Combustion Chamber Simulation Model Parameter Optimization Method, Device, Equipment and Medium

Abstract

The present disclosure relates to the technical field of three-dimensional numerical simulation of combustion chambers, and in particular, provides a method, apparatus, device, and medium for optimizing the parameters of a combustion chamber simulation model. The method includes: determining a sample model parameter set according to the initial parameter ranges of multiple model parameters of multiple combustion chamber simulation models; performing combustion chamber simulation calculations based on the sample model parameter set to obtain a simulation result data set of multiple combustion chamber performance parameters; constructing an objective quantity surrogate model of each combustion chamber performance parameter with respect to multiple model parameters based on the sample model parameter set and the simulation result data set; constructing a mathematical model of a multi-objective optimization problem based on the objective quantity surrogate models of multiple combustion chamber performance parameters with respect to multiple model parameters and constraint conditions, and using an optimization algorithm to perform parameter optimization to obtain an optimal parameter solution set of multiple model parameters; the present disclosure can improve the reliability of the determined optimal parameter solution set of multiple model parameters.

Description

Technical Field

[0001] The present disclosure relates to the technical field of three-dimensional numerical simulation of combustion chambers, and in particular, to a method, device, equipment, and medium for optimizing parameters of a combustion chamber simulation model. Background Art

[0002] As one of the three core components of an aeroengine, the combustion chamber is the core area where fuel-air mixing and chemical reactions occur, and the quality of its design directly affects the overall performance of the engine; among them, due to the complex physical and chemical process characteristics in the combustion chamber, it is necessary to specifically select physical models such as atomization, evaporation, turbulence, and combustion to carry out coupled simulation calculations of multiple models.

[0003] In the related art, before simulating the combustion chamber, it is necessary to optimize the model parameters of multiple selected physical models to determine appropriate values of the model parameters, thereby improving the accuracy of the combustion chamber simulation.

[0004] However, in the related art, in the process of optimizing the model parameters of multiple physical models, usually only the influence of a small number of model parameters of a single physical model on the combustion chamber simulation results is concerned, which cannot meet the actual needs of multi-physical model joint simulation, resulting in low accuracy of the determined combustion chamber simulation. Summary of the Invention

[0005] In view of the above problems, the present disclosure is proposed. The present disclosure provides a method, device, equipment, and medium for optimizing parameters of a combustion chamber simulation model, which can improve the reliability of the optimal parameter solution set of multiple determined model parameters to ensure the accuracy of the combustion chamber simulation results determined based on the optimal parameter solution set of multiple model parameters.

[0006] According to one aspect of the present disclosure, there is provided a method for optimizing parameters of a combustion chamber simulation model, including:

[0007] Determine a sample model parameter set according to the initial parameter ranges of multiple model parameters of multiple combustion chamber simulation models;

[0008] Based on the sample model parameter set, perform simulation calculations of the combustion chamber to obtain a simulation result data set of multiple combustion chamber performance parameters;

[0009] Based on the sample model parameter set and the simulation result data set, construct a target quantity surrogate model of each combustion chamber performance parameter with respect to the multiple model parameters;

[0010] Based on the target quantity surrogate models of multiple combustion chamber performance parameters with respect to the multiple model parameters, and constraint conditions, construct a mathematical model of a multi-objective optimization problem, and use an optimization algorithm to perform parameter optimization to obtain the optimal parameter solution set of the multiple model parameters.

[0011] According to another aspect of the present disclosure, there is provided an apparatus for optimizing parameters of a combustion chamber simulation model, including:

[0012] A determination module, configured to determine a set of sample model parameters according to the initial parameter ranges of multiple model parameters of multiple combustion chamber simulation models;

[0013] A simulation module, configured to perform simulation calculations of the combustion chamber based on the set of sample model parameters to obtain a data set of simulation results of multiple combustion chamber performance parameters;

[0014] A construction module, configured to construct a target quantity surrogate model of each combustion chamber performance parameter with respect to the multiple model parameters based on the set of sample model parameters and the data set of simulation results;

[0015] A determination module, configured to construct a mathematical model of a multi-objective optimization problem based on the target quantity surrogate models of multiple combustion chamber performance parameters with respect to the multiple model parameters and constraint conditions, and perform parameter optimization using an optimization algorithm to obtain an optimal parameter solution set of the multiple model parameters.

[0016] According to still another aspect of the present disclosure, there is provided an electronic device, including a memory, a processor, and a computer program stored on the memory, where the processor executes the computer program to implement the above-mentioned method for optimizing parameters of a combustion chamber simulation model.

[0017] According to yet another aspect of the present disclosure, there is provided a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the above-mentioned method for optimizing parameters of a combustion chamber simulation model is implemented.

[0018] The method, apparatus, device, and medium for optimizing parameters of a combustion chamber simulation model provided by the present disclosure are directed to a combustion chamber simulation scenario involving multiple combustion chamber simulation models and multiple combustion chamber performance parameters. A target quantity surrogate model of each combustion chamber performance parameter with respect to multiple model parameters can be constructed, and a mathematical model of a multi-objective optimization problem is constructed in combination with constraint conditions, and an optimization algorithm is used for parameter optimization to determine an optimal parameter solution set of multiple model parameters. Since the optimization process simultaneously optimizes multiple model parameters of multiple combustion chamber simulation models, the reliability of the determined optimal parameter solution set of multiple model parameters can be improved to ensure the accuracy of the combustion chamber simulation results determined based on the optimal parameter solution set of multiple model parameters.

[0019] It should be understood that both the foregoing general description and the following detailed description are exemplary and intended to provide further explanation of the claimed technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The above and other objects, features, and advantages of the present disclosure will become more apparent by describing the embodiments of the present disclosure in more detail with reference to the accompanying drawings. The accompanying drawings are used to provide a further understanding of the embodiments of the present disclosure, and constitute a part of the specification. Together with the embodiments of the present disclosure, they are used to explain the present disclosure, and do not constitute a limitation to the present disclosure. In the drawings, the same reference numerals generally represent the same components or steps.

[0021] Figure 1 It is a flowchart illustrating a method for optimizing parameters of a combustion chamber simulation model according to an embodiment of the present disclosure.

[0022] Figure 2 It is a flowchart further illustrating the determination of a target quantity surrogate model in the method for optimizing parameters of a combustion chamber simulation model according to an embodiment of the present disclosure.

[0023] Figure 3 It is a flowchart further illustrating the determination of an optimal parameter solution set for multiple model parameters in the method for optimizing parameters of a combustion chamber simulation model according to an embodiment of the present disclosure.

[0024] Figure 4 It is a flowchart illustrating another method for optimizing parameters of a combustion chamber simulation model according to an embodiment of the present disclosure.

[0025] Figure 5 It is a schematic diagram illustrating a combustion chamber simulation result according to an embodiment of the present disclosure.

[0026] Figure 6 It is a schematic diagram illustrating a combustion chamber simulation result according to the related art.

[0027] Figure 7 It is a block diagram illustrating an apparatus for optimizing parameters of a combustion chamber simulation model according to an embodiment of the present disclosure.

[0028] Figure 8 It is a schematic diagram illustrating a computer program product according to an embodiment of the present disclosure.

