2-D and 3-D measurements of flame stretch and turbulence–flame interactions in turbulent premixed flames using DNS

Wang, Haiou; Hawkes, Evatt R.; Ren, Jiahao; Chen, Guo; Luo, Kun; Fan, Jianren

doi:10.1017/jfm.2020.1171

Cited by 15 publications

(26 citation statements)

References 62 publications

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“…If h 3 is the height of A ′ above the tangent plane (see Fig. 1b), it can be shown that (Hawkes et al 2011;Veynante et al 2010;Chakraborty and Hawkes 2011;Chakraborty et al 2013;Wang et al 2021):…”

Section: Mathematical Backgroundmentioning

confidence: 99%

“…where y e1 and y e2 are distances in the principal directions �� ⃗ e 1 and �� ⃗ e 2 corresponding to principal curvatures 1 and 2 in the tangent plane at the point A . Figure 1 suggests that the measured height h 2 above the tangent t 1 in the two-dimensional plane can be expressed as (Hawkes et al 2011;Veynante et al 2010;Chakraborty and Hawkes 2011;Chakraborty et al 2013;Wang et al 2021):…”

Section: Mathematical Backgroundmentioning

confidence: 99%

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Relations Between Statistics of Three-Dimensional Flame Curvature and its Two-Dimensional Counterpart in Turbulent Premixed Flames

Chakraborty

Rasool

Ahmed

et al. 2022

Flow Turbulence Combust

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The relations between the actual flame curvature probability density function (PDF) evaluated in three-dimensions and its two-dimensional counterpart based on planar measurements have been analytically derived subject to the assumptions of isotropy and statistical independence of various angles and two-dimensional curvature. These relations have been assessed based on Direct Numerical Simulation (DNS) databases of turbulent premixed (a) statistically planar and (b) statistically axisymmetric Bunsen flames. It has been found that the analytically derived relation interlinking the PDFs of actual three-dimensional curvature and its two-dimensional counterpart holds reasonably well for a range of curvatures around the mean value defined by the inverse of the thermal flame thickness for different turbulence intensities across different combustion regimes. The flame surface is shown to exhibit predominantly two-dimensional cylindrical curvature but there is a significant probability of finding saddle type flame topologies and this probability increases with increasing turbulence intensity. The presence of saddle type flame topologies affects the ratios of second and third moments of two-dimensional and three-dimensional curvatures. It has been demonstrated that the ratios of second and third moments of two-dimensional and three-dimensional curvatures cannot be accurately predicted based on two-dimensional measurements. The ratio of the third moments of two-dimensional and three-dimensional curvatures remains positive and thus the qualitative nature of curvature skewness can still be obtained based on two-dimensional curvature measurements. As the curvature skewness is often taken to be a marker of the Darrius-Landau instability, the conclusion regarding the presence of this instability can potentially be taken from the two-dimensional curvature measurements.

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Section: Mathematical Backgroundmentioning

confidence: 99%

Section: Mathematical Backgroundmentioning

confidence: 99%

Relations Between Statistics of Three-Dimensional Flame Curvature and its Two-Dimensional Counterpart in Turbulent Premixed Flames

Chakraborty

Rasool

Ahmed

et al. 2022

Flow Turbulence Combust

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“…The simulation details were given in our previous studies ,,, and are briefly introduced in this section. Three-dimensional DNS of methane/air freely propagating premixed flame with different turbulent intensities is performed.…”

Section: Dns Data Setsmentioning

confidence: 99%

“…The simulation details were given in our previous studies 29,33,34,49 and are briefly introduced in this section. Three- 1a shows a schematic of the DNS configuration.…”

Section: Dns Data Setsmentioning

confidence: 99%

A Priori Modeling of NO Formation with Principal Component Analysis and the Convolutional Neural Network in the Context of Large Eddy Simulation

et al. 2021

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Large eddy simulation (LES) plays a significant role in turbulent flame modeling. However, accurate prediction of nitrogen oxide (NO x ) formation in turbulent combustion is challenging in LES as the characteristic timescale of the NO reaction is much larger than that of fuel oxidation. In the present work, a machine learning-based model using principal component analysis (PCA) and the convolutional neural network (CNN) was proposed to predict the NO reaction rate in the framework of LES. Direct numerical simulation (DNS) of CH 4 /air freely propagating premixed flames with various turbulent intensities was employed to assess the model performance a priori. The input features of the CNN model include the filtered temperature and species mass fraction related to NO formation. PCA was used to reduce the data dimensions and to remove the noise of the input features. The presented model was trained using samples from a single case and was tested using samples from cases with various turbulent intensities and filter sizes. Various NO pathways, that is, thermal, prompt, N 2 O, and NO 2 pathways, were examined. The distributions of the modeled NO pathways were compared with those of the DNS. It was shown that the model performs well in predicting the thermal, prompt, and N 2 O pathways, with relative errors being smaller than 0.4 for these pathways. As for the NO 2 pathway, non-negligible errors were observed, and the relative errors can be larger than 0.6. The correlations of the actual and modeled total NO reaction rate are evident, with correlation coefficients being higher than 0.98 generally. The conditional means from the CNN model are in good agreement with those from the DNS. Overall, the CNN model performs well for NO prediction in turbulent flames with various turbulent intensities.

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“…For example, a recent study by Wang et al. (2021 b ) showed that 2-D and 3-D turbulence–flame interactions can differ qualitatively in turbulent premixed flames, with the flame normal vector being misaligned (weakly aligned) with the most extensive strain rate in 3-D (2-D) cases.…”

Section: Introductionmentioning

confidence: 99%

Flame edge structures and dynamics in planar turbulent non-premixed inclined slot-jet flames impinging at a wall

et al. 2021

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2-D and 3-D measurements of flame stretch and turbulence–flame interactions in turbulent premixed flames using DNS

Abstract: Abstract

Cited by 15 publications

References 62 publications

Relations Between Statistics of Three-Dimensional Flame Curvature and its Two-Dimensional Counterpart in Turbulent Premixed Flames

Relations Between Statistics of Three-Dimensional Flame Curvature and its Two-Dimensional Counterpart in Turbulent Premixed Flames

A Priori Modeling of NO Formation with Principal Component Analysis and the Convolutional Neural Network in the Context of Large Eddy Simulation

Flame edge structures and dynamics in planar turbulent non-premixed inclined slot-jet flames impinging at a wall

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