scholarly journals Soot Predictions with a Laminar Flamelet Combustion Model in SIERRA/Fuego on a Coflow Scenario.

2021 ◽  
Author(s):  
Andrew Kurzawski ◽  
Michael Hansen ◽  
John Hewson
Keyword(s):  
Combustion Model ◽  
Laminar Flamelet

scholarly journals A combustion model sensitivity study for CH4/H2 bluff-body stabilized flame

2007 ◽  
Vol 221 (11) ◽  
pp. 1377-1390 ◽  
Author(s):  
M Hossain ◽  
W Malalasekera
Keyword(s):  
Bluff Body ◽  
Mixture Fraction ◽  
Sensitivity Study ◽  
Combustion Model ◽  
Flamelet Model ◽  
Combustion Models ◽  
Mass Fractions ◽  
Experimental Values ◽  
Constituent Mass ◽  
Laminar Flamelet

The objective of the current work is to assess the performance of different combustion models in predicting turbulent non-premixed combustion in conjunction with the k-∊ turbulence model. The laminar flamelet, equilibrium chemistry, constrained equilibrium chemistry, and flame sheet models are applied to simulate combustion in a CH4/H2 bluff-body flame experimentally studied by the University of Sydney. The computational results are compared to experimental values of mixture fraction, temperature, and constituent mass fractions. The comparison shows that the laminar flamelet model performs better than other combustion models and mimics most of the significant features of the bluff-body flame.

Application of Laminar Flamelet Model to a Diffusion Flame Combustor

2004 ◽  
Author(s):  
L. Y. Jiang ◽  
I. Campbell ◽  
K. Su
Keyword(s):  
Diffusion Flame ◽  
Temperature Fields ◽  
Combustion Model ◽  
Discrete Ordinates ◽  
Post Processing ◽  
Zone Length ◽  
Flamelet Model ◽  
The Mean ◽  
Heat Transfers ◽  
Laminar Flamelet

The flow field of a propane-air diffusion flame combustor with interior and exterior conjugate heat transfers was numerically studied. Results obtained from a laminar flamelet combustion model (228 reactions and 31 species), together with the re-normalization group (RNG) k-ε turbulence model, the discrete ordinates radiation model, and the NOx model, are presented and discussed. These results are compared with a comprehensive database obtained from a series of experimental measurements. The flow patterns and recirculation zone length in the combustion chamber are accurately predicted, and the mean axial velocities are in fairly good agreement with the experimental data, particularly at downstream sections. The mean temperature profiles are also captured fairly well by the laminar flamelet combustion model. Based the velocity and temperature field solutions, NOx simulation was performed in a post-processing mode. The numerical results indicate that the semi-empirical, post-processing NOx model can provide valuable NOx simulations as long as the velocity and temperature fields are adequately predicted.

Application of a Partially Premixed Laminar Flamelet Model to a Low Emissions Gas Turbine Combustor

1998 ◽  
Author(s):  
T. J. Held ◽  
H. C. Mongia
Keyword(s):  
Scalar Field ◽  
Turbulent Flame ◽  
Combustion Model ◽  
Measured Velocity ◽  
Combustion Dynamics ◽  
Turbulent Flame Speed ◽  
Basic Fluid ◽  
Partially Premixed ◽  
Premixed Laminar ◽  
Laminar Flamelet

Computational combustion dynamics simulations have been used widely for the design and analysis of conventional rich dome combustors using a fast chemistry assumed shape PDF approach (Shyy et al. 1986) and/or an eddy-breakup model (Valachovic, 1993, Danis et al., 1996). The application of these tools to ultra-low emissions combustors such as the GE LM6000 DLE has been hampered by the inadequacies of the eddy break-up combustion model. In the present work, a partially-premixed laminar flamelet combustion model, based initially on the model of Müller et al. (1994), is applied to an LM6000 single cup combustor. The basic fluid mechanical code is ACC, using the k-ε turbulence model (Prakash, et al., 1998). Assumed-shape PDF models are used for mixture fraction Z(x), and the scalar field G(x), whose level surfaces G = G0 represent the flame location. The model includes the effects of local strain rate on flame propagation rate and extinction through modification of the turbulent flame speed correlation, which determines the rate of propagation of the scalar field variable G. The effects of variable inlet fuel/air ratio variance (unmixedness) on predicted NOx emissions are included through the moments of a calculated NO source term on the PDF’s of Z, and include the contributions of flame-front production of NO in premixed flames. Comparisons to measured velocity and emissions data are shown.

scholarly journals Combustion Optimization for Coal Fired Power Plant Boilers Based on Improved Distributed ELM and Distributed PSO

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 1036 ◽  
Author(s):  
Xinying Xu ◽  
Qi Chen ◽  
Mifeng Ren ◽  
Lan Cheng ◽  
Jun Xie
Keyword(s):  
Power Plant ◽  
Combustion Process ◽  
Optimization Method ◽  
Combustion Efficiency ◽  
Pollutant Emissions ◽  
Combustion Model ◽  
Nox Emissions ◽  
Coupling Characteristics ◽  
Learning Machine ◽  
Combustion Optimization

