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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

ANALYZING SHADOWGRAPH IMAGES: A NEW ALGORITHM

Volume 24, Numéro 1-4, 2017, pp. 291-314
DOI: 10.1615/JFlowVisImageProc.v24.i1-4.180
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RÉSUMÉ

A new algorithm for constructing shadowgraph images from approximate density fields is presented with the primary motivation of performing accurate laboratory shadowgraph analysis. Available image construction algorithms are noisy and produce discontinuous errors even for small gradient density fields. Discontinuity errors are serious, being indistinguishable from real optical focusing which occurs frequently. The new algorithm completely eliminates these errors. Image improvements are demonstrated for realistic synthetic refractive index fields. Favorable comparisons of the new algorithm are also demonstrated with laboratory shadowgraph of natural convection flows in a cavity which feature large density gradients. A second motivation of the paper is to accurately analyze approximate shadowgraph images derived from a linearized analytical model for refraction. The linearized shadowgraph images are correlated with the artificial shadowgraph images of the new algorithm. Preliminary results indicate that quantitative information from shadowgraph images of larger gradient density fields could be obtained by iterating about the linear solution.

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