[0029] Figure 9 It is a hardware block diagram illustrating an electronic device according to an embodiment of the present disclosure. Detailed Embodiments

[0030] In order to make the objectives, technical solutions, and advantages of the present disclosure more apparent, exemplary embodiments according to the present disclosure will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments of the present disclosure. It should be understood that the present disclosure is not limited by the exemplary embodiments described herein.

[0031] To solve the above problems, embodiments of the present disclosure provide a method for optimizing parameters of a combustion chamber simulation model. This method for optimizing parameters of a combustion chamber simulation model can be applied to a terminal device, which can be an electronic device such as a computer, a notebook, or a server. For example, Figure 1 As shown, the method for optimizing parameters of a combustion chamber simulation model includes:

[0032] Step S101: Determine a sample model parameter set according to the initial parameter ranges of multiple model parameters of multiple combustion chamber simulation models;

[0033] Step S102: Perform combustion chamber simulation calculations based on the sample model parameter set to obtain a simulation result data set of multiple combustion chamber performance parameters;

[0034] Step S103: Based on the sample model parameter set and the simulation result data set, construct a target quantity surrogate model for each combustion chamber performance parameter with respect to the multiple model parameters;

[0035] Step S104: Based on the target quantity surrogate models of multiple combustion chamber performance parameters with respect to the multiple model parameters, and constraint conditions, construct a mathematical model of a multi-objective optimization problem, and use an optimization algorithm to perform parameter optimization to obtain an optimal parameter solution set of the multiple model parameters.

[0036] In summary, the method for optimizing parameters of a combustion chamber simulation model provided by the embodiments of the present disclosure can construct a target quantity surrogate model for each combustion chamber performance parameter with respect to multiple model parameters for a combustion chamber simulation scenario involving multiple combustion chamber simulation models and multiple combustion chamber performance parameters, and construct a mathematical model of a multi-objective optimization problem in combination with constraint conditions, and use an optimization algorithm to perform parameter optimization to determine an optimal parameter solution set of multiple model parameters. Since the optimization process simultaneously optimizes multiple model parameters of multiple combustion chamber simulation models, the reliability of the determined optimal parameter solution set of multiple model parameters can be improved to ensure the accuracy of the combustion chamber simulation results determined based on the optimal parameter solution set of multiple model parameters.

[0037] The following elaborates in detail on the specific implementation manners of each step in the Figure 1 shown embodiments:

[0038] In step S101, the terminal device determines a sample model parameter set according to the initial parameter ranges of multiple model parameters of multiple combustion chamber simulation models.

[0039] In the embodiments of the present disclosure, the combustion chamber simulation model is a physical model that can be used for combustion chamber simulation, and the model parameter is a parameter related to the combustion chamber simulation model; it can be understood that multiple combustion chamber simulation models, and the initial parameter ranges of multiple model parameters can be determined based on actual needs, and the embodiments of the present disclosure do not limit this.

[0040] Exemplarily, in the transient calculation scenario of the combustion chamber simulation, the selectable combustion chamber simulation models, the model parameters related to the combustion chamber simulation models, and the initial parameter ranges can be as shown in Table 1; and, in the steady-state calculation scenario of the combustion chamber simulation, the selectable combustion chamber simulation models, the model parameters related to the combustion chamber simulation models, and the initial parameter ranges can be as shown in Table 2.

[0041] Table 1

[0042]

[0043] Table 2

[0044]

[0045] In an alternative implementation, before the terminal device determines the sample model parameter set according to the initial parameter ranges of the multiple model parameters of the multiple combustion chamber simulation models, it can further: in response to obtaining a combustion chamber simulation instruction, determine the multiple combustion chamber simulation models selected by the combustion chamber simulation instruction, and obtain the initial parameter ranges of the multiple model parameters in the combustion chamber simulation model configuration information table; further, determine the sample model parameter set according to the initial parameter ranges of the multiple model parameters of the multiple combustion chamber simulation models.

[0046] In an alternative implementation, the process by which the terminal device determines the sample model parameter set according to the initial parameter ranges of the multiple model parameters of the multiple combustion chamber simulation models may include: performing normalization processing on the initial parameter ranges of the multiple model parameters of the multiple combustion chamber simulation models to obtain the normalized parameter ranges of the multiple model parameters of the multiple combustion chamber simulation models; further, repeatedly performing optimal Latin hypercube sampling in the normalized parameter ranges of the multiple model parameters of the multiple combustion chamber simulation models to obtain a candidate model parameter set, and based on the AE (Audze–Eglais) potential energy criterion, determining the system potential energy of the candidate model parameter set, and determining the candidate model parameter set with the minimum system potential energy among the candidate model parameter sets obtained by multiple samplings as the sample model parameter set. The dataset with the optimal sample quality can be selected as the sample model parameter set among the parameter ranges of the multiple model parameters based on the AE potential energy criterion, so that the combustion chamber performance parameter based on the sample model parameter set is more accurate with respect to the target quantity surrogate model of the multiple model parameters.

[0047] Among them, the process of determining the system potential energy of the candidate model parameter set based on the AE potential energy criterion can be implemented based on the first formula, where the first formula is:

[0048]

[0049] In Formula 1, U(x) is the system potential energy, and x i is the i-th sampling value related to a model parameter in the candidate model parameter set, and x i is the j-th sampling value related to the same model parameter in the candidate model parameter set. N is the total number of sampling values related to multiple model parameters. ||x i - x j || 2 is the Euclidean distance between the i-th sampling value and the j-th sampling value related to the same model parameter.

[0050] It should be noted that the sample model parameter set can include multiple groups of model parameter subsets. Among them, each group of model parameter subsets includes sampling values of multiple model parameters.

[0051] Exemplarily, the sample model parameter set can be expressed as where the number of model parameters is m, and the number of sampling values of each model parameter is p. Then the sample model parameter set can include p groups of model parameter subsets.

[0052] Step S102: The terminal device performs combustion chamber simulation calculations based on the sample model parameter set to obtain a simulation result data set of multiple combustion chamber performance parameters.

[0053] In the embodiments of the present disclosure, the combustion chamber simulation calculations can be implemented based on simulation software; the multiple combustion chamber performance parameters can include combustion chamber pressure loss, combustion efficiency, and outtemperature distribution factor (OTDF), etc. Specifically, it can be determined based on actual needs, and the embodiments of the present disclosure do not limit this.

[0054] In an alternative embodiment, the process by which the terminal device performs combustion chamber simulation calculations based on the sample model parameter set to obtain a simulation result data set of multiple combustion chamber performance parameters can include: performing combustion chamber simulation calculations based on each group of model parameter subsets to obtain simulation values of multiple combustion chamber performance parameters associated with each model parameter subset.

[0055] Among them, the simulation result data set of multiple combustion chamber performance parameters can be expressed as f j =[f j1 , f j2 , …, f jp T , where j = 1, 2, …, n, and n is the total number of multiple combustion chamber performance parameters.

[0056] In step S103, the terminal device constructs a target quantity surrogate model of each combustion chamber performance parameter with respect to the multiple model parameters based on the sample model parameter set and the simulation result data set. ​

[0057] In an alternative embodiment, as Figure 2 shown, the process by which the terminal device constructs a target quantity surrogate model of each combustion chamber performance parameter with respect to the plurality of model parameters based on the sample model parameter set and the simulation result data set may include:

[0058] Step S201, for each combustion chamber performance parameter, process the first simulation values of the combustion chamber performance parameter in the sample model parameter set and the simulation result data set based on the active subspace method, and determine the low-dimensional subspace direction associated with the combustion chamber performance parameter.

