Numerical calculation method and system for multi-fuel blended turbulent combustion differential diffusion

Abstract

The application discloses a numerical calculation method and system for fuel blending turbulent combustion differential diffusion, and belongs to the field of turbulent combustion numerical simulation. The method comprises the following steps: a small flame table is established based on one-dimensional counterflow flame equation; through a mapping algorithm, thermodynamic state is parameterized as a function of mixing fraction and reaction process variable; a differential diffusion term of multi-fuel component is derived; control equations of a differential diffusion two-equation small flame model suitable for multi-fuel component under a large eddy simulation framework are established and solved; the model is discretized and coupled with a pressure-velocity field; in the process, simulation results of a small flame model without considering the differential diffusion effect are used as a comparison by the same method to highlight the influence of the differential diffusion effect. The application extends the differential diffusion two-equation model to multi-fuel blending conditions, takes a hydrogen-ammonia blended combustion two-fuel turbulent diffusion flame as an example, compares the mass fraction and temperature distribution of main components through a priori and posteriori method, and quantitatively analyzes the influence of the differential diffusion effect.

Description

Technical Field

[0001] This invention belongs to the field of numerical simulation of turbulent combustion, specifically a numerical calculation method and system for differential diffusion in turbulent combustion of multiple fuel mixtures. Background Technology

[0002] Today, ammonia is seen as a promising fuel due to its carbon-free nature and ease of storage and transportation; however, it has low flammability, low radiation intensity, and emits large amounts of NO during combustion. x Pollutants such as N2O and residual NH3 are present. Studies have shown that partially cracked ammonia can achieve properties similar to methane fuel, addressing the aforementioned challenges to some extent. However, the hydrogen diffusion rate at the cracking site is much higher than that of ammonia, and a strong differential diffusion effect is expected, significantly impacting flame structure and characteristics. Therefore, for dual-fuel flames with significant differential diffusion effects, accurate numerical models are needed to calculate these effects. Currently, most differential diffusion small-flame models heavily rely on interpolation algorithms and the number of grid points in the manifold coordinates. While the recently proposed simplified differential diffusion two-equation small-flame model offers advantages in computational stability and efficiency, it is only applicable to single-component pure fuel flames and cannot simulate the combustion of hydrogen-ammonia blended systems. Therefore, extending this differential diffusion two-equation small-flame model to multi-fuel blended combustion systems is of significant research importance. It will contribute to the design and optimization of novel hydrogen-ammonia burners and provide theoretical support for future research on other multi-fuel blended systems. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a numerical calculation method and system for differential diffusion in turbulent combustion of multiple fuel blends. By deriving the differential diffusion term for multiple fuel components, the traditional two-equation small flame model of differential diffusion is extended to make it applicable to multi-fuel conditions. The accuracy of the extended model is fully verified by performing large eddy simulation on a hydrogen-ammonia blended turbulent combustion system with dual fuel components.

[0004] To achieve the above objectives, the present invention adopts the following technical solution:

[0005] A numerical calculation method for differential diffusion in turbulent combustion with multiple fuel blends includes the following steps:

[0006] Step 1: Establish a small flame database based on the one-dimensional counter-current flame equation, considering the differential diffusion effect, covering different scalar dissipation rate conditions;

[0007] Step 2: Map the small flame database to the trajectory variable space composed of the mixing fraction Z and the reaction process variable C, and generate the small flame lookup table FLT;

[0008] Step 3: Derive the expression for the differential diffusion term of multiple fuel components, establish the governing equation for differential diffusion of multiple fuel components under the framework of large eddy simulation, including the differential diffusion term characterizing the difference in diffusion rates of multiple fuel components; the differential diffusion term is expressed in a closed form through the fuel blending ratio coefficient and the stoichiometric coefficient of the multi-component purification; using a subgrid-scale turbulence model, the distribution of mixing fraction and reaction process variables is described by the probability density function method; the impact of differential diffusion effect is quantitatively analyzed by comparing experimental data through prior and posterior verification.

[0009] The present invention also provides a multi-fuel blending combustion simulation system, comprising:

[0010] The small flame database building module is used to generate one-dimensional counter-flame data;

[0011] The trajectory variable mapping module converts the database into a small flame lookup table (FLT) in the mixed fractional-process variable space;

[0012] The large eddy simulation solver integrates the numerical calculation methods for differential diffusion in turbulent combustion of multiple fuel blends mentioned above, and solves the governing equations for differential diffusion of multiple fuel components.

