The linear stability of a thermally stratified fluid layer between horizontal walls, where non-Brownian thermal particles are injected continuously at one boundary and extracted at the other – a system known as particulate Rayleigh–Bénard (pRB) – is studied. For a fixed volumetric particle flux and minimal thermal coupling, reducing the injection velocity stabilises the system when heavy particles are introduced from above, but destabilises it when light particles are injected from below. For very light particles (bubbles), low injection velocities can shift the onset of convection to negative Rayleigh numbers, i.e. heating from above. Particles accumulate non-uniformly near the extraction wall and in regions of strong vertical flow, aligning with either wall-impinging or wall-detaching zones depending on whether injection is at sub- or super-terminal velocity. The increase of the volumetric particle flux always enhances these effects.

In this work, we study the effect of flow curvature, or angular momentum, on the propagation and trapping characteristics of near-inertial waves (NIWs) in a curved front. The curved front is idealised as a baroclinic vortex in cyclogeostrophic balance. Motivated by ocean observations, we employ a Gaussian base flow, which by construction possesses a shield of oppositely signed vorticity surrounding its core, and we consider both cyclonic and anticyclonic representations of this flow. Following two main assumptions, i.e. that (i) the horizontal wavelength of the NIW is smaller than the length scale of the background flow (the WKBJ approximation), and (ii) the vertical wavelength of the NIW is smaller than the radial distance of interest, we derive the NIW dispersion relation and discuss the group velocity and direction of energy propagation. We show that the curvature can (i) increase the critical depth and horizontal extent of the trapping region, (ii) reduce NIW activity at the centre of the anticyclonic vortex core and enhance it in the cyclonic shield surrounding the core for high curvatures, (iii) lead to NIW trapping in the anticyclonic shield surrounding the cyclonic core, and (iv) increase the available band of NIW frequencies that are trapped. The solutions from the ray-tracing method are supported by numerical solutions of the governing equations linearised about the cyclogeostrophic base state. Finally, these methods are applied to an idealised model of oceanic mesoscale Arctic eddies showing an increase in the critical depth of trapping. Our results – while applied to polar eddies – equally apply at lower latitudes in both oceans and atmospheres, highlighting the potential importance of flow curvature in controlling the propagation of NIW energy.

Stochastic resonance (SR) is universal phenomenon, where noise amplifies a weak periodic signal in bistable nonlinear systems, with wide applications in biology, climate science, engineering etc., although in fluid dynamics it remains underexplored. Recently, we unexpectedly found SR above non-modal elastic instability onset in an inertialess viscoelastic channel flow, where it emerges on the top of a chaotic streamwise velocity power spectrum $E_u$ due to its interaction with white-noise spanwise velocity power spectrum $E_w$ and weak elastic waves. These three conditions necessary for SR emergence differ from those required for the classical SR emergence mentioned above. Here, we consider SR in an inertialess viscoelastic channel flow with a smoothed inlet causing order of magnitude lower noise intensity than in our former studies. Our observations reveal that SR appears at the same conditions mentioned above, where SR is found just upon the instability onset in a lower subrange of a transition regime, in contrast, here, SR persists across all flow regimes – transition, elastic turbulence and drag reduction. Furthermore, we provide experimental evidence that SR, presented by a sharp peak in $E_u$, manifests as either a standing or propagating wave in the $x$-direction, with a rather uniform amplitude of streamwise velocity fluctuations and zero propagation velocity in the $z$-direction. These findings reveal a new mechanism underpinning the transition to a chaotic channel flow of viscoelastic fluids and establish SR as a robust framework for understanding complex flow dynamics. This work opens new avenues for exploring SR in other nonlinear systems and practical applications such as mixing enhancement and flow control in industrial and biological contexts.

Ultra-thin liquid sheets generated by impinging two liquid jets are crucial high-repetition-rate targets for laser ion acceleration and ultra-fast physics, and serve widely as barrier-free samples for structural biochemistry. The impact of liquid viscosity on sheet thickness should be comprehended fully to exploit its potential. Here, we demonstrate experimentally that viscosity significantly influences thickness distribution, while surface tension primarily governs shape. We propose a thickness model based on momentum exchange and mass transport within the radial flow, which agrees well with the experiments. These results provide deeper insights into the behaviour of liquid sheets and enable accurate thickness control for various applications, including atomization nozzles and laser-driven particle sources.

