A method, apparatus, device and medium for predicting surface peak heat flux of an aircraft

Abstract

The application provides a surface peak heat flow prediction method, device and equipment of an aircraft and a medium, relates to the technical field of aircraft design, and comprises the following steps: obtaining a parameter value of a first target parameter and a parameter range of all second target parameters for predicting a surface peak heat flow in a target application scenario; determining a target parameter combination according to the parameter range of the second target parameter and matching a corresponding target prediction model; inputting the target parameter combination, the parameter range of the second target parameter except the third target parameter and the parameter value of the first target parameter into the target prediction model to obtain a surface peak heat flow prediction value corresponding to the target parameter combination; and determining a surface peak heat flow prediction result based on each target parameter combination and the corresponding surface peak heat flow prediction value. The application greatly reduces the calculation time, reduces the use of computing resources, improves the calculation efficiency, and realizes efficient and accurate prediction of the surface peak heat flow of the aircraft.

Description

Technical Field

[0001] This application relates to the field of aircraft design technology, and more specifically, to a method, apparatus, device, and medium for predicting the surface peak heat flux of an aircraft. Background Technology

[0002] In recent years, aerodynamic heating of hypersonic vehicles has become a research hotspot. Stagnation points often exhibit heat flux concentration zones under the influence of shock waves generated by hypersonic incoming flows. Simultaneously, heat flux concentration zones also appear at the vehicle's shoulders and corners due to flow separation. Therefore, determining the location of peak heat flux on the overall vehicle surface is a crucial consideration for researchers. Furthermore, the location and intensity of peak heat flux vary with shape and operating parameters. Efficient and accurate prediction of peak heat flux on the vehicle surface is of great significance for improving the aerodynamic performance of hypersonic vehicles.

[0003] Existing technology (a method for determining the optimal flight angle of attack for an aircraft) discloses a method for determining the optimal flight angle of attack for an aircraft. This method improves the thermal environment of the surfaces of key parts of the aircraft by changing the flight angle of attack, thereby bringing the thermal environment of these key parts to an optimal state, i.e., minimizing the heat flux in the thermal environment. This belongs to the field of aircraft flight control technology. However, this method does not specify the location and problem of peak heat flux on the aircraft surface, and cannot solve the initial configuration design.

[0004] Meanwhile, most current methods for predicting the peak heat flux density on the surface of aircraft focus on predicting the heat flux density at the stagnation point at the nose of the aircraft, without considering the heat flux density at flow separation points such as the shoulder of the aircraft, resulting in predicted values ​​that are much smaller than the actual values. Furthermore, current methods for calculating the heat flux density on the surface of aircraft through numerical simulation consume too many computational resources and take too long for a single numerical simulation, making them inconvenient to use during aerodynamic shape optimization. Summary of the Invention

[0005] The purpose of this application is to provide a method, apparatus, device and medium for predicting the surface peak heat flux of an aircraft, so as to solve the above-mentioned problems existing in the prior art, and to accurately and efficiently predict the surface peak heat flux of an aircraft.

[0006] Firstly, a method for predicting the peak surface heat flux of an aircraft is provided, the method including:

[0007] Obtain the parameter values ​​of the first target parameter used to predict the peak surface heat flux in the target application scenario, as well as the parameter ranges of all second target parameters;

[0008] Extract at least one third objective parameter from all the second objective parameters;

[0009] For any third target parameter, multiple parameter values ​​of the third target parameter are obtained by taking values ​​within the parameter range of the third target parameter;

[0010] By combining the different parameter values ​​of each third objective parameter, multiple objective parameter combinations are obtained;

[0011] For any combination of target parameters, the target prediction model corresponding to the target parameter combination is matched from a pre-constructed table of different parameter combinations and corresponding prediction models.

[0012] The target parameter combination, the parameter range of each second target parameter excluding the third target parameter, and the parameter value of the first target parameter are input into the target prediction model to obtain the predicted surface peak heat flux value corresponding to the target parameter combination.

[0013] Based on the combination of each target parameter and the corresponding predicted value of the surface peak heat flux, the predicted result of the surface peak heat flux is determined.

[0014] In an optional implementation, at least one third objective parameter is extracted from all second objective parameters, including:

[0015] Obtain the parameter category to which the second target parameter belongs, and the key parameters under the parameter category; wherein, the key parameters are obtained by analyzing data from a pre-built aircraft heat flux density distribution database;

[0016] For any second target parameter, if the second target parameter is the key parameter, then the second target parameter is used as the third target parameter.

[0017] In an optional implementation, the method for constructing the aircraft heat flux density distribution database includes:

[0018] The historical parameter ranges and parameter categories to which the first target parameter and the second target parameter belong are obtained respectively; wherein the parameter categories of the first target parameter and the second target parameter are different; the parameter categories include working condition category and shape category;

[0019] For any target parameter, values ​​are taken within the historical parameter range of the target parameter to obtain multiple historical parameter values ​​of the target parameter;

[0020] For any parameter category, the different historical parameter values ​​of each target parameter included in the parameter category are combined to obtain multiple historical parameter combinations;

[0021] By combining different historical parameter combinations under each parameter category, multiple historical parameter vectors are obtained;

[0022] For any historical parameter vector, a 3D model of the aircraft corresponding to the historical parameter vector is constructed based on the historical parameter combination of the shape class in the historical parameter vector;

[0023] The three-dimensional model is used to perform simulations under the historical parameter combinations of the operating conditions to obtain the heat flux density distribution data of the aircraft corresponding to the historical parameter vector.

