1267 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 reveals that in rotating turbulence, the sign-specific conservation of helicity in fast inertial waves drives an inverse energy transfer to large-scale 2D structures, while slower modes exchange helicity across signs to sustain a forward transfer, thereby unifying the understanding of bi-directional energy fluxes across all rotation rates.
This paper combines experiments and simulations to characterize droplet breakup against an isolated obstacle in a quasi-2D microfluidic chamber, identifying key influencing factors and defining a nondimensional breakup number (Bk) that predicts the transition from non-breaking to breaking behavior based on flow velocity, droplet size, surface tension, collision symmetry, and chamber height.
This paper demonstrates that by applying specific assumptions regarding angular momentum and Korteweg capillary stress to an Euler-Korteweg vortex model, the resulting fluid-mechanical equations become mathematically equivalent to the Schrödinger and Klein-Gordon equations, thereby establishing a formal analogy that reproduces fundamental quantum mechanical relations such as the de Broglie wavelength and the uncertainty principle.
This paper introduces WAKESET, a novel large-scale dataset comprising over 4,000 high-fidelity simulations of high-Reynolds number turbulent flows during underwater vehicle recovery, designed to overcome data scarcity and advance machine learning applications in computational fluid dynamics.
This study extends the feature-enhanced neural network (FENN) to successfully solve forward and parametric problems involving compressible viscous flows governed by the Navier-Stokes equations, overcoming the limitations of existing physics-informed neural network methods that have previously failed in this challenging scenario.
This study demonstrates the creation of twisted multilayer moiré water waves carrying robust skyrmionic topologies, revealing that trilayer configurations offer superior stability and energy concentration compared to bilayers, thereby establishing water waves as a macroscopic platform for exploring topological physics.
This paper presents an interpretable, data-driven model using dynamic mode decomposition (DMD) to accurately reconstruct and extrapolate the complex flight dynamics of hawks by identifying a common set of simple, linearly combinable modal structures that characterize various maneuvers with minimal parameters.
This paper proposes a unified Smoothed Particle Hydrodynamics (SPH) framework that utilizes a novel imaginary particle projection strategy and a fluid-analogous contact model to efficiently and accurately simulate diverse interactions involving thin shells, including fluid-shell, solid-shell, and shell-shell contacts.
This study employs Recurrence Quantification Analysis and Statistical-Spectral analysis of OH* chemiluminescence and acoustic pressure signals to extract dynamical features from mesoscale combustor flames, which are then utilized in a stacking ensemble machine learning framework to accurately classify distinct flame regimes such as stable, extinction-ignition, and propagating flames.
This paper demonstrates that while deterministic closures fail to capture the rapid growth of uncertainty in coarse-grained turbulence models, incorporating data-driven stochastic closures is essential for accurately restoring the correct timing and magnitude of variance growth across scales.