一種用於微粒紋痕影像測速儀之新型紋痕影像解析演算法

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研究生：	MUMTAZ HUSSAIN QURESHI MUMTAZ HUSSAIN QURESHI
論文名稱：	一種用於微粒紋痕影像測速儀之新型紋痕影像解析演算法 A novel streak-resolving algorithm for particle streak velocimetry
指導教授：	田維欣 Wei-Hsin Tien
口試委員:	林顯群 Sheam-Chyun Lin 林怡均 Yi-Jiun Peter LIN 周一志 Yi-Chih Chow 葉思沂 Yeh,Szu-I siyeh
學位類別：	博士 Doctor
系所名稱：	工程學院 - 機械工程系 Department of Mechanical Engineering
論文出版年：	2022
畢業學年度：	110
語文別：	英文
論文頁數：	148
中文關鍵詞：	微粒影像測速儀、微粒循跡測速儀、微粒紋痕測速儀、流場可視化、最小平方擬合法
外文關鍵詞：	particle image velocimetry, particle tracking velocimetry, particle streak velocimetry, flow visualization, least-square curve fitting
相關次數：	點閱：266 下載：2
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微粒紋痕測速儀 (Particle Streak Velocimetry, PSV) 是有別於流行的微粒影像測速儀 (Particle Image Velocimetry, PIV) 和微粒循跡測速儀 (Particle Tracking Velocimetry, PTV) 之外的另一種用於定量測量流場的實驗方法。在許多實際應用中，硬體設備限制導致需要更長的曝光時間，從而導致拍攝到的移動微粒影像拉長為紋痕影像。以PIV 和 PTV分析這些影像將導致準確度降低並且喪失有關流體旋轉的資訊，因其需要利用一對影像與近似瞬時速度的假設。針對這項限制，本研究提出了一種用於 PSV 的新型紋痕影像解析算法，利用單幀影像即可解析具有多個微粒紋痕的速度場。首先透過模擬合成以及實際實驗之影像，以系統性地方式探討了PTV 和 PIV 應用於微粒紋痕影像時之性能表現。對於模擬合成之微粒紋痕影像，係透過利用古典二維高斯函數近似之微粒模型影像並加以時間積分生成。透過引入無因次曝光時間〖" E" 〗_"T" ^"*" 來正規化每幀影像的曝光時間，並讓微粒在流場中分別依照一維均勻流和二維旋轉之希爾渦旋流(Hill’s vortex)進行運動。其他參數的影響，例如微粒影像直徑、訊號強度和微粒紋痕密度均進行了探討。結果顯示基於視覺的（VB）-PTV演算法可以成功擬合微粒紋痕影像輪廓及取得微粒中心位置的低擬合誤差直到〖" E" 〗_"T" ^"*" =10，但隨著〖" E" 〗_"T" ^"*" 從 10 增加到 70，其產量略有下降而效能表現則顯著下降。與 PTV 相比， PIV 對長曝光時間的微粒紋痕影像處理更為強健，對微粒影像直徑和強度的影響不太敏感。根據此結果所提出的新PSV算法則利用相同的時間積分高斯紋痕模型函數，但在曝光期間內假設其微粒之軌跡可用多項式加以擬合，並應用多變數最小平方法之演算程序對微粒紋痕之資訊和相應的微粒軌跡進行重建。如此可實現拉格朗日觀點之循跡(Lagrangian tracking)，並且可以對時間微分的方式解析微粒紋痕軌跡上任意點的瞬時速度。加速平行流及希爾渦旋流被使用來生成模擬合成之微粒紋痕影像，進行了演算法之性能測試且探討了誤差來源。實驗結果表明當影像噪訊低於 1.0%時，平均誤差和標準偏差 (SD) 可以忽略不計。而對於 5.0% 的噪訊水準，平均誤差和標準偏差 (SD)則會分別達到10% 和12%。兩種流動型態下的微粒影像強度和微粒影像直徑的誤差在無噪訊情況下為 0% ~7%，而在有噪訊的情形下則可達 12%。對流過圓柱體的流場實驗其微粒紋痕影像的處理結果則顯示，這些影像確可以使用此算法進行解析，其殘差平均誤差為 4.38，SD 為 9.48。實驗結果亦顯示，微粒紋痕之速度方向可以透過加入照明強度在總持續時間的100% 到 50%之變化並加入模型擬合函數來決定。結果顯示修改過的演算法成功地預測了從高紋痕強度到低條紋強度的大多數正確微粒紋痕速度向量。這些結果表明本研究所提出PSV 算法僅用單幀影像即可解析速度場且不受限於影像擷取與照明之硬體，因此具有廣泛應用的潛力。

Particle streak velocimetry (PSV) is an alternative experimental methods other than the popular particle image velocimetry (PIV) and particle tracking velocimetry (PTV) to quantitatively measure flow fields. In many practical applications, hardware limitations result in a requirement of longer exposure times, causing captured images of moving particles to elongate into particle streak images. To analyze such images with PIV or PTV can be less accurate and the information of the fluid rotation is lost because of the instantaneous velocity approximation using frame pairs. In this study, a novel streak-resolving algorithm is proposed for PSV to resolve velocity fields from a single image frame with multiple particle streaks to overcome this limitation. The performances of PTV and PIV on particle streak images were first evaluated systematically by both synthetic and experimental images. Synthetic particle streak images were created by a time integral of the model particle image, which is approximated by the classical two-dimensional Gaussian function. The exposure time of each frame is normalized by introducing the non-dimensional exposure time "E" _"T" ^"*" , and the particle were set to move in the analytical flows of the one-dimensional uniform flow and the rotational two-dimensional Hill’s vortex flow, respectively. Other parameters, such as the particle image diameter, intensity, and the particle streak density were also investigated. The results show that performances of vision-based (VB)-PTV algorithm can fit