1224 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 demonstrates theoretically that the tangential interactions and friction-like behavior observed in suspensions of rough particles arise not from direct contact but from highly singular hydrodynamic forces generated by fluid flows between surface asperities, which effectively constrain particle rotation and translation in dense suspensions.
This paper introduces a novel auxiliary-time extension method that transforms unsteady oscillatory flow problems into a two-time-variable system, enabling the direct application of established steady-flow moment solutions to analytically characterize transient dispersion phenomena such as skewness and kurtosis.
This paper proposes Discontinuity-aware Physics-Informed Neural Networks (DPINNs), a novel framework that integrates adaptive Fourier-feature embeddings, a discontinuity-aware Kolmogorov-based architecture, mesh transformation, and learnable artificial viscosity to overcome spectral bias and instability, thereby achieving superior accuracy in solving partial differential equations with sharp discontinuities and complex geometries.
This study demonstrates that in evaporating respiratory droplets containing mucin, the formation of a protein-rich coffee ring is governed by a feedback loop between local solute concentration and evaporation rate mediated by water activity, a mechanism captured by a new theoretical model that explains the humidity-dependent stability and infectivity of such droplets.
This paper presents a design equation that leverages the non-ideality of polyisobutylene-based Boger fluids to systematically fabricate model viscoelastic fluids with precisely controlled shear moduli, relaxation times, and first normal stress difference coefficients by calculating the required polymer concentration, molecular weight, and solvent viscosity.
This paper reports the first experimental detection of a rare upstream-directed jet burst in a backward-facing-step flow at Re=2100, achieved through long-duration Live Optical Flow Velocimetry that successfully captured a single event characterized by vortex collapse and heavy-tailed statistics.
This paper presents a first-principles computational framework based on the Navier-Stokes-Korteweg equations to simulate surfactant effects in liquid-vapor phase-transforming fluids, successfully demonstrating how surfactants reduce surface tension and influence bubble coalescence and condensation.
This paper presents a quantitative theory demonstrating that Johnson-Nyquist noise-induced density fluctuations in charge-neutral graphene drive a fluctuating hydrodynamic flow, which generates a size-dependent fluctuation conductivity that diverges logarithmically at zero magnetic field and is rapidly suppressed by external fields, resulting in a giant magnetoresistance effect.
The paper proposes DiAFNO, a novel model integrating an Implicit Adaptive Fourier Neural Operator with a diffusion framework to achieve accurate, stable, and efficient autoregressive predictions of three-dimensional turbulence across various flow regimes, outperforming both existing diffusion models and traditional large-eddy simulations.
This paper introduces physics-informed Transformer operators (PITO and PIITO) that leverage the Vision Transformer architecture and embedded LES equations to accurately and efficiently predict 3D turbulence with superior stability, lower memory usage, and fewer parameters compared to existing methods like PIFNO, all without requiring labeled training data.