Method for Determining Radial Double-V Groove Flow Vortex Flame Stability of Jet Propulsion Device

Abstract

The present application relates to a method for determining the flame stability of a radial double-V groove flow vortex in a jet propulsion device. The method includes: obtaining the flow field data of the afterburner and the nozzle; performing dimensionality reduction processing on the flow field data to determine the low-order modal characteristic data; determining the target characteristic data from the low-order modal characteristics according to a preset optimization method, and the preset optimization method is implemented based on an adaptive multi-response surface model and a genetic algorithm; inputting the target characteristic data into a flame stability prediction model, and determining the flame stability according to the output of the flame stability prediction model. Using this method can accurately determine the flame stability.

Description

Technical Field

[0001] The present application relates to the technical field of jet propulsion devices, and in particular to a method for determining the stability of a radial double V-groove flow vortex flame of a jet propulsion device. Background Art

[0002] As a key component of military aviation engines, the afterburner can significantly increase thrust in a short period of time, supporting fighters to achieve short-distance takeoff, air maneuvers and supersonic cruise. The radial double V-groove flame stabilizer is an important component of the afterburner, and its flame stability is affected by many factors, including oil-gas ratio, electrode position, parasitic capacitance and installation size. In addition, factors such as violent fluctuations in aerodynamic heat caused by sudden changes in engine operating conditions, structural deformation and electrical system abnormalities will also affect its stability.

[0003] Therefore, when determining the flame stability of the radial double V-groove, a method that can accurately determine the flame stability is urgently needed. Summary of the invention

[0004] Based on this, it is necessary to provide a method for determining the radial double V-groove flow vortex flame stability of a jet propulsion device that can accurately determine the flame stability in response to the above technical problems.

[0005] In a first aspect, the present application provides a method for determining the stability of a radial double V-groove flow vortex flame of a jet propulsion device, comprising:

[0006] Obtain flow field data of afterburner and nozzle;

[0007] Perform dimension reduction processing on flow field data to determine low-order modal characteristic data;

[0008] Determining target feature data from low-order modal features according to a preset optimization method, wherein the preset optimization method is implemented according to an adaptive multi-response surface model and a genetic algorithm;

[0009] The target characteristic data is input into the flame stability prediction model, and the flame stability is determined according to the output of the flame stability prediction model.

[0010] In one embodiment, acquiring flow field data of an afterburner and a nozzle includes:

[0011] Acquire the geometric model of the afterburner and the nozzle; determine the flow field data of the afterburner and the nozzle according to the preset aerodynamic thermal coupling calculation method and the geometric model; wherein the preset aerodynamic thermal coupling calculation method is an aerodynamic thermal coupling calculation method corrected according to the experimental method, and the experimental method includes particle image velocimetry and thermocouple temperature measurement.

[0012] In one embodiment, the low-order mode feature data includes low-order mode basis coefficients and low-order mode basis vectors. Determining target feature data from the low-order mode features according to a preset optimization method includes:

[0013] Determining a predicted value corresponding to the low-order mode basis coefficient according to the low-order mode basis coefficient and the preset optimization method, where the predicted value corresponding to the low-order mode basis coefficient is a value within a preset numerical range; determining the target feature data according to the product of the predicted value of the low-order mode basis coefficient and the low-order mode basis vector.

[0014] In one embodiment, the genetic algorithm is the Non-dominated Sorting Genetic Algorithm II. Determining a predicted value corresponding to the low-order mode basis coefficient according to the low-order mode basis coefficient and the preset optimization method includes:

[0015] Determining the mapping relationship between the low-order mode basis coefficient and the optimization objective according to the adaptive multi-response surface model, where the optimization objective includes flow field data; taking the low-order mode basis coefficient as the input variable, and determining the predicted value corresponding to the low-order mode basis coefficient according to the Non-dominated Sorting Genetic Algorithm II and the mapping relationship.

[0016] In one embodiment, determining the mapping relationship between the low-order mode basis coefficient and the optimization objective according to the adaptive multi-response surface model includes:

[0017] Sampling the low-order mode basis coefficient according to a preset sampling method to determine sample points, where the sampling method includes the Latin hypercube sampling method, the central composite sampling method, or the Box-Behnken sampling method; updating the sample points according to the distribution of the sample points and the adaptive grid refinement technique; determining the mapping relationship between the updated sample points and the corresponding optimization objective according to the adaptive multi-response surface model; determining the accuracy of the mapping relationship, and when the accuracy does not meet the preset requirements, continuing to sample the low-order mode basis coefficient according to the distribution of the sample points and the adaptive grid refinement technique until the accuracy of the mapping relationship between the low-order mode basis coefficient and the corresponding optimization objective meets the preset requirements.

[0018] In one embodiment, performing dimensionality reduction processing on the flow field data to determine low-order mode feature data includes:

[0019] Performing dimensionality reduction processing on the flow field data according to proper orthogonal decomposition to determine low-order mode feature data.

[0020] In one embodiment, the target feature data includes the flow vortex feature information in the radial double V-groove region. Inputting the target feature data into the flame stability prediction model, and determining the flame stability according to the output of the flame stability prediction model includes:

[0021] Obtain the flow - direction vortex feature information in the target feature data; input the flow - direction vortex feature information into the flame stability prediction model, and determine the flame stability according to the output of the flame stability prediction model.

[0022] In a second aspect, the present application also provides a device for determining the flame stability of a radial double - V - groove flow - direction vortex in a jet propulsion device, including:

[0023] An acquisition module, configured to acquire the flow - field data of the afterburner and the nozzle;

[0024] A dimensionality - reduction module, configured to perform dimensionality - reduction processing on the flow - field data to determine the low - order modal feature data;

[0025] A first determination module, configured to determine the target feature data from the low - order modal features according to a preset optimization method, and the preset optimization method is implemented based on an adaptive multi - response surface model and the non - dominated sorting genetic algorithm II;

[0026] A second determination module, configured to input the target feature data into the flame stability prediction model, and determine the flame stability according to the output of the flame stability prediction model.

