Papers

We investigate the propagation of counter-rotating convection rolls in Rayleigh-Bénard convection initiated locally in a quiescent fluid layer under supercritical conditions. The velocity of the front separating quiescent fluid from the forming convection rolls, and the wavenumber of the convection rolls remaining behind the front, are explored. We numerically investigate fronts of forming convection rolls over five orders of magnitude of the reduced Rayleigh number, $\epsilon$, in 2D and 3D domains, for a broad range of boundary conditions, and for different front initiation approaches. In all cases, the front velocity increases as $\epsilon^{1/2}$ with increasing $\epsilon$ for $\epsilon \lesssim 1$ in agreement with predictions using the amplitude equation. The amplitude equation description of the front velocity remains accurate for $\epsilon \lesssim 10$ except when the Prandtl number is large which yields a velocity that is faster than predicted for a fluid layer far from threshold. The wavenumber of the convection rolls increases linearly with $\epsilon$ in agreement with the wavenumber that maximizes the growth rate of perturbations in the linear regime. Farther from onset, the wavenumber growth transitions to a reduced scaling of $\epsilon^{1/4}$ in agreement with predictions using the Swift-Hohenberg equation in the large $\epsilon$ limit. The scalings describing the wavenumber variation with $\epsilon$ are independent of the domain geometry, boundary conditions, and front initiation method. However, the front-selected wavenumber at criticality does not equal the critical wavenumber of the bulk instability, in general, and depends significantly upon these details. We compare our results with experimental measurements where possible.

Localized forcing in the fluid inside or outside a viscous drop can drive drop translation. Using the Lorentz reciprocal theorem, we derive an integral expression for the translational velocity of a spherical Newtonian drop subject to localized force and source distributions in either fluid and to interfacial traction. For a clean drop, we obtain explicit responses to Stokeslets, force dipoles, rotlets, general second force moments, and source dipoles as functions of position, orientation, and viscosity ratio. Interior forcing obeys a finite selection rule: only force moments through second order and the first source moment contribute directly to translation. Exterior forcing can couple to multipoles of all orders and produces distance-dependent responses. Although different enclosed singularities can produce the same drop velocity, resolving their exterior flows in drop-centered spherical Stokes modes provides additional constraints on the underlying forcing. We also distinguish regularized force distributions, governed by prescribed kernel moments, from resolved rigid particles, governed by low-order surface-traction moments and prescribed slip. The framework unifies these representations and shows how exterior-flow measurements provide information beyond drop translation, laying the foundation for constructing squirmer-like viscous drop solutions with controllable far-field behaviors.

Cold-to-hot heat transfer is a conspicuous non-equilibrium feature of rarefied cavity flows and is often used to judge whether a continuum closure captures higher-order transport. We examine what such agreement actually establishes. The exact heat-flux balance shows that the fourth-order contribution enters only through the divergence of the composite tensor (A_{ij}=R^{\mathrm{cl}}*{ij}+\Delta\delta*{ij}/3). A compactly supported Airy construction yields an infinite-dimensional family of symmetric divergence-free perturbations that preserve the flux-side wall trace. A two-dimensional-physical, three-dimensional-velocity (2D3V) description also leaves the transverse component (A_{zz}) unobserved. Heat flux therefore cannot uniquely identify the underlying fourth-order state. We test the consequences using direct simulation Monte Carlo (DSMC) ensembles and independent regularized 13-moment (R13) and regularized 26-moment (R26) solutions at two rarefaction levels. Counter-gradient transport and the dominant tensorial channel persist under changes in grid and particle number, whereas the local scalar fourth moment and the spatial organization of the counter-gradient region are more sensitive. A finite-particle-corrected fourth-moment audit shows that more than 95% of the resolved R26–DSMC composite-tensor discrepancy at the higher rarefaction lies in the discrete divergence-null space, whereas projected sampling noise hides only about half of its energy. R26 captures the circulation and heat-flux direction more closely than R13, yet the remaining differences concentrate in the wall and corner layers.

