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 demonstrates that the apparent diffusivity governing pore-pressure evolution and flow mobility in granular-fluid mixtures is not an intrinsic material property but emerges from the coupling between pore-pressure diffusion and granular compaction, a mechanism that successfully explains the thickness-dependent decay of pore pressure and runout behavior observed in experiments.
This paper presents a fully coupled boundary integral formulation and numerical methodology for modeling steadily propagating semi-infinite plane strain fractures in poroelastic media, which is systematically verified against analytical solutions to provide a robust tool for analyzing coupled fracture-fluid interactions.
This paper introduces IncompressibleNavierStokes.jl, an open-source Julia package that enables efficient, differentiable, and high-resolution simulation of incompressible turbulent flows on both CPUs and GPUs, facilitating the training of neural network closure models within large-eddy simulations.
This study develops a physics-based data-driven strategy using a modified fully connected neural network that integrates upstream velocity measurements with optimized pressure sensor data to accurately predict vortex-induced drag on a circular cylinder under non-uniform inflow conditions at a Reynolds number of 4000.
This paper presents a component-based reduced-order modeling (CBROM) framework that decomposes large-scale rocket engines into individual components trained on small-domain high-fidelity simulations and coupled via an adaptive MP-LSVT projection to enable accurate, efficient parametric predictions of combustion dynamics in multi-injector configurations.
This paper presents a hybrid data-driven method that utilizes DNS data and a sampling estimator to numerically solve dimensionally reduced, linear kinetic transport equations for one-point and two-point vorticity probability density functions in two-dimensional homogeneous isotropic turbulence.
This study introduces a scalable probabilistic workflow that integrates Bayesian correction and deep learning surrogates to bridge image-based fracture geometries with uncertainty-aware hydraulic predictions, effectively capturing the impact of aperture heterogeneity and 3D void complexity on transmissivity while avoiding the computational cost of repeated high-fidelity simulations.
This study utilizes particle-resolved direct numerical simulation to demonstrate that a wall-mounted cylinder in turbulent open channel flow generates intense vortical structures and enhanced turbulence that significantly alter local wall shear stress, leading to preferential particle accumulation or depletion in the wake and increased wall-normal transport, effects that are further amplified by the addition of wall roughness.
This study demonstrates through an inviscid model and high-Reynolds-number simulations that secondary shear instability can onset early in Kelvin-Helmholtz braid regions at high stratification, potentially controlling turbulent transition and diapycnal mixing before primary billows saturate or pairing instabilities occur.
This paper demonstrates that Matrix Product State (MPS) methods can efficiently simulate 2D turbulent Rayleigh-Bénard convection up to Rayleigh numbers of , achieving accurate statistical observables with significantly fewer degrees of freedom than traditional methods and suggesting scalability for investigating the ultimate regime of turbulence.