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 the computational and experimental optimization of a multifunctional magnetic milli-spinner, demonstrating that its enhanced structural design enables record-breaking swimming velocities in blood-mimicking fluids, thereby establishing a robust untethered platform for navigating high-flow, tortuous vasculature to treat vascular diseases.
This paper proposes a closed-loop control framework based on a parametric reduced-order model that successfully suppresses vortex shedding in the gap and wake of tandem cylinders at low Reynolds numbers using limited sensing and volumetric forcing.
This study combines experiments, simulations, and analytical modeling to demonstrate that pore-scale disorder in porous media accelerates fluid stretching and mixing by localizing deformation near solid boundaries, leading to quadratic rather than linear growth in mean stretching over time.
This paper investigates the sensitivity of Physics-Informed Neural Networks (PINNs) to the choice between conservative and non-conservative PDE formulations when solving problems involving shocks and discontinuities, using benchmark cases like the Burgers and Euler equations to evaluate their effectiveness in bridging the gap between these two approaches.
This paper derives an asymptotically reduced model for rapidly rotating, internally heated convection at infinite Prandtl number to establish rigorous, optimized bounds on mean temperature and vertical heat transport that reveal two distinct scaling behaviors with respect to the Rayleigh and Ekman numbers.
This paper demonstrates that standard non-conservative Physics-Informed Neural Networks (PINNs) fail to capture correct shock speeds in unsteady fluid dynamics due to violations of Rankine–Hugoniot conditions, and proposes a path-integral framework based on DLM theory to successfully restore physical fidelity in primitive-variable formulations.
This paper introduces a novel, dimension-independent Lattice Boltzmann framework that solves Eulerian-Eulerian multiphase flow equations with large density ratios and realistic drag coefficients without finite difference corrections, demonstrating excellent agreement with traditional solvers and promising efficient implementation on high-performance computing systems.
Through numerical simulations of the two-dimensional Taylor-Couette system, this study characterizes the onset of elastic turbulence and reveals that the resulting fully nonlinear dynamics are strongly nonhomogeneous and confined to an active region near the inner wall, where statistical properties align reasonably well with theoretical expectations despite spatial deviations.
This paper introduces Predictor-Driven Diffusion, a novel framework that integrates renormalization-group-based spatial coarse-graining with path-integral temporal dynamics to enable a unified predictor for spatiotemporal generation, simulation, and super-resolution in multiscale turbulent systems.
This paper reveals that the dispersion of clean spherical bubbles rising in a chain follows a two-stage mechanism where initial destabilization is driven by wake-induced lift, while subsequent large-scale lateral dispersion is caused by a collective, self-induced mean flow that enhances shear-induced lift.