Multilevel non-volatile memory technologies face recurring trade-offs among information density, endurance, retention, and switching energy. We investigate an alternative state variable based on the discrete vortex configuration of self-organized vortices in a boundary-driven electron fluid. A dissipative point-vortex model derived from magnetohydrodynamic dynamics yields six reproducible vortex codewords, corresponding to 2.585 bits per cell, whose write fidelity, noise sensitivity, 100-cycle endurance, and effective information capacity are quantified. The ordering of their finite-amplitude basin radii $r_{50}$ differs from that predicted by both fixed-circulation and fully coupled linear-stability spectra. A nonlinear saddle-point construction based on the reduced dynamics likewise does not recover the measured basin ordering. The discrepancy is associated with escape pathways involving coupled position–circulation dynamics that are absent from the fixed-circulation description. These results show that local stability alone does not determine the finite perturbation tolerance of the vortex states considered here. The proposed memory requires active hold power, and passive retention remains to be established experimentally.

We present a variational formulation of the incompressible vorticity equation based on Gauss's principle of least constraint using the Gauss constraint functional $\Zvec_\omega$. The central result is the Euler–Lagrange equation $\mathbf{Z}_\omega = -\nabla h$, where $h = \mathbf{u} \cdot\boldsymbol{omega}$ is the helicity density. This reveals that the helicity gradient $\nabla h$ acts as the constraint force maintaining the solenoidality of the vorticity field, exactly as the pressure gradient $\nabla p$ maintains incompressibility in Taha et al.'s pressure-gradient minimization principle. The helicity density naturally emerges as the Lagrange multiplier enforcing $\nabla \cdot \boldysmbol{omega}=0$, and at the solution the flow minimizes the norm of the helicity gradient $\|\nabla h\|^2$. This establishes the exact duality: helicity is to vorticity as pressure is to velocity. We apply the variational principle to the Burgers vortex and verify the the Euler-Lagrange equation. The variational principle connects to Moffatt's helicity conservation theorem, Arnold's geometric formulation of ideal fluid flow, and Kambe's gauge-theoretic formulation. This work provides a unified variational framework for fluid dynamics that spans classical mechanics, geometric mechanics, and topological field theory. We discuss how this work may provide a theoretical foundation for understanding the role of helicity gradients in boundary layer dynamics, with potential implications for the formation of coherent structures and the onset of transition. These applications are reserved for future work.

During drop coalescence, a connecting bridge of fluid forms and rapidly expands due to surface tension. For spherical drops, these dynamics are well understood in both the viscous and inertial regimes. However, under strong confinement, fluid motion is fundamentally altered by geometric constraints, leading to dissipation on small lengthscales. We investigate the coalescence of drops confined in a Hele-Shaw cell (two parallel plates separated by a narrow gap). In this geometry, the depth-averaged flow is governed by Darcy's law while surface tension drives the interface motion. We identify two distinct temporal regimes in the evolution of the bridge radius that evolves as a power law ($R_b$). At early times, the bridge grows as $R_b \sim t^{1/2}$, which results from a confinement-dependent meniscus instability that determines the initiation of contact between droplets prior to bridge formation. At later times, the bridge growth slows substantially and follows $R_b \sim t^{1/5}$, consistent with recent theoretical predictions for Darcy-governed coalescence. We show that the transition between these regimes is controlled by several geometric lengthscales. In particular, the onset of the Darcy regime occurs when the interface radius of curvature becomes comparable to the plate spacing, such that the flow becomes fully confined. Using a boundary integral formulation, we find that both scaling laws for $R_b$ are determined by the bridge width. Together, these results identify a new universal regime of drop coalescence in a broad class of fluids obeying Darcy's law.

This work advances a fundamentally new approach to space-based construction by using liquid self organization in microgravity as a generative design and fabrication principle. Building on the LiquiFab method, we demonstrate how minimal surface architectures - traditionally dependent on complex additive manufacturing - can instead emerge directly from the physics of fluid interfaces shaped by programmable boundary conditions. We present a simulation-to-fabrication workflow, that includes a boundary-driven minimal-surface solver integrated in Grasshopper/Rhino, and an experimental system that implements in a neutral buoyancy environment simulating microgravity. This enables the generation of customizable Schwarz-P-inspired with tunable geometry and thickness, illustrating a scalable pathway for material-efficient, on-orbit fabrication. To validate performance in true microgravity, a flight experiment on the International Space Station is scheduled for the first quarter of 2027 and we here detail the additional considerations required for this experiment.