We study the dispersion of bubble swarms rising in initially quiescent water using three-dimensional Lagrangian tracking of deformable bubbles and tracer particles in an octagonal bubble column. Two different bubble sizes (3.5 mm and 4.4 mm) and moderate gas volume fractions ($0.52\,\%{-}1.20\,\%$) are considered. First, we compare the dispersion inside bubble swarms with that for single-bubble cases, and find that the horizontal mean squared displacement (MSD) in the swarm cases exhibits oscillations around the asymptotic scaling predicted for a diffusive regime. This occurs due to wake-induced bubble motion; however, the oscillatory behaviour is heavily damped compared to the single-bubble cases due to the presence of bubble-induced turbulence (BIT) and bubble–bubble interactions in the swarm. The vertical MSD in bubble swarms is nearly an order of magnitude faster than in the single-bubble cases, due to the much higher vertical fluctuating bubble velocities in the swarms. We also investigate tracer dispersion in BIT, and find that concerning the time to transition away from the ballistic regime, larger bubbles with a higher gas void fraction transition earlier than tracers, consistent with Mathai et al. (2018, Phys. Rev. Lett., vol. 121, 054501). However, for bubble swarms with smaller bubbles and a lower gas void fraction, they transition at the same time. This differing behaviour is due to the turbulence being more well-mixed for the larger bubble case, whereas for the smaller bubble case, the tracer dispersion is highly dependent on the wake fluctuations generated by the oscillating motion of nearby bubbles.

Previous studies claimed that the non-monotonic effects of wettability came mainly from the heterogeneity of geometries or flow conditions on multiphase displacements in porous media. For macroscopic homogeneous porous media, without permeability contrast or obvious preferential flow pathways, most pore-scale evidence showed a monotonic trend of the wettability effect. However, this work reports transitions from monotonic to non-monotonic wettability effects when the dimension of the model system rises from two-dimensional (2-D) to three-dimensional (3-D), validated by both the network modelling and the microfluidic experiments. The mechanisms linking the pore-scale events to macroscopic displacement patterns have been analysed through direct simulations. For 2-D porous media, the monotonic effect of wettability comes from the consistent transition pattern for the full range of capillary numbers $Ca$, where the capillary fingering mode transitions to the compact displacement mode as the contact angle $\theta$ decreases. Yet, it is indicated that the 3-D porous geometries, even though homogeneous without permeability contrast or obvious preferential flow pathways, introduce a different $Ca$–$\theta$ phase diagram with new pore-scale events, such as the coupling of capillary fingering with snap-off during strong drainage, and frequent snap-off events during strong imbibition. These events depend strongly on geometric confinements and capillary numbers, leading to the non-monotonicity of wettability effects. Our findings provide new insights into the multiphase displacement dependent on wettability in various natural porous media and offer design principles for engineering artificial porous media to achieve desired immiscible displacement behaviours.

We investigate the dynamics of a pair of rigid rotating helices in a viscous fluid, as a model for bacterial flagellar bundle and a prototype of microfluidic pumps. Combining experiments with hydrodynamic modelling, we examine how spacing and phase difference between the two helices affect their torque, flow field and fluid transport capacity at low Reynolds numbers. Hydrodynamic coupling reduces the torque when the helices rotate in phase at constant angular speed, but increases the torque when they rotate out of phase. We identify a critical phase difference, at which the hydrodynamic coupling vanishes despite the close spacing between the helices. A simple model, based on the flow characteristics and positioning of a single helix, is constructed, which quantitatively predicts the torque of the helical pair in both unbounded and confined systems. Finally, we show the influence of spacing and phase difference on the axial flux and the pump efficiency of the helices. Our findings shed light on the function of bacterial flagella and provide design principles for efficient low-Reynolds-number pumps.

We study the homogeneous isotropic turbulence of a shear-thinning fluid modelled by the Carreau model, and show how the variable viscosity affects the multiscale behaviour of the turbulent flow. We show that Kolmogorov theory can be extended to such non-Newtonian fluids, provided that the correct choice of average is taken when defining the mean Kolmogorov scale and dissipation rate, to properly capture the effect of the variable viscosity. Thus the classical phenomenology à la Kolmogorov can be observed in the inertial range of scale, with the energy spectra decaying as $k^{-5/3}$, with $k$ being the wavenumber, and the third-order structure function obeying the $4/5$ law. The changing viscosity instead strongly alters the small scale of turbulence, leading to an enhanced intermittent behaviour of the velocity field.

