This study proposes a novel super-resolution (or SR) framework for generating high-resolution turbulent boundary layer (TBL) flow from low-resolution inputs. The framework combines a super-resolution generative adversarial neural network (SRGAN) with down-sampling modules (DMs), integrating the residual of the continuity equation into the loss function. The DMs selectively filter out components with excessive energy dissipation in low-resolution fields prior to the super-resolution process. The framework iteratively applies the SRGAN and DM procedure to fully capture the energy cascade of multi-scale flow structures, collectively termed the SRGAN-based energy cascade reconstruction framework (EC-SRGAN). Despite being trained solely on turbulent channel flow data (via ‘zero-shot transfer’), EC-SRGAN exhibits remarkable generalization in predicting TBL small-scale velocity fields, accurately reproducing wavenumber spectra compared to direct numerical simulation (DNS) results. Furthermore, a super-resolution core is trained at a specific super-resolution ratio. By leveraging this pretrained super-resolution core, EC-SRGAN efficiently reconstructs TBL fields at multiple super-resolution ratios from various levels of low-resolution inputs, showcasing strong flexibility. By learning turbulent scale invariance, EC-SRGAN demonstrates robustness across different TBL datasets. These results underscore the potential of EC-SRGAN for generating and predicting wall turbulence with high flexibility, offering promising applications in addressing diverse TBL-related challenges.

High-dimensional dynamical systems projected onto a lower-dimensional manifold cease to be deterministic and are best described by probability distributions in the projected state space. Their equations of motion map onto an evolution operator with a deterministic component, describing the projected dynamics, and a stochastic one representing the neglected dimensions. This is illustrated with data-driven models for a moderate-Reynolds-number turbulent channel. It is shown that, for projections in which the deterministic component is dominant, relatively ‘physics-free’ stochastic Markovian models can be constructed that mimic many of the statistics of the real flow, even for fairly crude operator approximations, and this is related to general properties of Markov chains. Deterministic models converge to steady states, but the simplified stochastic models can be used to suggest what is essential to the flow and what is not.

We consider the long-time propagation of a Boussinesq inertia–buoyancy (large-Reynolds- number) gravity current released from a lock over a downslope of angle $\gamma$, affected by entrainment and drag. We show that the shallow-water (depth-averaged) equations with a Benjamin-type front-jump condition admit a similarity solution $x_N(t) = K t^{2/3}$ while $h, \phi, u$ change like $t$ to the power of $2/3, -4/3, -1/3$, respectively; here $x_N, h, \phi, u$ and $t$ are the position of the nose (distance from backwall), thickness, concentration of dense fluid, velocity and time, respectively, and K is a constant. Assuming that $\gamma$ and the coefficients of entrainment and drag are constant, we derive an analytical exact solution for the similarity profiles and show that $K \propto (\tan \gamma )^{1/3}$; the driving of the slope is balanced by entrainment and/or drag. The predicted $t^{2/3}$ propagation is in agreement with previously published experimental data but a conclusive quantitative assessment of the present theory cannot be performed due to various uncertainties (discussed in the paper) that must be resolved by future work.

The clustering of debris floating on liquid interfaces such as water surfaces is a complex phenomenon that finds its applications in numerous examples from industrial processing and environmental systems. The recent paper by Shin & Coletti (J. Fluid Mech., vol. 984, 2024, R7) presents an experimental campaign investigating the three effects of turbulence, particle interactions and interfacial effects, to elucidate how the three force scales drag, capillary forces and lubrication give rise to three distinct regimes of clustering in dense suspensions. The study, hence, provides a useful systematic to categorize the clustering mechanisms. As an important finding, it is shown that, depending on volume fraction and non-dimensional turbulent shear, particles either tend to cluster into aggregate sizes larger than the Kolmogorov scale or can break into pieces that are as small as the primary particle size.