[0027] In one embodiment, the acquisition module is specifically configured to acquire the geometric models of the afterburner and the nozzle; determine the flow - field data of the afterburner and the nozzle according to a preset aerodynamic - thermal coupling calculation method and the geometric models; wherein, the preset aerodynamic - thermal coupling calculation method is an aerodynamic - thermal coupling calculation method corrected according to an experimental method, and the experimental method includes particle image velocimetry and thermocouple temperature measurement.

[0028] In one embodiment, the low - order modal feature data includes low - order modal basis coefficients and low - order modal basis vectors. The first determination module is specifically configured to determine the predicted value corresponding to the low - order modal basis coefficient according to the low - order modal basis coefficient and the preset optimization method, and the predicted value corresponding to the low - order modal basis coefficient is a value within a preset numerical range; determine the target feature data according to the product of the predicted value of the low - order modal basis coefficient and the low - order modal basis vector.

[0029] In one embodiment, the genetic algorithm is the non - dominated sorting genetic algorithm II. The first determination module is specifically configured to determine the mapping relationship between the low - order modal basis coefficient and the optimization objective according to the adaptive multi - response surface model, and the optimization objective includes the flow - field data; use the low - order modal basis coefficient as the input variable, and determine the predicted value corresponding to the low - order modal basis coefficient according to the non - dominated sorting genetic algorithm II and the mapping relationship.

[0030] In one embodiment, the first determination module is specifically configured to sample the low-order modal basis coefficients according to a preset sampling method to determine sample points, where the sampling method includes the Latin hypercube sampling method, the central composite sampling method, or the Box-Behnken sampling method; update the sample points according to the distribution of the sample points and the adaptive grid refinement technique; determine the mapping relationship between the updated sample points and the corresponding optimization objectives according to the adaptive multi-response surface model; determine the accuracy of the mapping relationship, and when the accuracy does not meet the preset requirements, continue to sample the low-order modal basis coefficients according to the distribution of the sample points and the adaptive grid refinement technique until the accuracy of the mapping relationship between the low-order modal basis coefficients and the corresponding optimization objectives meets the preset requirements.

[0031] In one embodiment, the dimension reduction module is configured to perform dimension reduction processing on the flow field data to determine low-order modal feature data, including: performing dimension reduction processing on the flow field data according to the proper orthogonal decomposition to determine low-order modal feature data.

[0032] In one embodiment, the second determination module is specifically configured to obtain the streamwise vortex feature information in the target feature data; input the streamwise vortex feature information into the flame stability prediction model, and determine the flame stability according to the output of the flame stability prediction model.

[0033] In a third aspect, the present application further provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, it implements the method according to any one of the first aspects above.

[0034] In a fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the method according to any one of the first aspects above.

[0035] In a fifth aspect, the present application further provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the method according to any one of the first aspects above.

[0036] The method for determining the flame stability of the radial double-V groove flow vortex in the above jet propulsion device obtains the flow field data of the afterburner and the nozzle, then reduces the dimension of the flow field data to determine the low-order modal characteristic data, and then determines the target characteristic data from the low-order modal characteristics according to a preset optimization method. The preset optimization method is implemented based on an adaptive multi-response surface model and a genetic algorithm. Then, the target characteristic data is input into the flame stability prediction model, and the flame stability is determined according to the output of the flame stability prediction model. In this way, when determining the flame stability based on the flow field data of the afterburner and the nozzle, the flow field data can be dimensionally reduced and optimized to obtain the target characteristic data, which can optimize the low-order modal characteristic data, reduce the error caused by dimensional reduction, and based on the target characteristic data and the preset flame stability prediction model, an accurate prediction result can be obtained. Based on this, the flame stability of the jet propulsion device under different working conditions can be accurately determined. Description of the Drawings

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments of the present application or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0038] Figure 1 It is a schematic flowchart of the method for determining the flame stability of the radial double-V groove flow vortex in the jet propulsion device in one embodiment;

[0039] Figure 2 It is a schematic flowchart of the step of obtaining the flow field data of the afterburner and the nozzle in one embodiment;

[0040] Figure 3 It is a schematic flowchart of the step of determining the target characteristic data in one embodiment;

[0041] Figure 4 It is a schematic flowchart of the process of determining the predicted value corresponding to the low-order modal basis coefficient in one embodiment;

[0042] Figure 5 It is a schematic flowchart of the step of determining the mapping relationship between the low-order modal basis coefficient and the optimization target in one embodiment;

[0043] Figure 6 It is a schematic flowchart of the step of determining the flame stability in one embodiment;

[0044] Figure 7 It is a schematic flowchart of the method for determining the flame stability of the radial double-V groove flow vortex in the jet propulsion device in another embodiment;

[0045] Figure 8 It is a structural block diagram of a device for determining the flame stability of a radial double-V groove flow vortex in a jet propulsion device in an embodiment;

[0046] Figure 9 It is an internal structure diagram of a computer device in an embodiment. Specific embodiments

[0047] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0048] As a key component of a military aeroengine, the afterburner can significantly increase the thrust within a short time, supporting the fighter to achieve functions such as short takeoff, air maneuverability and supersonic cruise. However, due to its extremely harsh working environment (such as ultra-high temperature, low oxygen content, large disturbance, strong oscillation, etc.), ensuring the stability of the combustion flame has become the main challenge for improving performance. Once the flame goes out, it will not only cause the engine to be unable to continue providing additional thrust, but may also lead to takeoff interruption, failure of air maneuverability and even serious flight safety accidents. Therefore, enhancing the reliability of the afterburner flame stability under extreme conditions is crucial for improving the overall performance of aeroengines.

[0049] The flame stability of traditional radial double-V groove flame holders is affected by various factors, including the fuel-air ratio, electrode position, parasitic capacitance and installation dimensions, etc. In addition, factors such as the violent fluctuation of aerodynamic heat, structural deformation and electrical system abnormalities caused by sudden changes in engine operating conditions will also affect its stability, resulting in frequent occurrence of start-up connection failures. Such failures not only increase the maintenance cost and time consumption, but more importantly, reduce the equipment attendance rate.

