1169 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 proposes an approximate Hamiltonian simulation algorithm that significantly reduces circuit depth and two-qubit gate counts for quantum fluid simulations by optimizing operator truncation, thereby preserving macroscopic flow characteristics and balancing theoretical errors with hardware noise to enable feasible simulations on near-term quantum devices.
This paper demonstrates that for nonlinear spatiotemporal system identification in turbulent regimes, diffusion models utilizing clean-state prediction as a target parameterization significantly improve rollout stability and reduce long-horizon errors compared to traditional noise or velocity-based objectives.
This study utilizes simultaneous drag force measurements and multi-plane imaging to characterize three distinct air lubrication regimes (bubbly, transitional, and air layer) across varying flow conditions, identifying specific mechanisms for drag reduction, proposing a new scaling law for the critical air flow rate, and revealing how Froude-depth numbers dictate whether the air layer remains unbounded or transitions into a finite cavity.
This paper presents an analytical solution for the slow movement of small liquid droplets, gas bubbles, and aerosols by generalizing the Hadamard-Rybczynski equation to include both tangential and normal partial slip conditions at fluid-fluid interfaces, demonstrating that each fluid possesses its own slip length and deriving new equations for terminal velocity that account for non-uniform gas density.
This study utilizes direct numerical simulation and spectral proper orthogonal decomposition to reveal that increasing near-nozzle forcing in aspect ratio 2 elliptical jets accelerates axis-switching, causing the dominant pre-switch flapping mode to decay and transition into a post-switch wagging mode relative to the new axis, which subsequently gives way to a new flapping mode in the low-frequency spectrum.
This paper presents a physics-based numerical model that synthesizes seismic signals from gravel-bed rivers by integrating grain-scale particle dynamics with turbulent flow effects, demonstrating its ability to distinguish between sediment transport and flow-induced noise through comparison with observed flood data.
This paper proposes an information-theoretic variational autoencoder framework that decomposes the KL divergence to achieve disentangled, physically interpretable manifold learning of high-dimensional fluid flows, demonstrating superior performance over traditional methods in separating distinct physical effects across diverse unsteady flow scenarios.
This paper introduces AeroTransformer, a foundation-model paradigm that leverages pre-training on a large-scale diverse dataset followed by fine-tuning with limited task-specific samples to achieve highly accurate and data-efficient aerodynamic predictions for complex three-dimensional designs.
This paper introduces two deep learning autoregressive surrogate models—a Koopman-based Transformer and a ConvLSTM-UNet—that accurately and efficiently predict the temporal evolution of 2D ideal magnetohydrodynamic Kelvin-Helmholtz instabilities while preserving key physical structures and invariants at a substantially reduced computational cost compared to direct numerical simulations.
This paper demonstrates that the apparent diffusivity governing pore-pressure evolution and flow mobility in granular-fluid mixtures is not an intrinsic material property but emerges from the coupling between pore-pressure diffusion and granular compaction, a mechanism that successfully explains the thickness-dependent decay of pore pressure and runout behavior observed in experiments.