General & Applied Physics · Physics & Astronomy
Fluid Dynamics
The newest papers in Fluid Dynamics that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
101 papers, from 7 September 2026 to 5 November 2026: 101 arXiv preprints.
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Newest papers, page 3
Noise Steering: Exploiting Wake Steering and Acoustic Directivity for Noise-Constrained Wind Farm Control via Reinforcement Learning
Existing noise mitigation strategies for wind farms usually consist in reducing turbines power output, without accounting for the wake interactions between them and the noise emission directivity.Local Metrics for Exact Streaming on Curvilinear Lattices: A Curvilinear D3Q27 Cumulant Lattice-Boltzmann Method
A three-dimensional curvilinear lattice-Boltzmann method is introduced for simulations on smoothly deformed grids while retaining exact one-to-one streaming on a uniform computational lattice.DSMC Study on Unsteadiness of Supersonic Flow Over a Cylinder
Unsteady flow over a 30 $μ$m diameter cylinder at Mach 4 is investigated using Direct Simulation Monte Carlo across Reynolds numbers from 270 to 6750, spanning slip-flow to near-continuum regimes.Effects of Deep Learning Architecture and Pretraining on Automated Vocal Fold Segmentation in Ex Vivo High-Speed Phonation Images
Two-dimensional (2D) intraglottal particle image velocimetry (PIV) of ex vivo phonation experiments quantifies vocal fold medial surface dynamics and the intraglottal flow field.Predicting Sparse Observables in Parameterized Unsteady Flows Using Coarse-Mesh Graph Neural Networks
Full-order surrogates for high-dimensional nonlinear flows are often unnecessary when applications require only selected degrees of freedom.Generative AI in Turbulence: Foundations, Applications, Validations and Benchmarks
Generative AI offers new approaches to turbulence problems in which incomplete observations or unresolved d.o.f. admit multiple physically plausible solutions.A Graph-informed Kriging model for sample-efficient Bayesian optimization of wind farm layouts
Wind farm layout optimization becomes increasingly difficult as turbine count grows, especially when high-fidelity aerodynamic models are used.Quantum hydrodynamics from Nambu's unpublished fluid notes
We develop a quantum hydrodynamic theory based on Nambu's unpublished note \emph{A New Look at Fluid Dynamics}.CFL-aware edge design for graph neural solvers of hyperbolic transport
Hyperbolic partial differential equations (PDEs) transport information at finite speed along characteristics, often producing shocks and discontinuities that are challenging to capture numerically.Neural Eigenfluid Bases: Spectral Objectives, Gauge, and Topology
Reduced-order models simplify fluid simulation by representing complex flows with a small number of spatial modes.Solving and Learning Navier-Stokes Dynamics with the Neural Basis Method
Physics-informed machine learning has become a major direction in predictive modeling of fluid dynamics, combining governing physics with learned representations.CoPiT: A conditional Position-induced Transformer for aerodynamic field prediction on unstructured meshes
High-fidelity Reynolds-averaged Navier--Stokes (RANS) simulations remain a standard tool for aerodynamic analysis, yet each solve on an unstructured mesh is computationally expensive, making…Let's Do Turbulence Right: In Space and Time!
The three-dimensional Navier-Stokes equations govern fluid turbulence, a chaotic and non-linear physical phenomenon characterized by complex eddies interacting across vast spatial and temporal scales.Physics Informed Neural Network for Multi-dimensional Turbulent Self-gravitating Flows
Molecular clouds exhibit complex, multi-scale dynamics driven by the interplay of turbulence, self-gravity, and gas pressure.Ideal interaction dynamics of defects and background fields in condensed matter
While defect dynamics in condensed matter systems is widely studied and several methods are available to track the defect position over time, the formulation of dynamical models for the coupled…Flow Mesh: A Velocity-Regularized Flow Matching Framework for Stable Distributional Modeling of Physical Fields on Unstructured Meshes
Thermal-fluid field distributions underpin the analysis and operation of energy infrastructure, from nuclear-reactor cooling channels to industrial heat exchangers, but resolving these distributions…Neural Operator Splitting for Fluid Dynamics in Complex Geometry
Solving dynamical systems in complex and diverse geometries remains challenging for both numerical solvers and learning-based surrogate models.GeoDiv KAN Solver for Time Fractional Navier Stokes Equations on Complex Domains to Realistic CFD Reconstruction
Scientific machine learning provides a promising framework for solving complex problems in science and engineering.How the elastic axis folds the transonic flutter boundary: shock energy transfer and conformally calibrated active learning
Isogai's case A, a pitch-plunge aerofoil whose elastic axis lies half a chord ahead of the leading edge, has a folded transonic flutter boundary in inviscid flow: near the bottom of the transonic…Probing wetting and solidification dynamics of neon thin films with a high-$T_c$ superconducting circuit
Integrating condensed noble gases with superconducting circuits offers new opportunities for operating hybrid quantum devices.
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