Product Information
Description
An annual survey of the physics.flu-dyn arXiv category, covering research published from September 2025 – August 2026. Built on a close-reading set of 132 papers and organised into 37 chapters, it maps the year by the field's research sub-directions.
Book framework (share of the year's output):
- Experimental and Numerical Fluid Dynamics Fundamentals (51.4%) — This cluster covers foundational studies on viscous, compressible, and multiphase flows, including experimental setups, heat transfer, and basic flow evolution in channels and walls.
- Turbulence Modeling and High-Reynolds-Number Simulations (21.2%) — Focuses on direct numerical simulations, large-eddy simulation techniques, and the investigation of turbulent transitions, instabilities, and Reynolds-number scaling effects.
- Machine Learning and Data-Driven Flow Prediction (11.1%) — Explores the application of neural networks, reduced-order models, and diffusion models for predicting flow fields, drag reduction, and active control in complex fluid systems.
- Multiphase Transport and Porous Media Dynamics (4.6%) — Addresses particle transport, interface dynamics, and flow control in porous media, granular fluids, and multiphase systems with strong coupling effects.
- Physics-Informed Neural Networks and Differential Equation Solvers (4.3%) — Highlights the use of physics-informed machine learning and novel numerical solvers for solving Navier-Stokes equations and related differential problems in various domains.
- Aerodynamic Design and Vortex Structure Optimization (2.8%) — Investigates vortex formation, wake structures, and aerodynamic performance optimization for wind turbines, wings, and other geometrically complex bodies.
- Oceanic and Internal Gravity Wave Dynamics (2.4%) — Studies surface and internal gravity waves, wave turbulence, and their interactions with water bodies, ice, and submerged vegetation in oceanic and shallow water contexts.
- Flow Measurement and Reduced-Order Reconstruction Techniques (1.4%) — Focuses on data assimilation, super-resolution reconstruction, and dimensionality reduction methods for capturing and modeling flow features from sparse or measured data.
- Theoretical Energy Dissipation and Kinetic Theory (0.5%) — Examines theoretical frameworks for energy distribution, dissipation rates, and kinetic theory applications in weakly compressible and multiphase turbulent flows.
- Large-Eddy Simulation Closure and Subgrid Modeling (0.3%) — Develops and evaluates subgrid-scale models and data-driven closures for large-eddy simulations, focusing on realizability and generalization across flow regimes.
- Surface Roughness Effects on Lift and Drag Mechanisms (0.0%) — A niche study identifying how asymmetric surface roughness influences lift generation and drag invariance on spherical bodies.
Each sub-direction is covered across three chapters — how it framed its questions, the strength of its key findings, and its open debates and frontiers — so the reader gets a traceable picture of what this part of the field actually produced over the year.
