Knowledge of bubble and drop size distributions in two-phase flows is important for characterizing a wide range of phenomena, including combustor ignition, sonar communication, and cloud formation. The physical mechanisms driving the background flow also drive the time evolution of these distributions. Accurate and robust identification and tracking algorithms for the dispersed phase are necessary to reliably measure this evolution and thereby quantify the underlying mechanisms in interface-resolving flow simulations. The identification of individual bubbles and drops traditionally relies on an algorithm used to identify connected regions. This traditional algorithm can be sensitive to the presence of spurious structures. A cost-effective refinement is proposed to maximize volume accuracy while minimizing the identification of spurious bubbles and drops. An accurate identification scheme is crucial for distinguishing bubble and drop pairs with large size ratios. The identified bubbles and drops need to be tracked in time to obtain breakup and coalescence statistics that characterize the evolution of the size distribution, including breakup and coalescence frequencies, and the probability distributions of parent and child bubble and drop sizes. An algorithm based on mass conservation is proposed to construct bubble and drop lineages using simulation snapshots that are not necessarily from consecutive time-steps. These lineages are then used to detect breakup and coalescence events, and obtain the desired statistics. Accurate identification of large-size-ratio bubble and drop pairs enables accurate detection of breakup and coalescence events over a large size range. Together, these algorithms enable insights into the mechanisms behind bubble and drop formation and evolution in flows of practical importance.

中文翻译：

---

识别和跟踪模拟中的气泡和液滴：用于获取大小、谱系以及分裂和合并统计数据的工具箱

两相流中气泡和液滴尺寸分布的知识对于表征广泛的现象非常重要，包括燃烧器点火、声纳通信和云形成。驱动背景流的物理机制也驱动这些分布的时间演化。准确和稳健的分散相识别和跟踪算法对于可靠地测量这种演变是必要的，从而量化界面解析流动模拟中的潜在机制。单个气泡和液滴的识别传统上依赖于用于识别连接区域的算法。这种传统算法可能对虚假结构的存在很敏感。提出了一种具有成本效益的改进，以最大限度地提高体积精度，同时最大限度地减少对虚假气泡和液滴的识别。准确的识别方案对于区分具有大尺寸比的气泡和液滴对至关重要。需要及时跟踪识别出的气泡和液滴，以获得表征尺寸分布演变的破裂和合并统计数据，包括破裂和合并频率，以及父子气泡和液滴大小的概率分布。提出了一种基于质量守恒的算法，使用不一定来自连续时间步长的模拟快照来构建气泡和液滴谱系。然后使用这些谱系来检测分裂和合并事件，并获得所需的统计数据。准确识别大尺寸比例的气泡和液滴对能够准确检测大尺寸范围内的破裂和聚结事件。总之，这些算法可以深入了解具有实际重要性的流动中气泡和液滴的形成和演化背后的机制。