Differential diffusion small flame model construction method for buoyancy driven plume

Abstract

The invention provides a method for constructing a differential diffusion small flame model for buoyancy-driven plume, and the method is applied and expanded to a pool fire combustion process in the field of fire safety. According to the method, the buoyancy driven plume is solved by using the small flame model, the solving number of component equations of the buoyancy plume is greatly reduced through coordinate conversion dimensionality reduction processing, and the main characteristics of the buoyancy plume can be reproduced with relatively high precision and a faster calculation rate. According to the method, high-precision numerical solution of buoyancy plume is realized, and pool fire combustion of gas fuel and liquid fuel and numerical solution of all plume driven by buoyancy can be processed.

Description

Technical Field

[0001] The present invention belongs to the field of fire safety, and particularly relates to a method for constructing a differential diffusion small flame model for buoyancy-driven plumes. Background Art

[0002] Currently, in the field of fire safety, the numerical solution modeling process of buoyant plumes usually uses the large eddy simulation method of computational fluid dynamics, and the combustion model selects the infinitely fast chemical reaction model. However, the above models do not consider the interaction between the flame structure and turbulence, that is, the influence of the finite chemical reaction rate. The traditional finite chemical reaction rate model solves a large number of component equations and consumes a relatively large amount of computing resources. However, the small flame model reduces the number of equation solutions in the plume numerical modeling process by dimensionality reduction through coordinate transformation, reduces the time required for calculation, and reduces the consumption of computing resources. At the same time, the differential diffusion small flame model considers the influence of the differential diffusion effect on the distribution of substance components compared with the general small flame model, improving the accuracy and precision of the buoyant plume calculation. Therefore, the differential diffusion small flame model has advantages such as accurate calculation, high efficiency, and saving computing resources, and can well reproduce the main characteristics of the buoyant plume. However, currently, the application of the small flame model is mostly concentrated in the fields of energy and combustion engineering, and its application in the field of fire safety is very few. There is an urgent need for a method for constructing a differential diffusion small flame model for buoyancy-driven plumes. Summary of the Invention

[0003] To overcome the shortcomings of the prior art, the present invention proposes a method for constructing a differential diffusion small flame model for buoyancy-driven plumes and extends its application to the pool fire combustion process in the field of fire safety. The present invention uses the small flame model to solve the buoyancy-driven plume. Through dimensionality reduction by coordinate transformation, the number of component equation solutions of the buoyant plume is greatly reduced, and the main characteristics of the buoyant plume can be reproduced with high accuracy and faster calculation rate. The present invention realizes the high-precision numerical solution of the buoyant plume and can handle the pool fire combustion of gas fuels and liquid fuels and the numerical solution of all plumes driven by buoyancy.

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

[0005] A method for constructing a differential diffusion small flame model for buoyancy-driven plumes, comprising the following steps:

[0006] Step 1, establish an unsteady, viscous multi-dimensional plume mathematical model, and design a computational domain and divide the grid;

[0007] Step 2: Based on the grid division in Step 1, physically discretize the multi-dimensional plume mathematical model established in Step 1 to obtain a system of equations including the mixture fraction equation, process variable equation, density equation, velocity equation, enthalpy equation, and pressure equation, and couple the differential diffusion term into the small flame model, that is, consider the differential diffusion term when solving the control parameter equations of enthalpy, process variables, mixture fraction, and second moment of mixture fraction;

[0008] Step 3: First, set the initial conditions and boundary conditions, and then solve the system of equations of the mixture fraction equation, process variable equation, density equation, velocity equation, enthalpy equation, and pressure equation mentioned in Step 2. After solving the control parameter equations, input the calculated results into the pre-calculated small flame library, and look up the table to obtain the components, temperature, chemical reaction source term, and differential diffusion term; then solve the pressure equation inside the predictor-corrector iteration and perform pressure correction.

[0009] Through the above three steps, the construction process of the differential diffusion small flame model for buoyancy-driven plume is established.