[0059] Among them, the process by which the terminal device processes the first simulation values of the combustion chamber performance parameter in the sample model parameter set and the simulation result data set based on the active subspace method to obtain a low-dimensional parameter sample set associated with the combustion chamber performance parameter may include: using an approximation method to construct the gradient of the combustion performance parameter with respect to the model parameter, and obtaining the covariance matrix of the gradient, where the covariance matrix is;

[0060]

[0061] In Equation 2, is the gradient of the combustion performance parameter with respect to the model parameter, and ρ(x) is the joint probability density function of the model parameter.

[0062] Further, solve the eigenvalues and eigenvector matrix of the moment covariance matrix based on the Jacobi iterative method, then C = WΛW T , where W is the eigenvector matrix and Λ is the diagonal matrix composed of eigenvalues. In the eigenvalue matrix, λ1, λ2,..., λ m are arranged in descending order, and m represents the data dimension; among them, if the r-th eigenvalue in the eigenvalue matrix is much larger than the (r + 1)-th eigenvalue, then the space spanned by the first r eigenvectors in the eigenvector matrix is determined as the low-dimensional subspace direction.

[0063] It can be understood that in the case where the r-th eigenvalue in the eigenvalue matrix is much larger than the (r + 1)-th eigenvalue, the eigenvalue matrix is eigenvector matrix where

[0064] It should be noted that in the embodiments of the present disclosure, the active variable associated with the combustion chamber performance parameter is w1 T x = w 1,1 x1 + w 1,2 x2 + … + w 1,i x i , where i = 1, 2,..., m.

[0065] The low-dimensional response surface model is as follows:

[0066]

[0067] In Equation 3, X is the active variable, and X = w1 T x, which is a combustion chamber performance parameter, f(X) is the basis function of the regression part, β is the regression coefficient vector, and z(X) is the error with a random distribution.

[0068] Step S202: Project multiple model parameters in the sample model parameter set in the direction of the low-dimensional subspace to obtain the active variables associated with the combustion chamber performance parameters.

[0069] In the embodiment of the present disclosure, the active variables are composed of the active direction vector components of the multiple model parameters. Among them, the linear model of the combustion chamber performance parameter with respect to the active variables is:

[0070] f(w1 T x) = a(w1 T x) + b; (Equation 6)

[0071] Step S203: Determine the candidate target quantity surrogate model of the combustion chamber performance parameter with respect to the multiple model parameters according to the active variables associated with the combustion chamber performance parameter and the low-dimensional response surface model.

[0072] In the embodiment of the present disclosure, the low-dimensional response surface model is the initial relationship model of the combustion chamber performance parameter with respect to the active variables determined based on the Kriging interpolation method. The low-dimensional response surface model includes a regression part and a random part.

[0073] Among them, the low-dimensional response surface model satisfies:

[0074] E(z i (x)) = 0; (Equation 4)

[0075]

[0076] Cov[z(x i ), z(x j )] = δ 2 R[x i , x j , θ](1 ≤ i, j ≤ n); (Equation 6)

[0077] In Equations 4 to 6, E is the expectation, Var is the variance, Cov is the covariance, R[x i , x j , θ] is the correlation function, and the correlation function can be a Gaussian function, z(xi ) is the error of the random distribution of multiple model parameters associated with the i-th combustion chamber performance parameter among multiple combustion chamber performance parameters, z(x j ) is the error of the random distribution of multiple model parameters associated with the j-th combustion chamber performance parameter among multiple combustion chamber performance parameters.

[0078] In an alternative embodiment, the process by which the terminal device determines the candidate target quantity surrogate model of the combustion chamber performance parameter with respect to the multiple model parameters according to the active variables associated with the combustion chamber performance parameter and the low-dimensional response surface model may include: when it is determined that the low-dimensional subspace associated with the combustion chamber performance parameter is a one-dimensional subspace and the combustion chamber performance parameter is linearly distributed in the active direction, updating the low-dimensional response surface model to a linear model of the combustion chamber performance parameter with respect to the active variables; further, substituting the active variables associated with the combustion chamber performance parameter into the linear model to obtain the candidate target quantity surrogate model of the combustion chamber performance parameter with respect to the multiple model parameters. When the low-dimensional subspace is a one-dimensional subspace and the combustion chamber performance parameter is linearly distributed in the active direction, the low-dimensional response surface model can be further simplified to reduce the complexity of the determined candidate target quantity surrogate model of the combustion chamber performance parameter with respect to the multiple model parameters, and further improve the optimization efficiency of the combustion chamber simulation model parameters.

[0079] It can be understood that when the terminal device substitutes the active variables associated with the combustion chamber performance parameter into the linear model, the obtained candidate target quantity surrogate model of the combustion chamber performance parameter with respect to the multiple model parameters is:

[0080] f(x) = a(w 1,1 x1 + w 1,2 x2 + … + w 1,i x i ) + b; (Formula 7)

[0081] It should be noted that in the embodiments of the present disclosure, when it is determined that the low-dimensional subspace associated with the combustion chamber performance parameter is not a one-dimensional subspace, or the combustion chamber performance parameter is non-linearly distributed in the active direction, the active variables associated with the combustion chamber performance parameter can be substituted into the low-dimensional response surface model to obtain the candidate target quantity surrogate model of the combustion chamber performance parameter with respect to the multiple model parameters.

[0082] Step S204, if it is determined that the candidate target quantity surrogate model meets the model accuracy requirements, then determine the candidate target quantity surrogate model as the target quantity surrogate model of the combustion chamber performance parameter with respect to the multiple model parameters.

[0083] In the embodiments of the present disclosure, the sample model parameter set and the simulation result data set can be processed based on the active subspace method to reduce the sample model parameter set, reduce the data processing volume in the modeling process of the target quantity surrogate model, and improve the efficiency of determining the optimal parameter solution set of multiple model parameters; at the same time, the low-dimensional parameter sample set is determined based on the active subspace method, which has the same reliability as the sample model parameter set before dimension reduction, and can ensure the reliability of the established target quantity surrogate model.

[0084] In an alternative embodiment, the process for the terminal device to determine whether the candidate target quantity surrogate model meets the model accuracy requirement may include: inputting each group of model parameter subsets in the sample model parameter set into the candidate target quantity surrogate model respectively to obtain the predicted values of the combustion chamber performance parameters associated with each group of model parameter subsets; and, in the simulation result data set, reading the simulation values of the combustion chamber performance parameters associated with each group of model parameter subsets; further, according to the predicted values and simulation values of the combustion chamber performance parameters respectively associated with multiple groups of model parameter subsets, determining the model accuracy index value of the candidate target quantity surrogate model; wherein, if the model accuracy index value is less than the model accuracy index value threshold, it is determined that the candidate target quantity surrogate model meets the model accuracy requirement. The accuracy of the candidate target quantity surrogate model of the combustion chamber performance parameters with respect to the multiple model parameters can be tested through the sample model parameter set, and when the accuracy requirement is met, the candidate target quantity surrogate model is determined as the target quantity surrogate model of the combustion chamber performance parameters with respect to the multiple model parameters, further ensuring the reliability of the constructed target quantity surrogate model.

