KnowledgeAssisted Visualization of Turbulent Combustion Simulations

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Knowledge-Assisted Visualization of Turbulent
Combustion Simulations

• Chaoli Wang, Hongfeng Yu, Kwan-Liu Ma

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Turbulent combustion simulations

• Direct numerical simulations
• Time-varying, multivariate data
• 800 686 217, 450MB
• 53 time steps
• 4 variables mixfrac, chi, HO2, and OH
• 93GB in total

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Visualization-specific task

• Scientific interests
• Observe variable relationships close to the
  mixfrac surface
• Bring out more the lower values of other variables

The mixed rendering of the mixfrac (isovalue
0.2) and the HO2 variables
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Challenges

• Data ranges of other variables close to the
  surface are unknown
• Only value-based transfer function may bring out
  undesired visualization contents
• Lack of control over the amount of information
  shown around the surface

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Our solution
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Transfer function specification
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d 0.02
Visualization result
d 0.05
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d 0.10
Visualization result
d 0.20
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Video demos
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Summary

• Knowledge-assisted visualization
• Domain knowledge isovalue, ranges of interests
• Derived knowledge distance volume and partial
  histogram
• Importance-driven visualization
• Future work
• Time-varying, multivariate data compression
• Utilize domain knowledge
• Visualization-specific compression

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Thank you!
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Extra slides
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Visualization-specific compression

• Regions of interest are around the given surface
• Data precision can thus vary according to the
  distance to the surface
• Our solution
• Non-uniform quantization
• Space-time coherence utilization
• Decompression on the fly using graphics hardware

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Compression result

• Compression ratio 20x
• Each voxel only uses less than 2 bits per
  variable on the average
• Advantages
• Reduce data transfer among disk to main memory,
  and main memory to video memory
• Fast offline compression and online decompression
• Preserve fine details near the surface and
  maintain the overall image quality

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Compression result
original
compressed (20x)
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Compression result
original
compressed (20x)