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.
This paper introduces WellPINN, a novel workflow that utilizes sequentially trained physics-informed neural networks on shrinking subdomains to accurately model fluid pressure diffusion around wells throughout the entire injection period, overcoming previous limitations in capturing early-stage pressure dynamics.
This paper presents a one-dimensional shear-force-driven droplet formation model that utilizes a flux-based error estimator derived from mixed finite element gradients to drive an adaptive mesh refinement algorithm, significantly reducing computational cost while maintaining accuracy in capturing droplet interface dynamics.
This paper presents an enhanced octree-based sampling algorithm () that significantly reduces the storage requirements of large-scale CFD simulation data by 35% to 95% while preserving dominant flow dynamics, thereby enabling efficient post-processing on local workstations instead of requiring high-performance computing resources.
This study presents a Deep Learning-accelerated Lennard-Jones DSMC framework that combines a viscosity-consistent Variable Effective Diameter collision-selection model with a DeepONet surrogate for rapid scattering-angle prediction, successfully resolving complex rarefied flows in cryogenic and hypersonic regimes while significantly reducing computational costs.
This paper formulates and solves the Taylor--Aris dispersion problem for Brownian rods in arbitrary regular-polygonal ducts by coupling pressure-driven shear alignment with a tensorial diffusion model, revealing that while rod alignment causes only minor shifts in mean speed, it significantly enhances dispersion by reducing transverse mixing, with finite-time dynamics governed by a biorthogonal spectral decomposition of the resulting cell problem.
This paper introduces U-FlowPET, an unsupervised physics-informed framework that overcomes the underdetermined nature of optical tomography to reconstruct all six components of the three-dimensional fluid stress tensor without relying on constitutive assumptions or labeled training data, thereby enabling direct quantification of forces in complex fluid systems.
This paper presents a non-invasive, real-time pressure sensing method for microfluidic channels that utilizes quantitative phase imaging to measure PDMS deformation, enabling accurate pressure distribution mapping without requiring embedded sensors or device modifications.
This paper introduces "soft mobility theory," a framework combining virtual power and the Lorentz reciprocal theorem to derive configuration-dependent equations for deformable bodies in viscous flows, enabling efficient gradient-based inverse design and validated through differentiable JAX simulations of both rigid and flexible swimmers.
This study demonstrates that transparent 3D-printed vascular models combined with micro-particle image velocimetry (microPIV) provide a robust experimental framework for investigating microscale cerebrovascular hemodynamics, successfully capturing flow features and wall shear stress in geometries as small as 500 microns with high accuracy compared to analytical predictions.
This paper presents and validates a bond-based peridynamics framework for accurately modeling coupled heat transfer and pressure-driven water flow with phase change in saturated porous media, offering a robust non-local approach to predict phase interfaces and thermodynamic distributions in complex scenarios like soil freezing and thawing.