Small flame modeling method accounting for curvature and differential diffusion for hydrogen-rich fuel backfiring

Abstract

The application discloses a small flame modeling method considering curvature and differential diffusion for hydrogen-rich fuel quenching, and belongs to the field of energy and combustion engineering. The method comprises the following steps: a database is established by solving one-dimensional premixed small flame equations by using ULF software; the database is mapped to a control parameter space (mixing fraction, process variable, enthalpy and hydrogen mass fraction) by an OpenFOAM mapping program, wherein the hydrogen mass fraction represents the curvature effect; a differential diffusion term is introduced when solving the control parameter equation set, and the thermochemical variable is obtained by table lookup. The application firstly considers the influence of the curvature effect and the differential diffusion effect on the quenching process, takes the hydrogen mass fraction as the curvature proxy variable, and significantly improves the prediction accuracy of the quenching speed. Through dimension reduction processing, the number of equations to be solved is reduced, the calculation efficiency is improved while the accuracy is ensured, and the method is suitable for quenching process simulation of all gas fuels.

Description

Technical Field

[0001] This invention belongs to the field of energy and combustion engineering, specifically relating to a small flame modeling method that considers curvature and differential diffusion for the backfire of hydrogen-rich fuels. Background Technology

[0002] With the increasing global demand for reducing carbon emissions, hydrogen energy has attracted widespread attention and discussion due to its cleanliness and high energy density. However, because the flame propagation speed of hydrogen combustion is very fast, hydrogen is prone to backfire during application. Once backfire occurs, it can cause significant damage to the burner and, in severe cases, even cause injury or death. Therefore, reducing backfire and understanding its occurrence process has become a key focus for researchers. Currently, in the field of energy and combustion engineering, the modeling process for solving the backfire process typically utilizes the large eddy simulation method of computational fluid dynamics. The combustion model chosen is the finite chemical reaction rate model, which includes simplified models such as detailed finite chemical reaction rate models and small flame models. Compared to detailed finite chemical reaction models, small flame models reduce the number of equations to be solved by solving a set of control parameter equations instead of component equations, thus reducing computational resource consumption and computation time. During tempering, differential diffusion and curvature effects have a significant impact on flame structure and tempering rate. Differential diffusion affects component distribution, which in turn affects fuel content, thereby altering chemical reaction rates and flame structure. Fuel preferentially diffuses into regions with positive curvature, resulting in higher stoichiometry and temperatures in these regions, while the opposite occurs in regions with negative curvature. Therefore, curvature also influences flame structure and tempering rate. Considering both curvature and differential diffusion effects would improve the accuracy of researchers' tempering process predictions; however, currently, no method simultaneously considers the influence of both differential diffusion and curvature effects on the tempering process. Summary of the Invention

[0003] To overcome the shortcomings of existing technologies, this invention proposes a small-flame modeling method for hydrogen-rich fuel tempering that considers curvature and differential diffusion, simultaneously taking into account the effects of curvature and differential diffusion during the tempering process. This invention uses a small-flame model to solve the tempering process, representing curvature with the hydrogen mass fraction, and considers the curvature effect during tempering, enabling the reproduction of the tempering process with high accuracy and faster computation speed. This invention achieves high-precision numerical solutions for hydrogen-rich fuel tempering processes and can handle numerical solutions for tempering processes of all gaseous fuels.

[0004] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0005] A small flame modeling method considering curvature and differential diffusion for hydrogen-rich fuel backfire includes the following steps:

[0006] Step 1: Solve the one-dimensional premixed small flame equation to obtain a database of laminar small flames under different operating conditions;

[0007] Step 2: Using a mapping procedure, map the laminar small flame database to the control parameter space, which includes the mixing fraction, process variables, enthalpy, and hydrogen mass fraction; the mixing fraction represents the equivalence ratio, the process variables represent the reaction progress, the enthalpy represents the heat loss, and the hydrogen mass fraction represents the curvature; obtain a small flame table containing thermochemical variables;

[0008] Step 3: Design the computational domain and divide it into grids based on the experimental data to establish a discretized mathematical model; solve the system of equations including the control parameter equations, density equations, velocity equations, and pressure equations; add a differential diffusion term when solving the control parameter equations, obtain the component, temperature, chemical reaction source terms, and differential diffusion term by looking up the small flame table, and perform pressure correction.

