1219 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 utilizes direct numerical simulations to demonstrate that spanwise wall oscillation with extended, out-scale actuation periods can enhance drag reduction performance in turbulent boundary layers at high Reynolds numbers, challenging the conventional view of inevitable deterioration and offering a new analytical framework linking drag reduction to mean velocity shifts.
This paper introduces a novel high-order compact finite volume method for unstructured grids that utilizes a "scheme space" formulation to systematically control dispersion and dissipation properties and incorporates WENO concepts to robustly capture strong discontinuities without unphysical oscillations.
This paper proposes a surrogate-assisted model predictive control framework that utilizes a trained Fourier Neural Operator to efficiently forecast spatiotemporal dynamics and optimize control inputs for multiphase processes, thereby overcoming the computational limitations of traditional numerical models.
This paper establishes a predictive framework for transient buoyancy-driven mixing in porous media by deriving exact time-dependent balances that reveal localized dynamics, enabling a universal, parameter-free transport law validated by high-resolution direct numerical simulations.
This study introduces and validates a modified Bernoulli equation that incorporates Reynolds-number-dependent loss coefficients to more accurately estimate trans-stenotic pressure gradients across varying flow regimes compared to traditional simplified and extended formulations, while also demonstrating that peak-velocity-based estimations are more robust to MRI pixel size limitations than bulk flow rate measurements.
This paper presents a novel, scalable mechanism that converts vertical waste heat into horizontal fluid motion via symmetry-breaking and heterogeneous thermal conductivities to enable autonomous, self-powered pumping for microtechnological applications and waste heat recovery.
This paper introduces a discrete variance decay framework to quantify spurious mixing in numerical advection-diffusion schemes, revealing the non-uniqueness of implied second-moment fluxes, the necessity of temporal averaging for high-order methods, and the correlation of spurious mixing with eddy kinetic energy distributions.
This paper presents a GPU-accelerated, quantum-inspired fluid simulation method using matrix product states (MPS) and the cuQuantum library to efficiently model 2D turbulence at high Reynolds numbers, demonstrating up to a 12.1-fold speedup over traditional approaches and establishing a theoretical scaling law for the required bond dimension based on turbulent energy spectra.
This study utilizes high-Reynolds-number wind tunnel experiments with controlled pressure gradients to demonstrate that while the von Karman coefficient in the logarithmic law remains invariant, the additive coefficient varies systematically with both local adverse pressure gradients and upstream pressure-gradient history, which energize motions in the wake region without penetrating the inner layer.
This paper presents a data assimilation framework that successfully demonstrates the joint estimation of carrier flow fields and unknown particle properties (such as position, size, and density) from sparse and noisy Lagrangian particle tracking data across turbulent, inertial, and compressible supersonic flow regimes.