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 study demonstrates that a model-based control framework combining interpolatory model order reduction with a quadratic-quadratic regulator (QQR) effectively stabilizes the fluidic pinball's unstable wake at Reynolds numbers of 30 and 50, outperforming traditional linear controllers by achieving faster convergence and successfully suppressing vortex shedding where linear methods fail.
This study demonstrates that ensemble-variational assimilation of wall-pressure measurements from sensors spanning the entire separation region is essential for accurately predicting Mach 6 flow separation and downstream disturbances over a cone-flare geometry, revealing shock-boundary layer interactions and quantifying uncertainties caused by low-frequency shock unsteadiness.
This paper develops a coarse-graining framework to derive multi-scale evolution equations for local available potential energy, including cross-scale flux terms, thereby enabling a complete analysis of energy cycles across spatial scales and reservoirs in stratified flows.
This paper presents a highly efficient, multi-GPU-accelerated implementation of the Discontinuous Galerkin ocean model SLIM that achieves massive speedups over CPU-based systems and enables ultra-high-resolution coastal simulations, such as a five-fold resolution improvement for the Great Barrier Reef.
This paper investigates a one-dimensional alternating particle system with elastic collisions, demonstrating that while equidistant initial positions with a mass ratio of 2 exhibit hydrodynamic shock front behavior similar to random initial conditions, specific mass ratios induce a unique "staggering domino-like" regime where only a single triplet moves at any time, resulting in ballistic shock front propagation.
This paper proposes ARCTIC, a lightweight, adaptive real-time forecasting framework that integrates sensor data with precomputed simulations to significantly improve the accuracy and autonomy of cryogenic fluid management in space systems, as validated by both synthetic scenarios and NASA experimental data.
This paper introduces an unsupervised physics- and equality-constrained neural network framework utilizing a pressure-Poisson objective and adaptive augmented Lagrangian method to successfully simulate high-Reynolds-number incompressible flows without labeled data, overcoming previous limitations in enforcing strict divergence-free constraints and boundary conditions.
This study utilizes high-fidelity lattice-Boltzmann very-large-eddy simulations to reveal that grazing flow fundamentally alters the noise dissipation mechanisms of an acoustic liner by modifying the near-wall flow topology, which increases viscous losses at low sound pressure levels while introducing phase-dependent vortex shedding that generates energy during outflow, ultimately reducing the liner's net acoustic dissipation.
This paper proposes a Quantum Physics-Informed Neural Network (QPINN) framework utilizing quantum trainable embeddings to solve the lid-driven cavity problem, demonstrating that this approach achieves stable training and competitive accuracy with significantly fewer parameters than classical PINNs, thereby highlighting the potential of trainable quantum embeddings for parameter-efficient physics-informed learning.
Using an idealized reentrant channel model, this study demonstrates that the dominant mechanism behind eddy saturation in the Antarctic Circumpolar Current shifts from a combination of standing meander and eddy diffusivity adjustments to solely standing meander adjustment as wind stress relative to friction exceeds a critical threshold, thereby explaining divergent findings in previous research.