The classical Helmholtz–Smoluchowski (HS) model of electroosmosis holds for homogeneously charged interfaces in contact with a fluid layer bearing an equal and opposite net charge. However, inhomogeneities in the surface charge and topography are inevitable, either as practical materials and fabrication artefacts, or at times as deliberately introduced modulations for flow control. In an effort to arrive at an analytically tractable theoretical framework for addressing the underlying electro-mechanical coupling, here, we generalize the traditional HS theory to an extent where both the surface charge and topographies may bear arbitrary and independent periodic forms. Using a spectral-asymptotic approach, we further arrive at closed-form expressions for describing the resulting electroosmotic pumping for topographic features with small characteristic amplitude to pattern period ratio, as relevant for most practical scenarios. We subsequently execute full-scale numerical simulations without any restrictions on the surface charge and topography variations to assess the efficacy of the theoretical framework. The corresponding test beds include distinctive signature patterns – for example, a square-wave surface charge distribution on trapezoidal pit topographies. Our results reveal that the charge–topography interplay induces an anisotropic flow drift, deviating from the classical HS paradigm. This, in turn, provides new quantitative insights into highly selective electroosmotic flow control via judicious design of the charge and topographical patterns, resulting in controllable accentuation, attenuation, nullification, deflection and even complete reversal of the flow. Our analysis further establishes a provision of estimating the zeta potentials of naturally ‘contaminated’ surfaces, as well as explaining the electrophoresis of large inhomogeneous particles; a paradigm that remained to be explored thus far.

Large amounts of small inertial particles embedded in a turbulent flow are known to modify the turbulent statistics and structures, a phenomenon referred to as turbulence modulation. While particle electrification is ubiquitous in particle-laden turbulence and significantly alters particle behaviour, the effects of inter-particle electrostatic forces on turbulence modulation and the underlying physical mechanisms remain unclear. To fill this gap, we perform a series of point-particle direct numerical simulations of turbulent channel flows at a friction Reynolds number of approximately 540, laden with uncharged and charged bidisperse particles. The results demonstrate that, compared to flows laden with uncharged particles, the presence of inter-particle electrostatic forces leads to substantial changes in both turbulent intensities and structures. In particular, the inner-scaled mean streamwise fluid velocity is found to shift towards lower values, indicating a noticeable increase in fluid friction velocity. Turbulent intensities appear to be further suppressed through facilitating the particles to extract momentum from the fluid phase and increasing extra turbulent kinetic dissipation by particles. Importantly, the overall drag is enhanced by indirectly strengthening the contribution of particle stress, even though the contribution of the total fluid stress is decreased. On the other hand, the magnitude of the large-scale motions is weakened by simultaneously reducing turbulent production and increasing particle feedback around the scales of the large-scale motions. Meanwhile, the average streaky fluid structures in the streamwise–spanwise planes and inclined fluid structures in the streamwise–wall-normal planes become expanded and flattened, respectively.

A simulation method has been developed to efficiently evaluate the motion of colloidal particles in a low-Reynolds-number confined microchannel flow using a Lagrangian-based approach. In this method, the background velocity within the channel, in the absence of suspended particles, is obtained from a fluid dynamics solver and is used to update the velocity at the particle centres using the Stokesian dynamics (SD) method, which incorporates multi-body hydrodynamic interactions. As a result, instead of computing the momentum of both the fluid and particles throughout the entire computational domain, the microscopic balance equation is solved only at the particle centres, increasing the computational efficiency. To accommodate complex boundary conditions within the SD framework, imaginary particles are placed on the channel walls, allowing the mobility relation to be reformulated to apply velocity constraints to immobilized wall particles. By employing this constrained SD approach, global mobility interactions that need to be computed at each time step are limited to the interior particles, resulting in a significant reduction in computational cost. The efficiency of this study is demonstrated through case studies on particulate flows in contraction and cross-flow microchannels. By using colloidal particles that incorporate Brownian motion and inter-particle attraction, observations through the entire stages of fouling dynamics are possible, from particle inflow to channel blockage. The fouling patterns observed in the simulations are consistent with experiments conducted under the same flow conditions. This study provides an efficient approach for analysing the effect of hydrodynamic interactions on particle dynamics in microfluidics and materials processing fields while allowing for predictions about structural changes over long-time scales, including complex phenomena such as clogging.

Serrations are commonly employed to mitigate the turbulent boundary layer trailing-edge noise. However, significant discrepancies persist between model predictions and experimental observations. In this paper, we show that this results from the frozen turbulence assumption. A fully developed turbulent boundary layer over a flat plate is first simulated using the large-eddy simulation method, with the turbulence at the inlet generated using the digital filter method. The space–time correlations and spectral characteristics of wall pressure fluctuations are examined. The simulation results demonstrate that the coherence function decays in the streamwise direction, deviating from the constant value of unity assumed in the frozen turbulence assumption. By considering an exponential decay function, we relax the frozen turbulence assumption and develop a prediction model that accounts for the intrinsic non-frozen nature of turbulent boundary layers. To facilitate a direct comparison with frozen models, a correction coefficient is introduced to account for the influence of non-frozen turbulence. The comparison between the new and original models demonstrates that the new model predicts lower noise reductions, aligning more closely with the experimental observations. The physical mechanism underlying the overprediction of the noise model assuming frozen turbulence is discussed. The overprediction is due to the decoherence of the phase variation along the serrated trailing edge. Consequently, the ratio of the serration amplitude to the streamwise frequency-dependent correlation length is identified as a crucial parameter in determining the correct prediction of far-field noise.

