Hologra g ph ra hic Particle hic Particle Image Velocimetry locimetry Michael Barry, Alish ha Schor, Anna Shih May 6, 2009

Transcription

1 Holograph hic Particle Image Ve elocimetry Michael Barry, Alisha Schor, Anna Shih May 6, 2009

2 Motivation Image removed due to copyright restrictions. Please see Fig. 7 in Meng, Hui, et al. "Holographic Particle Image Velocimetry." Measurement Science and Technology 15 (2004): Near field of a laminar jet image from Meng et. al, 2000 Turbulent flow in a square duct Image from Courtesy of Bo Tao. Used with permission.

3 Holography - Basic Principles Recording Reconstruction Image removed due to copyright restrictions. Please see Fig. 1 in Meng, Hui, et al. "Holographic Particle Image Velocimetry." Measurement Science and Technology 15 (2004): d information to 2d representation Reconstruct at discrete z depths image from Meng et. al, 2000

4 Basic Illumin nation Types In-Line in-line reference beam simpler set up small diameter particles Image removed due to copyright restrictions. Please see Fig. 6a,b in Meng, Hui, et al. "Holographic Particle Image Velocimetry." Measurement Science and Technology 15 (2004): Off-Axis obliquely incident reference beam real and virtual images separate during reconstruction higher seeding densities for digital holography: incident id angle < 2 image from Meng et. al, 2000

5 Experimental Desi gn Considerations Recording distance (Zhang 2006) - If large z: low pass filter - If small z: limited FOV, aliasing Particle concentrati ion (Kim, 2008) - Speckle noise vs. flow information recovery CCD: size, pixel siz ze

6 Experimen ntal Setup Camera Flow tank Iris Spatial filter Neutral density filter Diode laser Collimating lens

7 Raw I mage Cylinder Flow Direction

8 Crop initial image to square Reconstruction Code (Convolution Method) Take the Fourier Transform Multiply this by the Fresnel diffraction kernel Reconstructed t image Take the inverse Fourier Transform exp{-iπλz (u 2 +v 2 )} note the dependence on z depth

9 Reconstructed cted Image Out of focus particle In focu In focus particle

10 Reconstruction tion In Depth

11 Particle Detection t 1 t2 t n 1. Image Preprocessing and Reconstr uction: Subtract t backg ground Reconstructionn Threshold Convert to B&W Input: Time series of raw holograms 2. Locate Partic les in x-y: bwlabel (MAT LAB) Bounding box and centroid of cont iguous white zones Output: matrix of labeled particles with x-y position of centers and area for each z-plane 3. Eliminate inval lid particles Outside of expected size Double-counting a single particle Lab el 1 2 n x y are a

12 Particle Detection (cont d) x-y info z 1 z 2 z n 1. Load output of previous step z-plane is dete rmined by step size Input: x-y coordinate matrices at each z-plane 2. Eliminate doubles: Particles may a ppear in focus in multiple planes Choose based on minimum radius Output: matrix of labeled particles with x-y-z position of centers and radius for each time step Label x y z radius 1 2 n t

13 Result: 3D Flow Flow

14 Calculating Velocity Maps HPIV typically uses 3 3D coordinates as data - Computing cost of images is high (96 GB) Tradeoffs depending on complexity of flow, mean velocity - Correlation: compl ex flows, dense particle seeding - Particle tracking: lo ow velocity flows, sparse seeding (reduces speckle noise)

15 Velocity Maps by Correlation Correlation (Pu & Meng, 2000) noise t 1 t 2 Two spatially shifted images (time sequence) t 1 t 2 t 2 t 1 t t 2 t 2 Mean Mean displacement displacement vector vector Shift images according to mean velocity vector Within discrete interrogat ion windows, pair particles based on closest distance, group deformation threshold

16 E(m,t, A) = Velocity Map ps by Particle Y = AX r r r i + t Trac cking Pair particles by mi inimizing a cost function (Stellmacher & Obermayer 2000) Utilize knowledge about flow: Y i Cost function J K m jk j =1 k =1 Y j t AX k 2 Y k Maximize expectation J K k =1 + g(a) + α m j(k +1) + α m (J +1)k j =1 re egularizer Cost for unmatched particles

17 Velocity Ma aps - Others Genetic algorithm pa article pairing (Sheng and Meng 1998) - Treat pairings i like chromosomes - Choose most fit pairs to propogate p Optical correlation (Coupland and Halliwell 1997) - Expand 2D autocorrelation to 3D - Can utilize FFT

18 Velocity Maps Image from Courtesy of Bo Tao. Used with permission.

19 Questions?

20 Bibliog graphy Arroyo M; Hinsch K (2008) Recent de velopments of PIV towards 3d measurements. Topics Appl. Physics (112) Hinsch KD (2002) Holographic image R72 velocimetry. Meas. Sci. Technol. (13) R61- Kim S; Lee S (2008) Effect of particle number density in in-line digital holographic particle velocimetry (44) Meng H; Pan G; Pu Y; Woodward S (2004) Holographic particle image velocimetry: from film to digital recording. Meas. Sci. Technol. (15) Pu Y., Meng H. (2000) An advanced off-axis holographic partcile image velocimetry (HPIV) system). Exp in F luids (29) Pu Y; Song X; Meng H (2000) Off-axis holographic particle image velocimetry for diagnosing particulate flows. Exp in Fluids [Suppl.] S117-S128 Sheng, J., Meng, H. (1998) A genetic algorithm particle pairing technique for 3D velocity field extraction in holographic particle image velocimetry. Exp in Fluids (25) Stellmacher, M; Obermayer, K. (2000) A new particle tracking algorithm based on deterministic annealing and alternativee distance measures. Exp in Fluids (28) Zhang Y; Shen G; Schroder A (2006) Influe nce of some recording parameters on digital holographic particle image velocimetry. Opt ical Engineering (45)

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