1262 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 uses numerical simulations of the three-dimensional incompressible Navier-Stokes equations to demonstrate that Kelvin waves in classical viscous vortex filaments align with Lord Kelvin's predictions, while also revealing the existence and collision dynamics of solitons and proposing a feasible experimental method for their generation.
This paper investigates the evolution of two-dimensional incompressible Navier-Stokes solutions initialized as polygonal vortex crystals, with or without a central vortex, by leveraging symmetry and stability to characterize their behavior up to sub-diffusive time scales before vortex merging occurs.
This study investigates the rheological properties and shear-induced structural transitions of three ferroelectric nematic liquid crystals, revealing distinct shear-rate-dependent viscosity behaviors, flow-alignment regimes, and the unique tendency of the polarization vector to remain parallel to the shear direction to avoid splay deformations.
This study utilizes large-eddy simulations to investigate the spatial structure and intermittency of soot in a swirl-stabilized ethylene flame, identifying flow recirculation and flame-vortex interactions as key drivers while comparing the performance and cost of on-the-fly versus pre-tabulated soot modeling approaches.
This paper demonstrates that a generative diffusion model, trained on turbulent Navier-Stokes data and modified to enforce physical symmetries, can discover and refine 111 previously unknown short-period orbits, establishing generative AI as a powerful complementary tool for exploring the complex solution spaces of nonlinear dynamical systems.
This paper proposes an efficient real-time adaptation strategy for a VAE-transformer-based Reduced Order Model that, by identifying latent manifold distortions as the primary source of error, enables lightweight retraining of only the autoencoder using sparse data and ensemble Kalman filtering to achieve accurate, uncertainty-quantified predictions for unsteady flows across out-of-sample parameter regimes.
This paper proposes CoK-PCA, a dimensionality reduction method based on the co-kurtosis tensor that outperforms traditional PCA in accurately capturing stiff chemical dynamics and extreme-valued ignition events in combustion datasets by better identifying critical directions in the thermo-chemical state space.
This study proposes a novel Bi-LSTM-VAE-WDC framework that effectively reduces high-dimensional spatiotemporal combustion data to a low-dimensional phase space and utilizes Wasserstein distance-based classification to accurately recognize and distinguish dynamical oscillation modes in circular flame arrays, outperforming traditional PCA and VAE methods.
This paper presents a hybrid turbulence model that integrates Physics Informed Neural Networks (PINN) and standard Neural Networks (NN) to correct the underprediction of turbulent kinetic energy by improving the turbulent diffusion term and recalibrating model coefficients, resulting in accurate flow predictions across various channel and boundary layer configurations.
This paper introduces a two-dimensional Non-Reciprocal Cahn-Hilliard-Navier-Stokes (NRCHNS) model to explore non-reciprocal binary-fluid turbulence, revealing a novel turbulent state characterized by an inverse energy cascade with a spectrum and a non-reciprocal flux that is suppressed at high Reynolds numbers.