This Article 
 Bibliographic References 
 Add to: 
Visual Analysis of Particle Behaviors to Understand Combustion Simulations
January/February 2012 (vol. 32 no. 1)
pp. 22-33
Jishang Wei, Univ. of California, Davis, CA, USA
Kwan-Liu Ma, Univ. of California, Davis, CA, USA
Web Extra: View Supplemental Material
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.

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.
1. Z. Fang et al., "Visualization and Exploration of Time-Varying Medical Image Data Sets," Proc. Graphics Interface 2007, ACM Press, 2007, pp. 281–288.
2. J.J. van Wijk and E.R. van Selow, "Cluster and Calendar Based Visualization of Time Series Data," Proc. 1999 IEEE Symp. Information Visualization (InfoVis 99), IEEE CS Press, 1999, pp. 4–9.
3. H. Hochheiser and B. Shneiderman, "Interactive Exploration of Time Series Data," Proc. 4th Int. Conf. Discovery Science, Springer, 2001, pp. 441–446.
4. H. Akiba and K.-L. Ma, "A Tri-space Visualization Interface for Analyzing Time-Varying Multivariate Volume Data," Proc. Eurographics/IEEE VGTC Symp. Visualization, Eurographics Assoc., 2007, pp. 115–122.
5. Z. Konyha et al., "Interactive Visual Analysis of Families of Function Graphs," IEEE Trans. Visualization and Computer Graphics, vol. 12, no. 6, 2006, pp. 1373–1385.
6. P. Muigg et al., "A Four-Level Focus+Context Approach to Interactive Visual Analysis of Temporal Features in Large Scientific Data," Computer Graphics Forum, vol. 27, no. 3, 2008, pp. 775–782.
7. E. Bertini and D. Lalanne, "Investigating and Reflecting on the Integration of Automatic Data Analysis and Visualization in Knowledge Discovery," SIGKDD Explorations, vol. 11, no. 2, 2009, pp. 9–18.
8. D.A. Keim, F. Mansmann, and J. Thomas, "Visual Analytics: How Much Visualization and How Much Analytics?" SIGKDD Explorations, vol. 11, no. 2, 2009, pp. 5–8.
9. J. Woodring and H.-W. Shen, "Semi-automatic Time-Series Transfer Functions via Temporal Clustering and Sequencing," Computer Graphics Forum, vol. 28, no. 3, 2009, pp. 791–798.
10. T.-Y. Lee and H.-W. Shen, "Visualization and Exploration of Temporal Trend Relationships in Multivariate Time-Varying Data," IEEE Trans. Visualization and Computer Graphics, vol. 15, no. 6, 2009, pp. 1359–1366.
11. T. Schreck et al., "Visual Cluster Analysis of Trajectory Data with Interactive Kohonen Maps," Information Visualization, vol. 8, no. 1, 2009, pp. 14–29.
12. W. Aigner et al., "Visual Methods for Analyzing Time-Oriented Data," IEEE Trans. Visualization and Computer Graphics, vol. 14, no. 1, 2008, pp. 47–60.
13. T.W. Liao, "Clustering of Time Series Data—a Survey," Pattern Recognition, vol. 38, no. 11, 2005, pp. 1857–1874.
14. E. Keogh, "A Decade of Progress in Indexing and Mining Large Time Series Databases," Proc. 32nd Int'l Conf. Very Large Data Bases (VLDB 06), VLDB Endowment, 2006, pp. 1268–1268.
1. S. Gaffney and P. Smyth, "Joint Probabilistic Curve Clustering and Alignment," Advances in Neural Information Processing Systems 17, MIT Press, 2004, pp. 473–480.
1. J.H. Chen et al., "Terascale Direct Numerical Simulations of Turbulent Combustion Using S3D," Computational Science and Discovery, vol. 2, no. 015001, 2009.
2. G.K. Batchelor, An Introduction to Fluid Dynamics, Cambridge Univ. Press, 1967.
3. S.H. Lamb, Hydrodynamics, 6th ed., Cambridge Univ. Press, 1994.
4. P. Yeung, "Lagrangian Investigations of Turbulence," Ann. Rev. Fluid Mechanics, vol. 34, 2002, pp. 115–142.

Index Terms:
Data visualization,Data models,Artificial intelligence,Clustering algorithms,Computer graphics,Analytical models,Computational modeling,graphics and multimedia,visualization,simulation,modeling,machine learning,artificial intelligence,computer graphics
Jishang Wei, Hongfeng Yu, R. W. Grout, J. 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, Jan.-Feb. 2012, doi:10.1109/MCG.2011.108
Usage of this product signifies your acceptance of the Terms of Use.