1169 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 introduces an LSTM-PINN framework that leverages depth-recursive memory mechanisms to overcome numerical stiffness and gradient disparities in strongly coupled steady-state electrothermal systems, thereby achieving superior accuracy and multi-field consistency compared to state-of-the-art baselines across diverse convective and drag regimes.
This numerical study demonstrates that kerosene-based mist film cooling, particularly when combined with air, offers superior thermal protection for rotating detonation combustors compared to conventional air cooling by forming a more persistent near-wall layer with enhanced heat removal through phase change and improved resistance to detonation wave interaction.
This study experimentally and theoretically investigates how surfactant concentration and substrate wettability influence evaporation rates, thermo-solutal transport, and deposition patterns in sessile droplets, revealing that Marangoni solutal advection dominates the flow dynamics while viscous resistance and surfactant crowding can dampen these effects.
This paper demonstrates that reformulating the Deep Reinforcement Learning control problem for Rotating Detonation Engines in a moving reference frame to exploit timescale separation enables more reliable and robust induction of mode transitions compared to stationary frame approaches.
This paper proposes a non-intrusive, physics-informed spatio-temporal surrogate modeling framework that leverages Koopman autoencoders to accelerate engineering design by efficiently predicting complex dynamical system behaviors, such as fluid flow around a cylinder, while ensuring generalizability beyond the training data distribution.
This paper demonstrates that tensor invariants of velocity and magnetic field gradients serve as quantifiable proxies and bounds for specific energy flux mechanisms in magnetohydrodynamic turbulence, a relationship validated through 3D simulations of freely decaying flows.
This paper theoretically and numerically investigates the collective dynamics of self-propelled particles on a curved viscous interface, demonstrating that the interplay between interfacial active stress and geometric constraints leads to a finite-wavelength instability governed by the competition between the vesicle radius and the Saffman-Delbrück length.
This study utilizes biohybrid microelectronics to demonstrate that two scyphozoan jellyfish species exhibit a shared speed-frequency relationship peaking near 0.5 Hz, suggesting their natural stroke frequencies are optimized for feeding rather than locomotion, and validates a new paddling-based analytical model that outperforms existing jet-propulsion models in predicting their swimming dynamics.
This study demonstrates that reinforcement learning enables inertial particles to efficiently navigate turbulent Rayleigh–Bénard convection by exploiting flow reorganization at high Rayleigh numbers, where fragmented barriers and plume-assisted pathways allow for successful traversal with lower energy consumption compared to constant-heading strategies.
This paper presents a discrete adjoint gas-kinetic scheme for turbulent continuum flows, which is rigorously verified against a linearized solver and demonstrated to be highly efficient and accurate for aerodynamic shape optimization across various benchmark cases.