[0013] The present invention also provides a computer-readable storage medium storing program instructions, which, when executed by a processor, implement the above-mentioned numerical calculation method for differential diffusion in turbulent combustion of multiple fuel blends.

[0014] Compared with the prior art, the present invention has the following advantages:

[0015] This invention is based on a simplified and stable two-equation differential diffusion small flame model, extending its applicability to multi-fuel component conditions. Using large eddy simulation of a hydrogen-ammonia mixed turbulent diffusion flame as an example, the model is fully validated by comparing it with experimental data. It can accurately predict the main components and temperature distribution. Furthermore, comparison with the differential diffusion term of a complex, high-precision, and computationally demanding differential diffusion small flame model verifies the model's correctness. Compared to the aforementioned complex differential diffusion small flame models, this model effectively and significantly reduces computational complexity and increases computational stability. Compared to traditional two-equation differential diffusion small flame models, this model can be widely used in various fuel-mixed flames, providing research ideas and methods for subsequent simulations of complex fuel-mixed flames with differential diffusion effects. Attached Figure Description

[0016] Figure 1 A flowchart illustrating the numerical calculation method for differential diffusion in turbulent combustion of various fuel blends according to the present invention;

[0017] Figure 2This is a table showing the mass fraction and temperature distribution of the main components at the flow direction 6D (D is the diameter of the central fuel jet) in the prior verification of an embodiment of the present invention.

[0018] Figure 3 This is a partial mass fraction distribution diagram of the main components in the experimental data at the flow direction 6D (D is the diameter of the central fuel jet) during the prior verification in an embodiment of the present invention;

[0019] Figure 4 This is a comparison of the mass fraction distribution of the main components in the large eddy simulation of the mixing fraction space at the flow directions of 6D, 15D, and 30D in the post-hoc verification of an embodiment of the present invention.

[0020] Figure 5 This is a comparison chart of the main component mass fraction distribution of experimental data in physical space at the 6D, 15D, and 30D flow directions in the post-hoc verification of embodiments of the present invention.

[0021] Figure 6 This is a quantitative comparison of the differential diffusion terms between the differential diffusion two-equation model derived in the embodiments of the present invention and the existing complex and high-precision differential diffusion small flame model. Detailed Implementation

[0022] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. However, the following embodiments are only for explaining the present invention, and the scope of protection of the present invention should include all the contents of the claims. Moreover, through the description of the following embodiments, those skilled in the art can fully implement all the contents of the claims of the present invention.

[0023] Example:

[0024] Within the framework of large eddy simulation, this invention derives trajectory variable transport equations for various fuel components, considering the differential diffusion effect, based on an extended differential diffusion two-equation small flame model. These equations are then applied to the simulation of a dual-fuel hydrogen-ammonia blended turbulent flame where the differential diffusion effect is significant. Furthermore, a comparison is made with a model that does not consider the differential diffusion effect to demonstrate the importance of differential diffusion. Finally, the correctness of the model is further verified by comparing the differential diffusion term calculated by this model with a complex, high-precision differential diffusion small flame model.

[0025] like Figure 1As shown, the embodiment of the numerical calculation method for differential diffusion in turbulent combustion of multiple fuel blends according to the present invention mainly consists of the following steps: Step 1: Establish a one-dimensional small flame database; Step 2: Map the small flame table to the trajectory variable dimension and convert it into an OpenFOAM readable form; Step 3: Derive the two-equation small flame model for differential diffusion of multiple fuel components in detail and perform model verification, which is divided into prior and posterior. The prior verifies the small flame table for use in the posterior. The posterior is the large eddy simulation. According to the experimental design, computational domain, mesh generation, initial conditions, and boundary conditions, iteratively solve the density, velocity, energy, and pressure equations, as well as the derived governing equations of the two-equation small flame model for differential diffusion of multiple fuel components. The prediction results are obtained by looking up the small flame table. Finally, the results are compared with the experimental data, and the differential diffusion term of the model is quantitatively analyzed.