[0009] The beneficial effects of this invention compared to the prior art are as follows:

[0010] The small flame model proposed in this invention, which considers curvature and differential diffusion, first takes into account the influence of differential diffusion and curvature effects on the distribution of material components, flame structure, and tempering rate during tempering, thereby improving the accuracy and precision of the calculation. By using small flame table mapping and coordinate transformation for dimensionality reduction, the number of governing equations to be solved is reduced, greatly improving the solution efficiency without reducing the solution accuracy. This helps to reduce the consumption of computing resources and expands the ideas and methods for studying the tempering process. Attached Figure Description

[0011] Figure 1 The flowchart shows the small flame modeling method of the present invention that considers curvature and differential diffusion for hydrogen-rich fuel backfire.

[0012] Figure 2 This is a schematic diagram illustrating the design of the computational domain setting and mesh division in an embodiment of the present invention;

[0013] Figure 3 This is a comparison of the axial velocity at position z = -0.029 m and experimental data in the cold unreacted fluid computational simulation results of the model in the embodiment of the present invention.

[0014] Figure 4 The figure shows a comparison between the axial velocity of the cold, unreacted fluid simulation results of the model in the embodiment of the present invention at the position z = -0.054 m and the experimental data:

[0015] Figure 5 This is a comparison of the circumferential velocity at z = -0.029 m with experimental data from the cold-state unreacted fluid computational simulation results of the model in an embodiment of the present invention.

[0016] Figure 6 The following is a comparison of the circumferential velocity at position z = -0.054 m in the cold-state unreacted fluid simulation results of the model in the embodiment of the present invention with experimental data:

[0017] Figure 7 This is a comparison chart of the simulation results and experimental data of tempering rate calculation in an embodiment of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments described below can be combined with each other as long as they do not conflict with each other. To facilitate understanding of this invention, a more comprehensive and detailed description of the invention will be provided below with reference to embodiments, but the scope of protection of this invention is not limited to the following specific embodiments.

[0019] This invention achieves high-precision numerical solutions for the tempering process of hydrogen-rich fuels within the framework of large eddy simulation, and can handle numerical solutions for the tempering processes of all gaseous fuels.

[0020] like Figure 1 As shown, the small flame modeling method of the present invention for hydrogen-rich fuel backfire considering curvature and differential diffusion includes the following steps:

[0021] Step 1: Use ULF software to solve the one-dimensional premixed small flame equation to obtain laminar small flame data under different operating conditions;

[0022] Step 2: Using a mapping program developed based on OpenFOAM software, the small flame database obtained in Step 1 is mapped to the control parameter space, namely the space of mixing fraction, process variable, enthalpy and hydrogen mass fraction. The equivalence ratio is represented by the mixing fraction, the reaction progress is represented by the process variable, the heat loss is represented by the enthalpy, and the curvature is represented by the hydrogen mass fraction. The small flame table is obtained by solving the problem.

[0023] Step 3: Based on the experimental data, design the computational domain, divide the grid, and establish a data model. Considering the grid division, discretize the data model to obtain a set of equations including control parameter equations, density equations, velocity equations, and pressure equations. When solving the control parameter process variables and the mixture fraction equations, consider the differential diffusion term. Then solve the equations. After solving the control parameter equations, input the results into the small flame library obtained in Step 2, and look up the components, temperature, chemical reaction source terms, and differential diffusion terms. Next, solve the pressure equation and use the pressure-velocity coupled iterative correction (PIMPLE) algorithm to correct the pressure.

[0024] Through the above three steps, a small flame model considering curvature and differential diffusion for hydrogen-rich fuel backfire was established.

[0025] Specifically, in step 1, a multi-component space model was established, and the ULF software was used to solve the one-dimensional premixed small flame equations under different operating conditions, i.e., different temperatures, different equivalence ratios, and different curvatures. The equations for the components, temperature, and reaction progress gradient variables were solved to obtain a data file containing information such as component mass fraction, temperature, equivalence ratio, and curvature. The component and temperature equations respectively describe the changes of each component and temperature in the process variable space, and a laminar small flame database was established.