the particle streak profile successfully with low fitting error to the particle central location up to "E" _"T" ^"*" =10, but the performance dropped significantly as "E" _"T" ^"*" further increased to 70 with a slightly lower yield. In comparison with PTV, PIV is more robust in processing streak images with long-exposure times, and less sensitive to the effects of particle image diameter and intensity. The proposed new PSV algorithm based on these results utilized the same temporal-integrated model Gaussian streak function, but set the particle trajectory during the exposure period based on polynomial functions and applied a multivariable least-square fit procedure to reconstruct the streak information and the corresponding particle trajectory. Lagrangian tracking can be achieved and the instantaneous velocity at any given point on the streak can be evaluated by differentiating the resolved particle trajectory with respect to time. Accelerating parallel flow and the rotational flow of Hill’s vortex were used to generate synthetic streaks for the performance tests of the algorithm and the sources of errors were discussed. The results show that the mean error and standard deviation (SD) were negligible if the image noise is below 1.0%. For the noise level of 5.0%, the mean error is up to 10% and SD 12%, respectively. The errors of the particle image intensity and particle image diameter for both flow types were 0% ~7% for the cases without-noise and up to 12% for noisy images. The processing results for experimental streak images of flow past a cylinder reveal that these images can be resolved using the proposed algorithm with a residual mean error of 4.38 and SD of 9.48. The streak velocity direction can be correctly determined by applying a variation of illumination intensity from 100% to 50% of the total duration and adding this variation into the model fitting function. The results shows that the revised algorithm can successfully predict most of the correct streak velocity vectors from high streak intensity to low streak intensity. These results suggest that the proposed PSV algorithm can be used to resolve velocity fields with only a single image frame and not limited by imaging and illumination hardware, and thus has the potential for diverse applications.

TABLE OF CONTENTS
摘要    I
ABSTRACT    III
ACKNOWLEDGMENTS    V
CHAPTER 1 INTRODUCTION    1
1 MOTIVATION    1
2 LITERATURE REVIEW    2
3 RESEARCH OBJECTIVES    10
4 ORGANIZATION OF THE THESIS    11
CHAPTER 2  METHODS    12
1 SYNTHETIC PARTICLE IMAGE GENERATION    12
2. SIMULATED ANALYTICAL FLOW PATTERN    13
3 SYNTHETIC STREAK IMAGE GENERATION    14
4 PIV AND PTV ALGORITHMS    15
5 STREAK RESOLVING  ALGORITHM    19
5.1 Accelerating parallel flow and rotational flow    20
5.2 Streak resolved by least square curve-fitting    22
6 TYPES OF ERRORS    24
7 DIRECTION OF STREAK VELOCITY VECTOR    25
CHAPTER 3  PERFORMANCES OF PTV AND PIV ON STREAK IMAGES    27
1 UNIFORM TRANSLATIONAL FLOW    27
1.1 Performance of PTV with synthetic streak images    27
1.2 Performance of PIV with synthetic streak images    28
1.3 Comparisons between RMS displacement errors of PTV and PIV    29
2 ROTATIONAL FLOW (HILL’S VORTEX)    30
2.1 Performance of PTV with synthetic streak images    31
2.2 Performance of PIV with synthetic streak images    33
3 PIV AND PTV PERFORMANCES ON EXPERIMENTAL IMAGES    35
CHAPTER 4  RECONSTRUCTING VELOCITY FIELD OF STREAK IMAGES USING PSV    39
1 DETERMINATION OF UPPER AND LOWER BOUNDS OF STREAK FITTINGS    39