[0050] In view of this, the present application provides a method for determining the flame stability of a radial double-V groove flow vortex in a jet propulsion device that can accurately determine the flame stability. The method for determining the flame stability of a radial double-V groove flow vortex in a jet propulsion device provided by the embodiments of the present application may have an execution subject that is a device for determining the flame stability of a radial double-V groove flow vortex in a jet propulsion device. The device for determining the flame stability of a radial double-V groove flow vortex in a jet propulsion device can be implemented by software, hardware or a combination of software and hardware. It can be embedded in or independent of the processor in a computer device in the form of hardware, or stored in the memory of the computer device in the form of software. In the following method embodiments, the execution subject is taken as a computer device for illustration. The computer device can be a server or a desktop computer. The specific type of the computer device is not limited in the embodiments of the present application.

[0051] In an exemplary embodiment, as Figure 1 shown, a method for determining the flame stability of a radial double-V groove flow in a jet propulsion device is provided, including the following steps 101 to 104. Among them:

[0052] Step 101, obtain the flow field data of the afterburner and the nozzle.

[0053] Optionally, the flow field data can be a set of various parameters describing the flow state of the fluid in space and time during flame combustion, and the flow field data can be used to characterize phenomena such as the motion characteristics of the fluid, energy conversion, and heat transfer.

[0054] Optionally, the flow field data can include velocity field data, pressure field data, temperature field data, turbulence parameters, etc., which are not limited in the embodiments of the present application.

[0055] Optionally, obtaining the flow field data of the afterburner and the nozzle can be to obtain the pre-stored flow field data from a database or a storage medium, or the flow field data obtained by solving according to a numerical simulation method. Alternatively, the flow field data can also be determined through a machine learning or neural network model.

[0056] Exemplarily, the pre-stored flow field data can be obtained through experimental measurements. For example, the velocity field of the flow field can be calculated by photographing the movement of tiny particles in the flow field.

[0057] It can be understood that the determined flow field data is the flow field characteristics and flow behavior of the radial double-V groove region in the afterburner and the nozzle under the afterburning state.

[0058] Step 102, perform dimensionality reduction processing on the flow field data to determine the low-order modal feature data.

[0059] Optionally, after obtaining the flow field data, dimensionality reduction processing can be performed to reduce noise and redundant information. Compressing high-dimensional data to a lower dimension can improve the subsequent calculation efficiency. At the same time, after mapping the high-dimensional data to a low-dimensional space, the flow field data is easier to visualize and understand.

[0060] In a possible implementation manner, perform dimensionality reduction processing on the flow field data according to the proper orthogonal decomposition to determine the low-order modal feature data.

[0061] Optionally, when performing the proper orthogonal decomposition (POD), the flow field data can be preprocessed first, such as data normalization processing, noise removal, and data alignment, etc., to ensure that the format and quality of the flow field data are suitable for subsequent analysis.

[0062] Optionally, when performing proper orthogonal decomposition, a data matrix is constructed based on the flow field data; and the covariance matrix of the data matrix is calculated, where the covariance matrix is used to characterize the correlation between the features in the flow field data; then the covariance matrix is eigen-decomposed to solve its eigenvalues and eigenvectors. The eigenvalue represents the variance size corresponding to each eigenvector, and the eigenvector represents the main direction of the data. The larger the eigenvalue, the more important the corresponding eigenvector; Next, according to the size of the eigenvalues, the first k largest eigenvalues and their corresponding eigenvectors are selected. These eigenvectors are called proper orthogonal basis (POD basis), which form the basis of the low-dimensional space. Usually, the selected k is much smaller than the dimension m of the flow field data; Finally, the flow field data is projected into the low-dimensional space formed by the proper orthogonal basis to obtain the reduced-dimension data matrix. Among them, the projection process can be realized by matrix multiplication.

[0063] Among them, the low-order mode feature data can include eigenvalues (basis coefficients) and corresponding eigenvectors (basis vectors).

[0064] Optionally, truncating the high-order modes according to the POD method can reduce the data dimension while retaining the key features.

[0065] In another possible implementation, the flow field data can also be reduced in dimension according to singular value decomposition. Important components are selected according to certain metrics (such as the size of singular values), and unimportant components are discarded, so as to achieve the purpose of dimension reduction.

[0066] Step 103, determining the target feature data from the low-order mode features according to a preset optimization method, where the preset optimization method is implemented according to an adaptive multi-response surface model and a genetic algorithm.

[0067] Optionally, there are differences between the low-order mode feature data obtained after dimension reduction and the acquired flow field data, which may bring errors in subsequent calculations. Therefore, the basis coefficients can be adjusted according to a preset optimization algorithm to reduce the errors caused by the dimension reduction process.

[0068] Optionally, there are multiple groups of low-order mode features. One group of low-order mode features can include a basis coefficient and a corresponding basis vector.

[0069] Optionally, an adaptive multi-response surface model (AMRSM) can be used to characterize the mapping relationship between the low-order mode features and the optimization objective, and the target feature data can be determined from the low-order mode features according to the genetic algorithm and this mapping relationship.

[0070] Optionally, the target feature data can be the reduced-order feature data of the flow field data.

[0071] Optionally, the adaptive multi-response surface model can be trained through multiple response surface models and combined with a cross-validation strategy to ensure the prediction accuracy and generalization ability of the adaptive multi-response surface model. In the embodiments of the present application, the adaptive multi-response model can be pre-trained or obtained by training based on low-order modal features and corresponding target values. Among them, the target value can be determined according to the flow field data. The embodiments of the present application do not limit the establishment process of the adaptive multi-response surface model.

[0072] In a possible implementation manner, a preset adaptive multi-response surface model can be obtained, and then after encoding, initializing the population, fitness evaluation, selection operation, crossover operation, mutation operation, and iterative update through the genetic algorithm, the individual with the highest fitness in the population is output as the optimal solution, and the target feature data is determined according to the optimal solution. Among them, an individual can be substituted into the adaptive multi-response surface model, and the individual fitness is determined according to the output of the adaptive multi-response surface model.

