Flow Visualization Overview

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Flow VisualizationOverview

• Line Integral Convolution

Szigyarto Tamas Peter, Saint-Petersburg State
University, Faculty of Applied Mathematics
Control Processes Department of Computer Modeling
and Multiple Processors Systems
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Agenda

• Introduction
• Mathematical Model
• Classification of visualization approach
• LIC technique
• Conclusion

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Introduction

• Application
• Automotive industry
• Aerodynamics
• Turbo machinery design
• Weather simulation
• Medical visualization
• Climate modeling

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Mathematical Model

• Basic definitions
• Particle-Tracing
• Numerical model

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Vector fields and Integral curves

• Time-dependent vector field
• Integral curves
• The collection of all possible integral curves
  for a vector field constitutes the corresponding
  flow

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Two types of flow fields

• Steady flows
• Unsteady flows

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Streamlines, pathlines and streaklines

• Pathlines
• Streamlines
• Streaklines

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Reconstruction of flow data

• velocity is usually not given in analytic form,
  but requires reconstruction from the discrete
  simulation output
• The output of flow simulation usually represented
  by many sample vectors , which are
  discretely represent the solution of the
  simulation process on large-sized grids
• Reconstruction filter
• we need to get a continuous velocity

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Numerical integration
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Grids

• Grids involved in flow simulation
• cartesian, (b) regular,
• (c) general rectilinear,
• (d) structured or curvilinear,
• (e) unstructured,
• (f) unstructured triangular.

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Classification of visualization approach

• Overview
• Point-based direct flow visualization
• Sparse representation for particle-tracing
  technique
• Dense representation for particle-tracing
  technique
• Feature-based visualization approach

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Overview

• Direct flow visualization common approaches are
  drawing arrows or color coding velocity.
  Intuitive pictures can be provided, especially in
  the case of two dimensions. Solutions of this
  kind allow immediate investigation of the flow
  data.
• Dense, texture-based flow visualization similar
  to direct flow visualization, a texture is
  computed that is used to generate a dense
  representation of the flow. A notion of where the
  flow moves is incorporated through co-related
  texture values along the vector field.
• Geometric flow visualization integration-based
  approaches first integrate the flow data and then
  use geometric objects as a basis for flow
  visualization. Examples include streamlines,
  streaklines, and pathlines.
• Feature-based flow visualization another
  approach makes use of an abstraction and/or
  extraction step which is performed before
  visualization. Special features are extracted
  from the original dataset, such as important
  phenomena or topological information of the flow.

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Example of circular flow at the surface of a ring
direct visualization by the use of arrow glyphs
texture-based by the use of LIC
visualization based on geometric objects, here
streamlines
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Point-Based Direct Flow Visualization

• Traditional techniques
• arrow plots based on glyphs
• direct line segments (the length represent the
  magnitude of the velocity)
• Additional features
• applying arrow-plots to time-dependent flow
  fields
• illumination and shadows
• use complex glyphs with respect to velocity,
  acceleration, curvature, local rotation, shear,
  or convergence

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Examples
Glyph-based 3D flow visualization, combined with
illuminated streamlines
Traditional arrow plot
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Problems

• 3D representation issues
• the position and orientation of an arrow is more
  difficult to understand due to the projection
  onto the 2D image plane
• arrow might occlude other arrows in the
  background
• the problem of clutter
• Solutions
• use of semi-transparency to avoid occlusion
  problems
• highlighting arrows with orientations in a range
  specified by the user, or by selectively seeding
  the arrows to avoid clutter problem

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Feature-based visualization approach

• Basic concept
• seek to compute a more abstract representation
  that already contains the important properties in
  a condensed form and suppresses superfluous
  information
• Examples of the abstract data
• flow topology based on
• critical points
• vortices
• shockwaves
• Methods
• to emphasize special attributes for each type of
  feature, suitable representations must be used
• glyphs or icons can be employed for vortices or
  for critical points
• ellipses or ellipsoids to encode the rotation
  speed and other attributes of vortices