We show that living microswimmers mechanically reinforce dense emulsions. Castor-oil-in-water emulsions laden with the microalga Chlamydomonas reinhardtii are compared in three states: without algae, with immobilized algae, and with motile algae, over a broad range of oil fractions spanning the jamming transition. Oscillatory rheology reveals that motile algae systematically increase the yield stress, by up to a factor of two, whereas immobilized cells at the same concentration leave it essentially unchanged. The reinforcement thus originates from activity rather than from the mere presence of inclusions. Single-cell tracking shows that the droplet network confines the swimmers in pores that shrink as the oil fraction increases, and confinement is known to amplify the propulsion force of C. reinhardtii. A simple estimate based on this confinement-enhanced force accounts for the measured excess yield stress and indicates an effective, activity-induced depletion-like attraction between the passive droplets. These results identify a feedback loop: the microstructure confines the swimmers, confinement amplifies the forces they exert, and these forces stiffen the microstructure. Active emulsions thus emerge as a model platform for programming the mechanics of disordered soft solids through activity.

The possibility of convection in a wall-heated unstirred simple fluid in zero-gravity conditions is discussed. It is shown that a low-Prandtl-number fluid possesses a stable circulating state in zero gravity, without destabilization of the diffusive state, provided geometric inhomogeneity allows the thermal cycle to perform net mechanical work. The analysis provides a quantitative explanation of why purely volumetric effects cannot induce by themselves convection in conventional fluids under zero-gravity conditions.

Interaction between energy-abundant mesoscale eddies and internal waves can lead to convection-turbulence generation and may prove important for deep-sea life and circulation. However, the size of scales of interacting flows is not well known. In this paper, a diagnostic tool of tilt is tested near the single top-buoyancy of 40 mooring lines 9.5 m apart horizontally and compared with 50-m scale relative vorticity and waterflow above a 2500-m deep flat Northwestern-Mediterranean seafloor. Whilst tilt relates to first order with flow-speed squared induced by mooring-line drag, considerable deviations from this relationship and larger tilt occur when the amplitude of relative vorticity attains values O(f), f the inertial frequency of planetary vorticity. The sign of relative vorticity is of no importance. Such larger-tilt events occur during most intense convection turbulence via warm-water slanting from above. During these events, variations in tilt-angle magnitude are as large as the average tilt O(0.1)degree, thereby reducing variational scales from 50 to 9.5 m. Thus, in a deep-sea environment where flow speeds are <0.07 m s^-1, O(0.01) m s^-1 flow-speed variations provide important turbulent mixing, without deep dense-water formation.

This study develops a low-fidelity framework for aerofoil shape optimisation under curvilinear blade kinematics, using a hovering cyclorotor as a representative case. Aerofoil optimisation can improve cyclorotor efficiency by suppressing leading-edge vortex separation during dynamic stall, but conventional approaches rely on computationally expensive CFD-based optimisation. The proposed method uses a single-streamtube model to estimate the rotor throughflow and optimises the aerofoil camberline using separate leading- and trailing-edge criteria. Assessed across configurations with varying blade counts and chord lengths, the framework consistently identifies aerofoils that improve hover efficiency, quantified by Figure of Merit. For the baseline four-bladed configuration, the low-fidelity optimum achieves 77% of the Figure of Merit improvement obtained using high-fidelity optimisation at a fraction of the computational cost. The analysis also reveals an additional torque-minimising design family at increased chord lengths, highlighting the influence of trailing-edge loading. Aerofoil optimisation is also compared to blade-pitch kinematics optimisation, which improves the efficiency through similar control of the leading-edge vortex separation. While both approaches produce comparable improvements in efficiency, the optimised kinematics substantially reduces thrust. Aerofoil optimisation may therefore be more practical, as maintaining a target thrust with optimised pitch kinematics would require higher rotational speeds, potentially introducing structural and noise issues.

Periodic surface-wave forcing can reorganise turbulent flows through coherent spectral response, synchronization, intermittency, and changes in short-term predictability, so its dynamical effect need not vary monotonically with forcing frequency. We reanalyse laboratory acoustic Doppler velocimetry records obtained at constant discharge under four conditions (0, 0.5, 0.67, and 1~Hz) using one common KNN–GMAE forecast-error-growth protocol. A distance-weighted $k$-nearest-neighbour predictor generates out-of-sample forecasts over multiple horizons, and the slope of the early quasi-linear region of $\ln(\mathrm{GMAE})$ versus physical forecast time is reported as a finite-horizon forecast-error-growth rate, $\lFEG$. For the full 120-s records, $\lFEG$ is 5.68, 0.85, 3.39, and 4.11~s$^{-1}$ for 0, 0.5, 0.67, and 1~Hz, respectively. The ordering 0~Hz $>$ 1~Hz $>$ 0.67~Hz $>$ 0.5~Hz is preserved in all seven nearby parameter configurations, indicating that the comparative result is not an artefact of a single KNN setting. A 40-s sliding-window analysis with a 10-s step reveals substantial temporal structure. The no-wave condition remains predominantly high and the 0.5-Hz condition predominantly low, whereas the 1-Hz record has weaker local support for a single exponential-growth regime: only 3 of 9 windows satisfy the adopted early-fit criterion $\RFEG\geq0.90$, compared with 8/9, 7/9, and 7/9 for 0, 0.5, and 0.67~Hz.

