Reconstructing bubble profiles from gas-liquid two-phase flow data using agglomerative hierarchical clustering method

基于气液两相流数据凝聚层次聚类的气泡形态重构（英文）

The knowledge of bubble profiles in gas-liquid two-phase flows is crucial for analyzing the kinetic processes such as heat and mass transfer, and this knowledge is contained in field data obtained by surface-resolved computational fluid dynamics (CFD) simulations. To obtain this information, an efficient bubble profile reconstruction method based on an improved agglomerative hierarchical clustering (AHC) algorithm is proposed in this paper. The reconstruction method is featured by the implementations of a binary space division preprocessing, which aims to reduce the computational complexity, an adaptive linkage criterion, which guarantees the applicability of the AHC algorithm when dealing with datasets involving either non-uniform or distorted grids, and a stepwise execution strategy, which enables the separation of attached bubbles. To illustrate and verify this method, it was applied to dealing with 3 datasets, 2 of them with pre-specified spherical bubbles and the other obtained by a surface-resolved CFD simulation. Application results indicate that the proposed method is effective even when the data include some non-uniform and distortion.

气泡形态对气液两相流中的传热、传质等动力学过程的影响重大,基于计算流体力学(CFD)的流动模拟结果中包含有此类信息。为了提取该信息,提出一种改进的凝聚层次聚类算法,并据此构建了气泡形态重构方法。该方法具有三个重要特征:1)聚类阈值自适应调整,适用于离散尺度不均一的两相流数据;2)空间软分割,降低了算法的计算复杂度;3)纯气相单元、混合相单元分步聚类的执行策略,可以准确识别和重构识毗邻的气泡/气泡群。基于两组预定义的两相流数据和一组基于CFD 的两相流模拟数据,展示了所提出方法的实现过程并对相关功能进行了测试。测试结果表明,提出的方法易于实现,需要人为设置的参数少,即使在离散网格尺度不均一、部分网格单元有一定扭曲的条件下,仍可高效地识别气液和重构两相流中的气体的聚集形态(气泡/气泡群)。