[0073] In another possible implementation manner, the adaptive multi-response surface model can be first trained according to the low-order modal features and the corresponding target values. The process of determining the target feature data from the low-order modal features according to the genetic algorithm and the mapping relationship is the same as the process in the above implementation manner and will not be repeated.

[0074] Step 104, input the target feature data into the flame stability prediction model, and determine the flame stability according to the output of the flame stability prediction model.

[0075] Optionally, the flame stability prediction model can be trained based on a machine learning method. During the training process, multi-condition combustion data and corresponding stability data are used as training parameters, and then the initial flame stability prediction model is trained by extracting features. Through cross-validation and optimizing the model parameters, when the prediction accuracy meets the preset requirements, the flame stability prediction model is obtained to ensure the model accuracy and generalization ability, so as to improve the accuracy and reliability of the flame stability prediction.

[0076] Exemplarily, the flame stability prediction model can be a classification model and can be trained based on a decision tree method, a random forest method, or a neural network.

[0077] Optionally, when the determined flame stability is poor, the geometric parameters of the radial double V-grooves can be optimized, thereby improving the flame stability in the afterburner.

[0078] The method for determining the flow - vortex flame stability of the radial double - V - groove of the above - mentioned jet propulsion device obtains the flow - field data of the afterburner and the nozzle, then performs dimensionality reduction processing on the flow - field data to determine the low - order modal characteristic data, and then determines the target characteristic data from the low - order modal characteristics according to a preset optimization method. The preset optimization method is implemented based on an adaptive multi - response surface model and a genetic algorithm. Then, the target characteristic data is input into the flame stability prediction model, and the flame stability is determined according to the output of the flame stability prediction model. In this way, when determining the flame stability based on the flow - field data of the afterburner and the nozzle, the flow - field data can be dimensionally reduced and optimized to obtain the target characteristic data, which can optimize the low - order modal characteristic data, reduce the error caused by dimensionality reduction processing, and based on the target characteristic data and the preset flame stability prediction model, an accurate prediction result can be obtained. Based on this, the flame stability of the jet propulsion device under different working conditions can be accurately determined.

[0079] In an exemplary embodiment, as Figure 2 shown, optionally, obtaining the flow - field data of the afterburner and the nozzle includes the following steps 201 to 202. Among them:

[0080] Step 201, obtain the geometric models of the afterburner and the nozzle.

[0081] In a possible implementation manner, the geometric models of the afterburner and the nozzle can be pre - stored in a storage medium, and the identification information of the geometric model input by the user can be queried and obtained from the storage medium.

[0082] In another possible implementation manner, the key dimensions and shape parameters of the afterburner and the nozzle can also be determined by a parametric design method. By calling the relevant interfaces of the modeling software, a geometric model is generated according to the key dimensions and shape parameters. When generating the geometric model, Boolean operations are used to achieve the precise combination between components, and the generated geometric model is inspected and optimized to obtain the geometric models of the afterburner and the nozzle.

[0083] Step 202, determine the flow - field data of the afterburner and the nozzle according to the preset aerodynamic - thermal coupling calculation method and the geometric model.

[0084] Among them, the preset aerodynamic - thermal coupling calculation method is an aerodynamic - thermal coupling calculation method corrected according to the experimental method. The experimental methods include particle image velocimetry and thermocouple temperature measurement.

[0085] Optionally, the aerodynamic-thermal coupling calculation method can be implemented based on the Navier-Stokes equations. The accuracy of flow behavior prediction can be improved by setting the turbulent intensity distribution, the simulation accuracy of temperature can be enhanced by adjusting parameters such as the flame propagation speed, the convergence can be accelerated and the solution process can be stabilized by adjusting the relaxation factor, and the iterative convergence criterion can be optimized by adjusting the physical quantity change threshold. The specific expression of the Navier-Stokes equations is as follows:

[0086]

[0087]

[0088]

[0089] where, is the fluid density, t is the time, which is used to measure the sequence and duration of physical processes, is the Hamiltonian operator, is the fluid velocity vector, p is the fluid pressure, is the dynamic viscosity, is the second viscosity coefficient, is the strain rate tensor, is the external force vector acting on the fluid per unit mass, h is the specific enthalpy, is the dissipation function, is the internal heat source intensity per unit mass of the fluid, is the heat flux vector, is the term related to dissipation, is the thermal conductivity, and T represents the fluid temperature.

[0090] Optionally, the numerical values of key physical parameters such as velocity and temperature in the radial double-V groove region can be obtained through experimental methods, and the aerodynamic-thermal coupling calculation method can be corrected according to the numerical values of the key physical parameters to improve the calculation accuracy of the aerodynamic-thermal coupling calculation method.

[0091] Exemplarily, the velocity field in the radial double-V groove region can be measured by Particle Image Velocimetry (PIV), the temperature distribution can be obtained by thermocouples, the experimental data of the flow field and temperature field in the radial double-V groove region can be compared with the calculation data obtained by the aerodynamic-thermal coupling calculation method to identify the deviation, and the turbulent parameters and combustion performance influence parameters in the aerodynamic-thermal coupling calculation method can be adjusted based on the experimental data to correct the aerodynamic-thermal coupling calculation method. When comparing and identifying the deviation, the Root Mean Square Error (RMSE) is used for evaluation, where:

[0092]

[0093] Among them, is the experimental value, is the calculated value, and n is the number of samples.

[0094] The geometric models of the afterburner and the nozzle are obtained above. According to the preset aerodynamic-thermal coupling calculation method and the geometric models, the flow field data of the afterburner and the nozzle are determined. Among them, the preset aerodynamic-thermal coupling calculation method is the aerodynamic-thermal coupling calculation method corrected according to the experimental method. The experimental method includes particle image velocimetry and thermocouple temperature measurement. In this way, according to the corrected aerodynamic-thermal coupling calculation method and the obtained geometric models, the flow field data of the radial double V-groove region can be accurately calculated, so as to accurately determine the flame stability.

