Conditional statistics of inert droplet effects on turbulant combustion in reacting mixing layers
Conditional statistics of inert droplet effects on turbulant combustion in reacting mixing layers
Direct numerical simulation (DNS) of turbulent reacting mixing layers laden with evaporating inert droplets is used to assess the droplet effects in the context of the conditional moment closure (CMC) for multiphase turbulent combustion. The temporally developing mixing layer has an initial Reynolds number of 1000 based on the vorticity thickness with more than 16 million Lagrangian droplets traced. An equivalent mixture fraction incorporating the inert vapour mass fractions clearly demonstrates the effects of vapour dilution on the mixture. Instantaneous fields and conditional statistics, such as the singly conditioned scalar dissipation rate, the gas temperature ? Tg|? ?, conditional variance of the gas temperature ? Tg”2|? ? and conditional covariance between the fuel mass fraction and gas temperature ? Yf” Tg”|? ? show considerable droplet effects. Comparison between the droplet-free and droplet-laden reacting mixing layer cases suggests significant extinction in the latter case. The resulting large conditional fluctuations around the conditional means contradict the basic assumption behind the first-order singly conditioned CMC. More sophisticated CMC approaches, such as doubly conditioned or second-order CMCs are, in principle, better able to incorporate the effects of evaporating droplets, but significant modelling challenges exist. The scalar dissipation rate doubly conditioned on the mixture fraction and a normalized gas temperature ? ? | ?, ? ? exemplifies the modelling complexity in the CMC of multiphase combustion.
multiphase combustion, conditional moment closure, droplets, reacting mixing layer, DNS
901-920
Xia, J.
ab23d6f5-1c9b-4f1e-8d6a-47606c9429d7
Luo, Kai
36a077c3-a413-4b2e-8cd0-0d2a0c3ce28f
October 2009
Xia, J.
ab23d6f5-1c9b-4f1e-8d6a-47606c9429d7
Luo, Kai
36a077c3-a413-4b2e-8cd0-0d2a0c3ce28f
Xia, J. and Luo, Kai
(2009)
Conditional statistics of inert droplet effects on turbulant combustion in reacting mixing layers.
Combustion Theory and Modelling, 13 (5), .
(doi:10.1080/13647830903288381).
Abstract
Direct numerical simulation (DNS) of turbulent reacting mixing layers laden with evaporating inert droplets is used to assess the droplet effects in the context of the conditional moment closure (CMC) for multiphase turbulent combustion. The temporally developing mixing layer has an initial Reynolds number of 1000 based on the vorticity thickness with more than 16 million Lagrangian droplets traced. An equivalent mixture fraction incorporating the inert vapour mass fractions clearly demonstrates the effects of vapour dilution on the mixture. Instantaneous fields and conditional statistics, such as the singly conditioned scalar dissipation rate, the gas temperature ? Tg|? ?, conditional variance of the gas temperature ? Tg”2|? ? and conditional covariance between the fuel mass fraction and gas temperature ? Yf” Tg”|? ? show considerable droplet effects. Comparison between the droplet-free and droplet-laden reacting mixing layer cases suggests significant extinction in the latter case. The resulting large conditional fluctuations around the conditional means contradict the basic assumption behind the first-order singly conditioned CMC. More sophisticated CMC approaches, such as doubly conditioned or second-order CMCs are, in principle, better able to incorporate the effects of evaporating droplets, but significant modelling challenges exist. The scalar dissipation rate doubly conditioned on the mixture fraction and a normalized gas temperature ? ? | ?, ? ? exemplifies the modelling complexity in the CMC of multiphase combustion.
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Published date: October 2009
Keywords:
multiphase combustion, conditional moment closure, droplets, reacting mixing layer, DNS
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Local EPrints ID: 147317
URI: http://eprints.soton.ac.uk/id/eprint/147317
PURE UUID: c6a44fd6-d9ba-4c6b-ab64-5b4be44d3b8e
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Date deposited: 23 Apr 2010 13:48
Last modified: 14 Mar 2024 00:58
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Author:
J. Xia
Author:
Kai Luo
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