This research investigates the hydrodynamics of a physical boundary transition from free slip to no slip, which usually occurs in ice-jams, large wood and debris accumulation in free-surface flows. Using direct numerical simulation coupled with a volume penalisation method, a series of numerical simulations is performed for an open-channel flow covered with a layer of floating spherical particles, replicating the laboratory set-up of Yan Toe et al. (2025 J. Hydraul. Eng., vol. 151, 04025010). Flow transition from the open channel to the closed channel induces a new boundary-layer development at the top surface, accompanied by a flow separation and an increased bottom shear stress that enhances particle mobility at the bottom. Analysis of a fully developed flow in an asymmetric roughness channel (rough surface at the top boundary and smooth surface at the bottom boundary) also shows that the vertical position of maximum velocity is higher than the position of zero Reynolds shear stress, which supports the experimental observation of Hanjalić & Launder (J. Fluid Mech., vol. 51, 1972, pp. 301–335), demonstrating the shortcoming of traditional turbulence closure models such as the $k{-}\varepsilon$ model. Finally, the stagnation force acting on a particle at the leading edge of the accumulation layer is compared with the analytical prediction of Yan Toe et al. Understanding the flow transition improves the prediction of the stability threshold of the accumulation layer and design criteria for debris-collection devices.

We investigate and model the initiation of motion of a single particle on a structured substrate within an oscillatory boundary layer flow, following a mechanistic approach. By deterministically relating forces and torques acting on the particle to the instantaneous ambient flow, the effects of flow unsteadiness are captured, revealing rich particle dynamics. Laboratory experiments in an oscillatory flow tunnel characterise the initiation and early stages of motion, with particle imaging velocimetry measurements yielding the flow conditions at the motion threshold. The experiments validate and complement results from particle-resolved direct numerical simulations, combining an immersed boundary method with a discrete element method that incorporates a static friction contact model. Within the parameter range just above the motion threshold, the mobile particle rolls without sliding over the substrate, indicating that motion initiation is governed by an unbalanced torque rather than a force. Both experimental and numerical results show excellent agreement with an analytical torque balance including hydrodynamic torque derived from the theoretical Stokes velocity profile, and contributions of lift, added mass and externally imposed pressure gradient. In addition to static and rolling particle states, we identify a wiggling regime where the particle moves but does not leave its original pocket. Our deterministic approach enables prediction of the phase within the oscillation cycle at which the particle starts moving, without relying on empirical threshold estimates, and can be extended to a wide range of flow and substrate conditions, as long as turbulence is absent and interactions with other mobile particles are negligible.

The stress tensor is calculated for dilute active suspensions composed of colloidal Janus particles propelled by self-diffusiophoresis and powered by a chemical reaction. The Janus particles are assumed to be spherical and made of catalytic and non-catalytic hemispheres. The reaction taking place on the catalytic part of each Janus particle generates local molecular concentration gradients at the surface of the particle and, thus, an interfacial velocity slippage between the fluid and the particle, which is the propulsion mechanism of self-diffusiophoresis. In the dilute-system limit, the contributions of the suspended particles to the stress tensor are calculated by solving the linearised chemohydrodynamic equations for the fluid velocity and the molecular concentrations around every Janus particle considered as isolated and far apart from each other. The results are the following. First, the well-known Einstein formula for the effective shear viscosity of colloidal suspensions is recovered, including the effect of a possible uniform Navier slip length. Next, two further contributions are obtained, which depend on the molecular concentrations of the fuel and product species of the reaction, on the concentration gradients, and on the orientation of the Janus particles. The second contribution is caused by simple diffusiophoresis, which already exists in passive suspensions with global concentration gradients and no reaction. The third contribution is due to the self-diffusiophoresis generated by the chemical reaction, which arises in active suspensions. The calculation gives quantitative predictions based on the geometry of the Janus particles and on the constitutive properties of the fluid and the fluid–solid interfaces.

We study flows generated within a two-dimensional corner by the chemical activity of the confining boundaries. Catalytic reactions at the surfaces induce diffusio-osmotic motion of the viscous fluid throughout the domain. The presence of chemically active sectors can give rise to steady eddies reminiscent of classical Moffatt vortices, which are mechanically induced in similar confined geometries. In our approach, an exact analytical solution of the diffusion problem in a wedge geometry is derived and coupled to the diffusio-osmotic slip-velocity formulation, yielding the stream function of associated Stokes flow. In selected limiting cases, simple closed-form expressions provide clear physical insight into the underlying mechanisms. Our results open new perspectives for the design of microscale mixing strategies in dead-end pores and cornered microfluidic channels, and offer benchmarks for numerical simulations of confined (diffusio-)osmotic systems.

