1 ieee visualization 2006 vortex visualization for practical engineering applications ieee...
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Vortex Visualization for Practical Engineering Applications
IEEE Visualization 2006IEEE Visualization 2006 M. Jankun-Kelly, M. Jiang,M. Jankun-Kelly, M. Jiang,
D. S. Thompson, R. MachirajuD. S. Thompson, R. Machiraju
We thank NSF and DOD for funding our research
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OverviewOverview
Goal: Feature Based Vortex Goal: Feature Based Vortex VisualizationVisualization
Challenge: Practical Engineering Challenge: Practical Engineering DataData
Existing TechniquesExisting Techniques Our MethodOur Method Results & ConclusionsResults & Conclusions Ongoing WorkOngoing Work
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Feature Based Vortex Feature Based Vortex VisualizationVisualization
Vortex: a swirling flow featureVortex: a swirling flow feature Characterization: high level feature Characterization: high level feature
descriptiondescription
Vortex visualization schematic: wing (green),vortex core line with sense of rotation (twisted ribbon), vortex extent & local tangential velocity (shaded surface)
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Practical Engineering DataPractical Engineering Data
largelarge unstructured meshunstructured mesh low levellow level noisynoisy complex vortical flowscomplex vortical flows resolutionresolution
spinning missile with dithering canards
[Blades & Marcum 2004]
serrated wing [Hammons 2006]
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Existing TechniquesExisting Techniques
swirl parameter isosurfacing
core line segments [Sujudi & Haimes 1995]
streamlines
Feature BasedFeature Based– Stegmeier et al. 2005Stegmeier et al. 2005– Garth, Laramee, Tricoche et al. 2005Garth, Laramee, Tricoche et al. 2005– Tricoche et al. 2005Tricoche et al. 2005
Line BasedLine Based– Sujudi & Haimes method Sujudi & Haimes method
(line segments) 1995(line segments) 1995– streamlines from critical pointsstreamlines from critical points– Banks & Singer method 1995Banks & Singer method 1995– Jiang’s combinatorial method Jiang’s combinatorial method
20022002– Sahner/Weinkauf/Hege Sahner/Weinkauf/Hege λλ22 and and
scalar field method 2005scalar field method 2005
Region BasedRegion Based– Vorticity magnitudeVorticity magnitude– Swirl parameter Swirl parameter
[Berdahl & Thompson 1993] [Berdahl & Thompson 1993]
– λλ22 [Jeong & Hussain 1995] [Jeong & Hussain 1995]
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Overview of Our MethodOverview of Our Method
1.1. Vortex detectionVortex detection
2.2. Topology IdentificationTopology Identification
3.3. Core line extractionCore line extraction
4.4. Extent computationExtent computation
Characteristics are Characteristics are found in stages 3,4.found in stages 3,4.
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Stage 1: Vortex DetectionStage 1: Vortex Detection
1.1. Vortex detectionVortex detection2.2. Topology IdentificationTopology Identification
3.3. Core line extractionCore line extraction
4.4. Extent computationExtent computation
Characteristics are found Characteristics are found in stages 3,4. in stages 3,4.
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Vortex DetectionVortex DetectionLocal Extrema Method (LEM)Local Extrema Method (LEM)
Vortex core candidate cells
Scalar field whose extrema coincide with vortex core lines
Detection of line-type local extrema
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Vortex Detection: Vortex Detection: AggregationAggregation
low level data
(candidate cells)
high level data
(aggregates)
Aggregation moves the level of abstraction from mesh data towards feature data.
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Stage 2: Topology Stage 2: Topology IdentificationIdentification
1.1. Vortex detectionVortex detection
2.2. TopologyTopology IdentificationIdentification
3.3. Core line extractionCore line extraction
4.4. Extent computationExtent computation
Characteristics are found Characteristics are found in stages 3,4. in stages 3,4.
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3
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Topology IdentificationTopology 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.
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Stage 3: Core Line Stage 3: Core Line ExtractionExtraction
1.1. Vortex detectionVortex detection
2.2. Topology IdentificationTopology Identification
3.3. Core line extractionCore line extraction4.4. Extent computationExtent computation
Characteristics are found Characteristics are found in stages 3,4. in stages 3,4.
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Core Line ExtractionCore 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.
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Correction Step: Function Correction Step: Function FittingFitting
Goal: locate extreme value Goal: locate extreme value in the swirl planein the swirl plane
2D conical fitting function, 2D conical fitting function, one extreme value one extreme value expectedexpected
Best fit: minimal standard Best fit: minimal standard deviation of fit error deviation of fit error (red high, blue low)(red high, blue low)
Locate vortex core line with Locate vortex core line with subcell resolutionsubcell resolution
known function sample point
local extremum (not a data point)
predicted local extremum
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Stage 4: Extent Stage 4: Extent ComputationComputation
1.1. Vortex detectionVortex detection
2.2. Topology IdentificationTopology Identification
3.3. Core line extractionCore line extraction
4.4. Extent computationExtent computation
Characteristics are found Characteristics are found in stages 3,4. in stages 3,4.
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Vortex ExtentVortex Extent
vortex core lines
vortex extent surfaces
extent is the surface of maximum tangential velocity
Dacles-Mariani 1995
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Feature Based Visualization: Feature Based Visualization: Serrated WingSerrated Wing
visualization goal schematic
Extent: purple surfaceExtent: purple surface Core lines: ribbonsCore lines: ribbons Rotation sense: ribbon twistRotation sense: ribbon twist Circulation: ribbon colorCirculation: ribbon color
visualization result
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Feature Based Visualization: Feature Based Visualization: Spinning MissileSpinning Missile
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Feature Based Visualization: Feature Based Visualization: Spinning Missile MovieSpinning Missile Movie
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Timing on Sun UltraSPARC Timing on Sun UltraSPARC IIIIII
DatasetDataset Mesh Size Mesh Size (nodes)(nodes)
Feature Feature CountCount
Feature Feature Extraction Extraction
TimeTime
Serrated wingSerrated wing 900,000900,000 2525 < 2 min< 2 min
Spinning Spinning missilemissile
9 million9 million 1,800+1,800+ 33 min33 min
Bronchial tubeBronchial tube 12 million12 million 800+800+ 11 min11 min
Helicopter Helicopter rotorrotor
10.2 10.2 millionmillion
172172 22 min22 min
(12 min on Apple XServe G5)
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ConclusionsConclusions
Vortex core lines resolved through novel Vortex core lines resolved through novel function fitting techniquefunction fitting technique
Individual vortices identified with novel k-Individual vortices identified with novel k-means techniquemeans technique
These techniques work on practical data: These techniques work on practical data: large, noisy, unstructured, not ideally large, noisy, unstructured, not ideally sampledsampled
Feature based visualization of interesting, Feature based visualization of interesting, complex vortex behavior made possiblecomplex vortex behavior made possible
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Ongoing WorkOngoing Work
Improve Core Line QualityImprove Core Line Quality– reduce swirl vector field noisereduce swirl vector field noise– improve local extremum detectionimprove local extremum detection– repair Crepair C00 discontinuities discontinuities
Improve Extent QualityImprove Extent Quality– local repair of outlierslocal repair of outliers– better extent modelbetter extent model
