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Journal of Flow Visualization and Image Processing

Publicou 4 edições por ano

ISSN Imprimir: 1065-3090

ISSN On-line: 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

CONCENTRATION ESTIMATION IN TWO-DIMENSIONAL BLUFF BODY WAKES USING IMAGE PROCESSING AND NEURAL NETWORKS

Volume 8, Edição 2-3, 2001, 19 pages
DOI: 10.1615/JFlowVisImageProc.v8.i2-3.30
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RESUMO

Knowledge of the variation of scalar quantities like concentration is required to understand the mixing and dilution characteristics of environmentally important flow fields. The present study deals with the development of a nonintrusive flow visualization technique using neural networks to study the topology of dye concentration distribution in two-dimensional flow fields. Aflow fast a bluff body in a shallow open-channel flow is used to illustrate the technique developed. The present study aims to eliminate problems associated with previous image to concentration conversion techniques. The flow field is captured using a video camera. The captured images are then converted into concentration data with the aid of neural network. To this end, the use of four different networks with varying input data was investigated. The estimations were validated using concentration measurements conducted by a light absorption probe.

CITADO POR
  1. Hocˇevar Marko , Sˇirok Brane , Grabec Igor , Experimental Turbulent Field Modeling by Visualization and Neural Networks , Journal of Fluids Engineering, 126, 3, 2004. Crossref

  2. Young David L., Larsson Ann I., Webster Donald R., Structure and mixing of a meandering turbulent chemical plume: concentration and velocity fields, Experiments in Fluids, 62, 12, 2021. Crossref

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