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 introduces Lie Generator Network-Koopman (LGN-KM), a neural operator that lifts nonlinear partial differential equation dynamics into a stable, interpretable linear latent space by learning a decomposed continuous-time generator, thereby enabling accurate long-horizon prediction, spectral analysis, and physics-informed transfer on complex systems like Navier-Stokes turbulence without physics supervision.
This paper proposes a self-scaling tensor basis neural network (STBNN) that utilizes an invariant velocity-gradient normalization to achieve robust, geometry-independent Reynolds stress modeling for wall-bounded turbulence, demonstrating superior accuracy and generalization across Reynolds numbers and unseen flow configurations compared to existing data-driven and traditional closure models.
This study utilizes two-dimensional direct numerical simulations and global linear stability analyses to investigate viscous flow past a circular array of six-fold symmetric cylinders, identifying three distinct instability regimes based on patch density that transition from independent cylinder behavior to porous medium characteristics and finally to solid cylinder dynamics.
This paper investigates gravitational wave turbulence within the Hadad-Zakharov metric by addressing the compatibility of Einstein equations and utilizing a new GPU-based code to demonstrate the emergence of Kolmogorov-Zakharov spectra, dual cascades, and intermittent coherent structures, thereby confirming the propagation of genuine physical degrees of freedom.
This paper proposes a hard-constrained neural Riemann solver (HCNRS) that enforces five physical constraints—positivity, consistency, mirror symmetry, Galilean invariance, and scaling invariance—to accurately approximate exact Riemann solvers for fluid dynamics while overcoming the conservation errors and symmetry breaking issues found in unconstrained neural approaches.
This paper presents a Bayesian physics-informed neural network approach for flow field tomography that leverages governing fluid dynamics equations to reconstruct 2D flows from sparse line-of-sight measurements while providing comprehensive uncertainty quantification to address semi-convergence issues caused by noise.
This paper introduces a novel physics-informed background-oriented schlieren (BOS) workflow that utilizes physics-informed neural networks to simultaneously reconstruct accurate density, velocity, and pressure fields in supersonic flows by integrating measurement data with governing Euler and irrotationality equations, thereby overcoming the limitations of conventional methods and achieving the first PINN-based reconstruction of supersonic flow from experimental data.
This paper introduces Stochastic Particle Advection Velocimetry (SPAV), a novel framework utilizing a statistical data loss and physics-informed neural networks to significantly improve the accuracy of particle tracking velocimetry by explicitly modeling particle advection and accounting for localization uncertainties, thereby reducing reconstruction errors by approximately 50% in both simulated and experimental fluid flow measurements.
This paper introduces a novel "cone-ray" model for background-oriented schlieren imaging that significantly improves the robustness and accuracy of density field reconstructions by accounting for depth-of-field effects, outperforming traditional "thin-ray" methods across various aperture settings and flow conditions.
This paper introduces Neural Optical Flow (NOF), a continuous neural-implicit framework that enhances the accuracy, robustness, and data compression of particle image velocimetry (PIV) by integrating differentiable image warping, physical constraints like Navier-Stokes residuals, and tailored network expressivity to enable advanced analysis of both planar and stereo flows.