A combustion model of vegetation burning in “Tiger” fire propagation tool

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.

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

International Journal of Engine Research ◽

10.1177/1468087420972903 ◽

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.

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Reconstruction of Grenfell Tower fire. Part 2: A numerical investigation of the fire propagation and behaviour from the initial apartment to the façade

Fire and Materials ◽

10.1002/fam.2765 ◽

2019 ◽

Vol 44 (1) ◽

pp. 15-34 ◽

Cited By ~ 3

Author(s):

Eric Guillaume ◽

Virginie Dréan ◽

Bertrand Girardin ◽

Maxime Koohkan ◽

Talal Fateh

Keyword(s):

Numerical Investigation ◽

Fire Propagation

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

Entropy ◽

10.3390/e23050567 ◽

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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