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Interfacial Phenomena and Heat Transfer

Publicou 4 edições por ano

ISSN Imprimir: 2169-2785

ISSN On-line: 2167-857X

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: 0.5 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 0.8 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: 0.2 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: 0.00018 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: 0.11 SJR: 0.286 SNIP: 1.032 CiteScore™:: 1.6 H-Index: 10

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NEURAL NETWORK APPROACH FOR PLUG FLOW ANALYSIS IN MICROCHANNELS

Volume 10, Edição 1, 2022, pp. 15-24
DOI: 10.1615/InterfacPhenomHeatTransfer.2022043493
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RESUMO

Liquid-liquid and gas-liquid flows in microchannels are widely utilized in various technological fields. The plug/droplet flow regime is preferable in many applications. The features of plugs and droplets, such as the length, volume, and velocity, are critical parameters when developing new microchannel devices. The general approach employed to define plug features is based on image processing algorithms, in which the spatial filters are used in edge detection. This approach's main drawback consists of manually adjusting the parameters, such as the filter type, threshold, background removal procedure, etc. Here, we present a neural network approach for plug/droplet detection. A comprehensive data set for neural network training was compiled. The results of the neural network training are discussed, and a comparison with the image processing algorithm is provided. The proposed method has shown consistent numerical measurements. The average deviations of the measured plug size and velocity did not exceed 1.71% and 0.91%, respectively. New data on the plug size and velocity for an extremely low-viscosity ratio of the phases have been obtained.

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CITADO POR
  1. Vasyukov A. V., Nikitin I. S., Stankevich A. S., Golubev Vasily I., DEEP CONVOLUTIONAL NEURAL NETWORKS IN SEISMIC EXPLORATION PROBLEMS, Interfacial Phenomena and Heat Transfer, 10, 3, 2022. Crossref

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