[0095] In an exemplary embodiment, as Figure 3 shown, optionally, the low-order mode feature data includes low-order mode basis coefficients and low-order mode basis vectors. Determining the target feature data from the low-order mode features according to the preset optimization method includes the following steps 301 to 302. Among them:

[0096] Step 301, determine the predicted value corresponding to the low-order mode basis coefficient according to the low-order mode basis coefficient and the preset optimization method.

[0097] Among them, the predicted value corresponding to the low-order mode basis coefficient is a value in the preset numerical range.

[0098] Exemplarily, taking the low-order mode basis coefficient as k for example, for each mode basis coefficient, a value can be determined from the preset numerical range according to the preset optimization method as the predicted value of the low-order mode basis coefficient.

[0099] It can be understood that a predicted value can be determined from the preset numerical range according to the genetic algorithm, and this predicted value is the optimal solution, that is, the error between the data reconstructed according to this predicted value and the low-order mode basis vectors and the flow field data is the smallest.

[0100] Optionally, the genetic algorithm can be selected according to the number of optimization objectives. For example, the standard genetic algorithm can be selected when the optimization objective is one. In multi-objective optimization problems, there may be conflicts between the objectives, and a multi-objective genetic algorithm can be used, such as the multi-objective evolutionary algorithm based on decomposition (Multi-Objective Evolutionary Algorithmbased on Decomposition, MOEA / D), etc.

[0101] In a possible implementation manner, the genetic algorithm is the Non-dominatedSorting Genetic Algorithm II (NSGA-II), asFigure 4 As shown, determining the predicted value corresponding to the low-order modal basis coefficient according to the low-order modal basis coefficient and the preset optimization method includes the following steps 401 to step 402. Wherein:

[0102] Step 401: Determine the mapping relationship between the low-order modal basis coefficient and the optimization objective according to the adaptive multi-response surface model.

[0103] Among them, the optimization objective includes the flow field data, which may include the position, intensity, and evolution information of the streamwise vortices in the flow field, etc.

[0104] Optionally, the adaptive multi-response surface model AMRSM can sample the input data, train different response surface models, and randomly select a certain proportion of samples as the validation set to test the prediction errors of each model. Subsequently, for different optimization objectives, select the model with the smallest average relative error for the final response surface construction.

[0105] Optionally, AMRSM can be selected but is not limited to Gaussian process response surface, neural network response surface, Kriging response surface, radial basis function response surface, etc.

[0106] Optionally, the mapping relationship between the low-order modal basis coefficient and the optimization objective can be determined through AMRSM.

[0107] Step 402: Use the low-order modal basis coefficient as the input variable, and determine the predicted value corresponding to the low-order modal basis coefficient according to the non-dominated sorting genetic algorithm II and the mapping relationship.

[0108] Optionally, for a low-order modal basis coefficient, determining the predicted value corresponding to the low-order modal basis coefficient according to the non-dominated sorting genetic algorithm II and the mapping relationship may include the following steps:

[0109] The first step: Initialize the population. A random initial population P can be generated within the range of the low-order modal basis coefficient, and the population size is N;

[0110] The second step: Substitute each individual into the mapping relationship and calculate the fitness of each individual;

[0111] The third step: Divide the population into different non-dominated levels. The individuals in the first level are not dominated by other individuals, and the subsequent level individuals are sequentially dominated by the individuals in the previous level;

[0112] The fourth step: Calculate the crowding distance. For each individual within each non-dominated level, calculate its crowding distance to measure the density of the individual in the objective space;

[0113] Step 5: Selection operation. Use methods such as binary tournament selection to select parent individuals for crossover. Compare the non-dominated levels of individuals. The one with a lower level is preferred; if the levels are the same, compare the crowding distances, and the one with a larger distance is preferred.

[0114] Step 6: Crossover and mutation. Perform crossover operations (exchanging parts of chromosomes to generate new individuals) and mutation operations (introducing small random changes in chromosomes) on the selected parent individuals.

[0115] Step 7: Generate the next generation. Merge the parent and offspring populations to form an intermediate population, perform non-dominated sorting on it, and select excellent individuals based on the non-dominated rank and crowding distance to form the next generation population.

[0116] Step 8: Termination judgment. Continuously repeat the processes of selection, crossover, mutation, and population update until the termination conditions are met (such as reaching the specified number of generations, convergence threshold, or exhausting computational resources, etc.). The final population contains the Pareto optimal solutions representing the optimal trade-off between objectives.

[0117] Next, the concepts involved in the above steps are explained:

[0118] Non-dominated sorting is a technique for dividing individuals in a population into different levels (Pareto front levels). If there are no other individuals in the population that are superior to individual A in all objectives, then individual A is a non-dominated individual. Classify non-dominated individuals into the first-level front; after removing the first-level front individuals, find the non-dominated individuals among the remaining individuals and classify them into the second-level front, and so on. This sorting can identify and retain high-quality individuals and is crucial for multi-objective optimization.

[0119] Crowding distance can be used to measure the distribution density of individuals within the Pareto front to maintain population diversity. When calculating, first sort the individuals under each objective. The crowding distance of boundary individuals is set to infinity, and the crowding distance of intermediate individuals is calculated through the normalized difference of the objective values of adjacent individuals. Individuals with a larger crowding distance are more favored in selection, which can make individuals evenly distributed in the objective space and avoid aggregation.

[0120] In step 302, determine the target feature data according to the product of the predicted value of the low-order modal basis coefficient and the low-order modal basis vector.

[0121] Optionally, for a low-order modal basis coefficient, multiply the predicted value of the low-order modal basis coefficient by the corresponding low-order modal basis vector, and use the product as the target feature data.

[0122] The predicted values corresponding to the low-order modal basis coefficients are determined based on the low-order modal basis coefficients and a preset optimization method. The target feature data is determined according to the product of the predicted values of the low-order modal basis coefficients and the low-order modal basis vectors. By processing the flow field data, the target feature data is obtained. In this way, by adjusting the low-order modal basis coefficients, the error between the obtained target feature data and the flow field data is reduced, that is, the target feature data can accurately represent the flow field data. Thus, the flame stability performance determined based on the target feature data is more accurate. At the same time, since the prediction of flame stability involves the rated interaction of multiple physical quantities, these variables can be comprehensively considered through the preset optimization method, and the influence of each variable on flame stability can be fully reflected, which can effectively improve the accuracy of flame stability prediction.

