scholarly journals C213 Variations of Flame Surface Density of Spherically Propagating Premixed Turbulent Flame with Flame Propagation and Pressure

2012 ◽  
Vol 2012 (0) ◽  
pp. 361-362
Author(s):  
Akihiro Hayakawa ◽  
Toshihiko Kubo ◽  
Shiori Tanaka ◽  
Yukihide Nagano ◽  
Toshiaki Kitagawa
Keyword(s):  
Flame Propagation ◽  
Surface Density ◽  
Turbulent Flame ◽  
Flame Surface ◽  
Flame Surface Density ◽  
Premixed Turbulent Flame

104 Variation of flame surface density with flame propagation of spherically propagating premixed turbulent flame

2014 ◽  
Vol 2014.67 (0) ◽  
pp. _104-1_-_104-2_
Author(s):  
Tatsuya KAI ◽  
Yotaro WAKABAYASHI ◽  
Taiki TSUKAMOTO ◽  
Akira NOMO ◽  
Yukihide NAGANO ◽  
...  
Keyword(s):  
Flame Propagation ◽  
Surface Density ◽  
Turbulent Flame ◽  
Flame Surface ◽  
Flame Surface Density ◽  
Premixed Turbulent Flame

scholarly journals The evolution equation for the flame surface density in turbulent premixed combustion

1994 ◽  
Vol 278 ◽  
pp. 1-31 ◽  
Author(s):  
Arnaud Trouvé ◽  
Thierry Poinsot
Keyword(s):  
Evolution Equation ◽  
Flame Propagation ◽  
Surface Density ◽  
Turbulent Flame ◽  
Premixed Combustion ◽  
Flame Surface ◽  
Turbulent Premixed Combustion ◽  
Flame Surface Density ◽  
Propagation Effects ◽  
Turbulent Premixed

One basic effect of turbulence in turbulent premixed combustion is for the fluctuating velocity field to wrinkle the flame and greatly increase its surface area. In the flamelet theory, this effect is described by the flame surface density. An exact evolution equation for the flame surface density, called the Σ-equation, may be written, where basic physical mechanisms like production by hydrodynamic straining and destruction by propagation effects are described explicitly. Direct numerical simulation (DNS) is used in this paper to estimate the different terms appearing in the Σ-equation. The numerical configuration corresponds to three-dimensional premixed flames in isotropic turbulent flow. The simulations are performed for various mixture Lewis numbers in order to modify the strength and nature of the flame-flow coupling. The DNS-based analysis provides much information relevant to flamelet models. In particular, the flame surface density, and the source and sink terms for the flame surface density, are resolved spatially across the turbulent flame brush. The geometry as well as the dynamics of the flame differ quite significantly from one end of the reaction zone to the other. For instance, contrary to the intuitive idea that flame propagation effects merely counteract the wrinkling due to the turbulence, the role of flame propagation is not constant across the turbulent brush and switches from flame surface production at the front to flame surface dissipation at the back. Direct comparisons with flamelet models are also performed. The Bray-Moss-Libby assumption that the flame surface density is proportional to the flamelet crossing frequency, a quantity that can be measured in experiments, is found to be valid. Major uncertainties remain, however, over an appropriate description of the flamelet crossing frequency. In comparison, the coherent flame model of Marble & Broadwell achieves closure at the level of the Σ-equation and provides a more promising physically based description of the flame surface dynamics. Some areas where the model needs improvement are identified.

One-Dimensional Propagation of a Premixed Turbulent Flame With a Balance Equation for the Flame Surface Density

1993 ◽  
Vol 90 (1-4) ◽  
pp. 35-60 ◽  
Author(s):  
FLORIAN FICHOT ◽  
FRANÇOIS LACAS ◽  
DENIS VEYNANTE ◽  
SÉBASTIEN CANDEL
Keyword(s):  
Balance Equation ◽  
Surface Density ◽  
Turbulent Flame ◽  
Flame Surface ◽  
Flame Surface Density ◽  
One Dimensional ◽  
Premixed Turbulent Flame

Development of a RANS Premixed Turbulent Combustion Model Based on the Algebraic Flame Surface Density Concept

2018 ◽  
Vol 141 (2) ◽  
Author(s):  
Usman Allauddin ◽  
Michael Pfitzner
Keyword(s):  
Experimental Data ◽  
Surface Density ◽  
Turbulent Flame ◽  
Theoretical Models ◽  
Elevated Pressure ◽  
Navier Stokes ◽  
Combustion Model ◽  
Flame Surface ◽  
Flame Surface Density ◽  
Premixed Turbulent Combustion