Temporal fluctuations of wind farm-generated power arise from the interaction between atmospheric turbulence, turbine properties, and wind-farm layout. However, accurately characterizing these fluctuations remains an open challenge. We here present and extend an analytical framework to predict the temporal spectrum of wind farm power-fluctuations, and compare its predictions with detailed large-eddy-simulations (LES) of a wind farm operating within a conventionally neutral boundary layer. The modeling framework assembles several established concepts from turbulent boundary layer physics: a spatio-temporal turbulence spectral model accounting for mean advection and assuming random sweeping by large eddies, a top-down wind farm model of a fully developed wind turbine array boundary layer flow providing the required mean-flow and turbulence scales, and a spatial sampling kernel representing turbine positions and finite rotor size. The latter is extended to three dimensions to represent filtering of spatial fluctuations of turbulence along the vertical direction. Using only atmospheric, turbine, and layout parameters, the model predictions are evaluated against an extensive LES database of a large wind farm on flat terrain. The model accurately predicts the aggregate power frequency spectrum, including peaks associated with advection between turbine rows, the decay of inertial-range turbulence fluctuations due to rotor averaging, and spectra of aggregate power signals from various arrangements of groups of turbines within the array (e.g. staggered or random subsets). The ability to predict wind power fluctuation spectra from fundamental fluid dynamics and existing boundary layer turbulence models could help improve wind farm grid integration.

Actuator-disk models (ADM) are widely used in wind-farm simulations, but their accuracy in reproducing power and thrust temporal fluctuation statistics has not yet been compared to the more accurate and detailed predictions of actuator-line models (ALM). This work provides a detailed comparison of such model predictions under three distinct atmospheric conditions. Data is obtained from a database (JHTDB-Wind) containing actuator-line turbine-response and time-resolved flow field data from a large-eddy simulation of a small windfarm operating over a full diurnal cycle. Power and thrust time series corresponding to ADM are constructed from disk-averaged velocity signals and compared with the corresponding more detailed actuator-line signals over three temporal windows within the diurnal cycle. The analysis compares time series, power spectral densities, and both the fluctuation and increment probability density distributions. The results show that the ADM time-series capture the dominant temporal, spectral, and statistical features of the ALM values at resolved and disk-averaged flow-field time scales, i.e. slower than the rotor frequency. Effects of temporal filtering that are often applied to ADM inputs are examined. Overall, the results provide strong support for the use of ADM for predicting power and thrust fluctuation statistics in wind-farm simulations.

In this paper, we identify a new mechanism for rogue wave formation in a shallow-water setting. We study a bidirectional shallow-water wave field in the context of the Kaup-Boussinesq equation, and introduce a weak high-wavenumber dissipative perturbation that breaks the underlying integrability of the system. In this setting, dominant solitons grow through successive interactions with weaker, co-propagating solitons, leading to the formation of a "champion soliton" in each direction of propagation. This behaviour is in contrast to the general intuition that dissipation damps coherent structures, and instead shows that weak dissipation can induce their intensification. Moreover, we find that weak dissipation alone is not sufficient for champion soliton formation; the presence of random waves plays a crucial role in the intensification process, catalysing the transfer of energy into dominant coherent structures. While champion solitons have previously been studied in non-integrable systems, these works primarily consider perturbations introduced through modifications of the nonlinear terms (e.g., higher-order Korteweg-de Vries and Schrödinger-type models). In the present work, high-wavenumber dissipation provides a more physically natural perturbation, since such small-scale damping is a common feature in many systems.