2 SINGLE STREAK RESOLVED AT MULTIPLE POINTS    40
3 RESIDUAL ERRORS FOR THE RESOLVED SINGLE STREAK    40
4 ACCELERATING PARALLEL FLOW    41
4.1 PSV analysis of single streak    41
4.2 Effects of particle image intensity and particle image diameter    42
4.3 Analysis of multiple streaks of accelerating parallel flow    42
5 ROTATIONAL FLOW (HILL’S VORTEX)    43
5.1 PSV analysis of single streak    43
5.2 Effect of particle image intensity (µ) and Particle image diameter (σ)    44
5.3 Analysis of multiple streaks of Hill’s Vortex (rotational flow)    44
6 ANALYSIS OF EXPERIMENTAL STREAK IMAGE    45
7 DISCUSSION    45
CHAPTER 5  DETERMINING THE DIRECTIONS OF VELOCITY FIELD FROM STREAK IMAGES IN PSV    48
1 PARALLEL FLOW    48
1.1 Direction of parallel flow at constant velocity    48
1.2 Direction of accelerating parallel flow    48
2 DIRECTION OF STREAK VELOCITY VECTORS FOR ROTATIONAL FLOW (HILL’S VORTEX)    48
3 DISCUSSION AND LIMITATIONS    50
CHAPTER 6  CONCLUSIONS AND FUTURE WORK    52
1 CONCLUSIONS    52
2 FUTURE WORK    54
REFERENCES    55


                                

REFERENCES

1. Gencler, B., An Exploratory Study on Continuous Wave Laser and High-Speed Camera Combination in Planar Particle Image Velocimetry for Low Speed Flows. 2019.
2. Ganapathisubramani, B. and N.T. Clemens, Effect of laser pulse duration on particle image velocimetry. AIAA journal, 2006. 44(6): p. 1368-1371.
3. Dimotakis, P.E., F.D. Debussy, and M.M. Koochesfahani, Particle streak velocity field measurements in a two‐dimensional mixing layer. The Physics of Fluids, 1981. 24(6): p. 995-999.
4. Rosenstiel, M. and R.-R. Grigat, Reconstruction of curved particle streak trajectories. Journal of Flow Visualization and Image Processing, 2008. 15(4).
5. Rosenstiel, M. and R.-R. Grigat, Segmentation and classification of streaks in a large-scale particle streak tracking system. Flow Measurement and Instrumentation, 2010. 21(1): p. 1-7.
6. Dickey, T., et al., Measurement of fluid flow using streak photography. American Journal of Physics, 1984. 52(3): p. 216-219.
7. Tsukamoto, Y. and S. Funatani, Application of feature matching trajectory detection algorithm for particle streak velocimetry. Journal of Visualization, 2020. 23(6): p. 971-979.
8. Wang, Z., D. Wang, and Y. Liu, Comparison of Three Particle Based Velocimetry Techniques. Transactions, 2017. 117(1): p. 1707-1708.
9. Fu, S., P.H. Biwole, and C. Mathis, Particle tracking velocimetry for indoor airflow field: A review. Building and Environment, 2015. 87: p. 34-44.
10. Friman, S.I., Laboratory investigations of concentration and wind profiles close to the wind-driven wavy water surface. 2020.
11. Rajasegar, R. and D.C. Kyritsis, Experimental investigation of coal combustion in coal-laden methane jets. Journal of Energy Engineering, 2015. 141(2): p. C4014012.
12. Cottle, A.E., et al., Investigation of Air Injection and Cavity Size Within a Circumferential Combustor to Increase G-Load and Residence Time. Journal of Engineering for Gas Turbines and Power, 2018. 140(1).
13. Wang, B., et al., Radiation Spectral Analysis and Particle Temperature/Velocity Measurement in Plasma Spray with One-Color Camera. Journal of Thermal Spray Technology, 2017. 26(7): p. 1632-1640.
14. Willits, J.D., Particle Streak Velocimetry: A Diagnostic for High Speed Flows. 2018, Purdue University.
15. Prasad, S., D.K. Mandal, and S. Narayanan, On the suppression of oscillatory circulation inside an evaporating bi-component drop through acoustic streaming. International Journal of Multiphase Flow, 2020. 129: p. 103314.
