1230 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 presents a review of a statistical mechanics approach, based on Jaynes' information-theoretical entropy, which provides a manageable continuum-scale description of immiscible two-phase flow in porous media by deriving thermodynamics-like relations and identifying new emergent variables such as co-moving velocity.
This paper proposes and numerically validates an experimentally feasible protocol for the deterministic nucleation and manipulation of stable quantum vortex rings in Bose-Einstein condensates using a sweeping laser-sheet barrier, enabling precise control over their properties and the generation of Kelvin-wave excitations for the study of three-dimensional vortices and quantum turbulence.
This paper presents a physics-based pore-network model for yield-stress fluid flow in disordered porous media that accurately captures nonlinear transport and channelization by deriving pressure-flow relations from pore-scale mechanics, revealing that near-yield pressure losses are governed by constriction statistics rather than obstacle-scale length.
This paper extends the Theory of Porous Media to model non-isothermal material injection into porous structures, specifically for percutaneous vertebroplasty, by incorporating local thermal non-equilibrium conditions and demonstrating thermodynamic consistency through numerical simulations.
This paper analyzes the dynamics of a water droplet on a vertical sugar fiber, presenting a model and phase diagram that predict whether the droplet will fall or be propelled upward as the fiber dissolves, depending on the droplet's volume and the fiber's diameter.
This study combines theoretical analysis and numerical simulations to demonstrate that a stationary triangular strain field induces resonant instability in a Batchelor vortex, where the introduction of axial flow reduces critical layer damping to activate additional unstable mode pairs and ultimately shifts the dominant instability from a specific mode combination to one involving the first branches of both azimuthal wavenumbers.
This paper proposes a molecular kinetic model for Lennard-Jones fluids that accurately reproduces equilibrium properties and reveals significant deviations from the classical Hertz-Knudsen relation under non-equilibrium evaporation conditions, thereby advancing the development of efficient computational frameworks for real fluid flows.
This study demonstrates that directional light stimuli can trigger bioconvection rolls in suspensions of phototactic microalgae (*Chlamydomonas reinhardtii*) to achieve the controlled collective transport of hundreds of large passive particles, offering potential applications in targeted drug delivery and decontamination.
This study demonstrates that symmetric reservoir topologies significantly enhance prediction accuracy for low-dimensional nonlinear dynamical systems with limited input dimensions, whereas high-dimensional chaotic systems like turbulent shear flow exhibit minimal sensitivity to such structural symmetries.
This paper establishes the theoretical framework for solving axial symmetric Navier-Stokes equations in a cylindrical topology by constructing a complete basis of harmonic 1-forms comprising Beltrami, anti-Beltrami, and closed components, thereby reducing the problem to a hierarchy of quadratic relations suitable for future optimization via Physics-Informed Neural Networks.