[0123] In an exemplary embodiment, as Figure 5 shown, optionally, according to the adaptive multi-response surface model, the mapping relationship between the low-order modal basis coefficients and the optimization objective is determined, including the following steps 501 to 504. Among them:

[0124] Step 501: Sample the low-order modal basis coefficients according to a preset sampling method to determine sample points.

[0125] Among them, the sampling method includes the Latin hypercube sampling method, the central composite sampling method, or the Box-Behnken sampling method.

[0126] Optionally, for a low-order modal basis coefficient, sampling can be performed within a preset numerical range according to a preset sampling method to obtain sample points.

[0127] Exemplarily, the Latin hypercube sampling method can be to divide the preset numerical range into several intervals with equal probabilities, and then randomly select a sample point within each interval, and it is required that each hyperplane perpendicular to the axis contains at most one sample point.

[0128] Step 502: Update the sample points according to the distribution of the sample points and the adaptive grid refinement technique.

[0129] Optionally, after sampling the low-order modal basis coefficients to determine sample points, the adaptive grid refinement technique can dynamically refine the sampling interval for key regions according to the distribution of the sample points and increase the sample points.

[0130] Optionally, when the accuracy of the mapping relationship does not meet the preset requirements, the sampling interval can also be dynamically refined in the region with a large gap from the actual value to increase the sample points.

[0131] Step 503: Determine the mapping relationship between the updated sample points and the corresponding optimization objective according to the adaptive multi-response surface model.

[0132] Optionally, after determining the sample points, the flow field data can be used to determine the target value corresponding to the sample points, and this target value is the actual target value.

[0133] Exemplarily, taking the response surface function as a quadratic polynomial response surface function as an example, with the input being the sample points and the output being the target values, the coefficients in the response surface function can be estimated by methods such as the least squares method, so that the response surface function can fit the sample points and the target values as much as possible.

[0134] Step 504: Determine the accuracy of the mapping relationship. When the accuracy does not meet the preset requirements, according to the distribution of the sample points and the adaptive grid refinement technique, continue to sample the low-order modal basis coefficients until the accuracy of the mapping relationship between the low-order modal basis coefficients and the corresponding optimization objectives meets the preset requirements.

[0135] Optionally, when determining the mapping relationship between the updated sample points and the corresponding optimization objectives, the updated sample points can be divided into two groups according to a preset ratio, one group is the training samples and the other group is the test samples. After determining the mapping relationship based on the training samples, the mapping relationship can be tested according to the test samples to determine the accuracy of the mapping relationship.

[0136] Exemplarily, for each test sample point in the test samples, the error between the actual target value and the predicted target value determined by the adaptive multi-response surface model can be calculated. Commonly used error metrics include the mean square error (MSE), the mean absolute error (MAE), etc. When the error exceeds the preset error threshold, it can be considered that the accuracy of the mapping relationship does not meet the preset requirements.

[0137] Optionally, new sampling points can also be obtained, and whether the accuracy of the mapping relationship meets the preset requirements can be determined according to the error between the actual target value and the predicted target value of the new sampling points.

[0138] Optionally, when the accuracy meets the preset requirements, the above steps 502 to 504 can be repeated until a mapping relationship that meets the preset requirements is obtained.

[0139] The low-order modal basis coefficients are sampled according to the preset sampling method to determine sample points. Based on the distribution of the sample points and the adaptive grid refinement technique, the sample points are updated. According to the adaptive multi-response surface model, the mapping relationship between the updated sample points and the corresponding optimization objectives is determined, and the accuracy of the mapping relationship is determined. When the accuracy does not meet the preset requirements, the low-order modal basis coefficients are continuously sampled according to the distribution of the sample points and the adaptive grid refinement technique until the accuracy of the mapping relationship between the low-order modal basis coefficients and the corresponding optimization objectives meets the preset requirements. An approximate mapping relationship of the complex flame physical process can be constructed based on less data. By reasonably selecting sample points and using the response surface function to approximate the mapping relationship, the mapping efficiency can be effectively improved. At the same time, the mapping relationship can be further refined by the distribution of the sample points and the adaptive grid refinement technique to obtain a mapping relationship that can accurately describe the low-order modal basis coefficients and the corresponding optimization objectives under different working conditions.

[0140] In an exemplary embodiment, as Figure 6 shown, optionally, the target feature data includes the flow vortex feature information of the radial double V-groove region. The target feature data is input into the flame stability prediction model, and the flame stability is determined according to the output of the flame stability prediction model, including the following steps 601 to step 602. Wherein:

[0141] Step 601, obtain the flow vortex feature information in the target feature data.

[0142] Optionally, the target feature data includes the flow vortex feature information of the radial double V-groove region. Therefore, after determining the target feature data, the flow vortex feature information can be extracted from the target feature data.

[0143] Step 602, input the flow vortex feature information into the flame stability prediction model, and determine the flame stability according to the output of the flame stability prediction model.

[0144] Optionally, the flow vortex feature information can be input into a pre-trained flame stability prediction model. The output result of the flame stability prediction model can include flame stable or flame unstable, and can also include the stability level of the flame stability. The flame stability is determined according to the stability level of the flame stability.

[0145] Exemplarily, when the output result of the flame stability prediction model includes the stability level of the flame stability, the higher the stability level value, the more stable the predicted flame stability.

[0146] The above-mentioned method for obtaining the flow-vortex feature information in the target feature data, inputting the flow-vortex feature information into the flame stability prediction model, and determining the flame stability according to the output of the flame stability prediction model can determine the flame stability based on the flow-vortex feature information in the target data obtained through dimensionality reduction processing, adaptive multi-response surface model, and non-dominated sorting genetic algorithm II, effectively improving the determination accuracy.

