A solid rocket charge performance uncertainty design optimization method and related device

Abstract

The present application provides a solid motor charge performance uncertainty design optimization method and related devices, relating to the field of solid motor technology. The method takes the maximum impulse-to-mass ratio as the objective function, the solid motor working thrust and working time as the constraints, and the front wing length, front wing circumscribed circle diameter, rear wing length, rear wing circumscribed circle diameter, maximum combustion chamber pressure, front wing inclination angle, rear wing inclination angle, wing width, first-stage charge reference burning rate, and expansion ratio as the design variables. An initial Kriging model of the solid motor charge performance is established; the Kriging model of the solid motor charge performance is updated according to the selection of the minimum performance target point according to the adaptive strategy, and the solid motor charge performance is optimized and solved using an adaptive single-loop optimization method; the scientific nature and effectiveness of the proposed uncertainty design optimization method are verified by comparative analysis with traditional optimization results.

Description

Technical Field

[0001] The present application relates to the technical field of solid motors, and in particular to a solid motor charge performance uncertainty design optimization method and related devices. Background Art

[0002] Solid rocket engines are important power equipment in aerospace propulsion engineering. They have the characteristics of simple structure, easy use and long-term storage, and are widely used in military and aerospace fields.

[0003] As the energy source for solid rocket motors, the design level of the grain determines the motor's interior ballistic performance and other technical indicators. However, due to the objective uncertainty factors such as geometric parameters, propellant burning rate, pressure index, and temperature sensitivity coefficient during the solidification, cooling, storage, transportation, and flight of the grain, the change pattern of the grain burning surface deviates significantly from the design requirements, reducing the reliability of the grain performance. Summary of the Invention

[0004] The present application provides a solid motor charge performance uncertainty design optimization method and related devices, which can improve the reliability of solid motor charge performance.

[0005] To achieve the above objectives, this application adopts the following technical solutions:

[0006] In a first aspect, the present application provides a method for design optimization of solid motor charge performance uncertainty, the method comprising:

[0007] According to the k-1th group of target uncertainty parameters, obtaining the k-1th round of normalized gradient vectors and the kth group of parameter mean values, wherein the working thrust corresponding to the kth group of parameter mean values ​​is greater than a preset thrust threshold, and the working time corresponding to the kth group of parameter mean values ​​is greater than a preset time threshold;

[0008] Determine whether the mean value of the kth group of parameters and the mean value of the k-1th group of parameters meet a first preset condition, and obtain a first determination result;

[0009] If the first judgment result indicates that the mean of the kth group of parameters and the mean of the k-1th group of parameters do not meet the first preset condition, determining the kth group of first uncertainty parameters based on the mean of the kth group of parameters, the parameter variance, the target reliability index, and the k-1th round of standardized gradient vector, and determining the kth round prediction gradient of the kth round kriging model for the kth group of first uncertainty parameters;

[0010] Determine whether the k-th group of first uncertainty parameters and the k-th round prediction gradient meet a second preset condition, and obtain a second determination result;

[0011] If the second judgment result indicates that the kth group of first uncertainty parameters and the kth round prediction gradient meet a second preset condition, then the kth group of first uncertainty parameters is determined as the kth group of target uncertainty parameters; wherein the uncertainty parameters include front wing length, front wing circumscribed circle diameter, rear wing length, rear wing circumscribed circle diameter, maximum combustion chamber pressure, front wing inclination angle, rear wing inclination angle, wing width, first-stage charge reference burning rate, and expansion ratio; and the prediction dimension of the kth round Kriging model includes a working thrust dimension or a working time dimension;

[0012] If the first judgment result indicates that the mean value of the kth group of parameters and the mean value of the k-1th group of parameters meet the first preset condition, the solid motor charge performance is optimized according to the mean value of the kth group of parameters; wherein k is an integer greater than 1.

[0013] Optionally, the method further includes:

[0014] If the second judgment result represents that the kth group of first uncertainty parameters and the kth round prediction gradient do not meet the second preset condition, then the kth group of second uncertainty parameters is determined based on the kth group of first uncertainty parameters, and the kth group of second uncertainty parameters is determined as the kth group of target uncertainty parameters, and the accuracy of the second uncertainty parameters is higher than the accuracy of the first uncertainty parameters.

