Article information
2018 , Volume 23, ¹ 1, p.33-45
Zaripov D.I., Mikheev N.I., Dushin N.S., Aslaev A.K., Shakirov R.R.
Application of projection method to speed-up a new algorithm for the measurement of instantaneous flow velocity field
Applicability of projection method has been investigated to speed-up a new optical method for the measurement of flow velocity fields using smoke visualization. The algorithm is based on the analysis of integral projections of images on coordinate axes. The variation of random and bias measurement errors while changing the magnitude of uniform displacement of particles has been studied for the particle diameters of 2.2 and 10 pix. Synthetic images with sizes 16×16, 32×32 and 64×64 pix have been used to show that the application of projection method to image processing speeds up the processing time of the measurements by approximately 16, 140 and 1060 times, respectively. However, if compared with a new method of Smoke Image Velocimetry, it reduces the accuracy of the obtained data when processing the images with large particles (10 pix) imitating smoke structures. Nevertheless, the results obtained by the proposed speedup algorithm are not inferior in accuracy to similar data obtained by Particle Image Velocimetry. It is noted that the proposed speed-up algorithm can be applied to preliminary evaluation of initial displacement field with its subsequent refinement using more accurate computational algorithms and methods.
[full text] Keywords: computational cost reduction, optical method, smoke visualization, PIV, SIV, accuracy assessment, velocity field measurement
doi: 10.5072/ICT.2018.1.11850
Author(s): Zaripov Dinar Ilyasovich Office: KazanSc of RAS Address: 420111, Russia, Kazan
Mikheev Nikolay Ivanovich Office: KazanSc of RAS Address: 420111, Russia, Kazan
Dushin Nikolay Sergeevich Office: KazanSc of RAS Address: 420111, Russia, Kazan
Aslaev Albert Kamilovich Office: KazanSc of RAS Address: 420111, Russia, Kazan
Shakirov Radif Rustyamovich Office: KazanSc of RAS Address: 420111, Russia, Kazan
References: [1] Raffel, M., Willert, C., Wereley, S., Kompenhans, J. Particle image velocimetry. A practical Guide. Springer; 2007: 468.
[2] Tokarev, M.P., Markovich, D.M., Bil’skiy, A.V. Adaptive algorithms for PIV image processing. Computational Technologies. 2007; 12(3):109–131. (In Russ.)
[3] Westerweel, J., Elsinga, G.E., Adrian, R.J. Particle image velocimetry for complex and turbulent flow. Annual review of fluid mechanics. 2013; (45):409–436.
[4] Kim, B.J., Sung, H.J. A further assessment of interpolation schemes for window deformation in PIV. Experiments in Fluids. 2006; (41):499–511.
[5] Jambunathan, K., Ju, X.Y., Dobbins, B.N., Ashforth-Frost, S. An improved cross correlation technique for particle image velocimetry. Measurement Science & Technology. 1995; (6):507–514.
[6] Nasibov, H., Kholmatov, A., Akselli, B., Nasibov, A., Baytaroglu, S. Performance analysis of the CCD pixel binning option in particle-image velocimetry measurements. IEEE/ASME Transactions on Mechatronics. 2010; 15(4):527–540.
[7] Cierpka, C., Hain, R., Buchmann, N.A. Flow visualization by mobile phone cameras. Experiments in Fluids. 2016; (57):108.
[8] Willert C.E. High-speed particle image velocimetry for the efficient measurement of turbulence statistics. Experiments in Fluids. 2015; (56):17.
[9] Willert, C., Stasicki, B., Klinner, J., Moessner, S. Pulsed operation of high-power light emitting diodes for imaging flow velocimetry. Measurement Science and Technology. 2010; 21(7):075402.
[10] Zaripov, D.I., Aslaev, A.K., Mikheev, N.I., Dushin, N.S. Estimation of accuracy of new optical method of instantaneous flow velocity field measurement. Transactions of Academenergo. 2016; (1):42–52. (In Russ.)
[11] Mikheev, N.I., Dushin, N.S. A method for measuring the dynamics of velocity vector fields in a turbulent flow using smoke image-visualization videos. Instruments and experimental techniques. 2016; 59(6):882–889. [12] Scharnowski, S., Kahler, C.J. On the effect of curved streamlines on the accuracy of PIV vector fields. Experiments in Fluids. 2013; (54):1435.
[13] Discetti, S., Astarita, T. Fast 3D PIV with direct sparse cross-correlations. Experiments in Fluids. 2012; (53):1437–1451.
[14] Bilsky, A.V., Dulin, V.M., Lozhkin, V.A., Markovich, D.M., Tokarev, M.P. Twodimensional correlation algorithms for tomographic PIV. Proc. of 9 th Intern. Symp. on Particle Image Velocimetry. Kobe, Japan: Kobe Univ.; 2011.
[15] Earl, T., Jeon, Y.J., Lecordier, B., Laurent, D. Accuracy and speed assessment of 3D cross-correlation algorithms for two-frame and multi-frame PIV. Proc. of 11th Intern. Symp. on Particle Image Velocimetry. Santa Barbara, California; 2015.
[16] Melling A. Tracer particles and seeding for particle image velocimetry. Measurement Science & Technology. 1997; (8):1406–1416.
[17] Westerweel, J., Scarano, F. Universal outlier detection for PIV data. Experiments in Fluids. 2005; (39):1096–1100.
[18] Westerweel, J., Draad, A.A., van der Hoeven, J.G.Th., van Oord, J. Measurement of fully-developed turbulent pipe flow with digital particle image velocimetry. Experiments in Fluids. 1996; (20):165–177.
Bibliography link: Zaripov D.I., Mikheev N.I., Dushin N.S., Aslaev A.K., Shakirov R.R. Application of projection method to speed-up a new algorithm for the measurement of instantaneous flow velocity field // Computational technologies. 2018. V. 23. ¹ 1. P. 33-45
|