[0024] A database of aircraft heat flux density distribution is constructed based on different historical parameter vectors and corresponding aircraft heat flux density distribution data.

[0025] In an optional implementation, the method for determining the key parameters includes:

[0026] By analyzing different historical parameter vectors and corresponding heat flux density distribution data of the aircraft, importance scores for different target parameters are obtained.

[0027] For any parameter category, sort the target parameters contained in the parameter category from largest to smallest according to their respective importance scores;

[0028] From the sorted target parameters, extract the first preset number of target parameters to obtain the key parameters of the parameter category.

[0029] In an optional implementation, after constructing the spacecraft heat flux density distribution database, the method further includes:

[0030] For any historical parameter vector in the aircraft heat flux density distribution database, the stationary point coordinates and shoulder coordinates of the aircraft are determined based on the three-dimensional model of the aircraft corresponding to the historical parameter vector.

[0031] From the heat flux density distribution data of the aircraft corresponding to the historical parameter vector, extract the heat flux density at the stationary point and the heat flux density at the shoulder corresponding to the stationary point coordinates and the shoulder coordinates, respectively.

[0032] If the stagnation point heat flux density is greater than the shoulder heat flux density, then the heat flux density distribution data of the aircraft corresponding to the historical parameter vector is stored in the configured first dataset;

[0033] If the stagnation point heat flux density is not greater than the shoulder heat flux density, then the heat flux density distribution data of the aircraft corresponding to the historical parameter vector is stored in the configured second dataset.

[0034] In an optional implementation, the prediction model includes a first prediction model and a second prediction model;

[0035] The first prediction model is obtained by training a pre-built initial prediction model using the first dataset;

[0036] The second prediction model is obtained by training a pre-built second initial prediction model using a second dataset.

[0037] In an optional implementation, the target application scenarios include: aircraft design scenarios and aircraft surface peak heat flux analysis scenarios;

[0038] When the target application scenario is an aircraft design scenario, the surface peak heat flux prediction result is determined based on the combination of each target parameter and the corresponding predicted surface peak heat flux value, including:

[0039] From each combination of target parameters, extract the target parameter combination with the smallest predicted surface peak heat flux value and the corresponding predicted surface peak heat flux value to obtain the predicted surface peak heat flux result.

[0040] When the target application scenario is the peak heat flux analysis scenario of the aircraft surface, the combination of each target parameter and the corresponding predicted peak heat flux value of the surface are used as the predicted peak heat flux of the aircraft surface.

[0041] In a second aspect, a surface peak heat flux prediction device for an aircraft is provided, the device comprising:

[0042] The acquisition unit is used to acquire the parameter values ​​of the first target parameter for predicting the peak surface heat flux in the target application scenario and the parameter ranges of all second target parameters.

[0043] An extraction unit is used to extract at least one third target parameter from all second target parameters;

[0044] The combination unit is used to take values ​​within the parameter range of any third target parameter to obtain multiple parameter values ​​of the third target parameter; and to combine the different parameter values ​​of each third target parameter to obtain multiple target parameter combinations.

[0045] The matching unit is used to match the target prediction model corresponding to any target parameter combination from a pre-built table of different parameter combinations and corresponding prediction models.

[0046] The prediction unit is used to input the target parameter combination, the parameter range of each second target parameter excluding the third target parameter, and the parameter value of the first target parameter into the target prediction model to obtain the predicted value of the surface peak heat flux corresponding to the target parameter combination.

[0047] The determination unit is used to determine the predicted surface peak heat flux based on the combination of each target parameter and the corresponding predicted surface peak heat flux value.

[0048] Thirdly, an electronic device is provided, which includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0049] Memory, used to store computer programs;

[0050] When a processor executes a program stored in memory, it implements any of the steps described in the first aspect above.

[0051] Fourthly, a computer-readable storage medium is provided, wherein a computer program is stored therein, and when executed by a processor, the computer program implements the steps of any of the methods described in the first aspect above.

[0052] This application solves the problem of predicting the location of peak heat flux density on the surface of an aircraft. It classifies the data according to the different locations of peak heat flux density and trains different prediction models using the classified data. When the combination of target parameters to be predicted appears, the corresponding prediction model is selected according to the distribution range of the classified data. The peak heat flux density of the aircraft surface under the corresponding shape and operating conditions can be quickly obtained. It can be used for aerodynamic shape optimization with the goal of minimizing the peak heat flux density of the aircraft under fixed operating conditions, and can also be used for outputting and predicting the curve of peak heat flux of the aircraft with ballistic operating conditions under fixed shape.

[0053] This application innovatively compares and classifies stagnation heat flux and shoulder heat flux, quickly locates the position where the peak heat flux density occurs, and calculates the heat flux density only once during the prediction model generation process, reducing the amount of computation.