[0026] Specifically, in step 2, using a mapping program developed based on OpenFOAM, the small flame database established in step 1 is read, and a suitable component mass fraction is selected as a process variable to represent the reaction progress of the chemical reaction. Generally, key products and key reactants are selected, which can accurately reflect the reaction progress of the chemical reaction. Curvature is represented by a suitable component mass fraction, and hydrogen mass fraction is generally selected to represent the change in curvature. During the mapping process, data such as differential diffusion term, component mass fraction term, and temperature are mapped to the control parameter space and stored in a table. Since the flame surface thickness of the premixed flame is smaller than the mesh size that can be solved by large eddy simulation, the flame is thickened. The thickening factor, efficiency factor, and flame sensor coefficient are also calculated and stored in the small flame table. In the subsequent step 3, only the control parameter equation needs to be solved, instead of solving the component equation. The control parameter results obtained are input into the table to calculate the thermochemical variables.

[0027] like Figure 1 As shown, step 3 specifically includes the following steps:

[0028] Step (1): Design the computational domain, divide the grid, and establish a mathematical model based on the experimental facilities;

[0029] Step (2): Based on the mesh division in step (1), the mathematical model established in step (1) is physically discretized to obtain a set of equations including the control parameter equations, density equations, velocity equations and pressure equations. When solving the control parameter process variables and the mixed fractional equations, the differential diffusion term is considered.

[0030] Step (3): Solve the density and velocity transport equations to obtain the density and velocity estimates;

[0031] Step (4): Solve the control parameter equations, including the equations for mixture fraction, process variables, enthalpy and hydrogen mass fraction, to obtain the predicted control parameter values;

[0032] Step (5): Input the results of step (4) into the small flame table obtained in step 2, and look up the results of the components, temperature, differential diffusion term and chemical reaction source term.

[0033] Step (6): Solve the pressure transport equation and use the PIMPLE algorithm to input the pressure calculation results into the density and velocity equations for iterative correction;

[0034] Step (7): Determine whether the convergence has occurred or the specified end time has been reached. Otherwise, advance the time step and repeat steps (3) to (6).

[0035] The equation solved in step (4) is in the form of:

[0036] ;

[0037] in, It is a spatial average quantity. This represents the Farve average. Density; The variables to be determined include process variables, mixture fraction, hydrogen mass fraction, and enthalpy; Gradient operator; Represents the velocity vector; Represents time; E is the efficiency factor, and F is the thickening factor. For flame detectors; This is the diffusion coefficient at the subgrid scale, and in the enthalpy equation, it is the thermal diffusivity. The remaining equations contain the mass diffusion coefficient. , It is the specific heat capacity at constant pressure. It is thermal conductivity; The source term corresponding to the variable is 0 in the enthalpy and mixed fractional equation; It is a difference diffusion term. ,in It is the difference diffusion coefficient.

[0038] In the above solution process, since hydrogen combustion is a coupled process of heat and mass transfer and flow in the energy and engineering fields, the velocity equation and density equation are solved first, followed by the set of governing parameter equations. When solving the governing parameter equations—namely, the process variable equations, mixing fraction equations, hydrogen mass fraction equations, and enthalpy equations—differential diffusion terms are added to couple the differential diffusion effect. Applying a small flame model decouples the flow and combustion processes. By using coordinate transformation to reduce the dimensionality, the governing parameter equations are solved instead of the component equations, reducing the number of governing equations, accelerating convergence, and improving solution efficiency.

[0039] Example:

[0040] Taking the numerical calculation of the three-dimensional hydrogen / methane / air premixed swirling flame tempering process as an example, this invention is described in a comprehensive and detailed manner. This method is not limited to hydrogen combustion scenarios and is applicable to all gaseous fuels. In this embodiment, a small flame model considering curvature and differential diffusion is used to model the hydrogen / methane / air premixed swirling flame tempering process to analyze the influence of coupled differential diffusion and curvature effects on the results.

[0041] The embodiments of this invention are mainly divided into the following steps: Step (1) Establishment of the small flame database and equation description; Step (2) Mapping and establishing the small flame table, converting it into an OpenFOAM readable form; Step (3) Performing large eddy simulation, designing the computational domain according to the experiment, dividing the mesh, and then performing numerical solution. Afterwards, the characteristics such as cold state velocity and tempering velocity are analyzed based on the model.

