995 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 introduces a generative framework that synthesizes physically feasible, incompressible 2D flows under arbitrary obstacle geometries by integrating boundary-conditioned diffusion, physics-informed training, and a projection-constrained reverse process to enforce exact divergence-free constraints.
This study demonstrates that an optimized higher-order harmonic surface tessellation, developed through an analytical and numerical framework, outperforms conventional gyroid TPMS structures in turbulent flow regimes by achieving a superior balance of high thermal effectiveness and lower pressure drop, with secondary surface wave frequency identified as the critical design parameter.
This study experimentally demonstrates that turbulent Rayleigh-Benard convection in a large-aspect-ratio cell exhibits multiple self-organized flow states with varying numbers of stacked rolls, where the initial conditions and Prandtl number significantly influence the flow structure, global momentum transport scaling, and heat transfer efficiency.
This paper investigates the bifurcation mechanisms of vortex breakdown in a laminar swirling jet within a hydro turbine draft tube, demonstrating how wall friction influences the transition from axisymmetric flow to helical vortex ropes through supercritical Hopf, transcritical, and hysteresis-inducing subcritical bifurcations across different turbine load regimes.
This study combines experimental observations and a modified theoretical model to demonstrate how temperature, humidity, and airflow-induced shape oscillations significantly alter the evaporation dynamics and lifetime of freely levitated water droplets, extending the classical -law to accurately predict their behavior in convective environments.
This study demonstrates that fluid viscoelasticity governs acoustic streaming in microchannels by modulating shear waves and the interplay between Reynolds and viscoelastic stresses, enabling the enhancement, suppression, or reversal of flow regimes based on the Deborah number, viscous diffusion number, and a derived Streaming Coefficient.
This study investigates the conversion between kinetic and surface energy in periodically forced multiphase turbulence through numerical simulations and an enhanced total energy model, revealing that while kinetic energy exhibits non-equilibrium phase lags, surface energy remains in equilibrium with its destruction, indicating the absence of a surface energy cascade.
This study demonstrates that impact-induced liquid jets achieve greater penetration depth in skin-simulating materials than laser-induced jets at similar velocities due to their focused cylindrical structure, and proposes a shear deformation model that successfully explains this penetration mechanism by accounting for liquid viscosity, jet inertia, and material elasticity.
This paper provides a rigorous mathematical justification for damped-driven wave turbulence theory by proving that the stochastic dynamics of the nonlinear Schrödinger equation converge to a deterministic kinetic equation under specific scaling limits, thereby establishing a formal framework for the turbulent energy cascade observed in empirical studies.
This paper presents an adaptive sampling framework that couples a classifier network with a deep generative model (KRnet) to efficiently detect hydrodynamic bifurcation boundaries by iteratively concentrating computational resources in high-uncertainty regions, thereby significantly reducing the number of required Navier-Stokes simulations.