Simplified modeling method and device for complex mixed fuel combustion reaction kinetic model and computer program product

Abstract

The invention provides a simplified modeling method and device for a complex mixed fuel combustion reaction kinetic model and a computer program product. The simplified modeling method comprises the steps of model coupling, primary model simplification by adopting a directed relation graph method based on error transfer and a species sensitivity analysis method, and optimization of the primary simplified model by taking an optimal pre-exponential factor, a temperature index and activation energy of a three-parameter modified Arranius equation as optimization objects. And the optimization model is simplified again. According to the method, a method of directly simplifying a complex model is adopted, and the model does not need to be manually disassembled and constructed; the method does not need deep reaction path analysis, does not need programming, is suitable for combustion simulation engineering practitioners without the basis of combustion reaction kinetics research, and is used for rapidly making a fuel mechanism model and carrying out reasonable verification.

Description

Technical Field

[0001] The present invention relates to the technical field of reaction kinetic model modeling, and in particular to a simplified modeling method, device and computer program product for a combustion reaction kinetic model of a complex mixed fuel. Background Art

[0003] Currently, the design process of a combustion chamber is inseparable from three-dimensional CFD (Computational Fluid Dynamics) numerical simulation, and the combustion model in the numerical simulation requires the participation of a reaction kinetic mechanism model. In a model with the direct solution of the reaction rate as the core calculation method, the calculation efficiency is negatively correlated with the number of components and the number of elementary reactions of the mechanism model used. If a chemical reaction kinetic model with a large number of elementary reaction steps is used, problems such as too long calculation time and even insufficient calculation resources will occur. Therefore, when technicians perform simulation calculations, the elementary reactions of the reaction kinetic model used generally have less than 100 steps. However, when performing combustion simulation calculations on new component fuels, existing traditional fuel mechanism models often cannot be directly used for simulation calculations. This requires researchers to use alternative component mechanism models of new fuels or splice other admixture mechanism models to traditional fuel mechanism models to achieve the purpose of making a new fuel mechanism model. Since a detailed mechanism model is generally used for splicing when making a new fuel mechanism model, in order to reduce the complexity of simulation calculations, it is necessary to simplify the chemical reaction kinetic model.

[0004] Conventional simplification methods generally combine methods such as sensitivity analysis, path analysis, decoupling method, and isomer lumping method, such as Chinese patent applications with publication numbers CN 117524330 A, CN 118335212 A, and CN 116978473 A. These methods generally require researchers to have relatively in-depth professional knowledge of chemical kinetics to perform kinetic calculations or switch to use multiple software and methods for programming and modeling. For general engineering technicians when performing simulation calculations, it is extremely difficult to construct and simplify proxy models using traditional methods in a short time. Summary of the Invention

[0005] Due to the above-mentioned defects in the prior art, the present invention provides a simplified modeling method, device and computer program product for a combustion reaction kinetic model of a complex mixed fuel. By directly simplifying the complex model, there is no need to artificially disassemble and construct the model; there is no need to conduct in-depth reaction path analysis, and there is no need to write programs. It can be automatically realized only by relying on ANSYS software, which is suitable for combustion simulation engineering practitioners who do not have a research foundation in combustion reaction kinetics, so as to quickly make a fuel mechanism model and conduct reasonable verification.

[0006] To achieve the above object, in a first aspect, the present invention provides a simplified modeling method for a complex mixed fuel combustion reaction kinetics model, comprising the following steps: Step S1, model coupling: Select a model according to the mechanism of complex mixed fuel combustion reaction for coupling to obtain a detailed reaction kinetics model of fuel combustion; Eliminate duplicate components and duplicate elementary reactions to obtain a detailed reaction kinetics model of fuel combustion to be simplified; Step S2, perform a first simplification on the model obtained in S1: Adopt the directed graph method based on error transfer and the species sensitivity analysis method to remove redundant components and elementary reactions; The target components are selected as the surrogate components of the fuel, oxidant, main combustion products, and main intermediate products; Step S3, optimize the model obtained in S2: Identify the elementary reactions to be optimized through reaction rate and sensitivity analysis; Adopt the genetic algorithm to calculate and optimize the pre-exponential factor A, temperature exponent N, and activation energy E of the three-parameter modified Arrhenius equation for each elementary reaction to be optimized a ; Substitute the optimal pre-exponential factor A, temperature exponent N, and activation energy E a into the rate constant equation corresponding to the elementary reaction to be optimized to obtain the optimized elementary reaction; Step S4, perform a second simplification on the model obtained in S3: Conduct a rationality analysis of the chemical reaction path; Perform sensitivity analysis on the optimized model with a reasonable reaction path to find the key components of the model; Add the key components to the target component set for model simplification; Adopt the directed graph method based on error transfer and the species sensitivity analysis method to perform model simplification again; Step S5, verify the model obtained in S4: When the verification fails, return to the above steps S2 to S4.

