A priori assessment of convolutional neural network and algebraic models for flame surface density of high Karlovitz premixed flames

2021 ◽  
Vol 33 (3) ◽  
pp. 036111
Author(s):  
Jiahao Ren ◽  
Haiou Wang ◽  
Kun Luo ◽  
Jianren Fan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Surface Density ◽  
A Priori ◽  
Premixed Flames ◽  
Flame Surface ◽  
Algebraic Models ◽  
Flame Surface Density

scholarly journals Effects of Turbulent Reynolds Number on the Performance of Algebraic Flame Surface Density Models for Large Eddy Simulation in the Thin Reaction Zones Regime: A Direct Numerical Simulation Analysis

2012 ◽  
Vol 2012 ◽  
pp. 1-13 ◽  
Author(s):  
Mohit Katragadda ◽  
Nilanjan Chakraborty ◽  
R. S. Cant
Keyword(s):  
Numerical Simulation ◽  
Reynolds Number ◽  
Fractal Dimension ◽  
Direct Numerical Simulation ◽  
Surface Density ◽  
Flame Surface ◽  
Algebraic Models ◽  
Flame Surface Density ◽  
Turbulent Reynolds Number ◽  
Reaction Zones

A direct numerical simulation (DNS) database of freely propagating statistically planar turbulent premixed flames with a range of different turbulent Reynolds numbers has been used to assess the performance of algebraic flame surface density (FSD) models based on a fractal representation of the flame wrinkling factor. The turbulent Reynolds number Rethas been varied by modifying the Karlovitz number Ka and the Damköhler number Da independently of each other in such a way that the flames remain within the thin reaction zones regime. It has been found that the turbulent Reynolds number and the Karlovitz number both have a significant influence on the fractal dimension, which is found to increase with increasing Retand Ka before reaching an asymptotic value for large values of Retand Ka. A parameterisation of the fractal dimension is presented in which the effects of the Reynolds and the Karlovitz numbers are explicitly taken into account. By contrast, the inner cut-off scale normalised by the Zel’dovich flame thicknessηi/δzdoes not exhibit any significant dependence on Retfor the cases considered here. The performance of several algebraic FSD models has been assessed based on various criteria. Most of the algebraic models show a deterioration in performance with increasing the LES filter width.

scholarly journals Flame surface density based modelling of head-on quenching of turbulent premixed flames

2017 ◽  
Vol 36 (2) ◽  
pp. 1817-1825 ◽  
Author(s):  
Johannes Sellmann ◽  
Jiawei Lai ◽  
Andreas M Kempf ◽  
Nilanjan Chakraborty
Keyword(s):  
Surface Density ◽  
Premixed Flames ◽  
Flame Surface ◽  
Turbulent Premixed Flames ◽  
Flame Surface Density ◽  
Turbulent Premixed

Measurement of flame surface density for turbulent premixed flames using PLIF and DNS

2007 ◽  
Vol 31 (1) ◽  
pp. 1319-1326 ◽  
Author(s):  
Johan Hult ◽  
Sara Gashi ◽  
Nilanjan Chakraborty ◽  
Markus Klein ◽  
Karl W. Jenkins ◽  
...  
Keyword(s):  
Surface Density ◽  
Premixed Flames ◽  
Flame Surface ◽  
Turbulent Premixed Flames ◽  
Flame Surface Density ◽  
Turbulent Premixed
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