A mixedness-reactedness flamelet model for turbulent diffusion flames

Bradley, D.; Gaskell, Philip; Lau, A. K. C.

doi:10.1016/s0082-0784(06)80317-6

Cited by 54 publications

(52 citation statements)

References 18 publications

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“…The mean scalar quantities such as density and temperature can now be written as (9) In this equation the last term is the isothermal value of the scalar, and the factor in front of it is the probability that the flame base is located downstream of x. The probability that the instantaneous flame base is upstream of x is denoted by P(x > xL).…”

Section: Modeling Of the Fluctuating Flame Basementioning

confidence: 99%

“…Due to the fluctuations of the flame base, there might be a back coupling from downstream to upstream positions in the flame [9]. This necessitates the use of an elliptical computercode.…”

Section: Turbulent Flow Calculationsmentioning

confidence: 99%

“…A third concept for predicting liftoff heights of turbulent diffusion flames was proposed by Bradley et al [9] with an alternative, so called mixedness-reactedness, flamelet model. Here the combustion is considered to take place in a premixed mode in the diffusion flame.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Modeling and calculation of turbulent lifted diffusion flames

Sanders

Lamers

1994

Combustion and Flame

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Section: Modeling Of the Fluctuating Flame Basementioning

confidence: 99%

“…Due to the fluctuations of the flame base, there might be a back coupling from downstream to upstream positions in the flame [9]. This necessitates the use of an elliptical computercode.…”

Section: Turbulent Flow Calculationsmentioning

confidence: 99%

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Modeling and calculation of turbulent lifted diffusion flames

Sanders

Lamers

1994

Combustion and Flame

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“…There have been significant successes in the mathematical modelling of lift-off distances, L, and plume heights for pure fuel jet flames, and in the associated formulation of appropriate dimensionless groups for the correlation of experimental data [2][3][4][5][6]. The region between the exit plane of a fuel jet discharging into the atmosphere and the flame leading edge is one of intense mixing that generates high strain rates.…”

Section: Introductionmentioning

confidence: 99%

“…The stretched laminar flamelet modelling in [2][3][4], in conjunction with experimental jet flame data, have led to more practical, generalised, correlations of experimental jet flame data, involving a dimensionless flow number, U*, that is closely related to the Karlovitz stretch factor, employed in premixed turbulent combustion [6], where…”

Section: Introductionmentioning

confidence: 99%

Lift-off and blow-off of methane and propane subsonic vertical jet flames, with and without diluent air

Palacios

Bradley

2016

Fuel

Self Cite

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The paper seeks to increase understanding of subsonic jet flame blow-off phenomena, through experimental studies that include the controlled introduction of air into the fuel jet. As the molar concentration of air in the jet flame gas, Aj, is increased the reaction zone becomes leaner, and the flame lift-off distance increases.Eventually, flame oscillations develop and are followed by flame blow-off. A jet mixing analysis enables the extent of the leaning-off of the mixture to be estimated. From this, the reduced mean flamelet burning velocity, ua, is found at the location of the pure fuel jet flame. The conditions for blow-off are correlated with the last measured stable values of the dimensionless flow number, Ub*, for methane and propane jet flames, with and without added air. Values of Ub* decline as the proportion of added air increases, more markedly so with methane. This is attributed to the leaning-off of the flame, and the associated decrease in the flame extinction stretch rate. As Ub* declines in value, with increasing air dilution, the emissions of unburned hydrocarbons just prior to blow-off increase. An underlying generality of the findings is revealed when ua is introduced into the expression for Ub*, and Aj is normalised by the moles of air required to burn a mole of fuel.

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On the Use of Machine Learning for Subgrid Scale Filtered Density Function Modelling in Large Eddy Simulations of Combustion Systems

Iavarone

Yang

et al. 2023

Lecture Notes in Energy

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The application of machine learning algorithms to model subgrid-scale filtered density functions (FDFs), required to estimate filtered reaction rates for Large Eddy Simulation (LES) of chemically reacting flows, is discussed in this chapter. Three test cases, i.e., a low-swirl premixed methane-air flame, a MILD combustion of methane-air mixtures, and a kerosene spray turbulent flame, are presented. The scalar statistics in these test cases may not be easily represented using the commonly used presumed shapes for modeling FDFs of mixture fraction and progress variable. Hence, the use of ML methods is explored. Particularly, deep neural network (DNN) to infer joint FDFs of mixture fraction and progress variable is reviewed here. The Direct Numerical Simulation (DNS) datasets employed to train the DNNs in each test case are described. The DNN performances are shown and compared to typical presumed probability density function (PDF) models. Finally, this chapter examines the advantages and caveats of the DNN-based approach.

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A mixedness-reactedness flamelet model for turbulent diffusion flames

Cited by 54 publications

References 18 publications

Modeling and calculation of turbulent lifted diffusion flames

Modeling and calculation of turbulent lifted diffusion flames

Lift-off and blow-off of methane and propane subsonic vertical jet flames, with and without diluent air

On the Use of Machine Learning for Subgrid Scale Filtered Density Function Modelling in Large Eddy Simulations of Combustion Systems

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