Gas phase combustion model construction method based on dynamic adaptive grid

Abstract

The invention discloses a gas-phase combustion model construction method based on a dynamic self-adaptive grid, which comprises the following steps: establishing a steady-state flame control equation set according to the property of target fuel, and obtaining laminar flame velocities and laminar flame thicknesses under different conditions; establishing an unsteady-state reactive Navier-Stokes equation set, performing spatial filtering on each variable in the equation set, and constructing a sub-lattice scale turbulence model; coupling a thickened flame model and a disturbance factor model in the filtered equation set; limiting the encrypted area of the self-adaptive grid near a flame surface through a flame detection function; and solving the equation set to obtain a variable of a next time step, updating a disturbance factor and the number of layers of the adaptive grid, and re-dividing the adaptive grid of the layered block structure to realize dynamic tracking of the flame position and real-time adjustment of the grid resolution. According to the method, the disturbance factor and the grid encryption layer number can be adjusted in real time based on the laminar flame speed and the flame thickness in the current state, and the combustion modeling precision and the calculation efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of computational fluid dynamics, and in particular to a method for constructing a gas phase combustion model based on a dynamic adaptive grid. Background Art

[0002] Numerical simulation of gas-phase combustion processes has important applications in fields such as energy, aerospace, and security. Accurately and efficiently simulating the flame structure and combustion dynamics during combustion processes is crucial for improving energy efficiency and optimizing combustion equipment safety and protection research. However, gas-phase combustion processes, including minute flame thickness, intense chemical reactions, and strong coupling with turbulent flows, typically require very fine meshes to accurately simulate these characteristics, making it difficult to meet computational efficiency and resource requirements in practical applications.

[0003] Existing technologies primarily employ thickened flame models, adjusting the chemical reaction rate and diffusion coefficient. While maintaining a constant flame propagation speed, the flame thickness is increased to reduce mesh resolution requirements. However, increasing flame thickness alters the interaction characteristics between the flame and the flow field, making the flame less sensitive to turbulence. Vortices smaller than the flame thickness can no longer interact with the flame, resulting in reduced flame structure simulation accuracy. Adaptive Mesh Refinement (AMR) technology dynamically adjusts mesh resolution, refining the mesh near the flame surface while maintaining a coarser mesh in non-critical areas, significantly reducing overall computational cost. However, determining the degree of mesh refinement is highly empirical and requires manual adjustment for different combustion conditions. Insufficient refinement may fail to accurately capture the flame structure, while excessive refinement significantly increases the computational effort and reduces efficiency. Therefore, dynamically updating the mesh refinement and perturbation factor model as flame characteristic parameters change is a key challenge in achieving high-precision and efficient gas-phase combustion simulations. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for constructing a gas phase combustion model based on a dynamic adaptive grid. This method can adjust the disturbance factor and the number of grid encryption layers in real time based on the laminar flame speed and flame thickness in the current state, ensuring that there is always a sufficient number of grids in the flame area, thereby improving the accuracy and computational efficiency of combustion modeling.

[0005] The purpose of the present invention is achieved through the following technical solutions: A method for constructing a gas phase combustion model based on a dynamic adaptive grid, the method comprising: Step 1: Based on the steady-state flame control equations, the laminar flame speed and laminar flame thickness of the target fuel under different conditions are obtained, and a data table for reference is established; Step 2: Establish the unsteady-state reactive Navier-Stokes equations, perform spatial filtering on the variables in the equations, and construct a subgrid-scale turbulence model; Step 3, coupling the thickening flame model and the disturbance factor model to the filtered unsteady reactive Navier-Stokes equations; Step 4: Construct the computational domain according to the requirements of the computational scenario and divide the computational domain into a hierarchical block structure adaptive grid to discretize the computational domain. The flame detection function is used to limit the encrypted area of ​​the adaptive grid to the vicinity of the flame surface. Step 5: Solve the unsteady-state reactive Navier-Stokes equations obtained in step 3 to obtain the variables for the next time step, and update the disturbance factor and the number of adaptive grid layers. Based on the flame surface position and the updated number of adaptive grid layers, re-divide the hierarchical block structure adaptive grid to achieve dynamic tracking of the flame position and real-time adjustment of the grid resolution.