[0054] This application addresses multidimensional parameters by using orthogonal experimental design and sensitivity analysis to select at least one key parameter with the greatest impact on the output results for each parameter category for parameter inversion. This provides a basis for judging the combination of target parameters and supports classification prediction.

[0055] This application has a wide range of applications. It can be combined with optimization algorithms to find the minimum aerodynamic shape for peak heat flux under fixed operating conditions, and it can also obtain the variation law of peak heat flux under different operating conditions for a fixed shape based on ballistic conditions.

[0056] Compared to numerical simulation calculations, the introduction of a prediction model in this application directly establishes the relationship between input parameters and output performance parameters, which greatly reduces computation time, lowers the use of computing resources, and improves computational efficiency compared to CFD simulation calculations. Attached Figure Description

[0057] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0058] Figure 1 A flowchart illustrating a method for predicting the surface peak heat flux of an aircraft, provided in an embodiment of this application;

[0059] Figure 2 A schematic diagram illustrating a method for predicting the surface peak heat flux of an aircraft, provided in an embodiment of this application;

[0060] Figure 3 A flowchart for finding the minimum aerodynamic shape under fixed operating conditions peak heat flux is provided in the embodiments of this application.

[0061] Figure 4 A schematic diagram illustrating the peak heat flux variation under different operating conditions for a fixed shape, provided in an embodiment of this application;

[0062] Figure 5 A schematic diagram of the structure of a surface peak heat flux prediction device for an aircraft provided in this application embodiment;

[0063] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0064] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0065] The surface peak heat flux prediction method for aircraft provided in this application can be applied to servers or terminals with strong computing capabilities. The server can be a physical server, a server cluster composed of multiple physical servers, or a distributed system. It can also be a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms. The terminal can be user equipment (UE) such as mobile phones, smartphones, laptops, digital radio receivers, personal digital assistants (PDAs), and tablet computers (PADs), handheld devices, in-vehicle devices, wearable devices, computing devices, or other processing devices connected to a wireless modem, mobile stations (MS), and mobile terminals. The terminal and server can be directly or indirectly connected via wired or wireless communication methods, which is not limited herein.

[0066] The preferred embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit this application. Furthermore, the embodiments and features in the embodiments of this application can be combined with each other without conflict.

[0067] Figure 1 This is a flowchart illustrating a method for predicting the surface peak heat flux of an aircraft, provided as an embodiment of this application. Figure 1 As shown, the method may include:

[0068] Step S110: Obtain the parameter values ​​of the first target parameter used to predict the peak surface heat flux in the target application scenario and the parameter ranges of all second target parameters; extract at least one third target parameter from all second target parameters.

[0069] In this application embodiment, there are two target application scenarios: one is the aerodynamic shape optimization scenario of aircraft, and the other is the peak heat flux analysis scenario of aircraft surface. Regardless of the application scenario, predicting the peak heat flux of the surface requires inputting two types of parameters, namely the first target parameter and the second target parameter. The first target parameter requires inputting a specific parameter value, while the second target parameter requires inputting a parameter range. The first target parameter and the second target parameter are different in the two application scenarios.

[0070] In the embodiments of this application, the first target parameter and the second target parameter each correspond to a parameter category, and the parameter categories of the first target parameter and the second target parameter are different; when the target application scenario is the aerodynamic shape optimization scenario of an aircraft, the parameter category to which the first target parameter belongs is the operating condition parameter category, and the parameter category to which the second target parameter belongs is the shape category; when the target application scenario is the peak heat flux analysis scenario of an aircraft surface, the parameter category to which the first target parameter belongs is the shape category, and the parameter category to which the second target parameter belongs is the operating condition parameter category.

[0071] In the embodiments of this application, the operating parameters include: flight altitude, flight Mach number and angle of attack, etc.; the shape parameters include: head radius, shoulder radius and overall length, etc.

[0072] In practical applications, the purpose of aircraft aerodynamic shape optimization scenarios is to find the optimal shape parameters of an aircraft under specific operating conditions; the purpose of aircraft surface peak heat flux analysis scenarios is to analyze the distribution law of the corresponding surface peak heat flux for an aircraft with a specific shape.

[0073] In this embodiment of the application, the parameter value of the first target parameter and the parameter range of all second target parameters are user-defined inputs or configurations.

[0074] In practical applications, since there are multiple second objective parameters, and the amount of computation required to match the corresponding prediction model after taking values ​​from multiple second objective parameters is large in the early stage, this application performs dimensionality reduction processing on multiple second objective parameters, extracts at least one third objective parameter from them, and uses only the third objective parameter to match the prediction model, thereby reducing the amount of data and improving the overall efficiency.

[0075] In this embodiment of the application, at least one third target parameter is extracted from all second target parameters, including:

[0076] Obtain the parameter category to which the second target parameter belongs, and the key parameters under that parameter category; for any second target parameter, if the second target parameter is a key parameter, then use the second target parameter as the third target parameter.

[0077] In the embodiments of this application, the parameter categories to which the first target parameter and the second target parameter belong both include at least one key parameter; for example, the second target parameter is flight altitude, flight Mach number and angle of attack, and the parameter category to which the second target parameter belongs is the operating condition category; the key parameters under the operating condition category are flight altitude and angle of attack, then the flight altitude and angle of attack are extracted from the second target parameter as the third target parameter.