[0010] Further, in Step 1, the small flame model is used to model the plume;

[0011] Further, in Step 1, referring to the diameter and height of the combustion chamber of the experimental facility and other necessary facilities such as fuel nozzles, design the computational domain and divide the grid;

[0012] Further, in Step 2, using the finite volume method, according to the grid division, perform volume integration of the mixture fraction transport equation, process variable transport equation, density transport equation, second moment of process variable transport equation, velocity transport equation, enthalpy transport equation, process variable transport equation, and pressure transport equation established in Step 1 within the grid, and perform time integration within the time step. Finally, introduce the spatial and time discretization formats to obtain the algebraic equations defined at the grid center points.

[0013] Further, for the transport equations of the process variables, mixture fraction, enthalpy, and second moment of mixture fraction, that is, the control parameter equations, consider the influence of the differential diffusion effect when solving the control parameter equations and couple the differential diffusion term.

[0014] Further, in Step 2, when solving the pressure transport equation, consider pressure correction and correct the velocity field and density field.

[0015] Further, Step 3 specifically includes the following steps:

[0016] Step (1): Design the initial conditions and boundary conditions according to the experimental facility;

[0017] Step (2), determine the global time step and perform time marching calculations;

[0018] Step (3), solve the density transport equation to obtain the density value;

[0019] Step (4), solve the velocity transport equation to obtain the velocity prediction value;

[0020] Step (5), solve the process variable transport equation to obtain the process variable prediction value;

[0021] Step (6), solve the mixture fraction transport equation to obtain the mixture fraction prediction value;

[0022] Step (7), solve the mixture fraction second moment transport equation to obtain its prediction value;

[0023] Step (8), solve the enthalpy transport equation to obtain the enthalpy prediction value;

[0024] Step (9), input the results of steps (5) to (8) into the pre-solved small flame library, and look up the table to obtain the results of the component, temperature, chemical reaction source term, and differential diffusion term;

[0025] Step (10), solve the pressure transport equation, substitute the calculation result of the pressure into the velocity equation and solve it to obtain the velocity correction value;

[0026] Step (11), loop step (10) to continuously correct the velocity value until the specified number of loops is reached;

[0027] Step (12), loop steps (4) to (11) until convergence or the maximum number of loops is reached;

[0028] Step (13), solve the ideal gas state equation to update the density value;

[0029] Step (14), determine whether the specified end time is reached, otherwise perform time step marching and repeat steps (3) to (13).

[0030] Furthermore, it is used to handle the unsteady term, convective term, diffusion term, and source term.

[0031] The beneficial effects of the present invention compared with the prior art are as follows:

[0032] The differential diffusion small flame model proposed by the present invention, for buoyancy-driven plumes, considers the influence of the differential diffusion effect in buoyancy-driven plumes on the distribution of substance components, improves the calculation accuracy and precision, and through coordinate transformation and dimension reduction processing, reduces the number of component control equations to be solved, greatly improves the solution efficiency, and reduces the consumption of computing resources. Brief Description of the Drawings

[0033] Figure 1 It is a flowchart of the method for constructing a differential diffusion small flame model for buoyancy-driven plume according to the present invention;

[0034] Figure 2 It is a schematic diagram of mesh division, initial conditions and boundary conditions design in the embodiment of the present invention;

[0035] Figure 3 It is a comparison diagram of the calculation simulation results and experimental data of three models in the embodiment of the present invention. The left figure is the axial velocity comparison diagram, and the right figure is the radial velocity comparison diagram. Among them, the dots correspond to the experimental data results, the black solid line corresponds to the calculation results of the infinitely fast chemical reaction model, the black dotted line corresponds to the calculation results of the model without considering the influence of differential diffusion effect, and the gray dotted line corresponds to the calculation results of the model considering the influence of differential diffusion effect;

[0036] Figure 4 It is a comparison diagram of the distribution of the calculation results of the mass fraction of product carbon dioxide in the laminar (left figure) and turbulent (right figure) regions in the embodiment of the present invention. Among them, the dotted line corresponds to the model without considering the influence of differential diffusion effect, and the solid line corresponds to the model considering the influence of differential diffusion effect. Detailed implementation manners

[0037] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present 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 only used to explain the present invention and are not used to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other. For the convenience of understanding the present invention, the present invention will be described more comprehensively and meticulously below with reference to the embodiments, but the protection scope of the present invention is not limited to the following specific embodiments.