[0085] If the model accuracy index value is greater than or equal to the model accuracy index value threshold, re-determine the sample model parameter set to obtain the updated sample model parameter set, and repeat the above steps S102 to S103 using the updated sample model parameter set until the target quantity surrogate models of each combustion chamber performance parameter with respect to the multiple model parameters are obtained.

[0086] Among them, the model accuracy index can be Mean Absolute Error (MAE), error ratio (MAE / mean value), Root Mean Square Error (RMSE), or correlation coefficient, etc.; the model accuracy index value threshold can be determined based on actual needs, and the embodiments of the present disclosure do not limit this.

[0087] In an alternative embodiment, for each combustion chamber performance parameter, after the terminal device obtains the active variables associated with the combustion chamber performance parameter, it may further: based on the sensitivity analysis strategy and the active direction vector components of each model parameter in the active variables associated with the combustion chamber performance parameter, determine the sensitivity analysis result of the combustion chamber performance parameter to each model parameter. After obtaining the active variables associated with the combustion chamber performance parameter, the sensitivity of the combustion chamber performance parameter to each model parameter can be analyzed, so that in the process of determining the optimal parameter solution set of multiple model parameters, the Pareto optimal parameter solution set initially obtained by optimizing the mathematical model of the multi-objective optimization problem can be secondarily screened according to the sensitivity analysis result, further improving the reliability of the obtained optimal parameter solution set of multiple model parameters.

[0088] Among them, the sensitivity analysis strategy may include that the magnitude of the active direction vector component w 1,i represents the sensitivity of the combustion chamber performance parameter to the i-th model parameter among multiple model parameters, and the positive or negative of the active direction vector component w 1,i can predict the optimization direction of the parameter. Among them, the larger the component value of the active direction vector component, the greater the influence of the i-th model parameter on the combustion chamber performance parameter. On the contrary, the smaller the component value of the active direction vector component, the smaller the influence of the i-th model parameter on the combustion chamber performance parameter; when the active direction vector component value is positive, it indicates that a positive perturbation of the input model parameter will cause an increase in the simulation value of the combustion chamber performance parameter. On the contrary, when the active direction vector component value is negative, it indicates that a positive perturbation of the input model parameter will cause a decrease in the simulation value of the combustion chamber performance parameter.

[0089] Then, the process by which the terminal device determines the sensitivity analysis results of the combustion chamber performance parameters with respect to each model parameter based on the sensitivity analysis strategy and the component of the active direction vector of each model parameter among the active variables associated with the combustion chamber performance parameters may include: for each model parameter, if the component value of the active direction vector component of the model parameter is greater than the component value threshold, it is determined that the influence degree of the model parameter on the combustion chamber performance parameter is high; or, if the component value of the active direction vector component of the model parameter is less than or equal to the component value threshold, it is determined that the influence degree of the model parameter on the combustion chamber performance parameter is low; and, if the component value of the active direction vector component of the model parameter is positive and the characteristic of the combustion chamber performance parameter is the higher the better, the adjustment direction of the model parameter is determined as: increase, or, if the component value of the active direction vector component of the model parameter is positive and the characteristic of the combustion chamber performance parameter is the smaller the better, the adjustment direction of the model parameter is determined as: decrease, or, if the component value of the active direction vector component of the model parameter is negative and the characteristic of the combustion chamber performance parameter is the higher the better, the adjustment direction of the model parameter is determined as: decrease, or, if the component value of the active direction vector component of the model parameter is negative and the characteristic of the combustion chamber performance parameter is the smaller the better, the adjustment direction of the model parameter is determined as: increase.

[0090] It can be understood that the sensitivity analysis results of the combustion chamber performance parameters with respect to each model parameter include: the influence degree information of each model parameter on the combustion chamber performance parameter, and the adjustment direction information of each model parameter, where the influence degree information may be: high or low, and the adjustment direction information may be: increase or decrease.

[0091] In step S104, the terminal device constructs a multi-objective optimization problem mathematical model based on the target quantity surrogate models of multiple combustion chamber performance parameters with respect to the multiple model parameters and the constraint conditions, and uses an optimization algorithm to perform parameter optimization to obtain the optimal parameter solution set of the multiple model parameters.

[0092] In the embodiment of the present disclosure, in order to improve the reliability of the optimal parameter solution set of the multiple model parameters determined, the multi-objective optimization problem mathematical model may not only include the target quantity surrogate models of multiple combustion chamber performance parameters with respect to the multiple model parameters, but may also include constraint conditions, where the constraint conditions include the initial parameter ranges of the multiple model parameters and the combustion chamber performance parameter thresholds of each combustion chamber performance parameter among the multiple combustion chamber performance parameters, so as to evaluate the parameter ranges of the multiple model parameters obtained after optimizing the multi-objective optimization problem mathematical model in combination with the evaluation index of the combustion chamber performance parameter (i.e., the combustion chamber performance parameter threshold).

[0093] In an alternative embodiment, as Figure 3As shown, the process by which the terminal device constructs a mathematical model of a multi-objective optimization problem based on the objective quantity surrogate models of multiple combustion chamber performance parameters with respect to the multiple model parameters and the constraint conditions, and uses an optimization algorithm to perform parameter search to obtain the optimal parameter solution set of the multiple model parameters may include:

[0094] Step S301, construct a mathematical model of a multi-objective optimization problem based on the objective quantity surrogate models of multiple combustion chamber performance parameters with respect to multiple model parameters and the constraint conditions.

[0095] Among them, the mathematical model of the multi-objective optimization problem is:

[0096]

[0097] In Formula 8, f j (x) is the relationship model of the jth combustion chamber performance parameter among the multiple combustion chamber performance parameters with respect to the multiple model parameters, x i (L) is the minimum value of the ith model parameter among the m model parameters, x i U is the maximum value of the ith model parameter among the m model parameters; f j (x) (L) is the minimum threshold of the jth combustion chamber performance parameter among the n combustion chamber performance parameters, and f j (x) (U) is the maximum threshold of the jth combustion chamber performance parameter among the n combustion chamber performance parameters.

[0098] Step S302, optimize the mathematical model of the multi-objective optimization problem based on the genetic algorithm to obtain the Pareto optimal parameter solution set of the multiple model parameters.

[0099] In the embodiments of the present disclosure, when the terminal device optimizes the mathematical model of the multi-objective optimization problem based on the genetic algorithm to obtain the Pareto optimal parameter solution set of the multiple model parameters, the selected genetic algorithm can be determined based on actual needs, and the embodiments of the present disclosure do not limit this.

[0100] In an alternative embodiment, the terminal device may determine a Pareto optimal parameter solution set of multiple model parameters based on the Non-dominated Sorting Genetic Algorithm III (NSGA-III). Wherein, the process of the terminal device optimizing the mathematical model of the multi-objective optimization problem based on the NSGA-III algorithm to obtain the Pareto optimal parameter solution set of multiple model parameters may include: performing non-dominated sorting on the individuals in the initial population composed of the initial parameter ranges of multiple objective parameters to obtain multiple non-dominated levels, and creating a population reference point; then, solving the minimum value of each dimension objective of the individuals in the current population to form the ideal point of the current population, and moving the origin of the solution space to the ideal point, and, solving the ASF equation to find the extreme points:

[0101]

[0102] In Equation 9, is the extreme point, is the unit direction vector of the coordinate axis.