Physics-informed neural networks (PINNs) embed governing partial differential equations directly into the training loss, offering a promising alternative to costly CFD solvers for unsteady flows. Yet the growing list of techniques proposed to improve PINN training is typically validated one at a time, leaving open whether these techniques actually compose. We study this question in depth on the DFG/Schafer-Turek unsteady cylinder wake benchmark. In isolation, nearly every technique performs no better than an untreated baseline. However, combining periodic (SIREN) activations with causal weighting unlocks a previously inaccessible regime, reconstructing velocity and pressure fields to within 4.1% average relative L2 error against an OpenFOAM reference solution. Adding further techniques instead causes catastrophic performance degradation, demonstrating that individually effective PINN interventions can interact nonlinearly and that more elaborate training recipes are not necessarily better.

We investigate the dynamics and stability of three immiscible viscous liquid layers coating the interior of a vertical cylindrical tube, a configuration relevant to stratified core–annular transport processes. A long-wave asymptotic analysis yields a coupled system of nonlinear evolution equations governing the motion of the three interfaces. Linear stability analysis predicts a persistent long-wave instability, the capillary (Rayleigh–Plateau) instability of the air–core interface, together with secondary finite-wavenumber instability bands that emerge from interfacial coupling in certain parameter regimes. These stability characteristics depend sensitively on the layer thicknesses, viscosity ratios, and surface tension parameters, and include mode-switching associated with competing maxima in the dispersion relation. Nonlinear simulations reveal three distinct dynamical outcomes: saturation to finite-amplitude travelling waves, air-core closure through plug formation, and rupture of the intermediate liquid layer while the air core remains open. The intermediate-layer rupture mechanism is unique to the three-layer configuration which has no analogue in one- or two-interface cylindrical film flows. Numerical continuation is used to compute branches of travelling-wave solutions and their associated limit points. Comparison with time-dependent simulations shows that travelling-wave branches successfully predict the transition from saturated waves to plug formation, but do not capture the distinct rupture mechanism associated with collapse of the intermediate layer.

The influence of microscale flow structures (smaller than the pore size) on turbulent heat transfer in porous media has not been yet investigated. The goal of this study is to determine the influence of the micro-vortices on convection heat transfer in turbulent porous media flow. Turbulent flow in a homogeneous porous medium was investigated using Large Eddy Simulation (LES) at a Reynolds number of 300. We observed that the convection heat transfer characteristics are dependent on whether the micro-vortices are attached or detached from the surface of the obstacle. There is a spectral correlation between the Nusselt number and the pressure instabilities due to vortex shedding. A secondary flow instability occurs due to high pressure regions forming periodically near the converging pathway between obstacles. This causes local adverse pressure gradient, affecting the flow velocity and convection heat transfer. This study has been performed for obstacles with shapes of square and circular cylinders at porosities of 0.50 and 0.87. Understanding the dominant modes that affect convection heat transfer can aid in finding an optimum geometry for the porous medium.

The near-field shock structures of underexpanded sonic jets exiting into a subsonic coflow are investigated over a range of nozzle pressure ratio (NPR) and coflow-to-nozzle-exit velocity ratio ($U_c$), representative of a propulsive nozzle in subsonic flight. Time-averaged statistics from fully-resolved axisymmetric simulations and inviscid method-of-characteristics (MOC) analysis are used to understand how coflow alters the shock-cell structures, in particular the Mach-disk formation. It is well established that increasing NPR transitions the centerline reflection from regular (characterized by oblique shocks) to Mach reflection (characterized by a near-normal Mach disk). We find that coflow has the opposite influence: a strong coflow shrinks the Mach disk until it vanishes, reverting Mach reflection to regular reflection, so the NPR for this transition increases with $U_c$. This effect has previously been attributed to a reduction in the jet-boundary inclination at the nozzle lip, which confines the lip Prandtl-Meyer fan to a smaller angle and weakens the embedded shock. We show instead that this inclination is determined by the non-uniform pressure the coflow imposes along the jet boundary, which is the primary driver of the shock-structure transitions in coflowing jets. The non-uniform pressure weakens the boundary-reflected compression waves and orients them at shallower angles, so the embedded shock reflects regularly or fails to form. A simulation-informed MOC analysis with this non-uniform pressure boundary condition reproduces the transition behavior with increasing coflow. Coflow also lengthens the first shock cell linearly, which is accurately estimated by a simple correction to Prandtl classical shock-cell length scaling.

