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Journal of Flow Visualization and Image Processing

Published 4 issues per year

ISSN Print: 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

EVALUATION OF THE RAINBOW VOLUMIC VELOCIMETRY (RVV) PROCESS BY SYNTHETIC IMAGES

Volume 14, Issue 1, 2007, pp. 1-15
DOI: 10.1615/JFlowVisImageProc.v14.i1.10
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ABSTRACT

The main goal of this paper is to present recent improvements of the original optical method dedicated to 3D flows, named R VV (Rainbow Volumic Velocimetry). The authors propose a new way to evaluate the importance of the image quality in flow visualization. A method for generating synthetic images has been developed in order to simulate the RVV technique. With this approach, some factors can be tested such as image noise, image contrast, and spectrum characteristics. Moreover, the performance of image processing tools specifically developed for RVV applications can be also estimated. The principles of both PTV and PSV have been investigated synthetically.

CITED BY
  1. Rosenstiel Marcus, Grigat Rolf-Rainer, Segmentation and classification of streaks in a large-scale particle streak tracking system, Flow Measurement and Instrumentation, 21, 1, 2010. Crossref

  2. Zappa Emanuele, Malavasi Stefano, Negri Marco, Uncertainty budget in PSV technique measurements, Flow Measurement and Instrumentation, 30, 2013. Crossref

  3. Aguirre-Pablo A. A., Aljedaani A. B., Xiong J., Idoughi R., Heidrich W., Thoroddsen S. T., Single-camera 3D PTV using particle intensities and structured light, Experiments in Fluids, 60, 2, 2019. Crossref

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