We consider the problem of a cylindrical (quasi-two-dimensional) droplet impacting on a hard surface. Cylindrical droplet impact can be engineered in the laboratory, and a theoretical model of the system can also be used to shed light on various complex experiments involving the impact of liquid sheets. We formulate a rim-lamella model for the droplet-impact problem. Using Gronwall’s inequality applied to the model, we establish theoretical bounds for the maximum spreading radius $\mathcal{R}_{\textit{max}}$ in droplet impact, specifically $k_1 {\textit{Re}}^{1/3}-k_2(1-\cos \vartheta _a)^{1/2}({\textit{Re}}/{\textit{We}})^{1/2}\leq \mathcal{R}_{\textit{max}}/R_0\leq k_1{\textit{Re}}^{1/3}$, valid for ${\textit{Re}}$ and ${\textit{We}}$ sufficiently large. Here, ${\textit{Re}}$ and ${\textit{We}}$ are the Reynolds and Weber number based on the droplet’s pre-impact velocity and radius $R_0$, $\vartheta _a$ is the advancing contact angle (assumed constant in our simplified analysis) and $k_1$ and $k_2$ are constants. We perform several campaigns of simulations using the volume of fluid method to model the droplet impact, and we find that the simulation results fall within the theoretical bounds.

Extreme weather events, combined with human-induced factors, such as expanding impervious surfaces and inadequate drainage infrastructure, are driving escalating urban flood risks worldwide. In this study, we present a novel spatiotemporal Long Short-Term Memory (LSTM)-based surrogate of the U.S. Environmental Protection Agency (EPA)’s Storm Water Management Model (SWMM) to predict maximum water depth and inflow at the asset level within urban drainage networks. The high-resolution SWMM model, encompassing the full network of conduits and manholes, was first calibrated and validated using U.S. Geological Survey (USGS) observations. The LSTM surrogate was then trained on data from 5,000 rainfall events across seven Annual Recurrence Intervals (ARIs) ranging from 1 to 100 years. The SWMM-LSTM surrogate model consistently achieves high predictive performance for both water depth and inflow, highlighting its robustness across diverse storm scenarios and ARI conditions. Hyperparameter optimization via grid search revealed task-specific configurations: larger hidden layers with moderate dropout improved water depth predictions, while deeper network architectures with minimal dropout optimized inflow forecasts. By providing rapid, computationally efficient predictions without compromising accuracy, the SWMM-LSTM surrogate offers a practical tool for real-time flood risk assessment, scenario evaluation and actionable decision-making in complex urban drainage systems.

This paper extends the two-layer high-level Green–Naghdi (HLGN) internal-wave model to study boundary time-varying problems, involving moving bottom or surface disturbances. The equations for the two-layer HLGN model with time-varying boundaries are presented, accompanied by a time-domain algorithm for solving these equations. The wave profiles predicted by the HLGN model for internal waves generated by boundary disturbances, whether occurring at the bottom or at the surface, show excellent agreement with results obtained by the fully nonlinear potential-flow (FNPF) solution. For internal waves generated by a surface moving disturbance, the results obtained by the HLGN model show good agreement with the experimental observations and the FNPF solution, including the relationships between the disturbance speed and the resulting wave amplitude and phase speed. Furthermore, the HLGN model is applied to analyse the evolution of the wave profiles and speed generated by the surface disturbance with different moving speeds. In addition, the extended HLGN model incorporates background linear shear currents to examine internal waves generated by a moving bottom disturbance with a linear shear current. The results reveal that background vorticity exerts a pronounced modulation effect on the wave profile and velocity field. Counter-flow narrows the waves and increases their phase speed, whereas co-flow broadens the waves and enhances their amplitude.

The effects of wall temperature on hypersonic boundary layer transition are investigated by analysing the kinematics (acoustic ray trajectories) and mechanics (fluctuation energy production and transport) of second-mode instabilities. The disturbance energy formulation is taken from Roy & Scalo (J. Fluid Mech. 2025, vol. 1007, A49). Flow conditions are taken from a Mach 6 boundary layer over a $3^\circ$ cone, with varying degrees of wall-to-adiabatic temperature ratios, $\varTheta =T_w/T_{\textit{ad}}=0.25{-}1.75$. Boundary layer-resolved axisymmetric direct numerical simulations with companion Laguerre polynomials-based linear stability theory provide the supporting numerical datasets. It was found that second-mode instabilities comprise two decks, separated by the pressure node location $(y=y_\pi )$. The upper deck ($y\gt y_\pi$) is characterised by temperature ($T^{\prime}$) and density ($\rho'$) fluctuations working with in-phase wall-normal velocity fluctuations ($v'$) to sustain the total disturbance energy production term, $-(\rho _0 v'T'\partial T_0/\partial y+\rho ' u_0 v' \partial u_0/\partial y$), which peaks at the generalised inflection point $y=y_i$. The downward-oriented energy flux peaks below the critical layer, $y\lt y_c$, and sustains acoustic energy accumulation in the lower deck. Effective energy transfer requires the streamwise and wall-normal fluxes to maintain a $90^\circ$ phase difference. This is satisfied especially for colder walls, whereas heated walls yield out-of-phase $v'$–$T'$ and in-phase pressure ($p'$) – streamwise velocity ($u'$) fluctuations, reducing the disturbance energy production and discouraging the coupling between the two decks. Ray tracing reveals the trajectory of purely acoustic wave paths emanating from the wall, as trapping occurs below the generalised inflection line $(y_i)$, governed by the mean flow velocity gradients $(\partial u_0/\partial y)$.

