1274 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 presents a robust numerical method using higher-order surface finite elements and a nonlocal tangent-point energy to simulate the self-avoiding, curvature-adaptive evolution of fluid deformable surfaces under active growth, effectively modeling complex epithelial shape changes like discocyte-to-stomatocyte transitions and sphere inversions.
This paper demonstrates that a nonlinear, nonlocal asymptotic model derived from the Navier-Stokes equations accurately predicts and characterizes the bistability of unimodal and bimodal travelling waves observed in two-layer Couette flow experiments, while also revealing new symmetry-breaking branches and time-periodic orbits through comprehensive bifurcation analysis.
This study demonstrates that machine learning models trained on high-speed images of droplet pinch-off can accurately predict key fluid properties like viscosity and surface tension, offering a simplified and automated alternative to conventional measurement techniques.
LAViG-FLOW is a latent autoregressive video generation framework that efficiently models and forecasts subsurface multiphase fluid flow fields by learning the coupled evolution of saturation and pressure, offering a solution two orders of magnitude faster than traditional numerical solvers for applications like CO2 sequestration.
This paper presents numerical evidence using Clean Numerical Simulation suggesting that the Navier-Stokes equations may admit distinct global solutions arising from initial conditions differing by as little as , thereby challenging the uniqueness of smooth solutions and offering new insights into the associated Millennium Prize Problem.
This paper derives a higher-order mean velocity profile for the convective atmospheric boundary layer using matched asymptotic expansions and field data from the MHATS campaign, demonstrating excellent agreement with measurements and validating the convective logarithmic friction law to at least second order while accounting for deviations from standard similarity theories.
This paper demonstrates that in super-Alfvénic plane shear flows, large-scale velocity shear suppresses magnetohydrodynamic turbulence imbalance through linear non-modal dynamics, driving initially imbalanced systems toward a balanced state of counter-propagating Alfvén waves.
This paper presents a high-fidelity, machine learning-accelerated Direct Simulation Monte Carlo framework that integrates a Lennard-Jones potential via a universal Variable Effective Diameter model and a Deep Operator Network surrogate to efficiently resolve complex rarefied flows, revealing significant physical discrepancies in cryogenic and hypersonic regimes compared to traditional models.
This paper presents a mathematical model of a microbial droplet growing on a viscous fluid, revealing that while growth forces stabilize its axisymmetric expansion, buoyancy forces induce morphological instability, a finding that aligns with experimental observations.
This paper proposes a preconditioned adjoint data assimilation method for two-dimensional decaying isotropic turbulence that redefines the adjoint operator's inner product using Fourier-space weighting kernels to suppress the exponential growth of small-scale structures in backward time, thereby significantly improving the reconstruction of initial conditions from sparse measurements.