[0015] Optionally, determining whether the mean value of the kth group of parameters and the mean value of the k-1th group of parameters meet a first preset condition to obtain a first determination result includes:

[0016]

[0017] in, represents the mean value of the kth group of parameters, represents the mean of the k-1th group of parameters, represents the first condition threshold; if the above formula is satisfied, a first judgment result is obtained that the mean of the kth group of parameters and the mean of the k-1th group of parameters satisfy the first preset condition; if the above formula is not satisfied, a first judgment result is obtained that the mean of the kth group of parameters and the mean of the k-1th group of parameters do not satisfy the first preset condition.

[0018] Optionally, determining the kth group of first uncertainty parameters according to the kth group of parameter means, parameter variances, target reliability indicators, and the k-1th round of normalized gradient vectors includes:

[0019]

[0020] in, represents the kth group of first uncertainty parameters in the i-th dimension, represents the mean value of the kth group of parameters, represents the parameter variance, is the target reliability index, is the k-1th normalized gradient vector for the i-th dimension.

[0021] Optionally, the determining whether the k-th group of first uncertainty parameters and the k-th round prediction gradient satisfy a second preset condition to obtain a second determination result includes:

[0022]

[0023] in, represents the kth group of first uncertainty parameters in the i-th dimension, represents the k-th round prediction gradient of the i-th dimension, Represents the second conditional threshold; if the above formula is satisfied, a second judgment result is obtained that the k-th group of first uncertainty parameters and the k-th round prediction gradient satisfy the second preset condition; if the above formula is not satisfied, a second judgment result is obtained that the k-th group of first uncertainty parameters and the k-th round prediction gradient do not satisfy the second preset condition.

[0024] Optionally, the method further includes:

[0025] Simulating the kth group of target uncertainty parameters to obtain response values ​​of multiple dimensions corresponding to the kth group of target uncertainty parameters, the multiple dimensions including a working thrust dimension and a working time dimension;

[0026] The kth group of target uncertainty parameters and the corresponding response values ​​of multiple dimensions are used to update the kth round Kriging model corresponding to each dimension respectively to obtain the k+1th round Kriging model.

[0027] Optionally, the method further includes:

[0028] Obtaining sample uncertainty parameters of the solid rocket motor, wherein the sample uncertainty parameters include a sample front wing length, a sample front wing circumscribed circle diameter, a sample rear wing length, a sample rear wing circumscribed circle diameter, a sample maximum combustion chamber pressure, a sample front wing inclination angle, a sample rear wing inclination angle, a sample wing width, a sample first-stage charge reference burning rate, and a sample expansion ratio;

[0029] Simulating the sample uncertainty parameters to obtain response values ​​of multiple dimensions corresponding to the sample uncertainty parameters;

[0030] Based on the sample uncertainty parameter and the response values ​​of the multiple dimensions, initial round kriging models corresponding to the multiple dimensions are respectively constructed.

[0031] In a second aspect, the present application provides a solid motor charge performance uncertainty design optimization device, which includes:

[0032] an acquisition module, configured to acquire, based on the k-1th group of target uncertainty parameters, a k-1th round of normalized gradient vectors and a kth group of parameter mean values, wherein the working thrust corresponding to the kth group of parameter mean values ​​is greater than a preset thrust threshold, and the working time corresponding to the kth group of parameter mean values ​​is greater than a preset time threshold;

[0033] A first judgment module is used to judge whether the mean value of the kth group of parameters and the mean value of the k-1th group of parameters meet a first preset condition, and obtain a first judgment result;

[0034] a processing module, configured to determine, if the first judgment result indicates that the mean of the kth group of parameters and the mean of the k-1th group of parameters do not meet a first preset condition, a kth group of first uncertainty parameters based on the mean of the kth group of parameters, the parameter variance, the target reliability index, and the k-1th round of standardized gradient vector, and determine a kth round prediction gradient of the kth round kriging model for the kth group of first uncertainty parameters;

[0035] A second judgment module is used to judge whether the k-th group of first uncertainty parameters and the k-th round prediction gradient meet a second preset condition, and obtain a second judgment result;

[0036] The processing module is further configured to, if the second judgment result indicates that the kth group of first uncertainty parameters and the kth round prediction gradient satisfy a second preset condition, determine the kth group of first uncertainty parameters as the kth group of target uncertainty parameters; wherein the uncertainty parameters include front wing length, front wing circumscribed circle diameter, rear wing length, rear wing circumscribed circle diameter, maximum combustion chamber pressure, front wing inclination angle, rear wing inclination angle, wing width, first-stage charge reference burning rate, and expansion ratio; and the prediction dimension of the kth round Kriging model includes a working thrust dimension or a working time dimension;

[0037] An optimization module is used to optimize the solid motor charge performance according to the kth group parameter mean if the first judgment result indicates that the kth group parameter mean and the k-1th group parameter mean meet the first preset condition; wherein k is an integer greater than 1.