[0147] As an alternative implementation, as Figure 7 shown, the method for determining the flame stability of the radial double-V groove flow-vortex of the jet propulsion device provided in the embodiment of the present application may include the following specific steps:

[0148] Step 701: Obtain the geometric models of the afterburner and the nozzle.

[0149] Step 702: Determine the flow field data of the afterburner and the nozzle according to the preset aerodynamic-thermal coupling calculation method and the geometric models.

[0150] Among them, the preset aerodynamic-thermal coupling calculation method is the aerodynamic-thermal coupling calculation method corrected according to the experimental method, and the experimental method includes particle image velocimetry and thermocouple temperature measurement.

[0151] Step 703: Perform dimensionality reduction processing on the flow field data according to proper orthogonal decomposition to determine the low-order mode feature data.

[0152] Among them, the low-order mode feature data includes low-order mode basis coefficients and low-order mode basis vectors.

[0153] Step 704: Sample the low-order mode basis coefficients according to the preset sampling method to determine the first sample points.

[0154] Among them, the sampling method includes Latin hypercube sampling method, central composite sampling method or Box-Behnken sampling method.

[0155] Step 705: Determine the sample points according to the distribution of the first sample points and the adaptive grid refinement technique.

[0156] Step 706: Determine the mapping relationship between the sample points and the corresponding optimization objectives according to the adaptive multi-response surface model.

[0157] Step 707: Determine whether the accuracy of the mapping relationship meets the preset requirements. When the accuracy of the mapping relationship does not meet the preset requirements, execute Step 705.

[0158] Step 708: When the accuracy meets the preset requirements, use the low-order mode basis coefficients as input variables, and determine the predicted values corresponding to the low-order mode basis coefficients according to the non-dominated sorting genetic algorithm II and the mapping relationship.

[0159] Among them, the predicted value corresponding to the low-order modal basis coefficient is a value within a preset numerical range.

[0160] Step 709: Determine the target feature data according to the product of the predicted value of the low-order modal basis coefficient and the low-order modal basis vector.

[0161] Step 710: Obtain the flow-direction vortex feature information in the target feature data.

[0162] Step 711: Input the flow-direction vortex feature information into the flame stability prediction model, and determine the flame stability according to the output of the flame stability prediction model.

[0163] It should be understood that although the steps in the flowcharts involved in the above-mentioned embodiments are sequentially shown according to the indication of the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or steps or stages in other steps.

[0164] Based on the same inventive concept, the embodiments of the present application also provide a device for determining the radial double-V groove flow-direction vortex flame stability of a jet propulsion device for implementing the above-mentioned method for determining the radial double-V groove flow-direction vortex flame stability of a jet propulsion device. The implementation solutions provided by this device to solve problems are similar to the implementation solutions described in the above method. Therefore, the specific limitations in one or more embodiments of the device for determining the radial double-V groove flow-direction vortex flame stability of a jet propulsion device provided below can refer to the limitations on the method for determining the radial double-V groove flow-direction vortex flame stability of a jet propulsion device in the above text, and will not be repeated here.

[0165] In an exemplary embodiment, as Figure 8 shown, a device 800 for determining the radial double-V groove flow-direction vortex flame stability of a jet propulsion device is provided, including: an acquisition module 801, a dimensionality reduction module 802, a first determination module 803, and a second determination module 804, where:

[0166] The acquisition module 801 is configured to acquire the flow field data of the afterburner and the nozzle;

[0167] The dimensionality reduction module 802 is configured to perform dimensionality reduction processing on the flow field data to determine low-order modal feature data;

[0168] The first determination module 803 is configured to determine target feature data from low-order modal features according to a preset optimization method, where the preset optimization method is implemented based on an adaptive multi-response surface model and the non-dominated sorting genetic algorithm II;

[0169] The second determination module 804 is configured to input the target feature data into a flame stability prediction model and determine the flame stability according to the output of the flame stability prediction model.

[0170] In one embodiment, the acquisition module 801 is specifically configured to acquire the geometric models of the afterburner and the nozzle; determine the flow field data of the afterburner and the nozzle according to a preset aerodynamic-thermal coupling calculation method and the geometric models; where the preset aerodynamic-thermal coupling calculation method is an aerodynamic-thermal coupling calculation method corrected according to an experimental method, and the experimental method includes particle image velocimetry and thermocouple temperature measurement.

[0171] In one embodiment, the low-order modal feature data includes low-order modal basis coefficients and low-order modal basis vectors. The first determination module 803 is specifically configured to determine the predicted value corresponding to the low-order modal basis coefficient according to the low-order modal basis coefficient and the preset optimization method, where the predicted value corresponding to the low-order modal basis coefficient is a value within a preset numerical range; determine the target feature data according to the product of the predicted value of the low-order modal basis coefficient and the low-order modal basis vector.

[0172] In one embodiment, the genetic algorithm is the non-dominated sorting genetic algorithm II. The first determination module 803 is specifically configured to determine the mapping relationship between the low-order modal basis coefficient and the optimization objective according to the adaptive multi-response surface model, where the optimization objective includes flow field data; use the low-order modal basis coefficient as the input variable, and determine the predicted value corresponding to the low-order modal basis coefficient according to the non-dominated sorting genetic algorithm II and the mapping relationship.

[0173] In one embodiment, the first determination module 803 is specifically configured to sample the low-order modal basis coefficient according to a preset sampling method to determine the first sample points, where the sampling method includes the Latin hypercube sampling method, the central composite sampling method, or the Box-Behnken sampling method; update the sample points according to the distribution of the sample points and the adaptive grid refinement technique; determine the mapping relationship between the updated sample points and the corresponding optimization objective according to the adaptive multi-response surface model; determine the accuracy of the mapping relationship, and when the accuracy does not meet the preset requirements, continue to sample the low-order modal basis coefficient according to the distribution of the sample points and the adaptive grid refinement technique until the accuracy of the mapping relationship between the low-order modal basis coefficient and the corresponding optimization objective meets the preset requirements.

[0174] In one embodiment, the dimensionality reduction module 802 is configured to perform dimensionality reduction processing on the flow field data to determine low-order modal feature data, including: performing dimensionality reduction processing on the flow field data according to proper orthogonal decomposition to determine low-order modal feature data.

