1236 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 global linear stability analysis and simulations to reveal that the stability of rising oblate bubble pairs is primarily governed by inclination-induced rotational feedback and lift rather than deformation, while also identifying distinct short-range and long-range coupling mechanisms for different instability modes and a new oscillatory mode driven by unsteady recirculation.
This paper establishes the global existence and uniqueness of generalized solutions for the three-dimensional inviscid quasi-geostrophic equation on a cylindrical domain with a multiply connected cross-section under specific Neumann and no-flux boundary conditions, assuming the initial potential vorticity is essentially bounded.
This paper extends the Statistical State Dynamics framework to shallow water magnetohydrodynamic turbulence, demonstrating how the interplay of Reynolds and Maxwell stresses leads to the formation and equilibration of zonal jet-toroidal field structures that explain both steady phenomena like solar super-rotation and time-dependent events such as the solar cycle.
This paper presents a reduced mathematical model based on center manifold theory for open cavity flow, which successfully captures key dynamics such as unstable quasi-periodic edge states and limit-cycle switching by explaining the stability exchange through cross-coupling terms in amplitude equations.
This paper proposes a Meta-PINNs framework that integrates meta-learning to enhance physics-informed neural networks, demonstrating significantly improved convergence, generalization, and accuracy with reduced computational costs for predicting turbomachinery flows under varying operating conditions compared to standard methods.
This paper establishes a hydrodynamic framework for force dipoles in compressible membranes with odd viscosity by deriving an exact Green tensor that reveals distinct dynamical regimes and chiral observables, such as transverse drift and relative motion, arising from the interplay of shear, compressional, and odd-viscous modes.
This paper proposes a trajectory-driven global optimization framework, inspired by CMA-ES, that enables unified, high-fidelity structural-hydrodynamic modeling of underwater underactuated and soft robotic systems by simultaneously identifying coupled internal and external parameters, achieving accurate real-to-sim consistency across diverse mechanisms without manual retuning.
This paper demonstrates that a shape-changing microswimmer, guided by reinforcement learning to adapt its aspect ratio based on local flow signals, can robustly maximize its displacement in turbulent environments, outperforming fixed-shape strategies and revealing a physically interpretable control paradigm for navigation in complex flows.
This paper demonstrates that the transition between weak- and strong-field dynamo regimes in spherical shells is characterized by a breaking of equatorial symmetry, which is triggered by the sudden growth of the magnetic field and subsequently sustains the strong-field branch.
This study demonstrates that aerofoil shape optimisation for curvilinear blade kinematics is effective only in high-solidity configurations where light dynamic stall allows geometric modifications to suppress leading-edge vortex separation, whereas deep dynamic stall renders such optimisation ineffective due to dominant flow separation.