We present the measurements of the decay of stationary turbulence at Reynolds numbers based on the Taylor microscale $Re_{\lambda }=493, 599, 689$ produced in a large-scale von Kármán flow using stereoscopic particle image velocimetry. First, steady-state conditions were established, after which the impellers were simultaneously and abruptly stopped, and the turbulent decay was measured over 10–20 impeller rotation periods. A total of 258 decay experiments were performed. The temporal evolution of the ensemble-averaged turbulent kinetic energy (TKE) showed excellent agreement over all $Re_{\lambda }$ and exhibited two distinct phases: a short, initial transition phase where the TKE remained almost constant due to the inertia of the flow and lasted approximately $0.4$ impeller rotations, followed by a classical power-law decay. To extract the decay exponent $n$, a curve-fitting function based on a one-dimensional energy spectrum was used, and successfully captured the entire measured decay process. A value $n=1.62$ was obtained based on ensemble-averaged TKE. However, different decay exponents were found for individual velocity components: $n=1.38$ for the axial component consistent with various reports in the literature and Loitsiansky’s prediction ($n=1.43$), and $n=1.99$ for the radial and circumferential components indicating saturation/confinement effects. Similarly, the longitudinal integral length scale in the axial direction grew as $L\propto t^{2/7}$, whereas it remained nearly constant in the radial direction. Finally, the evolution of the ensemble-averaged velocity gradients showed that after the impellers were stopped, the mean flow pattern persisted for a short time before undergoing a large-scale reversal before the onset of the turbulent decay.

The skin-friction coefficient is a dimensionless quantity defined by the wall shear stress exerted on an object moving in a fluid, and it decreases as the Reynolds number increases for wall-bounded turbulent flows over a flat plate. In this work, a novel transformation, based on physical and asymptotic analyses, is proposed to map the skin-friction relation of high-speed turbulent boundary layers (TBLs) for air described by the ideal gas law to the incompressible skin-friction relation. Through this proposed approach, it has been confirmed theoretically that the transformed skin-friction coefficient $C_{f,i}$, and the transformed momentum-thickness Reynolds number $Re_{\theta ,i}$ for compressible TBLs with and without heat transfer, follow a general scaling law that aligns precisely with the incompressible skin-friction scaling law, expressed as $ (2/C_{f,i} )^{1/2}\propto \ln Re_{\theta ,i}$. Furthermore, the reliability of the skin-friction scaling law is validated by compressible TBLs with free-stream Mach number ranging from $0.5$ to $14$, friction Reynolds number ranging from $100$ to $2400$, and the wall-to-recovery temperature ratio ranging from $0.15$ to $1.9$. In all of these data, $ (2/C_{f,i} )^{1/2}$ and $\ln Re_{\theta ,i}$ based on the present theory collapse to the incompressible relation, with a squared Pearson correlation coefficient reaching an impressive value $0.99$, significantly exceeding $0.85$ and $0.86$ based on the established van Driest II and the Spalding–Chi transformations, respectively.

We present a unified framework derived from the total heat flux equation, enabling the direct formulation of the relationship between mean temperature and velocity fields, as well as the development of mean temperature scalings in compressible turbulent channel flows. The proposed mean temperature–velocity relationship, combined with a simple damping function model for the mixed Prandtl number, demonstrates high efficacy in channels with both symmetric and asymmetric thermal boundary conditions across a range of Mach and Reynolds numbers. In contrast, the state-of-the-art generalised Reynolds analogy (GRA) relation (Zhang et al., 2014, J. Fluid Mech., vol. 739, pp. 392–420) is shown to be insufficient for asymmetric cases due to mismatched boundary conditions at the effective boundary layer edge. By introducing a mean temperature decomposition, we clarify that while the GRA relation effectively characterises the component associated with turbulence production and viscous dissipation, it fails to account for the contribution arising from non-zero edge total heat flux. Furthermore, we rigorously derive mean temperature transformations compatible with arbitrary velocity scalings for the first time. These findings provide some physical insights into the mean momentum and heat transport in compressible wall-bounded turbulence, and may be helpful for developing near-wall models.