In this work, particle-resolved direct numerical simulations are performed to investigate flow past the five Platonic solids, which represent a progression in particle sphericity with an increasing number of faces. The simulations cover particle Reynolds numbers in the range 0.1 <= Re_p <= 300 and multiple particle orientations relative to the incoming flow. Based on the numerical data, new correlations are developed for the drag, lift, and torque coefficients. The proposed drag correlation explicitly accounts for both Reynolds number and particle orientation, whereas the lift and torque coefficients are represented by orientation-dependent trigonometric and exponential basis functions whose coefficients vary with Reynolds number. The simulations are conducted using the immersed boundary method, and the resulting drag correlation accurately reproduces the numerical data. The lift and torque correlations capture the principal trends observed in the numerical simulations, including the strong dependence on particle orientation. The proposed correlations provide a computationally efficient framework for incorporating orientation-dependent hydrodynamic forces and torques into Euler–Lagrange and point-particle simulations, enabling a more realistic representation and predictions of non-spherical particle transport in multiphase flows.

We experimentally and theoretically study the hydrodynamic analog of the classical brachistochrone problem: the time- and energy-minimizing paths for a spherical particle rolling down an incline within a viscous fluid. We show that in the presence of viscous dissipation, the paths of minima diverge from the classical cycloid, into curves of opposing curvature for time and energy, and are characterized by an effective dimensionless parameter, $St_p$, representing the ratio of the particle's viscous response time scale to its gravitational time scale. Using a generalized variational framework, we show that the fastest path reduces to {nearly straight ramps}, however, beginning and terminating in localized cycloids of curvature, $\kappa_c \sim St_p^{-2}$. Remarkably, the path of fastest descent on a given energy budget requires navigating a non-monotonic path ({\it“S-shaped”}) with an interior point of inflection. Our findings reveal a unification of temporal and energetic optimality for transport through dissipative media, and expand the celebrated brachistochrone solutions to the hydrodynamic regime.

Wind turbines operating downstream of others in a farm are routinely exposed to waked inflow, with reduced mean velocity and elevated turbulence driving power deficits and additional structural fatigue. The direct effect of wakes on blade-level structural loading remains under-explored experimentally, owing partly to the sparse spatial coverage of conventional point-based strain sensors. Here, we present a wind-tunnel study of wake-induced blade dynamics using two $1\,\mathrm{m}$-diameter turbine models, in which one blade of a downstream turbine ($WT_2$) is instrumented with distributed Rayleigh-backscattering fibre-optic strain sensors, providing spatially continuous strain measurement across the blade span. By changing the relative position of the upstream turbine ($WT_1$) to the downstream, waked turbine $WT_2$ across the streamwise and spanwise extent, we map power output, spanwise strain, and accumulated representative fatigue relevant loading across the wake profile. Full wake impingement suppresses blade loading through the associated velocity deficit, while partial wake overlap generates the strongest load intermittency and highest relative fatigue relevant loading, despite an intermediate power recovery. A combined performance-to-loading metric shows this partial-wake regime offers the least favourable trade-off between energy yield and structural loading. These results show that minimising partial-wake exposure, not only mean velocity deficits, should be a design consideration for wind-farm layout and turbine spacing.

Granular flows are ubiquitous in natural and industrial systems, yet their complex dynamics remain difficult to characterize. For inverse problems involving unknown inlet, outlet, and wall boundary conditions, where CFD simulations are challenging, reconstructing complete flow fields from sparse observations constitutes a challenging inverse problem. In this study, a physics-informed neural network framework driven by both physical mechanisms and measurement data is developed to reconstruct the steady-state full-field distribution of granular flows in a pipe. The proposed approach integrates sparse measurement data with governing equations and constitutive relations and is trained using high-fidelity datasets generated by CFD solutions of a continuum model. The framework incorporates a dimensionless loss formulation, physics-informed initialization, dynamic global weighting, and a locally weighted granular temperature data-loss strategy. These treatments enable accurate reconstruction of the complete flow-field evolution. This work establishes a robust methodological framework for flow-field reconstruction in complex granular flow systems.