[0007] Adopting the above technical solution, in step S2, the directed graph method based on error transfer and the species sensitivity analysis method are used for model simplification, which can effectively avoid the problems of huge computational amount and non-convergence of calculation caused by global sensitivity analysis and reaction path analysis, ensure the model simplification accuracy and significantly reduce the simplification calculation amount.

[0008] In general optimization methods, the simplified model under the target working condition is usually made to match the detailed model by directly optimizing the form of the rate constant. In chemical kinetics, the reaction rate constant K is a function of temperature T. Therefore, directly modifying the rate constant will cause the simulation results of the optimized model to be distorted under wide working conditions and the effect is not good when simulating diffusion flames with a large temperature change range. The optimization process in step S3 takes the optimal pre-exponential factor, temperature exponent, and activation energy of the three-parameter modified Arrhenius equation as the optimization objects, avoiding the problem of model distortion under a wide temperature range caused by directly optimizing the rate constant.

[0009] Furthermore, step S2 includes the following sub-steps: Step S2-1: Import the model obtained in step S1 into the simulation software and perform subsequent simulation calculations using a closed homogeneous reactor model; Step S2-2: Use the directed graph method based on error transfer and species sensitivity analysis to remove redundant components and elementary reactions; during the simplification process, the target components are selected as surrogate components of the fuel, oxidizer, main combustion products, and main intermediate products; Step S2-3: Set the simplified initial conditions according to the typical operating conditions of the engineering calculations, and then select a fault tolerance threshold to obtain a fuel skeletal mechanism model as small as possible. A relatively large value is selected for this fault tolerance threshold to obtain a fuel skeletal mechanism model as small as possible.

[0010] Furthermore, step S3 includes the following sub-steps: Step S3-1: Set the boundary conditions and select the target parameter as the ignition delay time; Step S3-2: Identify the elementary reactions that need to be optimized through reaction rate and sensitivity analysis; Step S3-3: Use the genetic algorithm to calculate and optimize the pre-exponential factor A, temperature exponent N, and activation energy E of the three-parameter modified Arrhenius equation for each elementary reaction to be optimized identified in step S3-2 a ; Step S3-4: Substitute the optimal pre-exponential factor, temperature exponent, and activation energy parameters obtained in S3-3 into the rate constant equation corresponding to the elementary reaction to be optimized, and thus obtain the optimized elementary reaction; replace the corresponding elementary reaction in the simplified model with the optimized elementary reaction to obtain the optimized model.

[0011] Furthermore, step S4 includes the following sub-steps: Step S4-1: Use the closed homogeneous reactor model to perform a chemical reaction path analysis on the model obtained in S3 to verify the rationality of its reaction path; if the reaction path is unreasonable, it is necessary to return to step S2; Step S4-2: Set the boundary conditions according to the typical operating conditions of the engineering calculations, perform sensitivity analysis on the optimized model with a reasonable reaction path, and find the key components of the model; Step S4-3: Add the key components determined in S4-2 to the target component set for mechanism simplification; Step S4-4: Use the directed graph method based on error transfer and species sensitivity analysis to perform model simplification again to obtain a simplified complex mixed fuel combustion reaction kinetic model.

[0012] Further, the verification in step S5 includes, but is not limited to, zero-dimensional flame verification and one-dimensional flame verification.

[0013] Furthermore, step S5 includes the following sub-steps: Step S5-1: Conduct zero-dimensional flame verification on the model obtained in S4; Step S5-2: Parametrize the boundary conditions of combustion according to the target working conditions of the engineering calculation; Step S5-3: Calculate the ignition delay time, and compare the simulation value of the ignition delay time with the simulation values and experimental values of the ignition delay time of the detailed reaction kinetics model of fuel combustion; if the ignition delay calculation result of the simplified mechanism model is within the allowable range compared with the detailed value and the experimental value, it can be determined that the simplified mechanism model has passed the zero-dimensional flame verification, otherwise, return to step S2; Step S5-4: On the basis of the verification in S5-3, conduct one-dimensional flame verification: calculate the laminar flame propagation speed, and compare the simulation values of the laminar flame propagation speed of the simplified mechanism model and the detailed mechanism model with the experimental measurement value of the laminar flame propagation speed of the fuel; if the calculation result of the laminar flame propagation speed of the simplified mechanism model is within the allowable range compared with the detailed value and the experimental value, it can be determined that the simplified mechanism model has passed the one-dimensional flame verification, otherwise, return to step S2.