[0026] Step 1: Establishing a one-dimensional small flame database. The detailed process is as follows:

[0027] The Cantera software was used to solve for a one-dimensional counterflow flame in physical space by setting the fuel temperature (T), pressure (P), and component mass fraction (Y). i The initial conditions of the inlet state are continuously changed by altering the velocity u of the counter-current flame (indirectly changing the scalar dissipation rate). The one-dimensional laminar diffusion small flame equation is solved iteratively, outputting important thermodynamic parameters. Finally, the counter-flame velocity is increased until the first extinguishing state data is output, at which point the cycle ends. During this process, a detailed hydrogen-ammonia co-combustion chemical reaction mechanism is used, involving 38 components and 263 chemical reactions. Furthermore, a mixture-averaging (MA) method considering differential diffusion effects is employed in the solution process. This solution process yields results including component mass fractions. ,temperature Scalar dissipation rate Component diffusion coefficient The data file containing information such as the one-dimensional laminar small flame database can be represented by the following function:

[0028] (1)

[0029] To highlight the effects of differential diffusion, additional small flame libraries are established using a similar approach, based on the unified Lewis number assumption (le=1) that does not consider differential diffusion effects.

[0030] Step 2: Mapping to the small flame table of trajectory variable dimension. Since the small flame library format in Step 1 cannot be directly recognized by OpenFOAM, a mapping solver is needed to convert it and map the thermochemical quantities in the small flame library to the trajectory variable dimension, where the mixing fraction... The solution is based on the mass fraction of the main components; for the case of hydrogen-ammonia mixed combustion, since water is the main product and can fully represent the reaction progress, the reaction process variable is defined as follows: By reading the key contents of the small flame library in step 1, the calculation of physical quantities such as enthalpy and generation rate is completed, generating a data linked list that OpenFOAM can read—the Small Flame Lookup Table (FLT), which can be represented in the following form:

[0031] (2)

[0032] in, This is the representation of the small flame lookup table.

[0033] For the prior verification in step 3, the small flame lookup table is directly used as input. For the large eddy simulation (posterior verification) in step 3, the assumed probability density function (PDF) method is further used to process the mixed fractions. - PDF probability density function integration and data interpolation, i.e., using The -PDF method describes the subgrid-scale distribution of the mixed fraction Z of the trajectory variable, using... The PDF method describes the subgrid-scale distribution of the trajectory variable process variable C. In this process, the mixture fractional variance is further converted into a mixture fraction using a small flame lookup table. Variables in the reaction process Mixed fractional variance Functions, such as those in the following form:

[0034] (3)

[0035] in, This is the representation of the small flame lookup table in the transformed large eddy simulation.

[0036] In the above mapping process, the corresponding mixed average (MA) method is used for small flame libraries that consider differential diffusion to calculate in detail the changes in material transport and reaction caused by differential diffusion effect; for small flame libraries that do not consider differential diffusion, the corresponding method based on the unified Lewis number assumption (le=1) is used to compare and show the importance of differential diffusion effect.

[0037] Step 3, model validation, is divided into prior validation and posterior validation. Posterior validation is large eddy simulation.

[0038] The specific steps of prior analysis are as follows: First, use the experimental data as input to calculate the trajectory variables. Based on the small flame lookup table in the above trajectory variable lookup step 2, the lookup result is output as formula (4), which makes the thermochemical state space a function of the trajectory variable, as shown in formula (5).

[0039] (4)

[0040] (5)

[0041] in, For table lookup formula, This indicates its functional expression.

[0042] The accuracy of the Flamelet Look-up Table (FLT) obtained in step 2 is verified by comparing the mass fractions of the main components obtained from the lookup table with those from known experimental data. (Comparison of flow direction) (D is the diameter of the central jet flame) The temperature and some major components at the location, namely the fuel represented by hydrogen, the oxidant represented by oxygen, and the product represented by water, are compared as follows: Figure 2 and Figure 3 As shown, where Figure 2 The first row of the legend compares temperatures (T), and the second row compares oxygen levels. Figure 3 The first row of the legend compares the mass fraction of hydrogen, and the second row compares the mass fraction of water. The curly braces {} indicate that the data are averages. Exp. (abbreviation for Experiment) indicates experimental data, MA (abbreviation for Mixture average) indicates the mixed averaging method, and Le1 (abbreviation for Lewis Number=1) indicates the uniform Lewis number method. The color and direction of the arrows are used to distinguish between the two axes: a red arrow pointing to the left indicates the use of the red axis on the left, and a blue arrow pointing to the right indicates the use of the blue axis on the right. The value represents the adiabatic temperature (ad is an abbreviation for Adiabatic), and is represented by a horizontal gray dashed line. The Bilger mixture fraction is represented by the first vertical gray dashed line, indicating the inlet fuel side. The value represents the complete combustion mixture fraction, and the corresponding value is shown by the second vertical gray dashed line. The graph shows a good fit with the experimental data, proving the accuracy of the small flame lookup table, which can be used for subsequent large eddy simulations.

