Open-source BOS tomography dataset of high-speed flow over a flight body

TL;DR

This paper introduces an open-source high-speed flow dataset over a flight body using background-oriented schlieren imaging, along with neural-implicit reconstruction and data assimilation techniques for flow analysis.

Contribution

It provides a novel open-source dataset and demonstrates new neural-implicit and data assimilation methods for 3D flow reconstruction from schlieren data.

Findings

Limited views can resolve sharp shocks accurately.

Data assimilation recovers unmeasured flow fields.

NIRT enables efficient uncertainty quantification.

Abstract

We present an open-source background-oriented schlieren dataset with 70 views of high-speed flow over a flight body. Sample analyses are performed using a neural-implicit reconstruction technique (NIRT) with total variation regularization as well as data assimilation via the 3D compressible Euler equations. Limited-data reconstructions based on nine views resolve sharp shocks that are consistent with the geometry, reproduce validation deflections with high fidelity, and exhibit minimal artifacts. Data assimilation recovers unmeasured fields, marking the first demonstration of 3D state estimation directly from experimental schlieren measurements. The NIRT also enables efficient uncertainty quantification, providing insight into well-resolved flow features and guiding design-of-experiments efforts. Public access to the data and code repositories is detailed at the end of this correspondence.