1 / 16

Knowledge-Assisted Visualization of Turbulent Combustion Simulations

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

hayes-padilla
Download Presentation

Knowledge-Assisted Visualization of Turbulent Combustion Simulations

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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, HO2, 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 HO2 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 = 0.02 Visualization result d = 0.05

  8. d = 0.10 Visualization result d = 0.20

  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 original compressed (~20x)

  16. Compression result original compressed (~20x)

More Related