1. Robert S. Laramee r.s.laramee@swansea.ac.uk 1 http://cs.swan.ac.uk/~csbob/teaching/ Visualization Lecture Flow visualization, An Introduction

2. Robert S. Laramee r.s.laramee@swansea.ac.uk 2 http://cs.swan.ac.uk/~csbob/teaching/ Review: Previous Lecture Shear-Warp Factorization  The shear-warp factorization algorithm  A comparison of direct volume visualization algorithms: splatting, ray casting, 3D texture mapping, shear-warp factorization

3. Robert S. Laramee r.s.laramee@swansea.ac.uk 3 http://cs.swan.ac.uk/~csbob/teaching/ Overview: Current Lecture  Flow Visualization, Part 1:  introduction, overview  flow data  simulation vs. measuring vs. modelling  2D vs. surfaces vs. 3D  steady vs time-dependent  experimental FlowVis

4. Robert S. Laramee r.s.laramee@swansea.ac.uk 4 http://cs.swan.ac.uk/~csbob/teaching/ Flow Visualization Introduction, Overview

5. Robert S. Laramee r.s.laramee@swansea.ac.uk 5 http://cs.swan.ac.uk/~csbob/teaching/ What is Flow Visualization?  A classic topic within scientific visualization  The depiction of vector quantities (as opposed to scalar quantities)  Applications include automotive simulation, aerodynamics, turbomachinery, meteorology, oceanography, medical visualization Challenges: ● to effectively visualize both magnitude + direction, often simultaneously ● large data sets ● time-dependent data ● What should be visualized? (data filtering/feature extraction)

6. Robert S. Laramee r.s.laramee@swansea.ac.uk 6 http://cs.swan.ac.uk/~csbob/teaching/ What is Flow Visualization? Challenge: to effectively visualize both magnitude + direction often simultaneously magnitude only direction only

7. Robert S. Laramee r.s.laramee@swansea.ac.uk 7 http://cs.swan.ac.uk/~csbob/teaching/ 1. direct: overview of vector field, minimal computation, e.g. glyphs, color mapping 2. texture-based: covers domain with a convolved texture, e.g., Spot Noise, LIC, ISA, IBFV(S) 3. geometric: a discrete object(s) whose geometry reflects flow characteristics, e.g. streamlines 4. feature-based: both automatic and interactive feature-based techniques, e.g. flow topology Flow Visualization Classification

8. Robert S. Laramee r.s.laramee@swansea.ac.uk 8 http://cs.swan.ac.uk/~csbob/teaching/ Flow Data Data origins:  flow simulation:  airplane- / ship- / car-design  weather simulation (air-, sea-flows)  medicine (blood flows, etc.)  flow measurement:  wind tunnels, water channels  optical measurement techniques  flow models:  differential equation systems (dynamic systems)

9. Robert S. Laramee r.s.laramee@swansea.ac.uk 9 http://cs.swan.ac.uk/~csbob/teaching/ Flow Data Simulation:  flow: set of samples (n dims. of data), e.g., given on a curvi-linear grid  most important primitive: tetrahedron (cell) Measurement:  vectors: taken from instruments, often computed on a regular grid Modelling:  flow: analytic formula, evaluated where ever needed

10. Robert S. Laramee r.s.laramee@swansea.ac.uk 10 http://cs.swan.ac.uk/~csbob/teaching/ Notes on Computational Fluid Dynamics  We often visualize Computational Fluid Dynamics (CFD) simulation data  CFD is the discipline of predicting flow behavior, quantitatively  data is (often) the result of a simulation of flow through or around an object of interest ● some characteristics of CFD data: ● large, often gigabytes ● unsteady, time-dependent ● unstructured, adaptive resolution grids ● smooth

11. Robert S. Laramee r.s.laramee@swansea.ac.uk 11 http://cs.swan.ac.uk/~csbob/teaching/ Comparison with Reality Experiment Simulation

12. Robert S. Laramee r.s.laramee@swansea.ac.uk 12 http://cs.swan.ac.uk/~csbob/teaching/ Data Origin – Examples 1/2

13. Robert S. Laramee r.s.laramee@swansea.ac.uk 13 http://cs.swan.ac.uk/~csbob/teaching/ Data Origin – Examples 2/2

14. Robert S. Laramee r.s.laramee@swansea.ac.uk 14 http://cs.swan.ac.uk/~csbob/teaching/ Simulation vs. Measurement vs. Modelling Simulation:  fit (non-regular) grid to flow domain  FEM (finite element method), CFD (computational fluid dynamics) Measurement:  optical methods + image recognition, e.g.: PIV (particle image velocimetry) Models:  differential equation systems dx/dt

15. Robert S. Laramee r.s.laramee@swansea.ac.uk 15 http://cs.swan.ac.uk/~csbob/teaching/ 2D vs. 2.5D Surfaces vs. 3D 2D flow visualization  2D  2D flows  models, flow layers (2D section through 3D) Visualization of flows on surfaces (2.5D)  3D flows around obstacles  boundary flows on surfaces (locally 2D) 3D flow visualization  3D  3D flows  simulations, 3D models

16. Robert S. Laramee r.s.laramee@swansea.ac.uk 16 http://cs.swan.ac.uk/~csbob/teaching/ 2D/Surfaces/3D – Examples 2D Surface 3D

