Dual space analysis of turbulent combustion particle data

Jishang Wei, Hongfeng Yu, R. Grout, Jacqueline H. Chen, K. Ma
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引用次数: 19

Abstract

Current simulations of turbulent flames are instrumented with particles to capture the dynamic behavior of combustion in next-generation engines. Categorizing the set of many millions of particles, each of which is featured with a history of its movement positions and changing thermo-chemical states, helps understand the turbulence mechanism. We introduce a dual-space method to analyze such data, starting by clustering the time series curves in the phase space of the data, and then visualizing the corresponding trajectories of each cluster in the physical space. To cluster time series curves, we adopt a model-based clustering technique in a two-stage scheme. In the first stage, the characteristics of shape and relative position are particularly concerned in classifying the time series curves, and in the second stage, within each group of curves, clustering is further conducted based on how the curves change over time. In our work, we perform the model-based clustering in a semi-supervised manner. Users' domain knowledge is integrated through intuitive interaction tools to steer the clustering process. Our dual-space method has been used to analyze particle data in combustion simulations and can also be applied to other scientific simulations involving particle trajectory analysis work.
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紊流燃烧颗粒数据的双空间分析
目前紊流火焰的模拟是用粒子来捕捉下一代发动机燃烧的动态行为。对数以百万计的粒子进行分类有助于理解湍流机制,每个粒子都有其运动位置和热化学状态变化的历史。我们引入一种双空间方法来分析这些数据,首先在数据的相空间中对时间序列曲线进行聚类,然后在物理空间中可视化每个聚类的相应轨迹。为了对时间序列曲线进行聚类,我们采用了基于模型的两阶段聚类技术。第一阶段主要关注时间序列曲线的形状特征和相对位置特征进行分类,第二阶段在每组曲线内,根据曲线随时间的变化情况进行聚类。在我们的工作中,我们以半监督的方式执行基于模型的聚类。通过直观的交互工具整合用户的领域知识,引导聚类过程。我们的双空间方法已经用于分析燃烧模拟中的颗粒数据,也可以应用于其他涉及颗粒轨迹分析工作的科学模拟。
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Copyright page An advanced network visualization system for financial crime detection Static correlation visualization for large time-varying volume data Keynote address: Why everyone seems to be using spring embedders for network visualization, and should not Dual space analysis of turbulent combustion particle data
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