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.

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.

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.

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.

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.

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.

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.

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.