In the present study, we perform direct numerical simulations of compressible turbulent boundary layers at free stream Mach numbers $2\unicode{x2013}6$ laden with dilute phase of spherical particles to investigate the Mach number effects on particle transport and dynamics. Most of the phenomena observed and well-recognized for inertia particles in incompressible wall-bounded turbulent flows – such as near-wall preferential accumulation and clustering beneath low-speed streaks, flatter mean velocity profiles, and trend variation of the particle velocity fluctuations – are identified in the compressible turbulent boundary layer as well. However, we find that the compressibility effects are significant for large inertia particles. As the Mach number increases, the near-wall accumulation and the small-scale clustering are alleviated, which is probably caused by the variations of the fluid density and viscosity that are crucial to particle dynamics. This can be affected by the fact that the forces acting on the particles with viscous Stokes number greater than 500 are modulated by the comparatively high particle Mach numbers in the near-wall region. This is also the reason for the abatement of the streamwise particle velocity fluctuation intensities with the Mach numbers.

Cryogenic carbon capture (CCC) is an innovative technology to desublimate $\text {CO}_2$ out of industrial flue gases. A comprehensive understanding of $\text {CO}_2$ desublimation and sublimation is essential for widespread application of CCC, which is highly challenging due to the complex physics behind. In this work, a lattice Boltzmann (LB) model is proposed to study $\text {CO}_2$ desublimation and sublimation for different operating conditions, including the bed temperature (subcooling degree $\Delta T_s$), gas feed rate (Péclet number $Pe $) and bed porosity ($\psi$). The $\text {CO}_2$ desublimation and sublimation properties are reproduced. Interactions between convective $\text {CO}_2$ supply and desublimation/sublimation intensity are analysed. In the single-grain case, $Pe $ is suggested to exceed a critical value $Pe _c$ at each $\Delta T_s$ to avoid the convection-limited regime. Beyond $Pe _c$, the $\text {CO}_2$ capture rate ($v_c$) grows monotonically with $\Delta T_s$, indicating a desublimation-limited regime. In the packed bed case, multiple grains render the convective $\text {CO}_2$ supply insufficient and make CCC operate under the convection-limited mechanism. Besides, in small-$\Delta T_s$ and high-$Pe $ tests, $\text {CO}_2$ desublimation becomes insufficient compared with convective $\text {CO}_2$ supply, thus introducing the desublimation-limited regime with severe $\text {CO}_2$ capture capacity loss ($\eta _d$). Moreover, large $\psi$ enhances gas mobility while decreasing cold grain volume. A moderate porosity $\psi _c$ is recommended for improving the $\text {CO}_2$ capture performance. By analysing $v_c$ and $\eta _d$, regime diagrams are proposed in $\Delta T_s$–$Pe $ space to show distributions of convection-limited and desublimation-limited regimes, thus suggesting optimal conditions for efficient $\text {CO}_2$ capture. This work develops a viable LB model to examine CCC under extensive operating conditions, contributing to facilitating its application.

As a promising machine learning method for active flow control (AFC), deep reinforcement learning (DRL) has been successfully applied in various scenarios, such as the drag reduction for stationary cylinders under both laminar and weakly turbulent conditions. However, current applications of DRL in AFC still suffer from drawbacks including excessive sensor usage, unclear search paths and insufficient robustness tests. In this study, we aim to tackle these issues by applying DRL-guided self-rotation to suppress the vortex-induced vibration (VIV) of a circular cylinder under the lock-in condition. With a state space consisting only of the acceleration, velocity and displacement of the cylinder, the DRL agent learns an effective control strategy that successfully suppresses the VIV amplitude by $99.6\,\%$. Through systematic comparisons between different combinations of sensory-motor cues as well as sensitivity analysis, we identify three distinct stages of the search path related to the flow physics, in which the DRL agent adjusts the amplitude, frequency and phase lag of the actions. Under the deterministic control, only a little forcing is required to maintain the control performance, and the VIV frequency is only slightly affected, showing that the present control strategy is distinct from those utilizing the lock-on effect. Through dynamic mode decomposition analysis, we observe that the growth rates of the dominant modes in the controlled case all become negative, indicating that DRL remarkably enhances the system stability. Furthermore, tests involving various Reynolds numbers and upstream perturbations confirm that the learned control strategy is robust. Finally, the present study shows that DRL is capable of controlling VIV with a very small number of sensors, making it effective, efficient, interpretable and robust. We anticipate that DRL could provide a general framework for AFC and a deeper understanding of the underlying physics.