[0006] It can be seen from the technical solution provided by the present invention that the above method can adjust the disturbance factor and the number of grid encryption layers in real time based on the laminar flame speed and flame thickness in the current state, ensuring that there is always a sufficient number of grids in the flame area, thereby improving the accuracy and computational efficiency of combustion modeling. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0008] Figure 1 A schematic flow chart of a method for constructing a gas phase combustion model based on a dynamic adaptive grid provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of a hierarchical block structure adaptive grid structure according to an embodiment of the present invention; Figure 3 A schematic diagram of some data of an example of the present invention; Figure 4 This is a schematic diagram of the flame distribution over time in the pipeline at different times calculated according to the example of the present invention; Figure 5 This is a schematic diagram of the changes in the grid near the flame surface during the flame development process of the example of the present invention. DETAILED DESCRIPTION

[0009] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments, and do not constitute a limitation of the present invention. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0010] like Figure 1 FIG. 1 is a flow chart of a method for constructing a gas phase combustion model based on a dynamic adaptive grid according to an embodiment of the present invention. The method includes: Step 1: Based on the steady-state flame control equations, the laminar flame speed and laminar flame thickness of the target fuel under different conditions are obtained, and a data table for reference is established; In this step, a standard one-dimensional steady-state laminar flame physics model is specifically used, which accurately describes the internal structure of the premixed flame at constant pressure by solving a set of coupled ordinary differential equations.

[0011] Assume that the steady-state flame x Directional propagation, the steady-state flame control equations include: Continuity equation: ; Component conservation equation: ; Energy conservation equation: ; in, is the density; is the gas velocity in the x-direction of the spatial coordinate; is the mass flux; For components k The quality score of is the diffusion mass flux; For components k The molar chemical reaction rate; For components k molar mass; is temperature; 、 mixture and components respectively k Specific heat capacity at constant pressure; is the thermal conductivity of the mixture; is the specific enthalpy of component k; is the total number of components; For one-dimensional steady-state flow, the mass flux is constant in the flame propagation direction, so the laminar flame speed to be solved is Satisfies the relationship: ,in is the density of the unburned area; Laminar flame thickness Calculated from the temperature, it is defined as: ,in T b and T u are the temperatures of the burned and unburned areas, respectively; The above equations are solved under different initial temperatures, initial pressures and equivalence ratios to obtain the corresponding laminar flame speed and laminar flame thickness, and a data table is established for subsequent steps.

[0012] Step 2: Establish the unsteady-state reactive Navier-Stokes equations, perform spatial filtering on the variables in the equations, and construct a subgrid-scale turbulence model; In this step, the unsteady-state reactive Navier-Stokes equations are established as follows: Continuity equation: (1) Momentum equation: (2) Energy equation: (3) Component transport equation: (4) in, is the density; For time; is the vector differential operator, , Represents the spatial coordinates x 、 y 、 z The partial derivative of is the velocity vector; For pressure; is the Kronecker symbol, , i and j Represents the direction index, which is used to represent the three-dimensional space x 、 y 、 z direction; is the viscous stress tensor; For the ratio of total energy; is temperature; is the concentration of reactant components; is the thermal conductivity of the mixture; is the component diffusion coefficient; The heat of reaction is released; is the chemical reaction rate, ; is the pre-exponential factor; is the activation energy; is the gas constant; A discrete explicit filter is used to perform spatial filtering on the variables in the established unsteady-state reactive Navier-Stokes equations. The variables are decomposed into large-scale and small-scale parts. The large-scale part is directly solved, and the small-scale part is modeled. The expression of the discrete explicit filter is: (5) in, is any variable; is any variable after spatial filtering; N is the filter half-width; subscript o is the center grid index; l The sum index is used to specify the position offset relative to the center grid; is the filter coefficient, satisfying ; Considering the significant density change in compressible reacting flow, the velocity vector , than the total energy , component concentration Using Favre filtering, they are defined as 、 、 ; 、 、 are the velocity vector, specific total energy, and component concentration after Favre filtering, respectively; is the density after spatial filtering; Represents the value after spatial filtering of the product of density and velocity vector; Represents the value after spatial filtering of the product of density and specific total energy; Represents the value after spatial filtering of the product of density and component concentration; The spatially filtered unsteady reactive Navier-Stokes equations are shown below: Continuity equation: (6) Momentum equation: (7) Energy equation: (8) Component transport equation: (9) in, is the viscous stress tensor after spatial filtering; is the chemical reaction rate after spatial filtering; is the pressure after spatial filtering; is the temperature after spatial filtering; is the heat release of the reaction after Favre filtering, defined as , represents the value after spatial filtering of the product of density and reaction heat release; 、 、 、 They are the stress term, heat flux term, turbulent diffusion term and mass flux term at the sub-grid scale.