16. Dersoir, B., et al., Capture and release of particles inside a narrow slit pore.
17. Salipante, P.F., V.L. Dharmaraj, and S.D. Hudson, Entrance effects and high shear rate rheology of shear-banding wormlike micelle fluids in a microcapillary flow. Journal of rheology, 2020. 64(3): p. 481-492.
18. Qian, T., et al., Adaptable pulsatile flow generated by quantitative imaging of stem-cell derived cardiomyocytes for disease modeling. bioRxiv, 2020.
19. Raffel, M., et al., Particle image velocimetry: a practical guide. 2018: Springer.
20. Adrian, L., R.J. Adrian, and J. Westerweel, Particle image velocimetry. 2011: Cambridge university press.
21. Ghosal, S. and R. Mehrotra, Robust optical flow estimation using semi-invariant local features. Pattern recognition, 1997. 30(2): p. 229-237.
22. Ruhnau, P., et al., Variational optical flow estimation for particle image velocimetry. Experiments in Fluids, 2005. 38(1): p. 21-32.
23. Rabault, J., J. Kolaas, and A. Jensen, Performing particle image velocimetry using artificial neural networks: a proof-of-concept. Measurement Science and Technology, 2017. 28(12): p. 125301.
24. Cai, S., et al., Particle image velocimetry based on a deep learning motion estimator. IEEE Transactions on Instrumentation and Measurement, 2019. 69(6): p. 3538-3554.
25. Millier, D. and U. Renz, A particle streak tracking system (PST) to measure flow fields in ventilated rooms. Air Distribution in Rooms: Ventilation for Health and Sustainable Environment, 2000. 1: p. 325.
26. Müller, D., B. Müller, and U. Renz, Three-dimensional particle-streak tracking (PST) velocity measurements of a heat exchanger inlet flow A new method to measure all three air-flow velocity components in a plane is applied to a steady-state three-dimensional flow. Experiments in Fluids, 2001. 30(6): p. 645-656.
27. Zappa, E., S. Malavasi, and M. Negri, Uncertainty budget in PSV technique measurements. Flow Measurement and Instrumentation, 2013. 30: p. 144-153.
28. Wang, H., et al., Measurement of airflow pattern induced by ceiling fan with quad-view colour sequence particle streak velocimetry. Building and Environment, 2019. 152: p. 122-134.
29. Texier, A. and L. David, A kinematic study of hydrodynamic flows using particle streak velocimetry technique. 1996. 3(4): p. 279-298.
30. Sciacchitano, A., Uncertainty quantification in particle image velocimetry. Measurement Science and Technology, 2019. 30(9): p. 31.
31. Huang, H., D. Dabiri, and M. Gharib, On errors of digital particle image velocimetry. Measurement Science and Technology, 1997. 8(12): p. 1427.
32. Bhattacharya, S. and P.P. Vlachos, Volumetric Particle Tracking Velocimetry (PTV) Uncertainty Quantification. arXiv preprint arXiv:1911.12495, 2019.
33. Lei, Y.C., et al., A vision-based hybrid particle tracking velocimetry (PTV) technique using a modified cascade correlation peak-finding method. Experiments in Fluids, 2012. 53(5): p. 1251-1268.
34. Brady, M.R., S.G. Raben, and P.P. Vlachos, Methods for Digital Particle Image Sizing (DPIS): Comparisons and improvements. Flow Measurement and Instrumentation, 2009. 20(6): p. 207-219.
35. Kahler, C.J., S. Scharnowski, and C. Cierpka, On the uncertainty of digital PIV and PTV near walls. Experiments in Fluids, 2012. 52(6): p. 1641-1656.
36. Ruhnau, P., et al., A variational approach for particle tracking velocimetry. Measurement Science and Technology, 2005. 16(7): p. 1449.
37. Guo, T.Q., A.M. Ardekani, and P.P. Vlachos, Microscale, scanning defocusing volumetric particle-tracking velocimetry. Experiments in Fluids, 2019. 60(6): p. 14.
38. Voss, B.M., Novel single Camera Techniques for 3D3C Lagrangian Trajectory Measurements in Interfacial Flows. 2012.
39. Zhou, K.C., et al., Particle streak velocimetry-optical coherence tomography: a novel method for multidimensional imaging of microscale fluid flows. Biomedical optics express, 2016. 7(4): p. 1590-1603.
