989 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 proposes a new framework for characterizing canopy density regimes by linking turbulence penetration to the interplay between the effective spanwise gap and the size of overlying eddies, demonstrating that traditional frontal density metrics fail to capture the physics of anisotropic layouts and Reynolds-number-dependent behavior.
This paper proposes an "embed-learn-lift" machine learning framework that utilizes nonlinear manifold learning (specifically Diffusion Maps) and Gaussian Process regression to construct minimal-dimensional surrogate models, enabling efficient numerical bifurcation and stability analysis of high-fidelity Navier-Stokes flows while preserving symmetries and outperforming traditional POD-based methods.
Using refractive index-matched stereo PIV, this study reveals that gradual () pipe expansions generate higher turbulence levels and stronger Reynolds stress anisotropy than abrupt () expansions due to geometry-induced modulation of the return flow, which sustains shear layer interaction and turbulence production in the former while confinement limits it in the latter.
This study evaluates tabulated-chemistry models for strained lean premixed hydrogen flames, identifying limitations in traditional approaches and proposing novel strained flamelet manifolds and correction methodologies that improve reaction rate and consumption speed predictions in turbulent settings without increasing computational cost.
This paper presents a consistent kinetic modeling and discretization strategy using a double-distribution quasi-equilibrium approach that enables accurate, stable, and Galilean-invariant simulations of compressible flows across all Prandtl numbers and specific heat ratios, successfully recovering Navier-Stokes-Fourier physics for both moderate supersonic speeds and complex discontinuities.
This paper establishes strong stability estimates for the velocity and physical densities of the two-dimensional semigeostrophic system relative to the incompressible Euler equations on a flat torus, while also proving a lifespan lower bound of that improves upon the standard hyperbolic scale.
This paper introduces novel, locally computable refinement sensors based on the one-particle distribution function to enable efficient and scalable adaptive mesh and algorithm refinement for kinetic models like lattice Boltzmann methods, effectively resolving complex features in compressible, turbulent, and non-equilibrium flows.
By training an autoregressive conditional diffusion model on Kolmogorov flow simulations, this study reveals a state-dependent hierarchy in the predictability of turbulence extremes, demonstrating that forecast horizons are governed by the persistence of large-scale coherent structures and the lifetime of pre-extreme strain cores.
This paper investigates how sign-indefinite helicity conservation shapes weak turbulence in rotating and odd-viscous flows by demonstrating that helical decomposition reorganizes energy cascades into sign-definite branch-specific transfers, leading to systematic backscatter and unique scale-invariant spectra validated by numerical analysis.
This paper resolves the unbounded wave energy issue in the limit of linear Kelvin wave theory by introducing a modified kernel with elliptic spanwise integration and a fast contour-deformation evaluator, enabling physically consistent and computationally efficient predictions for flat-ship applications.