[0043] Large eddy simulation (posterior verification), i.e., numerical simulation of a three-dimensional hydrogen-ammonia mixed turbulent flame. This section derives a two-equation differential diffusion small flame model for multiple components, which is also the core of this invention. The specific process is as follows:

[0044] Step (1): Referring to the turbulent hydrogen-ammonia mixing experiment conducted by the KAUST laboratory, the computational domain, geometry, initial conditions, and boundary conditions were designed. The computational domain was a cylindrical field, dimensionlessly represented by the diameter D (D=7.5mm) of the central jet flame nozzle, with a diameter of 20D and a length of 40D. The burner consisted of three fuel inlets: the central jet was a hydrogen-ammonia mixed fuel with a mixing ratio of NH3 / H2 / N2=40 / 45 / 15 (volume fraction), an equivalence ratio of 3, a diameter of 7.5mm, and a jet velocity of 61.07m / s; surrounding the central jet fuel were annular igniter nozzles with the same fuel mixture composition as the central fuel, an equivalence ratio of 0.9, a diameter of 18mm, and a velocity of 1.03m / s; the outermost annular co-current nozzles had a diameter of 150mm, providing a clean airflow with a velocity of 0.8m / s. A non-orthogonal mesh was selected, with a total of approximately six million elements. The initial temperature of the premixed fuel is 293K, and the pressure is set to atmospheric pressure.

[0045] Step (2): Using the finite volume method, the control equations are numerically discretized and solved, including the density equation, velocity equation, etc. The time integral adopts the first-order Euler implicit integral, and the spatial integral adopts the second-order linear restricted integral scheme.

[0046] Step (3): Determine whether differential diffusion should be considered. If differential diffusion is considered, derive the diffusion term for the multi-fuel component according to the following steps, and substitute it into the governing equation to derive the trajectory variable output equation applicable to the multi-fuel component. The specific derivation process is as follows:

[0047] Step (a) Introducing the fuel blending ratio coefficient Defined as the proportion of fuel mole fractions mixed, based on the assumption of a single-step irreversible global chemical reaction, it is expressed as:

[0048] (6)

[0049] Wherein, F, O, and P represent fuel, oxidant, and product, respectively. and Let be the stoichiometric coefficients of fuel and oxidant, respectively, and n represent the total amount of fuel. This leads to the decomposition coefficients of the multi-fuel components. Defined as:

[0050] (7)

[0051] in, The molecular weight of the oxidant is... The total molecular weight of the first fuel component is... denoted as the total molecular weight of the i-th fuel component.

[0052] Furthermore, a simplified definition of the mixing fraction of multiple fuel components can be derived:

[0053] (8)

[0054] in, and These represent the mass fractions of local fuel and oxidizer, respectively, indicated by subscripts. Represents fuel flow, subscript Represents the oxidant flow, n is the total number of fuel component types, and i is the index of the fuel component.

[0055] Step (b) derives the mixing fraction diffusion terms corresponding to various fuel components under different operating conditions based on the component transport equations for each fuel and oxidant, expressed as:

[0056] (9)

[0057] in, and Representing the first The diffusion fluxes of the fuel components and oxidizers are calculated based on the mixture averaging method. Substituting these values ​​into the diffusion flux formulas for the fuel components and oxidizers, a detailed expanded expression for the mixture fraction difference diffusion term is obtained, expressed as:

[0058] (10)

[0059] For density, For purification measurement coefficients, This refers to the local fuel mass fraction of the first component fuel stream. This is the fuel blending ratio coefficient. Let i be the local fuel mass fraction of the i-th component fuel stream. The mass fraction of the oxidant in the oxidant stream. For gradient operators, Molecular mass The first component represents the local fuel mass fraction. The mass fraction of the oxidant. The diffusion coefficient of the first fuel component is... Let be the diffusion coefficient of the i-th fuel component. The value represents the diffusion coefficient of the oxidant component, with the superscript T indicating thermal diffusion, u being the velocity, and c being a constant.