[0103] Furthermore, construct a hyperplane containing the extreme points and the ideal point (origin) of each objective direction, obtain the intercepts of the hyperplane with the coordinate axes, and normalize the objective quantities; and, calculate the Euclidean distance between the reference point and the individuals, associate all individuals to the reference point, extract individuals from the non-dominated levels and add them to the next generation population, and repeat the above process until the convergence condition is reached.

[0104] Among them, the population reference point is created using the reference point generation method proposed by Deb and Jain. The reference point includes two layers of reference points, namely the boundary layer and the inner layer, which can ensure the wide distribution of the reference point and improve the calculation efficiency.

[0105] Step S303, according to the combustion chamber simulation accuracy condition and the sensitivity analysis results of each model parameter with respect to multiple combustion chamber performance parameters, perform a secondary screening on the Pareto optimal parameter solution set of the multiple model parameters to obtain the secondary optimal parameter solution set of the multiple model parameters.

[0106] In an alternative embodiment, the process by which the terminal device performs a secondary screening on the Pareto optimal parameter solution set of the multiple model parameters according to the combustion chamber simulation accuracy condition and the sensitivity analysis results of each model parameter with respect to multiple combustion chamber performance parameters may include: sampling from the Pareto optimal parameter solution set of the multiple model parameters to obtain a set of model parameters to be verified, and then, based on each subset of the model parameters to be verified in the set of model parameters to be verified, performing simulation calculations on the combustion chamber to obtain verified simulation values of multiple combustion chamber performance parameters associated with each subset of the model parameters to be verified; at the same time, in the Pareto optimal parameter solution set of the multiple model parameters, deleting the verified sampling values included in the target subset of the model parameters to be verified to obtain an initial secondary optimal parameter solution set of the multiple model parameters; further, based on the adjustment direction information in the sensitivity analysis results of each combustion chamber performance parameter with respect to each model parameter, increasing or decreasing the initial secondary optimal parameter solution of each model parameter in the initial secondary optimal parameter solution set of the multiple model parameters to obtain a secondary optimal parameter solution set of the multiple model parameters.

[0107] Among them, the set of model parameters to be verified includes verified sampling values of multiple model parameters. Among the verified simulation values of multiple combustion chamber performance parameters associated with the target subset of the model parameters to be verified, there is at least one verified simulation value of a combustion chamber performance parameter that does not meet the combustion chamber performance parameter threshold; among them, a verified simulation value of a combustion chamber performance parameter meeting the combustion chamber performance parameter threshold means that the verified simulation value of the combustion chamber performance parameter is greater than the combustion chamber performance parameter threshold, or the verified simulation value of the combustion chamber performance parameter is less than or equal to the combustion chamber performance parameter threshold. Among them, the combustion chamber performance parameter threshold and the degree of increasing or decreasing the initial secondary optimal parameter solution of each model parameter can both be determined based on actual needs, and the embodiments of the present disclosure do not limit this.

[0108] Step S304: Using the secondary optimal parameter solution set of the multiple model parameters to perform simulation calculations on the combustion chamber to obtain second simulation values of the multiple combustion chamber performance parameters.

[0109] In an alternative embodiment, the process by which the terminal device uses the secondary optimal parameter solution set of the multiple model parameters to perform simulation calculations on the combustion chamber to obtain second simulation values of the multiple combustion chamber performance parameters may include: sampling from the secondary optimal parameter solution set of the multiple model parameters to obtain a subset of model parameters to be simulated, and then, using the subset of model parameters to be simulated to perform simulation calculations on the combustion chamber to obtain second simulation values of the multiple combustion chamber performance parameters. Among them, the subset of model parameters to be simulated includes verified sampling values of multiple model parameters.

[0110] Step S305: If the second simulation values of multiple combustion chamber performance parameters respectively meet the thresholds of each combustion chamber performance parameter, then determine the quadratic optimization optimal parameter solution set of the multiple model parameters as the optimal parameter solution set of the multiple model parameters.

[0111] In an embodiment of the present disclosure, after optimizing the mathematical model of the multi-objective optimization problem to obtain the Pareto optimal parameter solution set of multiple model parameters, a secondary screening is performed on the Pareto optimal parameter solution set of the multiple model parameters to improve the reliability of the finally determined optimal parameter solution set of the multiple model parameters; on the other hand, after the secondary screening of the Pareto optimal parameter solution set of the multiple model parameters, continue to evaluate the Pareto optimal parameter solution set of the multiple model parameters after the secondary screening based on the accuracy requirements for the combustion chamber performance parameters during the simulation calculation of the combustion chamber, so as to finally determine the optimal parameter solution set of the multiple model parameters, which can ensure the accuracy of the simulation results during the simulation calculation of the combustion chamber based on the optimal parameter solution set of the multiple model parameters while improving the reliability of the determined optimal parameter solution set of the multiple model parameters.

[0112] In an alternative embodiment, the terminal device determines whether the second simulation values of multiple combustion chamber performance parameters respectively meet the thresholds of each combustion chamber performance parameter, including: for each combustion chamber performance parameter, the second simulation value of the combustion chamber performance parameter is greater than the combustion chamber performance parameter threshold, or the second simulation value of the combustion chamber performance parameter is less than or equal to the combustion chamber performance parameter threshold.

[0113] In an alternative embodiment, if, among the multiple combustion chamber performance parameters, the second simulation value of at least one combustion chamber performance parameter does not meet the corresponding combustion chamber performance parameter threshold, then add the subset of model parameters to be simulated to the sample model parameter set, and add the second simulation values of the multiple combustion chamber performance parameters to the simulation result dataset of the multiple combustion chamber performance parameters, and repeat the above steps S103 to S104 until the optimal parameter solution set of the multiple model parameters is obtained.

[0114] In an alternative embodiment, the process by which the terminal device uses an optimization algorithm to perform parameter optimization to obtain the optimal parameter solution set of the multiple model parameters may include: optimizing the mathematical model of the multi-objective optimization problem based on a genetic algorithm to obtain the Pareto optimal parameter solution set of the multiple model parameters; and performing a secondary screening on the Pareto optimal parameter solution set of the multiple model parameters according to the combustion chamber simulation accuracy condition and the sensitivity analysis results of each model parameter with respect to the multiple combustion chamber performance parameters, so as to obtain the optimal parameter solution set of the multiple model parameters; this can reduce the data processing flow in the process of determining the optimal parameter solution set of the multiple model parameters and improve the efficiency of determining the optimal parameter solution set of the multiple model parameters.

[0115] In an alternative embodiment, the process by which the terminal device uses an optimization algorithm to optimize parameters and obtain the optimal parameter solution set of the multiple model parameters may include: optimizing the mathematical model of the multi-objective optimization problem based on a genetic algorithm to obtain the Pareto optimal parameter solution set of the multiple model parameters; and performing a secondary screening on the Pareto optimal parameter solution set of the multiple model parameters according to the sensitivity analysis results of each model parameter with respect to multiple combustion chamber performance parameters, to obtain the optimal parameter solution set of the multiple model parameters; which can further reduce the processing flow in the process of determining the optimal parameter solution set of the multiple model parameters and further improve the efficiency of determining the optimal parameter solution set of the multiple model parameters.