Closed-form expressions for aerodynamic force on an accelerating aerofoil were presented in the 1930s, relating instantaneous force to geometric and kinematic parameters under the following assumptions: a thin aerofoil, small-amplitude motions, planar wake development, and a flow that is inviscid, incompressible and two-dimensional. The present work is a step towards analogous closed-form expressions for large-amplitude motions of thick foils when the flow remains attached and boundary-layer thickness approaches (but does not equal) zero. A mathematical framework is derived from vortical flow theory to highlight the finite degrees of freedom that must be solved or predicted in order to yield a predictive aerodynamic model under the stated conditions. The special case of periodic motion is further considered, and an equation is derived to calculate mean forces from known or assumed time histories of circulation, vorticity-weighted mean wake convection velocity and trailing-edge velocity.

Jet penetration into soft gels is essential for optimising fluid delivery in medical therapies, biomedical engineering, and soft robotics. In this work, we visualise the jet flow of a Newtonian fluid into a soft viscoplastic gel using camera imaging and time-resolved tomographic particle image velocimetry (PIV) systems. The flow is primarily governed by the Reynolds number ($Re = 350-5000$) and the effective viscosity ratio ($m$ up to 22). We observe three flow regimes – mixing, jellyfish, and fingering – with transitions between them quantified in the $Re-m$ plane. An experimentally informed, systematic, practical, semi-analytical modelling framework is developed to estimate jet penetration depth over time, incorporating PIV results and an approximate functional decomposition approach to describe the velocity distribution and Reynolds stress contributions. The model provides reasonable estimations across all three regimes.

The effect of nucleation on cavitation inception in a high-Reynolds-number von Kármán wake from a bluff two-dimensional hydrofoil is studied experimentally in a variable pressure water tunnel. Nucleation effects are studied by seeding the flow with sparse monodisperse nuclei populations, with the critical pressure nominally equal to vapour pressure. The injected nuclei population and incipient cavitation events were imaged simultaneously using high-speed cameras to precisely quantify the number of activated nuclei of the total available. Three-dimensional spatial characterisation (orientation and location) of the incipient structures is obtained using two high-speed cameras mounted to the side and below the tunnel test section. Inception was observed predominantly in the stretched cores of secondary structures, with a negligible proportion of events occurring in the primary vortices. A broad peak in the vertical angle distribution is observed about the streamwise axis; however, events at all angles are seen. A symmetric distribution was observed for the horizontal angle, with a dominant orientation $45^{\circ }$ from the free-stream direction. The majority of events occur at approximately one hydrofoil thickness downstream of the hydrofoil trailing edge, with a bimodal symmetric distribution about the hydrofoil vertical centre plane. Nuclei activation rate is determined from the acoustic measurements, and was found to be proportional to the number of the injected nuclei. A power law increase in activation rate was observed following a decrease in cavitation number and an increase in Reynolds number. The nuclei activation rate was of the order of $0.1{-}10 \, \mathrm {s^{-1}}$, which combined with seeding rates of the orderof $100{-}1000 \, \mathrm {s^{-1}}$ reveals inception to be a rare occurrence (0.001 %–10 % of nuclei being activated), requiring the confluence of two unlikely events, the occurrence of a subvapour pressure vortex core with capture of a sufficiently weak nuclei. The presented study provides new insights into the physics of cavitation nucleation and inception and provides a comprehensive dataset for development of computational models.

We solve a Bayesian inverse Navier–Stokes (N–S) problem that assimilates velocimetry data by jointly reconstructing a flow field and learning its unknown N–S parameters. We devise an algorithm that learns the most likely parameters of a Carreau shear-thinning viscosity model, and estimates their uncertainties, from velocimetry data of a shear-thinning fluid. We conduct a magnetic resonance velocimetry experiment to obtain velocimetry data of an axisymmetric laminar jet in an idealised medical device (US Food and Drug Administration’s benchmark nozzle) for a blood analogue fluid. The algorithm successfully reconstructs the flow field and learns the most likely Carreau parameters. Predictions from the learned model agree well with rheometry measurements. The algorithm accepts any differentiable algebraic viscosity model, and can be extended to more complicated non-Newtonian fluids (e.g. Oldroyd-B fluid if a viscoelastic model is incorporated).

