1 / 27

Instructor: Lichuan Gui lichuan-gui@uiowa lcgui

Students are encouraged to attend the class. You may not be able to understand by just reading the lecture notes. Measurements in Fluid Mechanics 058:180:001 (ME:5180:0001) Time & Location: 2:30P - 3:20P MWF 218 MLH Office Hours: 4:00P – 5:00P MWF 223B-5 HL. Instructor: Lichuan Gui

bree
Download Presentation

Instructor: Lichuan Gui lichuan-gui@uiowa lcgui

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Students are encouraged to attend the class. You may not be able to understand by just reading the lecture notes. Measurements in Fluid Mechanics058:180:001 (ME:5180:0001)Time & Location: 2:30P - 3:20P MWF 218 MLHOffice Hours: 4:00P – 5:00P MWF 223B-5 HL Instructor: Lichuan Gui lichuan-gui@uiowa.edu http://lcgui.net

  2. Lecture 34. Multi-phase flow PIV techniques

  3. True color recordings Fluorescent Technique • Phase separation Example: Orange solid particles in water and air flow using green laser Red channel Green channel Blue channel

  4. Fluorescent Technique • Background noise elimination Example: Fluorescent particles used in Micro PIV Micro channel Micro PIV recording

  5. Digital Mask Technique • Phase separation according to particle image size Example: Solid particle in seeded water flow - Identify particle images in the recording and compute size of each particle image; - Extract image of the dispersed phase: Keep particle images bigger than a given threshold and fill the rest with zero - Extract image of the continuous phase: Set pixel values of the big particle images to zero or background intensity 2 phase recording Image for big particles Image for small particles

  6. Digital Mask Technique • Phase separation according to particle image size Evaluation results of the correlation-based interrogation - Without phase separation, uncertainty arises around interface of different phases; - Influence of dispersed phase (big particles) cannot be completely eliminated by just removing the big particle images Results w/o phase separation Results of small particle image

  7. j (i,j) j g(i,j) Define: o i o i 2-Phase PIV recording G(x,y) Phase mask (x,y) y y o o x x Digital Mask Technique • Phase mask

  8. Digital Mask Technique • Phase mask applied to dispersed phase Schematic illumination of the masking procedure

  9. Define: Digital Mask Technique • Phase mask applied to dispersed phase Masked evaluation function - MQD function

  10. Define: and correlation-based mask tech. MQD-based mask tech. Define: Digital Mask Technique • Phase mask applied to dispersed phase Masked evaluation function

  11. Digital Mask Technique • Phase mask applied to continuous phase Schematic illumination of the masking procedure

  12. - MQD function averaged with effective pixel numbers Digital Mask Technique • Phase mask applied to continuous phase Masked evaluation function

  13. Define: Digital Mask Technique • Phase mask applied to continuous phase Masked evaluation function

  14. Define: MQD-based evaluation function: C1, C2, C3 and C4 are correlation travcking functions Correlation-based evaluation function: For both correlation interrogation and tracking Digital Mask Technique • Phase mask applied to continuous phase Masked evaluation function

  15. Test samples: a – Original double exposed evaluation sample, b – Superimposed with a big particle image, c – Big particle image removed, d – Phase mask. Test results: a – (m,n) for the original, b – (m,n) for sample b, c – (m,n) for sample c, d – (m,n) phase masked. Digital Mask Technique • Test of the phase mask for continuous phase

  16. Digital Mask Technique • Phase-separated evaluation with digital mask Evaluation results of the correlation-based interrogation - Without phase separation, uncertainty arises around interface of different phases; - With phase separation, velocity difference between 2 phases clarified. Results w/o phase separation Results of masked correlation

  17. Digital Mask Technique • Application examples Two phase flows Bubbly water flow Solid/water flow

  18. Digital Mask Technique • Application examples Elimination of visible background influence One of the PIV recording pairs at phase=0 (200400 pixels / 13.326.7 mm2) Phase averaged velocity Flow around a vibrating cantilever

  19. Digital Mask Technique • Application examples Elimination of invisible background influence Flow around a blood cell

  20. References • Gui L, Merzkirch W (1996) Phase-separation of PIV measurements in two-phase flow by applying a digital mask technique. ERCOFTAC Bulletin 30: 45-48  • Gui L, Wereley ST, Kim YH (2003) Advances and applications of the digital mask technique in Particle Image Velocimetry (PIV) experiments. Meas. Sci. Technol. 14, 1820-1828

  21. Fast computation of unsharp mask Definition - used to effectively remove low-frequency background noise in PIV recordings

  22. Fast computation of unsharp mask - Compute Gsm(x,y) close to the edges of the image forx  r+1 or x> nx-r or y< r+1 or y>ny-r A

  23. Fast computation of unsharp mask - Compute Gsm(x,y) away from the edges of the image

  24. 4-P CDIC 24 Central difference window shift & image corection f1(i,j) f2(i,j) Correlation function improved with window shift (red) & image correction (blue) Clear correlation function high peak at the particle image displacement

  25. 4-P CDIC Pixel displacement functions

  26. 4-P CDIC 7 8 9 4 5 6 1 2 3 4-point image corection method Interrogation window - Particle image sisplacements at center and 4 corners (i.e. S1,S3,S5,S7,S9) determined according to a previus evaluation - Window shift determined with displacement in the window center, i.e. Sws=S5 - Image distortion at the 4 points determined as - Sdis(i,j) determined with bilinear interpolation according to Sdis(k) - f(i,j) determined with bilinear interpolation according to Sws and Sdis(i,j) - Mutipass interrogation with iterated number aropund 6.

  27. 4-P CDIC 7 8 9 4 5 6 1 2 3 - 50% interrogation overlapp to determine particle image displacements at 5 points. Sx(4)=U(i-1,j) Sx(1)=U(i-1,j-1) Sx(7)=U(i-1,j+1) Interrogation window Sx(5)=U(i, j) Sx(2)=U(i , j-1) Sx(8)=U(i, j+1) Sx(6)=U(i+1,j) Sx(3)=U(i+1,j-1) Sx(9)=U(i+1,j+1) wsx=Sx(5) S_dis_x(k)=Sx(k)-(Sx(1)+Sx(3)+Sx(7)+Sx(9))/4; - Bilinear interpolation to determine distortion function at each pixel in the interrogation window. A=(M-i)*(N-j)/double((M-1)*(N-1)); B=(i-1)*(N-j)/double((M-1)*(N-1)); C=(M-i)*(j-1)/double((M-1)*(N-1)); D=(i-1)*(j-1)/double((M-1)*(N-1)); s_dis_x(i,j)=S_dis_x (1)*A+S_dis_x (3)*B+S_dis_x (7)*C+S_dis_x (9)*D; X=xm±swx/2 ±s_dis_x(i,j)/2 - Bilinear interpolation to determine gray value of ech pixel. A=(1-x)*(1-y); B=x*(1-y); C=(1-x)*y; D=x*y; g(i,j)=A*Ga+B*Gb+C*Gc+D*Gd;% bilinear interpolation

More Related