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Journal of Flow Visualization and Image Processing

Erscheint 4 Ausgaben pro Jahr

ISSN Druckformat: 1065-3090

ISSN Online: 1940-4336

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 0.6 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.6 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.00013 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.14 SJR: 0.201 SNIP: 0.313 CiteScore™:: 1.2 H-Index: 13

Indexed in

CORRECTION METHOD OF ERRONEOUS VECTORS IN PIV

Volumen 2, Ausgabe 2, 1995, pp. 173-185
DOI: 10.1615/JFlowVisImageProc.v2.i2.60
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ABSTRAKT

A new method that can detect and correct erroneous velocity vectors unexpectedly appeared in particle imaging velocimetry (PIV) is proposed. Two or three sets of four divergence values independently defined at a measuring point are used to examine local continuity of flow. The fundamental idea of the method is that some of these absolute divergence values must become greater than a certain threshold level if the local continuity is destroyed by erroneous vectors. Two criteria for the error detection are proposed and their efficiencies are examined by the simulation of uniform vector fields containing specified number of erroneous vectors. They are found to be satisfactory if the ratio of erroneous vectors is less than 0.5. It is also demonstrated that excellent performances are achieved when the presented method is applied to an experimental data of a complicated open-channel flow and a remote sensing data of flood flow.

REFERENZIERT VON
  1. Shinneeb A-M, Bugg J D, Balachandar R, Variable threshold outlier identification in PIV data, Measurement Science and Technology, 15, 9, 2004. Crossref

  2. Kim Y., Muste M., Hauet A., Krajewski W. F., Kruger A., Bradley A., Stream discharge using mobile large-scale particle image velocimetry: A proof of concept, Water Resources Research, 44, 9, 2008. Crossref

  3. Bradley A. Allen, Kruger Anton, Meselhe Ehab A., Muste Marian V. I., Flow measurement in streams using video imagery, Water Resources Research, 38, 12, 2002. Crossref

  4. Tianding Chen, An affine-model-based technique for fast DPIV computation, Image and Vision Computing, 24, 5, 2006. Crossref

  5. Wang Xin, Yan Xijun, Lv Guofang, Fan Tanghuai, Balloon-borne spectrum–polarization imaging for river surface velocimetry under extreme conditions, Infrared Physics & Technology, 58, 2013. Crossref

  6. Druault Philippe, Guibert Philippe, Use of turbulent flow statistical properties for correcting erroneous velocity vectors in PIV, Comptes Rendus Mécanique, 332, 9, 2004. Crossref

  7. Holland K.T, Puleo J.A, Kooney T.N, Quantification of swash flows using video-based particle image velocimetry, Coastal Engineering, 44, 2, 2001. Crossref

  8. Ettema Robert, Fujita Ichiro, Muste Marian, Kruger Anton, Particle-image velocimetry for whole-field measurement of ice velocities, Cold Regions Science and Technology, 26, 2, 1997. Crossref

  9. Achyut Sapkota , Kazuo Ohmi , Detection of PIV outliers using rule-based fuzzy logic, 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence), 2008. Crossref

  10. Weng W.G., Fan W.C., Liao G.X., Qin J., Wavelet-based image denoising in (digital) particle image velocimetry, Signal Processing, 81, 7, 2001. Crossref

  11. Nogueira J, Lecuona A, Rodríguez P A, Data validation, false vectors correction and derived magnitudes calculation on PIV data, Measurement Science and Technology, 8, 12, 1997. Crossref

  12. Muste M., Kim Y., Kruger A., Krajewski W., Bradley A., Papanicolaou T., Watershed-Scale Cybertools: Real-time Stream Monitoring at Ungaged Sites, Managing Watersheds for Human and Natural Impacts, 2005. Crossref

  13. Leitão João P., Peña-Haro Salvador, Lüthi Beat, Scheidegger Andreas, Moy de Vitry Matthew, Urban overland runoff velocity measurement with consumer-grade surveillance cameras and surface structure image velocimetry, Journal of Hydrology, 565, 2018. Crossref

  14. Fujita Ichiro, Muste Marian, Kruger Anton, Large-scale particle image velocimetry for flow analysis in hydraulic engineering applications, Journal of Hydraulic Research, 36, 3, 1998. Crossref

  15. Di Cristo Cristiana, Particle Imaging Velocimetry and Its Applications in Hydraulics: A State-of-the-Art Review, in Experimental Methods in Hydraulic Research, 1, 2011. Crossref

  16. Merzkirch Wolfgang, Particle Image Velocimetry, in Optical Measurements, 2001. Crossref

  17. Scharnowski Sven, Kähler Christian J., Particle image velocimetry - Classical operating rules from today’s perspective, Optics and Lasers in Engineering, 135, 2020. Crossref

  18. Fujita Ichiro, Kanda Tohru, Kadowaki Masao, Morita Takamitsu, ANALYSIS OF TURBULENT FLOW IN A RECTANGULAR TRENCH BY PIV AND LES, Doboku Gakkai Ronbunshu, 1996, 539, 1996. Crossref

  19. MIYAZAWA Naoki, INFLUENCE OF FINE COMPONENT ON MOVEMENT OF DEBRIS FLOW FRONT, Doboku Gakkai Ronbunshu, 2001, 677, 2001. Crossref

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