[0116] For example, Figure 4 as shown Figure 4 shows a flowchart of a method for optimizing combustion chamber simulation model parameters provided by an embodiment of the present disclosure, including:

[0117] Step S401, determine a sample model parameter set according to the initial parameter ranges of the multiple model parameters of multiple combustion chamber simulation models.

[0118] Step S402, perform combustion chamber simulation calculations based on the sample model parameter set to obtain a simulation result data set of multiple combustion chamber performance parameters.

[0119] Step S403, for each combustion chamber performance parameter, based on the active subspace method, process the first simulation values of the combustion chamber performance parameter in the sample model parameter set and the simulation result data set, and determine the low-dimensional subspace direction associated with the combustion chamber performance parameter.

[0120] Step S404, project the multiple model parameters in the sample model parameter set in the low-dimensional subspace direction to obtain the active variables associated with the combustion chamber performance parameter.

[0121] Step S405, according to the active variables associated with the combustion chamber performance parameter and the low-dimensional response surface model, determine a candidate objective quantity surrogate model of the combustion chamber performance parameter with respect to the multiple model parameters.

[0122] Step S406, determine whether the candidate objective quantity surrogate model meets the model accuracy requirement.

[0123] Step S407, if it is determined that the candidate objective quantity surrogate model does not meet the model accuracy requirement, re-determine the sample model parameter set to obtain an updated sample model parameter set, and repeat the above process starting from step S402 using the updated sample model parameter set until the candidate objective quantity surrogate model meets the model accuracy requirement.

[0124] Step S408, if it is determined that the candidate target quantity surrogate model meets the model accuracy requirement, then determine the candidate target quantity surrogate model as the target quantity surrogate model of the combustion chamber performance parameters with respect to the multiple model parameters.

[0125] Step S409, based on the target quantity surrogate models of the multiple combustion chamber performance parameters with respect to the multiple model parameters respectively, and the constraint conditions, construct a mathematical model of the multi-objective optimization problem.

[0126] Step S410, optimize the mathematical model of the multi-objective optimization problem based on the genetic algorithm to obtain the Pareto optimal parameter solution set of the multiple model parameters.

[0127] Step S411, according to the combustion chamber simulation accuracy condition, and the sensitivity analysis results of each model parameter with respect to the multiple combustion chamber performance parameters respectively, perform a secondary screening on the Pareto optimal parameter solution set of the multiple model parameters to obtain the secondary optimized optimal parameter solution set of the multiple model parameters.

[0128] Step S412, use the secondary optimized optimal parameter solution set of the multiple model parameters to perform simulation calculations on the combustion chamber to obtain the second simulation values of the multiple combustion chamber performance parameters.

[0129] Step S413, determine whether the second simulation values of the multiple combustion chamber performance parameters respectively meet the thresholds of each combustion chamber performance parameter.

[0130] Step S414, if among the multiple combustion chamber performance parameters, the second simulation value of at least one combustion chamber performance parameter does not meet the corresponding combustion chamber performance parameter threshold, then add the subset of the model parameters to be simulated to the sample model parameter set, and add the second simulation values of the multiple combustion chamber performance parameters to the simulation result data set of the multiple combustion chamber performance parameters, and repeat the above steps from Step S403 until the optimal parameter solution set of the multiple model parameters is obtained.

[0131] Step S415, if the second simulation values of the multiple combustion chamber performance parameters respectively meet the thresholds of each combustion chamber performance parameter, then determine the secondary optimized optimal parameter solution set of the multiple model parameters as the optimal parameter solution set of the multiple model parameters.

[0132] As Figure 5 shown, Figure 5Shows the optimal parameter solution sets of multiple model parameters of multiple combustion chamber simulation models (atomization model, evaporation model, turbulence model, and combustion model) determined by using the combustion chamber simulation model parameter optimization method provided by the embodiments of the present disclosure. During the combustion chamber simulation calculation process, the distribution schematic diagram of the combustion chamber outlet temperature, compared with the distribution schematic diagram of the combustion chamber outlet temperature during the simulation calculation process of the combustion chamber by using the combustion chamber simulation model parameter optimization method provided by the related technology, respectively determining the optimal parameter solution sets of multiple model parameters of multiple combustion chamber simulation models (atomization model, evaporation model, turbulence model, and combustion model) (as Figure 6 shown), the distribution accuracy of the combustion chamber outlet temperature has been improved by 5% to 10%.

[0133] An exemplary embodiment of the present disclosure provides a combustion chamber simulation model parameter optimization device, and the combustion chamber simulation model parameter optimization device can be a chip of a terminal device. Figure 7 Shows a schematic block diagram of the functional modules of the combustion chamber simulation model parameter optimization device according to an exemplary embodiment of the present disclosure. As Figure 7 shown, the combustion chamber simulation model parameter optimization device 700 includes:

[0134] A first determination module 701, configured to determine a sample model parameter set according to the initial parameter ranges of multiple model parameters of multiple combustion chamber simulation models;

[0135] A simulation module 702, configured to perform combustion chamber simulation calculation based on the sample model parameter set to obtain a simulation result data set of multiple combustion chamber performance parameters;

[0136] A construction module 703, configured to construct a target quantity surrogate model of each combustion chamber performance parameter with respect to the multiple model parameters based on the sample model parameter set and the simulation result data set;

[0137] A second determination module 704, configured to construct a multi-objective optimization problem mathematical model based on the target quantity surrogate models of multiple combustion chamber performance parameters with respect to the multiple model parameters and constraint conditions, and perform parameter optimization using an optimization algorithm to obtain the optimal parameter solution sets of the multiple model parameters.

[0138] Optionally, the first determination module 701 is configured to:

[0139] Perform normalization processing on the initial parameter ranges of multiple model parameters of multiple combustion chamber simulation models to obtain the normalized parameter ranges of multiple model parameters of multiple combustion chamber simulation models;

[0140] The process of repeatedly performing optimal Latin hypercube sampling within the normalized parameter ranges of multiple model parameters of multiple combustion chamber simulation models to obtain a set of candidate model parameters, and determining the system potential energy of the set of candidate model parameters based on the AE potential energy criterion, determines the set of candidate model parameters with the minimum system potential energy among the sets of candidate model parameters obtained from multiple samplings as the set of sample model parameters.

[0141] Optionally, the construction module 703 is configured to:

[0142] For each combustion chamber performance parameter, based on the active subspace method, process the first simulation values of the combustion chamber performance parameters in the set of sample model parameters and the simulation result dataset to determine the low-dimensional subspace direction associated with the combustion chamber performance parameter;

[0143] Project the multiple model parameters in the set of sample model parameters onto the low-dimensional subspace direction to obtain the active variables associated with the combustion chamber performance parameter, where the active variables are composed of the active direction vector components of the multiple model parameters;

[0144] According to the active variables associated with the combustion chamber performance parameter and the low-dimensional response surface model, determine the candidate target quantity surrogate model of the combustion chamber performance parameter with respect to the multiple model parameters, where the low-dimensional response surface model is the initial relationship model of the combustion chamber performance parameter with respect to the active variables determined based on the Kriging interpolation method;

[0145] If it is determined that the candidate target quantity surrogate model meets the model accuracy requirements, then determine the candidate target quantity surrogate model as the target quantity surrogate model of the combustion chamber performance parameter with respect to the multiple model parameters.