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Examples
Large vortex formed by detatching flow at the
stay vane leading edge
Topology-based visualization
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Sparse Representations for Particle-Tracing
Techniques

• Traditional approach
• compute characteristic curves (streamlines,
  pathlines, streaklines) and draws them as thin
  lines
• streamlets lines generated by particles traced
  for a very short time
• use of geometric objects of finite extent
  perpendicular to the particle trace
• streamribbon
• an area swept out by a deformable line segment
  along a streamline. The strip-like shape of a
  streamribbon displays the rotational behavior of
  a 3D flow.
• streamtubes
• is a thick tube-shaped streamline whose radial
  extent shows the expansion of the flow
• stream polygons

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Examples
Combination of streamlines, streamribbons,
arrows, and color coding for a 3D flow
(courtesy of BMW Group and Martin Schulz).
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Examples
Sparse representation based on the use of
streamlets
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Dense Representations for Particle-Tracing
Techniques

• Dense representation typically built upon
  texture-based techniques among their
• Spot Noise
• Line Integral Convolution (LIC)

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Spot noise

• produces a texture by generating a set of spots
  on the spatial domain (spot is an ellipse or
  another shape that wrapes and distributed over
  domain)
• each spot represents a particle moving over a
  short period of time and results in a streak in
  the direction of the flow at the position of the
  spot
• enhanced spot noise adds the visualization of the
  velocity magnitude and allows for curved spots
• common form

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Examples
A snapshot of the unsteady spot noise algorithm.
Image courtesy of De Leeuw and Van Liere
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Line Integral Convolution (LIC)

• common form
• LIC was one of the first dense, texture-based
  algorithms able to accurately reflect velocity
  fields with high local curvature

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LIC-based hierarchy

• LIC extends directions
• adding flow orientation cues
• (2) showing local velocity magnitude
• (3) adding support for non-rectilinear grids
• (4) animating the resulting textures such that
  the animation shows the upstream and downstream
  flow direction
• (5) allowing real-time and interactive
  exploration
• (6) extending LIC to 3D
• (7) extending LIC to unsteady vector fields

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Curvilinear and unsteady LIC

• Basic challenges for original LIC
• LIC portrays a vector field with uniform velocity
  magnitude
• LIC operates over a steady flows
• LIC uses only a Cartesian grids
• Solutions (by Forsell and Cohen)
• curvilinear LIC introduces technique for
  displaying vector magnitude
• use streaklines instead streamlines, so the LIC
  can trace a path that incorporates multiple time
  steps

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Fast LIC (by Stalling and Hege)

• Fast LIC comparison with original technique
• Fast LIC approximately one order magnitude faster
  than original LIC
• Key parts of the fast LIC
• fast LIC minimizes the computation of redundant
  streamlines present in the original method
• fast LIC exploits similar convolution integrals
  along a single streamline and thus reuses parts
  of the convolution computation from neighboring
  streamline texels

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Fast LIC modifications

• Parallel fast LIC computes animation sequences
  on a massively parallel distributed memory
  computer.
• Fast LIC on the surfaces The approach by
  Forssell and Cohen was limited to surfaces
  represented by curvilinear grids. The proposed
  method works by tessellating a given surface
  representation with triangles.
• Volume LIC introduces the use of halos in order
  to enhance depth perception such that the user
  has a better chance at perceiving the 3D space
  covered in the visualization
• Enhanced fast LIC and LIC with normal Hege and
  Stalling experiment with higher order filter
  kernels in order to enhance the quality of the
  resulting LIC textures. Scheuermann address this
  missing orthogonal vector field component by
  extending fast LIC to incorporate a normal
  component into the visualization.

