1229 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 study utilizes spatial dynamic mode decomposition to demonstrate that while surface pores alone minimally affect shear layer instabilities behind a sphere, the sustained presence of a superhydrophobic plastron significantly alters the flow dynamics in the near wake.
This paper employs a Lyapunov-based approach to derive linear matrix inequality bounds on transient energy growth in accelerating and decelerating wall-driven flows, revealing that decelerating base flows exhibit significantly larger growth while providing certificates of uniform stability and invariant sets.
This paper demonstrates that applying a finite-scale coarse-graining procedure to the irrotational Madelung equations of quantum hydrodynamics yields a macroscopic description featuring non-zero vorticity with a vortex-stretching term and a novel stress term analogous to artificial viscosity.
This paper demonstrates that the Lyapunov method, utilizing a time-dependent weighting matrix and temporal discretization, effectively identifies the growth rate of thermohaline convection with time-varying shear flow, yielding results that converge with Floquet theory and numerical simulations while offering insights into computational efficiency and dangerous disturbances.
This study employs 3D numerical simulations using a diffusive flux model to analyze a passive trifurcated microchannel for separating platelet-enriched plasma and red blood cells, revealing that smaller channel widths, extended inlets, and lower hematocrit concentrations significantly enhance separation efficiency while performance remains less sensitive to flow rates and geometric angles.
This paper demonstrates that using a quasi-Lagrangian velocity correlator within the Kazantsev theory, rather than the standard Eulerian one, yields quantitative agreement with numerical simulations of small-scale turbulent dynamos in low-Prandtl number fluids, while also attributing the observed decrease in the critical magnetic Reynolds number to Reynolds-dependent intermittency.
This paper introduces a multimodal learning framework that leverages 4D micro-velocimetry data to create a rapid, autoregressive surrogate model for simulating pore-scale multiphase flow dynamics, enabling efficient "digital experiments" for subsurface energy applications like CO and hydrogen storage.
This paper proposes an adaptive diffusion posterior sampling framework that combines a multi-scale graph transformer with a multi-step autoregressive diffusion objective to probabilistically forecast complex nonlinear dynamical systems, while simultaneously enabling adaptive sensor placement and retraining-free data assimilation for chaotic flows.
This paper establishes the inviscid limit of Yudovich solutions for the heat-conductive Boussinesq equations on a two-dimensional periodic domain, proving that diffusive solutions converge to the Euler-Boussinesq solutions in as viscosity vanishes, provided the initial data converge strongly in .
This paper introduces a physics-constrained diffusion model (PCDM) that successfully synthesizes statistically faithful 3D turbulent velocity fields by directly incorporating physical constraints like incompressibility into the generative dynamics, thereby overcoming the statistical deviations and slow convergence observed in standard diffusion models.