[0038] The present invention realizes high-precision numerical solution of buoyancy plume under the framework of the finite volume method, and can handle the numerical solution of pool fire combustion of gaseous fuel and liquid fuel and all plumes driven by buoyancy.

[0039] As Figure 1 shown, the method for constructing a differential diffusion small flame model for buoyancy-driven plume according to the present invention includes the following steps:

[0040] Step 1, establish an unsteady, viscous multi-dimensional plume mathematical model, and design a computational domain and divide the mesh;

[0041] Step 2: Based on the grid division in Step 1, physically discretize the multi-dimensional plume mathematical model established in Step 1 to obtain a system of equations including a mixture fraction equation, a process variable equation, a density equation, a velocity equation, an enthalpy equation, and a pressure equation. Couple the differential diffusion term into the small flame model, that is, consider the differential diffusion term when solving the control parameter equations of enthalpy, process variables, mixture fraction, and the second moment of the mixture fraction.

[0042] Step 3: First, set the initial conditions and boundary conditions. Then, solve the system of equations of the mixture fraction equation, process variable equation, density equation, velocity equation, enthalpy equation, and pressure equation mentioned in Step 2. After solving the control parameter equations of enthalpy, process variables, mixture fraction, and the second moment of the mixture fraction, input the calculated results into the pre-calculated small flame library, and look up the table to obtain the components, temperature, chemical reaction source term, and differential diffusion term. Then, solve the pressure equation inside the predictor-corrector iteration and perform pressure correction. Through the above three steps, the solution process of the differential diffusion small flame model for buoyancy-driven plumes is established.

[0043] Specifically, in Step 1, the established multi-dimensional plume mathematical model uses the small flame mathematical model, including a pressure equation, a density equation, a mixture fraction equation, a process variable equation, a velocity equation, an enthalpy equation, and a second moment equation of the mixture fraction. Among them, the process variable equation, mixture fraction equation, enthalpy equation, and second moment equation of the mixture fraction are the control parameter equations solved in the small flame model. The process variable can reflect the progress of the chemical reaction, and different parameters can be set according to different cases. Generally, it is set as the sum of the mass fractions of key products and key reactants. The more reasonable the setting, the more efficient and accurate the calculation. The second moment of the mixture fraction is the variance of the mixture fraction. Solving this parameter can take into account the turbulent pulsation at any position in space.

[0044] Specifically, in Step 2, use the finite volume method. According to the grid division, perform volume integration of the mixture fraction transport equation, process variable transport equation, density transport equation, second moment transport equation of the process variable, velocity transport equation, enthalpy transport equation, process variable transport equation, and pressure transport equation established in Step 1 within the grid, and perform time integration within the time step. Finally, introduce the spatial and temporal discretization formats to obtain the algebraic equations defined at the grid center points. The transport equations of process variables, mixture fraction, enthalpy, and the second moment of the mixture fraction, that is, the control parameter equations, consider the influence of the differential diffusion effect when solving the control parameter equations and couple the differential diffusion term. When solving the pressure transport equation, consider pressure correction and correct the velocity field and density field. The solution order between each system of equations will be described in Step 3.

