Wednesday, 4 November 2026: 9 new papers, 4 cross-lists and 7 replacements in Fluid Dynamics (physics.flu-dyn), in arXiv's order, with full abstracts. The same papers as arXiv's own listing, set as a newspaper; every title links to arXiv.
Gas transport driven by thermal transpiration under temperature gradients, known as a Knudsen pump, has shown strong potential in gas separation and delivery applications. However, its performance and operational stability are strongly affected by structural design, system integration, and manufacturing complexity, highlighting the need for efficient and manufacturable multi-stage configurations. In this study, a novel spiral-structured Knudsen pump is proposed. Within the microchannels, a temperature gradient drives gas from the low- to high-temperature region, inducing pressure rise and thermal transpiration. In the main channel, a pressure gradient drives gas from the high- to low-temperature region, forming Poiseuille flow. Numerical methods are employed to investigate the flow mechanisms, and the effects of varying the numbers of series and parallel microchannels on gas transmission behavior are evaluated. The results indicate that bends of the main channel lead to nonlinear variations in pressure and temperature, as well as flow deceleration in the curved regions. Increasing the number of serial channels reduces the pressure load across the microchannel, thereby enhancing the overall transport performance. For instance, a ten-stage serial configuration improves performance by 3.35% compared with the four-stage case, reaching 3.10E10-7 kg/(m*s). In contrast, increasing the number of parallel microchannels reduces pressure loading while enhancing gas rarefaction, thereby strengthening thermal transpiration. A five-stage parallel configuration yields an 70.80% improvement over the two-stage case, reaching 3.07E10-7 kg/(m*s). These results provide valuable guidance for the design and fabrication of high-performance Knudsen pumps.
Direct numerical simulations (DNS) are performed for a Mach 8 flow over a 15 degree compression ramp undergoing ablation to analyze fluid-ablation interaction (FAI) effects on instability growth and transition of the high-speed boundary layer. Wind-tunnel experiments at these conditions show characteristic heating streaks which are first replicated for laminar flow over an inert ramp. Perturbations superimposed on the laminar baseflow are informed by stability calculations. The ramp is then replaced by a low-temperature ablator in our DNS and the interaction of the streaks with the ablating surface is examined. Ablation models implemented in two independently-developed fluid dynamics solvers are used to study this problem. Overall, both solvers have shown an excellent agreement regarding heat fluxes, mass blowing rates, and recession patterns. Numerical experiments are performed to assess the impact of accelerating recession and of higher amplitude perturbations. We demonstrate accurate predictions of ablative recession and present novel findings regarding its influence on the perturbation evolution for this configuration.
Predicting the history force on a sphere beyond the creeping-flow limit remains challenging. Existing formulations have been derived or calibrated for selected flow histories, with limited evidence of their predictive accuracy under more general conditions. We develop a systematic approach to extract the history kernel of a fixed sphere in spatially uniform, unsteady flow from axisymmetric Navier-Stokes simulations of finite-duration acceleration and deceleration ramps at initial Reynolds numbers from 0.2 to 200. The resulting kernel surface recovers the Basset limit at short lags and in the creeping-flow limit and is continued beyond the resolved lags using far-wake asymptotics. At higher Reynolds numbers, the kernel develops non-monotonic behaviour at intermediate lags, associated with delayed adjustment of the boundary layer and wake. For the cases tested, the present kernel substantially improves history-force predictions for finite ramps, oscillatory forcing and non-periodic composite velocity histories relative to the literature formulations considered, without case-specific adjustment. These findings indicate that lag and Reynolds number capture the dominant memory response.