[0013] In the constructed subgrid-scale turbulence model, the standard Smagorinsky model is used for calculation, as follows: (10) (11) (12) (13) in, is the sub-grid stress viscosity, ; is the strain rate tensor, , Tr represents the transpose of the vector; is a tensor traces; is the filter size; is the specific heat capacity at constant pressure; is the Prandtl number; is the Schmidt number; and is the model constant.

[0014] Step 3, coupling the thickening flame model and the disturbance factor model to the filtered unsteady reactive Navier-Stokes equations; In this step, the coupling between the thickening flame model and the perturbation factor model is completed by modifying the chemical reaction rate and diffusion coefficient in the filtered unsteady-state reactive Navier-Stokes equations. The specific process is as follows: The energy equation is modified to: (14) The component transport equation is modified as follows: (15) in, is the flame thickening multiple, the expression is: (16) In the above formula, It is a user-defined thickening factor, and its relationship with the mesh size and flame thickness is: ; Indicates the number of grids within the flame thickness; Indicates the size of the finest grid element in the flame surface; is the laminar flame thickness; represents the flame detection function, , is the hyperbolic tangent function, To thicken the flame, is the chemical reaction rate, The value range is [0,1], the closer to 1, the closer to the flame, through the flame detection function Identify the flame area and thicken only in the flame area while reducing the impact on the non-flame area; coefficient This means turning off the subgrid turbulence model in the flame region to prevent excessive dissipation when solving the flame. is a disturbance factor used to correct the decrease in flame wrinkling ability caused by the thickening flame model, and is specifically defined as: (17) in, is the filter size; represents the laminar flame speed; is the efficiency function, calculated as: (18) in: is the turbulence intensity effect term, which is used to describe the contribution of unresolved small-scale vortices to flame wrinkles and is defined as: ; is the scale separation effect term, which is used to describe the contribution of eddies between the flame thickness and the filter scale to the flame wrinkles and is defined as: ; is the viscous dissipation effect term, which is used to describe the dissipative effect of molecular viscosity on small-scale vortices and is defined as: ; 、 and is the model constant; in the specific implementation:

[0015] ; ; is the Reynolds number of the filter scale, ; is the subgrid-scale turbulent velocity, calculated as: (19) is the model coefficient, which is calculated by the Germano identity. The specific expression is: (20) in, is a logarithmic function; To test the filter size and filter size The ratio of Indicates the filtered size Chemical reaction rates after spatial filtering; express Then the value after testing the filter size; Indicates the tested filter size Filtered chemical reaction rate; symbol Represents volume averaging operation.

[0016] Step 4: Construct the computational domain according to the requirements of the computational scenario and divide the computational domain into a hierarchical block structure adaptive grid to discretize the computational domain. The flame detection function is used to limit the encrypted area of ​​the adaptive grid to the vicinity of the flame surface. In this step, if Figure 2 The figure shows the schematic diagram of the hierarchical block structure adaptive grid structure of the embodiment of the present invention. All grids are square / cube grids. In actual application, the flame detection function is used to limit the adaptive grid encryption area to the vicinity of the flame surface. When the set threshold is reached, the adaptive grid is refined; When mesh encryption is in progress, the original underlying mesh remains unchanged, and a separate layer of fine mesh is generated where encryption is required. The encryption process is then repeated until the maximum number of encryption layers is reached. The mesh refinement ratio between adjacent layers is 2. n Layer grid cell size for: (twenty one) in, is the base grid size; The number of mesh encryption layers is determined by the flame thickening multiple and flame thickness, specifically: (twenty two) Among them, int points to the integer; is the laminar flame thickness; It is a user-defined thickening multiple; Indicates the number of grids within the flame thickness; Grid passes The density of the layers is such that the finest mesh layer in the flame area has at least A grid is used to ensure the accuracy of flame solution.

[0017] Step 5: Solve the unsteady-state reactive Navier-Stokes equations obtained in step 3 to obtain the variables for the next time step, and update the disturbance factor and the number of adaptive grid layers. Based on the flame surface position and the updated number of adaptive grid layers, re-divide the hierarchical block structure adaptive grid to achieve dynamic tracking of the flame position and real-time adjustment of the grid resolution.

