1229 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 a novel kernel-based operator learning method that analytically enforces incompressibility and other physical properties for incompressible Navier-Stokes flows, achieving significantly higher accuracy and faster training speeds on desktop GPUs compared to traditional neural operators.
This paper proposes a physics-informed video diffusion framework that integrates physical constraints directly into the generative process to simultaneously produce realistic, temporally coherent water flow videos and accurate physical states based on shallow water equations, outperforming both purely data-driven models and traditional simulation pipelines in terms of fidelity and speed.
This paper proposes a unified framework that combines space-dependent aggregation of multiple turbulence models with non-intrusive reduced order models and neural network-based weighting to create efficient, accurate surrogate models for turbulent flows that outperform individual RANS and ROM predictions at near real-time computational costs.
This paper demonstrates that viscoelastic turbulence induced by macromolecules in low Reynolds number microfluidic channels overcomes diffusion-limited mixing to significantly enhance both solvent folding for reaction rates and macromolecule mixing, offering an efficient alternative to conventional turbulence for applications ranging from chemical synthesis to biomedical assays.
This study reveals that the wettability, density, and size of floating microparticle rafts critically govern raindrop impact dynamics—specifically splash onset, jet formation, and microplastic aerosolization—by establishing distinct interfacial regimes that ultimately collapse under a unified geometric-inertial-capillary scaling law.
This paper presents a physics-constrained neural network closure for Lattice Boltzmann Large-Eddy Simulation that, by integrating data-driven learning with physical constraints like rotational equivariance and energy transfer matching, achieves superior statistical accuracy and production-ready performance compared to traditional Smagorinsky baselines.
This paper establishes a general framework to analyze nonlinear mode-mode interactions in a Mach 6 transitional boundary layer, revealing that multiple coexisting primary instabilities drive complex energy transfers and early secondary growth through triadic forcings and base-flow-dependent resolvent leverage, challenging the applicability of conventional secondary stability analysis.
This paper investigates the flow of a Bingham yield-stress fluid through a blocked pore network, identifying two distinct regimes where flow properties are deterministic above the percolation threshold but become non-self-averaging and governed solely by the critical backbone at the threshold.
This paper introduces a physics-integrated neural differentiable framework that combines structural physical principles with learned implicit corrections to achieve stable, high-fidelity, and 200-fold faster long-horizon predictions of immersed-boundary flows compared to conventional solvers and purely data-driven models.
This paper introduces UrbanFlow-3K, a comprehensive open-source dataset comprising 3,000 two-dimensional lattice-Boltzmann simulations of urban flow around randomized building layouts, designed to facilitate the development, benchmarking, and transfer learning of machine learning models for predicting urban flow fields.