Hybrid binomial Langevin-multiple mapping conditioning modeling of a reacting mixing layer

Wandel, Andrew P. and Lindstedt, R. Peter (2009) Hybrid binomial Langevin-multiple mapping conditioning modeling of a reacting mixing layer. Physics of Fluids, 21 (1). 015103-015116. ISSN 1070-6631

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A novel, stochastic, hybrid binomial Langevin-multiple mapping conditioning (MMC) model—that utilizes the strengths of each component—has been developed for inhomogeneous flows. The implementation has the advantage of naturally incorporating velocity-scalar interactions through the binomial Langevin model and using this joint probability density function (PDF) to define a reference variable for the MMC part of the model. The approach has the advantage that the difficulties encountered with the binomial Langevin model in modeling scalars with nonelementary bounds are removed. The formulation of the closure leads to locality in scalar space and permits the use of simple approaches (e.g., the modified Curl’s model) for transport in the reference space. The overall closure was evaluated through application to a chemically reacting mixing layer. The results show encouraging comparisons with experimental data for the first two moments of the PDF and plausible results for higher moments at a relatively modest computational cost.

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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Publisher: American Institute of Physics
Item Status: Live Archive
Additional Information (displayed to public): Author's version deposited in accordance with the copyright policy of the publisher (American Institute of Physics).
Depositing User: Dr Andrew Wandel
Faculty / Department / School: Historic - Faculty of Engineering and Surveying - Department of Mechanical and Mechatronic Engineering
Date Deposited: 17 Mar 2010 12:27
Last Modified: 01 Sep 2014 02:39
Uncontrolled Keywords: hybrid binomial Langevin-multiple mapping conditioning model
Fields of Research (FoR): 09 Engineering > 0913 Mechanical Engineering > 091305 Energy Generation, Conversion and Storage Engineering
09 Engineering > 0915 Interdisciplinary Engineering > 091508 Turbulent Flows
09 Engineering > 0915 Interdisciplinary Engineering > 091501 Computational Fluid Dynamics
Socio-Economic Objective (SEO): E Expanding Knowledge > 97 Expanding Knowledge > 970102 Expanding Knowledge in the Physical Sciences
Identification Number or DOI: doi: 10.1063/1.3041716
URI: http://eprints.usq.edu.au/id/eprint/6422

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