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 presents a numerical and experimental study of a single-layer PDMS microfluidic device that achieves controllable micron-to-millimeter scale ceiling deformations through geometric parameter optimization, enabling versatile applications such as fully closing valves and tunable optical lenses.
This paper demonstrates that Topological Data Analysis (TDA), utilizing persistence diagrams and Wasserstein-distance metrics on vorticity and length-scale fields, offers a more sensitive and effective framework than traditional statistical methods for identifying the spatiotemporal signatures of strong turbulent fluctuations and intermittency in low-resolution helically rotating flows.
This paper proposes a self-similar solution to the inviscid Euler equations demonstrating that finite-time singularities are driven by a self-focusing mechanism of helicity, which separates the flow into a shrinking tubular region and an outer zero-vorticity zone, with the singularity's geometry (point-like or line-like) determined by the axial dynamics of this tube.
This paper develops a tensorial Taylor–Aris dispersion theory for Brownian rods in circular Poiseuille flow, revealing how shear-induced streamwise alignment in high-shear annular layers reduces radial diffusivity and amplifies the Taylor coefficient by up to 30% compared to classical scalar predictions.
This paper introduces the Ray-Column IPRM, a structure-based turbulence model that restores radial spectral scale information by projecting conditional states onto finite wavenumber bands, thereby enabling more accurate closure evaluations and the formation of filtered observables compared to traditional orientation-only approaches.
This study demonstrates that using two-color Laguerre-Gaussian laser pulses to drive plasma wakefields fundamentally alters their topology by redistributing longitudinal field energy off-axis into hollow, ring-shaped structures, thereby offering new mechanisms for controlling transverse plasma dynamics and enabling off-axis particle acceleration.
This paper proposes a Physics Informed Neural Network (PINN)-based computational method that directly solves for time-periodic flow states by optimizing over a single period rather than simulating transient initial conditions, thereby achieving significant reductions in computational time while maintaining accuracy comparable to traditional mesh-based solvers.
This study presents a methodology to solve the Riemann problem for three-phase foam flow in porous media under local equilibrium conditions, overcoming the challenges of an umbilic point to classify wave structures and analyze oil bank formation for applications in enhanced oil recovery and carbon storage.
This paper proposes a theoretical framework for scalar mixing fronts in smooth chaotic flows that identifies a characteristic length scale where dispersion and stretching-enhanced diffusion balance, yielding a parameter-free expression for concentration variance that accurately matches numerical simulations across a broad range of Péclet numbers.
This study validates the applicability of the Rheo-SINDy framework to extensional flows by demonstrating its ability to accurately recover the Giesekus model and derive a predictive approximate constitutive model for FENE dumbbell data through a manually designed library.