[0078] In this embodiment, the key parameters for each parameter category are obtained by analyzing data from a pre-built aircraft heat flux density distribution database. Specific steps include:

[0079] The importance scores of different target parameters are obtained by analyzing the different historical parameter vectors and corresponding aircraft heat flux density distribution data stored in the aircraft heat flux density distribution database. For any parameter category, the target parameters included in the parameter category are sorted from largest to smallest according to their respective importance scores. From the sorted target parameters, a preset number of target parameters at the top of the sorted list are extracted to obtain the key parameters of that parameter category.

[0080] For example, assuming a preset quantity of 2, analyzing the data stored in the aircraft heat flux density distribution database yields importance scores of 9, 5, 6, 4, 2, and 1 for different target parameters such as flight altitude, flight Mach number, angle of attack, nose radius, shoulder radius, and overall length. For the operational condition category, after sorting the flight altitude, flight Mach number, and angle of attack according to their respective importance scores from largest to smallest, the top two parameters are extracted to obtain the key parameters for the operational condition category: flight altitude and angle of attack. For the shape category, after sorting the nose radius, shoulder radius, and overall length according to their respective importance scores from largest to smallest, the top two parameters are extracted to obtain the key parameters for the shape category: nose radius and shoulder radius.

[0081] In the embodiments of this application, the data stored in the aircraft heat flux density distribution database can be analyzed using various methods such as correlation analysis, regression analysis, feature importance assessment, partial derivative dependence plots, Shapley values, and / or sensitivity analysis.

[0082] In the embodiments of this application, the key parameter of each parameter category is actually the parameter that has the greatest impact on the final heat flux density distribution on the aircraft surface among the parameters of that parameter category.

[0083] In this embodiment of the application, the method for constructing a spacecraft heat flux density distribution database includes:

[0084] Obtain the historical parameter range and parameter category of the first target parameter and the second target parameter respectively; for any target parameter, take a value within the historical parameter range of the target parameter to obtain multiple historical parameter values ​​of the target parameter; for any parameter category, combine the different historical parameter values ​​of each target parameter included in the parameter category to obtain multiple historical parameter combinations; combine the different historical parameter combinations under each parameter category to obtain multiple historical parameter vectors.

[0085] For any historical parameter vector, a 3D model of the aircraft corresponding to the historical parameter vector is constructed based on the historical parameter combination of the shape class in the historical parameter vector; the 3D model is used to perform simulation under the historical parameter combination of the operating condition class to obtain the heat flux density distribution data of the aircraft corresponding to the historical parameter vector; a heat flux density distribution database of the aircraft is constructed based on different historical parameter vectors and the corresponding heat flux density distribution data of the aircraft.

[0086] In the embodiments of this application, historical parameter values ​​or parameter values ​​can be combined using orthogonal experimental design methods, thereby reducing the amount of data and ensuring a balanced combination of different parameter values ​​or different historical parameter values ​​for each target parameter.

[0087] In this embodiment, the historical parameter ranges of all operating condition parameters and shape parameters are obtained in advance, and each parameter is assigned a value within its parameter range using an orthogonal experimental design method, thereby obtaining multiple parameter vectors composed of the corresponding historical parameter values ​​for different parameters. For example, the ranges for flight altitude, flight Mach number, angle of attack, nose radius, shoulder radius, and total length are 3000-5000, 100-200, 30-50, 20-30, 60-80, and 10-15, respectively. Assuming that each parameter takes two values, namely 3500, 4000; 150, 160; 30, 40; 22, 23; 70, 75; 12, 13, then using orthogonal experimental design, eight historical parameter vectors are obtained: (3500, 150, 30, 22, 70, 12), (3500, 150, 40, 13), and (3500, 150, 40, 13). 23,75,13), (3500,160,30,23,70,13), (3500,160,40,22,75,12), (4000,150,30,23,75,12), (4000,150,40,22,70,13), (4000,160,30,22,75,13) and (4000,160,40,23,70,12); In the above historical vectors, the parameter combination composed of the first 3 parameter values ​​is the parameter combination of the operating condition class, and the parameter combination composed of the last 3 parameter values ​​is the parameter combination of the shape class. Based on the parameter combination of the shape class, a three-dimensional model of the aircraft is constructed. Based on the parameter combination of the operating condition class and the three-dimensional model, CFD numerical simulation is performed to obtain the corresponding aircraft heat flux density distribution data and construct an aircraft heat flux density distribution database.

[0088] In this embodiment of the application, the historical parameter ranges of all operating condition parameters and shape parameters can be determined according to aircraft design specifications or other design specifications, or can be customized by the user.

[0089] In this embodiment of the application, the heat flux density distribution data of the aircraft refers to the heat flux density distribution of the aircraft along the axis of symmetry corresponding to each historical parameter vector.

[0090] In this embodiment of the application, the stationary point coordinates and shoulder coordinates of the corresponding aircraft can be determined according to the three-dimensional model corresponding to each historical parameter vector. Based on the determined coordinates, the corresponding stationary point heat flux density and shoulder heat flux density can be extracted from the heat flux density distribution along the axis of symmetry of the aircraft.

