1. Scanning Multiple Projection Digital Image Processor Stroboscope v Through 2D Recording v Through 3D Recording Paths to 3D PIV  Holography

2. Principle of HPIV Displacement Velocity Holocine (time resolved) t1t1 t2t2 t3t3 Hologram 8ns Laser Pulse 3D flow seeded with particles Recording CCD Interrogation camera Laser Beam Reconstruction Double Exposure t 1 t 1 +  t t 2 t 2 +  t

3. Advantage of holography vTrue 3D imaging vInstantaneous Volumetric vHigh Information Capacity (10 6 - 10 9 Particles) vReal-Time Recording but Off-line Data Transfer & Processing

4. How to get true 3D imaging? Phase Preservation O=Oexp[i(  -  t)] or: O=Osin(  t) How to record  ? Any light sensitive media records intensity I=|O| 2 =O 2 Need to “encode” phase  into some intensity modulation

5. Encoding Phase -- Use interference of coherent light! E=R+O Reference waveObject wave where R = R exp[i(  -  t)], O=Oexp[i(  -  t)] Recorded Intensity: I=|R+O| 2 = R 2 + O 2 +2ROsin(  )

6. Real Image Principle of Holography R e f e r e n c e B e a m Virtual Image  x z Hologram 0 Recording R e f e r e n c e B e a m Reconstruction y x z Hologram 0  Object I =|R+O| 2 = R 2 + O 2 +2ROsin (  ) R O O I =(R+O)(R+O)* = R 2 + O 2 + R*O+RO* O*O* T ~ R 2 + O 2 + R*O+RO* Usually R= exp(-i  t) T ~ 1 + O 2 + O + O*

7. Experimental Demonstration Reference beam, object beam Virtual, real image *Transmission or Reflection Hologram? Setup Considerations: Coherence length vs. path length difference Exposure energy: In the linear range R:O ratio

8. Transmission or Reflection Hologram Transmission hologram created by 2 plane waves traveling towards the same side Reflection hologram created by 2 plane waves traveling towards opposite sides (Volume Hologram)

9. Reflection Hologram Bragg Condition 2dsin  =m

10. In-line (Gabor) Holography n Simple geometry n Low coherence & energy requirement Traditional for particle fields n Speckle noise (limit seeding density & seeding depth) n Large depth of focus (practically only 2D vectors) Reference wave Object wave LASER Real Image Viral Image

11. Speckle Noise (in-line hologram) Reconstruction field of an in-line hologram for an ensemble of particles: B +  o k +  o* k Type-I speckle -- interference between B and the scattered waves  Major Source of Speckle Type-II speckle -- self-interference of the scattered waves. O k =  o k =  k  ex p ( i  k ) : Random Walk

12. Speckle noise: decrease Signal-to- Noise Ratio 1 particle /mm 3 6 particles /mm 3 40 particles /mm 3

13. Off-Axis Holography as Solution Off-axis HPIV:  Higher SNR  Higher Seeding Density  Complex Geometry  Higher Coherence Required Reconstruction Virtual Image Real Image Hologram Reference Beam Hologram Illuminating Beam Reference Beam Recording Off-axis HPIV

14. IROV  In-line Recording Off-axis Viewing Holography IROV: Use side scattering  Suppresses speckle noise  Reduces image depth of focus Making In-line based HPIV feasible Meng & Hussain (1995): Appl. Opt. 34, 1827

15. Recording Reconstruction IROV Experimental Setup

16. Use of High-Frequency Fringes on In-Line Holograms Negligible influence of forward scattering: Since |O L | << |R|, I L << I sig

17. IROV suppresses speckle noise Completely avoids type-I speckle greatly reduces type-II speckle Reconstruction field of an in-line hologram for an ensemble of particles: B +  o k +  o* k Off-axis Viewing: receives only  o* k

18. Improved SNR by IROV IROV In-line Viewed

19. Reduction of Depth of Focus by IROV 0 degree 20 degree In focus +100  m -100  m In-line: Fraunhofer diffraction

20. Proof of Principle Experiment

21. IROV Measurement of a Vortex Ring

22. Post Processing

23.  Low density requires intelligent pairing  GA searches large solution space IROV Data Processing: Genetic Algorithm Particle Pairing 2’ 1’ 3’ 4’ 5’ 6’ 7’ 1 2 3 4 5 6 7 Interrogation Cell

24. Genetic Algorithm Particle Pairing

25. Why Genetic Algorithm? Many possibilities to pair particles Need to numerate and filter Conventional searching methods þComputation intensive þDifficult to incorporate intelligence þTime consuming Genetic Algorithm þEfficient in searching large space þBuilt-in intelligence to follow fluid dynamics þFast and inherent parallel processing speed Large solution space

27. Two Approaches of HPIV Developed at LFD Off-axis HPIV high-end In-line (IROV) HPIV low-cost

28. Digital In-line Holography

29. Dual-Reference Off-Axis Technique High Seeding Density Allowed Small Depth of Focus Image Separation Removes Direction Ambiguity Complex Optical Geometry High Energy Laser Required High Coherence of Beam Needed

30. Gemini Off-axis HPIV System

31. Concise Cross Correlation (CCC) Algorithm Matching by particle groups Uses particle centroids only Group shifting for matching Decomposition of operation Low data volume / high compression rate High-speed processing

32. System Test Flow  Excited Air Jet

33. Phase-Locked Vortex Side ViewTop View

34. Vorticity

35. Vorticity Iso-surface To be re-made

36. HPIV Measurement of Tab Wake

37. Vortab Flow: HPIV Measurement Result  Amount of Data: 400,000 Vectors  Mean Velocity: 16.67 cm/sec.

38. Vortab Flow: Vorticity Iso-Surfaces

39. Hologram captures 3D instantly Turbulent Flow Field HPIV = 3D Information Transfer & Processing Fundamental Challenges  3D Signal Decoding  Complex Flow Mapping  Large Data Quantity  User-friendly? Flow Field Reconstruction

40. Holographic Flow Visualization a Tool for Studying 3D Coherent Structures and Instabilities Kansas State University, ISSI, Wright Laboratory, WP/AFB

41. (a) (b) (c) Holographic Images of Three Vortex-Flame Systems Photographed from Two Angles (a) or Using Two Magnifications (b and c). Off-Axis HFV of Vortex Flame

42. Holographic Images of A Milk Drop Undergoing Bag Instability (a, b) Holographic Images of A Turbulent Milk Drop (a) and Its Downstream Breakdown (b, c) IROV HFV of Turbulent Milk Drop

43. Naturally, HPIV is an ideal diagnostic tool for studying particulate phase  3D and dynamically