[0018] In this step, the unsteady reactive Navier-Stokes equations of the coupled thickening flame model and the perturbation factor model obtained in step 3 are numerically discretized on a hierarchical block structured grid. Specifically: The equations are decomposed into three parts: convection term, diffusion term and source term by using operator splitting method; The convection term is calculated using the 5th-order Weighted Essentially Non-Oscillatory (WENO) format to obtain discontinuous structures such as flame fronts and shock waves. The diffusion term is calculated using the second-order central difference format; The source term is calculated using direct integration; The variables of the next time step are obtained through iterative solution.

[0019] In the specific implementation, after completing the advancement of each time step, the laminar flame speed and flame thickness are queried in the data table established in step 1 according to the current state parameters. If there is no corresponding data entry in the data table, the required laminar flame speed and flame thickness are obtained by interpolation through adjacent data points; Update the perturbation factor and adaptive grid layer number according to the obtained laminar flame speed and flame thickness; Based on the encrypted area determined by the flame detection function and the updated number of adaptive grid layers, the hierarchical block structure adaptive grid is re-divided to achieve dynamic tracking of the flame position and real-time adjustment of the grid resolution; Repeat step 5 until the specified number of calculation steps or calculation time is reached.

[0020] It should be noted that the contents not described in detail in the embodiments of the present invention belong to the prior art known to those skilled in the art.

[0021] The following is a specific example to illustrate the process and application of the method of the present invention. In this example, a hydrogen-air premixed combustion process with an equivalence ratio of 1 in a two-dimensional closed pipe is calculated: Step 1. Based on the target fuel properties, a steady-state flame control equation group is established to describe the free-propagating laminar flame. The hydrogen-air detailed chemical reaction mechanism is used for calculation, which includes 9 chemical species and 23 chemical reactions. The initial pressure range is 0.1 MPa-4.0 MPa, with an interval of 0.001 MPa; the initial temperature range is 290 K-3000 K, with an interval of 10 K. Solve the steady-state flame control equation group to obtain the corresponding laminar flame speed and laminar flame thickness data, and establish the corresponding data table, as shown in the following figure: Figure 3 Shown is a schematic diagram of part of the data of the examples given in the present invention.

[0022] Step 2: Establish the unsteady-state reactive Navier-Stokes equations, perform spatial filtering operations on each variable, and construct a subgrid-scale turbulence model.

[0023] In this embodiment, the spatial filtering adopts a second-order Gaussian filter, which is defined as: (twenty three) in, For any variable, is any variable after spatial filtering, is the ratio of filter size to grid size, subscript o Represents the center grid index.

[0024] Step 3, coupling the thickening flame model and the disturbance factor model to the filtered unsteady reactive Navier-Stokes equations; Thickening multiple The relationship between the grid size and the flame thickness is In order to avoid physical distortion caused by excessive flame thickening, it is generally required In this embodiment, ; is the number of grids within the flame thickness. To ensure accurate flame calculation, it is generally required In this embodiment, ; is the size of the finest grid element in the flame surface; is the laminar flame thickness.

[0025] The disturbance factor is calculated as follows: (twenty four) is the laminar flame speed, and this step and subsequent steps involve and All of them are obtained by querying the data table created in step 1.

[0026] Step 4: Construct the computational domain according to the requirements of the computational scenario. Set the pipe size to 2×14 cm and divide the computational domain into hierarchical block structure adaptive grids to discretize the computational domain. The basic grid size is Setting it too large will result in too many encryption layers, affecting the calculation accuracy and efficiency. .

[0027] The flame detection function is used to limit the adaptive mesh encryption area to the vicinity of the flame surface. The flame detection function encryption threshold is set to 0.3. The number of adaptive mesh encryption layers is determined by the flame thickening multiple and the flame thickness, specifically: (25) Among them, int points to the integer, and the grid passes The density of the layers is such that the finest mesh layer in the flame area has at least A grid is used to ensure the accuracy of flame solution.

[0028] Step 5, numerically solving the unsteady-state reactive Navier-Stokes equations of the coupled thickening flame model and the disturbance factor model; After each time step, the laminar flame speed and laminar flame thickness are calculated according to the state in the computational domain, and the disturbance factor and the number of adaptive grid layers are updated. According to the flame surface position and the updated number of adaptive grid layers, the hierarchical block structure adaptive grid structure is re-divided to achieve dynamic tracking of the flame position and real-time adjustment of the grid resolution.

