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Atomization and Sprays

Publicou 12 edições por ano

ISSN Imprimir: 1044-5110

ISSN On-line: 1936-2684

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: 1.2 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: 1.8 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: 0.3 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: 0.00095 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: 0.28 SJR: 0.341 SNIP: 0.536 CiteScore™:: 1.9 H-Index: 57

Indexed in

EXTRACTION OF DROPLET GENEALOGIES FROM HIGH-FIDELITY ATOMIZATION SIMULATIONS

Volume 29, Edição 8, 2019, pp. 709-739
DOI: 10.1615/AtomizSpr.2020031624
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RESUMO

Many research groups are performing high-fidelity simulations of atomizing jets that are taking advantage of the continually increasing speed and size of computational resources and advances in numerical methods. These high-fidelity simulations produce extremely large data sets characterizing the flow and giving the ability to gather a better understanding of atomization. However, a significant challenge with these data sets is their large size, which limits the usefulness of the results. The main goal of the present work is to create a physics extraction technique to compute the genealogy of atomization and store the data in a format that can be easily and quickly queried. This information will characterize the process of the coherent liquid core breaking into droplets and ligaments, which may proceed to break up further. Each event in the atomization process will be combined with detailed information, such as droplet size, shape, flow field characteristics, etc. The extracted information is stored in a neo4j database that allows queries. The ultimate goal of the work is to use this information to improve our understanding of atomization and enhance the development and testing of low-fidelity atomization models.

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CITADO POR
  1. Chan Wai Hong Ronald, Dodd Michael S., Johnson Perry L., Moin Parviz, Identifying and tracking bubbles and drops in simulations: A toolbox for obtaining sizes, lineages, and breakup and coalescence statistics, Journal of Computational Physics, 432, 2021. Crossref

  2. Chan Wai Hong Ronald, Johnson Perry L., Moin Parviz, Urzay Javier, The turbulent bubble break-up cascade. Part 2. Numerical simulations of breaking waves, Journal of Fluid Mechanics, 912, 2021. Crossref

  3. Gaylo Declan B., Hendrickson Kelli, Yue Dick K.P., An Eulerian label advection method for conservative volume-based tracking of bubbles/droplets, Journal of Computational Physics, 470, 2022. Crossref

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