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International Journal for Multiscale Computational Engineering
Импакт фактор: 1.016 5-летний Импакт фактор: 1.194 SJR: 0.452 SNIP: 0.68 CiteScore™: 1.18

ISSN Печать: 1543-1649
ISSN Онлайн: 1940-4352

Выпуски:
Том 17, 2019 Том 16, 2018 Том 15, 2017 Том 14, 2016 Том 13, 2015 Том 12, 2014 Том 11, 2013 Том 10, 2012 Том 9, 2011 Том 8, 2010 Том 7, 2009 Том 6, 2008 Том 5, 2007 Том 4, 2006 Том 3, 2005 Том 2, 2004 Том 1, 2003

International Journal for Multiscale Computational Engineering

DOI: 10.1615/IntJMultCompEng.2017017084
pages 1-17

PARTICLE-FILTER BASED UPSCALING FOR TURBULENT REACTING FLOW SIMULATIONS

Shubham Srivastava
Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC 27695, USA
Tarek Echekki
Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, North Carolina 27695, USA

Краткое описание

The particle filter is used to couple a coarse-grained (CG) deterministic solution for a reacting flow with a fine-grained (FG) stochastic solution. The proposed method investigates the feasibility of implementing a multiscale approach for turbulent reacting flows based on large-eddy simulation (LES) coupled with a low-dimensional fine-grained stochastic solution for the subfilter scales reaction and transport. In this study, a model for the turbulent transport in the FG solution is implemented using the linear-eddy model (LEM), which combines a deterministic implementation for reaction, diffusion, and large-scale transport with a stochastic implementation for fine-scale transport. The solution for the continuity and momentum (the Burgers' equation) equations are implemented in 1D. The filtered densities obtained through the FG and the CG solutions are combined using the particle filter to obtain an updated density for the coarse solution that combines the effects of heat release (from the FG solution) and flow dynamics (from the CG solution). The results demonstrate that the particle filter may be a viable tool to couple deterministic CG solutions and stochastic FG solutions in reacting flow applications.


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