[0014] Furthermore, for the verification of the axial combustion emission concentration of the flame, two-dimensional flame verification is conducted; step S5 further includes: Step S5-5: On the basis of the verification in S5-4, use the coaxial jet flame model in open-source code or simulation software to conduct two-dimensional flame verification: set the boundary conditions according to the target parameters of the engineering calculation, calculate the flame height, temperature field, soot concentration field, and OH radical concentration field, and compare and verify with the experimental results of the coaxial jet flame; if the error is within the allowable range, it can be determined that the model has passed the two-dimensional flame verification and can be used for the simulation calculation of engine pollutant emissions.

[0015] In a second aspect, the present invention provides an electronic device, which includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the simplified modeling method of the complex hybrid fuel combustion reaction kinetics model as described above.

[0016] In a last aspect, the present invention provides a computer program product which, when running on a computer, causes the computer to execute the method for simplifying the modeling of the complex hybrid fuel combustion reaction kinetics model as described above.

[0017] Compared with the prior art, the present invention has the following advantages or beneficial effects: (1) The present invention automatically simplifies and models with a complete detailed reaction model as the object of simplification, without the need for reaction class splitting and isomer lumping, which can effectively avoid the problem of human operation errors caused by the lack of a foundation in chemical reaction kinetics research by engineering and technical personnel; it is particularly suitable for quickly simplifying a complex chemical reaction kinetics model containing 5,000 to 20,000 elementary reactions to less than 300 steps. (2) The present invention uses the directed relationship graph method based on error transfer and the species sensitivity analysis method for model simplification, which can effectively avoid the problems of huge computational amount and non-convergence of calculations caused by using global sensitivity analysis and reaction path analysis, ensuring the model simplification accuracy and significantly reducing the simplification calculation amount. (3) In the mechanism optimization process of the present invention, the optimal pre-exponential factor, temperature exponent, and activation energy of the three-parameter modified Arrhenius equation are used as the optimization objects, avoiding the problem of model distortion in a wide temperature range caused by directly optimizing the rate constant. Description of the Drawings

[0018] By reading the detailed description of the non-limiting embodiments with reference to the following drawings, the present invention and its features and advantages will become more obvious. The same reference numerals indicate the same parts in all the drawings. The drawings are not drawn to scale in their entirety, and the emphasis is on showing the gist of the present invention.

[0019] Figure 1 It is a flowchart of the method for simplifying the modeling of the complex hybrid fuel combustion reaction kinetics model in an embodiment of the present invention; Figure 2 It is a main reaction path diagram of the complex hybrid fuel combustion reaction kinetics model in an embodiment of the present invention; Figure 3 It is the verification result of the shock tube ignition delay time of the complex hybrid fuel combustion reaction kinetics model in an embodiment of the present invention. Detailed Embodiments

[0020] Hereinafter, the exemplary embodiments of the present disclosure will be described in more detail with reference to the drawings. It should be understood that all these described exemplary embodiments are only partial embodiments and examples of the present invention, rather than all. On the contrary, these exemplary embodiments are provided so that those skilled in the art can more thoroughly understand the present disclosure and can more completely convey the technical content of the present disclosure to those skilled in the art.

[0021] In the following detailed description, many specific details are set forth to provide a more thorough understanding of the present invention. However, it is obvious to those skilled in the art that well-known algorithms or models (such as genetic algorithms, directed relation graph methods, species sensitivity analysis methods, etc.) do not show detailed processes to avoid obscuring the gist of the present invention.

[0022] Example 1 See Figure 1 , this example provides a simplified modeling method for a complex hybrid fuel combustion reaction kinetics model, including the following steps: S1 Coupling of the model S1-1. According to the different simulation calculation objects faced by the mechanism model, select appropriate model coupling objects. If the original model can simulate the characteristics of the calculation object, there is no need to couple other models.

[0023] S1-2. Remove duplicate components and duplicate elementary reactions from the fuel detailed reaction kinetics model obtained by coupling in S1-1 to obtain a fuel detailed reaction kinetics model for simplification.