The prototypical diffuse-interface model for incompressible fluid mixtures is the Navier–Stokes Cahn–Hilliard (Allen–Cahn) model. Despite its foundation in continuum mixture theory, it is not fully compatible with this theory due to the diffusive flux approximation. This paper introduces a class of thermodynamically consistent diffuse-interface incompressible fluid mixture models that is fully compatible with the continuum theory of mixtures. The proposed models can be formulated in either constituent or mixture quantities, enabling a direct comparison with the Navier–Stokes Cahn–Hilliard (Allen–Cahn) model with non-matching densities. This comparison reveals the key modelling simplifications employed in the latter.

This study investigates the potential use of an active device to efficiently absorb water waves propagating in a channel. The active device comprises a dipole source consisting of two sources in quasi-opposition of phase. We explore the feasibility of this approach to achieve perfect absorption of guided waves through interference phenomena. To accomplish this, we establish the law governing the waves emitted by the dipole source to optimize the absorption of specific incident waves. The validity of this law is demonstrated through numerical simulations and laboratory experiments, encompassing both the harmonic and transient regimes of the experimental set-up.

We consider the dynamic wetting and dewetting processes of films and droplets of complex liquids on planar surfaces, focusing on the case of colloidal suspensions, where the particle interactions can be sufficiently attractive to cause agglomeration of the colloids within the film. This leads to an interesting array of dynamic behaviours within the liquid and of the liquid–air interface. Incorporating concepts from thermodynamics and using the thin-film approximation, we construct a model consisting of a pair of coupled partial differential equations that represent the evolution of the liquid film and the effective colloidal height profiles. We determine the relevant phase behaviour of the uniform system, including finding associated binodal and spinodal curves, helping to uncover how the emerging behaviour depends on the particle interactions. Performing a linear stability analysis of our system enables us to identify parameter regimes where agglomerates form, which we independently confirm through numerical simulations and continuation of steady states, to construct bifurcation diagrams. We obtain various dynamics such as uniform colloidal profiles in an unstable situation evolving into agglomerates and thus elucidate the interplay between dewetting and particle aggregation in complex liquids on surfaces.

This study explores the dynamics of flexible ribbons with an added weight $G$ at the tail in uniform flow, considering key parameters like inflow Reynolds number ($Re_u$), mass ratio ($M_t$) and aspect ratio (${A{\kern-4pt}R}$). For two-dimensional ribbons, a simplified theoretical model accurately predicts equilibrium configurations and forces. Inspired by Barois & De Langre (J. Fluid Mech., vol. 735, 2013, R2), we introduce an important control parameter ($C_G$) that effectively collapses normalized forces and angle data. Vortex-induced vibration is observed, and Strouhal number ($St$) scaling laws with $C_G$ are identified. In three-dimensional scenarios, the model effectively predicts lift, but its accuracy in predicting drag is limited to situations with small $Re_u$ values. The flow along the side edges mitigates pressure differences, thereby suppressing vibration and uplift, particularly noticeable in the case of narrow ribbons. This study offers new insights into the dynamics of flexible bodies in uniform flow.

Resonant standing waves excited on the water surface in a deep narrow rectangular cavity by a fully immersed cylinder harmonically oscillating in the vertical direction are studied theoretically and experimentally. The effect of the finite wavemaker size is considered in the framework of the potential two-dimensional flow theory. Nonlinearities and weak dissipation at solid surfaces are accounted for. The spatio-temporal structure of the waves in the presence of detuning between the forcing and the natural frequency of the system is analysed. The variation of the surface shape in space and time studied in experiments supports the assumption of two-dimensional flow. The finite size of the wavemaker causes a downshift of the effective resonant frequency of the cavity; this effect is enhanced by the nonlinearity. For small amplitude waves, the surface elevation evolution in time is decomposed into the sum of the time-periodic function, corresponding to the forcing frequency, and its second harmonic; the shape of the wavenumber spectra of these components depends on the forcing frequency. For larger wave amplitudes, additional peaks in the frequency spectrum appear. The theoretical predictions are compared with the experimental results.

