Method for simplifying large-scale detailed chemical reaction model of high-carbon fuel
阅读说明：本技术 一种简化高碳燃料的大规模详细化学反应模型的方法 (Method for simplifying large-scale detailed chemical reaction model of high-carbon fuel ) 是由 常亚超 贾明 牛波 王朋志 于 2021-01-21 设计创作，主要内容包括：本发明属于燃烧学科数值仿真领域,涉及一种简化高碳燃料的大规模详细化学反应模型的方法,简化高碳燃料的大规模详细化学反应模型,获得在精度和规模同时满足燃烧多维模拟的简化反应动力学模型。此方法以子模型/反应类为对象,进行高碳燃料的大规模详细化学反应模型简化,极大地降低了输入变量数目,降低了全局敏感分析的计算时间,使全局敏感性分析应用于大规模详细化学反应模型成为可能。(The invention belongs to the field of combustion subject numerical simulation, and relates to a method for simplifying a large-scale detailed chemical reaction model of a high-carbon fuel, which is used for simplifying the large-scale detailed chemical reaction model of the high-carbon fuel and obtaining a simplified reaction kinetic model which meets the requirement of combustion multidimensional simulation on precision and scale. The method takes the sub-models/reaction classes as objects to simplify the large-scale detailed chemical reaction model of the high-carbon fuel, greatly reduces the number of input variables, reduces the calculation time of global sensitivity analysis, and makes the application of the global sensitivity analysis to the large-scale detailed chemical reaction model possible.)
1. A method of simplifying a large-scale detailed chemical reaction model of a high carbon fuel, characterized by: the method comprises the following steps:
(1) large scale detailed chemical reaction model pretreatment of high carbon fuels
Dividing the reaction into different sub-models according to the large-scale detailed chemical reaction model structure of the high-carbon fuel and the maximum number of carbon atoms of the components involved in the reaction; then, dividing the reactions in the submodels into different reaction classes according to a rate rule; in order to reduce the amount of model simplification, in the subsequent calculations, the submodels/reaction classes are considered as a whole, i.e. for all reactions belonging to the jth submodel/reaction class
Wherein m isjFor the total number of reactions contained in the jth sub-model/reaction class,standard uncertainty rate parameter for nth reaction
knFor the nth in a single simulationReaction rate of the reaction, fnAs the uncertainty factor f of the nth reactionn＝log10(kn，max/kn，0)＝log10(kn，0/kn，min)，kn,0、kn,maxAnd kn,minRespectively, the standard reaction rate, the maximum value and the minimum value of the nth reaction;
(2) assessing importance of submodels using global sensitivity analysis
To perform a global sensitivity analysis, first, an input variable x is inputjThe dispersion is (0,1/(p-1),2/(p-1), …, 1); subsequently, a set of x is randomly generated, and the change Δ of the elements in x one by one is calculated, and a reduction target y is calculatedi(ii) a The jth input variable pair simplification target yiHas a single influence of
The average effect is obtained through k times of calculation
Sum variance
The larger the influence of the rate constant for disturbing the reaction in the jth submodel/reaction class on the ith simplified target predicted value is, the larger the sigma isijThe larger the reaction class is, the stronger nonlinear relation exists between the jth reaction class and the ith simplified target or the stronger coupling relation exists between the jth reaction class and other reaction classes;
based onAnd the number of carbon atoms in the submodel, and dividing the submodel into a small molecule submodel (the number of carbon atoms is less than or equal to 4), an important large molecule submodel and an unimportant large molecule submodel;
(3) assessment of the importance of reactive species in important macromolecular submodels using global sensitivity and pathway sensitivity analysis
The path sensitivity coefficient is calculated by equation (6),
yi,jand removing the predicted value of the simplified chemical reaction model of the ith reaction class to the jth simplified target.
The significance of the reaction classes being standardisedAndare jointly determined, i.e.
