A mixture-fraction-based hybrid binomial Langevin-multiple mapping conditioning model

Wandel, Andrew P. and Lindstedt, R. Peter (2019) A mixture-fraction-based hybrid binomial Langevin-multiple mapping conditioning model. Proceedings of the Combustion Institute, 37 (2). pp. 2151-2158. ISSN 1540-7489

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Generalized Multiple Mapping Conditioning (MMC) allows for the use of any physical quantity to represent the required reference variable provided that it delivers the desired behavior. The binomial Langevin model (BLM) has been shown to predict higher statistical moments with good accuracy. However, joint-scalar modeling for many scalars becomes problematic because scalar bounds must be specified as conditional on other scalars to preserve elemental balances. The resulting volumes in state space become exceptionally complex for realistic problem sizes. In the current work, this central difficulty is avoided by using only velocity and mixture fraction statistics from the BLM with the latter used as the MMC reference variable. The principal advantage of this method is that the implementation of the binomial Langevin mixture fraction is relatively straightforward and provides a direct physical link to MMC. The MMC model is closed using an augmented modified Curl's model where the selection of particle pairs for (turbulent) mixing ensures proximity in reference space and a corresponding closeness in physical space. The method is evaluated for a lifted methane jet flame undergoing auto-ignition in a vitiated coflow. Most of the major features of the flow are well reproduced and found to generally outperform other modeling approaches, including Large Eddy Simulations using simplified treatments of turbulence--chemistry interactions such as unsteady flamelet/progress variable descriptions.

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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Submitted version displayed in accordance with the copyright policy of the publisher.
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Mechanical and Electrical Engineering
Date Deposited: 14 Jun 2019 01:27
Last Modified: 24 Jun 2019 06:08
Uncontrolled Keywords: turbulent combustion, multiple mapping conditioning, MMC, langevin models, lifted flame
Fields of Research : 09 Engineering > 0913 Mechanical Engineering > 091305 Energy Generation, Conversion and Storage Engineering
09 Engineering > 0902 Automotive Engineering > 090201 Automotive Combustion and Fuel Engineering (incl. Alternative/Renewable Fuels)
Socio-Economic Objective: B Economic Development > 85 Energy > 8507 Energy Conservation and Efficiency > 850702 Energy Conservation and Efficiency in Transport
Identification Number or DOI: 10.1016/j.proci.2018.06.122
URI: http://eprints.usq.edu.au/id/eprint/35714

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