ALT Data-driven reduced-order modelling (ROM) of turbulent flows is based on an efficient and physical low-dimensional representation of the flow process. Such a dimension reduction can be achieved using proper orthogonal decomposition (POD) or non-linear autoencoders. Capturing the inherent non-linearities in turbulent flows offers the potential for improved compression rates. This work proposes the local tangent residual (LTR) as an a priori estimate of the potential benefit of non-linear dimension reduction for a given dataset. The LTR is based on the local geometry of the data points, requiring neither prior model training nor the definition of a threshold parameter. For all datasets investigated in this work, namely, two vortex street cases and the turbulent wake flow of a wind turbine, the dominant trends in autoencoder compression performance are reproduced by the proposed LTR. The results are further contextualised through the decoder linearisation error (DLE). The DLE quantifies t

ALT We developed a machine learning (ML) surrogate trained on instantaneous large eddy simulation (LES) data filtered using proper orthogonal decomposition (POD) to predict mean flow fields in natural rivers with complex geometries. The surrogate enables efficient prediction of mean flow fields downstream of marine hydrokinetic (MHK) turbine arrays at a fraction of the computational cost of high fidelity simulations, facilitating turbine layout optimization in natural waterways where bathymetry, turbulence, and turbine interactions influence the available energy resource. We demonstrate the capabilities of the model at a real world tidal river site under ebb tide conditions. Complex bed bathymetry and planform geometry generate substantial background turbulence that can obscure turbine wake signatures. POD is employed to extract relevant flow structures, improving the ability of the ML model to learn wake dynamics. First, LES is performed at the tidal site to generate training data. POD is

ALT Twin tandem jets in crossflow are examined by 40 kHz time-resolved particle image velocimetry (PIV) over nine spacing–velocity-ratio conditions (L=2d–4d, r≈3–7; 68 twin cases, 11 single-jet records). The front jet is diluted: its trajectory lies below the single-jet reference, with a near-field segment up to 40% shorter, and its velocity ratio falls to an effective value r_eff<r, while the shielded rear jet rises above the reference. This paper identifies the carrier of that dilution and the unsteady dynamics that sustains it. The rear-jet blockage closes a gap recirculation whose reversed flow puts the two gap-side layers in the counter-current shear-layer configuration, the classical route to enhanced entrainment. The measured Reynolds stress and Prandtl mixing length show that the absolute exchange is nearly independent of the velocity ratio and exceeds the single-jet leeward zone like-for-like by up to 1.5× (zone peak, across conditions) and 2.8× (area-integrated) at r≈7. The gain

ALT We construct a second-order, linear, and fully decoupled scheme for Rayleigh–Bénard convection between Navier-slip walls. The nonlinear terms are treated explicitly and multiplied by a scalar with exact value 1 and no energy contributed to the continuous problem. As a result, each BDF2 step, with a rotational pressure correction, solves only constant-coefficient linear problems. For the time discretization, we prove that, for any time-step, the energy decays when the walls have equal temperatures, under a condition on the thermal weight, and the energy is bounded on finite time intervals when heated from below. Relaxing the scalar towards 1 preserves both estimates. Energy stability does not control the accuracy of the scalar; the velocity and temperature fields are computed with its unrelaxed value, whose deviation from 1 scales like (E/C)(δt/τ)³ for kinetic energy E, time-scale τ of the flow, and weight C of the scalar equation. In our tests, choosing C comparable to E keeps this dev

ALT Structures with large global contributions to a flow field are not necessarily those that are most closely associated with a specified engineering observable. We propose a time-lagged cross-correlation (TCC) ranking criterion to extract target-related dynamical structures from a fixed modal expansion while preserving existing spatial modes and temporal coefficients. The criterion ranks mode groups by the mean squared norm of cross-responses between their flow contributions and the observable over a lag window. It is tested separately using dynamic mode decomposition (DMD) and proper orthogonal decomposition (POD). TCC-DMD analysis shows that TCC recovers three low-contribution target structures from a dominant background in a synthetic flow. The reconstruction error for the target subfield is 2.15%. In the cylinder wake, different force observables select structures associated with the vortex-shedding fundamental, slow drag variations, and the second harmonic. In the cavity case, selec

