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International Journal for Multiscale Computational Engineering

Publicado 6 números por año

ISSN Imprimir: 1543-1649

ISSN En Línea: 1940-4352

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: 1.4 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: 1.3 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: 2.2 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: 0.00034 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: 0.46 SJR: 0.333 SNIP: 0.606 CiteScore™:: 3.1 H-Index: 31

Indexed in

UPSCALING AND DOWNSCALING APPROACHES IN LES-ODT FOR TURBULENT COMBUSTION FLOWS

Volumen 16, Edición 1, 2018, pp. 45-76
DOI: 10.1615/IntJMultCompEng.2018021350
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SINOPSIS

The multiscale LES-ODT approach in turbulent combustion is based on a hybrid temporal and spatial coupling of a coarse 3D large-eddy simulation (LES) with embedded fine-grained 1D solutions for momentum and thermochemical scalars using the one-dimensional turbulence (ODT) model. Schemes for coupling LES and ODT using wavelet-based approaches for downscaling (information passing from LES to ODT) and the Kalman filter for upscaling (passing statistics from ODT to LES) are developed. Comparison between direct numerical simulation (DNS) and LES-ODT predictions are performed for various turbulence and turbulent combustion cases, including autoignition in non-homogeneous mixtures and twin premixed flames. The results show that the developed upscaling and downscaling schemes predict reasonably well statistics of the combustion problems investigated.

CITADO POR
  1. Hoffie Andreas F., Echekki Tarek, A coupled LES-ODT model for spatially-developing turbulent reacting shear layers, International Journal of Heat and Mass Transfer, 127, 2018. Crossref

  2. Gitushi Kevin M., Ranade Rishikesh, Echekki Tarek, Investigation of deep learning methods for efficient high-fidelity simulations in turbulent combustion, Combustion and Flame, 236, 2022. Crossref

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