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 investigates how dissipation and toroidal geometry influence the motion of vortex binaries in periodic fluid domains, demonstrating that while local dynamics allow for analytical solutions, the periodic boundary conditions induce geometric corrections such as angular drift in dipoles and a unique nonlinear frequency chirp during inspiral.
This paper presents an analytic theory of quenched force-dipole interactions in viscous membranes, demonstrating that the Saffman crossover fundamentally reorganizes the system's Hamiltonian dynamics from an effectively one-dimensional near-field regime to a fully coupled, two-dimensional far-field regime characterized by universal late-time aggregation scaling.
This study demonstrates that Fourier Neural Operators (FNOs) outperform Physics-Informed Neural Networks (PINNs) in predicting the multi-scale dynamic wakes of floating offshore wind turbines by more accurately capturing high-frequency turbulent structures and higher-order harmonics while offering significantly faster training speeds.
The paper demonstrates that while embedding a fractal geometric prior into a machine learning model improves the prediction of subgrid interfacial area density in multiphase flows, the effectiveness of this physics-based inductive bias is regime-dependent, performing well in corrugation-dominated flows but failing during topology-changing fragmentation.
By analyzing high-Reynolds-number turbulence simulations, this study demonstrates that intermittency extends the Markov-Einstein coherence length of the energy cascade to approximately three times the canonical estimate, suggesting that the standard Markovian assumption used in cascade modeling is insufficient for describing intermittent events.
The paper proposes the Synchronized Molecular Dynamics (SMD) method, a multiscale computational framework that efficiently simulates thin-layer flows of complex fluids by coupling sparse local molecular dynamics simulations with a macroscopic lubrication description through iterative synchronization of conservation laws.
This paper introduces an enhanced "MMS-Sparse" hybrid framework that utilizes multilevel radial basis functions (RBFs) to provide noise-robust, automated, and geometrically flexible coupling between DSMC and CFD solvers for simulating rarefied gas flows.
This paper presents an extended direct-forcing immersed boundary method (DF-IBM) that couples Navier-Stokes equations with Newton-Euler equations through an implicit, computationally efficient algorithm to stably simulate free-moving fluid-rigid body interactions across various density ratios.
This paper derives an exact, implicit dispersion relation for linear surface waves on an arbitrary vertical shear profile by employing a Green's function framework for the Rayleigh equation, providing a general solution that reduces to known analytical and asymptotic limits.
By combining extreme turbulence with a minute reduction in surface tension via surfactants, the researchers demonstrated a scalable method to suppress bubble coalescence and promote breakup, resulting in smaller, more uniform microbubbles and enhanced mass transfer without altering the underlying fluid hydrodynamics.