[0091] In this embodiment of the application, for each data point stored in the aircraft heat flux density distribution database, the extracted stagnation point heat flux density and shoulder heat flux density are compared. If the stagnation point heat flux density is greater than the shoulder heat flux density, the heat flux density distribution data of the aircraft corresponding to the historical parameter vector is stored in the configured first dataset; if the stagnation point heat flux density is not greater than the shoulder heat flux density, the heat flux density distribution data of the aircraft corresponding to the historical parameter vector is stored in the configured second dataset, thereby obtaining two datasets containing different data.

[0092] In this embodiment of the application, after obtaining two different datasets, it is also necessary to determine the distribution range of the key parameters of each parameter category in each dataset. Taking the database composed of the above-obtained 8 historical parameter vectors as an example, assuming that the key parameter of the working condition category is flight altitude, the first 4 sets of data are the first dataset, and the last 4 sets of data are the second dataset; then for flight altitude, its distribution range in the first dataset is a value of 3500, and its distribution range in the second dataset is also a value of 400; the key parameters of the shape category are shoulder radius and total length. The distribution range of shoulder radius in the second dataset is (70, 75), and the distribution range of total length in the second dataset is (12, 13). For the parameter combination of the key parameters of the shape category, its distribution range is (75, 12), (75, 13), (70, 12), and (70, 13); based on this, the distribution range of the key parameters of each parameter category in the corresponding dataset is determined.

[0093] In practical applications, if the peak heat flux of the head and shoulders is compared and selected each time, the computational load will be greatly increased. Therefore, this application classifies the peak heat flux of the aircraft in the early stage of building the prediction model, and outputs the peak heat flux of the aircraft directly based on the previous classification when it is used, thereby improving the computational and optimization efficiency.

[0094] In this embodiment of the application, the prediction model includes a first prediction model and a second prediction model; the two prediction models can be the same model or different models, preferably the same model; specifically, the prediction model can be selected from radial basis, kriging or BP neural network models, etc.

[0095] Step S120: For any third target parameter, take values ​​within the parameter range of the third target parameter to obtain multiple parameter values ​​of the third target parameter; combine the different parameter values ​​of each third target parameter to obtain multiple target parameter combinations.

[0096] In the embodiments of this application, the third target parameter is actually a key parameter of the parameter category to which the second target parameter belongs; and the number of key parameters of the parameter category can be one or more. If there are multiple key parameters, the third target parameter needs to be combined after taking values ​​to obtain the corresponding target parameter combination; the method of taking and combining values ​​can be the orthogonal experimental design method.

[0097] Step S130: For any combination of target parameters, match the target prediction model corresponding to the combination of target parameters from the pre-built table of different parameter combinations and corresponding prediction models.

[0098] In this embodiment of the application, the method for constructing a comparison table of different parameter combinations and corresponding prediction models includes: for any parameter category in any dataset, extracting historical parameter values ​​of each key parameter of the parameter category from the dataset; based on the extracted historical parameter values, determining the historical parameter combinations of each key parameter of the parameter category in the dataset; using the determined historical parameter combinations as parameter combinations, and using the prediction model trained on the dataset as the prediction model corresponding to the parameter combinations, to obtain a comparison table of different parameter combinations and corresponding prediction models.

[0099] In this embodiment of the application, two datasets are used to train two models respectively. The first dataset is used to train the first prediction model, and the second dataset is used to train the second prediction model. Since the distribution range of the key parameters of each parameter category in the two datasets is different, the correlation can be constructed based on the distribution range of the parameter combination of each parameter category in the dataset and the training objective of the dataset.

[0100] For example, the parameter combinations of the key parameters of the shape class in the second dataset are distributed in the range of (75,12), (75,13), (70,12), and (70,13). The second dataset is used to train the second prediction model. Therefore, we establish a correlation between the parameter combinations (75,12), (75,13), (70,12), and (70,13) of the shoulder radius and total length and the second prediction model. Similarly, we establish a correlation between the distribution range of the flight altitude of the working condition parameters in the second dataset and the second prediction model, and establish a correlation between the parameter combinations of the working condition parameters and the parameter combinations of the shape parameters in the first dataset and the first prediction model. This results in a table comparing different parameter combinations with the corresponding prediction models.

[0101] In the embodiments of this application, for any combination of target parameters of the third target parameter, it is possible to determine whether its value is within the distribution range of the key parameters of the corresponding parameter category in the first dataset. For example, if the parameter category of the third target parameter is the working condition class and the target parameter combination is (60,12), it can be determined that the target parameter combination does not belong to the distribution range of the key parameters of the working condition class in the second dataset. Therefore, it should be within the distribution range of the first dataset, that is, the first prediction model is the target model.

[0102] In this embodiment of the application, it is sufficient to determine the distribution range of key parameters of working condition class and shape class in the first dataset. As long as the target parameter combination does not conform to the distribution range of key parameters of the corresponding parameter category in the first dataset, it is directly determined to conform to the distribution range of key parameters of the corresponding parameter category in the second dataset; that is, as long as it does not match the range corresponding to the first prediction model, the second prediction model is directly used as the target model.