Therein, standardizedAndcalculated by equations (8) to (10)
Based on xijDeleting the reaction classes one by one from small to large until the predicted value of the simplified chemical reaction model reaches the uncertain prediction boundary of the detailed chemical reaction model on any simplified target;
(4) construction of framework macromolecular submodel
Firstly, collecting isomers in the reaction classes reserved in the step (3) as a representative component; then, reactions in the unimportant submodel are lumped to obtain a framework macromolecule submodel;
(5) simplified small molecule submodels
The reactions in the small molecule submodels were evaluated using equation (7) and based on ξjDeleting the reactions one by one until the predicted value of the simplified chemical reaction model reaches the uncertain boundary of the detailed chemical reaction model for predicting any simplified target, and obtaining an initial simplified model;
(6) reaction rate optimization
And (3) optimizing the reaction rate constant in the fuel submodel within an uncertain range by using a multi-objective genetic algorithm to obtain a final simplified model.
2. A method of simplifying a large scale detailed chemical reaction model of high carbon fuel as claimed in claim 1, characterized in that: the high-carbon fuel is isohexadecane.
Technical Field
The invention belongs to the field of combustion subject numerical simulation, and relates to a method for simplifying a large-scale detailed chemical reaction model of a high-carbon fuel.
Background
Engines require higher thermal efficiency and lower pollutant emissions to meet increasingly stringent emission regulations. With the development of computer technology, multi-dimensional combustion simulation becomes an important tool for the design and optimization of new engines. In order to ensure the reliability of the multidimensional combustion simulation, a simplified chemical reaction model with compact structure and reliable performance is particularly important. The components of real fuel oil are extremely complex, and in order to reproduce the physicochemical characteristics of the real fuel oil, the characterization fuel is usually composed of components with huge molecular structures, so that the detailed chemical reaction model is extremely large in scale. The simplified chemical reaction model which can satisfy the multidimensional combustion simulation at the same time of scale and performance cannot be obtained based on the current detailed chemical reaction model simplification method. To solve this problem, a method for simplifying a large-scale detailed chemical reaction model of high-carbon fuel is needed to obtain a simplified reaction model satisfying the needs of multi-dimensional combustion simulation.
Disclosure of Invention
In order to simplify a large-scale detailed chemical reaction model of the high-carbon fuel and obtain a simplified reaction kinetic model which meets the multi-dimensional simulation of combustion at the same time of precision and scale, the invention provides a method for simplifying the large-scale detailed chemical reaction model of the high-carbon fuel.
The technical scheme adopted by the invention for solving the technical problems is as follows: a method for simplifying a large-scale detailed chemical reaction model of high-carbon fuel comprises the following specific steps:
(1) large scale detailed chemical reaction model pretreatment of high carbon fuels
Dividing the reaction into different sub-models according to the large-scale detailed chemical reaction model structure of the high-carbon fuel and the maximum number of carbon atoms of the components involved in the reaction; then, dividing the reactions in the submodels into different reaction classes according to a rate rule; in order to reduce the amount of model simplification, in the subsequent calculations, the submodels/reaction classes are considered as a whole, i.e. for all reactions belonging to the jth submodel/reaction class
Wherein m isjFor the total number of reactions contained in the jth sub-model/reaction class,standard uncertainty rate parameter for nth reaction
knReaction rate for the nth reaction in a single simulation, fnAs the uncertainty factor f of the nth reactionn＝log10(kn，max/kn，0)＝log10(kn，0/kn，min)，kn,0、kn,maxAnd kn,minRespectively, the standard reaction rate, the maximum value and the minimum value of the nth reaction;
(2) assessing importance of submodels using global sensitivity analysis
To perform a global sensitivity analysis, first, an input variable x is inputjThe dispersion is (0,1/(p-1),2/(p-1), …, 1); subsequently, a set of x is randomly generated, and the change Δ of the elements in x one by one is calculated, and a reduction target y is calculatedi(ii) a The jth input variable pair simplification target yiHas a single influence of
The average effect is obtained through k times of calculation
Sum variance
The larger the influence of the rate constant for disturbing the reaction in the jth submodel/reaction class on the ith simplified target predicted value is, the larger the sigma isijThe larger the reaction class is, the stronger nonlinear relation exists between the jth reaction class and the ith simplified target or the stronger coupling relation exists between the jth reaction class and other reaction classes;
based onAnd the number of carbon atoms in the submodel, and dividing the submodel into a small molecule submodel (the number of carbon atoms is less than or equal to 4), an important large molecule submodel and an unimportant large molecule submodel;
(3) assessment of the importance of reactive species in important macromolecular submodels using global sensitivity and pathway sensitivity analysis
The path sensitivity coefficient is calculated by equation (6),
yi,jand removing the predicted value of the simplified chemical reaction model of the ith reaction class to the jth simplified target.
