🔬 Category

physics.flu-dyn

1221 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.

Characterisation of rough-wall drag in compressible turbulent boundary layers

This study investigates the applicability of incompressible roughness parameters in compressible turbulent boundary layers across a wide range of Mach and Reynolds numbers, revealing that while velocity transformations have limited impact, the fully rough regime exhibits a Mach-number-dependent shift that is best addressed by a temperature-ratio-based correction factor, thereby highlighting the need for custom rough-wall transformations.

Dea Daniella Wangsawijaya, Rio Baidya, Sven Scharnowski, Bharath Ganapathisubramani, Christian Kähler2026-03-26🔬 physics

Evaporative cooling and deposition patterns of evaporating Al2O3Al_2O_3 nanofluid droplets

This study investigates the evaporative cooling and deposition patterns of sessile Al2O3Al_2O_3 nanofluid droplets on hydrophobic substrates, revealing that thermocapillary flow driven by evaporative cooling governs internal circulation and dictates the transition from unique polygonal networks to classical coffee-ring or dual-ring patterns as substrate temperature increases.

S. K. Saroj, P. K. Panigrahi2026-03-26🔬 physics

Learning Response-Statistic Shifts and Parametric Roll Episodes from Wave--Vessel Time Series via LSTM Functional Models

This paper presents a data-driven LSTM surrogate model capable of learning nonlinear mappings from wave-vessel time series to accurately reproduce both parametric roll episodes and their associated statistical shifts, utilizing training data from either physical experiments or high-fidelity simulations to enhance operability and risk assessment.

Jose del Aguila Ferrandis2026-03-26🤖 cs.LG

Geometric Memory Generates Irreversible Transport in Time-Periodic Irrotational Flows

This paper demonstrates that irreversible transport can emerge in strictly time-periodic and locally irrotational flows through a purely geometric mechanism called "geometric memory," where causal self-transport over a finite memory time creates a geometric connection whose holonomy generates a finite Lagrangian drift that quantitatively matches experimental observations without fitting.

Mounir Kassmi2026-03-26🔬 physics

Project and Generate: Divergence-Free Neural Operators for Incompressible Flows

This paper introduces a unified framework for learning-based fluid dynamics that enforces exact incompressibility by integrating a differentiable spectral Leray projection for deterministic models and constructing a divergence-free Gaussian reference measure via curl-based pushforward for generative models, thereby eliminating spurious divergence and ensuring long-term physical stability.

Xigui Li, Hongwei Zhang, Ruoxi Jiang, Deshu Chen, Chensen Lin, Limei Han, Yuan Qi, Xin Guo, Yuan Cheng2026-03-26🤖 cs.LG

Scaling Laws Governing Droplet Spreading and Merging Dynamics on Solid Surfaces: A Molecular Simulation Study

This molecular dynamics study investigates the jumping behavior and energy conversion of merged droplets struck from above, establishing new scaling laws for spreading time, spreading factor, and restitution coefficients that depend on impact velocity, droplet size, surface texture, and wettability, particularly highlighting constant energy conversion efficiency at high velocities on superhydrophobic surfaces.

Ertiza Hossain Shopnil, Jahid Emon, Md Nadeem Azad, AKM Monjur Morshed2026-03-25🔬 physics

Bridging advection and diffusion in the encounter dynamics of sedimenting marine snow

By reconciling ballistic interception and advective-diffusive capture models through theoretical analysis and numerical simulations, this study reveals that diffusion significantly enhances encounter rates between sinking marine snow and suspended particles even at high Péclet numbers, suggesting that key biological and physical processes like bacterial colonization and mass accretion occur much faster than previously estimated.

Jan Turczynowicz, Radost Waszkiewicz, Jonasz Słomka, Maciej Lisicki2026-03-25🔬 cond-mat