ALT We present a thorough theoretical investigation of the shear force exerted on a horizontal plate undergoing fast (MHz range) longitudinal oscillations in presence of a spherical particle suspended in the liquid at close proximity to the plate. This hydrodynamic problem is relevant toward the analysis of frequency response (i.e., complex impedance) of Quartz Crystal Microbalance (QCM) to discrete (particulate) adsorbates. For low areal density of adsorbates, the contributions of individual particles remain additive, rendering the theory applicable towards quantitative interpretation of the experimental measurements. For adsorbates much smaller than the viscous penetration depth, the closed-form analytical expressions for the complex impedance were derived for particles of arbitrary density, assuming a lubricated contact (i.e., non-localized adsorption). The theoretical predictions were shown to be in an excellent agreement with accurate Finite Element numerical computations. The validit

ALT Nozzle-free droplet generation eliminates the need for a dispensing orifice, but useful printing also requires control over where droplets are deposited. Here we demonstrate predictable droplet deposition using a nozzle-free, angle-controlled surface acoustic wave jetting platform. Opposing focused interdigital transducers steer droplets from a pendant parent drop toward a collection substrate, with the ejection angle set by the time-integrated acoustic acceleration imbalance. The overall acceleration ratio identifies the operating window for single-droplet ejection and separates it from no-ejection, satellite-droplet, and multiple-droplet regimes. We then connect ejection angle to landing position through a geometric deposition model referenced to a balanced shot in each experiment. At a 5.2 mm parent-drop-to-substrate spacing, 80 steered shots across twenty experiments show strongly anisotropic relative placement scatter: 0.59 mm in the steering direction and 0.06 mm transverse to it

ALT Scientific machine learning provides a promising framework for solving complex problems in science and engineering. Notably, physics informed neural networks (PINNs) have shown considerable potential for solving differential equations and modelling engineering systems. Solving time fractional Navier Stokes equations (TFNSEs) on complex domains is challenging due to temporal nonlocality, nonlinear flow dynamics, geometric complexity, and incompressibility constraints. To address these challenges, we propose a geometry gated, divergence-free Gaussian radial basis function Kolmogorov Arnold network (GeoDiv KAN). The proposed architecture uses geometry dependent feature gating and a streamfunction formulation to preserve incompressibility by construction. GeoDiv KAN is evaluated for the time fractional incompressible Navier-Stokes equations on square, circular, and star-shaped domains. Numerical experiments show that GeoDiv KAN achieves higher predictive accuracy than PINN, PIKAN, RBF KAN,

ALT The rotating detonation engine (RDE) faces severe thermal protection challenges. This study numerically investigates a head-end dual-purpose injection configuration in which the film-cooling holes act as both fuel and coolant injectors. An Eulerian-Lagrangian framework is employed, in which the turbulence closure, the film-cooling effectiveness prediction, and the droplet evaporation model are validated against experimental benchmarks. The validated framework is then applied to examine the effects of coolant flow rate, atomization quality, and the fuel allocation between the main flow and the cooling system. Beyond a threshold coolant flow rate the wall temperature drops substantially, whereas an excessively low flow rate elevates it by intensifying local deflagration, revealing a competition between evaporative cooling and secondary combustion that is inherent to this dual-purpose configuration. Fine droplets cool effectively but only over a limited distance downstream, and fuel alloc

ALT This experimental study demonstrates MHz-order acoustic control of Rayleigh-regime liquid-jet breakup using a compact through-hole lithium niobate thickness-mode transducer integrated at the nozzle exit and driven near its 6.7 MHz resonance. Jet dynamics were measured by high-speed shadowgraphy at 50,000 fps, and breakup lengths were obtained from instantaneous jet-length records using an autocorrelation-based method that identifies statistically separated breakup events. Acoustic actuation produced visible interfacial corrugations and reduced the normalized breakup length, Lb/d, for both water and glycerol–water jets. In water, the response was threshold-like at low Weber number and weakened as the jet velocity increased to We 50. A reduced-order threshold correlation, fit to the water amplitude and velocity sweeps, captured this abrupt shortening and the subsequent loss of actuation efficacy. More viscous glycerol–water mixtures shortened immediately at low forcing, indicating a dist

