1212 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 provides a rigorous mathematical proof, supported by numerical simulations, that a concentrated vortex blob in a 2D inviscid fluid moves along the gradient of an underlying non-constant vorticity field, offering a unique Lagrangian explanation for the aggregation of same-sign vortex structures and extending this result to nearly vertical vortex filaments in 3D domains.
This paper investigates the sensitivity of Physics-Informed Neural Networks (PINNs) to the choice between conservative and non-conservative PDE formulations when solving problems involving shocks and discontinuities, using benchmark cases like the Burgers and Euler equations to evaluate their effectiveness in bridging the gap between these two approaches.
This paper proposes a novel Residual Guided Training strategy that combines a decoder-only Transformer with a residual-aware Generative Adversarial Network to enforce temporal causality and dynamically prioritize high-residual regions, significantly improving the accuracy of Physics-Informed Neural Networks in solving nonlinear partial differential equations.
This paper presents a framework that repurposes the Discrete Empirical Interpolation Method (DEIM) to serve as both an interpretable diagnostic tool for identifying physically meaningful structures and failure modes in Neural ODEs, and a guide for an adaptive data assimilation strategy that significantly improves predictive accuracy and stability in out-of-distribution flow scenarios.
This paper demonstrates that active flow networks composed of pumping and elastic units can spontaneously generate and transmit solitary waves, revealing how simple fluidic elements collectively create, shape, and transport information through emergent dynamics.
This paper advances the application of continuous branching stochastic processes to the Navier-Stokes equations, providing novel propagator representations and enabling efficient Backward Monte Carlo simulations for complex fluid transport in confined domains.
This paper extends the Shakhov-based Bhatnagar-Gross-Krook (SBGK) model within the PICLas code to simulate polyatomic molecules and their mixtures with non-equilibrium internal degrees of freedom, demonstrating through supersonic and hypersonic test cases that it accurately captures transport properties and shock structures with improved precision over the ESBGK model compared to DSMC results.
This paper derives an asymptotically reduced model for rapidly rotating, internally heated convection at infinite Prandtl number to establish rigorous, optimized bounds on mean temperature and vertical heat transport that reveal two distinct scaling behaviors with respect to the Rayleigh and Ekman numbers.
This paper presents an experimental realization of a heart-shaped floating object based on Zindler curves that maintains neutral equilibrium in any orientation when its density is half that of the surrounding liquid, demonstrating the interplay between geometry and buoyancy while highlighting the effects of density variations and capillarity.
This paper proposes a method to solve the Kazantsev small-scale dynamo equation across the full spectrum of hydrodynamic turbulence, revealing that the critical magnetic Reynolds number saturates at approximately 300 for high hydrodynamic Reynolds numbers while the growth rate slows and saturates at low magnetic Prandtl numbers.