识别和跟踪模拟中的气泡和液滴：用于获取大小，谱系，分裂和合并统计数据的工具箱
Identifying and tracking bubbles and drops in simulations: a toolbox for obtaining sizes, lineages, and breakup and coalescence statistics
摘要：
两相流中气泡和液滴尺寸分布的知识对于表征包括燃烧室点火、声纳通信和云形成在内的各种现象是重要的。驱动背景流的物理机制也驱动这些分布的时间演化。准确和强大的识别和跟踪算法的分散相是必要的，可靠地测量这种演变，从而量化的基本机制，在界面解析流模拟。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。这种传统的算法可能对虚假结构的存在敏感。提出了一种具有成本效益的改进方法，以最大限度地提高体积精度，同时最大限度地减少虚假气泡和液滴的识别。准确的识别方案对于区分大尺寸比的气泡和液滴对至关重要。需要及时跟踪所识别的气泡和液滴，以获得表征尺寸分布的演变的破裂和聚结统计，包括破裂和聚结频率，以及父气泡和子气泡和液滴尺寸的概率分布。提出了一种基于质量守恒的算法，利用不一定来自连续时间步的模拟快照来构建气泡和液滴谱系。然后，这些谱系用于检测分裂和合并事件，并获得所需的统计数据。大尺寸比气泡和液滴对的准确识别使得能够在大尺寸范围内准确检测破裂和聚结事件。总之，这些算法能够洞察气泡和液滴形成和演变背后的机制，在具有实际意义的流动。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