Rarefied gas--solid interactions determine mechanical loads, heat transfer, and material exchange in microscale devices and low-pressure processings. Modeling these interactions becomes challenging when gas non-equilibrium effect and complex moving boundaries must be resolved together. We develop a diffuse-interface kinetic model (DIKM) that describes rarefied gas transport and gas--solid interactions in one kinetic formulation on a single fixed Eulerian mesh. A smooth order parameter represents solid geometry, while a localized molecular flux operator incorporates boundary interactions directly into the kinetic equation by distinguishing incident and reflected distributions in the interface region and specifying their mass, momentum, and energy exchange, including prescribed surface absorption. Matched asymptotic analysis shows that the diffuse formulation recovers the corresponding partially absorbing diffuse-reflection boundary condition for smooth stationary or moving surfaces in the sharp-interface limit. Discretization uses Strang splitting, combining an order-parameter-weighted discrete unified gas kinetic scheme for the transport substep with a positivity-preserving update of the interfacial source. Benchmarks over planar and curved, stationary and moving boundaries reproduce rarefaction-dependent velocity slip and wall heat transfer, absorption-driven flow, and feedback between gas forces and solid translation and rotation. In final simulations, the order parameter evolves by a Cahn--Hilliard equation with degenerate mobility, and thin-film growth exhibits distinct surface profiles at different vapor incidence angles. By connecting implicit geometry to molecular surface exchange, DIKM provides a basis for coupling rarefied gas dynamics to solid motion and material evolution.
The gas dynamics of high speed flows over several compression-expansion ramp geometries with large separation regions were investigated using a particle kinetic approach and data driven methods. These large separation regions allow study of the role of scaled angle predicted by triple deck theory on recirculation strength, separation size, and appearance of secondary recirculation regions, all of which lead to flow unsteadiness. Examining four different free stream conditions for the same geometry using the direct simulation Monte Carlo (DSMC) approach, we show that the stability of single recirculation in the separation region is more sensitive to wall temperature and free stream velocity than physical ramp angle. Stability in this context refers to appearance of secondary separation regions. In addition, configurations with similar types of recirculation regions from literature considered, we find that general trends regarding unsteadiness, such as appearance of secondary recirculation regions, mostly hold; however, the flows are too complex to be characterized by only scaled angle. Thus, stochastic flowfields generated by DSMC were investigated using data driven methods, namely spectral proper orthogonal decomposition and dynamic mode decomposition. We found that both methods yield similar frequency components and mode shapes for spatio-temporal coherent structures of unsteady DSMC flows. With the help of these structures, the main mechanisms of unsteadiness were identified as oscillations of the reattachment shock, Kelvin-Helmholtz like instability of the shear layer, and c-shaped mode of the separation bubble. This is the first time SPOD has been applied to a DSMC generated flowfield and compared with DMD.
Turbulence is defined by a set of emergent and singular phenomena: a constant-flux energy cascade, intermittency, a dissipation rate that survives the vanishing of viscosity, and the spontaneous loss of determinism of the inviscid limit. Which physical ingredients produce them remains unclear, because in fluids they always come together with incompressibility, spatial structure and vortex dynamics. Here we show that a hierarchy of inertia, coupled by the purely geometric nonlinearity of rigid constraints, is enough. A planar chain of rigid pendula whose lengths and masses decrease geometrically, coupled only through nearest-neighbor constraints, develops a far-from-equilibrium cascade that replaces thermal equipartition, with statistically local and time-irreversible energy transfer despite intrinsically nonlocal coupling. Its fluctuations are intermittent, yet self-similarity is restored by a hidden scale symmetry. As the viscosity vanishes, the dissipation rate saturates at a finite value, and thermal noise consistent with fluctuation-dissipation balance is amplified from the smallest to the largest scales within a fraction of a large-scale period. Because the hierarchy can be tuned and the same dynamics can be realized mechanically or electrically, turbulence becomes a property that can be designed and measured in the laboratory.
Reconstructing the full state of a multiscale chaotic system from observations of only a subset of its degrees of freedom is a central problem in data assimilation. Turbulent Rayleigh--B\'enard convection offers a paradigmatic example, for which Charney conjectured that temperature measurements alone should suffice to determine the velocity field. Synchronization-based methods such as nudging and direct replacement nonetheless fail in chaotic regimes, because they require the measurement of the determining modes of the system, a set to which the velocity belongs but the temperature does not. Here we show that the Ensemble Kalman Filter (EnKF), equipped with a proper inflation scheme, removes this asymmetry by exploiting the flow-dependent cross-covariances between temperature and vorticity, and recovers the velocity from noisy and spatio-temporally sparse temperature data alone. We assess the filter in two chaotic regimes of two-dimensional bulk convection by means of a scale-by-scale spectral analysis. When the whole temperature field is observed, both fields synchronize with the true state at all resolved scales, with the temperature estimate becoming even more accurate than the measurements themselves for sufficiently frequent observations. Equispaced observation networks preserve this full-spectrum synchronization down to a threshold density, below which only the largest scales remain synchronized, as also happens when observations are confined to a subdomain. We conclude that Charney's conjecture is numerically verified in two-dimensional turbulent convection when the assimilation is performed using a properly calibrated EnKF.