[0024] S2 Simplification of the model S2-1. Import the fuel detailed reaction kinetics model obtained in S1 into the Reaction Workbench model simplification working domain of an extension module of Chemkin Pro software (a part of the ANSYS software package), and use a closed homogeneous reactor model for subsequent simulation calculations.

[0025] S2-2. Use the directed relation graph method and species sensitivity analysis method based on error transfer to remove redundant components and elementary reactions.

[0026] S2-3. During the simplification process, the target components are selected as the surrogate components of the fuel, oxidants, main combustion products, and main intermediate products. Set the initial conditions for simplification according to the typical working conditions of the engineering calculations performed, and then select a relatively large value for the fault tolerance threshold to obtain a fuel skeletal mechanism model as small as possible.

[0027] S3 Optimization of the model Import the skeletal mechanism model obtained in S2 into the Reaction Workbench model optimization working domain, and use a closed homogeneous reactor model for subsequent simulation calculations.

[0028] S3-1. Set boundary conditions and select the target parameter as the ignition delay time.

[0029] S3-2. On the basis of S3-1, identify the elementary reactions that need to be optimized through reaction rate and sensitivity analysis.

[0030] S3-3. Based on the genetic algorithm, on the basis of S3-2, the software automatically calculates and optimizes the pre-exponential factor A, temperature exponent N, and activation energy E of the three-parameter modified Arrhenius equation (Equation 1) for each elementary reaction to be optimized. a .

[0031] (1) In Equation 1, k is the chemical reaction rate constant, A is the frequency factor, also known as the pre-exponential factor, which is a constant independent of temperature and represents the maximum possible rate constant of the reaction if there is no activation energy barrier; T is the thermodynamic temperature, and R is the gas constant.

[0032] S3-4. Substitute the optimal pre-exponential factor, temperature exponent, and activation energy parameters obtained in S3-3 into the rate constant equation corresponding to the elementary reaction to be optimized, and the optimized elementary reaction is obtained; replace the corresponding elementary reaction in the simplified model with the optimized elementary reaction to obtain the optimized model.

[0033] In chemical reaction kinetics, the rate constant of an elementary reaction is determined by the Arrhenius equation, and the pre-exponential factor A, temperature exponent N, and activation energy E a jointly control the trend of the reaction rate changing with temperature. By optimizing these three parameters through an algorithm, the experimental data and the theoretical model can be accurately matched, making the kinetic behavior (such as product generation path, energy barrier) of the elementary reaction in the complex mechanism closer to the actual system, thereby improving the prediction accuracy and reliability of the overall reaction mechanism model. Replace the corresponding elementary reaction in the simplified model with the optimized elementary reaction to obtain the optimized skeletal mechanism model.

[0034] S4 Secondary simplification of the model S4-1. In the Chemkin Pro software, use the closed homogeneous reactor model to perform chemical reaction path analysis on the optimized skeletal mechanism model obtained in S3 to verify the rationality of its reaction path. If the reaction path is unreasonable, it is necessary to return to S2.

[0035] S4-2. Set the boundary conditions according to the typical working conditions of the engineering calculations performed, and perform sensitivity analysis on the optimized skeletal mechanism model with a reasonable reaction path to find the key components of this optimized skeletal mechanism model.

[0036] S4-3. Add the key components determined in S4-2 to the target component set for mechanism simplification.

[0037] S4-4. Use the directed graph method based on error transfer and species sensitivity analysis method to perform mechanism model simplification again to obtain the simplified mechanism model.

[0038] Verification of S5 Model S5-1. Perform zero-dimensional flame verification on the simplified fuel mechanism model obtained in S4. Use the closed homogeneous reactor model in Chemkin Pro software to perform simulation calculations of zero-dimensional flames.

[0039] S5-2. Parametrize the boundary conditions of combustion according to the target operating conditions of the engineering calculations.

[0040] S5-3. Calculate the ignition delay time, and compare the simulation value of the ignition delay time with the simulation value of the ignition delay time of the detailed reaction kinetics model and the experimental value of the ignition delay time. If the calculation result of the ignition delay of the simplified mechanism model differs from the detailed value and the experimental value within the allowable range, it can be determined that the simplified mechanism model has passed the zero-dimensional flame verification. Otherwise, the model simplification needs to be carried out again.

