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incompressible flow (incompressible flows)

Incompressible flow is fluid motion in which the material density of any fluid parcel remains constant over time. While all physical fluids exhibit some degree of compressibility, many practical scenarios involving liquids and low-speed gases are accurately modeled as incompressible because their density variations are negligible. In computational physics and scientific computing, incompressible flow is governed by the incompressible Navier-Stokes equations and characterized by a divergence-free velocity field that represents mass conservation. Simulating these flows computationally requires specialized numerical techniques, such as projection methods and staggered grid arrangements, to resolve the strong coupling between velocity and pressure while strictly enforcing the volume-preserving constraint across the simulation domain.

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AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier-Stokes Solutions

AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier-Stokes Solutions

Florent Bonnet, Jocelyn Ahmed Mazari, Paola Cinnella, Patrick Gallinari

Why you should read this

Presents a high-fidelity dataset of Reynolds-Averaged Navier–Stokes simulations over 2D airfoils alongside specialized surface force evaluation metrics and deep learning benchmarks to standardize surrogate modeling for aerodynamic design.

Surrogate models are necessary to optimize meaningful quantities in physical dynamics as their recursive numerical resolutions are often prohibitively expensive. It is mainly the case for fluid dynamics and the resolution of Navier–Stokes equations. However, despite the fast-growing field of data-driven models for physical systems, reference datasets representing real-world phenomena are lacking. In this work, we develop AIRFRANS, a dataset for studying the two-dimensional incompressible steady-state Reynolds-Averaged Navier–Stokes equations over airfoils at a subsonic regime and for different angles of attacks. We also introduce metrics on the stress forces at the surface of geometries and visualization of boundary layers to assess the capabilities of models to accurately predict the meaningful information of the problem. Finally, we propose deep learning baselines on four machine learning tasks to study AIRFRANS under different constraints for generalization considerations: big and scarce data regime, Reynolds number, and angle of attack extrapolation.

Added

2026-09-26