[0175] In one embodiment, the second determination module 804 is specifically configured to obtain the streamwise vortex feature information in the target feature data; input the streamwise vortex feature information into the flame stability prediction model, and determine the flame stability according to the output of the flame stability prediction model.

[0176] Each module in the above radial double-V groove streamwise vortex flame stability determination device of the jet propulsion device can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in the processor in the computer device in the form of hardware or be independent of the processor, or can be stored in the memory in the computer device in the form of software, so as to facilitate the processor to call and execute the operations corresponding to the above respective modules.

[0177] In an exemplary embodiment, a computer device is provided. The computer device can be a server, and its internal structural diagram can be as Figure 9 shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements a method for determining the radial double-V groove streamwise vortex flame stability of a jet propulsion device.

[0178] Those skilled in the art can understand that Figure 9 the structure shown in

[0179] In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps described in any of the above method embodiments are implemented.

[0180] In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps described in any of the above method embodiments are implemented.

[0181] In an embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps described in any of the above method embodiments are implemented.

[0182] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include Read-Only Memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, Resistive Random Access Memory (ReRAM), Magnetoresistive Random Access Memory (MRAM), Ferroelectric Random Access Memory (FRAM), Phase Change Memory (PCM), graphene memory, etc. Volatile memory can include Random Access Memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), etc. The databases involved in the embodiments provided in this application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in this application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, Artificial Intelligence (AI) processors, etc., without limitation.

[0183] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope recorded in this application.

[0184] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation to the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A method for determining the flow - vortex flame stability of a radial double - V groove in a jet propulsion device, characterized in that, The method includes: Obtaining the flow field data of the afterburner and the nozzle; Performing dimensionality reduction processing on the flow field data to determine low-order modal feature data; Determining target feature data from the low-order modal features according to a preset optimization method, where the preset optimization method is implemented based on an adaptive multi-response surface model and a genetic algorithm; Inputting the target feature data into a flame stability prediction model, and determining the flame stability according to the output of the flame stability prediction model; The low-order modal feature data includes low-order modal basis coefficients and low-order modal basis vectors. Determining target feature data from the low-order modal features according to the preset optimization method includes: Determining a predicted value corresponding to the low-order modal basis coefficient according to the low-order modal basis coefficient and the preset optimization method, where the predicted value corresponding to the low-order modal basis coefficient is a value within a preset numerical range; Determining the target feature data according to the product of the predicted value of the low-order modal basis coefficient and the low-order modal basis vector; The target feature data includes the flow vorticity feature information of the radial double V-groove region. Inputting the target feature data into a flame stability prediction model and determining the flame stability according to the output of the flame stability prediction model includes: Obtaining the flow vorticity feature information in the target feature data; Inputting the flow vorticity feature information into a flame stability prediction model, and determining the flame stability according to the output of the flame stability prediction model.

2. The method according to claim 1, characterized in that, The obtaining the flow field data of the afterburner and the nozzle includes: Obtaining the geometric models of the afterburner and the nozzle; Determining the flow field data of the afterburner and the nozzle according to a preset aerodynamic-thermal coupling calculation method and the geometric models; Wherein, the preset aerodynamic-thermal coupling calculation method is an aerodynamic-thermal coupling calculation method corrected according to an experimental method, and the experimental method includes particle image velocimetry and thermocouple temperature measurement.

3. The method according to claim 1, characterized in that, The genetic algorithm is the Non-dominated Sorting Genetic Algorithm II. Determining the predicted value corresponding to the low-order modal basis coefficient according to the low-order modal basis coefficient and the preset optimization method includes: Determining the mapping relationship between the low-order modal basis coefficient and the optimization objective according to the adaptive multi-response surface model, where the optimization objective includes the flow field data; Taking the low-order modal basis coefficient as an input variable, and determining the predicted value corresponding to the low-order modal basis coefficient according to the Non-dominated Sorting Genetic Algorithm II and the mapping relationship.

4. The method according to claim 3, wherein Determining the mapping relationship between the low-order modal basis coefficient and the optimization objective according to the adaptive multi-response surface model includes: Sampling the low-order modal basis coefficient according to a preset sampling method to determine sample points, where the sampling method includes the Latin hypercube sampling method, the central composite sampling method or the Box-Behnken sampling method; Updating the sample points according to the distribution of the sample points and the adaptive grid refinement technique; Determining the mapping relationship between the updated sample points and the corresponding optimization objective according to the adaptive multi-response surface model; Determine the accuracy of the mapping relationship. When the accuracy does not meet the preset requirements, continue to sample the low-order modal basis coefficients according to the distribution of the sample points and the adaptive grid refinement technique until the accuracy of the mapping relationship between the low-order modal basis coefficients and the corresponding optimization objective meets the preset requirements.

5. The method according to claim 1, wherein The dimensionality reduction processing of the flow field data to determine the low-order modal feature data includes: Perform dimensionality reduction processing on the flow field data according to proper orthogonal decomposition to determine the low-order modal feature data.

6. The method according to claim 1, characterized in that The inputting the streamwise vortex feature information into the flame stability prediction model and determining the flame stability according to the output of the flame stability prediction model includes: The output of the flame stability prediction model includes the stability level of the flame stability. The higher the value of the stability level, the more stable the flame stability.

7. A device for determining the radial double-V groove flow vortex flame stability of a jet propulsion device using the method according to any one of claims 1 to 6, characterized in that, The device includes: An acquisition module, configured to acquire the flow field data of the afterburner and the nozzle; A dimensionality reduction module, configured to perform dimensionality reduction processing on the flow field data to determine the low-order modal feature data; A first determination module, configured to determine the target feature data from the low-order modal features according to a preset optimization method, and the preset optimization method is implemented according to an adaptive multi-response surface model and a non-dominated sorting genetic algorithm II; A second determination module, configured to input the target feature data into the flame stability prediction model and determine the flame stability according to the output of the flame stability prediction model.

8. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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