[0041] S5-4. On the basis of the completion of the verification in S5-3, perform one-dimensional flame verification. Select the opposed flame model in Chemkin Pro software for simulation calculations. Calculate the laminar flame propagation speed, and compare the simulation values of the laminar flame propagation speed of the simplified mechanism model and the detailed mechanism model with the experimentally measured value of the laminar flame propagation speed of this fuel. If the calculation result of the laminar flame propagation speed of the simplified mechanism model differs from the detailed value and the experimental value within the allowable range, it can be determined that the simplified mechanism model has passed the one-dimensional flame verification. Otherwise, the mechanism simplification needs to be carried out again.

[0042] S5-5. If it is necessary to verify the axial combustion emission concentration of the flame, two-dimensional flame verification is required; otherwise, this step is skipped. On the basis of S5-4, use the coaxial jet flame model in open-source code or simulation software to perform two-dimensional flame verification. Set the boundary conditions according to the target parameters of the engineering calculations, calculate the flame height, temperature field, soot concentration field, and OH radical concentration field, and compare and verify with the experimental results of the coaxial jet flame. If the error is within the allowable range, it can be determined that the simplified mechanism model has passed the two-dimensional flame verification and can be used for the simulation calculation of engine pollutant emissions.

[0043] The following takes aviation kerosene RP-3 as an example to illustrate the above simplified modeling method in detail, so as to facilitate those skilled in the art to understand the technical solution and technical effect of the present invention.

[0044] Coupling of S1 Model S1-1. Select a suitable model coupling object according to the different simulation calculation objects faced by the mechanism model.

[0045] The target chemical reaction kinetic simplified model of this example is required for the simulation calculation of the soot particle characteristics of jet flames. Therefore, the effectively verified RP-3 combustion reaction kinetic model is coupled with the model including polycyclic aromatic hydrocarbon growth. In this example, a four-component substituted RP-3 detailed reaction kinetic model is selected. The substituted components of this model are composed of 27.44% (mole fraction) n-dodecane, 28.81% isododecane, 26.12% decalin, and 17.63% butylbenzene. The model contains 3,056 components and 11,898 elementary reactions.

[0046] S1-2. Remove the duplicate components and duplicate elementary reactions from the RP-3 detailed reaction kinetic model obtained by coupling S1-1 to obtain the RP-3 detailed reaction kinetic model including polycyclic aromatic hydrocarbon growth. This model contains 3,017 components and 12,168 elementary reactions.

[0047] Simplification of the model S2-1. Import the fuel detailed kinetic model obtained in S1 into the ReactionWorkbench model simplification workspace of Chemkin Pro software, and use the closed homogeneous reactor model for subsequent simulation calculations.

[0048] S2-2. Use the directed relation graph method based on error transfer and species sensitivity analysis method to remove the redundant components and elementary reactions.

[0049] During the simplification process, the target components are selected as the fuel substitute components NC12H26 (n-dodecane), PMH (isododecane), DECALIN (decalin), C6H5C4H9 (butylbenzene), the oxidants N2 and O2, the main combustion products CO2, CO, H2O, and the main intermediate products C2H2, C2H4, CH4, HO2, OH, and polycyclic aromatic hydrocarbon precursor components BAPYR, BAPYR*S, BGHIF.

[0050] Set the simplified initial working conditions according to the engineering calculation target working conditions. In this example, the initial working conditions are selected as shown in Table 1. Take the fault tolerance threshold as 20% to obtain the RP-3 fuel skeletal mechanism model, which contains 439 components and 2,206 reactions.

[0051] Table 1 Initial working condition table for mechanism model simplification calculation

[0052] Optimization of the model S3-1. Import the RP-3 skeletal mechanism model obtained in S2 into the mechanism optimization workspace of Reaction Workbench, and use the closed homogeneous reactor model for subsequent simulation calculations.

[0053] S3-2. On the basis of S3-1, set boundary conditions and select the target parameter as the ignition delay time.

[0054] S3-3. On the basis of S3-2, identify the elementary reactions that need to be optimized through rate and sensitivity analysis.

[0055] S3-4. On the basis of S3-3, based on the genetic algorithm, the software automatically calculates the optimal pre-exponential factor, temperature exponent, and activation energy of the three-parameter modified Arrhenius equation for each elementary reaction to be optimized.

[0056] S3-5. Replace the corresponding elementary reactions in the simplified model with the optimized elementary reactions in S3-3 to obtain an optimized skeletal mechanism model, and the tolerance between this model and the original model is 0.5%.

[0057] S4 Secondary simplification of the model S4-1. Conduct a chemical reaction path analysis on the optimized skeletal mechanism model obtained in S3 to verify the rationality of its reaction path. The reaction path of this example is as Figure 2 shown.

