Neural refractive index field: Unlocking the potential of background-oriented schlieren tomography in volumetric flow visualization

Background-oriented schlieren tomography is a prevalent method for visualizing intricate turbulent flows, appreciated for its ease of implementation and ability to capture three-dimensional distributions of a multitude of flow parameters. However, the voxel-based meshing scheme leads to significant challenges, such as inadequate spatial resolution, substantial discretization errors, poor noise immunity, and excessive computational costs. This study presents an innovative reconstruction approach termed neural refractive index field (NeRIF), which implicitly represents the flow field using a neural network trained with specialized strategies. Numerical simulations and experimental results on turbulent Bunsen flames demonstrate that this approach can substantially improve the reconstruction accuracy and spatial resolution while concurrently reducing computational expenses. Although showcased in the context of background-oriented schlieren tomography here, the key idea embedded in the NeRIF can be readily adapted to various other tomographic modalities including tomographic absorption spectroscopy and tomographic particle imaging velocimetry, broadening its potential impact across different domains of flow visualization and analysis.