Large eddy simulation of a growing turbulent premixed flame kernel using a dynamic flame surface density model

Wang, Gaofeng; Boileau, Matthieu; Veynante, Denis; Truffin, Karine

doi:10.1016/j.combustflame.2012.02.018

Cited by 58 publications

(78 citation statements)

References 56 publications

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“…may be used as a tuning constant or modelled using an algebraic expression or a balance equation. Applications of this model in modelling early stage flame development has been reported in [22] and in coupled radiative heat transfer calculations in [42]. Another promising approach is to solve a transport equation for the wrinkling factor as in [43].…”

Section: A Dynamic Fsd Model For the Fully Developed Phase Of Combustionmentioning

confidence: 99%

“…For example, if the combustion filter width ̅ is taken to be 4 times the grid filter width ̅ , the progress variable gradient is dispersed over ̅ ̅ ⁄ grid points ( ̅ ̅ ⁄ in this case). In practice, the flame front is thickened by a factor in the order of ̅ ⁄ [22] making it possible to resolve with a sufficiently large filter width.…”

Section: Combustion Filter Widthmentioning

confidence: 99%

“…Following [22], is modelled as the ratio between the combustion filter scale and the inner cut off scale. ̅ and are respectively the largest and the smallest possible flame wrinkling scales.…”

Section: A Dynamic Fsd Model For the Fully Developed Phase Of Combustionmentioning

confidence: 99%

“…For better results this can be up to fifty times [17]. Hence, compared to the other methods, thicken flame approach is more demanding in terms of the mesh resolution requirement [22].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Large Eddy Simulation of Premixed Combustion in Spark Ignited Engines Using a Dynamic Flame Surface Density Model

Ranasinghe

Malalasekera

Clarke

2013

SAE Int. J. Engines

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Section: A Dynamic Fsd Model For the Fully Developed Phase Of Combustionmentioning

confidence: 99%

Section: Combustion Filter Widthmentioning

confidence: 99%

Section: A Dynamic Fsd Model For the Fully Developed Phase Of Combustionmentioning

confidence: 99%

“…For better results this can be up to fifty times [17]. Hence, compared to the other methods, thicken flame approach is more demanding in terms of the mesh resolution requirement [22].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Large Eddy Simulation of Premixed Combustion in Spark Ignited Engines Using a Dynamic Flame Surface Density Model

Ranasinghe

Malalasekera

Clarke

2013

SAE Int. J. Engines

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“…These approaches can be broadly categorized into two classes, namely, flamelets and non-flamelets or geometrical and statistical (Gicquel et al, 2012). The geometrical category of flamelets includes thickened flame (Colin et al, 2000;De and Acharya, 2009), flame surface density or flame-wrinkling (see, for example, Boger et al 1998;Chakraborty and Cant 2007;Chakraborty and Klein 2008;Gubba et al, 2012;Hawkes and Cant 2001;Knikker et al, 2004;Wang et al, 2012), and level-set or G equation (Moureau et al, 2008;Pitsch, 2005). The statistical category of flamelets includes approaches, such as EBU (eddy-break-up model), algebraic closure involving scalar dissipation rate (Butz et al, 2015;Dunstan et al, 2013;Gao et al, 2014;Langella et al, 2015;Ma et al, 2014), and presumed probability density function (PDF) methodology with laminar flamelets.…”

Section: Introductionmentioning

confidence: 99%

Large Eddy Simulation of Premixed Combustion: Sensitivity to Subgrid Scale Velocity Modeling

Langella

Swaminathan

Gao

et al. 2016

Combustion Science and Technology

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An algebraic reaction rate closure involving filtered scalar dissipation rate of reaction progress variable is studied. The filtered scalar dissipation rate closure requires a model for sub-grid scale velocity, u 0 Δ , which is estimated using four algebraic models and transported subgrid scale kinetic energy. A priori analyses using direct numerical simulation (DNS) data show that the filtered dissipation rate, and thus the reaction rate closure, has some sensitivity to the u 0 Δ model. The sensitivity of various statistics obtained from large eddy simulation (LES) of three piloted Bunsen flames of stoichiometric methaneair mixture to the modeling of u 0 Δ is observed to be weaker compared to that for the DNS analysis. Moreover, analysis using transported sub-grid scale kinetic energy does not indicate a necessity to include flame-generated turbulence in the modeling of u 0 Δ for the Bunsen flames in the thin reaction zones regime. The measured and computed flame brush structures are compared and studied and the algebraic closure for the filtered reaction rate is found to be quite good. ARTICLE HISTORY

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Deep Convolutional Neural Networks for Subgrid-Scale Flame Wrinkling Modeling

Xing

Lapeyre

2023

Lecture Notes in Energy

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Subgrid-scale flame wrinkling is a key unclosed quantity for premixed turbulent combustion models in large eddy simulations. Due to the geometrical and multi-scale nature of flame wrinkling, convolutional neural networks are good candidates for data-driven modeling of flame wrinkling. This chapter presents how a deep convolutional neural network called a U-Net is trained to predict the total flame surface density from the resolved progress variable. Supervised training is performed on a database of filtered and downsampled direct numerical simulation fields. In an a priori evaluation on a slot burner configuration, the network outperforms classical dynamic models. In closing, challenges regarding the ability of deep convolutional networks to generalize to unseen configurations and their practical deployment with fluid solvers are discussed.

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Large eddy simulation of a growing turbulent premixed flame kernel using a dynamic flame surface density model

Cited by 58 publications

References 56 publications

Large Eddy Simulation of Premixed Combustion in Spark Ignited Engines Using a Dynamic Flame Surface Density Model

Large Eddy Simulation of Premixed Combustion in Spark Ignited Engines Using a Dynamic Flame Surface Density Model

Large Eddy Simulation of Premixed Combustion: Sensitivity to Subgrid Scale Velocity Modeling

Deep Convolutional Neural Networks for Subgrid-Scale Flame Wrinkling Modeling

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