The significance of the reaction classes being standardisedAndare jointly determined, i.e.
Therein, standardizedAndcalculated by equations (8) to (10)
Based on xijDeleting the reaction classes one by one from small to large until the predicted value of the simplified chemical reaction model reaches the uncertain prediction boundary of the detailed chemical reaction model on any simplified target;
(4) construction of framework macromolecular submodel
Firstly, collecting isomers in the reaction classes reserved in the step (3) as a representative component; then, reactions in the unimportant submodel are lumped to obtain a framework macromolecule submodel;
(5) simplified small molecule submodels
The reactions in the small molecule submodels were evaluated using equation (7) and based on ξjDeleting the reactions one by one until the predicted value of the simplified chemical reaction model reaches the uncertain boundary of the detailed chemical reaction model for predicting any simplified target, and obtaining an initial simplified model;
(6) reaction rate optimization
And (3) optimizing the reaction rate constant in the fuel submodel within an uncertain range by using a multi-objective genetic algorithm to obtain a final simplified model.
The preferred embodiment of the above process is where the high carbon fuel is isohexadecane.
Compared with the prior art, the invention has the beneficial effects that:
1. the sub-models/reaction classes are taken as objects to simplify the large-scale detailed chemical reaction model of the high-carbon fuel, so that the number of input variables is greatly reduced, the calculation time of global sensitivity analysis is shortened, and the application of the global sensitivity analysis to the large-scale detailed chemical reaction model is possible.
2. By taking the sub-model/reaction class as an object, the problems of too many isomers and huge reaction models caused by reaction path analysis can be effectively avoided. The global sensitivity analysis can accurately capture the nonlinear behavior in the chemical reaction model and the coupling relation information during the reaction, and can ensure the reliability of the final simplified model. Therefore, the simplified chemical reaction kinetic model based on the method can maintain a compact structure and reliable performance and meet the requirement of multi-dimensional combustion simulation.
Drawings
FIG. 1 is a flow diagram of a large scale detailed chemical reaction model using the present method to simplify high carbon fuels.
FIG. 2 is a block diagram of a large scale detailed chemical reaction model of isohexadecane.
FIG. 3 is a comparison of a large scale detailed chemical reaction model and a simplified chemical reaction model of isohexadecane to the predicted value of the stagnation period.
FIG. 4 is a comparison of a large scale detailed chemical reaction model and a simplified chemical reaction model of isohexadecane to the predicted values of the concentrations of the HMN, CO and CO2 components in JSR.
Detailed Description
The invention is described in detail below with reference to the accompanying drawings and technical solutions.
A method for simplifying a large-scale detailed chemical reaction model of high-carbon fuel is used for simplifying a detailed chemical reaction model of isohexadecane, and a flow chart is shown in figure 1.
(1) Large-scale detailed chemical reaction model pretreatment of isohexadecane
Firstly, dividing an isohexadecane large-scale detailed chemical reaction model into 17 sub-models according to the maximum number of carbon atoms of components involved in the reaction; the reactions in the submodel are then divided into 26 reaction classes according to the rate rule, as shown in FIG. 2. In the subsequent calculations, the reaction class/submodel as a whole, i.e. for all reactions belonging to the jth reaction class/submodel
Wherein m isjFor the total number of reactions contained in the jth sub-model/reaction class,standard uncertainty rate parameter for nth reaction
knReaction rate for the nth reaction in a single simulation, fnAs an indeterminate factor of the nth reaction, fn＝log10(kn，max/kn，0)＝log10(kn，0/kn，min)，kn,0、kn,maxAnd kn,minThe standard reaction rate, maximum and minimum values for the nth reaction, respectively. By the formula (2), the reaction rate of each reaction in the detailed chemical reaction model can be converted into a number between 0 and 1 in an uncertain space for subsequent global sensitivity analysis; uncertain factor f of reaction in C0-C4 submodelnUncertainty factor f from reaction in NIST database, C5-C16 submodelnSet to 0.6.