[0058] S4-2. Set boundary conditions according to the target working conditions of engineering calculations, and conduct temperature sensitivity analysis on the optimized skeletal mechanism model with a reasonable reaction path in Chemkin Pro to find the key components of this optimized skeletal mechanism model.

[0059] S4-3. Add the key components determined in S4-2 to the target component set for mechanism simplification.

[0060] S4-4. Use the directed graph method based on error transfer and species sensitivity analysis to conduct mechanism model simplification again to obtain a simplified mechanism model. In this example, through the above simplification method, the finally obtained simplified mechanism contains 288 components and 1619 elementary reactions.

[0061] S5 Verification of the model S5-1. Conduct zero-dimensional flame verification on the RP-3 simplified mechanism model obtained in S4. Use the closed homogeneous reactor model in Chemkin Pro to conduct simulation calculations of zero-dimensional flames.

[0062] S5-2. Parametrize the boundary conditions of combustion according to the target working conditions of engineering calculations.

[0063] S5-3. Calculate the ignition delay time, and compare and verify the simulated value of the ignition delay time with the simulated values and experimental values of the ignition delay time of the detailed reaction kinetic model. If the calculated result of the ignition delay of the simplified mechanism model is within the allowable range compared with the detailed value and the experimental value, it can be determined that the simplified mechanism model has passed the zero-dimensional flame verification; otherwise, the model simplification needs to be carried out again.

[0064] In this example, under the working conditions of T = 1100 - 1600 K and P = 1 atm, the ignition delay time of the mixture of RP-3 and air with 21% oxygen concentration is calculated successively under three different equivalence ratios of lean fuel (Φ = 0.5), stoichiometric ratio (Φ = 1.0), and rich fuel (Φ = 1.5), and compared and verified with the ignition delay time data obtained from the shock tube experiments published in the papers by Chen et al. (Chen, B. H.; Liu, J. Z.; Yao, F.; He, Y.; Yang, W. J., Ignition delay characteristics of RP-3 under ultra-low pressure(0.01–0.1 MPa). Combustion and Flame 2019, 210, 126-133.) and Liu et al. (Liu, J.; Hu, E.; Yin, G.; Huang, Z.; Zeng, W., An experimental and kinetic modeling study on the low-temperature oxidation, ignition delay time, and laminar flame speed of a surrogate fuel for RP-3 kerosene. Combustion and Flame 2022, 237.). The results are as Figure 3 shown. The results show that the synthesized detailed mechanism can reflect the dependence of the ignition delay of the mixture of RP-3 and air with 21% oxygen concentration on the equivalence ratio, which is consistent with the experimental measurement trend; the ignition delay time obtained by the numerical calculation model of the simplified mechanism can predict the experimentally measured ignition delay time.

[0065] S5-4. On the basis of the verification in S5-3, one-dimensional flame verification is carried out. Select the opposed jet flame model in Chemkin Pro for simulation calculation. Calculate the laminar flame speed, and compare the simulated values of the laminar flame speed of the simplified mechanism model and the detailed mechanism model with the experimental measurement values of the laminar flame speed of this fuel. If the calculation results of the laminar flame speed of the simplified mechanism model are within the allowable range compared with the detailed values and the experimental values, it can be determined that the simplified mechanism model has passed the one-dimensional flame verification. Otherwise, mechanism simplification needs to be carried out again.

[0066] S5-5. On the basis of S5-4, two-dimensional flame verification is carried out. In this example, the selected calculation model is an open-source two-dimensional coaxial jet flame calculation model. According to the experimental parameters, set the boundary conditions as the inner diameter of the fuel pipe is 5 mm, the fuel outlet velocity is 10.49 cm / s, the air outlet velocity is 21.62 cm / s, the fuel temperature is 540 K, the air temperature is 540 K, and the volume fractions of oxygen are 23.5%, 21%, 20%, 19%, and 18.5% in turn. Then calculate the flame height, temperature field, soot concentration field, and OH radical concentration field, and compare and verify with the experimental results of the coaxial jet flame. If the error is within the allowable range, it can be determined that the simplified mechanism model has passed the two-dimensional flame verification and can be used for the simulation calculation of engine pollutant emissions.

[0067] In this example, aiming at the calculation requirements of soot particle emissions, a mechanism coupling method applicable to complex fuels containing benzene is designed, and the formation mechanism of soot precursors of the model is verified from the perspective of soot emissions.

