1210 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 study employs Detached Eddy Simulation and Spectral Proper Orthogonal Decomposition to analyze unsteady transonic flow in a complex cavity-sub-cavity system derived from a scramjet-integrated launch vehicle, revealing feedback-driven pressure oscillations and demonstrating that a ventilated sub-cavity is the most effective passive control strategy for suppressing these loads.
This paper proposes using Deep Operator Networks (DeepONets) as a computationally efficient surrogate for the SWAN numerical wave model to accurately predict wave-induced forces and significant wave heights, thereby enabling more precise and efficient coupling with circulation models for storm surge prediction.
This paper establishes the first global well-posedness result for the one-phase Muskat problem with surface tension, proving that sufficiently small initial data in (where ) yields a unique global strong solution that decays to zero in the Lipschitz norm as time approaches infinity.
This paper introduces an image-based pore-network model (iPNM) that overcomes the limitations of existing quasi-static models by coupling two-phase flow, solute transport, and Ostwald ripening to accurately simulate the evolution of partially miscible ganglia in porous media, a capability validated against high-resolution microfluidic experiments without adjustable parameters.
This study experimentally investigates shock wave-turbulent boundary layer interactions on a cryogenically cooled wall at Mach 2.0, demonstrating that the cooled condition shifts the flow separation point downstream and reduces wall heat flux at separation, while validating cryogenic temperature-sensitive paint as an effective tool for analyzing these thermal and flow field effects.
The paper introduces MENO, a novel framework that leverages MeanFlow to enhance neural operators for dynamical systems, achieving accurate multi-scale predictions with significantly improved inference speed and physical fidelity compared to existing diffusion-based methods.
This paper presents a dataset derived from steady-state RANS simulations using the SST turbulence model in OpenFOAM, which quantifies flow separation characteristics—including surface pressure, skin friction, and separation points—for 2D flow around an ellipse and a von-Kármán-Trefftz airfoil across various angles of attack and Reynolds numbers to serve as a benchmark for extended potential flow models.
This numerical study investigates the formation and evolution of soliton-like coherent structures (SCS) in the transitional wake of a sphere across four Reynolds numbers, revealing that these structures emerge as wave packets from Tollmien-Schlichting waves, reach maximum amplitude following three-dimensional breakdown, and are sustained by surrounding vortex structures and high-shear layers rather than causing them.
This paper presents an analytical solution for the space-time correlation of passive scalars in colored-noise flows, validating the elliptic approximation model and demonstrating that mean-flow advection and large-scale sweeping drive Gaussian temporal decorrelation while small-scale distortion governs spatial decorrelation.
This paper introduces and validates two robust methods, based on Physics-Informed Neural Networks and the Adjoint State Method, for reconstructing bottom topography and surface velocity in shallow water flows using only surface deformation measurements, demonstrating their effectiveness in both 1D and 2D synthetic scenarios even with noisy or sparse data.