Small finite-size particles suspended in fluid flow through an enclosed curved duct can focus to points or periodic orbits in the two-dimensional duct cross-section. This particle focusing is due to a balance between inertial lift forces arising from axial flow and drag forces arising from cross-sectional vortices. The inertial particle focusing phenomenon has been exploited in various industrial and medical applications to passively separate particles by size using purely hydrodynamic effects. A fixed size particle in a circular duct with a uniform rectangular cross-section can have a variety of particle attractors, such as stable nodes/spirals or limit cycles, depending on the radius of curvature of the duct. Bifurcations occur at different radii of curvature, such as pitchfork, saddle-node and saddle-node infinite period (SNIPER), which result in variations in the location, number and nature of these particle attractors. By using a quasi-steady approximation, we extend the theoretical model of Harding et al. (J. Fluid Mech., vol. 875, 2019, pp. 1–43) developed for the particle dynamics in circular ducts to spiral duct geometries with slowly varying curvature, and numerically explore the particle dynamics within. Bifurcations of particle attractors with respect to radius of curvature can be traversed within spiral ducts and give rise to a rich nonlinear particle dynamics and various types of tipping phenomena, such as bifurcation-induced tipping (B-tipping), rate-induced tipping (R-tipping) and a combination of both, which we explore in detail. We discuss implications of these unsteady dynamical behaviours for particle separation and propose novel mechanisms to separate particles by size in a non-equilibrium manner.

Depinning of liquid droplets on substrates by flow of a surrounding immiscible fluid is central to applications such as cross-flow microemulsification, oil recovery and waste cleanup. Surface roughness, either natural or engineered, can cause droplet pinning, so it is of both fundamental and practical interest to determine the flow strength of the surrounding fluid required for droplet depinning on rough substrates. Here, we develop a lubrication-theory-based model for droplet depinning on a substrate with topographical defects by flow of a surrounding immiscible fluid. The droplet and surrounding fluid are in a rectangular channel, a pressure gradient is imposed to drive flow and the defects are modelled as Gaussian-shaped bumps. Using a precursor-film/disjoining-pressure approach to capture contact-line motion, a nonlinear evolution equation is derived describing the droplet thickness as a function of distance along the channel and time. Numerical solutions of the evolution equation are used to investigate how the critical pressure gradient for droplet depinning depends on the viscosity ratio, surface wettability and droplet volume. Simple analytical models are able to account for many of the features observed in the numerical simulations. The influence of defect height is also investigated, and it is found that, when the maximum defect slope is larger than the receding contact angle of the droplet, smaller residual droplets are left behind at the defect after the original droplet depins and slides away. The model presented here yields considerably more information than commonly used models based on simple force balances, and provides a framework that can readily be extended to study more complicated situations involving chemical heterogeneity and three-dimensional effects.

Understanding settling motion of coral grains is important in terms of protection of coral reef systems and resotoration of the associated ecosystems. In this paper, a series of laboratory experiments was conducted to investigate the settling motion, using optical microscopy to measure shape parameters of coral grains and the particle-filtering-based object tracking to reconstruct the three-dimensional trajectory. Three characteristic descent regimes, namely, tumbling, chaotic and fluttering, are classified based on the three-dimensional trajectory, the spiral radius variation and the velocity spectrum. It is demonstrated that if one randomly picks up one coral grain, then the probabilities of occurrence of the three regimes are approximately $26\,\%$, $42\,\%$ and $32\,\%$, respectively. We have shown that first, the dimensionless settling velocity generally increases with the non-dimensional diameter and Corey shape factor and second, the drag coefficient generally decreases with the Reynolds number and Corey shape factor. Based on this, the applicability of existing models on predicting settling velocity and drag coefficient for coral grains is demonstrated further. Finally, we have proposed extended models for predicting the settling velocity. This study contributes to better understanding of settling motion and improves our predictive capacity of settling velocity for coral grains with complex geometry.

We investigate the phenomena of crater formation and gas release caused by projectile impact on underwater beds, which occurs in many natural, geophysical and industrial applications. The bed in our experiment is constructed of hydrophobic particles, which trap a substantial amount of air in the pores of the bed. In contrast to dry beds, the air–water interface in a submerged bed generates a granular skin that provides rigidity to the medium by producing skin over the bulk. The projectile's energy is used to reorganize the grains, which causes the skin to crack, allowing the trapped air to escape. The morphology of the craters as a function of impact energy in submerged beds exhibits different scaling laws than what is known for dry beds. This phenomenon is attributed to the contact line motion on the hydrophobic fractal-like surface of submerged grains. The volume of the gas released is a function of multiple factors, chiefly the velocity of the projectile, depth of the bed and depth of the water column.