Twilight clouds are small, optically faint warm clouds whose droplets fluctuate between haze and activated growth. We develop a nonequilibrium statistical field theory linking this regime to cloud-field organization through K\"ohler microphysics, supersaturation transport, and turbulent mixing. At parcel level, supersaturation rapidly tracks slow droplet growth with a finite lag. Mixing-induced supersaturation removal drives a saddle-node bifurcation, shifting the activation threshold and supporting intermittent hovering under turbulent fluctuations. Starting from turbulent transport of supersaturation coupled to droplet growth, we derive a droplet-supersaturation field system and formulate it within the Martin-Siggia-Rose framework. A Gurari-type variational reduction inspired by polaron theory eliminates the fast bath. Turbulence dresses the slow droplet field, shrinks the forbidden zone below the equilibrium curve, and raises the mixing threshold required to suppress activation. Each droplet population induces a screened supersaturation halo that couples neighboring cloud elements; its delayed adjustment generates dynamic inertia and temporally correlated noise. The resulting nonlinear, non-Markovian pseudoparabolic stochastic equation has a bounded frozen-background response at short wavelengths. Its long-wave coupling amplifies contrasts where the dressed slow manifold turns downward. Comparisons with the parent two-field system show that a spatially correlated Markovian closure preserves droplet-radius distributions and cloudlet organization. Near the activation fold, restoring residual-bath fluctuation memory improves recurrent-hovering frequency and patch extent without recalibrating the drift or fitting a correlation time. More broadly, the framework identifies how slow fields acquire interactions and inertia through the relaxing, diffusive environments they modify.
Stable stratification reorganizes turbulence, suppressing vertical motion and altering the balance between vortical and strain-dominated flow structures. How these changes affect the temporal asymmetry of kinetic-energy exchange along fluid-particle trajectories remains incompletely understood. Using direct numerical simulations of homogeneous Boussinesq turbulence, we investigate Lagrangian power statistics across four local velocity-gradient topology classes and three nonzero stratification strengths, with an unstratified reference case. With increasing stratification, the global power variance decreases while the standardized skewness becomes more negative, indicating enhanced asymmetry despite weaker fluctuations. Conditioning on local topology reveals distinct responses: axial-strain-dominated regions exhibit the most negative total-power skewness at all stratification strengths, whereas biaxial-strain-dominated regions change sign from positive to negative under stratification. Horizontal and vertical component powers also respond differently, revealing directional dependence. Among the dynamical contributions to power, the pressure contribution, nearly symmetric in the unstratified case, becomes more negatively skewed with increasing stratification. Energy-conditioned pressure statistics further indicate altered pressure-mediated energy redistribution. Finally, tracer persistence-time distributions exhibit exponential tails, with time scales that differ across topology classes and respond differently to stratification. Increasing stratification is thus accompanied by three concurrent changes: enhanced asymmetry of Lagrangian power, modified energy redistribution by pressure, and altered temporal sampling of local flow structures by tracers. These co-occurring signatures characterize the response to stratification, though their causal relations remain to be established.
The transmission of shock waves generated by underwater electrical foil explosions across solid materials with varying acoustic impedances and thicknesses is investigated. Copper foils with thicknesses of 10 and 18 $\mu$m are exploded on planar substrates of aluminum, copper, and 3D-printed PLA with thicknesses up to 5 mm using a multi-gigawatt pulsed-power electrical discharge. The resulting shocks are visualized in water using high-speed shadowgraphy at 5 million frames per second, while high-resolution fiber optic hydrophone measurements provide direct quantitative measurements of shock strength after transmission through the impedance-mismatched materials. One-dimensional magnetohydrodynamic simulations are used to reproduce the foil explosion dynamics and provide insight into the thermodynamic, acoustic, and electrical properties of the foil during the explosion. The results show that shock transmission is governed not only by the impedance mismatch at the solid--water interface but also by the finite thickness and dynamic response of the supporting material. These findings provide a quantitative framework for understanding and controlling shock transmission in underwater electrical foil explosions and establish the feasibility and limitations of using solid substrates as structural supports in complex shock-generating geometries.