[0038] In a third aspect, the present application provides a computing device, including a memory and a processor;

[0039] One or more computer programs are stored in the memory, and the one or more computer programs include instructions; when the instructions are executed by the processor, the computing device executes the method as described in any one of the first aspects.

[0040] In a fourth aspect, the present application provides a computer-readable storage medium for storing a computer program for executing the method as described in any one of the first aspects.

[0041] It can be seen from the above technical solution that this application has at least the following beneficial effects:

[0042] The present application provides a method for optimizing the performance uncertainty of a solid rocket charge. The method first obtains a parameter mean value and a normalized gradient vector of the previous round based on the target uncertainty parameter of the previous round. The working thrust corresponding to the parameter mean value of the current round is greater than a preset thrust threshold, and the corresponding working time is also greater than a preset time threshold. The parameter mean value of the current round and the parameter mean value of the previous round are then judged to see whether they meet a first preset condition. If so, the solid rocket is optimized based on the parameter mean value of the current round. If not, the first uncertainty parameter of the current round is determined based on the parameter mean value, parameter variance, target reliability index, and normalized gradient vector of the previous round. The predicted gradient of the first uncertainty parameter of the current round is also determined based on the Kriging model of the current round. If the first uncertainty parameter of the current round and the predicted gradient of the current round meet a second preset condition, the first uncertainty parameter of the current round is determined as the target uncertainty parameter of the current round, and the next cycle is entered. Uncertain parameters include the front wing length, front wing circumscribed circle diameter, rear wing length, rear wing circumscribed circle diameter, maximum combustion chamber pressure, front wing inclination angle, rear wing inclination angle, wing width, first-stage charge reference burning rate, and expansion ratio. The Kriging model's prediction dimensions include operating thrust or operating time. This method can optimize the reliability of solid rocket charge performance, providing effective guidance for solid rocket rocket design.

[0043] It should be understood that the description of technical features, technical solutions, beneficial effects or similar language in this application does not imply that all features and advantages can be realized in any single embodiment. On the contrary, it is understood that the description of a feature or beneficial effect means that a specific technical feature, technical solution or beneficial effect is included in at least one embodiment. Therefore, the description of a technical feature, technical solution or beneficial effect in this specification does not necessarily refer to the same embodiment. Furthermore, the technical features, technical solutions and beneficial effects described in the present embodiment can also be combined in any appropriate manner. Those skilled in the art will understand that the embodiment can be implemented without one or more specific technical features, technical solutions or beneficial effects of a specific embodiment. In other embodiments, additional technical features and beneficial effects can also be identified in specific embodiments that do not embody all embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 A schematic diagram of a solid rocket engine provided in an embodiment of the present application;

[0045] Figure 2 A flow chart of a solid motor charge performance uncertainty design optimization method provided in an embodiment of the present application;

[0046] Figure 3 A schematic diagram of the convergence process of the impulse-to-mass ratio optimization of a solid rocket motor provided in an embodiment of the present application;

[0047] Figure 4 A schematic diagram of a solid motor charge performance uncertainty design optimization device provided in an embodiment of the present application;

[0048] Figure 5 A schematic diagram of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0049] The terms "first", "second" and "third" in this application specification and the accompanying drawings are used to distinguish different objects rather than to limit a specific order.

[0050] In the embodiments of this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0051] like Figure 1 As shown in FIG. , this figure is a schematic diagram of a solid rocket motor provided by an embodiment of the present application. As can be seen from the figure, the solid rocket motor includes a data set of front wing length 11, front wing circumscribed circle diameter 12, rear wing length 13, rear wing circumscribed circle diameter 14, internal ballistics-maximum combustion chamber pressure, front wing inclination angle 15, rear wing inclination angle 16, wing width 17, first-stage charge reference burning rate, and expansion ratio.

