用神经网络提升流场重构精度与效率，解决传统方法分辨率低、计算贵问题。
Neural refractive index field: Unlocking the Potential of Background-oriented Schlieren Tomography in Volumetric Flow Visualization
- 用神经网络隐式表示折射率场，替代传统网格划分。
- 数值与实验验证显示重建精度提升，计算成本降低。
- 方法可推广至多种光学流场测量技术，适用面广。
背景导向的阴影层析术（BOST）是一种广泛用于可视化复杂湍流流动的方法，因其易于实现且能捕捉多类流动参数的三维分布而备受青睐。然而，基于体素的网格划分方式导致空间分辨率不足、离散误差大、抗噪能力差及计算开销过高。本文提出一种新型重建方法——神经折射率场（NeRIF），通过神经网络隐式表征流场，并采用定制化训练策略。在湍流本生火焰的数值模拟与实验中均表明，该方法显著提升了重建精度与空间分辨率，同时降低了计算成本。尽管本文聚焦于背景导向阴影层析术，但其核心思想可轻松拓展至其他层析技术，如层析吸收光谱与层析粒子成像测速，具有跨领域的应用潜力。
原文摘要 · Abstract (English)
Background-oriented Schlieren tomography (BOST) is a prevalent method for visualizing intricate turbulent flows, valued for its ease of implementation and capacity 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 work presents an innovative reconstruction approach termed neural refractive index field (NeRIF) which implicitly represents the flow field with a neural network, which is trained with tailored strategies. Both numerical simulations and experimental demonstrations on turbulent Bunsen flames suggest that our approach can significantly 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.
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