[0103] Step S140: Input the target parameter combination, the parameter range of each second target parameter excluding the third target parameter, and the parameter value of the first target parameter into the target prediction model to obtain the predicted surface peak heat flux value corresponding to the target parameter combination.

[0104] In the embodiments of this application, during the prediction process, the parameter values ​​of the first target parameter, the combination of target parameters, and the parameter ranges of the second target parameters other than the third target parameter are all taken as inputs to obtain the predicted surface peak heat flux value of the corresponding target parameter combination.

[0105] Step S150: Based on the combination of each target parameter and the corresponding predicted value of the surface peak heat flux, determine the predicted result of the surface peak heat flux.

[0106] In this embodiment of the application, when the target application scenario is an aircraft design scenario, the target parameter combination with the smallest predicted surface peak heat flux value and the corresponding predicted surface peak heat flux value are extracted from each target parameter combination to obtain the predicted surface peak heat flux result.

[0107] In this embodiment of the application, when the target application scenario is an aircraft design scenario, the parameter category to which the second target parameter belongs is the shape category, and the first target parameter is the operating condition category. Its core is to select the aircraft configuration corresponding to the smallest predicted value of surface peak heat flux under fixed operating conditions from the combination of target parameters of multiple shape categories, and output the corresponding target parameter combination and the corresponding predicted value of surface peak heat flux.

[0108] In this embodiment of the application, when the target application scenario is the peak heat flux analysis scenario of the aircraft surface, the combination of each target parameter and the corresponding predicted peak heat flux value of the surface are used as the predicted peak heat flux of the aircraft surface.

[0109] In the embodiments of this application, in the scenario of peak heat flux analysis on the surface of an aircraft, the shape parameters of the aircraft are fixed. The core is to analyze the distribution of peak heat flux on the surface of an aircraft with a certain shape parameter under different operating conditions.

[0110] In another embodiment of this application, such as Figure 2 As shown, the method for predicting the surface peak heat flux of this aircraft includes:

[0111] 1. Construct a prediction model:

[0112] Step 1: Determine the parameter range: The parameters affecting aerodynamic simulation and heat flux density calculation of aircraft are mainly divided into two categories: one is structural shape parameters, such as nose radius, shoulder radius, and overall length; the other is operating condition parameters, such as flight altitude, flight Mach number, and angle of attack. Determining the range of each parameter facilitates sampling and experimental design.

[0113] Step 2: Orthogonal experimental design: Sampling is carried out within the parameter range using the orthogonal experimental design method to obtain calculation sample points, which facilitates the next step of simulation calculation.

[0114] Step 3: CFD numerical simulation: Perform CFD numerical simulation on each sample point with the determined shape and working condition to form a database.

[0115] Step 4: Obtain heat flux density distribution: After completing the calculation, output the heat flux density distribution of the aircraft along the axis of symmetry at each sample point, which is convenient for classification, comparison and screening.

[0116] Step 5: Heat flux classification and prediction: Classify the heat flux density distribution of each sample point according to its different distribution. When the heat flux density at the stagnation point Qo is greater than that at the shoulder Qr, the sample point is placed in the training sample set of the first prediction model. When the heat flux density at the stagnation point Qo is less than or equal to that at the shoulder Qr, the sample point is placed in the training sample set of the second prediction model.

[0117] Step Six: Establish Predictive Models: The first predictive model is constructed with the stagnation heat flux density Qoa as the output and the shape and operating parameters as inputs. The second predictive model is constructed with the shoulder heat flux density Qrb as the output and the shape and operating parameters as inputs. Predictive models can be selected from radial basis function, kriging, BP neural network, etc. After training and testing, the final predictive model is generated.

[0118] Step 7: Parameter sensitivity analysis: For the first and second prediction models, perform parameter sensitivity analysis on their input variables to obtain the shape parameters x1, x2 and operating parameters y1, y2 that have the greatest impact on their respective output heat flux densities, and determine the range of the above parameters according to the classification.

[0119] 2. Use predictive models for forecasting.

[0120] Once the prediction model is established, it can be applied in two ways.

[0121] (1) Optimization of aerodynamic shape to minimize peak heat flux under fixed operating conditions

[0122] like Figure 3 As shown, when optimizing the aerodynamic shape of an aircraft, optimization is often performed at a single operating point, i.e., with fixed operating parameters and the input variable Y determined. Based on the (x1, x2) parameter range of a new sample point, it can be determined whether the sample point belongs to the first or second prediction model. If it belongs to the first prediction model, the peak heat flux Qoa is calculated according to the prediction model; otherwise, the peak heat flux Qrb is calculated, quickly determining the location and value of the peak heat flux at this sample point. After calculation, a judgment is made. If this heat flux is not the minimum heat flux among all sample points, the shape parameters are re-inputted, a new sample point is constructed, and the calculation loop begins. If it is the minimum heat flux, the result is directly output. The result obtained is the aircraft configuration and heat flux value corresponding to the minimum peak heat flux under fixed operating conditions.