Prestress governs the stability, stiffness, and dynamic behavior of tensegrity structures, but it is not directly observable from sparse structural measurements, and it may decrease during service life. Under strong excitation, the cables may slacken and the dynamics become nonsmooth. Existing identification methods are limited to the taut regime, and physics-informed networks for tensegrities solve only the forward problem of predicting the response from known member parameters. This study formulates the inverse problem. Neural networks are trained on the equation-of-motion residual in constrained Cartesian coordinates, where the mass matrix is constant and the force densities enter linearly. The force densities, damping, and axial stiffness are trainable parameters, and the tension-only cable law is kept. A Fisher-information analysis shows, before inversion, which member parameters a record can determine. The method is tested on simulated planar tensegrity frames with up to five modules and a three-dimensional prism, and on measured records of a biomimetic leg and a full-scale tensegrity simplex. On the one-module frame, the identifiable force densities are recovered within 1.5% from noisy accelerations at half the free coordinates, on records with and without taut-slack transitions. Records with transitions determine members that taut records leave undetermined. On the five-module frame, a 16-cable two-span bridge, the median force-density error rises moderately, to 4.2%. On measured records, an identified spring stiffness is within 0.1% of its independently measured value on average, and the identified simplex prestress at 13 levels agrees with its cable force transducers to 2.9% root-mean-square.
Inferring upper-ocean velocity from altimetric sea surface height (SSH) remains challenging below 50~km, scales now resolved by the Surface Water and Ocean Topography (SWOT) mission. Conventional methods rely on geostrophic or cyclogeostrophic balance. Here, we demonstrate that deep learning can predict the full velocity vector from SSH and environmental data and recover derivative-based diagnostics, including spectral energy flux. We trained a U-Net encoder--decoder on hourly fields from the pre-SWOT Cape Basin extraction of the LLC4320 ocean simulation, with 1.5-km grid spacing ($11.8$--$15.8^\circ$E, $41$--$45^\circ$S). Inputs were SSH, sea surface temperature (SST), and wind stress; targets were horizontal velocities at the surface and 50~m, where wind influence is weaker. At 50~m, the best model achieves a speed RMSE of $0.049\,\mathrm{m\,s^{-1}}$, 29% lower than the geostrophic approximation. Predicted relative vorticity has a correlation of 0.93, compared with 0.75 for the geostrophic approximation. The model also recovers divergence with a correlation of 0.66, whereas geostrophic velocity is nondivergent by definition. Tests of training strategies yield four findings: SSH gradients outperform raw SSH as inputs; wind stress is the most useful covariate for total velocity prediction; SST benefits vary seasonally; and including vorticity and divergence in the loss improves accuracy. These results offer a path for transferring high-fidelity simulation physics to SWOT applications, where kilometer-scale SSH snapshots contain superposed balanced and unbalanced signals.
In this paper, a compact high-order gas-kinetic scheme (HGKS) is developed for compressible arbitrary Lagrangian-Eulerian (ALE) computations on three-dimensional unstructured meshes. To achieve high-order spatial accuracy, a large number of matrix inversions are required for spatial reconstruction. In ALE computations, the mesh moves and the geometric information must be recalculated at each time step, which reduces computational efficiency. To improve efficiency, a memory-reduction reconstruction strategy is employed to construct a quadratic polynomial on unstructured meshes. By differentiating the candidate polynomial, the quadratic terms are obtained by the least-squares method using the average gradient values of the cell itself and its neighboring cells. The linear terms of the polynomial are also determined by the least-squares method. On three-dimensional unstructured meshes, a $3\times3$ coefficient matrix must be stored, and four least-squares procedures are required to construct a quadratic polynomial. To suppress numerical oscillations in flows with discontinuities, generalized essentially non-oscillatory (GENO) reconstruction and a gradient compression factor are adopted. To accelerate the computation, a multi-GPU accelerated code is implemented using Compute Unified Device Architecture (CUDA) and Message Passing Interface (MPI). The current scheme is fully explicit, and the acceleration framework can be developed based on cell-level parallelism. Numerical examples are presented to evaluate the accuracy and robustness of the scheme, including flows with strong discontinuities, pitching wings, and helicopter rotors. Numerical results confirm the excellent performance of the compact ALE scheme on unstructured meshes.