[0045] As Figure 1 shown, Step 3 specifically includes the following steps:

[0046] Step (1), design the initial conditions and boundary conditions according to the experimental facilities;

[0047] Step (2), determine the global time step and perform time marching calculations;

[0048] Step (3), solve the density transport equation to obtain the density value;

[0049] Step (4), solve the velocity transport equation to obtain the estimated velocity value;

[0050] Step (5), solve the transport equation of the process variable to obtain the estimated value of the process variable;

[0051] Step (6), solve the mixture fraction transport equation to obtain the estimated mixture fraction value;

[0052] Step (7), solve the second moment transport equation of the mixture fraction to obtain its estimated value;

[0053] Step (8), solve the enthalpy transport equation to obtain the estimated enthalpy value;

[0054] Step (9), input the results of steps (5) to (8) into the pre-solved small flame library, and look up the table to obtain the results of the components, temperature, chemical reaction source term, and differential diffusion term;

[0055] Step (10), solve the pressure transport equation, substitute the calculated result of the pressure into the velocity equation and solve it to obtain the velocity correction value;

[0056] Step (11), loop step (10) to continuously correct the velocity value until the specified number of loops is reached;

[0057] Step (12), loop steps (4) to (11) until convergence or the maximum number of loops is reached;

[0058] Step (13), solve the ideal gas state equation to update the density value;

[0059] Step (14), determine whether the specified end time has been reached. Otherwise, perform time step marching and repeat steps (3) to (13).

[0060] Among them, the equation forms solved in steps (5) to (8) above are:

[0061] ;

[0062] Among them, is the density; is the variable to be solved, including the process variable, mixture fraction, second moment of the mixture fraction, and enthalpy; is the mass diffusion coefficient, , where c p is the specific heat capacity at constant pressure, is the thermal conductivity; is the source term corresponding to the variable; refers to the gradient operator, where refers to the Cartesian coordinate in the j-th direction; represents the velocity vector, refers to the component of the velocity in the j-th direction; represents time, is the differential diffusion term, , where .

[0063] In the above solution process, since the buoyant plume is a process of mutual coupling of heat transfer, mass transfer and flow processes in the field of fire safety, the velocity solution equation and the density solution equation are placed before the control parameter solution equations. When solving the control parameter equations, that is, the process variable equation, the mixture fraction equation, the second moment equation of the mixture fraction and the enthalpy equation, a differential diffusion term is added to the equation to couple the differential diffusion effect. The establishment of the small flame library is obtained by solving the one-dimensional small flame equation. Therefore, in the solution process, only the control variable equations need to be solved, and then input into the small flame library to calculate parameters such as component temperature. Through the coordinate transformation and dimensionality reduction process, the number of control equations to be solved can be greatly reduced, and the solution efficiency can be improved.

[0064] Example:

[0065] Taking the numerical calculation of a three-dimensional methane / air counterflow diffusion flame as an example, the present invention is described comprehensively and in detail. This method is not limited to the pool fire combustion scenario and is applicable to all buoyancy-driven plumes. In this embodiment, three different combustion models are used to model the methane / air combustion process respectively to compare the effects of finite chemical reaction rate and differential diffusion effect on the results. Among them, the infinitely fast chemical reaction model selects the EDM model, the finite fast chemical reaction model does not consider the differential diffusion effect, and the small flame model (FPV_Le1) based on the unified Lewis number hypothesis is used for calculation. Finally, the small flame model with differential diffusion effect (FPV_DD) is used for calculation to compare and analyze the influence of the differential diffusion effect.

[0066] The embodiments of the present invention are mainly divided into the following steps: Step (1) description of the mathematical model; Step (2) establishment and description of the computational domain, initial conditions and boundary conditions; Step (3) grid division and calculation process description. Then, based on the model, the flame characteristics such as the temperature field and the component field are analyzed.

[0067] Step (1) First, the mathematical model is described, which is divided into 3 parts, specifically including:

[0068] A non-steady, viscous multi-dimensional small flame mathematical model is established. Among them, the continuity equation is established according to the law of conservation of mass, the momentum equation is established according to Newton's second law of motion, and the energy equation is established according to the law of conservation of energy. Thus, the control equations that govern fluid flow and heat and mass transfer are formed.

[0069] The control equations for solving velocity and density are the same for the three models. Among them, the control equation for solving density is:

[0070] ;

[0071] is the density; refers to the gradient operator, where refers to the Cartesian coordinate in the j-th direction; represents the velocity vector, refers to the component of the velocity in the j-th direction; represents time.