[0060] Since the equation in step (b) is not closed, the function in step (b) is expressed as a function of the trajectory variable Z and the process variable C. This leads to the derivation of a function with only the trajectory variable Z and the process variable C as parameters, as follows:

[0061] (11)

[0062] Among them, coefficient , , For density, For purification measurement coefficients, This refers to the local fuel mass fraction of the first component fuel stream. This is the fuel blending ratio coefficient. Let i be the local fuel mass fraction of the i-th component fuel stream. The mass fraction of the oxidant in the oxidant stream. For gradient operators, Molecular mass The first component represents the local fuel mass fraction. The mass fraction of the oxidant. The diffusion coefficient of the first fuel component is... Let be the diffusion coefficient of the i-th fuel component. This represents the diffusion coefficient of the oxidant component, with the superscript T indicating thermal diffusion. If assumed to be a single-component fuel, then the blending ratio coefficient... Therefore, it can be reduced to the diffusion term of the traditional two-equation small flame model applicable to a single pure fuel component.

[0063] Note that the single-step chemical assumptions described above are only used for the closed-loop mixed fraction Z equation, while the establishment of the small flame library is based on detailed chemical reaction mechanisms, as described in step 1.

[0064] Step (d) substitutes the closed-form expression for the mixing fraction from step (c) into the governing equation for the mixing fraction and introduces turbulence-chemical interactions, i.e., it introduces a subgrid-scale term for calculation, to derive a general expression within the large eddy simulation framework. This expression includes a standard diffusion term. Difference diffusion term Thermal diffusion term .

[0065] The Farve-averaged mixed fraction control equation is expressed as:

[0066] (12)

[0067] in, , , These are the standard diffusion term coefficient, the differential diffusion term coefficient, and the thermal diffusion term coefficient, respectively, which are given by formula (11). This is the subgrid scale term.

[0068] in, For Reynolds average density, Farve's average mixed score, For Farve's average speed, For gradient operators, For divergence operators, For subgrid scale terms, , , The diffusion coefficient of the component is . For mixed fractions The gradient vector in space, Where is the thermal diffusivity, This is the standard diffusion term. This is the differential diffusion term. This is the thermal diffusion term, with the superscript T indicating thermal diffusion. For the reaction process variables, This represents the Farve average temperature.

[0069] Similarly, step (e) yields the general expression for the control equations of the process variables:

[0070] (13)

[0071] , The diffusion coefficient of the component is . This is the Farve average source term for component C.

[0072] Step (4): By solving the mixed fractional variance control equation in step (3), the Farve mean of the trajectory variable is obtained.

[0073] Step (5): Based on the trajectory variables obtained in step (4), look up the small flame lookup table (FLT) obtained in step 2 using the assumed probability density function (PDF) method to obtain the components, temperature, chemical reaction source term, differential diffusion term, etc. The subgrid-scale turbulence model adopts... - PDF describes the mixed score distribution. -PDF is the Beta probability density function, using - The PDF describes the distribution of variables in the reaction process. -PDF is the Dirac Delta probability density function.

[0074] Step (6): Determine whether the calculation error is satisfied, i.e., whether it has converged or reached the specified end time. If not, continue to step (2) for iterative correction and solution. If satisfied, output the solution and table lookup results in step (5), which can be represented as the following function:

[0075] (14)

[0076] Since the experimental dataset contains the main components and temperatures, only the component mass fractions are shown here. and temperature ( (This is the Farve average), but other quantities can also be obtained by looking up a table, which will not be detailed here.