[0042] Step (1) First, the small flame database is established and described, specifically including:

[0043] A multi-component spatial model was established, and the one-dimensional premixed small flame equations were solved in the component space using ULF software under different operating conditions, namely different temperatures, equivalence ratios, and curvatures. These equations included gradient equations for component, temperature, and reaction progress variables, yielding data files containing information such as component mass fraction, temperature, equivalence ratio, and curvature. A laminar small flame database was established, with equivalence ratios ranging from 0.31 to 0.5, encompassing the range of equivalence ratios involved in the experiments. The component and temperature equations describe the changes of each component and temperature in the process variable space, but the equations are not completely closed. The gradient equations for the reaction progress variables solved for the unclosed terms in the component and temperature equations.

[0044] Step (2) maps the component and other parameter information in the small flame database to the control parameter space to establish the small flame table. The process includes:

[0045] The first part determines the schedule variable, selecting the sum of the mass fractions of carbon monoxide and carbon dioxide as the process variable. C is a process variable, representing the extent of the chemical reaction;

[0046] Part Two: Calculating the mixture fraction, Z as defined by Bilger et al. Bilger Z is the only variable that can characterize fuel stratification caused by differential diffusion, but Bilger Since the solution for Z is not found in the small flame database in step (1), it is necessary to solve for Z during the mapping and table creation process. Bilger The formula for calculating the mixing fraction in the equivalence ratio range involved in the small flame database in step (1) is as follows:

[0047] ;

[0048] in, , and These represent the partial mixing fractions of carbon, hydrogen, and oxygen elements, with the subscripts fu and ox indicating pure fuel and pure air fluid, respectively. , and The relative atomic masses of carbon, hydrogen and oxygen are represented. The mixing fraction is also normalized. The mixing fraction outside the equivalence range in step (1) is extrapolated using the functions in the Cantera software coupled with OpenFOAM.

[0049] The third part defines the control parameters for the curvature effect. Curvature is an instantaneous and precise physical quantity. It is difficult to consider the cumulative curvature effect changes caused by convection, diffusion, and reaction. Furthermore, the curvature parameter is related to process variables and requires a joint probability density function to consider the subgrid distribution of these trajectory variables, which is difficult to achieve. However, the hydrogen mass fraction has been tested and can reflect curvature changes well. Therefore, the hydrogen mass fraction is chosen to reflect the curvature effect.

[0050] In the fourth part, the heat loss during the reaction is represented by enthalpy. Since the range of enthalpy is too large, the enthalpy is normalized and fixed between 0 and 1 to improve the accuracy of the solution.

[0051] In the fifth part, since the flame surface thickness of the premixed flame is small and lower than the mesh size that can be solved by large eddy simulation, the premixed flame is thickened. The efficiency factor, thickening factor and flame detector of the premixed flame are obtained and stored in the small flame table for easy reading and solving in step 3.

[0052] The calculation process for the three-dimensional hydrogen / methane / air premixed flame tempering process in step (3) is as follows:

[0053] The first part, referencing the flashback experiment of a blunt-body swirling flame burner conducted by Clemens et al., sets up the computational domain and boundary conditions, such as... Figure 2 As shown, the burner is divided into four parts: combustion chamber, mixing tube, swirler, and central body. The mixing tube, central body, and combustion chamber are all 150 mm long, the mixing tube has a diameter of 52 mm, the combustion chamber has a diameter of 100 mm, and the central body has a diameter of 25.4 mm. The swirler consists of 8 blades, with the blade edges designed at a 60-degree angle to the mixing tube. The origin of the coordinate system is shown in the figure. A non-orthogonal grid is selected, with a total of 10 million grids. Five to six grids are guaranteed to be within positions where the wall coordinates are less than 10, and the first point of the grid is less than 1 from the wall coordinates.

