journal article·2019-08-11Open access

Flow Velocity Field Measurement of Vertical Upward Oil–Water Two-Phase Immiscible Flow Using the Improved DPIV Algorithm Based on ICP and MLS

Lianfu Han, Yao Cong, Xingbin Liu, Changfeng Fu

Applied Sciences· Volume 9· Issue 16

Abstract

Flow velocity field measurement is important for analyzing flow characteristics of oil–water two-phase immiscible flow in vertical well. Digital particle image velocimetry (DPIV) is an effective velocity field measurement method that has overcome single point measurement limitation of traditional instruments. However, multiphase flow velocity fields generated by DPIV are often accompanied by local false vectors caused by image mismatching, which leads to measurement results with low accuracy. In this paper, the reasons for oil–water two-phase immiscible flow image mismatching in inner diameter 125 mm vertical pipe is identified by studying the DPIV calculation process. This is mainly caused by image noise and poor window following performance that results from poor deformation performance of the interrogation window. To improve deformation performance of the interrogation window, and thus improve the accuracy of the algorithm, iterative closest point (ICP) and moving least squares (MLS) are introduced into the window deformation iterative multigrid algorithm in DPIV postprocessing algorithm. The simulation showed that the improved DPIV algorithm had good matching performance, and thus the false vector was reduced. The experimental results showed that, in light of the present investigation, on average, the improved DPIV algorithm is found to yield an accuracy improvement of ~6%; the measurement uncertainty and reproducibility of the improved DPIV algorithm were 0.149 × 10−3 m/s and 1.98%, respectively.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Flow Measurement and Analysis · Fluid Dynamics and Mixing · Hydrology and Sediment Transport Processes

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

College of Electronics Science, Northeast Petroleum University, Daqing 163318, China

Funding

the National Science Foundation of China · the China Postdoctoral Science Foundation

References (41)

Show source-provided references
1. Oil–water two-phase flow measurement with combined ultrasonic transducer and electrical sensors10.1088/0957-0233/27/12/125307 ↗
2. Numerical Modeling of Oil-Water Multiphase Flow Contained by an Oil Spill Barrier
3. Cross-correlation velocity measurement of horizontal oil-water two-phase flow by using parallel-wire capacitance probe10.1016/j.expthermflusci.2013.12.021 ↗
4. The influence on response of axial rotation of a six-group local-conductance probe in horizontal oil-water two-phase flow
5. Flow measurement of oil-in-water emulsions using arc-type conductivity probes and electromagnetic flowmeter10.1109/TIM.2017.2783998 ↗
6. Three-phase flow measurement in the petroleum industry10.1088/0957-0233/24/1/012003 ↗
7. PIV measurements in near-wake turbulent regions10.1142/S0217984918400262 ↗
8. Accurate turbulence level estimations using PIV/PTV10.1007/s00348-018-2646-5 ↗
9. Flow measurements around guide vanes of francis turbine: A PIV approach10.1016/j.renene.2018.03.042 ↗
10. Twenty years of particle image velocimetry10.1007/s00348-005-0991-7 ↗
11. Digital particle image velocimetry (DPIV) robust phase correlation
12. Measuring the velocity fields of granular flows–Employment of a multi-pass two-dimensional particle image velocimetry (2D-PIV) approach10.1016/j.apt.2018.08.014 ↗
13. An optical method for measuring the near-wall volume fraction in granular dispersions10.1007/s10035-016-0676-3 ↗
14. Study on laminar flow containing fibre particles in a stirred tank using DPIV10.1016/j.powtec.2017.07.079 ↗
15. Measurement of hydrodynamic force generation by swimming dolphins using bubble DPIV10.1242/jeb.087924 ↗
16. A DPIV study on the effects of separation distance upon the vortical behaviour of jet—Cylinder impingements
17. Experimental investigation of fibre particles in a turbulent stirred tank with DPIV10.1016/j.powtec.2016.10.065 ↗
18. Challenges and improvements in applying a particle image velocimetry (PIV) approach to granular flows10.1088/1742-6596/1249/1/012011 ↗
19. Estevadeordal, J., and Goss, L. (2005). PIV with LED: Particle shadow velocimetry (PSV). AIAA J., 1–10.10.2514/6.2005-37 ↗
20. Particle Shadow Velocimetry (PSV) in bubbly flows10.1016/j.ijmultiphaseflow.2018.04.015 ↗
21. Image processing techniques for the measurement of two-phase bubbly pipe flows using particle image and tracking velocimetry (PIV/PTV)10.1016/j.ces.2018.05.029 ↗
22. Investigation of particleladen turbulent pipe flow at high-Reynolds-number using particle image/tracking velocimetry (PIV/PTV)10.1016/j.ijmultiphaseflow.2016.06.023 ↗
23. Biases of PIV measurement of turbulent flow and the masked correlation-based interrogation algorithm10.1007/s003480000131 ↗
24. PIV algorithms for open-channel turbulence research: Accuracy, resolution and limitations10.1016/j.jher.2010.12.006 ↗
25. PIV uncertainty quantification by image matching10.1088/0957-0233/24/4/045302 ↗
26. The effect of a discrete window offset on the accuracy of cross-correlation analysis of digital PIV recordings10.1007/s003480050082 ↗
27. A correlation-based continuous window-shift technique to reduce the peak-locking effect in digital PIV image evaluation10.1007/s00348-001-0396-1 ↗
28. Limitation and Improvement of PIV10.1007/BF00223404 ↗
29. Advances in iterative multigrid PIV image processing10.1007/s003480070007 ↗
30. Iterative image deformation methods in PIV10.1088/0957-0233/13/1/201 ↗
31. Analysis of velocity interpolation schemes for image deformation methods in PIV10.1007/s00348-008-0475-7 ↗
32. A modified cross-correlation algorithm for PIV image processing of particle-fluid two-phase flow10.1016/j.flowmeasinst.2015.06.010 ↗
33. The application of PIV technique for the investigation of oil-water two phase flow
34. An improved particle image velocimetry algorithm for velocity measurement of oil-water two-phase flow10.4028/www.scientific.net/AMM.602-605.1654 ↗
35. Main results of the third international PIV Challenge10.1007/s00348-008-0462-z ↗
36. He, Y., Liang, B., Yang, J., Li, S.Z., and He, J. (2017). An Iterative Closest Points Algorithm for Registration of 3D Laser Scanner Point Clouds with Geometric Features. Sensors-Basel, 17.10.3390/s17081862 ↗
37. A Method for Registration of 3-D Shapes
38. Map-matching algorithms for robot self-localization: A comparison between perfect match, iterative closest point and normal distributions transform10.1007/s10846-017-0765-5 ↗
39. On the numerical solution of logarithmic boundary integral equations arising in laplace’s equations based on the meshless local discrete collocation method
40. A new kinematic wave model for interpreting cross correlation velocity measurements in vertically upward, bubbly oil-in-water flows10.1088/0957-0233/12/9/321 ↗
41. Flow pattern identification of oil-water two phase flow in vertical upward pipes