Increasing the combustion efficiency of power plant boilers and reducing pollutant emissions are important for energy conservation and environmental protection. The power plant boiler combustion process is a complex multi-input/multi-output system, with a high degree of nonlinearity and strong coupling characteristics. It is necessary to optimize the boiler combustion model by means of artificial intelligence methods. However, the traditional intelligent algorithms cannot deal effectively with the massive and high dimensional power station data. In this paper, a distributed combustion optimization method for boilers is proposed. The MapReduce programming framework is used to parallelize the proposed algorithm model and improve its ability to deal with big data. An improved distributed extreme learning machine is used to establish the combustion system model aiming at boiler combustion efficiency and NOx emission. The distributed particle swarm optimization algorithm based on MapReduce is used to optimize the input parameters of boiler combustion model, and weighted coefficient method is used to solve the multi-objective optimization problem (boiler combustion efficiency and NOx emissions). According to the experimental analysis, the results show that the method can optimize the boiler combustion efficiency and NOx emissions by combining different weight coefficients as needed.

scholarly journals Modelling combustion in spark ignition engines with special emphasis on near wall flame quenching

2020 ◽  
pp. 146808742097290
Author(s):  
CP Ranasinghe ◽  
W Malalasekera
Keyword(s):  
Combustion Rate ◽  
Fractal Theory ◽  
Pressure Rise ◽  
Spark Ignition ◽  
Spatial Inhomogeneity ◽  
Combustion Model ◽  
Spark Ignition Engines ◽  
Flame Acceleration ◽  
Flame Quenching ◽  
Near Wall

A flame front is quenched when approaching a cold wall due to excessive heat loss. Accurate computation of combustion rate in such situations requires accounting for near wall flame quenching. Combustion models, developed without considering wall effects, cannot be used for wall bounded combustion modelling, as it leads to wall flame acceleration problem. In this work, a new model was developed to estimate the near wall combustion rate, accommodating quenching effects. The developed correlation was then applied to predict the combustion in two spark ignition engines in combination with the famous Bray–Moss–Libby (BML) combustion model. BML model normally fails when applied to wall bounded combustion due to flame wall acceleration. Results show that the proposed quenching correlation has significantly improved the performance of BML model in wall bounded combustion. As a second step, in order to further enhance the performance, the BML model was modified with the use of Kolmogorov–Petrovski–Piskunov analysis and fractal theory. In which, a new dynamic formulation is proposed to evaluate the mean flame wrinkling scale, there by accounting for spatial inhomogeneity of turbulence. Results indicate that the combination of the quenching correlation and the modified BML model has been successful in eliminating wall flame acceleration problem, while accurately predicting in-cylinder pressure rise, mass burn rates and heat release rates.

scholarly journals Computational Modeling of Boundary Layer Flashback in a Swirling Stratified Flame Using a LES-Based Non-Adiabatic Tabulated Chemistry Approach

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 567
Author(s):  
Xudong Jiang ◽  
Yihao Tang ◽  
Zhaohui Liu ◽  
Venkat Raman
Keyword(s):  
Boundary Layer ◽  
Heat Loss ◽  
Boundary Layers ◽  
Gas Turbines ◽  
Predictive Accuracy ◽  
Safety Issue ◽  
Combustion Model ◽  
Tabulated Chemistry ◽  
Lean Fuel ◽  
Flame Flashback

When operating under lean fuel–air conditions, flame flashback is an operational safety issue in stationary gas turbines. In particular, with the increased use of hydrogen, the propagation of the flame through the boundary layers into the mixing section becomes feasible. Typically, these mixing regions are not designed to hold a high-temperature flame and can lead to catastrophic failure of the gas turbine. Flame flashback along the boundary layers is a competition between chemical reactions in a turbulent flow, where fuel and air are incompletely mixed, and heat loss to the wall that promotes flame quenching. The focus of this work is to develop a comprehensive simulation approach to model boundary layer flashback, accounting for fuel–air stratification and wall heat loss. A large eddy simulation (LES) based framework is used, along with a tabulation-based combustion model. Different approaches to tabulation and the effect of wall heat loss are studied. An experimental flashback configuration is used to understand the predictive accuracy of the models. It is shown that diffusion-flame-based tabulation methods are better suited due to the flashback occurring in relatively low-strain and lean fuel–air mixtures. Further, the flashback is promoted by the formation of features such as flame tongues, which induce negative velocity separated boundary layer flow that promotes upstream flame motion. The wall heat loss alters the strength of these separated flows, which in turn affects the flashback propensity. Comparisons with experimental data for both non-reacting cases that quantify fuel–air mixing and reacting flashback cases are used to demonstrate predictive accuracy.

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