HiLiftAeroML: High-Fidelity Computational Fluid Dynamics Dataset for High-Lift Aircraft Aerodynamics

TL;DR

This paper introduces the first open-source high-fidelity CFD dataset for high-lift aircraft, enabling AI surrogate model development with detailed simulations using GPU-accelerated LES methods.

Contribution

It provides a comprehensive, high-accuracy CFD dataset of a high-lift aircraft geometry, using advanced GPU-accelerated LES simulations, to support AI surrogate modeling research.

Findings

1800 samples from 180 geometry variants and 10 angles of attack.

Use of GPU-accelerated high-fidelity LES with solution-adapted grids.

Dataset includes geometries, flow variables, and forces, all openly available.

Abstract

This paper describes the first-ever open-source high-fidelity CFD dataset of a high-lift aircraft for the purpose of AI surrogate model development. The dataset is composed of 1800 samples, arising from 180 geometry variants and 10 angles of attack for the high-lift NASA Common Research Model (CRM) geometry, used within the AIAA High-Lift Prediction Workshop series. One of the novelties of this dataset is the use of a GPU-accelerated high-fidelity explicit, wall-modeled LES approach for each simulation, using solution-adapted grids between 300M and 500M cells. This ensures the greatest possible accuracy given known challenges in steady-state RANS approaches for these portions of the flight envelope. The entire dataset (geometries, time-averaged volume and surface variables and integral forces) are available, free of charge with a permissive open-source license (CC-BY-4.0). By making this data publicly available, we aim to accelerate the research and development of AI surrogate modeling within the aerospace industry.