1256 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.
Using active fast Stokesian dynamics simulations, this study reveals that shear flow enhances energy dissipation while reducing relative viscosity in semi-dilute to concentrated suspensions of apolar squirmers, driven by unique microstructural signatures like enhanced nematic order and anisotropic pair correlations that distinguish the behavior of pushers and pullers from passive or purely motile systems.
This study employs spatial linear instability analysis and energy budget methods to demonstrate that increasing Weber numbers and elasticity in a viscoelastic jet within a co-flowing gas stream drive a transition from axisymmetric to helical modes, revealing a distinct elasticity-enhanced shear-driven instability mechanism validated by experimental flow-focusing results.
This paper experimentally and theoretically demonstrates that introducing geometric steps and lateral offsets in microchannels enables passive, reversible control and synchronization of capillary-driven flows by manipulating the balance between surface tension and Laplace pressure.
This study numerically investigates pressure-driven flow in low aspect-ratio curved channels across a wide range of Dean numbers and curvature ratios, revealing that the flow remains stable with a single pair of counter-rotating vortices at lower Dean numbers while exhibiting transient structures at higher values, with key flow features such as velocity peaks and vortex centers shifting from the inner to the outer wall as the Dean number increases or curvature decreases.
This paper proposes a stable and general quantum fractional-step lattice Boltzmann method that overcomes the high Reynolds number instability of previous quantum approaches by combining a quantum predictor step with a classical corrector step, thereby enabling accurate simulations of both two- and three-dimensional incompressible thermal flows.
This paper employs asymptotic analysis in the lubrication limit to investigate how the size of a Janus particle's inert region governs its rotational stability and propulsion behavior when moving near a planar wall, overcoming the numerical difficulties associated with resolving steep concentration gradients in the narrow gap.
This paper introduces a fully data-free Physics-Informed Neural Network framework that solves compressible inviscid flows up to Mach 15 around a cylinder by integrating a hybrid convolutional architecture, Mach-guided dynamic residual scaling, and specialized loss constraints to overcome spectral bias, gradient pathologies, and shock-capturing instabilities.
This study utilizes an ensemble of 1000 perturbed Taylor-Green vortex simulations to statistically characterize the timing variability of curl-of-vorticity precursor peaks relative to dissipation peaks, employing extreme-value theory to model rare lagging events and confirming a strong dynamical coupling between high-curvature activity and dissipation bursts.
This study demonstrates that graph neural networks can effectively reconstruct fine-scale structures in turbulent reacting flows on complex, unstructured meshes, overcoming the limitations of traditional deep learning models restricted to uniform grids.
This paper proposes Structure-Preserving Randomized Neural Networks (SP-RaNN), a novel framework that reformulates the solution of incompressible magnetohydrodynamic equations into a linear least-squares problem to eliminate nonconvex optimization while automatically and exactly satisfying divergence-free constraints, thereby achieving superior accuracy, stability, and convergence compared to traditional and deep learning-based methods.