Modeling Differential Diffusion Effects in Turbulent Nonreacting/Reacting Jets with Stochastic Mixing Models

Chen, Jyh-Yuan; Chang, W.-C.

doi:10.1080/00102209808952039

Cited by 23 publications

(10 citation statements)

References 28 publications

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“…Models designed to account for the different dissipation rates among species have been considered in the context of non-premixed combustion. Chen and Chang [23] account for differential diffusion, effectively adjusting species mixing rates according to their individual diffusion rates in a hypothetical one-dimensional mixing layer. This approach has some success in diffusion flames, but may be limited by its neglect of the effects of reaction on mixing.…”

Section: Introductionmentioning

confidence: 99%

Numerical analysis of reaction–diffusion effects on species mixing rates in turbulent premixed methane–air combustion

Richardson

Sankaran

Grout

et al. 2010

Combustion and Flame

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The scalar mixing time scale, a key quantity in many turbulent combustion models, is investigated for reactive scalars in premixed combustion. Direct numerical simulations (DNS) of three-dimensional, turbulent Bunsen flames with reduced methane-air chemistry have been analyzed in the thin reaction zones regime. Previous conclusions from single step chemistry DNS studies are confirmed regarding the role of dilatation and turbulence-chemistry interactions on the progress variable dissipation rate. Compared to the progress variable, the mixing rates of intermediate species is found to be several times greater. The variation of species mixing rates are explained with reference to the structure of one-dimensional premixed laminar flames. According to this analysis, mixing rates are governed by the strong gradients which are imposed by flamelet structures at high Damköhler numbers. This suggests a modeling approach to estimate the mixing rate of individual species which can be applied, for example, in transported probability density function simulations. Flame turbulence interactions which modify the flamelet based representation are analyzed.

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Section: Introductionmentioning

confidence: 99%

Numerical analysis of reaction–diffusion effects on species mixing rates in turbulent premixed methane–air combustion

Richardson

Sankaran

Grout

et al. 2010

Combustion and Flame

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“…Closure models that account for differential diffusion have been suggested for pdf-methods [1], CMC methods [14,22] and laminar flamelet methods [20,25]. Chen and Chang's [1] results showed good agreement between simulation and experiments for nonreacting flows. For reacting flows the predictions were less satisfactory and differences were explained with re-laminarisation of the flow in the reaction zone.…”

Section: Introductionmentioning

confidence: 50%

The Effect of Preferential Diffusion on the Soot Initiation Process in Ethylene Diffusion Flames

Malik

Løvås

Mauß

2011

Flow Turbulence Combust

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The influence of differential diffusion of chemical species in the soot initiation process in turbulent flows is investigated through Direct Numerical Simulations coupled to a compact global chemical mechanisms for ethylene (C 2 H 4 ) flame combustion (Løvås et al., Combust Sci Tech 182(11):1945-1960 featuring the important reaction steps for acetylene production. Our focus is on the formation of acetylene (C 2 H 2 ) which is one of the most important species indicative of soot formation layers, especially in relation to the location of the H and H 2 layers. The effect of preferential diffusion is assessed by comparison of results from unity and non-unity Lewis number simulations. The results indicate that under moderate turbulent conditions, where preferential diffusion effects become prominent, and with the global scheme used preferential diffusion greatly enhances the spread of the radical H whose peak value in mass fraction is reduced by a factor of about two; the spread of H 2 is also enhanced though to a lesser extent. Importantly, the H and H 2 spread into a range of mixture fraction Z between 0.2 and 0.3 which contains the soot formation range, supporting the hypothesis that soot formation is enhanced by preferential diffusion. Nevertheless, the acetylene formation layers themselves show little adjustment in the presence of non-unity Lewis numbers suggesting that the acetylene formation is dominated under the current conditions by the direct thermal Flow Turbulence Combust (2011) 87:293-312 decomposition of ethylene to acetylene in the global chemistry used. The specific F i factors that appear in flamelet models are explicitly computed; only F H , F H2 and F CO show appreciable differences on the fuel lean range of mixture fraction due to nonunity Lewis numbers, suggesting that the effects of non-unity Lewis numbers could be incorporated by a selective inclusion of only a few of the F i factors in order to save computational time.

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“…Due to the high diffusivity of hydrogen relative to other species (because its much lower molecular weight) and the importance of hydrogen containing species on carbon monoxide oxidation, it is speculated that differential diffusion plays an important role in the burn-out of carbon monoxide (a dangerous pollutant), and also in flame extinction and re-ignition processes which affect combustor stability. 1 The ability to predict these effects is increasingly important as the focus turns to hydrogen containing fuels such as syngas. However, many existing predictive models neglect differential diffusion.…”

Section: Introductionmentioning

confidence: 99%

A multiple mapping conditioning model for differential diffusion

2014

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This work introduces modeling of differential diffusion within the multiple mapping conditioning (MMC) turbulent mixing and combustion framework. The effect of differential diffusion on scalar variance decay is analyzed and, following a number of publications, is found to scale as Re−1/2. The ability to model the differential decay rates is the most important aim of practical differential diffusion models, and here this is achieved in MMC by introducing what is called the side-stepping method. The approach is practical and, as it does not involve an increase in the number of MMC reference variables, economical. In addition we also investigate the modeling of a more refined and difficult to reproduce differential diffusion effect – the loss of correlation between the different scalars. For this we develop an alternative MMC model with two reference variables but which also makes use of the side-stepping method. The new models are successfully validated against DNS results available in literature for homogenous, isotropic two scalar mixing.

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Modeling Differential Diffusion Effects in Turbulent Nonreacting/Reacting Jets with Stochastic Mixing Models

Cited by 23 publications

References 28 publications

Numerical analysis of reaction–diffusion effects on species mixing rates in turbulent premixed methane–air combustion

Numerical analysis of reaction–diffusion effects on species mixing rates in turbulent premixed methane–air combustion

The Effect of Preferential Diffusion on the Soot Initiation Process in Ethylene Diffusion Flames

A multiple mapping conditioning model for differential diffusion

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