[0072] The control equation for solving velocity is:

[0073] ;

[0074] Among them, is the pressure, is the gravitational acceleration, and is the viscous stress tensor.

[0075] When solving the component and enthalpy using the infinitely fast chemical reaction model, the method of directly solving the equations is also adopted. Among them, the component equation is:

[0076] ;

[0077] is the density; is the mass fraction of each component; is the mass diffusion coefficient, , where c p is the specific heat capacity at constant pressure, is the thermal conductivity; is the component source term;

[0078] The enthalpy equation for solving is:

[0079] ;

[0080] Among them, is the standard enthalpy, is the thermal diffusion coefficient, is the pressure, is the radiation source term, refers to the heat release rate per unit volume of combustion.

[0081] In the two small flame models, enthalpy, composition, etc. are not directly solved. Instead, the control parameters are solved first, and the form of their equations is as follows:

[0082] ;

[0083] is the differential diffusion term, is the variable to be solved, including process variables, mixture fraction, second moment of mixture fraction, and enthalpy, , .

[0084] For the FPV_Le1 model with the unified Lewis number assumption, the differential diffusion term on the right side of the equation is 0. While for the differential diffusion small flame model FPV_DD during solution, the differential diffusion term on the right side of the equation is not 0.

[0085] Step (2) Establishment and description of the computational domain, mesh division, initial conditions, and boundary conditions:

[0086] First part, in this embodiment, the computational domain and boundary conditions of the methane / air diffusion flame are set with reference to the methane pool fire combustion experiment of the US Sandia Laboratory, as Figure 2 shown, where n refers to the number of meshes divided, R1 refers to the half radius of the fuel inlet, R2 refers to the radius of the pedestal-shaped plane, R3 refers to the radius of the entire computational domain. The computational domain is a cylinder with a diameter of 4.6 m and a height of 4.56 m. The cylinder is divided into three-dimensional orthogonal meshes. Among them, it is evenly divided into 350 meshes along the axial direction, stretched meshes are set along the radial direction, and according to the stretching parameter of 1.3, the computational domain is divided into 200 meshes, and the computational domain is evenly divided into 80 meshes along the tangential direction. The total number of meshes is 22.4 million.

[0087] Second part, methane fuel enters the computational domain at an initial velocity of 0.097 m / s through an inlet with a half radius of 0.5 m. At the same time, the fuel inlet is surrounded by a pedestal-shaped plane with a half radius of 1 m, and the surrounding of the pedestal-shaped plane is air fluid with an initial velocity of 0.327 m / s. The initial temperatures of the fuel and oxidizer in this embodiment are both 300 K, the pressure is set to a constant pressure of 81.1 kPa, the mixture fraction Z of the fuel at the inlet is set to 1, and the mixture fraction Z of the oxidizer at the inlet is set to 0; the remaining variables all adopt zero gradient conditions.

[0088] The calculation process of the three-dimensional methane / air pool fire combustion diffusion flame in step (3) is as follows:

[0089] First, divide the grid according to the parameters mentioned in step (2). Use the PIMPLE algorithm to numerically discretize the process variable equation, enthalpy equation, mixture fraction equation, and mixture fraction second moment equation on all grids by means of the finite volume method. Among them, the time integration is carried out using the first-order Euler implicit method, that is, both the convective term and the diffusive term are attributed to the unknown time level, and the spatial integration is carried out using the second-order total variation diminishing scheme.

[0090] Subsequently, solve the equations sequentially according to the plume model coupled with the small flame model and the differential diffusion effect. The specific steps are as follows:

[0091] Step 1, design the initial conditions and boundary conditions according to the experimental facilities;

[0092] Step 2, determine the global time step and perform time marching calculations;

[0093] Step 3, solve the density transport equation to obtain the density value;

[0094] Step 4, solve the velocity transport equation to obtain the velocity prediction value;

[0095] Step 5, solve the process variable transport equation to obtain the process variable prediction value;

[0096] Step 6, solve the mixture fraction transport equation to obtain the mixture fraction prediction value;

