989 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 novel convolutional autoencoder neural ODE (CAE-NODE) framework that successfully constructs a highly compressed, physically consistent latent manifold to accurately predict the full transient dynamics of 2D counterflow flames, including ignition and propagation, with relative errors below 2%.
This paper numerically and theoretically compares continuously stratified and bilayer models for oceanic flows, proving convergence to bilayer equations for near-piecewise constant profiles while demonstrating via normal mode analysis that Kelvin-Helmholtz instabilities in shear flows fundamentally limit the validity of widely used bilayer Euler and shallow-water models.
This paper demonstrates that synchrotron X-ray multi-projection imaging (XMPI) enables high-resolution, real-time 4D visualization of sub-second flow instabilities in porous media without the centrifugal artifacts of conventional tomography, thereby revealing critical limitations in current Lattice Boltzmann simulations regarding contact-line dynamics and boundary conditions.
This study utilizes direct numerical simulation to demonstrate that while turbulent lean premixed hydrogen jet flames exhibit similar macroscopic behavior across slot and round geometries at constant Reynolds numbers, fundamental discrepancies in flame propagation arise from the coupling of geometry-dependent curvature effects and pressure-induced increases in displacement speed sensitivity to local curvature.
This study establishes a unified scaling framework incorporating flame speed and shape factors that successfully describes the turbulent premixed jet flames of both hydrogen and methane across a wide range of operating conditions, demonstrating that despite hydrogen's distinct thermodiffusive effects, both fuels follow consistent turbulent scaling laws when analyzed within this common model.
Through direct numerical simulations of polydisperse droplets in turbulence, this study reveals how polydispersity differentially affects collision rates across droplet sizes, proposes improved models for the collision kernel and radial distribution function to address existing bidisperse errors, and demonstrates that turbulent intermittency accelerates droplet growth to help overcome the rain formation bottleneck.
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 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.