We studied the reconstruction of turbulent flow fields from trajectory data recorded by actively migrating Lagrangian agents. We propose a deep-learning model, track-to-flow (T2F), which employs a vision transformer as the encoder to capture the spatiotemporal features of a single agent trajectory, and a convolutional neural network as the decoder to reconstruct the flow field. To enhance the physical consistency of the T2F model, we further incorporate a physics-informed loss function inspired by the framework of physics-informed neural network (PINN), yielding a variant model referred to as T2F+PINN. We first evaluate both models in a laminar cylinder wake flow at a Reynolds number of $\textit{Re} = 800$ as a proof of concept. The results show that the T2F model achieves velocity reconstruction accuracy comparable to that of existing flow reconstruction methods, while the T2F+PINN model reduces the normalised error in vorticity reconstruction relative to the T2F model. We then apply the models in turbulent Rayleigh–Bénard convection at a Rayleigh number of $Ra = 10^{8}$ and a Prandtl number of $\textit{Pr} = 0.71$. The results show that the T2F model accurately reconstructs both the velocity and temperature fields, whereas the T2F+PINN model further improves the reconstruction accuracy of gradient-related physical quantities, such as temperature gradients, vorticity and the $Q$ value, with a maximum improvement of approximately 60 % compared to the T2F model. Overall, the T2F model is better suited for reconstructing primitive flow variables, while the T2F+PINN model provides advantages in reconstructing gradient-related quantities. Our models open a promising avenue for accurate flow reconstruction from a single Lagrangian trajectory.

We present a linear stability analysis of two-dimensional magnetoconvection considering the effects of spatial confinement (characterised by the aspect ratio $\varGamma$) and magnetic field (characterised by the Hartmann number $\textit{Ha}_{i=x,y,z}$ with subscript representing its direction). It is found that when the magnetic field is perpendicular to the convection domain ($y$-direction), it does not affect the onset of convection due to zero Lorentz force. With a magnetic field in the $z$ (vertical) or $x$ (horizontal) directions, the onset of convection is delayed, resulting in a larger critical Rayleigh number $Ra_c$ for the onset of convection. We outline phase diagrams showing the dominating factors determining $Ra_c$. When $\varGamma \leqslant 0.83\textit{Ha}_z^{-0.5}$ for vertical and $\varGamma \leqslant 0.66\textit{Ha}_x^{-1.01}$ for horizontal magnetic field, $Ra_c$ is mainly determined by the geometrical confinement with $Ra_c=502\varGamma ^{-4.0}$. When $\varGamma \geqslant 2^{1/6}\pi ^{1/3}\textit{Ha}_z^{-1/3}$ for vertical and $\varGamma \geqslant 5$ for the horizontal magnetic field, $Ra_c$ is mainly determined by the magnetic field with $Ra_c=\pi ^2\textit{Ha}^2$. In the intermediate regime, both the magnetic field and spatial confinement determine $Ra_c$, and a horizontal magnetic field is found to suppress convection more than a vertical magnetic field. In addition, under a horizontal magnetic field, there exists a subregime characterised by $Ra_c = 9.9\,\varGamma ^{-2.0} \textit{Ha}_x^2$, which is explained by a theoretical model. The magnetic field also modifies the length scale $\ell$. For a vertical magnetic field, $\ell$ decreases with increasing $\textit{Ha}_z$, following $\ell =2^{1/6}\pi ^{1/3}\textit{Ha}^{-1/3}$. For a horizontal magnetic field, when $\varGamma \lt 0.62\textit{Ha}_x^{0.47}$, the flow is a single-roll structure with $\ell$ being the width of the domain. The study thus shed new light on the interplay between magnetic field and spatial confinement.

We present a study on the melting dynamics of neighbouring ice bodies by means of idealised simulations, focusing on collective effects, with the goal of obtaining fundamental insight into how collective interactions influence the melting of ice. Two neighbouring (vertically or horizontally aligned), square-shaped and equally sized ice objects (size of the order of centimetres) are immersed in quiescent fresh water at a temperature of ${20}\,^\circ \textrm {C}$. By performing two-dimensional direct numerical simulations, and using the phase-field method to model the phase change, the collective melting of these objects is studied. When the objects are horizontally aligned, no significant influence of the neighbouring object on the melting time is observed. On the other hand, when vertically aligned, although the melting of the upper object is mostly unaffected, the melting time and the morphology of the lower ice body strongly depends on the initial inter-object distance. We report that the melting of the bottom object can be enhanced by more than 10 %, or delayed more than 20 %, displaying a non-monotonic dependence on the initial object size. We show that this behaviour results from a non-trivial competition between layering of cold fluid, which lowers the heat transfer, and convective flows, which favour mixing and heat transfer. For this melting in mixed convection, we were able to collapse our data onto a single curve.