[0054] In the second part, unreacted cold fluid enters the computational domain at a velocity of 2.5 m / s through a circular inlet with an outer diameter of 52 mm and an inner diameter of 25.4 mm to verify the mesh quality and the accuracy of the boundary condition settings. The calculation results are as follows: Figure 3 , Figure 4 , Figure 5 and Figure 6 As shown, the hydrogen / methane / air premixed fuel then enters the computational domain through the inlet, with a hydrogen to methane volume ratio of 95:5 and an equivalence ratio of 0.33. This initial condition serves as the initial condition for the flashback process. Once the fuel has stabilized and combustion is stable, the equivalence ratio is changed to 0.4 before entering the computational domain, initiating the flashback process. The initial temperature of the premixed fuel is 300 K, and the pressure is set to atmospheric pressure.

[0055] The third part uses the finite volume method to numerically discretize the control equations, including the density equation, velocity equation, control parameter equations, and pressure equation. The time integration uses the first-order Euler implicit integral, and the spatial integration uses the second-order linear restricted integral scheme.

[0056] Part Four involves solving the equations sequentially, with the following specific steps:

[0057] Step 1: Solve the density and velocity transport equations to obtain the density and velocity estimates;

[0058] Step 2: Solve the control parameter transport equations to obtain the control parameter estimates;

[0059] Step 3: Input the results of Step 2 into the small flame library obtained in Step (2), and look up the table to obtain the coefficient results of the components, temperature, chemical reaction source term, differential diffusion term and thickening model;

[0060] Step 4: Solve the pressure transport equation and use the PIMPLE algorithm to correct the pressure velocity.

[0061] Step 5: Determine whether the convergence has occurred or the specified end time has been reached. Otherwise, advance the time step and repeat steps 1 to 4.

[0062] In step 1, the governing equation for density is:

[0063] ;

[0064] It is a spatial average quantity. This represents the Farve average. Density; The gradient operator, Represents the velocity vector; Represents time.

[0065] The governing equations for velocity are:

[0066] ;

[0067] in, For pressure, For gravitational acceleration, and It is the viscous stress tensor.

[0068] The set of control parameter equations to be solved in step 4 is as follows:

[0069] ;

[0070] in, To control the parameters, including process variables, mixture fraction, hydrogen mass fraction, and enthalpy; E is the efficiency factor, and F is the thickening factor. For flame detectors; This is the diffusion coefficient at the subgrid scale, and in the enthalpy equation, it is the thermal diffusivity. The remaining equations contain the mass diffusion coefficient. c p It is the specific heat capacity at constant pressure. It is thermal conductivity; The reaction source term corresponding to the variable is 0 in the enthalpy and mixture fraction equation; It is a difference diffusion term. ,in It is the difference diffusion coefficient.

[0071] Based on the above steps, variable values ​​defined at the center point of each grid are obtained, enabling the numerical solution of the three-dimensional hydrogen / methane / air premixed flame tempering process using the curvature difference diffusion small flame model.

[0072] Subsequently, the cold unreacted velocity field and tempering velocity characteristics of the three-dimensional hydrogen / methane / air premixed flame tempering process were analyzed:

[0073] The calculated results are compared with experimental data to analyze the quality of the mesh and the rationality of the boundary condition settings. Based on the velocity field calculated and time-averaged according to this invention, such as... Figure 3 As shown, the data points are selected from the points on the annular cross-section located at z = -0.029 m and -0.054 m. r / D is the ratio of the distance from the selected data point on the annular cross-section to the center of the circle to the diameter. z Refers to axial velocity. This refers to circumferential velocity; points correspond to experimental data, and lines correspond to the calculation results of the model, such as... Figure 3 , Figure 4 , Figure 5 and Figure 6 As shown, the model calculation results match the experimental data very well at both locations. Therefore, we can conclude that the mesh quality meets the requirements and the boundary condition settings are reasonable. This setting can be used to simulate the tempering process.

[0074] By comparing axial tempering velocities, the effects of the coupled differential diffusion and curvature effects are analyzed, such as... Figure 7 As shown, the tempering rate in the simulation results is slightly higher than that in the experiment, but it is within the measurement error range. Therefore, it can be concluded that the combination of differential diffusion effect and curvature effect can predict the tempering rate in the tempering process well. Thus, both differential diffusion effect and curvature effect need to be considered in the tempering process.

