Transcript Visualisation of complex flows using texture-based techniques D. J. Warne1,2, J. Young1, N.

Visualisation of complex flows using
texture-based techniques
D. J. Warne1,2, J. Young1, N. A. Kelson1
1High
Performance Computing and Research Support, QUT
2School
of Electrical Engineering and Computer Science, QUT
CRICOS No. 00213J
Queensland University of Technology
Overview
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Background
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Vector Field Visualisation
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Traditional Techniques
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Problems for Complex Flows
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Advantages of Texture-Based Techniques
Texture-Based Algorithms
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Line Integral Convolution
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Image Based Flow Visualisation
Implementation and Application
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Visualisation Effectiveness
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Implementation Complexity
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Computational Aspects
Conclusions
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Vector Field Visualisation
Vectors are everywhere!
“A picture says a thousand words.”
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Traditional Techniques
We are all familiar with these:
• Arrow/Quiver plots.
• Streamlines/Pathlines.
• Iso-surfaces.
[1]
[2]
[1] http://www.mathworks.com.au/help/matlab/ref/quiver.html
[2] http://www.mathworks.com.au/help/matlab/visualize/visualizing-vector-volume-data.html#f5-7374
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Problems for Complex Flows
• Visual Clutter
• Choice of seed points
[3]
[4]
• Difficult to interpret time-dependent flows
[3] http://rgm2.lab.nig.ac.jp/RGM2/func.php?rd_id=CircSpatial:PlotVectors
[4] J. Ma et. Al. (2011) . Streamline Selection and Viewpoint Selection via Information Channel. IEEE VisWeek
Poster 2011, Providence, RI, Oct 2011.
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Texture-Based Techniques
• Warp an image by the underlying field
• Advantages
• Global/local flow regimes visible
• No issues with seed points
• Easily extend to capture time dependent features
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Line-Integral Convolution (LIC)
• Applies a convolution along streamlines.
• The final image at point p is the result of a
convolution of the kernel k(x) with noise along
the streamline s(x,p,t) = p at x = t.
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Line-Integral Convolution (LIC)
[4] B. Cabral, and C. Leedom (1993). Imaging vector fields using line integral convolution. SIGGRAPH 93, pp. 263-270.
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Image Based Flow Visualisation (IBFV)
• Basic extension of LIC.
• Here, I(x,t) is now a noise image modulated in
time.
• We convolve over a pathline P(x,p,t) rather than
streamline.
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Image Based Flow Visualisation (IBFV)
[5] A. Telea (2008). Data Visualization: Principles and practice. Wellesley, MA : A K Peters, Ltd, 2008.
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Case Study: Variable-density flow
through porous media
• Aquifer 600m x 200m fully saturated with fresh
water.
• Sitting on top, a region of more dense salt water.
• Salt water sinks into the aquifer.
• Causes complex up-welling and down-welling flows.
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Traditional Quiver Plot
Animation
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Line Integral Convolution Image
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Image Base Flow Visualisation
Animation
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Visualisation Effectiveness (LIC)
LIC
•Strengths
•Dense Coverage.
•Spatial Correlation.
•Clearly identifies extrema.
•Weaknesses
•No indicators of direction.
•No indicators of magnitude.
•Only applicable for steady-state
flows.
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Visualisation Effectiveness (IBFV)
IBFV
•Strengths
•Dense Coverage.
•Spatial/Temporal Correlation.
•Clearly identifies extrema.
•Identifies motion of extrema.
•Strong visual cues for flow direction
and magnitude.
•Weaknesses
•Requires animation.
•Care is needed to correctly set
texture speeds.
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Implementation Comparison
LIC Algorithm
IBFV Algorithm
1. For each pixel
1. Warp mesh by field
2. Render with previous
texture
3. Overlay next noise
texture and blend
4. Copy buffer.
1.1 Compute forward
streamline.
1.2 Compute backward
streamline.
1.3 Sum pixel intensities
1.4 Divide by the length
1.5 Assign result to output pixel.
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Extensions to IBFV
• Easily extends to advection of multiple textures
• Scalar data overlays. movie
• Dye injects (particle traces, similar to streaklines).
movie
• Jittered Grid (similar to quiver plot overlay). movie
• Timelines. movie
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Computational Aspects
• CPU based LIC can be expensive.
• Need to implement interpolation.
• Streamline tracing for every pixel.
• IBFV naturally implemented on GPU
• Hardware handles interpolation
• Convolution is written in terms of blending functions
• Only mesh nodes need be intergrated
• LIC  IBFV with I(x,t) = I(x)
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Future Work
• Improve accessibility to researchers.
• Integrate into popular tools such as MATLAB.
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Thank you!
Questions?
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