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

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Title: A mixture-fraction-based hybrid binomial Langevin-multiple mapping conditioning model
Authors: Wandel, AP
Lindstedt, RP
Item Type: Journal Article
Abstract: 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.
Issue Date: 1-Jan-2021
Date of Acceptance: 17-Jun-2018
URI: http://hdl.handle.net/10044/1/61514
DOI: 10.1016/j.proci.2018.06.122
ISSN: 0082-0784
Publisher: Elsevier
Start Page: 2151
End Page: 2158
Journal / Book Title: Proceedings of the Combustion Institute
Volume: 37
Issue: 2
Copyright Statement: © 2018 Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/
Keywords: Science & Technology
Physical Sciences
Technology
Thermodynamics
Energy & Fuels
Engineering, Chemical
Engineering, Mechanical
Engineering
Turbulent combustion
Multiple Mapping Conditioning
MMC
Langevin models
Lifted flame
PROBABILITY DENSITY-FUNCTION
LARGE-EDDY SIMULATION
FINITE RATE CHEMISTRY
TURBULENT
EXTINCTION
FLAME
REIGNITION
CLOSURE
LIMIT
0902 Automotive Engineering
0904 Chemical Engineering
0913 Mechanical Engineering
Publication Status: Published
Online Publication Date: 2018-07-06
Appears in Collections:Central Faculty