Phase discrimination and a high accuracy algorithm for PIV image processing of particle–fluid two-phase flow inside high-speed rotating centrifugal slurry pump

Abstract PIV technology is an efficient and powerful measurement method to investigate the characteristics of fluid flow field. But for PIV particle image post-processing, some problems still exit in two-phase particles discrimination and velocity field algorithm, especially for high-speed rotating centrifugal slurry pump. In this study, through summarization and comparison of the various phase discrimination methods, we proposed a two-phase identification method based on statistics of gray-scale level and particle size. The assessment of performance through experimental PIV images shows that a satisfying effect for particle identification. For high speed rotation of the impeller, a combination of adaptive cross-correlation window deformation algorithm and multistage grid subdivision is presented. The algorithm is applied to experimental PIV images of solid–liquid two-phase flow in a centrifugal slurry pump, the results show that the algorithm in the present study has less pseudo vector number and more matching particle pairs than those of fixed window and window translation methods, having the ability to remove pseudo vector efficiently. It confirmed that the algorithm proposed in the present study has good performance and reliability for PIV image processing of particle–fluid two-phase flow inside high-speed rotating centrifugal slurry pump.

[1]  Ronald Adrian,et al.  Multi-point optical measurements of simultaneous vectors in unsteady flow—a review , 1986 .

[2]  Fu Hao-ming Research on the Particle Image Velocimetry for Two-Phase Flow , 2003 .

[3]  Allan J. Acosta An Experimental and Theoretical Investigation of Two-Dimensional Centrifugal Pump Impellers , 1952 .

[4]  I Grant,et al.  The use of neural techniques in PIV and PTV , 1997 .

[5]  W. Schmidl,et al.  New tracking algorithm for particle image velocimetry , 1995 .

[6]  N. Malik,et al.  Particle tracking velocimetry in three-dimensional flows , 1993 .

[7]  J. Reboud,et al.  Experimental and Numerical Studies in a Centrifugal Pump With Two-Dimensional Curved Blades in Cavitating Condition , 2003 .

[8]  Christian Brix Jacobsen,et al.  Flow in a Centrifugal Pump Impeller at Design and Off-Design Conditions—Part II: Large Eddy Simulations , 2003 .

[9]  Paul E. Dimotakis,et al.  Image correlation velocimetry , 1995 .

[10]  Farid Bakir,et al.  Numerical Modelization of the Flow in Centrifugal Pump: Volute Influence in Velocity and Pressure Fields , 2005 .

[11]  C. Willert,et al.  Digital particle image velocimetry , 1991 .

[12]  Koji Okamoto,et al.  A novel algorithm for particle tracking velocimetry using the velocity gradient tensor , 2000 .

[13]  Ying Wei Li,et al.  An Improved Particle Image Velocimetry Algorithm for Velocity Measurement of Oil-Water Two-Phase Flow , 2014 .

[14]  R. A. Van den Braembussche,et al.  Experimental investigation of the flow in the vaneless diffuser of a centrifugal pump by particle image displacement velocimetry , 1989 .

[15]  Pavlos P. Vlachos,et al.  A method for automatic estimation of instantaneous local uncertainty in particle image velocimetry measurements , 2012 .

[16]  Dominique Pelletier,et al.  Denoising methods for time-resolved PIV measurements , 2011 .

[17]  H. E. Fiedler,et al.  Limitation and improvement of PIV , 1993 .

[18]  J. Westerweel,et al.  The effect of a discrete window offset on the accuracy of cross-correlation analysis of digital PIV recordings , 1997 .

[19]  Sang Joon Lee,et al.  A new two-frame particle tracking algorithm using match probability , 1996 .

[20]  Kitano Majidi Numerical Study of Unsteady Flow in a Centrifugal Pump , 2005 .

[21]  B. N. Dobbins,et al.  An improved cross correlation technique for particle image velocimetry , 1995 .

[22]  A. Cessou,et al.  Direct measurement of local instantaneous laminar burning velocity by a new PIV algorithm , 2011 .

[23]  Martin Sommerfeld,et al.  An advanced LIF-PLV system for analysing the hydrodynamics in a laboratory bubble column at higher void fractions , 2002 .

[24]  J. Soria An investigation of the near wake of a circular cylinder using a video-based digital cross-correlation particle image velocimetry technique , 1996 .

[25]  L. Vervisch,et al.  Estimation of the accuracy of PIV treatments for turbulent flow studies by direct numerical simulation of multi-phase flow , 2001 .

[26]  Richard D. Keane,et al.  Optimization of particle image velocimeters. I, Double pulsed systems , 1990 .

[27]  Fulvio Scarano,et al.  Advances in iterative multigrid PIV image processing , 2000 .

[28]  Ellen K. Longmire,et al.  Simultaneous two-phase PIV by two-parameter phase discrimination , 2002 .

[29]  Jinjia Wei,et al.  Experimental investigation on the turbulence channel flow laden with small bubbles by PIV , 2013 .

[30]  Fulvio Scarano,et al.  Iterative multigrid approach in PIV image processing with discrete window offset , 1999 .

[31]  G. Labonté,et al.  A new neural network for particle-tracking velocimetry , 1999 .

[32]  Jacob Benesty,et al.  A fast recursive algorithm for optimum sequential signal detection in a BLAST system , 2003, IEEE Trans. Signal Process..