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1 IEEE Visualization 2006 Vortex Visualization for Practical Engineering Applications IEEE Visualization 2006 M. Jankun-Kelly, M. Jiang, D. S. Thompson,

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Presentation on theme: "1 IEEE Visualization 2006 Vortex Visualization for Practical Engineering Applications IEEE Visualization 2006 M. Jankun-Kelly, M. Jiang, D. S. Thompson,"— Presentation transcript:

1 1 IEEE Visualization 2006 Vortex Visualization for Practical Engineering Applications IEEE Visualization 2006 M. Jankun-Kelly, M. Jiang, D. S. Thompson, R. Machiraju We thank NSF and DOD for funding our research

2 2 Overview Goal: Feature Based Vortex Visualization Goal: Feature Based Vortex Visualization Challenge: Practical Engineering Data Challenge: Practical Engineering Data Existing Techniques Existing Techniques Our Method Our Method Results & Conclusions Results & Conclusions Ongoing Work Ongoing Work

3 3 Feature Based Vortex Visualization Vortex: a swirling flow feature Vortex: a swirling flow feature Characterization: high level feature description Characterization: high level feature description Vortex visualization schematic: wing (green),vortex core line with sense of rotation (twisted ribbon), vortex extent & local tangential velocity (shaded surface)

4 4 Practical Engineering Data large large unstructured mesh unstructured mesh low level low level noisy noisy complex vortical flows complex vortical flows resolution resolution spinning missile with dithering canards [Blades & Marcum 2004] serrated wing [Hammons 2006]

5 5 Existing Techniques swirl parameter isosurfacing core line segments [Sujudi & Haimes 1995] streamlines Feature Based Feature Based –Stegmeier et al –Garth, Laramee, Tricoche et al –Tricoche et al Line Based Line Based –Sujudi & Haimes method (line segments) 1995 –streamlines from critical points –Banks & Singer method 1995 –Jiangs combinatorial method 2002 –Sahner/Weinkauf/Hege λ 2 and scalar field method 2005 Region Based Region Based –Vorticity magnitude –Swirl parameter [Berdahl & Thompson 1993] –λ 2 [Jeong & Hussain 1995]

6 6 Overview of Our Method 1. Vortex detection 2. Topology Identification 3. Core line extraction 4. Extent computation Characteristics are found in stages 3,4. Characteristics are found in stages 3,

7 7 Stage 1: Vortex Detection 1. Vortex detection 2. Topology Identification 3. Core line extraction 4. Extent computation Characteristics are found in stages 3,4. Characteristics are found in stages 3,

8 8 Vortex Detection Local Extrema Method (LEM) Vortex core candidate cells Scalar field whose extrema coincide with vortex core lines Detection of line-type local extrema

9 9 Vortex Detection: Aggregation low level data (candidate cells) high level data (aggregates) Aggregation moves the level of abstraction from mesh data towards feature data.

10 10 Stage 2: Topology Identification 1. Vortex detection 2. Topology Identification 3. Core line extraction 4. Extent computation Characteristics are found in stages 3,4. Characteristics are found in stages 3,

11 11 Topology Identification N vortices per aggregate, branching 1 vortex per aggregate, no branching (feature level data) Aggregates are split into non-branching pieces with a k-means clustering algorithm.

12 12 Stage 3: Core Line Extraction 1. Vortex detection 2. Topology Identification 3. Core line extraction 4. Extent computation Characteristics are found in stages 3,4. Characteristics are found in stages 3,

13 13 Core Line Extraction One core line is extracted from each aggregate with prediction / correction. The correction step locates the extreme value at the core line in the swirl plane.

14 14 Correction Step: Function Fitting Goal: locate extreme value in the swirl plane Goal: locate extreme value in the swirl plane 2D conical fitting function, one extreme value expected 2D conical fitting function, one extreme value expected Best fit: minimal standard deviation of fit error (red high, blue low) Best fit: minimal standard deviation of fit error (red high, blue low) Locate vortex core line with subcell resolution Locate vortex core line with subcell resolution known function sample point local extremum (not a data point) predicted local extremum

15 15 Stage 4: Extent Computation 1. Vortex detection 2. Topology Identification 3. Core line extraction 4. Extent computation Characteristics are found in stages 3,4. Characteristics are found in stages 3,

16 16 Vortex Extent vortex core lines vortex extent surfaces extent is the surface of maximum tangential velocity Dacles-Mariani 1995

17 17 Feature Based Visualization: Serrated Wing visualization goal schematic Extent: purple surface Extent: purple surface Core lines: ribbons Core lines: ribbons Rotation sense: ribbon twist Rotation sense: ribbon twist Circulation: ribbon color Circulation: ribbon color visualization result

18 18 Feature Based Visualization: Spinning Missile

19 19 Feature Based Visualization: Spinning Missile Movie

20 20 Timing on Sun UltraSPARC III Dataset Mesh Size (nodes) Feature Count Feature Extraction Time Serrated wing 900, ,00025 < 2 min Spinning missile 9 million 1, min Bronchial tube 12 million min Helicopter rotor 10.2 million min (12 min on Apple XServe G5)

21 21 Conclusions Vortex core lines resolved through novel function fitting technique Vortex core lines resolved through novel function fitting technique Individual vortices identified with novel k-means technique Individual vortices identified with novel k-means technique These techniques work on practical data: large, noisy, unstructured, not ideally sampled These techniques work on practical data: large, noisy, unstructured, not ideally sampled Feature based visualization of interesting, complex vortex behavior made possible Feature based visualization of interesting, complex vortex behavior made possible

22 22 Ongoing Work Improve Core Line Quality Improve Core Line Quality –reduce swirl vector field noise –improve local extremum detection –repair C 0 discontinuities Improve Extent Quality Improve Extent Quality –local repair of outliers –better extent model

23 23 Questions? Monika Jankun-Kelly


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