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 a hierarchical asynchronous iterative method that partitions the flow field into boundary, inner, and outer layers with distinct iteration steps, achieving identical simulation results to traditional approaches while reducing computational time by 46.8% (to 53.2% of the original) across various benchmark models.
This study employs high-order compact finite difference simulations to demonstrate that while the onset of viscous fingering in miscible slices is independent of boundary conditions, long-time solute mixing and instability growth are significantly enhanced by permeable boundaries, offering new insights for chromatography separation processes.
This paper demonstrates that a physics-guided surrogate learning approach, which trains reinforcement learning policies on turbulent channel flows matching wing boundary-layer statistics, enables zero-shot control of a NACA4412 wing that achieves significant drag reductions while drastically lowering computational costs compared to traditional on-wing training.
This study utilizes high-Reynolds-number DNS data to demonstrate that eddy viscosity in pressure-driven wall turbulence is configuration-dependent in the outer region, leading to a new Cess-type model with an outer correction function that significantly improves predictions for open-channel flow while maintaining accuracy for closed-channel and pipe flows.
This paper demonstrates that a multi-scale convolutional neural network offers the optimal balance of accuracy, robustness, and efficiency for enhancing coarse-grid atmospheric flow simulations via super-resolution, outperforming both baseline CNNs and state-of-the-art diffusion-based models.
Direct numerical simulations and theoretical analysis reveal that turbulent thermomagnetic convection of a high-Prandtl fluid in a square cavity exhibits an "ultimate-like" transport regime characterized by and , driven by the magnetic force's ability to enhance the ejection and advection of thermal plumes.
This paper validates the Shallow Recurrent Decoder network architecture for state estimation on the real-world DYNASTY experimental facility at Politecnico di Milano, demonstrating its ability to accurately reconstruct complex spatio-temporal dynamics of natural circulation systems using only sparse temperature measurements and high-fidelity RELAP5 simulation data.
This paper investigates the analytic properties of adjoint solutions for two-dimensional subcritical full potential flows by deriving an analytic solution via the Green's function approach, revealing connections to compressible adjoint Euler equations, and analyzing the role of the Kutta condition in determining aerodynamic lift.
This paper establishes a Hamiltonian framework for vortex clusters on a flat torus using the Schottky-Klein prime function, deriving a universal small-cluster expansion that reduces collective dynamics to a closed description governed by a single complex quadrupole moment, a prediction validated by numerical simulations.
This study utilizes a one-dimensional capillary fiber bundle model to demonstrate that hydraulic fracturing enhances fluid extraction efficiency by lowering capillary thresholds, identifying an optimal pressure gradient that maximizes flow rates and enables the detection of extraction conditions through computationally efficient analysis of local flow profiles and Shannon entropy.