967 papers
Fluid dynamics explores how liquids and gases move, shaping everything from weather patterns to the flow of blood through our veins. This field bridges the gap between abstract mathematical equations and the tangible forces that drive our physical world, offering insights into turbulence, aerodynamics, and fluid behavior in complex environments.
On Gist.Science, we process every new preprint in this category directly from arXiv to make cutting-edge research accessible to everyone. Each paper is transformed into a clear, plain-language overview alongside a detailed technical summary, ensuring both students and experts can grasp the latest findings without getting lost in dense jargon.
Below, you will find the most recent studies in fluid dynamics, curated and explained for a broader audience.
This paper proposes a hybrid data-driven framework that combines a probabilistic VAE-Transformer for learning large-scale coherent structures with Gaussian Process regression for stochastic closure, demonstrating superior stability and statistical accuracy in predicting chaotic Kolmogorov flow compared to state-of-the-art probabilistic baselines.
This study demonstrates that incorporating anisotropic subgrid-scale stress, particularly normal stress contributions, significantly improves the prediction of flow separation in wall-modeled large-eddy simulations of smooth-body separation under favorable pressure gradients by more accurately capturing the dissipation and diffusion of Reynolds stresses during grid refinement.
This paper introduces a flexible Koopman autoencoder surrogate model that incorporates meteorological forcings and boundary conditions to achieve stable, accurate, and computationally efficient long-term predictions for forced flexible mesh coastal-ocean models, outperforming traditional POD-based approaches in several cases while enabling practical applications like ensemble forecasting.
This paper demonstrates that metric-based mesh adaptation using the temperature Hessian enables accurate hypersonic aerothermal simulations of complex geometries, such as atmospheric entry capsules with RCS jets, while achieving surface heating predictions comparable to conventional block-structured methods.
This paper theoretically demonstrates that standing bulk acoustic waves can suppress Rayleigh-Taylor instability and significantly reduce fluid mixing in minichannels by satisfying specific conditions of critical acoustic energy density and perpendicular wave orientation relative to the fluid interface.
This paper advances Townsend's attached eddy hypothesis by formulating an inverse problem to derive influence kernels from DNS data, which are then used to construct and validate a minimal, Biot-Savart-consistent hairpin eddy model that successfully reproduces mean velocity and Reynolds stress statistics across high Reynolds numbers through a novel scale-by-scale decomposition.
This paper resolves the central problem of how finite-sized material lines stretch in chaotic and turbulent flows by demonstrating that elongation is governed by a finite-sampling process balancing ensemble and temporal averaging, mediated by particle dispersion, which necessitates a reassessment of existing experimental data and fluid-borne models.
This study utilizes direct numerical simulations to demonstrate that strong axial wall modulations in pipes induce early flow reversal, subcritical turbulence transitions, and fully rough turbulent regimes, necessitating the use of hydrodynamic concepts like effective hydraulic radius and sandgrain roughness to accurately characterize friction and stability across all flow regimes where classical models like the Moody diagram fail.
This study reveals that unlike viscoelastic drops which form stabilizing threads due to diverging polymer stresses, bubbles in dilute polymer solutions do not develop such threads because the polymer stress divergence is significantly weaker, with thread formation only occurring at high polymer concentrations where needle size becomes a critical factor.
This paper proposes a compact, continuous Gaussian field representation for turbulent flows that achieves high compression ratios, demonstrating that while isotropic kernels preserve velocity accuracy, anisotropic extensions are essential for capturing the geometric fidelity of vortical structures and recovering critical small-scale diagnostics like enstrophy.