[0029] like Figure 4 The figure shows the distribution of flames in the pipeline at different times over time calculated by the example of the present invention. Figure 4 (a) is the result of direct numerical simulation. Figure 4 (b) in the figure is the calculation result of the method proposed in this invention. It can be seen that the calculation results of the method proposed in this invention are in good agreement with the direct numerical simulation results, successfully capturing the dynamic process of flame propagation.

[0030] like Figure 5 The figure shows the change of the mesh near the flame surface during the flame development process of the example of the present invention. In the early stage of flame propagation (0.712 ms in the figure), the flame thickness is relatively large, and the dynamic encryption layer formula automatically calculates that only one layer of encryption is required, that is, , which can meet the resolution requirements and save computing resources. The burning area has a lower requirement for grid resolution. At this time, the adaptive grid maintains a coarse resolution and the number of grid refinement layers is Small, in order to reduce the computational overhead; as the flame continues to spread, the pressure in the pipe gradually rises, causing the flame thickness to become thinner (as shown in the figure at 2.108 ms). To ensure the accuracy of the flame surface solution, the adaptive grid technology of the present invention automatically increases the number of grid encryption layers to two, that is, , to adapt to the thinning flame scale; in the later stage of the simulation (such as 5.024 ms in the figure), the pressure rises further, the flame surface continues to thin, and the system further increases the number of encryption layers to level three, that is, , ensuring accurate capture of flame details.

[0031] This embodiment verifies that the method of the present invention can dynamically adjust the degree of grid density according to changes in flame thickness during the simulation of flame propagation, thereby ensuring calculation accuracy and effectively controlling calculation costs.

[0032] Based on the above method embodiment, an embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the method.

[0033] An embodiment of the present invention further provides a computer storage medium, wherein the computer storage medium stores a plurality of instructions, wherein the instructions are suitable for being loaded by a processor and executing the method.

[0034] In summary, the method described in the embodiment of the present invention overcomes the problems of insufficient grid resolution caused by flame thickness changes and physical distortion introduced by the thickened flame model in existing gas-phase combustion simulation technology, and solves the problem of large calculation errors and low calculation efficiency caused by adaptive grid encryption relying on experience.

[0035] The method uses a hierarchical block-structured adaptive grid near the flame surface, dynamically adjusting the number of grid densities based on flame thickness and the thickening factor, ensuring that the grid resolution within the flame region consistently meets requirements and accurately capturing the flame structure. A flame perturbation factor model is also introduced to correct the chemical reaction rate and diffusion coefficient in the thickened flame model, accurately simulating the perturbation effects of turbulence on the flame surface. This avoids physical distortion caused by excessive thickening and overcomes the limitation of traditional adaptive grid techniques that rely on empirical experience to determine the degree of densification.

[0036] In addition, the present invention has good applicability and compatibility, and can be conveniently coupled with existing programs. While ensuring simulation accuracy, it greatly reduces the amount of calculation and resource consumption, providing a reliable technical solution for efficient and high-precision numerical simulation of complex gas-phase combustion processes.

[0037] In addition, those skilled in the art will understand that all or part of the steps in the above-mentioned embodiment method can be implemented by instructing the relevant hardware through a program, and the corresponding program can be stored in a computer-readable storage medium. The above-mentioned storage medium can be a read-only memory, a disk or an optical disk, etc.

[0038] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims. The information disclosed in the background technology section of this article is only intended to deepen the understanding of the overall background technology of the present invention, and should not be regarded as an admission or any form of implication that the information constitutes prior art already known to those skilled in the art.

Claims

1. A method for constructing a gas phase combustion model based on a dynamic adaptive grid, characterized in that: The method comprises: Step 1: Based on the steady-state flame control equations, the laminar flame speed and laminar flame thickness of the target fuel under different conditions are obtained, and a data table for reference is established; Step 2: Establish the unsteady-state reactive Navier-Stokes equations, perform spatial filtering on the variables in the equations, and construct a subgrid-scale turbulence model; Step 3, coupling the thickening flame model and the disturbance factor model to the filtered unsteady reactive Navier-Stokes equations; Step 4: Construct the computational domain according to the requirements of the computational scenario and divide the computational domain into a hierarchical block structure adaptive grid to discretize the computational domain. The flame detection function is used to limit the encrypted area of ​​the adaptive grid to the vicinity of the flame surface. Step 5: Solve the unsteady-state reactive Navier-Stokes equations obtained in step 3 to obtain the variables for the next time step, and update the disturbance factor and the number of adaptive grid layers. Based on the flame surface position and the updated number of adaptive grid layers, re-divide the hierarchical block structure adaptive grid to achieve dynamic tracking of the flame position and real-time adjustment of the grid resolution.

