1. Knowledge-Assisted Visualization of Turbulent Combustion Simulations Chaoli Wang, Hongfeng Yu, Kwan-Liu Ma

2. Turbulent combustion simulations Direct numerical simulations Time-varying, multivariate data –800 * 686 * 217, 450MB –53 time steps –4 variables: mixfrac, chi, HO 2, and OH –93GB in total

3. 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 HO 2 variables

4. 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

5. Our solution

6. Transfer function specification

7. d = 0.02d = 0.05 Visualization result

8. d = 0.10d = 0.20 Visualization result

9. Video demos

10. 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

11. Thank you!

12. Extra slides

13. 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

14. 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

15. Compression result originalcompressed (~20x)

16. Compression result originalcompressed (~20x)