MegaFlow2D: A Parametric Dataset for Machine Learning Super-resolution in Computational Fluid Dynamics Simulations

Abstract

This paper introduces MegaFlow2D, a dataset of over 2 million snapshots of parameterized 2D fluid dynamics simulations of 3000 different external flow and internal flow configurations. It's worth noting that, simulation results on both low and high mesh resolutions are provided to facilitate the training of machine learning (ML) models for super-resolution purposes. This is the first large-scale multi-fidelity fluid dynamics dataset ever provided. We build the entire data generation and simulation workflow on open-source and efficient interfaces that can be utilized for a variety of data samples according to the user's specific needs. Finally, we provide a use case to demonstrate the potential value of the MegaFlow2D dataset in applications related to error correction.