17. Robert S. Laramee r.s.laramee@swansea.ac.uk 17 http://cs.swan.ac.uk/~csbob/teaching/ Steady vs. Time-dependent Steady (time-independent) flows:  flow itself constant over time  v(x), e.g., laminar flows  simpler case for visualization Time-dependent (unsteady) flows:  flow itself changes over time  v(x,t), e.g., turbulent flow  more complex case

18. Robert S. Laramee r.s.laramee@swansea.ac.uk 18 http://cs.swan.ac.uk/~csbob/teaching/ Time-independent (steady) Data  Dataset sizes over time:

19. Robert S. Laramee r.s.laramee@swansea.ac.uk 19 http://cs.swan.ac.uk/~csbob/teaching/ Time-independent (steady) Data

20. Robert S. Laramee r.s.laramee@swansea.ac.uk 20 http://cs.swan.ac.uk/~csbob/teaching/ Time-dependent (unsteady) Data  Dataset sizes over time:

21. Robert S. Laramee r.s.laramee@swansea.ac.uk 21 http://cs.swan.ac.uk/~csbob/teaching/ Time-dependent (unsteady) Data

22. Robert S. Laramee r.s.laramee@swansea.ac.uk 22 http://cs.swan.ac.uk/~csbob/teaching/ Direct vs. Geometric FlowVis Direct flow visualization:  overview of current state of flow  visualization with vectors popular  arrows, icons, glyph techniques Geometric flow visualization:  use of intermediate objects, e.g., after vector field integration over time  visualization of development over time  streamlines, stream surfaces  analogous to indirect (vs. direct) volume visualization

23. Robert S. Laramee r.s.laramee@swansea.ac.uk 23 http://cs.swan.ac.uk/~csbob/teaching/ Direct vs. Geometric – Example

24. Robert S. Laramee r.s.laramee@swansea.ac.uk 24 http://cs.swan.ac.uk/~csbob/teaching/ Experimental Flow Visualization Optical Methods, etc.

25. Robert S. Laramee r.s.laramee@swansea.ac.uk 25 http://cs.swan.ac.uk/~csbob/teaching/ With Smoke or Dye  Injection of dye, smoke, particles  Optical methods:  streaks, shadows

26. Robert S. Laramee r.s.laramee@swansea.ac.uk 26 http://cs.swan.ac.uk/~csbob/teaching/ Example: Car, Aerodynamics  Ferrari model, five-hole probe (no back-flows)

27. Robert S. Laramee r.s.laramee@swansea.ac.uk 27 http://cs.swan.ac.uk/~csbob/teaching/ PIV: Particle Image Velocimetry Terminology Particle Image Velocimetry (PIV) -an optical method used to measure velocities and related properties in fluids. The fluid is seeded with particles which, for the purposes of PIV, are generally assumed to faithfully follow the flow dynamics. It is the motion of these seeding particles that is used to calculate velocity information. – www.wikipedia.com Laser lighting & correlation analysis:  real flow, e.g., in wind tunnel  injection of particles (as uniformly as possible)  at interesting location: illuminate with two laser sheet pulses (rapidly after each other)  take pictures (high-speed camera), then correlation analysis of particle traces  reconstruct/derive vectors, normally only 2D vectors

28. Robert S. Laramee r.s.laramee@swansea.ac.uk 28 http://cs.swan.ac.uk/~csbob/teaching/ Example: Wing-Tip Vortex 1/7 Problem: Air behind planes spirals

29. Robert S. Laramee r.s.laramee@swansea.ac.uk 29 http://cs.swan.ac.uk/~csbob/teaching/ Wing-Tip Vortex 2/7 Vortices can be dangerous

30. Robert S. Laramee r.s.laramee@swansea.ac.uk 30 http://cs.swan.ac.uk/~csbob/teaching/ Wing-Tip Vortex 3/7 Hence: planes need to keep a distance

31. Robert S. Laramee r.s.laramee@swansea.ac.uk 31 http://cs.swan.ac.uk/~csbob/teaching/ Wing-Tip Vortex 4/7 Tests in wind channel:

32. Robert S. Laramee r.s.laramee@swansea.ac.uk 32 http://cs.swan.ac.uk/~csbob/teaching/ Wing-Tip Vortex 5/7 Then: visualization

33. Robert S. Laramee r.s.laramee@swansea.ac.uk 33 http://cs.swan.ac.uk/~csbob/teaching/ Wing-Tip Vortex 6/7

34. Robert S. Laramee r.s.laramee@swansea.ac.uk 34 http://cs.swan.ac.uk/~csbob/teaching/ Wing-Tip Vortex 7/7

35. Robert S. Laramee r.s.laramee@swansea.ac.uk 35 http://cs.swan.ac.uk/~csbob/teaching/ Flow Visualization Demos

36. Robert S. Laramee r.s.laramee@swansea.ac.uk 36 http://cs.swan.ac.uk/~csbob/teaching/ Acknowledgements For more information please see:  Data Visualization: Principles and Practice, Chapter 6-Vector Visualization by A. Telea, AK Peters Ltd., 2008  Interactive Data Visualization: Foundations, Techniques, and Applications Chapter 5- Visualization Techniques for Spatial Data, by M. Ward et al., AK Peters, Ltd., 2010 For material used in this lecture:  Hans-Georg Pagendarm  Roger Crawfis  Lloyd Treinish  David Kenwright  Terry Hewitt  Helwig Hauser