[0068] Example 2 This example provides an electronic device, and the electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the simplified modeling method of the complex hybrid fuel combustion reaction kinetics model as described in Example 1.

[0069] In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist physically alone, or two or more units may be integrated in one unit. The above integrated units may be implemented in the form of hardware or in the form of software functional units.

[0070] Optionally, the memory includes, but is not limited to, high-speed random access memory and non-volatile memory. For example, one or more disk storage devices, flash memory devices, or other non-volatile solid-state storage devices; the processor includes, but is not limited to, a central processing unit (CPU), a network processor (NP), a graphics processing unit (GPU), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0071] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing relevant hardware through a program, and this program can be stored in a computer-readable storage medium. When the computer program product runs on a computer, it causes the computer to execute the simplified modeling method of the complex hybrid fuel combustion reaction kinetics model as described in Embodiment 1. The computer-readable storage medium may include, but is not limited to, floppy disks, optical disks, CD-ROMs (compact disc read-only memories), magneto-optical disks, ROMs (read-only memories), RAMs (random access memories), EPROMs (erasable programmable read-only memories), EEPROMs (electrically erasable programmable read-only memories), magnetic cards or optical cards, flash memories, or other types of media / machine-readable media suitable for storing machine-executable instructions. The computer-readable storage medium can be a product not connected to a computer device, or a component already connected to a computer device for use.

[0072] In summary, the present application provides a simplified modeling method, device, and computer program product for a complex hybrid fuel combustion reaction kinetics model. The simplified modeling method includes steps such as model coupling, performing a primary simplification of the model using the directed relationship graph method based on error transfer and the species sensitivity analysis method, optimizing the primary simplified model with the optimal pre-exponential factor, temperature exponent, and activation energy of the three-parameter modified Arrhenius equation as the optimization objects, and performing a secondary simplification of the optimized model. The present invention uses a method of directly simplifying complex models, without the need for artificial disassembly and construction of the models; no in-depth reaction path analysis is required, and no program writing is needed, which is suitable for combustion simulation engineering practitioners who do not have a foundation in combustion reaction kinetics research, for quickly creating fuel mechanism models and conducting reasonable verification.

[0073] The preferred embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the above specific embodiments, and the devices and structures not described in detail therein should be understood to be implemented in a common manner in the art; any person skilled in the art can, without departing from the scope of the technical solution of the present invention, make many possible changes and modifications to the technical solution of the present invention by using the methods and technical contents disclosed above, or modify it into an equivalent embodiment with equivalent changes, which does not affect the essence of the present invention. Therefore, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A simplified modeling method for the combustion reaction kinetics model of a complex mixed fuel, characterized in that, It includes the following steps: Step S1, model coupling: Select a model according to the mechanism of complex mixed fuel combustion reaction for coupling to obtain a detailed reaction kinetic model of fuel combustion; eliminate duplicate components and duplicate elementary reactions to obtain a detailed reaction kinetic model of fuel combustion to be simplified; Step S2, perform a first simplification on the model obtained in S1: Use the directed graph method based on error transfer and species sensitivity analysis method to remove redundant components and elementary reactions; the target components are selected as the surrogate components of the fuel, oxidizer, main combustion products, and main intermediate products; Step S3. Optimize the model obtained in S2: Identify the elementary reactions that need to be optimized through reaction rate and sensitivity analysis; use the genetic algorithm to calculate and optimize the pre-exponential factor A, temperature exponent N, and activation energy E of the three-parameter modified Arrhenius equation for each elementary reaction to be optimized a ; Substitute the optimal pre-exponential factor A, temperature exponent N, and activation energy E a into the rate constant equation corresponding to the elementary reaction to be optimized to obtain the optimized elementary reaction; Step S4, perform a second simplification on the model obtained in S3: Conduct a rationality analysis of the chemical reaction path; perform a sensitivity analysis on the optimized model with a reasonable reaction path to find the key components of the model; add the key components to the target component set for model simplification; use the directed graph method based on error transfer and species sensitivity analysis method to perform model simplification again; Step S5, verify the model obtained in S4: When the verification fails, return to the above steps S2 to S4.

2. The simplified modeling method of a complex hybrid fuel combustion reaction kinetic model according to claim 1, characterized in that The above step S2 includes the following sub-steps: Step S2-1, import the model obtained in step S1 into the simulation software, and use the closed homogeneous reactor model for subsequent simulation calculations; Step S2-2, use the directed graph method based on error transfer and species sensitivity analysis method to remove redundant components and elementary reactions; during the simplification process, the target components are selected as the surrogate components of the fuel, oxidizer, main combustion products, and main intermediate products; Step S2-3, set the initial conditions for simplification according to the typical working conditions of the engineering calculations performed, and then select the fault tolerance threshold to obtain a fuel skeletal mechanism model as small as possible.