[0097] Step 7, solve the mixture fraction second moment transport equation to obtain its prediction value;

[0098] Step 8, solve the enthalpy transport equation to obtain the enthalpy prediction value;

[0099] Step 9, input the results of steps 4 to 7 into the pre-solved small flame library, and look up the table to obtain the results of the components, temperature, chemical reaction source term, and differential diffusion term;

[0100] Step 10, solve the pressure transport equation, substitute the calculation result of the pressure into the velocity equation and solve it to obtain the velocity correction value;

[0101] Step 11, loop step 10, continuously correct the velocity value until the specified number of loops is reached;

[0102] Step 12, loop steps 3 to 11 until convergence or the maximum number of loops is reached;

[0103] Step 13, solve the ideal gas state equation to update the density value;

[0104] Step 14, judge whether the specified end time is reached. Otherwise, perform time step marching and repeat steps 1 to 13.

[0105] According to the above steps, the variable values defined at the center points of each grid are obtained, and the numerical solution of the differential diffusion small flame model for the methane / air pool fire combustion diffusion flame is realized.

[0106] After that, the characteristics such as the velocity field and the substance distribution of the three-dimensional methane / air pool fire combustion diffusion flame are analyzed:

[0107] The calculated results are compared with the experimental data to analyze the accuracy of the model prediction. Based on the velocity field calculated according to the present invention and averaged over time, as Figure 3 shown, the data points are selected at the circular cross-section 0.9 m from the bottom of the calculation domain. r is the distance from the data point selected on the circular cross-section to the center of the circle, and u z refers to the axial velocity, and u r refers to the radial velocity. The points correspond to the experimental data. The three lines respectively correspond to three models: the EDM model, the small flame model FPV_Le1 based on the unified Lewis number hypothesis, and the differential diffusion small flame model FPV_DD, as Figure 3 shown. The data at the flame neck are selected. It can be seen that the three models are in good agreement with the experimental data. The data of the two small flame models are in better agreement with the EDM model and the experimental data. However, there is not much difference between the FPV_DD model considering the differential diffusion effect and the FPV_Le1 model. Therefore, it can be concluded from the present invention that the finite chemical reaction rate has an impact on the velocity field distribution, while the differential diffusion effect has little impact on the accuracy of the velocity field calculation results. Figure 3 In it, the left figure is the comparison diagram of the axial velocity, and the right figure is the comparison diagram of the radial velocity. Among them, the points correspond to the experimental data results, the black solid line corresponds to the calculation results of the infinitely fast chemical reaction model, the black dotted line corresponds to the calculation results of the model without considering the influence of the differential diffusion effect, and the gray dotted line corresponds to the calculation results of the model considering the influence of the differential diffusion effect.

[0108] By comparing the distributions of the carbon dioxide mass fraction in the laminar and turbulent regions, the influence of the differential diffusion effect is analyzed, as Figure 4 shown ( Figure 4 is the distribution comparison diagram of the laminar (left figure) and turbulent (right figure) regions. Among them, the dotted line corresponds to the model without considering the influence of the differential diffusion effect, and the solid line corresponds to the model considering the influence of the differential diffusion effect). Z is the mixture fraction, is the mass fraction of CO2. Lam refers to the laminar region, and Tur refers to the turbulent region. It can be seen from the figure that in both the laminar and turbulent regions, the predicted results of FPV_DD for the mass fraction of CO2 are higher than those of the FPV_Le1 model. This is because the differential diffusion effect is affected by the relative molecular mass of the substance, indicating that the differential diffusion effect has an impact on the component distribution. Therefore, both the finite fast chemical reaction rate and the differential diffusion effect need to be considered.

