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Journal of Flow Visualization and Image Processing

Publicado 4 números por año

ISSN Imprimir: 1065-3090

ISSN En Línea: 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

DISCUSSION OF THE CROSS-CORRELATION METHODS FOR PIV

Volumen 3, Edición 1, 1996, pp. 65-78
DOI: 10.1615/JFlowVisImageProc.v3.i1.50
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SINOPSIS

In the image cross-correlation method for the particle imaging velocimetry (PIV acronym), fluid flow velocity vectors are calculated on the grounds of distribution pattern of particle clouds or particle images between two-consecutive pictures using the cross-correlation coefficient. Cross-correlation methods are classified into two types: one is called the brightness distribution method, which uses the equation of cross-correlation defined in the field of primary statistics, and the other is called the binary image method, which uses the equation of cross-correlation devised for the convenience of high-speed calculation without strict mathematical derivation.
This article shows that an exact form of cross-correlation for the brightness distribution method and the conventional equation of cross-correlation for the binary image method can be derived mathematically by using set theory and step function from a general equation defined in an integral form. The characteristics of the two types of cross-correlation methods are discussed based on the derived equations of cross-correlation.
Furthermore, the authors propose a new relation among the correlation parameters of time interval, identification domain size, imaginary particle size, and the velocity gradient tensors in order to raise applicability of the cross-correlation method to any flow fields such as turbulent flows, which include velocity gradient tensors of expansion and compression, shearing, and rotation.

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