995 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 introduces a two-dimensional Non-Reciprocal Cahn-Hilliard-Navier-Stokes (NRCHNS) model to explore non-reciprocal binary-fluid turbulence, revealing a novel turbulent state characterized by an inverse energy cascade with a spectrum and a non-reciprocal flux that is suppressed at high Reynolds numbers.
This paper reviews and extends a perturbative theoretical framework to explain how self-organized turbulence forms large-scale condensates in various systems, including two-dimensional and rotating three-dimensional flows, by demonstrating the universal role of two conserved quantities and the transition between different asymptotic models based on interaction scales.
This paper presents a unified, physics-constrained neural-operator framework that accelerates Direct Simulation Monte Carlo simulations by replacing the Variable Hard Sphere model with a stochastic neural collision kernel for improved generalization and by introducing an efficient surrogate for ab initio Jäger potentials, collectively achieving high-fidelity predictions of rarefied gas dynamics with reduced computational cost.
This paper demonstrates that a two-dimensional fluid of hard disks with chiral, energy- and momentum-conserving collisions obeys the H-theorem despite breaking time-reversal symmetry, and derives analytical expressions for its transport coefficients via Chapman-Enskog expansion that are validated by molecular dynamics simulations.
Through high-resolution three-dimensional simulations, this study demonstrates that unstable magnetic reconnection in magnetised jets can self-sustainfully transition into fully developed turbulence via a current-sheet instability, where the coupling between turbulent electromotive force and magnetic mean shear drives persistent energy injection and subsequent nonlinear cascades.
This paper demonstrates that the Senftleben-Beenakker effect in a diatomic gas under a magnetic field induces anisotropic odd viscosities that generate distinct hydrodynamic forces on oblate and prolate spheroids, enabling their separation during sedimentation.
This study demonstrates that varying the actuation frequency of a time-dependent magnetic field in an evaporating ferrofluid droplet controls the deposition morphology by inducing a transition from coffee-ring formation to multi-ring patterns and finally to central deposition, a process governed by the competition between magnetic forcing, capillary flow, and particle diffusion.
This paper extends a multiscale modeling framework for sea-ice floes by generalizing the particle, kinetic, and hydrodynamic descriptions to include rotational dynamics and nonlinear contact forces, thereby deriving a comprehensive system of macroscopic equations that capture complex stress, transport, and dissipative mechanisms in sea-ice rheology.
This paper introduces a grid-size-invariant deep learning framework using UNet++ architectures as efficient surrogate models for rock-fluid interaction, demonstrating their superior performance and memory efficiency over reduced-order models in predicting fluid flow through dynamic porous media with non-static solid fields.
This paper introduces a generalized characteristic mapping method that utilizes a semi-Lagrangian approach with Duhamel integrals to efficiently solve non-linear advection with source terms, demonstrating third-order accuracy and exceptional resolution in ideal magnetohydrodynamics simulations.