Developing a consistent near-wall turbulence model remains an unsolved problem. The machine learning method has the potential to become the workhorse for turbulence modelling. However, the learned model suffers from limited generalisability, especially for flows without similarity laws (e.g. separated flows). In this work, we propose a knowledge-integrated additive (KIA) learning approach for learning wall models in large-eddy simulations. The proposed approach integrates the knowledge in the simplified thin-boundary-layer equation with a data-driven forcing term for the non-equilibrium effects induced by pressure gradients and flow separations. The capability learned from each flow dataset is encapsulated using basis functions with the corresponding weights approximated using neural networks. The fusion of capabilities learned from various datasets is enabled using a distance function, in a way that the learned capability is preserved and the generalisability to other cases is allowed. The additive learning capability is demonstrated via training the model sequentially using the data of the flow with pressure gradient but no separation, and the separated flow data. The capability of the learned model to preserve previously learned capabilities is tested using turbulent channel flow cases. The periodic hill and the 2-D Gaussian bump cases showcase the generalisability of the model to flows with different surface curvatures and different Reynolds numbers. Good agreements with the references are obtained for all the test cases.

The transition to chaos in the subcritical regime of counter-rotating Taylor–Couette flow is investigated using a minimal periodic domain capable of sustaining coherent structures. Following a Feigenbaum cascade, the dynamics is found to be remarkably well approximated by a simple discrete map that admits rigorous proof of its chaotic nature. The chaotic set that arises for the map features densely distributed periodic points that are in one-to-one correspondence with unstable periodic orbits (UPOs) of the Navier–Stokes system. This supports the increasingly accepted view that UPOs may serve as the backbone of turbulence and, indeed, we demonstrate that it is possible to reconstruct every statistical property of chaotic fluid flow from UPOs.

Planar entropy waves are commonly assumed for predicting indirect combustion noise. However, the non-planar and turbulent nature of flows found in most practical combustors challenges this assumption. In the present paper, we examine the indirect noise generated by non-planar and turbulent entropy fields through subsonic nozzles. Firstly, we introduce a new transfer function framework that accounts for the contribution of non-planar Fourier modes of the entropy field to the indirect noise spectra. When applied to a turbulent flow field, this method demonstrates a significant improvement in spectral predictions compared with a conventional approach that only considers the planar mode. Secondly, simulations show that non-planar Fourier modes become significant above a threshold frequency $f_{thresh}$, found in the mid- to high-frequency range. This contribution of non-planar modes is explained by two-dimensional shear effects that distort the entropy waves. A scaling relation that uses residence times along streamlines is developed for $f_{thresh}$, showing good agreement with simulation results. Finally, we show that the indirect noise from non-planar entropy modes found in aviation combustors can be significant at frequencies below 1 kHz, which might be relevant in situations of thermo-acoustic instabilities coupled to indirect noise.

This study seeks a low-rank representation of turbulent flow data obtained from multiple sources. To uncover such a representation, we consider finding a finite-dimensional manifold that captures underlying turbulent flow structures and characteristics. While nonlinear machine-learning techniques can be considered to seek a low-order manifold from flow field data, there exists an infinite number of transformations between data-driven low-order representations, causing difficulty in understanding turbulent flows on a manifold. Finding a manifold that captures turbulence characteristics becomes further challenging when considering multi-source data together due to the presence of inherent noise or uncertainties and the difference in the spatiotemporal length scale resolved in flow snapshots, which depends on approaches in collecting data. With an example of numerical and experimental data sets of transitional turbulent boundary layers, this study considers an observable-augmented nonlinear autoencoder-based compression, enabling data-driven feature extraction with prior knowledge of turbulence. We show that it is possible to find a low-rank subspace that not only captures structural features of flows across the Reynolds number but also distinguishes the data source. Along with machine-learning-based super-resolution, we further argue that the present manifold can be used to validate the outcome of modern data-driven techniques when training and evaluating across data sets collected through different techniques. The current approach could serve as a foundation for a range of analyses including reduced-complexity modelling and state estimation with multi-source turbulent flow data.

The flow behind impulsively started circular and polygonal plates is investigated experimentally, using particle image velocimetry at several azimuthal angles. Observing plates accelerating up to a steady Reynolds number $Re=27\,000$, the three invariants of the motion, circulation $\Gamma$, hydrodynamic impulse $I$ and kinetic energy $E$, were scaled against four candidate lengths: the hydraulic diameter, perimeter, circumscribed diameter and the square root of the area. Of these, the square root of the area was found to best collapse all the data. Investigating the three-dimensionality of the flow, it is found that, while a single-plane measurement can provide a reasonable approximation for $\Gamma$ behind plates, multiple planes are necessary to accurately estimate $E$ and $I$.