[0075] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A small flame modeling method considering curvature and differential diffusion for hydrogen-rich fuel backfire, characterized in that, Includes the following steps: Step 1: Solve the one-dimensional premixed small flame equation to obtain a database of laminar small flames under different operating conditions; Step 2: Using a mapping procedure, map the laminar small flame database to the control parameter space, which includes the mixing fraction, process variables, enthalpy, and hydrogen mass fraction; the mixing fraction represents the equivalence ratio, the process variables represent the reaction progress, the enthalpy represents the heat loss, and the hydrogen mass fraction represents the curvature; obtain a small flame table containing thermochemical variables; Step 3: Design the computational domain and divide it into grids based on the experimental data, and establish a discretized mathematical model; solve the system of equations including the control parameter equations, density equations, velocity equations, and pressure equations; When solving the control parameter equations, a differential diffusion term is added. The components, temperature, chemical reaction source terms, and differential diffusion term are obtained by looking up the small flame table, and pressure correction is performed.

2. The method for modeling small flames considering curvature and differential diffusion during hydrogen-rich fuel backfire according to claim 1, characterized in that, In step 1, the one-dimensional premixed small flame equation includes a component equation, a temperature equation, and a reaction progress gradient variable equation; the operating conditions include different temperatures, different equivalence ratios, and different curvatures.

3. The method for modeling small flames considering curvature and differential diffusion during hydrogen-rich fuel backfire according to claim 1, characterized in that, In step 2, the process variables are the component mass fractions of key products and key reactants; the hydrogen mass fraction is used to characterize the curvature effect; and the flame thickening factor, efficiency factor, and flame detector coefficient are calculated and stored during the mapping process.

4. The method for modeling small flames considering curvature and differential diffusion during hydrogen-rich fuel backfire according to claim 1, characterized in that, In step 3, the difference diffusion term is solved separately during the mapping process and mapped to the control parameter space.

5. The method for modeling small flames considering curvature and differential diffusion during hydrogen-rich fuel backfire according to claim 1, characterized in that, Step 3 includes: Step 3.1: Design the computational domain and mesh it; Step 3.2: Discretize the mathematical model to obtain a system of equations; Step 3.3: Solve the density equation and velocity equation to obtain the estimated density and velocity values; Step 3.4: Solve the system of control parameter equations to obtain the predicted values ​​of the control parameters; Step 3.5: Check the small flame table to obtain the thermochemical variables; Step 3.6: Solve the pressure equation and perform pressure-velocity correction; Step 3.7: Determine the convergence or termination time; otherwise, advance the time step and repeat steps 3.3-3.

6.

6. The method for modeling small flames considering curvature and differential diffusion during hydrogen-rich fuel backfire according to claim 5, characterized in that, The expression for the control parameter equation set is: ， in, It is a spatial average quantity. This represents the Farve average. Density; The variables to be determined include process variables, mixture fraction, hydrogen mass fraction, and enthalpy; Gradient operator; Represents the velocity vector; Represents time; E is the efficiency factor, and F is the thickening factor. For flame detectors; It is a difference diffusion term. ,in It is the difference diffusion coefficient. The diffusion coefficient at the subgrid scale. It is the specific heat capacity at constant pressure. It is thermal conductivity; This refers to the source term corresponding to the variable.

7. The method for modeling small flames considering curvature and differential diffusion during hydrogen-rich fuel backfire according to claim 6, characterized in that, In the enthalpy equation, it is the thermal diffusivity. In the remaining equations, it is the mass diffusion coefficient. , It is 0 in the enthalpy equation and the mixed fraction equation.

8. The method for modeling small flames considering curvature and differential diffusion during hydrogen-rich fuel backfire according to claim 1, characterized in that, In step 3, the mixed fraction is calculated using the Bilger mixed fraction formula: ， in, , and These represent the partial mixing fractions of carbon, hydrogen, and oxygen elements, with the subscripts fu and ox indicating pure fuel and pure air fluid, respectively. , and Represents the relative atomic masses of the elements carbon, hydrogen, and oxygen.

9. The method for modeling small flames considering curvature and differential diffusion during hydrogen-rich fuel backfire according to claim 1, characterized in that, The process variable is the mass fraction of carbon monoxide. With carbon dioxide mass fraction sum: C is a process variable.

10. The method for modeling small flames considering curvature and differential diffusion during hydrogen-rich fuel backfire according to claim 1, characterized in that, Numerical simulation of tempering processes applicable to all gaseous fuels.

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