[0052] The design of these parameters can affect the performance of solid motors, such as the performance of their charge. However, due to the objective existence of multiple sources of uncertainty in the grain during solidification, cooling, storage, transportation, and flight, such as geometric parameters, propellant burning rate, pressure exponent, and temperature sensitivity, the variation pattern of the charge's burning surface deviates significantly from design requirements, reducing the reliability of the charge's performance. Therefore, considering the impact of uncertain parameters on charge performance during solid motor charge design is an effective way to improve the reliability of solid motor charge performance. By optimizing these uncertain parameters, effective guidance can be provided for estimating the design of the motor, thereby optimizing the performance of the solid motor charge.

[0053] In view of this, an embodiment of the present application provides a solid motor charge performance uncertainty design optimization method, which can be executed by an optimization device, which can be a processing device with certain computing capabilities, such as a computer, server, etc.

[0054] In an embodiment of the present application, a solid motor charge performance uncertainty design optimization method is proposed to address the problem that uncertainty factors such as geometric tolerances and propellant performance parameters in solid motor charge design reduce charge performance reliability, thereby causing uneven propellant combustion and thrust fluctuations, resulting in poor design results. This method uses the maximum impulse-to-mass ratio (the ratio of total impulse to mass) as the objective function and the solid motor operating thrust and operating time as constraints to establish an initial Kriging model. The approximate minimum performance target point (first uncertainty parameter) and the precise minimum performance target point (second uncertainty parameter) obtained in each iteration are adaptively updated according to an adaptive strategy. A single-variable dimensionality reduction method based on the Kriging model is used to search for the precise minimum performance target point. An adaptive single-loop method assisted by the Kriging model is used to optimize and solve the solid motor charge performance. Based on the solution results, the solid motor charge performance is efficiently optimized, thereby improving the reliability of the solid motor charge performance.

[0055] In order to make the technical solution of the present application clearer and easier to understand, the technical solution of the present application is described in detail below with reference to the accompanying drawings. Figure 2 As shown in FIG, this figure is a flow chart of a solid motor charge performance uncertainty design optimization method provided in an embodiment of the present application, the method comprising:

[0056] S201. Optimize the equipment to build a sample set.

[0057] The sample set includes multiple samples, and the samples include sample features and sample responses corresponding to the sample features.

[0058] In the examples of this application, the sample characteristics used include: sample front wing length, sample front wing circumscribed circle diameter, sample rear wing length, sample rear wing circumscribed circle diameter, sample front wing inclination angle, sample rear wing inclination angle, sample wing width, sample first-stage charge reference burning rate, and sample internal trajectory maximum combustion chamber pressure. Sample responses include sample thrust response and sample time response.

[0059] In some embodiments, the optimization device can obtain the mean, standard deviation, upper bound, lower bound, and target reliability index of the uncertainty parameters. The mean, standard deviation, upper bound, and lower bound of these uncertainty parameters can be specified by the user based on actual needs. Furthermore, based on the mean, standard deviation, upper bound, and lower bound, the optimization device can use Latin hypercube sampling to obtain a feature set of design variables for the solid rocket motor. Then, through simulation, the optimization device can determine the response set corresponding to each set of features in the feature set, and combine each feature in the feature set with the corresponding response in the response set to obtain a sample set.

[0060] S202: The optimization device constructs an initial Kriging model based on the sample set.

[0061] In some embodiments, the optimization device obtains sample uncertainty parameters of a solid rocket motor, including sample front wing length, sample front wing circumscribed circle diameter, sample rear wing length, sample rear wing circumscribed circle diameter, sample maximum combustion chamber pressure, sample front wing inclination angle, sample rear wing inclination angle, sample wing width, sample first-stage charge reference burning rate, and sample expansion ratio. The sample uncertainty parameters are then simulated to obtain response values ​​corresponding to the sample uncertainty parameters in multiple dimensions. Based on the sample uncertainty parameters and the response values ​​in the multiple dimensions, initial round kriging models corresponding to each of the multiple dimensions are constructed.

[0062] In the embodiment of the present application, the initial Kriging model includes two categories. The first category is a Kriging model for working thrust, which is used to predict the working thrust of a solid rocket engine. The second category is a Kriging model for working time, which is used to predict the working time of a solid rocket engine. In other examples, the parameters output by the initial Kriging model may include working thrust and working time. The construction method and principle of the Kriging model in the above two methods are similar. For the sake of convenience, the Kriging model whose output parameter includes working thrust is taken as an example for introduction. The optimization device constructs an initial Kriging model of the working thrust dimension based on the sample features in the sample set and the sample responses of the working thrust dimension. It should be noted that the optimization device can also construct an initial Kriging model of the working time dimension based on the sample features in the sample set and the sample responses of the working time dimension.