[0123] (2) Variation law of peak heat flux under different operating conditions with fixed shape

[0124] like Figure 4 As shown, during the ballistic simulation of an aircraft, it is necessary to simulate the peak heat flux variation under different operating conditions for an aircraft with a given configuration. Therefore, the input variable X is determined, and the range of the operating parameters (y1, y2) at the new sample point is judged. Similarly, the corresponding peak heat flux is calculated according to the previous classification. After the calculation is completed, the corresponding operating parameters and peak heat flux are output. Then, the calculation is repeated until the calculation is completed, and the peak heat flux variation along the ballistic operating point can be obtained, providing a reference for the design of heat protection materials.

[0125] Corresponding to the above method, embodiments of this application also provide a surface peak heat flux prediction device for an aircraft, such as... Figure 5 As shown, the surface peak heat flux prediction device for this aircraft includes:

[0126] The acquisition unit 510 is used to acquire the parameter values ​​of the first target parameter for predicting the peak surface heat flux in the target application scenario and the parameter ranges of all second target parameters.

[0127] Extraction unit 520 is used to extract at least one third target parameter from all second target parameters;

[0128] The combination unit 530 is used to take values ​​within the parameter range of any third target parameter to obtain multiple parameter values ​​of the third target parameter; and to combine the different parameter values ​​of each third target parameter to obtain multiple target parameter combinations.

[0129] Matching unit 540 is used to match the target prediction model corresponding to any target parameter combination from a pre-built table of different parameter combinations and corresponding prediction models.

[0130] The prediction unit 550 is used to input the target parameter combination, the parameter range of each second target parameter excluding the third target parameter, and the parameter value of the first target parameter into the target prediction model to obtain the predicted value of the surface peak heat flux corresponding to the target parameter combination.

[0131] The determination unit 560 is used to determine the surface peak heat flux prediction result based on the combination of each target parameter and the corresponding surface peak heat flux prediction value.

[0132] The functions of each functional unit of the surface peak heat flux prediction device for aircraft provided in the above embodiments of this application can be implemented through the above method steps. Therefore, the specific working process and beneficial effects of each unit in the surface peak heat flux prediction device for aircraft provided in the embodiments of this application will not be repeated here.

[0133] This application also provides an electronic device, such as... Figure 6 As shown, it includes a processor 610, a communication interface 620, a memory 630, and a communication bus 640, wherein the processor 610, the communication interface 620, and the memory 630 communicate with each other through the communication bus 640.

[0134] Memory 630 is used to store computer programs;

[0135] When the processor 610 executes the program stored in the memory 630, it performs the following steps:

[0136] Obtain the parameter values ​​of the first target parameter used to predict the peak surface heat flux in the target application scenario, as well as the parameter ranges of all second target parameters;

[0137] Extract at least one third objective parameter from all the second objective parameters;

[0138] For any third target parameter, take values ​​within the parameter range of the third target parameter to obtain multiple parameter values ​​of the third target parameter; combine the different parameter values ​​of each third target parameter to obtain multiple target parameter combinations;

[0139] For any combination of target parameters, the target prediction model corresponding to the target parameter combination is matched from a pre-built table of different parameter combinations and corresponding prediction models.

[0140] Input the target parameter combination, the parameter range of each second target parameter excluding the third target parameter, and the parameter value of the first target parameter into the target prediction model to obtain the predicted surface peak heat flux value corresponding to the target parameter combination;

[0141] Based on the combination of each target parameter and the corresponding predicted value of the surface peak heat flux, the predicted result of the surface peak heat flux is determined.

[0142] The communication bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0143] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0144] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0145] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0146] The implementation methods and beneficial effects of the various components of the electronic device in the above embodiments for solving the problem can be found in [reference needed]. Figure 1 The steps in the illustrated embodiments are used to implement the electronic device. Therefore, the specific working process and beneficial effects of the electronic device provided in this application will not be repeated here.

[0147] In another embodiment provided in this application, a computer-readable storage medium is also provided, which stores instructions that, when executed on a computer, cause the computer to perform any of the surface peak heat flux prediction methods for aircraft in the above embodiments.

[0148] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute the surface peak heat flux prediction method for any of the above embodiments.

[0149] Those skilled in the art will understand that the embodiments in this application can be provided as methods, systems, or computer program products. Therefore, the embodiments in this application can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, the embodiments in this application can take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0150] This application describes embodiments of methods, apparatus (systems), and computer program products according to embodiments of this application with reference to flowchart illustrations and / or block diagrams. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0151] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0152] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0153] Although preferred embodiments have been described in this application, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of this application.

[0154] Obviously, those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the spirit and scope of the embodiments of this application. Therefore, if these modifications and variations to the embodiments of this application fall within the scope of the claims in this application and their equivalents, then this application also intends to include these modifications and variations.

Claims

1. A method for predicting the surface peak heat flux of an aircraft, characterized in that, The method comprises: obtaining a parameter value of a first target parameter and a parameter range of all second target parameters for predicting a surface peak heat flow in a target application scenario; wherein the parameter categories of the first target parameter and the second target parameter are different; the parameter categories include a working condition category and a shape category; extracting at least one third target parameter from all second target parameters; for any third target parameter, obtaining a plurality of parameter values of the third target parameter by taking values in the parameter range of the third target parameter; combining different parameter values of each third target parameter to obtain a plurality of target parameter combinations; for any target parameter combination, matching a target prediction model corresponding to the target parameter combination from a pre-constructed different parameter combination and corresponding prediction model matching table; inputting the target parameter combination, the parameter range of the second target parameter except the third target parameter and the parameter value of the first target parameter into the target prediction model to obtain a surface peak heat flow prediction value corresponding to the target parameter combination; determining a surface peak heat flow prediction result based on each target parameter combination and the corresponding surface peak heat flow prediction value.