Predicting flow-induced sound from moving and deforming bodies is computationally demanding because the near-field hydrodynamics and the far-field acoustics require substantially different spatial resolutions and domain extents. A fully parallel hybrid framework is developed to address this disparity by coupling an incompressible Navier-Stokes solver to an acoustic perturbation equation (APE) solver on independently generated, non-conforming Cartesian grids. A sharp-interface ghost-cell immersed boundary method, with radial-basis-function reconstruction, imposes the boundary conditions for complex moving geometries on both grids. The converged flow field supplies the acoustic source through a one-way, precomputed parallel interpolation operator. This arrangement confines the flow grid to the body and wake while allowing the acoustic grid to extend independently into the far field. The framework is validated for Gaussian-pulse propagation, pulse scattering by a rigid cylinder, tonal sound from flow past a cylinder, and radiation from a traveling wavy foil. The predicted waveforms, wavelengths, pressure amplitudes, and radiation patterns agree closely with analytical solutions and published reference data. Applications to eel and Jack fish locomotion, a four-eel school, a manta ray, and a harbor seal further demonstrate the treatment of realistic three-dimensional morphologies, large boundary deformation, and multiple interacting swimmers. The results resolve morphology-dependent acoustic signatures and interference-driven changes in far-field directivity without requiring the flow grid to span the acoustic far field.

This study compares three reduced-order modeling (ROM) approaches for flow simulations of generalized Newtonian fluids described by the Carreau rheological model. All three methods rely on offline snapshot generation in the rheological parameter space using the full-order model (FOM), followed by a proper orthogonal decomposition (POD) of the snapshot matrix to obtain a reduced basis, but they differ in how they reconstruct the solution for new parameter values in the online phase. The three ROM approaches examined are: (i) intrusive Galerkin projection onto the reduced basis with full operator reassembly (ROM-FULL), (ii) intrusive hyper-reduced Galerkin projection using the discrete empirical interpolation method with GappyPOD for the nonlinear term (ROM-DEIM), and (iii) a non-intrusive interpolation approach using radial basis function interpolation (ROM-RBF). We demonstrate these three ROM approaches on two benchmark flows: a lid-driven cavity and a sphere settling in a closed container, spanning boundary-driven and force-driven flows. ROM-FULL achieves the highest accuracy but requires reassembling the full-order nonlinear operator during the online phase, whereas ROM-RBF is fully non-intrusive, and its accuracy is closely tied to data availability and deteriorates outside the training data range. ROM-DEIM offers a balance between efficiency and accuracy, even when data are sparse. The results provide guidelines for selecting an appropriate ROM strategy based on solver accessibility, computational efficiency, and desired accuracy.

This paper investigates the feasibility of using residual learning to improve unsteady aerodynamic load prediction for aeroelastic applications. The machine learning technique selected for the study is the long short-term memory (LSTM) neural network, which is used for its suitability for sequential data with aerodynamic memory effects. The approach is investigated for the NLR 7301 airfoil benchmark using high-fidelity CFD lift data for prescribed pitch and plunge motions in the transonic flow regime in the presence of shock motion. An analytical unsteady aerodynamic model based on the Wagner function is used as a physics-based baseline, and the neural network is trained to learn the difference between the CFD lift coefficient and the Wagner prediction. The residual model is compared with a direct neural-network model trained to predict the CFD lift coefficient. The comparison includes feature and normalization studies, external benchmark cases, and leave-one-out and leave-family-out generalization tests across a range of sinusoidal and non-sinusoidal motions. The residual model performs best when its inputs align with the Wagner formulation variables, generally giving lower error and more consistent performance across training runs, though the direct model remains more accurate for some high-frequency cases. The residual model also generalizes better in the leave-one-out and leave-family-out tests, with a smaller increase in error than the direct model when entire motion families are withheld from training. Overall, the results indicate that residual learning shows promise as a modular approach for augmenting classical low-order aerodynamic theories, especially when the physics baseline removes a structured part of the aerodynamic response and leaves a lower-variance correction for the neural network to learn.