ALT Traditional turbulence-model development relies heavily on expert knowledge and is time-consuming. This study develops an agentic AI framework, DEScover (automated Discovery of DES closures), to explore subgrid length scales for detached eddy simulation (DES). The user provides only a modeling idea: the length scale is the grid width normal to the local vortex axis. Four large language model (LLM) agents then propose, review, implement, and evaluate candidate formulations and generate scripts for CFD simulations. The Discovery Agent infers the vortex axis from various vortex-identification methods. The candidates are tested on four cases spanning canonical and engineering flows: a flat plate, a backward-facing step, a free shear layer, and an iced airfoil. For all test cases, the best candidate consistently outperforms the standard length scale based on the maximum cell size. Its accuracy is similar to, or slightly better than, that of the expert-designed shear-layer-adapted length sca

ALT The fragmentation of a liquid jet in a crossflowing airstream arises from a complex interplay between aerodynamic forcing and interfacial instabilities acting across a wide range of length scales. We examine the spatial evolution of droplet size distributions along the jet trajectory using a combination of high-speed shadowgraphy and digital in-line holography, allowing simultaneous characterization of jet morphology and volumetric droplet size distributions. The results demonstrate a clear progression of breakup mechanisms, from near-field bag rupture to downstream rim, ligament, and node disintegration, each associated with distinct characteristic length scales. This evolution is reflected in the droplet size distributions, which broaden and become increasingly polydisperse downstream relative to the near-field distributions. By systematically varying the liquid-jet and airstream Weber numbers, we identify distinct breakup modes, and construct a regime map that delineates the column-

ALT Successful prediction of the interaction between turbulent flows and river ecosystems is essential for flood management, development of mitigation strategies, and infrastructure protection. Natural riverbeds consist of bed materials of multiple grain sizes that interact with turbulence differently, leading to spatial classification of particle sizes. Such particle sorting processes can only be captured by models capable of handling multiple grain sizes. We present a numerical framework that couples large-eddy simulation with a multi-grain-size sediment transport module to track multiple grain size classes, their local fractions within the bed-load layer, and bed evolution. A laboratory experiment was conducted in a flume with multi-size bed material to examine sediment particle redistribution and sorting. Measured sediment grain size distributions and bed elevations were used to validate the numerical framework. The results of the multi-grain-size sediment transport model for bed defor

ALT Bubble size, depth, and spatial distribution govern transport and uniformity in multiphase flows. A single shadowgraphic view cannot separate bubble diameter from focal-plane displacement, as image signatures depend on both, while multicamera systems add optical access, calibration, and cost. We develop a single-camera physics-guided framework combining direct numerical simulation (DNS), validated Monte Carlo ray tracing, preprocessing, and focus-conditioned multitask deep learning to recover three-dimensional bubble position and physical-equivalent diameter from two-dimensional shadowgraphs. DNS-resolved bubble label images rendered at multiple focal-plane settings. The ray tracer reproduces shadowgraphic signatures with errors below 3% (intensity), 1% (apparent size), 15% (blur), 6% (numerical aperture), and 4% (edge-spread-function shape), running ∼30× faster than the CPU implementation. The dataset has 900 frames and 87,728 labeled instances, partitioned by DNS timestamp to prevent

ALT This paper presents a grey-box framework for a robust deterministic, real-time prediction of coupled surge-heave-pitch motion in waves. The approach combines a physics-based online system identification with a nonlinear pseudo-spectral potential flow solver. Rather than prescribing a complete hydrodynamic model from captive model tests or relying on a black-box surrogate, three coupled compact, dynamics equations are identified with constrained ridge-regression of a short time-history of free-running body motion data in waves. The PSPF solver is then used to provide the future incident wave at the body's location. The PSPF and dynamic system models are coupled for continuous real-time future motion prediction in a receding horizon architecture, while the model coefficients are re-identified online to enable robust prediction to changing wave-conditions. This novel framework is demonstrated based on CFD data that has been validated for a freely floating two-dimensional rectangular box i

