967 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 constructs a generalized second-order relativistic magnetohydrodynamics framework using Zubarev's nonequilibrium statistical operator to derive all dissipative tensors and Kubo formulas for a parity- and charge-conjugation-symmetric magnetized plasma, while extending the formalism to include nonlocal contributions.
This study employs persistent homology, a topological data analysis technique, to objectively quantify and correlate the evolution of complex convective flow patterns with heat transport rates in porous media across various Rayleigh-Darcy numbers and domain sizes.
This review paper argues that traditional mean-field coagulation models fail to capture the complex dynamics of granular systems, proposing instead a unified multi-scale perspective that integrates dissipative interactions, spatial heterogeneity, and mechanical constraints like jamming to better describe aggregation in non-equilibrium particulate systems.
This study demonstrates that the turbulence driving parameter , commonly used to infer energy injection modes, is degenerate with the spatial locality and temporal correlation of the driving mechanism, as point source driving mimicking supernovae yields values typically associated with solenoidal driving despite being purely compressive.
This study utilizes direct numerical simulations to demonstrate that highly porous particles settle faster than solid ones at high volume fractions due to reduced fluid counterflows, while also exhibiting decreased clustering and altered velocity fluctuations driven by permeability-dependent hydrodynamic interactions.
ShipNet is a geometric deep learning surrogate model that leverages hull point clouds and speed to predict near-field pressure distributions and far-field wave patterns in approximately 0.15 seconds, achieving a 550x speedup over traditional potential-flow solvers while maintaining high accuracy (R² ≥ 0.91) for rapid ship design exploration.
This study utilizes spatial filtering on direct numerical simulation data of a supersonic reacting shear layer to demonstrate that while filtering only weakly alters the entropy field itself, it significantly distorts entropy transport and production statistics by preferentially removing high-wavenumber content and simplifying the geometry of the most dynamically and thermally active structures, thereby concentrating residual errors in the layer's core.
This study develops a local, scale-invariant subfilter wave drag model for Large Eddy Simulations to characterize how specific ocean wave parameters influence sea surface roughness and momentum flux within the Marine Atmospheric Boundary Layer, demonstrating that these relationships extend beyond simple monotonic dependencies between wind speed and surface stress.
This paper extends the averaged Lagrangian method to derive explicit variational formulas for solitary wave solutions of the extended Korteweg–de Vries (eKdV) equation and validates their accuracy in predicting the leading edge of dispersive shock waves through comparison with numerical simulations.
This paper presents and validates a GPU-native deep neural network surrogate that accelerates particle-based Fokker-Planck simulations of rarefied and hypersonic flows by replacing deterministic cubic closures with efficient batched computations, achieving substantial online speedups while preserving the kinetic model's macroscopic accuracy, stability, and entropy fidelity.