1147 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 elucidates the final states of two-dimensional topographic turbulence by demonstrating that localized vortices locked to topographic extrema follow Gaussian profiles with a "sinh"-like potential vorticity relation, and by revealing that the stability of these vortex-topography configurations depends critically on the background flow energy.
This paper introduces "turbuloscope," a physics-informed, three-stage geometric encoding method that overcomes the quantum state preparation bottleneck to enable efficient, ancilla-free, and logarithmically scaling end-to-end quantum simulation of complex turbulent flows, successfully demonstrating high-Reynolds-number turbulence generation on 30 qubits.
This study identifies spanwise-localised "arrowhead" travelling waves as the fundamental building blocks of elastic turbulence in rectilinear, sheared polymer flows, revealing how their interactions and splitting events drive chaotic dynamics while noting that the resulting flow remains a poor mixer due to small cross-shear and spanwise velocities.
This paper investigates the impact of two-way coupling in two-dimensional dusty turbulence, revealing enhanced intermittency and modified spectral scaling that are effectively captured by a proposed multiscale forcing framework modeling particle feedback as localized small-scale forcing.
This paper proposes a hybrid data-driven framework that combines a probabilistic VAE-Transformer for learning large-scale coherent structures with Gaussian Process regression for stochastic closure, demonstrating superior stability and statistical accuracy in predicting chaotic Kolmogorov flow compared to state-of-the-art probabilistic baselines.
This paper demonstrates that laminar-turbulent transition in canonical K-type flow is not a stochastic process but a deterministic sequence of temporal and spatial symmetry-breaking events driven by organized, energetically dominant coherent structures that progressively transform a harmonic response into broadband turbulence.
This study demonstrates that incorporating anisotropic subgrid-scale stress, particularly normal stress contributions, significantly improves the prediction of flow separation in wall-modeled large-eddy simulations of smooth-body separation under favorable pressure gradients by more accurately capturing the dissipation and diffusion of Reynolds stresses during grid refinement.
This paper introduces RedEigCD, a novel self-adaptive timestepping method for reduced-order models of incompressible flows that leverages exact spectral information to achieve stable timesteps up to 40 times larger than full-order models while maintaining accuracy and online efficiency.
This paper introduces a flexible Koopman autoencoder surrogate model that incorporates meteorological forcings and boundary conditions to achieve stable, accurate, and computationally efficient long-term predictions for forced flexible mesh coastal-ocean models, outperforming traditional POD-based approaches in several cases while enabling practical applications like ensemble forecasting.
This paper demonstrates that metric-based mesh adaptation using the temperature Hessian enables accurate hypersonic aerothermal simulations of complex geometries, such as atmospheric entry capsules with RCS jets, while achieving surface heating predictions comparable to conventional block-structured methods.