Simulations of turbulent flames have used particles to capture the dynamic behavior of combustion in next-generation engines. Each particle includes a history of its movement positions and changing thermochemical states. Analyzing such a set of many millions of particles helps scientists understand turbulence. A dual-space method enables effective visual analysis of both the spatial movement and attribute evolution of particles. A cluster-label-classify strategy categorizes particles' attribute evolution curves. Intuitive tools integrate users' domain knowledge to steer the classification. The dual-space method has been used to analyze particle data in combustion simulations and can be applied to other scientific simulations involving particle-data analysis. This video shows an expository movie that combustion scientists have used when discussing their simulation results with colleagues. This simulation employs visual analysis in both the physical space and phase space, with categorization driven by supervised learning.
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