ALT A graph neural network (GNN)-based autoencoder is leveraged to produce reduced latent graph representations of detonation-containing flows. In a two-dimensional hydrogen-air channel configuration with a uniform fuel-air mixture, it was found that latent graphs with effective resolutions of twice the induction length are capable of reconstructing both the wavefront and the post-shock region of the cellular detonation wave structure. Extrapolation studies using stratified and three-dimensional detonation configurations revealed an intrinsic property of GNN-based reconstruction from reduced latent graphs – a representational hierarchy, rooted in the local nature of message passing, that tends to prioritize features at the detonation front (i.e., the wavefront profile and triple points) over the downstream deflagrative regions. In particular, it was found that at a fixed ambient condition, the information contained in uniform two-dimensional detonations was sufficient to reconstruct the wa

ALT Cross helicity, the velocity–magnetic-field correlation, is an ideal invariant of incompressible magnetohydrodynamics and constrains energy cascades and magnetic-field generation. Although cross helicity typically decays via visco-resistive effects, we analytically demonstrate that it grows exponentially in sheared inhomogeneous turbulence. This cross-helical shear instability is confirmed using three-dimensional numerical simulations. Growth requires a nonzero gradient of the Reynolds–Maxwell stress to produce turbulent cross helicity; this production overcomes effective losses when shear exceeds a threshold set by the ratio of turbulent magnetic to momentum diffusivities.

ALT We study coherent structures extracted from time-resolved particle image velocimetry (TR-PIV) measurements of isothermal elliptical jets with aspect ratio of 1:2, at Mach numbers 0.4 and 0.7. The velocity snapshots are decomposed according to the D₂ symmetry. Spectral proper orthogonal decomposition (SPOD) is then used to identify coherent structures. Modelling is performed using three-dimensional parabolised stability equations (3D-PSE) based on linearisation about the time-averaged mean flows. A comparison between SPOD and 3D-PSE results shows alignment of SPOD and single-mode PSE structures of up to 70%. A linear combination of PSE solutions, obtained from the family of initially unstable local modes, leads to SPOD-PSE alignment of up to 90%. Limitations are observed at low frequencies and near the nozzle and are attributed to the existence of non-modal amplification mechanisms. The study shows that coherent structures in turbulent elliptical jets can be educed using the D₂-SPOD app

ALT The spatial evolution of a turbulent boundary layer at Reτ=652 developing across a finite spanwise oscillating wall is investigated with normalised oscillation period of T⁺ ≈ 101 and amplitude A⁺ ≈ 7, yielding a drag reduction of 32%. The spatial distribution of skin friction is estimated adopting a recently introduced near-wall data nonlinear regression method based on the arctangent function, which circumvents the requirement of high wall-normal resolution to capture the viscous sublayer. The method viability is assessed against reference DNS data, and the trends of measurement errors from experimental data are well reproduced. The local impact of the control action is examined by evaluating the spatial development of scalar fields of turbulence statistics as well as conditional quadrant analysis to link the onset and recovery transients to turbulence events. The boundary layer response is observed to follow an exponential decay law for the skin friction spatial evolution. The decay

ALT Accurate prediction of low-frequency responses is important for the dynamic assessment of floating offshore wind turbines (FOWTs), as second-order difference-frequency wave loads can contribute substantially to platform motions and mooring loads. Their relative importance, however, remains less well established when aerodynamic and hydrodynamic low-frequency excitations coexist under operating conditions. This study develops an efficient time-domain formulation for full difference-frequency quadratic transfer function (QTF) loading using bandwidth truncation and integrates it into a fully coupled model of the IEA 15 MW turbine on the VolturnUS-S semi-submersible platform. Numerical predictions are compared with 1:70-scale real-time hybrid wave-basin experiments covering both irregular wave-only and combined turbulent wind-wave conditions. Three hydrodynamic formulations are compared: linear wave excitation, Newman's approximation, and the full-QTF model. Under wave-only conditions, sec