1210 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 investigates the hydrodynamic interaction between cavitation bubbles and oil droplets in a confined thin water layer, revealing distinct rupture and non-rupture regimes and establishing a novel scaling law based on a non-dimensional Kelvin impulse to predict droplet breakup criteria.
This paper advances the theory of maximally mixed equilibria in two-dimensional perfect fluids by demonstrating that minimizers of strictly convex Casimirs under fixed energy constraints are maximally mixed states, while also showing that on symmetric domains, certain initial data near shear or radial flows fail to weakly converge to these equilibria despite satisfying all conservation laws.
This paper proposes a novel machine learning method that accurately estimates two-dimensional flow fields using only floating sensor locations without requiring ground-truth velocity data or governing equations, demonstrating performance comparable to physics-informed neural networks across diverse flow scenarios.
This paper presents a physically consistent numerical framework for viscous compressible multicomponent flows that adaptively combines THINC, MP, and WENO reconstruction schemes with a central scheme for tangential velocities to sharply capture material interfaces while minimizing dissipation and oscillations.
This paper presents a novel multidimensional upwinding approach for compressible multiphase flows that combines characteristic-space wave decomposition with physical-space reconstruction schemes, including THINC for interfaces and adaptive techniques for shocks, to significantly enhance accuracy, suppress numerical artifacts, and better capture complex physical phenomena compared to traditional methods.
This study demonstrates that using smaller capillary emitters (15–50 μm) with ionic liquids enables stable cone-jet operation at lower flow rates, effectively doubling specific impulse to 3000 s while revealing that propellant losses at low flow rates can compromise time-of-flight measurements.
This paper demonstrates that popular multi-phase depth-averaged models for debris flows are frequently ill-posed due to resonant phase interactions, rendering them unsuitable for prediction, and proposes a general framework showing that while small diffusive terms can theoretically regularize these models, existing formulations typically fail to meet the necessary conditions.
This paper extends the theory of elastoinertial rectification to two-dimensional deformable rectangular channels by combining a combined foundation model for the elastic layer with direct numerical simulations, demonstrating excellent agreement between the predicted and simulated cycle-averaged pressures and displacements across various dimensionless flow regimes.
This paper investigates the dynamics of dilute particle suspensions under shear by analyzing unstable equilibrium solutions to the coupled Navier-Stokes and advection-diffusion-settling equations, characterizing sediment transport in passive regimes and exploring symmetry-breaking bifurcations and Richardson number dependencies in stratified regimes.
This paper presents a novel physics-informed neural framework that accurately reconstructs unsteady fluid-structure interactions and structural deformations from sparse, off-body flow measurements without requiring a solid constitutive model or direct surface position data.