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.
The paper presents MeLISA, a scalable, latency-free autoregressive generative model based on MeanFlow in pixel space that achieves both high inference speed and accurate statistical fidelity over long time horizons for turbulent fluid dynamics through the use of block-wise stochastic transitions and specialized consistency losses.
This paper proposes a consistency-distilled flow matching framework that compresses high-fidelity generative models into efficient one-step architectures for scientific flow reconstruction, achieving significant inference speedups and improved training efficiency while maintaining physical fidelity across diverse fluid dynamics benchmarks.
This paper presents a multifidelity topology optimization framework that calibrates a computationally efficient Darcy flow-based low-fidelity model against a high-fidelity RANS model to design two-fluid turbulent heat exchangers, achieving up to a 22% improvement in performance over conventional designs by balancing enhanced heat transfer with manageable pressure drops.
This study utilizes numerical simulations of a stirred tank with miscible liquids to demonstrate that while mixing time correlates positively with the Richardson number, a derived exponential scaling law based on Power, Froude, and Richardson numbers successfully collapses all data onto a single master curve for intermediate-to-large density differences.
The paper introduces AI CFD Scientist, an open-source framework that leverages vision-language models to autonomously execute, validate, and refine computational fluid dynamics simulations on OpenFOAM, successfully discovering a Spalart-Allmaras correction that reduces error by 7.89% while detecting silent failures that traditional solver checks miss.
This study demonstrates that while elasticity-induced center modes in polymer-laden sheared convection yield negligible heat transfer gains, buoyancy-driven convective modes can be dramatically enhanced by up to 1100% through the formation of wall-attached polymer-stress "hooks" that reorganize flow into efficient counter-rotating rolls, offering a promising route for advanced thermal management systems.
This paper introduces a physics-informed coherent predictor that leverages Lagrangian coherent structures and surrounding particle dynamics to accurately forecast Lagrangian trajectories in turbulent flows from sparse temporal observations, demonstrating superior performance and topology-aware error characteristics across diverse 2D and 3D flow conditions.
This paper introduces an *ab initio* stochastic model that establishes a direct link between the Lagrangian velocity gradient tensor and fluid deformation measures by demonstrating that, despite non-Markovian velocity processes, temporal decorrelation in random unsteady flows leads to Fickian evolution of the gradient tensor, enabling closed-form predictions of the Cauchy-Green tensor and finite-time Lyapunov exponents.
Combining experiments and simulations, this study reveals that diffusiophoresis in porous media unexpectedly enhances longitudinal dispersion when colloids are attracted to a solute-rich blob and suppresses it when repelled, a counterintuitive mechanism driven by particle exchange between slow and fast streamlines.
This paper introduces a computationally efficient, linearised numerical method to model wind farm interactions, revealing that asymmetric turbulent entrainment causes wakes to rise vertically, thereby making downstream farms with higher hub heights more susceptible to upstream wake effects than those with lower hub heights.