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 demonstrates that maximal convective heat transport arises from the saturation of energy-stability constraints rather than turbulence intensity, predicting near-optimal heat flux scaling and revealing that such high-flux states can be maintained in motionless, non-turbulent flows through internal thermal forcing.
Using direct numerical simulations of stably stratified turbulence, this study reveals that non-trivial alignments between fluctuating and mean density gradients critically govern scale-dependent turbulent kinetic and available potential energy fluxes, dissipation rates, and mixing efficiency, demonstrating that these energetic mechanisms cannot be simply inferred from local flow stability.
This paper presents an asymptotic analysis of bacterial transport in a corrugated channel, deriving an analytical expression for adhesion rates that reveals how spatially varying wall shear rates cause bacterial adhesion to localize preferentially on surface peaks at low flow speeds and in valleys at high flow speeds.
This paper investigates the stability of a viscous liquid film coating a horizontal cylinder, revealing that the interplay between capillary and gravity forces determines a critical Bond number threshold for rupture and governs the transition between capillary-driven pearl formation and gravity-driven pendant drop modulations.
This paper demonstrates that while heat-flux residuals alone cannot uniquely identify the fourth-order closure variables in monatomic kinetic normal shocks due to a one-dimensional null space, combining them with a sparse scalar-excess budget enables accurate two-channel reconstruction of the tensorial anisotropy and isotropic tail intensity, significantly reducing recovery errors across various collision models.
This paper introduces a feature-preserving latent-EnKF that overcomes the Gaussian assumption limitations of traditional ensemble Kalman filters in compressible flows by performing ensemble updates in a learned low-dimensional latent space, thereby accurately recovering shocks and discontinuities without spurious oscillations.
This paper combines theoretical scaling arguments with magnetic actuation experiments to characterize the unsteady hydrodynamic resistance of oscillating planar bodies at an air-water interface, demonstrating that effective added mass and damping coefficients align with oscillatory Stokes boundary-layer theory while accurately predicting transient startup behavior via history integrals.
This study combines linear theory and direct numerical simulations to reveal that in strongly stratified flows with no initial vertical shear, horizontal shear instabilities inevitably drive diapycnal mixing through two distinct pathways—either via direct emergence of vertical shear or through a nonlinear evolution into columnar vortices—both of which ultimately trigger small-scale Kelvin-Helmholtz instabilities but yield different mixing efficiencies due to their excitation of distinct vertical scales.
This paper proposes a novel microfluidic separation method that utilizes elliptically wound ducts with periodically varying curvature to induce longitudinal particle clustering and size-based separation via SNIPER bifurcations, offering a distinct alternative to conventional cross-sectional inertial focusing.
This paper establishes a theoretical and experimental framework for practical quantum advantage in machine learning for chaotic systems, demonstrating that a two-copy quantum read-out protocol using higher-order quantum statistical priors can efficiently extract complex correlations and significantly improve weather forecasting accuracy compared to classical methods, even on current noisy hardware.