40. Malavasi, S., R. Corretto, and F. Fossati, PSV and marker detection techniques for investigating the wake of an oscillating cylinder in a large wind tunnel. Flow measurement and instrumentation, 2011. 22(5): p. 428-437.
41. Bergthorson, J.M., D.G. Goodwin, and P.E. Dimotakis, Particle streak velocimetry and CH laser-induced fluorescence diagnostics in strained, premixed, methane-air flames. Proceedings of the Combustion Institute, 2005. 30: p. 1637-1644.
42. Dimotakis, P.E., F.D. Debussy, and M.M. Koochesfahani, A particle streak velocity-field measurements in a two-dimensional mixing layer. Physics of Fluids, 1981. 24(6): p. 995-999.
43. Willert, C., et al., Prandtl’s flow visualization film C1 revisited. 2019.
44. Ganapathisubramani, B. and N. Clemens, Effect of laser pulse duration on particle image velocimetry. AIAA journal, 2006. 44(6): p. 1368-1371.
45. Qureshi, M.H., W.-H. Tien, and Y.-J.P. Lin, Performance comparison of particle tracking velocimetry (PTV) and particle image velocimetry (PIV) with long-exposure particle streaks. Measurement Science and Technology, 2020. 32(2): p. 024008.
46. Willits, J. and T. Pourpoint. Particle streak velocimetry of supersonic nozzle flows. in JANNAF Propulsion Meeting. 2016.
47. Willits, J.D. and T.L. Pourpoint. Development of Particle Streak Velocimetry with a Cold Flow Supersonic Nozzle. in 53rd AIAA/SAE/ASEE Joint Propulsion Conference. 2017.
48. Grayver, A.V. and J. Noir, Particle streak velocimetry using ensemble convolutional neural networks. Experiments in Fluids, 2020. 61(2): p. 1-12.
49. McKenna, S. and W. McGillis, Performance of digital image velocimetry processing techniques. Experiments in fluids, 2002. 32(1): p. 106-115.
50. Panton, R.L., Incompressible Flow. John Wiley& Sons. Inc., Hoboken, New Jersey, 2005.
51. Voss, B., et al., Bichromatic particle streak velocimetry bPSV. Experiments in Fluids, 2012. 53(5): p. 1405-1420.
52. Scott, G.L. and H.C. Longuet-Higgins, An algorithm for associating the features of two images. Proceedings of the Royal Society of London. Series B: Biological Sciences, 1991. 244(1309): p. 21-26.
53. Pilu, M. A direct method for stereo correspondence based on singular value decomposition. in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 1997. IEEE.
54. Thielicke, W. and E.J. Stamhuis, PIVlab – Towards User-friendly, Affordable and Accurate Digital Particle Image Velocimetry in MATLAB. Journal of Open Research Software, 2014. 2.
55. Westerweel, J. and F. Scarano, Universal outlier detection for PIV data. Experiments in Fluids, 2005. 39(6): p. 1096-1100.
56. Stevanus, W. and Y.J.P. Lin. An experimental study on the vertical flow past a finite-length horizontal cylinder at low Reynolds numbers. in Applied Mechanics and Materials. 2014. Trans Tech Publ.
57. McKenna, S.P. and W.R. McGillis, Performance of digital image velocimetry processing techniques. Experiments in Fluids, 2002. 32(1): p. 106-115.
58. Chuang, H.-S., S.T. Wereley, and L. Gui, Characterization of single pixel evaluation PIV for precise flow measurement.
59. Chuang, H.-S., S.T. Wereley, and L. Gui. Study of single pixel evaluation for experimental measurements in a microchannel. in ASME International Mechanical Engineering Congress and Exposition. 2006.
60. Lienhard, J.H., Synopsis of lift, drag, and vortex frequency data for rigid circular cylinders. Vol. 300. 1966: Technical Extension Service, Washington State University Pullman, WA.
61. Duncan, J., et al., Universal outlier detection for particle image velocimetry (PIV) and particle tracking velocimetry (PTV) data. Measurement Science and Technology, 2010. 21(5).
62. Thielicke, W. and E. Stamhuis, PIVlab–towards user-friendly, affordable and accurate digital particle image velocimetry in MATLAB. Journal of open research software, 2014. 2(1).
63. Duncan, J., et al., Universal outlier detection for particle image velocimetry (PIV) and particle tracking velocimetry (PTV) data. Measurement Science and Technology, 2010. 21(5): p. 057002.
64. Qureshi, M.H. and W.-H. Tien, Novel streak-resolving algorithm for particle streak velocimetry. Flow Measurement and Instrumentation, 2022. 87: p. 102208.

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