Recently, a fractal-based algebraic flame surface density (FSD) premixed combustion model has been derived and validated in the context of large eddy simulation (LES). The fractal parameters in the model, namely the cut-off scales and the fractal dimension were derived using theoretical models, experimental and direct numerical simulation (DNS) databases. The model showed good performance in predicting the premixed turbulent flame propagation for low to high Reynold numbers (Re) in ambient as well as elevated pressure conditions. Several LES combustion models have a direct counterpart in the Reynolds-averaged Navier–Stokes (RANS) context. In this work, a RANS version of the aforementioned LES subgrid scale FSD combustion model is developed. The performance of the RANS model is compared with that of the original LES model and validated with the experimental data. It is found that the RANS version of the model shows similarly good agreement with the experimental data.

Premixed flame–wall interaction in a turbulent channel flow: budget for the flame surface density evolution equation and modelling

1997 ◽  
Vol 349 ◽  
pp. 191-219 ◽  
Author(s):  
G. BRUNEAUX ◽  
T. POINSOT ◽  
J. H. FERZIGER
Keyword(s):  
Evolution Equation ◽  
Flame Propagation ◽  
Surface Density ◽  
Mass Fraction ◽  
Premixed Flame ◽  
Flame Surface ◽  
Flame Surface Density ◽  
Fuel Mass ◽  
Fuel Mass Fraction ◽  
Density Equation

Turbulent premixed flame propagation in the vicinity of a wall is studied using a three-dimensional constant-density simulation of flames propagating in a channel. The influence of the walls is investigated in terms of the flamelet approach, where flamelet speed and flame surface density transport are used to describe the flame. The walls have constant temperature and lead to flamelet quenching for sufficiently small wall–flame distances. Starting from the exact evolution equation for the surface density of propagating interfaces (Trouvé & Poinsot 1994; Candel & Poinsot 1990; Pope 1988), a budget for the flame surface density equation is presented before, during, and after the interaction with the wall. Before the flame interacts with the wall, flame propagation is controlled by a balance between surface production and annihilation. During the interaction, high flame surface density gradients near the wall are responsible for the predominance of the transport terms. Closures of all terms of the flame surface density equation are proposed. These models are based on flamelet ideas and take into account wall effects. Enthalpy loss through the wall affects flamelet speed, flamelet annihilation and flame propagation. Decrease of turbulent scales near the wall affects turbulent diffusion and flame strain. This model is compared to DNS results using two types of tests: (i) a priori tests, where individual terms of the modelled flame surface density equation are compared to the terms of the exact interface density propagation equation, calculated with the DNS; (ii) a posteriori tests, where the final model is used to obtain total reaction rate, mean fuel mass fraction, heat flux at the wall and fuel mass fraction at the wall in the configuration used in the DNS. For both types of tests the model compares well with the DNS results.

scholarly journals Data-Driven Combustion Modeling for a Turbulent Flame Simulated With a Computationally Efficient Solver

2020 ◽  
Author(s):  
Mohsen Talei ◽  
Man-Ching Ma ◽  
Richard Sandberg
Keyword(s):  
Surface Density ◽  
Turbulence Modeling ◽  
Gene Expression Programming ◽  
Turbulent Flame ◽  
Reacting Flows ◽  
Flame Surface ◽  
Algebraic Models ◽  
Computationally Efficient ◽  
Flame Surface Density ◽  
Combustion Modeling

Abstract The use of machine learning (ML) for modeling is on the rise. In the age of big data, this technique has shown great potential to describe complex physical phenomena in the form of models. More recently, ML has frequently been used for turbulence modeling while the use of this technique for combustion modeling is still emerging. Gene expression programming (GEP) is one class of ML that can be used as a tool for symbolic regression and thus improve existing algebraic models using high-fidelity data. Direct numerical simulation (DNS) is a powerful candidate for producing the required data for training GEP models and validation. This paper therefore presents a highly efficient DNS solver known as HiPSTAR, originally developed for simulating non-reacting flows in particular in the context of turbo-machinery. This solver has been extended to simulate reacting flows. DNSs of two turbulent premixed jet flames with different Karlovitz numbers are performed to produce the required data for training. GEP is then used to develop algebraic flame surface density models in the context of large-eddy simulation (LES). The result of this work introduces new models which show excellent performance in prediction of the flame surface density for premixed flames featuring different Karlovitz numbers.

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