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Journal of Flow Visualization and Image Processing

Publication de 4  numéros par an

ISSN Imprimer: 1065-3090

ISSN En ligne: 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

IDENTIFICATION OF GAS−LIQUID TWO-PHASE FLOW REGIMES USING VAGUE SETS AND IMAGE PROCESSING

Volume 20, Numéro 3, 2013, pp. 161-177
DOI: 10.1615/JFlowVisImageProc.2014011926
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RÉSUMÉ

A new method was developed for identifying gas−liquid two-phase flow regimes. The method was based on vague sets and image processing techniques. The gas−liquid two-phase flow images were captured by a camera in a transparent horizontal pipe. The characteristics of flow images such as area, width, length, and center coordinates were extracted using image processing techniques. The vague sets models of bubbly, stratified, slug, and annular flow regimes were built according to the typical flow image features. The cosine similarity measure was used to determine the degree of similarity between two vague sets. The flow pattern can be identified accurately according to the maximum cosine similarity degree of vague sets between the test flow regime and the typical flow regimes. The experimental results show that the proposed method is effective to identify the basic flow regimes in a horizontal pipe.

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