physics.flu-dyn papers | Gist.Science

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.

Sparse Müntz--Szász Recovery for Boundary-Anchored Velocity Profiles: A Short-Record Roughness Diagnostic in Turbulence

This paper introduces a sparse convex-relaxation framework using a Müntz–Szász/Jacobi dictionary to estimate effective local scaling exponents from short, boundary-anchored velocity profiles, demonstrating its utility as a finite-scale geometric diagnostic that reveals directional anisotropy and vorticity-aligned roughness structures in turbulent flows without requiring external calibration.

D Yang Eng2026-04-01🌀 nlin

Interplay between Temperature Oscillations and Melt Pool Dynamics in 3D Manufacturing Techniques

This paper presents a physically consistent analytical model that couples temperature oscillations with melt pool dynamics in laser melting, demonstrating that surface oscillations can occur without keyhole effects and providing closed-form formulas for real-time monitoring and industrial laser system design.

Stepan L. Lomaev, Georgii A. Gordeev, Marat A. Timirgazin, Dinara R. Fattalova, Mikhail D. Krivilyov2026-04-01🔬 physics.app-ph

The Closure Challenge: a benchmark task for machine learning in turbulence modelling

This paper introduces "The Closure Challenge," a standardized open-source benchmark comprising curated datasets, evaluation code, and specific test cases designed to establish a common metric for evaluating and advancing machine learning models in Reynolds-averaged Navier-Stokes turbulence modeling.

Ryley McConkey, Tyler Buchanan, Tess Smidt, Abigail Bodner, Richard Dwight, Paola Cinnella2026-04-01🔬 physics

A bounded-interval multiwavelet formulation with conservative finite-volume transport for one-dimensional Buckley--Leverett waterflooding

This paper presents a hybrid numerical method that combines a conservative finite-volume scheme with a bounded-interval multiwavelet basis to accurately solve the one-dimensional Buckley-Leverett equation for waterflooding, successfully capturing shock dynamics while providing a hierarchical multiresolution representation validated against benchmark data.

Christian Tantardini2026-04-01🔢 math

Data-informed lifting line theory

This paper presents a data-driven framework that enhances classical lifting-line theory by using a specialized neural network to incorporate high-fidelity panel method data, thereby accurately predicting aerodynamic loads in complex regimes like low aspect ratios and high sweep while maintaining the computational efficiency required for early-stage aircraft design.

Arjun Sharma, Jonas A. Actor, Peter A. Bosler2026-04-01🔬 physics

Piezoelectric tiles for passive flow rate monitoring across a surface

This paper introduces a non-invasive method for monitoring turbulent fluid flow rates in pipes using piezoelectric tiles to measure vibration-induced pressure fluctuations, demonstrating its ability to resolve specific velocity differences in water and air while suggesting potential applications for external flow navigation and enhanced noise suppression through sensor arrays.

S. Hales Swift, Ihab F. El-Kady2026-04-01🔬 physics.app-ph

Lie Generator Networks for Nonlinear Partial Differential Equations

This paper introduces Lie Generator Network-Koopman (LGN-KM), a neural operator that lifts nonlinear partial differential equation dynamics into a stable, interpretable linear latent space by learning a decomposed continuous-time generator, thereby enabling accurate long-horizon prediction, spectral analysis, and physics-informed transfer on complex systems like Navier-Stokes turbulence without physics supervision.

Shafayeth Jamil, Rehan Kapadia2026-04-01🔬 physics

LGFNet: Local-Global Fusion Network with Fidelity Gap Delta Learning for Multi-Source Aerodynamics

This paper proposes LGFNet, a novel deep learning framework that combines a spatial perception layer with self-attention mechanisms and a fidelity gap delta learning strategy to effectively fuse multi-source aerodynamic data, thereby achieving state-of-the-art accuracy in capturing both high-resolution local flow structures and global aerodynamic trends.

Qinye Zhu, Yu Xiang, Jun Zhang, Wenyong Wang2026-04-01🔬 physics

Nonlinear hydrodynamic response of a quantum Hall system

This paper argues that while the quantum Hall effect typically exhibits a linear current-voltage relationship due to Galilean invariance, spatially inhomogeneous electric fields can induce a nonlinear response in quantum Hall liquids through centrifugal forces and vorticity-induced density gradients within a hydrodynamic framework.

Hiroki Isobe2026-04-01🔬 cond-mat.mes-hall

A framework for diagnosing inertial lift generation in wall-bounded flows: application to eccentric rotating cylinders in Newtonian and shear-thinning fluids

This paper presents a generalized reciprocal theorem-based framework that decomposes steady inertial lift into vortex-force and viscous stress contributions, successfully diagnosing and explaining lift reversal mechanisms in eccentric rotating cylinder flows caused by increased eccentricity in Newtonian fluids and enhanced shear-thinning in non-Newtonian fluids.

Masafumi Hayashi, Kazuyasu Sugiyama2026-04-01🔬 physics

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