Spray atomisation rapidly creates large liquid-gas interfacial areas and is central to many industrial processes. However, predicting spray behaviour and surface area remains difficult: experiments cannot access all spray regions, while CFD becomes prohibitively expensive as finer structures develop. Data driven surrogates can learn interface evolution, enabling rapid design space exploration, operating condition ranking, and ultimately spray control. We investigate how state representation, neural architecture, and physics-informed regularisation affect long horizon autoregressive forecasting of spray interfaces, particularly conservation. Our principal model is a boundary-conditioned Fourier Neural Operator (FNO) that predicts the evolution of the signed distance function (SDF) from the liquid-gas interface. It is trained on 2D sharp interface Volume-of-Fluid CFD simulations spanning several atomisation regimes. The SDF-FNO retains interface fidelity better than an FNO trained directly on volume fraction, but is outperformed by a U-Net. Objective function ablation shows that a liquid inventory penalty improves conservation at a modest cost to local interface accuracy. We also introduce a physics-informed extension combining an open-domain target-increment liquid balance penalty, a narrowband Eikonal regulariser that preserves signed distance geometry, and a phase-boundedness penalty. Although this model trains stably, it leaves forecast error, interface overlap, and inventory behaviour essentially unchanged relative to the data driven baseline. Finally, we demonstrate the surrogate by ranking injection conditions according to interfacial area generated per unit gas injection power across the operating envelope of a fixed geometry.

We apply a modal framework for investigating the effect of density variations on gravity-type driven flows at the nanoscale. Using eigenfunction decomposition of the density and acceleration fields, each shear-pressure mode is separated into a homogeneous contribution and an inhomogeneous contribution determined by the Fourier amplitudes of the density and applied acceleration. This decomposition provides a direct means of identifying how density variations and external forcing couple and govern the flow behavior. We first revisit the Poiseuille flow and show that for channel heights larger than the characteristic intermolecular distance the homogeneous contribution dominates the long wave length (small wave vector) response, consistent with previous simulation results. In contrast, for sinusoidally driven flow, selective excitation of acceleration modes can produce the opposite behavior, with the inhomogeneous contribution dominating the long wave length response. The results show that the effect of the density variations depends on the specific flow; specifically the detailed mode coupling between the acceleration and density fields. The framework presented here provides a direct systematic approach for understanding and predicting the flow depending on the applied acceleration.

Poroelastic materials, such as polymer tissue scaffolds, porous rocks, and hydrogels, can weaken due to interactions between the solid skeleton and chemical species in the interstitial fluid. We develop a mathematical model for a poroelastic material to provide fundamental mechanistic insight into how weakening the material can affect the time-varying mechanics of the system. Our model couples large-deformation poroelasticity with an advection-diffusion equation for the solute. Furthermore, we introduce a decay equation for the material stiffness, whose rate of decay depends on the solute concentration. In this way, we describe a three-way coupling between poroelastic deformation, weakening of the skeleton and transport of solute through the material. We exploit numerical and analytical techniques to reveal the flow-driven uniaxial compression of a weakening poroelastic material and determine parameter regimes for which weakening the material facilitates pore closure at the downstream boundary. We identify parameter regimes in which (1) a steady state is attained without pore closure, (2) pore closure occurs at a finite time or (3) the pores close instantaneously; we uncover case (2) through the introduction of weakening into the system. We provide insights into the relationship between the differing behaviours and the separation between the timescales of the system. For systems with slow weakening, we derive a leading-order approximation for the time of pore closure, treating the ratio of the timescales of poroelastic relaxation and weakening as a small parameter, and investigate the accuracy of this approximation and the new behaviours that arise when these timescales become comparable.

In coastal wave boundary layers over gravel-scale roughness, with near-bed orbital excursions ten to a hundred times the roughness height, the boundary layer is only a few roughness heights thick. A roughness-controlled layer (RCL) of the steady-flow extent two to five element heights would then leave no room for a logarithmic layer, yet experiments over densely packed marbles recover logarithmic profiles within millimetres of the crests. We resolve this contradiction by re-analysing previous Particle Image Velocimetry (PIV) records with a triple decomposition that separates the marble-locked dispersive motion from the stochastic turbulence, across eleven wave, current and wave-current conditions. The boundary layer organises into an RCL, a transition region, and a logarithmic profile layer, with the RCL only one to two tenths of a marble diameter deep. This thinness is kinematic: above a periodic bed, the dispersive field decays over a length fixed by the element spacing, so close packing caps the layer at a fraction of a diameter. The layer is destroyed and rebuilt every half-cycle, tracking the near-bed velocity quasi-steadily, while the eddies within it stay locked to the inter-crest gap. Thinness does not imply weakness: within the layer, the dispersive kinetic energy rivals the turbulent kinetic energy, and the dispersive stress matches, near the crests exceeds, the Reynolds stress, showing that separated wakes carry organised momentum. The logarithmic layer survives because the decay length imposed by the packing is far smaller than the boundary-layer thickness, a margin set by the bed geometry rather than by the forcing.