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Fast LIC example
A result from the volume LIC method. Image
courtesy of Interrante and Grosch
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Dynamic LIC

• DLIC Sundquist presents an extension to fast LIC
  in order to visualize time-dependent
  electromagnetic fields
• Assumption the motion of the field is not
  necessarily along the direction of the field
  itself in the case of electromagnetic fields
• Result proposed algorithm handles the case of
  when the vector field and the direction of the
  motion of the field lines are independent

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Directional problems with LIC

• Dye injection Shen address the problem of
  directional cues in LIC by incorporating
  animation and introducing dye advection into the
  computation. The simulation of dye may be used to
  highlight features of the flow. But, modelling of
  dye transport is not always physically correct
  since dye is dispersed not only by advection, but
  also by diffusion.
• Oriented LIC address the problem of direction of
  flow in still images. By orientation, means the
  upstream and downstream directions of the flow,
  not visible in the original LIC implementation.
  Conceptually, the OLIC algorithm makes use of a
  sparse texture consisting of many separated spots
  that are smeared in the direction of the local
  vector field through integration.
• Fast Rendering OLIC A fast version of OLIC is
  achieved by Wegenkittl and Groller via a
  trade-off of accuracy for time.

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Dye injection examples

• Dye injection is used to highlight areas of the
  flow
• in combination on the boundary,
• in combination with a low-contrast LIC texture.
• The data set is a slice through an intake port
  and combustion chamber from CFD

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Unsteady Flow LIC

• UFLIC Shen and Kao extend the original LIC
  algorithm to handle unsteady flows
• Idea introduce a new convolution filter that
  better models the nature of unsteady flow
• Why? According to Shen and Kao, Forssell and
  Cohens approach (ULIC) has multiple limitations
  including
• lack of clarity with respect to spatial coherence
• deriving current flow values from future flow
  values
• unclear exposition with respect to temporal
  coherence
• lack of accurate time stepping
• All of these problems are addressed by
  UFLIC!!!

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UFLIC in action
Results from A Texture-Based Framework for
Spacetime-Coherent Visualization of
Time-Dependent Vector Fields, by D. Weiskopf, G.
Erlabacher, and T. Ertl.
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3D LIC

• Rezk-Salama propose rendering methods to
  effectively display the results of 3D LIC
  computations. They utilize texture-based volume
  rendering in an effort to provide exploration of
  3D LIC textures at interactive frame rates
• Proposed approach
• use of transfer functions
• allow user to see through portions of the LIC
  textures deemed uninteresting by the user
• use of clipping planes

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3D LIC examples
An LIC visualization showing a simulation of flow
around a wheel. The appropriate choice of
transfer function results in a sparser noise
texture. Image courtesy of Rezk-Salama.
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Spot Noise vs. LIC

• Spot noise is capable of reflecting velocity
  magnitude within the amount of smearing in the
  texture, thus freeing up hue for the
  visualization of another attribute such as
  pressure, temperature etc.
• LIC is more suited for the visualization of
  critical points which is a key element in
  conveying the flow topology. The vector
  magnitudes are normalized thus retaining lower
  spatial frequency texture in areas of low
  velocity magnitude

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Spot Noise vs. LIC (visual comparison)
Visualization of flow past a box using (left)
spot noise and (right) LIC.
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References

• 1 The State of the Art in Flow Visualization
  Dense and Texture-Based Techniques, Robert S.
  Laramee, Helwig Hauser, Helmut Doleisch, Benjamin
  Vrolijk, Frits H. Post, and Daniel Weiskopf,
  http//www.vrvis.at/ar3/pr2/star/
• 2 Flow Visualization Overview, Daniel Weiskopf
  and Gordon Erlebacher
• 3 Scientific Visualization of Large-Scale
  Unsteady Fluid Flows, David A. Lane
• 4 Analysis and Visualization of Features in
  Turbomachinery Fluid Flow, Turbomachinery CFD
  Flow Visualization, http//www.cg.inf.ethz.ch/eba
  uer/turbo/

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• Thanx for your attention!!!