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Atomization and Sprays

Publicado 12 números por año

ISSN Imprimir: 1044-5110

ISSN En Línea: 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

PRIMARY ATOMIZATION INSTABILITY EXTRACTION USING DYNAMIC MODE DECOMPOSITION

Volumen 28, Edición 12, 2018, pp. 1061-1079
DOI: 10.1615/AtomizSpr.2019029356
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SINOPSIS

Numerical methods have advanced to the point that many groups can perform detailed numerical simulations of atomizing liquid jets and replicate experimental measurements. However, the simulation results have not lead to a substantial advancement to our understanding of these flows due to the massive amount of data produced. In this work, a tool is developed to extract the physics that destabilize the jet's liquid core by leveraging dynamic mode decomposition (DMD). DMD is a data-driven reduced-order modeling technique that takes ideas from the Arnoldi method as well as the Koopman method to evaluate a nonlinear system with a low-rank linear operator. The method reduces the order of the simulation results from the original spatially and temporally varying data to a few key pieces of information. Most important of these are the dynamic modes, their time dynamics, and the DMD spectra. In this work, DMD is applied to the jet's liquid core outer radius, which is computed at azimuthal and streamwise locations, i.e., R(θ, x). With the DMD data, we obtain the dominant spatial modes of the system and their temporal characteristics. The dominant modes provide a useful way to collapse the large data set produced by the simulation into a length and time scale that can be used to initiate reduced-order models and numerically categorize the instabilities on the jet's liquid core.

CITADO POR
  1. Leask S. B., McDonell V. G., Samuelsen S., Modal extraction of spatiotemporal atomization data using a deep convolutional Koopman network, Physics of Fluids, 33, 3, 2021. Crossref

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