady asymmetry of an Ahmed body by central base bleed

Abstract: The injection of fluid into the wake of a bluff body, commonly referred to as base bleed, is a well-known method of drag reduction. In the current paper we demonstrate that this method provokes a complete suppression of the steady asymmetry which otherwise dominates the natural Ahmed body wake and causes additional drag. Independent force measurements corroborate the suppression of the wake asymmetry. Different scales of base blowing reveal similar maximum drag reduction and asymmetry suppression, where the optimal blowing coefficient is found to scale with bleed-to-base area ratio as $({S}_{j}/S{)}^{1/2}$.

Fluid-structure interaction of a bio-inspired passively deployable flap for lift enhancement

Abstract: Birds have a remarkable ability to perform complex maneuvers at post-stall angles of attack such as landing, take-off, hovering and perching. The passive deployment of self-actuating covert feathers in response to unsteady flow separation while performing such maneuvers provides a passive, adaptive flow control paradigm for these aerodynamic capabilities. Most studies involving covert-feathers-inspired passive flow control have modeled the feathers as a rigidly attached or a freely moving flap on a wing. A flap mounted via a torsional spring enables a configuration more emblematic of the finite stiffness associated with the covert-feather dynamics (the free-flap case is the zero-stiffness limit of this more general torsional spring configuration). The performance benefits and flow physics associated with this more general case remain largely unexplored. In this work, we model covert feathers as a passively deployable, torsionally hinged flap on the suction surface of a stationary airfoil. We numerically investigate this airfoil-flap system at a low Reynolds number of Re = 1,000 and angle of attack of 20◦ by performing high-fidelity nonlinear simulations using a projection-based immersed boundary method. A parametric study performed by varying the stiffness of the spring, mass of the flap and location of the hinge yielded lift improvements as high as 27% relative to the baseline flap-less case and revealed two dominant flow behavioral regimes. A detailed analysis revealed that the stiffness-dependent mean flap deflection and inertia-dependent amplitude and phase of flap oscillations altered the dominant flow characteristics in both the regimes. Of special interest for performance benefits were the flap parameters that enhanced the liftconducive leading-edge vortex while weakening the trailing-edge vortex and associated detrimental effect of upstream propagation of reverse flow. These parameters also yielded a favorable temporal synchronization of flap oscillations with the vortex-shedding process in both regimes.

Progressive, extrapolative machine learning for near-wall turbulence modeling

Abstract: Training/retraining a model against new data often breaks its good behavior. This left machine learning models open to criticism: machine-learned models do not fully preserve, e.g., the law of the wall (among other empirical facts), and they do not generalize to, e.g., high Reynolds numbers (among other conditions). This paper establishes a paradigm for machine learning, namely, progressive machine learning, allowing one to preserve the good behavior of an existing model in retraining. This paradigm is applied to progressively model flows in the constant stress layer, the wake layer, and with system rotation, with success.