Robert S. Laramee 1 Visualization Lecture Flow visualization, An Introduction

Robert S. Laramee 2 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

Robert S. Laramee 3 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

Robert S. Laramee 4 Flow Visualization Introduction, Overview

Robert S. Laramee 5 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)

Robert S. Laramee 6 What is Flow Visualization? Challenge: to effectively visualize both magnitude + direction often simultaneously magnitude only direction only

Robert S. Laramee 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

Robert S. Laramee 8 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)

Robert S. Laramee 9 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

Robert S. Laramee 10 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

Robert S. Laramee 11 Comparison with Reality Experiment Simulation

Robert S. Laramee 12 Data Origin – Examples 1/2

Robert S. Laramee 13 Data Origin – Examples 2/2

Robert S. Laramee 14 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

Robert S. Laramee D 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

Robert S. Laramee D/Surfaces/3D – Examples 2D Surface 3D

Robert S. Laramee 17 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

Robert S. Laramee 18 Time-independent (steady) Data  Dataset sizes over time:

Robert S. Laramee 19 Time-independent (steady) Data

Robert S. Laramee 20 Time-dependent (unsteady) Data  Dataset sizes over time:

Robert S. Laramee 21 Time-dependent (unsteady) Data

Robert S. Laramee 22 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

Robert S. Laramee 23 Direct vs. Geometric – Example

Robert S. Laramee 24 Experimental Flow Visualization Optical Methods, etc.

Robert S. Laramee 25 With Smoke or Dye  Injection of dye, smoke, particles  Optical methods:  streaks, shadows

Robert S. Laramee 26 Example: Car, Aerodynamics  Ferrari model, five-hole probe (no back-flows)

Robert S. Laramee 27 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. – 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

Robert S. Laramee 28 Example: Wing-Tip Vortex 1/7 Problem: Air behind planes spirals

Robert S. Laramee 29 Wing-Tip Vortex 2/7 Vortices can be dangerous

Robert S. Laramee 30 Wing-Tip Vortex 3/7 Hence: planes need to keep a distance

Robert S. Laramee 31 Wing-Tip Vortex 4/7 Tests in wind channel:

Robert S. Laramee 32 Wing-Tip Vortex 5/7 Then: visualization

Robert S. Laramee 33 Wing-Tip Vortex 6/7

Robert S. Laramee 34 Wing-Tip Vortex 7/7

Robert S. Laramee 35 Flow Visualization Demos

Robert S. Laramee 36 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