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Issue No.01 - January/February (2012 vol.32)
pp: 22-33
Jishang Wei , University of California, Davis
Hongfeng Yu , Sandia National Laboratories
Ray W. Grout , National Renewable Energy Laboratory
Jacqueline H. Chen , Sandia National Laboratories
Kwan-Liu Ma , University of California, Davis
ABSTRACT
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.
INDEX TERMS
visualization, simulation, modeling, machine learning, artificial intelligence, computer graphics, graphics and multimedia
CITATION
Jishang Wei, Hongfeng Yu, Ray W. Grout, Jacqueline H. Chen, Kwan-Liu Ma, "Visual Analysis of Particle Behaviors to Understand Combustion Simulations", IEEE Computer Graphics and Applications, vol.32, no. 1, pp. 22-33, January/February 2012, doi:10.1109/MCG.2011.108
REFERENCES
1. X. Zhu, Semi-supervised Learning Literature Survey, tech. report 1530, Computer Sciences, Univ. Wisconsin-Madison, 2005.
2. J. Wei et al., "Dual Space Analysis of Turbulent Combustion Particle Data," Proc. 2011 IEEE Pacific Visualization Symp. (PacificVis 11), IEEE Press, 2011, pp. 91–98.
3. A.P. Dempster, N.M. Laird, and D.B. Rubin, "Maximum Likelihood from Incomplete Data via the EM Algorithm," J. Royal Statistical Soc., Series B, vol. 39, no. 1, 1977, pp. 1–38.
4. N. Peters, "Laminar Diffusion Flamelet Models in Non-premixed Turbulent Combustion," Progress in Energy and Combustion Science, vol. 10, 1984, pp. 319–339.
5. J. Wei et al., "Parallel Clustering for Visualizing Large Scientific Line Data," Proc. IEEE Symp. Large Data Analysis and Visualization (LDAV 11), IEEE Press, 2011, pp. 68–76.
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