[0146] Optionally, the construction module 703 is configured to:

[0147] In the case where it is determined that the low-dimensional subspace associated with the combustion chamber performance parameter is a one-dimensional subspace and the combustion chamber performance parameter is linearly distributed in the active direction, update the low-dimensional response surface model to a linear model of the combustion chamber performance parameter with respect to the active variables;

[0148] Substitute the active variables associated with the combustion chamber performance parameter into the linear model to obtain the target quantity surrogate model of the combustion chamber performance parameter with respect to the multiple model parameters.

[0149] Optionally, the construction module 703 is further configured to:

[0150] Input each subset of model parameters in the sample model parameter set into the target quantity surrogate model respectively to obtain the predicted values of the combustion chamber performance parameters associated with each subset of model parameters, where each subset of model parameters includes sampling values of multiple model parameters;

[0151] In the simulation result dataset, read the simulation values of the combustion chamber performance parameters associated with each subset of model parameters;

[0152] Determine the model accuracy index value of the candidate target quantity surrogate model according to the predicted values and simulation values of the combustion chamber performance parameters respectively associated with multiple subsets of model parameters;

[0153] If the model accuracy index value is less than the model accuracy index value threshold, determine that the candidate target quantity surrogate model meets the model accuracy requirements.

[0154] Optionally, the construction module 703 is configured to:

[0155] Based on the sensitivity analysis strategy and the active direction vector components of each model parameter among the active variables associated with the combustion chamber performance parameters, determine the sensitivity analysis results of the combustion chamber performance parameters to each model parameter.

[0156] Optionally, the second determination module 704 is configured to:

[0157] Based on the target quantity surrogate models of multiple combustion chamber performance parameters with respect to multiple model parameters and the constraint conditions, construct a multi-objective optimization problem mathematical model;

[0158] Optimize the multi-objective optimization problem mathematical model based on the genetic algorithm to obtain the Pareto optimal parameter solution set of multiple model parameters;

[0159] According to the combustion chamber simulation accuracy condition and the sensitivity analysis results of multiple combustion chamber performance parameters to each model parameter, perform secondary screening on the Pareto optimal parameter solution set of multiple model parameters to obtain the secondary optimized optimal parameter solution set of multiple model parameters;

[0160] Use the secondary optimized optimal parameter solution set of multiple model parameters to perform simulation calculations on the combustion chamber to obtain the second simulation values of multiple combustion chamber performance parameters;

[0161] If the second simulation values of multiple combustion chamber performance parameters respectively meet the threshold of each combustion chamber performance parameter, determine the secondary optimized optimal parameter solution set of multiple model parameters as the optimal parameter solution set of multiple model parameters.

[0162] An exemplary embodiment of the present disclosure also provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor, and when executed by the at least one processor, the computer program is used to cause the electronic device to execute the method according to the embodiments of the present disclosure.

[0163] An exemplary embodiment of the present disclosure also provides a non-transitory computer-readable storage medium storing a computer program, wherein when executed by a processor of a computer, the computer program is used to cause the computer to execute the method according to the embodiments of the present disclosure.

[0164] As Figure 8 shown, an exemplary embodiment of the present disclosure also provides a computer program product 800, including a computer program 801, wherein when executed by a processor of a computer, the computer program is used to cause the computer to execute the method according to the embodiments of the present disclosure.

[0165] Refer to Figure 9 , the structural block diagram of an electronic device 900 that can be used as a terminal device of the present disclosure will now be described. It is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device is intended to represent various forms of digital electronic computer devices, such as, a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, a personal digital processor, a cellular phone, a smart phone, a wearable device, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0166] As Figure 9 shown, the electronic device 900 includes a computing unit 901, which can execute various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 902 or a computer program loaded from a storage unit 908 into a random access memory (RAM) 903. In the RAM 903, various programs and data required for the operation of the electronic device 900 can also be stored. The computing unit 901, the ROM 902, and the RAM 903 are connected to each other through a bus 904. An input / output (I / O) interface 905 is also connected to the bus 904.

[0167] Multiple components in the electronic device 900 are connected to the I / O interface 905, including: an input unit 906, an output unit 907, a storage unit 908, and a communication unit 909. The input unit 906 can be any type of device capable of inputting information into the electronic device 900. The input unit 906 can receive input digital or character information and generate key signal inputs related to the user settings and / or function controls of the electronic device. The output unit 907 can be any type of device capable of presenting information and can include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 908 can include, but is not limited to, a magnetic disk, an optical disk. The communication unit 909 allows the electronic device 900 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks and can include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a BluetoothTM device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0168] The computing unit 901 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 901 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 901 executes the various methods and processes described above. For example, in some embodiments, the methods of the exemplary embodiments of the present disclosure can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 908. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 900 via the ROM 902 and / or the communication unit 909. In some embodiments, the computing unit 901 can be configured to execute the methods of the exemplary embodiments of the present disclosure by any other suitable means (e.g., by means of firmware).

[0169] The program code for implementing the methods of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the program codes are executed by the processor or controller, the functions / operations specified in the flowchart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, executed partially on the machine and partially on a remote machine as an independent software package, or executed entirely on a remote machine or server.

[0170] In the context of this disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0171] As used in this disclosure, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and / or device that can be used to provide machine instructions and / or data to a programmable processor (e.g., a magnetic disk, an optical disk, a memory, a programmable logic device (PLD)), including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal that can be used to provide machine instructions and / or data to a programmable processor.

[0172] To provide for interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can also be used to provide for interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic, speech, or tactile input).

[0173] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected to each other by digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), and the Internet.

[0174] A computer system can include a client and a server. The client and the server are generally far from each other and typically interact through a communication network. The client-server relationship is created by computer programs that run on the respective computers and have a client-server relationship with each other.

[0175] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present disclosure are executed in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, a terminal, a user device, or other programmable devices. The computer program or instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer program or instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired or wireless manner. The computer-readable storage medium can be any available medium that the computer can access, or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium, such as a floppy disk, a hard disk, or a magnetic tape; it can also be an optical medium, such as a digital video disc (DVD); it can also be a semiconductor medium, such as a solid state drive (SSD).

[0176] Although the present disclosure has been described in connection with specific features and their embodiments, it will be apparent that various modifications and combinations can be made without departing from the spirit and scope of the present disclosure. Accordingly, the present specification and the drawings are merely exemplary illustrations of the present disclosure defined by the appended claims, and are considered to cover any and all modifications, variations, combinations or equivalents within the scope of the present disclosure. Obviously, those skilled in the art can make various changes and modifications to the present disclosure without departing from the spirit and scope of the present disclosure. Thus, if these modifications and variations of the present disclosure fall within the scope of the claims of the present disclosure and their equivalent technologies, the present disclosure is also intended to include these changes and modifications.