2. The method of claim 1, wherein, extracting at least one third target parameter from all second target parameters, comprising: obtaining a parameter category to which the second target parameter belongs and a key parameter under the parameter category; wherein the key parameter is obtained by analyzing data in a pre-constructed aircraft heat flow density distribution database; for any second target parameter, if the second target parameter is the key parameter, the second target parameter is taken as the third target parameter.

3. The method of claim 2, wherein, The method for constructing the aircraft heat flow density distribution database comprises: respectively obtaining a historical parameter range and a belonging parameter category of the first target parameter and the second target parameter; for any target parameter, obtaining a plurality of historical parameter values of the target parameter by taking values in the historical parameter range of the target parameter; for any parameter category, combining different historical parameter values of each target parameter included in the parameter category to obtain a plurality of historical parameter combinations; combining different historical parameter combinations under each parameter category to obtain a plurality of historical parameter vectors; for any historical parameter vector, constructing a three-dimensional model of an aircraft corresponding to the historical parameter vector based on the historical parameter combination of the shape category in the historical parameter vector; using the three-dimensional model to simulate under the historical parameter combination of the working condition category to obtain heat flow density distribution data of the aircraft corresponding to the historical parameter vector; constructing an aircraft heat flow density distribution database based on different historical parameter vectors and corresponding aircraft heat flow density distribution data.

4. The method of claim 3, wherein, The method for determining the key parameter comprises: analyzing different historical parameter vectors and corresponding aircraft heat flow density distribution data to obtain importance scores of different target parameters; for any parameter category, sorting each target parameter included in the parameter category according to the corresponding importance score from large to small; From the sorted target parameters, a preset number of target parameters ranked in the front are extracted to obtain key parameters of the parameter category.

5. The method of claim 3, wherein, After the aircraft heat flux density distribution database is constructed, the method further comprises: For any historical parameter vector in the aircraft heat flux density distribution database, based on the three-dimensional model of the aircraft corresponding to the historical parameter vector, the stagnation point coordinates and the shoulder coordinates of the aircraft are determined; From the heat flux density distribution data of the aircraft corresponding to the historical parameter vector, the stagnation point heat flux density and the shoulder heat flux density corresponding to the stagnation point coordinates and the shoulder coordinates are extracted respectively; If the stagnation point heat flux density is greater than the shoulder heat flux density, the heat flux density distribution data of the aircraft corresponding to the historical parameter vector is stored in the configured first data set; If the stagnation point heat flux density is not greater than the shoulder heat flux density, the heat flux density distribution data of the aircraft corresponding to the historical parameter vector is stored in the configured second data set.

6. The method of claim 5, wherein, The prediction model comprises a first prediction model and a second prediction model; The first prediction model is obtained by training a first initial prediction model using the first data set; The second prediction model is obtained by training a second initial prediction model using the second data set.

7. The method of claim 6, wherein, The target application scenario includes an aircraft design scenario and an aircraft surface peak heat flux analysis scenario; When the target application scenario is the aircraft design scenario, based on each target parameter combination and the corresponding surface peak heat flux prediction value, a surface peak heat flux prediction result is determined, comprising: From each target parameter combination, the target parameter combination with the minimum corresponding surface peak heat flux prediction value and the corresponding surface peak heat flux prediction value are extracted to obtain the surface peak heat flux prediction result; When the target application scenario is the aircraft surface peak heat flux analysis scenario, each target parameter combination and the corresponding surface peak heat flux prediction value are taken as the surface peak heat flux prediction result of the aircraft.

8. An aircraft surface peak heat flux prediction device, characterized by, The device comprises: An acquisition unit is configured to acquire parameter values of first target parameters and parameter ranges of all second target parameters for predicting surface peak heat flux in a target application scenario; wherein the parameter categories of the first target parameters and the second target parameters are different; the parameter categories include working condition categories and shape categories; An extraction unit is configured to extract at least one third target parameter from all second target parameters; A combination unit is configured to obtain multiple parameter values of any third target parameter by taking values in the parameter range of the third target parameter; and combine different parameter values of each third target parameter to obtain multiple target parameter combinations; A matching unit is configured to match a target prediction model corresponding to any target parameter combination from a pre-constructed different parameter combination and a corresponding prediction model matching table. The prediction unit is configured to input the target parameter combination, a parameter range of each second target parameter except the third target parameter, and a parameter value of the first target parameter into the target prediction model to obtain a surface peak heat flow prediction value corresponding to the target parameter combination. The determination unit is configured to determine a surface peak heat flow prediction result based on each target parameter combination and the corresponding surface peak heat flow prediction value.

9. An electronic device, comprising: The electronic device comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete mutual communication through the communication bus. The memory is configured to store a computer program. The processor is configured to execute the program stored on the memory to implement the method in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the method in any one of claims 1-7.

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