2. The method for constructing a gas phase combustion model based on a dynamic adaptive grid according to claim 1, characterized in that: In step 1, it is assumed that the steady-state flame x Directional propagation, the steady-state flame control equations include: Continuity equation: ; Component conservation equation: ; Energy conservation equation: ； in, is the density; is the gas velocity in the x-direction of the spatial coordinate; is the mass flux; For components k The quality score of is the diffusion mass flux; For components k The molar chemical reaction rate; For components k molar mass; is temperature; 、 mixture and components respectively k Specific heat capacity at constant pressure; is the thermal conductivity of the mixture; is the specific enthalpy of component k; is the total number of components; For one-dimensional steady-state flow, the mass flux is constant in the flame propagation direction, so the laminar flame speed to be solved is Satisfies the relationship: ,in is the density of the unburned area; Laminar flame thickness Calculated from the temperature, it is defined as: ,in T b and T u are the temperatures of the burned and unburned areas, respectively; The above equations are solved under different initial temperatures, initial pressures and equivalence ratios to obtain the corresponding laminar flame speed and laminar flame thickness, and a data table is established for subsequent steps.

3. The method for constructing a gas phase combustion model based on a dynamic adaptive grid according to claim 1, characterized in that: In step 2, the unsteady-state reactive Navier-Stokes equations are established as follows: Continuity equation: （1） Momentum equation: （2） Energy equation: （3） Component transport equation: （4） in, is the density; For time; is the vector differential operator, , Represents the spatial coordinates x 、 y 、 z The partial derivative of is the velocity vector; For pressure; is the Kronecker symbol, , i and j Represents the direction index, which is used to represent the three-dimensional space x 、 y 、 z direction; is the viscous stress tensor; For the ratio of total energy; is temperature; is the concentration of reactant components; is the thermal conductivity of the mixture; is the component diffusion coefficient; The heat of reaction is released; is the chemical reaction rate, ; is the pre-exponential factor; is the activation energy; is the gas constant; A discrete explicit filter is used to perform spatial filtering on the variables in the established unsteady-state reactive Navier-Stokes equations. The variables are decomposed into large-scale and small-scale parts. The large-scale part is directly solved, and the small-scale part is modeled. The expression of the discrete explicit filter is: （5） in, is any variable; is any variable after spatial filtering; N is the filter half-width; subscript o is the center grid index; l The sum index is used to specify the position offset relative to the center grid; is the filter coefficient, satisfying ; Considering the significant density change in compressible reacting flow, the velocity vector , than the total energy , component concentration Using Favre filtering, they are defined as 、 、 ; 、 、 are the velocity vector, specific total energy, and component concentration after Favre filtering, respectively; is the density after spatial filtering; Represents the value after spatial filtering of the product of density and velocity vector; Represents the value after spatial filtering of the product of density and specific total energy; Represents the value after spatial filtering of the product of density and component concentration; The spatially filtered unsteady reactive Navier-Stokes equations are shown below: Continuity equation: （6） Momentum equation: （7） Energy equation: （8） Component transport equation: （9） in, is the viscous stress tensor after spatial filtering; is the chemical reaction rate after spatial filtering; is the pressure after spatial filtering; is the temperature after spatial filtering; is the heat release of the reaction after Favre filtering, defined as , represents the value after spatial filtering of the product of density and reaction heat release; 、 、 、 They are the stress term, heat flux term, turbulent diffusion term and mass flux term at the sub-grid scale.

4. The method for constructing a gas phase combustion model based on a dynamic adaptive grid according to claim 3, characterized in that: In step 2, the standard Smagorinsky model is used to calculate the subgrid-scale turbulence model, as follows: （10） （11） （12） （13） in, is the sub-grid stress viscosity, ; is the strain rate tensor, , Tr represents the transpose of the vector; is a tensor traces; is the filter size; is the specific heat capacity at constant pressure; is the Prandtl number; is the Schmidt number; and is the model constant.