[0109] Those skilled in the art can easily understand that the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for constructing a differential diffusion flamelet model for buoyancy-driven plumes, characterized in that: The steps include: Step 1: Establish an unsteady, viscous multi-dimensional plume mathematical model, design the computational domain, and divide the grid; Step 2: Combined with the grid division in step 1, the multi-dimensional plume mathematical model established in step 1 is physically discretized to obtain a system of equations including mixture fraction equation, process variable equation, density equation, velocity equation, enthalpy equation and pressure equation, and the differential diffusion term is coupled to the small flame model, that is, the differential diffusion term is considered when solving the control parameter enthalpy, process variable, mixture fraction and mixture fraction second-order moment equation system; Step 3: First, set the initial conditions and boundary conditions, and then solve the equations of the mixture fraction equation, process variable equation, density equation, velocity equation, enthalpy equation and pressure equation mentioned in step 2. After solving the control parameter equations, input the calculated results into the pre-calculated small flame library, and look up the table to obtain the components, temperature, chemical reaction source terms and differential diffusion terms; The pressure equation is then solved within the estimation correction iteration and the pressure correction is performed.

2. A method for constructing a differential diffusion flamelet model for a buoyancy-driven plume according to claim 1, characterized in that: In the step 1, a small flame model is used to model the plume.

3. The method for constructing a differential diffusion flamelet model for a buoyancy-driven plume according to claim 1, characterized in that: In the step 1, the calculation domain is designed and the grid is divided with reference to the combustion chamber diameter and height of the experimental facility.

4. The method for constructing a differential diffusion flamelet model for a buoyancy-driven plume according to claim 1, characterized in that: In the step 2, the finite volume method is used to perform volume integration of the mixed fraction transport equation, process variable transport equation, density transport equation, process variable second-order moment transport equation, velocity transport equation, enthalpy transport equation, process variable transport equation, and pressure transport equation established in step 1 within the grid according to the grid division situation, and time integration within the time step.

5. A method for constructing a differential diffusion flamelet model for a buoyancy-driven plume according to claim 4, characterized in that: By introducing a spatial and temporal discretization format, we obtain algebraic equations defined at the center points of the grid.

6. A method for constructing a differential diffusion flamelet model for a buoyancy driven plume according to claim 5, characterized in that: The transport equations of the process variables, mixture fraction, enthalpy and second-order moment of the mixture fraction, namely the control parameter equation group, consider the influence of the differential diffusion effect and couple the differential diffusion term when solving the control parameter equation group.

7. A method for constructing a differential diffusion flamelet model for a buoyancy driven plume according to claim 1, characterized in that: In the second step, when solving the pressure transport equation, the pressure correction is considered, and the velocity field and the density field are corrected.

8. The method for constructing a differential diffusion flamelet model for a buoyancy driven plume according to claim 1, characterized in that: The step three comprises the following steps: Step (1), designing initial conditions and boundary conditions according to the experimental facilities; Step (2), determine the global time step and perform time advancement calculation; Step (3), solving the density transport equation to obtain the density value; Step (4), solving the velocity transport equation to obtain the velocity estimate; Step (5), solving the process variable transport equation to obtain the estimated value of the process variable; Step (6), solving the mixture fraction transport equation to obtain an estimated mixture fraction; Step (7), solving the mixed fractional second-order moment transport equation to obtain its estimated value; Step (8), solve the enthalpy transport equation to obtain the enthalpy estimate.

9. A method for constructing a differential diffusion flamelet model for a buoyancy driven plume according to claim 8, characterized in that: The step three also includes: Step (9), inputting the results of steps (5) to (8) into the pre-solved small flame library, and obtaining the results of components, temperature, chemical reaction source terms and differential diffusion terms by looking up the table; Step (10), solving the pressure transport equation, substituting the calculated pressure result into the velocity equation and solving it to obtain the velocity correction value; Step (11), looping step (10), continuously correcting the speed value until a specified number of loops is reached; Step (12), loop step (4) to step (11) until convergence or the maximum number of loops is reached; Step (13), solving the ideal gas state equation and updating the density value; Step (14), determine whether the specified end time has been reached, otherwise advance the time step and repeat steps (3) to (13).

10. The method for constructing a differential diffusion flamelet model for a buoyancy driven plume according to claim 1, characterized in that: Used to handle unsteady terms, convection terms, diffusion terms, and source terms.

Images