Measurements in Fluid Mechanics 058:180 (ME:5180) Time & Location: 2:30P - 3:20P MWF 3315 SC Office Hours: 4:00P – 5:00P MWF 223B-5 HL Instructor: Lichuan Gui lichuan-gui@uiowa.edu Phone: 319-384-0594 (Lab), 319-400-5985 (Cell) http://lcgui.net

Lecture 20. Particle image displacement methods and others

Particle image displacement methods - Optical, non- or minimally-intrusive, fluid flow measurement technique; - Instantaneous flow measurements in two-dimensional (2D) area or three-dimensional (3D) volume field of views; - Basic procedure of particle image displacement methods 1. Flow visualization - Flow field seeded with small tracer particles - Particles usually illuminated by a laser light sheet 2. Image recording - Particle images captured by an imaging system - Saved in photographic film or digital image file 3. Image evaluation - Young’s fringes method - Particle image identification - Correlation-based algorithm

Particle image displacement methods Example:

Particle image displacement methods Three groups of methods Particle tracking velocimetry (PTV) - flow seeded with tracer particles of very low concentration - very low image number density in photo or video recordings - single particle can be identified in image recording - particle image tracking possible from frame to frame - low information density in measurement plane Laser speckle velocimetry (LSV) - flow seeded with tracer particles of very high concentration - very high image number density in photo or video recordings - single particle can not be identified in image recording - particle image tracking impossible from frame to frame - high information density in measurement plane Particle image velocimetry (PIV) - flow seeded with tracer particles of high concentration - high image number density in photo or video recordings - single particle can be identified in image recording - particle image tracking impossible from frame to frame - high information density in measurement plane

Particle image displacement methods Single frame image recordings Single exposure - Long exposure time - Velocity determined by trajectory - Direction ambiguity - Low particle number density required Double exposure - Short exposure time - Velocity determined by displacement - Direction ambiguity - Methods to avoid direction ambiguity: a. color/intensity tagging b. Image shifting techniques Multi-exposure - Short exposure time - Velocity determined by displacement - Direction ambiguity - Used to increase particle image number - Limited in steady flow

Particle image displacement methods Multi frame image recordings - velocity determined with particle image displacement between frames - double/Multi exposure used to increase image number in steady flow

Particle image displacement methods Frequently used evaluation methods LID – low image density (PTV) HID – high image density (PIV) LS – laser speckle mode (LSV)

Particle image displacement methods Data reduction Image plane Objective Lens Laser light sheet Scale factor:  = L/L’ Time interval: t Velocity: V=S/t=·S’/ t Laser light sheet Image plane Objective Lens S S’ L L’

Particle image displacement methods Evaluation methods Particle trajectory identification Image recording - single frame - single long time exposure - low image density - film or digital recording Evaluation - read film recordings with a microscope system - identify particle trajectories in digital recording y x

Particle image displacement methods Evaluation methods Young’s fringes method Image recording - positive film - single frame - double/multiple exposed - HID & LS mode laser PC 2D traverse system CCD camera frosted glass Young’s fringes system - SM inversely proportional to SA - fringes perpendicular to particle image displacement

Particle image displacement methods Evaluation methods Particle image tracking PIV recording - Minimum 2 frames - Single exposure - LID mode - Film or digital recording Evaluation - Identify particle images & determine position of each particle image center - Pairing particles in two frames (many algorithms) - Velocity determined by position difference of paired particles & t t1 t2 o x y (x2, y2) (x1, y1)

Particle image displacement methods Evaluation methods Correlation-based interrogation m n (m, n) -S S o Auto- correlation (m’,n’) Cross-correlation

Particle image displacement methods Standard 2D PIV Light sheet t=t0 Lens Measurement volume Laser t=t0 Image #1 Single exposed recording Fluid flow seeded with small tracer particles Double exposed recording Exposure #1 Lens system & Camera

Particle image displacement methods Standard 2D PIV Light sheet t=t0+t Lens Measurement volume Laser Image #1 t=t0 Fluid flow seeded with small tracer particles t=t0+t Image #2 Exposure #2 Exposure #1 Lens system & Camera Single exposed recording Double exposed recording

Particle image displacement methods Micro-scale PIV (MPIV) Nd:YAG Laser Micro Device Flow in Flow out Glass cover CCD Camera (1280x1024 pixels) Beam Expander Epi-fluorescent Prism / Filter Cube Microscope Nd:YAG LASER MICROSCOPE BEAM EXPANDER CCD CAMERA MCROFLUIDIC DEVICE Flood Illumination l=532 nm Focal Plane l = 610 nm Micro-Fluidics Lab Purdue University Micro-PIV image pair

Particle image displacement methods Stereo PIV (SPIV) - 3 velocity components in a plane - Two cameras - Translation systems (lateral displacement) - Rotational systems (angular displacement) Scheimpflug condition

Particle image displacement methods Holographic PIV (HPIV) - 3 velocity components in a 3 dimensional volume - Complex and precise illumination a. Hologram recording b. Hologram reconstruction

Particle image displacement methods Other image-based methods Defocusing PIV (Pereira et al. 2000) Allow images to become defocused Single camera/ color CCD, particle image tracking Multiple-sheet PIV (Raffel et al.,1995 ) Multiple laser light sheet, single camera 3D scanning PIV (Brücker, 1997) Scanning a 3D volume with a laser beam Single high speed camera X-ray & Echo PIV Molecular Tagging Velocimetry Temperature measurement with particle Brownian motion More

Measurement of wind velocity Cup anemometers Propeller anemometers Vane anemometers Sonic anemometers

Homework - Read textbook 11.4-11.5 on page 275 - 284 Questions and Problems: 9 on page 287 - Due on 10/12

Try to write a Matlab program function [Cm Sx Sy]=peaksearch(C,sr) % INPUT PARAMETERS % C - correlation function % sr - search radius % OUTPUT PARAMETERS % Cm - high correlation peak value % (Sx,Sn) - displacement [M N]=size(C); i0=int16(M/2); % high peak search - begin j0=int16(N/2); ix=0; jy=0; Cm=0; for i=i0+1-sr:i0+1+sr for j=j0+1-sr:j0+1+sr max=0; for i1=i-1:i+1 for j1=j-1:j+1 if max<C(i1,j1) max=C(i1,j1); end if C(i,j)>=max & C(i,j)>Cm ix=i; jy=j; Cm=C(i,j); end % high peak search -end % SUB-PIXEL FIT dx=(C(ix+1,jy)-C(ix-1,jy))/(4*C(ix,jy)-2*C(ix+1,jy)-2*C(ix-1,jy)); dy=(C(ix,jy+1)-C(ix,jy-1))/(4*C(ix,jy)-2*C(ix,jy+1)-2*C(ix,jy-1)); %Displacement Sx=dx+double(ix-i0); Sy=dy+double(jy-j0); Main program: clear; A1=imread('A001_1.bmp'); G1=img2xy(A1); M=64; N=64; x=400; y=200; g1=sample01(G1,M,N,x,y); Sx=10.6; Sy=12.3; g2=sample01(G1,M,N,x+Sx,y+Sy); g1=g1-mean(mean(g1)); g2=g2-mean(mean(g2)); c=xcorr2(g1,g2); [cm Sx Sy]=peaksearch(c,20) % C=xy2img(c); % imwrite(C,'C.bmp','bmp'); Results: cm = 1.4730e+06 Sx = 10.7225 Sy = 12.1583 22