5. The method for constructing a gas phase combustion model based on a dynamic adaptive grid according to claim 3, characterized in that: In step 3, the coupling between the thickening flame model and the perturbation factor model is completed by modifying the chemical reaction rate and diffusion coefficient in the filtered unsteady-state reactive Navier-Stokes equations. The specific process is as follows: The energy equation is modified to: （14） The component transport equation is modified as follows: （15） in, is the flame thickening multiple, the expression is: （16） In the above formula, It is a user-defined thickening factor, and its relationship with the mesh size and flame thickness is: ; Indicates the number of grids within the flame thickness; Indicates the size of the finest grid element in the flame surface; is the laminar flame thickness; represents the flame detection function, , is the hyperbolic tangent function, To thicken the flame, is the chemical reaction rate, The value range is [0,1], the closer to 1, the closer to the flame, through the flame detection function Identify flame area; coefficient This means turning off the subgrid turbulence model in the flame region to prevent excessive dissipation when solving the flame. is a disturbance factor used to correct the decrease in flame wrinkling ability caused by the thickening flame model, and is specifically defined as: （17） in, is the filter size; represents the laminar flame speed; is the efficiency function, calculated as: （18） in: is the turbulence intensity effect term, which is used to describe the contribution of unresolved small-scale vortices to flame wrinkles and is defined as: ； is the scale separation effect term, which is used to describe the contribution of eddies between the flame thickness and the filter scale to the flame wrinkles and is defined as: ； is the viscous dissipation effect term, which is used to describe the dissipative effect of molecular viscosity on small-scale vortices and is defined as: ； 、 and is the model constant; is the Reynolds number of the filter scale; is the subgrid-scale turbulent velocity, calculated as: （19） is the model coefficient, which is calculated by the Germano identity. The specific expression is: （20） in, is a logarithmic function; To test the filter size and filter size The ratio of Indicates the filtered size Chemical reaction rates after spatial filtering; express Then the value after testing the filter size; Indicates the tested filter size Filtered chemical reaction rate; symbol Represents volume averaging operation.

6. The method for constructing a gas phase combustion model based on a dynamic adaptive grid according to claim 5, characterized in that: In step 4, when the flame detection function on the adaptive grid point When the set threshold is reached, the adaptive grid is refined; When mesh encryption is in progress, the original underlying mesh remains unchanged, and a separate layer of fine mesh is generated where encryption is required. The encryption process is then repeated until the maximum number of encryption layers is reached. The mesh refinement ratio between adjacent layers is 2. n Layer grid cell size for: （21） in, is the base grid size; The number of mesh encryption layers is determined by the flame thickening multiple and flame thickness, specifically: （22） Among them, int points to the integer; is the laminar flame thickness; It is a user-defined thickening multiple; Indicates the number of grids within the flame thickness; Grid passes The density of the layers is such that the finest mesh layer in the flame area has at least A grid is used to ensure the accuracy of flame solution.

7. The method for constructing a gas phase combustion model based on a dynamic adaptive grid according to claim 1, characterized in that: In step 5, the unsteady reactive Navier-Stokes equations of the coupled thickening flame model and the perturbation factor model obtained in step 3 are numerically discretized on a hierarchical block structured grid. Specifically: The equations are decomposed into three parts: convection term, diffusion term and source term by using operator splitting method; The convection term is calculated using a 5th-order weighted essentially non-oscillatory format to obtain the flame front and shock wave discontinuity structures; The diffusion term is calculated using the second-order central difference format; The source term is calculated using direct integration; The variables of the next time step are obtained through iterative solution.

8. The method for constructing a gas phase combustion model based on a dynamic adaptive grid according to claim 7, characterized in that: In step 5, after completing the advancement of each time step, the laminar flame speed and flame thickness are queried in the data table established in step 1 according to the current state parameters. If there is no corresponding data entry in the data table, the required laminar flame speed and flame thickness are obtained by interpolation through adjacent data points; Update the perturbation factor and adaptive grid layer number according to the obtained laminar flame speed and flame thickness; Based on the encrypted area determined by the flame detection function and the updated number of adaptive grid layers, the hierarchical block structure adaptive grid is re-divided to achieve dynamic tracking of the flame position and real-time adjustment of the grid resolution; Repeat step 5 until the specified number of calculation steps or calculation time is reached.

9. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to perform the method according to any one of claims 1 to 8.

10. A computer storage medium, characterized in that The computer storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the method according to any one of claims 1 to 8.

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