Abstract The connected component labeling technique (CCL), which labels regions of connected Eulerian field data, will inaccurately identify closely spaced components when applied to the volume-of-fluid function. We present two modifications to the CCL that improve its robustness and accuracy. This Informed Component Labeling algorithm (ICL) incorporates the normal and uses multilevel thresholding to improve and refine connectivity decisions for components with spacing just larger than the grid size. Through detailed verification and validation using synthetic volume fraction data, we show that the ICL algorithm removes the bias to larger components, provide guidelines for its use, and estimate its error bounds for the smallest components. The ICL produces zero standard deviation in the number of components identified for those with radius larger than twice the grid size and can reduce it by ∼ 38% for smaller components. The modifications that comprise the ICL can be applied to any existing CCL algorithm with a known increase in computational cost. It enables robust identification of connected components for accurate transfer of information in mixed Eulerian-Lagrangian methods and statistical analysis that use the volume-of-fluid function.

中文翻译：

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用流体体积法对连通分量进行鲁棒识别的知情分量标签算法

摘要 用于标记连接欧拉场数据区域的连通分量标记技术 (CCL) 在应用于流体体积函数时将不准确地识别紧密间隔的分量。我们对 CCL 进行了两项修改，以提高其鲁棒性和准确性。这种知情组件标记算法 (ICL) 结合了正态并使用多级阈值来改进和细化间距仅大于网格大小的组件的连接决策。通过使用合成体积分数数据的详细验证和验证，我们表明 ICL 算法消除了对较大组件的偏差，为其使用提供了指导，并估计了最小组件的误差范围。对于半径大于网格尺寸两倍的组件，ICL 在识别的组件数量中产生零标准偏差，对于较小的组件，可以将其减少约 38%。包含 ICL 的修改可以应用于任何现有的 CCL 算法，其中已知计算成本增加。它可以在混合欧拉-拉格朗日方法和使用流体体积函数的统计分析中可靠地识别连接组件，以准确传递信息。