The simplification aims are as follows: t is 600-,And a flame lag period at p-50 atm and T-600-1500K,And the concentration of isohexadecane (HMN), CO and CO2 components in JSR at p ═ 10 atm.
(2) Assessing importance of submodels using global sensitivity analysis
Inputting variable x by using sub-model as object and through global sensitivity analysis methodjThe dispersion is (0,1/(p-1),2/(p-1), …, 1); subsequently, a set of x is randomly generated, and the change Δ of the elements in x one by one is calculated, and a reduction target y is calculatedi(ii) a The jth input variable pair simplification target yiHas a single influence of
The average effect is obtained through k times of calculation
Sum variance
Calculating sub-models under different simplified objectivesOnly the C0-C4 submodel and the C16 submodel were found to be superior
(3) Assessment of the importance of reactive species in important macromolecular submodels using global sensitivity and pathway sensitivity analysis
Calculating PSCs of the reaction classes under different simplification targets by using global sensitivity analysis and path sensitivity analysis and using formulas (4) to (6) by taking the reaction class in the C16 submodel as an objectij、And σij，
yi,jThe importance of the simplified chemical reaction model to remove the ith reaction class to the predicted value reaction class of the jth simplified target is normalizedAndare jointly determined, i.e.
Therein, standardizedAndcalculated by equations (8) to (10)
Xi of each reaction class is obtained by the formula (7)jBased on xijAnd deleting the reaction classes one by one from small to large, and calculating and introducing errors until the predicted value of the simplified chemical reaction model for any simplified target reaches the prediction uncertainty boundary of the detailed chemical reaction model. A total of 10 reactive groups, namely reactive groups 1-3, 5, 10, 13, 14, 23-25, are retained.
(4) Construction of framework macromolecular submodel
Using linear lumped method, isomers in 10 reaction classes were lumped, and each class of isomers was lumped as a representative component. Reactions in the C5-C15 submodel were then lumped to obtain the framework macromolecule submodel.
(5) Simplified small molecule submodels
Calculation of PSC for each reaction in the C0-C4 submodels at different simplified targets using Global sensitivity analysis and Path sensitivity analysis equations (4) - (6)ij、And σijAnd calculated by the equations (8) to (10)Andxi of each reaction is obtained by the formula (7)j. Based on xijAnd deleting the reactions from small to large one by one, and calculating and introducing errors until the predicted value of the simplified chemical reaction model for any simplified target reaches the prediction uncertain boundary of the detailed chemical reaction model, thereby obtaining the initial simplified chemical reaction model.
(6) Reaction rate optimization
And (3) optimizing the rate constant of the reaction in the C16 submodel in the initial simplified chemical reaction model within an uncertain range by using a multi-objective genetic algorithm NSGA-II to obtain the final simplified chemical reaction model.
The detailed chemical reaction model of isohexadecane contained 1107 components and 4469 reactions, and the simplified chemical reaction model obtained using the present method contained only 56 components and 131 reactions. The number of reactions, the number of components and the introduced errors of each sub-model in the process of simplifying the chemical reaction model are shown in table 1. Then at T600-,And p is 10-80 atm to compare detailed chemical model and simplify chemical reactionModel pair stagnation and component concentrations of HMN, CO and CO2 in JSR, as shown in fig. 3 and 4, it can be seen that simplifying the chemical reaction model can well reproduce the predicted performance of the detailed chemical reaction model over the entire operating regime.
TABLE 1 variation of reaction number, component number and introduced error in different submodels during model simplification