1169 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 presents a novel, data-driven probabilistic model for binary droplet collisions, developed using LightGBM on a comprehensive experimental dataset and translated into a multinomial logistic regression framework to enable accurate, stochastic implementation in spray simulations.
This study demonstrates that electrokinetic flows driven by high-frequency dual-field excitation exhibit unexpected power-law scaling and nonlocal energy transfer characteristic of fully developed turbulence, revealing that intrinsic nonlinearities can govern system behavior even within nominally linear regimes and challenging the conventional validity of linear approximations in electrohydrodynamics.
This study demonstrates that horizontal oscillation of the bottom plate in Rayleigh-Bénard convection can enhance heat transport by over 60% by synchronizing large-scale circulation reversals with the wall oscillation period through a robust phase-locking mechanism that optimizes plume transport across a wide range of Rayleigh numbers.
This paper investigates the orientation dynamics of a settling spheroid in simple shear flow, revealing that the interplay between Jeffery and inertial torques leads to a saddle-node bifurcation on an invariant circle governing the transition to steady equilibrium, while stochastic analysis demonstrates that noise induces Kramers-type phase slips with rates exponentially sensitive to the Péclet number.
This paper introduces a nested Fourier-enhanced neural operator (Fourier-MIONet) framework that efficiently and accurately models radiation transfer in 3D fire simulations, achieving global relative errors of 2–4% while significantly reducing computational costs compared to traditional numerical methods.
This paper presents the flow characterization of TU Delft's newly commissioned Multiphase Flow Tunnel, utilizing Laser Doppler Anemometry to confirm a linear velocity-thruster relationship, low freestream turbulence (0.5–0.6%), high flow uniformity, and non-canonical turbulent boundary layer growth.
By integrating microfluidic experiments, numerical simulations, and theoretical modeling, this study demonstrates that diffusiophoresis driven by ubiquitous chemical gradients significantly alters colloid dispersion and transit times in porous media, necessitating a revision of classical transport models to improve predictive capabilities for applications like drug delivery and environmental remediation.
This paper presents a physics-based model that constructs wall turbulence as an ensemble of hierarchically organized hairpin vortex packets, successfully reproducing key statistical and structural features across a wide range of Reynolds numbers while offering a computationally efficient method for initializing high-fidelity simulations.
This paper presents a scalable method for generating tunable, monodisperse micro-scale emulsions by applying horizontal vibration to a rectangular container with stratified immiscible liquids, which excites ordered Faraday waves that break into regular droplet arrays whose critical breakup acceleration and size are governed by the container width, forcing frequency, and viscosity ratio.
This paper introduces an automated, reverse-mode automatic differentiation framework integrated into the Dedalus spectral PDE solver that enables efficient, code-free computation of model gradients for a wide range of time-dependent and nonlinear systems to facilitate gradient-based optimization and inverse problems.