3. A simplified modeling method for a complex hybrid fuel combustion reaction kinetic model according to claim 1, characterized in that The above step S3 includes the following sub-steps: Step S3-1, set the boundary conditions, and select the target parameter as the ignition delay time; Step S3-2, identify the elementary reactions to be optimized through reaction rate and sensitivity analysis; Step S3-3: Using the genetic algorithm, calculate and optimize the pre-exponential factor A, temperature exponent N, and activation energy E of the three-parameter modified Arrhenius equation for each primitive reaction to be optimized identified in Step S3-2 a ; Step S3-4, substitute the optimal pre-exponential factor, temperature exponent, and activation energy parameters obtained in S3-3 into the rate constant equation corresponding to the elementary reaction to be optimized, and then obtain the optimized elementary reaction; replace the corresponding elementary reaction in the simplified model with the optimized elementary reaction to obtain the optimized model.

4. A simplified modeling method for the combustion reaction kinetics model of a complex mixed fuel according to claim 1, characterized in that, The above step S4 includes the following sub-steps: Step S4-1, use the closed homogeneous reactor model to perform a chemical reaction path analysis on the model obtained in S3 to verify the rationality of its reaction path; if the reaction path is unreasonable, it is necessary to return to step S2 again; Step S4-2, set the boundary conditions according to the typical working conditions of the engineering calculations performed, perform a sensitivity analysis on the optimized model with a reasonable reaction path to find the key components of the model; Step S4-3, add the key components determined in S4-2 to the target component set for mechanism simplification; Step S4-4, use the directed graph method based on error transfer and species sensitivity analysis method to perform model simplification again to obtain a simplified detailed reaction kinetic model of complex mixed fuel combustion.

5. A simplified modeling method for a complex hybrid fuel combustion reaction kinetic model according to claim 1, characterized in that The verification in the above step S5 includes but is not limited to zero-dimensional flame verification and one-dimensional flame verification.

6. A simplified modeling method for a complex hybrid fuel combustion reaction kinetics model according to claim 5, characterized in that The above step S5 includes the following sub-steps: Step S5-1: Conduct zero-dimensional flame verification on the model obtained in S4; Step S5-2: Parameterize the boundary conditions of combustion according to the target operating conditions for the engineering calculations; Step S5-3: Calculate the ignition delay time, and compare the simulation value of the ignition delay time with the simulation values and experimental values of the ignition delay time of the detailed reaction kinetics model of fuel combustion; if the ignition delay calculation result of the simplified mechanism model is within the allowable range compared with the detailed value and the experimental value, it can be determined that the simplified mechanism model has passed the zero-dimensional flame verification; otherwise, return to Step S2; Step S5-4: On the basis of the verification in S5-3, conduct one-dimensional flame verification: calculate the laminar flame propagation speed, and compare the simulation values of the laminar flame propagation speed of the simplified mechanism model and the detailed mechanism model with the experimentally measured value of the laminar flame propagation speed of this fuel; If the calculation result of the laminar flame propagation speed of the simplified mechanism model is within the allowable range compared with the detailed value and the experimental value, it can be determined that the simplified mechanism model has passed the one-dimensional flame verification; otherwise, return to Step S2.

7. A simplified modeling method for a complex hybrid fuel combustion reaction kinetic model according to claim 6, characterized in that, For the verification of the axial combustion emission concentration of the flame, conduct two-dimensional flame verification; Step S5 further includes: Step S5-5: On the basis of the verification in S5-4, use the coaxial jet flame model in open-source code or simulation software to conduct two-dimensional flame verification: set the boundary conditions according to the target parameters of the engineering calculations, calculate the flame height, temperature field, soot concentration field, and OH radical concentration field, and compare and verify with the experimental results of the coaxial jet flame; if the error is within the allowable range, it can be determined that the model has passed the two-dimensional flame verification and can be used for the simulation calculation of engine pollutant emissions.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the simplified modeling method of the complex hybrid fuel combustion reaction kinetics model according to any one of claims 1 to 7.

9. A computer program product, characterized in that, When the computer program product runs on a computer, the computer is caused to execute the simplified modeling method of the complex hybrid fuel combustion reaction kinetics model according to any one of claims 1 to 7.

Images