[0077] Step (7): The accuracy of the model is verified by comparing the output of the table lookup in step (6) with the mass fractions and temperatures of the main components in the mixing fraction (Z) space and physical space (r) at the upstream (6D), midstream (15D), and downstream (30D) positions of the experimental data. This process is called posterior verification. In addition, to further verify the accuracy of the derived two-equation differential diffusion small flame model (Ext-Caltech), a complex and high-precision integrated differential diffusion small flame model (Ext-BSC) was used for large eddy simulation. At the same time, large eddy simulation was also performed on small flames based on the unified Lewis number (Le=1) without considering differential diffusion effects. The results of the three simulations are compared, such as Figure 4 and Figure 5 As shown, where Figure 4 To compare the Farve average of hydrogen mass fraction in the mixing fraction (Z) space with the Farve average of temperature, Figure 5To compare the Farve average of the mixing fraction Z in physical (r) space with the Farve average of the hydrogen mass fraction, curly braces {} represent time averages, and square brackets < > represent spatial averages; Exp. (abbreviation for Experiment) represents experimental data, indicated by hollow black dots; MA-Ext-Caltech represents the two-equation differential diffusion small flame model based on the Mixture Average (MA) method (Ext-Caltech), indicated by a solid red line; MA-Ext-BSC represents the complex, high-precision integrated differential diffusion small flame model based on the Mixture Average (MA) method (Ext-BSC), indicated by a dashed green line; and Le1-Ext-BSC represents the complex, high-precision integrated differential diffusion small flame model based on the Lewis number equal to 1 assumption method (Ext-BSC), indicated by a solid blue line. It can be seen that the two differential diffusion small flame models (models starting with MA, where MA stands for Mixture Average) fit the experimental data (Exp. data) well, while the unified Lewis number model (models starting with Le1, where Le1 represents the assumption that the Lewis number equals 1) has large errors in the upstream, midstream, and downstream sections, thus proving the importance of the differential diffusion effect in hydrogen-ammonia mixed turbulent flames. Furthermore, the two differential diffusion small flame models (models starting with MA, where MA stands for Mixture Average) are not significantly different from each other, and even differ in some locations. The simulation results of the simplified two-equation differential diffusion small flame model derived in this invention are more accurate than the complex differential diffusion model, thus proving the correctness and applicability of the model in this invention. This process is the posterior validation of the model.

[0078] Step (8): Quantitative analysis is performed on the differential diffusion terms in two differential diffusion models with different computational complexities to further verify the correctness of the multi-component two-equation differential diffusion small flame model derived in this invention. To compare the two differential diffusion models simultaneously, a normalization coefficient for the differential diffusion term is introduced for both models, defined as: Among them, for the differential diffusion model proposed in this invention and These are the standard diffusion term and the differential diffusion term in formula (12), respectively; for complex differential diffusion models, they also correspond to the standard diffusion term and the differential diffusion term in the complex differential diffusion model. The comparison results of the differential diffusion terms of the two models are as follows: Figure 6 As shown, the selection of the horizontal coordinate (x / D=6, 15, 30) corresponds to... Figure 4 and Figure 5 The three positions in the comparison, the vertical axis The normalization coefficient for the difference diffusion term; the gray dashed line is the 0 mark, used to distinguish between positive and negative values; the purple dashed line corresponds to the legend. The normalized coefficients of the differential diffusion term for the two-equation differential diffusion small flame model (Ext-Caltech) are shown by the solid magenta line, corresponding to the legend. The normalized coefficients of the differential diffusion term corresponding to the complex, high-precision integrated differential diffusion small flame model (Ext-BSC) show that their trends and orders of magnitude are basically consistent, with only minor errors, further proving the correctness of the simplified model proposed in this invention. Therefore, it can be concluded that the two-equation small flame model for differential diffusion in turbulent combustion with various fuel blends proposed in this invention can achieve prediction results that are largely consistent with complex differential diffusion models with relatively low computational cost. Through the detailed derivation of this invention, its applicability is further expanded, and it can be widely used for simulating complex fuel-blended flames with various strong differential diffusion effects.

[0079] The present invention also provides a multi-fuel blending combustion simulation system, comprising:

[0080] The small flame database building module is used to generate one-dimensional counter-flame data;

[0081] The trajectory variable mapping module converts the database into a small flame lookup table (FLT) in the mixed fractional-process variable space;

[0082] The large eddy simulation solver integrates the numerical calculation methods for differential diffusion in turbulent combustion of multiple fuel blends mentioned above, and solves the governing equations for differential diffusion of multiple fuel components.

[0083] The present invention also provides a computer-readable storage medium storing program instructions, which, when executed by a processor, implement the above-mentioned numerical calculation method for differential diffusion in turbulent combustion of multiple fuel blends.