Claims

1. A method for optimizing parameters of a combustion chamber simulation model, characterized in that, Including: Determine a sample model parameter set according to the initial parameter ranges of multiple model parameters of multiple combustion chamber simulation models; Based on the sample model parameter set, perform combustion chamber simulation calculations to obtain a simulation result data set of multiple combustion chamber performance parameters; Based on the sample model parameter set and the simulation result data set, construct a target quantity surrogate model for each combustion chamber performance parameter with respect to the multiple model parameters; Based on the target quantity surrogate models of multiple combustion chamber performance parameters with respect to the multiple model parameters respectively, and constraint conditions, construct a mathematical model of a multi-objective optimization problem, and use an optimization algorithm to perform parameter optimization to obtain the optimal parameter solution set of the multiple model parameters; Among them, the constructing a mathematical model of a multi-objective optimization problem based on the target quantity surrogate models of multiple combustion chamber performance parameters with respect to the multiple model parameters respectively, and constraint conditions, and using an optimization algorithm to perform parameter optimization to obtain the optimal parameter solution set of the multiple model parameters includes: Based on the target quantity surrogate models of multiple combustion chamber performance parameters with respect to multiple model parameters respectively, and constraint conditions, construct a mathematical model of a multi-objective optimization problem; Based on the genetic algorithm, optimize the mathematical model of the multi-objective optimization problem to obtain the Pareto optimal parameter solution set of the multiple model parameters; According to the combustion chamber simulation accuracy condition, and the sensitivity analysis results of multiple combustion chamber performance parameters with respect to each model parameter respectively, perform secondary screening on the Pareto optimal parameter solution set of the multiple model parameters to obtain the secondary optimized optimal parameter solution set of the multiple model parameters; Use the secondary optimized optimal parameter solution set of the multiple model parameters to perform combustion chamber simulation calculations to obtain the second simulation values of the multiple combustion chamber performance parameters; If the second simulation values of the multiple combustion chamber performance parameters respectively satisfy the thresholds of each combustion chamber performance parameter, then determine the secondary optimized optimal parameter solution set of the multiple model parameters as the optimal parameter solution set of the multiple model parameters.

2. The method for optimizing the parameters of the combustion chamber simulation model according to claim 1, wherein The determining a sample model parameter set according to the initial parameter ranges of multiple model parameters of multiple combustion chamber simulation models includes: Perform normalization processing on the initial parameter ranges of multiple model parameters of the multiple combustion chamber simulation models to obtain the normalized parameter ranges of multiple model parameters of the multiple combustion chamber simulation models; Repeat the process of performing optimal Latin hypercube sampling in the normalized parameter ranges of multiple model parameters of multiple combustion chamber simulation models to obtain a candidate model parameter set, and determining the system potential energy of the candidate model parameter set based on the AE potential energy criterion, and determine the candidate model parameter set with the minimum system potential energy among the candidate model parameter sets obtained by multiple samplings as the sample model parameter set.

3. The method for optimizing the parameters of the combustion chamber simulation model according to claim 1, wherein, The constructing a target quantity surrogate model for each combustion chamber performance parameter with respect to the multiple model parameters based on the sample model parameter set and the simulation result data set includes: For each combustion chamber performance parameter, based on the active subspace method, process the first simulation value of the combustion chamber performance parameter in the sample model parameter set and the simulation result data set to determine the low-dimensional subspace direction associated with the combustion chamber performance parameter; Project multiple model parameters in the sample model parameter set in the direction of the low-dimensional subspace to obtain active variables associated with the combustion chamber performance parameters, where the active variables are composed of the active direction vector components of the multiple model parameters; Based on the active variables associated with the combustion chamber performance parameters and the low-dimensional response surface model, determine a candidate target quantity surrogate model of the combustion chamber performance parameters with respect to the multiple model parameters, where the low-dimensional response surface model is an initial relationship model of the combustion chamber performance parameters with respect to the active variables determined based on the Kriging interpolation method; If it is determined that the candidate target quantity surrogate model meets the model accuracy requirements, then determine the candidate target quantity surrogate model as the target quantity surrogate model of the combustion chamber performance parameters with respect to the multiple model parameters.

4. The method for optimizing the combustion chamber simulation model parameters according to claim 3, characterized in that, The step of determining a candidate target quantity surrogate model of the combustion chamber performance parameters with respect to the multiple model parameters based on the active variables associated with the combustion chamber performance parameters and the low-dimensional response surface model includes: When it is determined that the low-dimensional subspace associated with the combustion chamber performance parameters is a one-dimensional subspace and the combustion chamber performance parameters are linearly distributed in the active direction, update the low-dimensional response surface model to a linear model of the combustion chamber performance parameters with respect to the active variables; Substitute the active variables associated with the combustion chamber performance parameters into the linear model to obtain a candidate target quantity surrogate model of the combustion chamber performance parameters with respect to the multiple model parameters.

5. The method for optimizing the parameters of the combustion chamber simulation model according to claim 3, characterized in that Determining whether the candidate target quantity surrogate model meets the model accuracy requirements includes: Input each subset of model parameters in the sample model parameter set into the candidate target quantity surrogate model respectively to obtain predicted values of the combustion chamber performance parameters associated with each subset of model parameters, where each subset of model parameters includes sampling values of multiple model parameters; In the simulation result data set, read the simulation values of the combustion chamber performance parameters associated with each subset of model parameters; Based on the predicted values and simulation values of the combustion chamber performance parameters respectively associated with multiple subsets of model parameters, determine the model accuracy index value of the candidate target quantity surrogate model; If the model accuracy index value is less than the model accuracy index value threshold, then determine that the candidate target quantity surrogate model meets the model accuracy requirements.

6. The method for optimizing the parameters of the combustion chamber simulation model according to claim 3, characterized in that The method further includes: Based on the sensitivity analysis strategy and the active direction vector components of each model parameter in the active variables associated with the combustion chamber performance parameters, determine the sensitivity analysis result of the combustion chamber performance parameters to each model parameter.

7. An apparatus for optimizing parameters of a combustion chamber simulation model, characterized in that including: A first determination module configured to determine a sample model parameter set according to the initial parameter ranges of multiple model parameters of multiple combustion chamber simulation models; A simulation module configured to perform combustion chamber simulation calculations based on the sample model parameter set to obtain a simulation result data set of multiple combustion chamber performance parameters; A construction module configured to construct a target quantity surrogate model of each combustion chamber performance parameter with respect to the multiple model parameters based on the sample model parameter set and the simulation result data set; A second determination module, configured to build a mathematical model of a multi-objective optimization problem based on a surrogate model of the target quantity of multiple combustion chamber performance parameters with respect to multiple model parameters respectively, and a constraint condition; Optimizing the mathematical model of the multi-objective optimization problem based on a genetic algorithm to obtain a Pareto optimal parameter solution set of multiple model parameters; According to the combustion chamber simulation accuracy condition and the sensitivity analysis results of multiple combustion chamber performance parameters with respect to each model parameter respectively, performing secondary screening on the Pareto optimal parameter solution set of the multiple model parameters to obtain a secondary optimized optimal parameter solution set of the multiple model parameters; Performing simulation calculation of the combustion chamber by using the secondary optimized optimal parameter solution set of the multiple model parameters to obtain second simulation values of the multiple combustion chamber performance parameters; If the second simulation values of the multiple combustion chamber performance parameters respectively satisfy each combustion chamber performance parameter threshold, determining the secondary optimized optimal parameter solution set of the multiple model parameters as the optimal parameter solution set of the multiple model parameters.

8. An electronic device, comprising a memory, a processor, and a computer program stored on the memory, characterized in that The processor executes the computer program to implement the combustion chamber simulation model parameter optimization method according to claim 1.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the combustion chamber simulation model parameter optimization method according to claim 1.

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