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 study demonstrates that a computationally efficient deep learning algorithm, when trained with annealed learning rates and utilizing synergistic surface inputs—particularly surface velocity—can effectively disentangle internal tides from balanced motions in satellite data, though residual errors persist at small scales due to information limitations and architectural constraints.
This study derives an analytical solution for the standard -- model in Rayleigh--Bénard convection to identify discrepancies in buoyancy treatment, leading to a reformulated model with two new algebraic functions that significantly improve predictions of mean temperature and turbulent heat flux across various buoyancy-driven flows.
This study employs wave topology to demonstrate that pressure extrema in astrophysical disks act as waveguides for specific topological modes, establishes analytical criteria for their existence, and highlights their potential as prime targets for future disk seismology.
This paper establishes the boundary -regularity for finite-energy weak solutions to the incompressible Navier–Stokes equations on a three-dimensional bounded smooth domain by proving that solutions are regular up to the boundary whenever their -norm is sufficiently small, thereby resolving a problem posed by Albritton, Barker, and Prange through a novel slicing construction near the boundary.
This work investigates Hamiltonian vortex dynamics on a catenoid and demonstrates that curvature gradients drive a rigid rotation and a secular drift for co-rotating vortex pairs, with numerical simulations confirming a linear instability in symmetric states as well as a reduced dynamics for generic configurations.
This study compares Local Thermal Non-Equilibrium (LTNE) models against a pore-resolved reference for purely conductive porous media, demonstrating that REV-scale models utilizing homogenization-based effective parameters accurately capture interfacial resistance effects, whereas dual-network models with fixed spatial resolution show greater deviation.
This paper establishes and validates a mechanical analogue for the natural oscillations of standing surface waves on a liquid pool, demonstrating that both systems share qualitatively similar nonlinear dynamics and stability characteristics, including a newly derived Mathieu-like equation for super-harmonic perturbations.
Using clean numerical simulations to eliminate artificial noise, this paper demonstrates that Navier-Stokes turbulence exhibits "ultra-chaotic" behavior where tiny initial disturbances drastically alter flow statistics, suggesting a fundamental logical paradox in current turbulence models that neglect such unavoidable small perturbations.
The paper argues that the Navier-Stokes equations face a logical paradox because numerical noise—driven by the choice of time-step—can fundamentally alter the resulting flow statistics, suggesting that small disturbances cannot be neglected in turbulence modeling.
This paper proposes a physics-informed machine learning framework that uses neural networks to infer non-Newtonian rheological models from either stress or velocity data, demonstrating its ability to accurately recover temperature-dependent land ice laws and concentration-dependent sea ice behaviors.