[0084] 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 numerical calculation method for differential diffusion in turbulent combustion of multiple fuel blends, characterized in that, include: Step 1: Establish a small flame database based on the one-dimensional counter-current flame equation, considering the differential diffusion effect, covering different scalar dissipation rate conditions; Step 2: Map the small flame database to the trajectory variable space composed of the mixing fraction Z and the reaction process variable C, and generate the small flame lookup table FLT; Step 3: Derive the expression for the differential diffusion term of multiple fuel components, establish the governing equation for differential diffusion of multiple fuel components under the framework of large eddy simulation, including the differential diffusion term characterizing the difference in diffusion rate of multiple fuel components; the differential diffusion term is expressed in a closed form through the fuel blending ratio coefficient and the stoichiometric coefficient of the multi-component purification; using a subgrid-scale turbulence model, the distribution of mixing fraction and reaction process variables is described by the probability density function method; the impact of differential diffusion effect is quantitatively analyzed by comparing experimental data through prior and posterior verification. The governing equation for differential diffusion of multiple fuel components is: (12) in, For Reynolds average density, Farve's average mixed score, For Farve's average speed, For gradient operators, For divergence operators, For subgrid scale terms, , , The diffusion coefficient of the component is . For mixed fractions The gradient vector in space, For standard diffusion terms, For the difference diffusion term, This is the thermal diffusion term, with the superscript T indicating thermal diffusion. For the reaction process variables, This represents the Farve average temperature.

2. The numerical calculation method for differential diffusion in turbulent combustion of multiple fuel blends according to claim 1, characterized in that, In step 1: The diffusion coefficients of the components were calculated using the mixed averaging method. The chemical reaction mechanism of hydrogen-ammonia co-combustion involves 38 components and 263 reactions; The scalar dissipation rate can be indirectly adjusted by changing the speed of the counter-firing.

3. The numerical calculation method for differential diffusion in turbulent combustion of multiple fuel blends according to claim 1, characterized in that, In step 2: Mixed fractions Defined as: (8) in, This refers to the local fuel mass fraction. The mass fraction of the oxidizing agent, indicated by subscript. Represents fuel flow, subscript Represents the oxidant flow. For purification measurement coefficients, This is the fuel blending ratio coefficient. This represents the total number of different types of fuel components. This is an index for fuel components.

4. The numerical calculation method for differential diffusion in turbulent combustion of multiple fuel blends according to claim 3, characterized in that, The purification measurement coefficient The calculation formula is: (7) Fuel blending ratio coefficient Determined by the mixing ratio of fuel mole fraction. The stoichiometric coefficient of the oxidizing agent. The stoichiometric coefficient of the fuel. The molecular weight of the oxidant is... The total molecular weight of the first component of the fuel is denoted as . denoted as the total molecular weight of the i-th component of the fuel composition.

5. The numerical calculation method for differential diffusion in turbulent combustion of multiple fuel blends according to claim 1, characterized in that, In step 3: Prior verification involves directly querying the small flame lookup table using experimental data and comparing the output components with the experimental data. Posterior validation compares the output results of large eddy simulation with experimental data in a mixed fractional space and physical space.

6. The numerical calculation method for differential diffusion in turbulent combustion of multiple fuel blends according to claim 1, characterized in that, Subgrid-scale turbulence model adopts - PDF describes the mixed score distribution -PDF is the Beta probability density function, using - The PDF describes the distribution of variables in the reaction process. -PDF is the Dirac Delta probability density function; the mixed fractional variance is used as the third dimension parameter of the small flame lookup table.

7. The numerical calculation method for differential diffusion in turbulent combustion of multiple fuel blends according to claim 6, characterized in that, Difference diffusion effect The quantification formula is: ,in, and These are the standard diffusion term and the differential diffusion term in the formula, respectively.

8. A multi-fuel blending combustion simulation system, characterized in that, include: The small flame database building module is used to generate one-dimensional counter-flame data; The trajectory variable mapping module converts the database into a small flame lookup table (FLT) in the mixed fractional-process variable space; The large eddy simulation solver integrates the numerical calculation method for differential diffusion in turbulent combustion of multiple fuel blends as described in any one of claims 1-7, and solves the governing equations for differential diffusion of multiple fuel components.

9. A computer-readable storage medium storing program instructions, characterized in that: When the program instructions are executed by the processor, they implement the numerical calculation method for differential diffusion in turbulent combustion of multiple fuels as described in any one of claims 1-7.

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