A sparse-Lagrangian multiple mapping conditioning model for turbulent diffusion flames

Cleary, M. J., Klimenko, A. Y., Janicka, J. and Pfitzner, M. (2009) A sparse-Lagrangian multiple mapping conditioning model for turbulent diffusion flames. Proceedings of the Combustion Institute, 32 1: 1499-1507.


Author Cleary, M. J.
Klimenko, A. Y.
Janicka, J.
Pfitzner, M.
Title A sparse-Lagrangian multiple mapping conditioning model for turbulent diffusion flames
Journal name Proceedings of the Combustion Institute   Check publisher's open access policy
ISSN 1540-7489
Publication date 2009
Year available 2009
Sub-type Article (original research)
DOI 10.1016/j.proci.2008.07.015
Volume 32
Issue 1
Start page 1499
End page 1507
Total pages 9
Editor P. Dagaut
V. Sick
Place of publication New York, United States of America
Publisher Elsevier
Collection year 2010
Language eng
Subject 091508 Turbulent Flows
091501 Computational Fluid Dynamics
C1
850799 Energy Conservation and Efficiency not elsewhere classified
299902 Combustion and Fuel Engineering
Abstract A sparse-Lagrangian multiple mapping conditioning (MMC) model for turbulent diffusion flames is presented and tested against experimental data for a piloted methane/air jet diffusion flame (Sandia Flame D). The model incorporates a large eddy simulation for the flow field and a stochastic multiple mapping conditioning (MMC) approach for the reactive scalars. The stochastic MMC models the filtered density function of the scalar composition field. The numerical implementation involves a sparse-Lagrangian particle scheme in which there are fewer particles than there are LES grid cells. Predictions of similar accuracy to previously published Flame D simulations are achieved using only 35,000 particles (of these only 10,000 are chemically active). Sub-filter conditional dissipation is modelled by interactions between pairs of particles which are closely located in a reference mixture fraction space interpolated from the underlying Eulerian filtered field. A model is developed for the mixing time-scale which is proportional to the distance between mixing particles. It is shown that the time-scale can be adjusted to achieve good predictions for time-averaged mean and fluctuating statistics of passive and reactive scalars. © 2009 Elsevier Inc. All rights reserved.
Keyword Multiple mapping conditioning
Sparse simulations
Diffusion flames
Probability density-function
Q-Index Code C1
Q-Index Status Confirmed Code

Document type: Journal Article
Sub-type: Article (original research)
Collections: 2010 Higher Education Research Data Collection
School of Mechanical & Mining Engineering Publications
 
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Created: Thu, 03 Sep 2009, 08:23:22 EST by Mr Andrew Martlew on behalf of School of Mechanical and Mining Engineering