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The JHU Turbulence Database Cluster Visualization with Matlab
Introduction Easy and fast accessibility to the database Main problem: request time limits number of points to fetch What has been done so far: 2D vector map 2D vector map with close-ups 3D iso-vorticity surfaces 5/18/11 2
Caching requests To prevent repetitive requests Cache files locally stored Fast development on custom applications Preloading data in cache 5/18/11 3
Built-in SOAP functions Very time consuming due to DOM approach Bottleneck: constructing XML request and parsing XML response Solution: fast rewritten SOAP functions Results: large time gain, more points to be requested 5/18/11 4
Proof of concept #1 Requested points: 64x64 = 4096 Requested area: 256x256 5/18/11 5
Proof of concept #2 Requested points: 32x32x32 = 32768 Requested area: 32x32x32 5/18/11 6
Example: 2D vector map 256x256 = 65536 points on area of 1024x1024 points Arbitrary time step High resolution in contours, low resolution in quiver Calculation time: ~1 min 5/18/11 7
5/18/11 8
Example: 2D vector map close-ups Four steps, zooming in to center of previous: 128x128 on 1024x1024 area 128x128 on 256x256 area 64x64 on 64x64 area 16x16 on 16x16 area Emphasizes large and small-scale structures Calculation time: ~ 1 min 5/18/11 9
5/18/11 10
Example: 3D isovorticity 32x32x32 = 32768 points in same volume Show vorticity structures in flow Cubic interpolation: smoother result, better readable Calculation time: ~15 sec 5/18/11 11
5/18/11 12
Future Q-value and Lambda-2 for vorticity Statistics Suggestions and ideas? 5/18/11 13

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Presentation TDG

  • 1. The JHU Turbulence Database Cluster Visualization with Matlab
  • 2. Introduction Easy and fast accessibility to the database Main problem: request time limits number of points to fetch What has been done so far: 2D vector map 2D vector map with close-ups 3D iso-vorticity surfaces 5/18/11 2
  • 3. Caching requests To prevent repetitive requests Cache files locally stored Fast development on custom applications Preloading data in cache 5/18/11 3
  • 4. Built-in SOAP functions Very time consuming due to DOM approach Bottleneck: constructing XML request and parsing XML response Solution: fast rewritten SOAP functions Results: large time gain, more points to be requested 5/18/11 4
  • 5. Proof of concept #1 Requested points: 64x64 = 4096 Requested area: 256x256 5/18/11 5
  • 6. Proof of concept #2 Requested points: 32x32x32 = 32768 Requested area: 32x32x32 5/18/11 6
  • 7. Example: 2D vector map 256x256 = 65536 points on area of 1024x1024 points Arbitrary time step High resolution in contours, low resolution in quiver Calculation time: ~1 min 5/18/11 7
  • 9. Example: 2D vector map close-ups Four steps, zooming in to center of previous: 128x128 on 1024x1024 area 128x128 on 256x256 area 64x64 on 64x64 area 16x16 on 16x16 area Emphasizes large and small-scale structures Calculation time: ~ 1 min 5/18/11 9
  • 11. Example: 3D isovorticity 32x32x32 = 32768 points in same volume Show vorticity structures in flow Cubic interpolation: smoother result, better readable Calculation time: ~15 sec 5/18/11 11
  • 13. Future Q-value and Lambda-2 for vorticity Statistics Suggestions and ideas? 5/18/11 13