[0063] Table 1 shows a table of uncertainty parameters:

[0064]

[0065] S203, optimizing equipment to construct a solid motor charge performance optimization model.

[0066] In some examples, a solid motor charge performance optimization model is characterized by the following equation:

[0067]

[0068] in, is the mean value of the design variables of the solid rocket, represents the lower bound of the mean of the design variable, represents the upper bound of the mean of the design variable, The mean of the design variables of the solid rocket is When the total impulse of the solid rocket is The mean of the design variables of the solid rocket is When, the mass of the solid rocket; The mean of the design variables of the solid rocket is The ratio of the total impulse to mass of the solid rocket is ; represents the probability operator, Represents the target reliability probability. Represents the kriging model of the first dimension (such as the kriging model of the thrust dimension) for the uncertainty parameter The predicted response value of Kriging models that represent the second dimension (for example, time dimension) are calculated for the uncertainty parameter The predicted response value of express This incident, express This incident.

[0069] In the actual processing process, the calculation cost of the above optimization model is relatively high, which can be converted according to the following formula: ,in, i It can be 1 or 2. is the design variable The joint probability function of For functional functions The target reliability index is used to express the target reliability probability, that is, , is the target reliability index, and the above probability constraint can be transformed into: . Then After inverse transformation, we get: . For the i After the above processing, the solid motor charge performance optimization model is characterized by the following formula:

[0070]

[0071] in, is the normalized gradient vector of the i-th dimension, For the i The predicted gradient of dimension, represents the parameter variance, represents the upper limit of the mean value of the design variables of the solid rocket, Represents the lower limit of the mean of the design variables of the solid rocket.

[0072] S204. The optimization device initializes the k-1th group of target uncertainty parameters.

[0073] Here, k is an integer greater than 1.

[0074] In some embodiments, when k=2, the optimization device initializes the first set of target uncertainty parameters, for example, the target uncertainty parameters can be the mean of the first set of parameters. represents the k-1th group of target uncertainty parameters in the i-th dimension, represents the mean value of the k-1th group of parameters. When k=2, = The case where k is greater than 2 will be described in the subsequent embodiments.

[0075] S205. The optimization device determines the mean value of the kth group of parameters and the k-1th round of normalized gradient vector according to the k-1th group of target uncertainty parameters.

[0076] After the optimization device determines the k-1th group of target uncertainty parameters, it can determine the kth group parameter mean and the k-1th round of normalized gradient vector based on the k-1th group of target uncertainty parameters.

[0077] In some embodiments, the k-1th group of target uncertainty parameters can be substituted into the above-mentioned solid motor charge optimization model, and the parameters of the solid motor can be optimized using the sequential quadratic programming method. When the working thrust of the solid motor is greater than the preset thrust threshold and the working time is greater than the preset time threshold, the mean value of the kth group of parameters is obtained, where the working time refers to the time that the solid motor can work continuously.

[0078] In some embodiments, the optimization device may simulate the kth set of target uncertainty parameters to obtain response values ​​for multiple dimensions corresponding to the kth set of target uncertainty parameters, the multiple dimensions including a working thrust dimension and a working time dimension. Using the kth set of target uncertainty parameters and the corresponding response values ​​for the multiple dimensions, the kth round kriging model corresponding to each dimension is updated to obtain a k+1th round kriging model.

[0079] In some examples, the optimization device may update the kriging model of the working thrust dimension based on the kth set of target uncertainty parameters and the response value of the working thrust dimension. In other examples, the optimization device may update the kriging model of the working time dimension based on the kth set of target uncertainty parameters and the response value of the working time dimension.

[0080] S206: The optimization device determines whether the mean value of the kth group of parameters and the mean value of the k-1th group of parameters meet a first preset condition.

[0081] If the first judgment result is obtained that the kth group parameter mean and the k-1th group parameter mean do not meet the first preset condition, then execute S207. If the first judgment result is obtained that the kth group parameter mean and the k-1th group parameter mean meet the first preset condition, then execute S212.

[0082] In some embodiments, the optimization device may obtain the first judgment result by:

[0083]

[0084] in, represents the mean value of the kth group of parameters, represents the mean of the k-1th group of parameters, represents the first condition threshold; if the above formula is satisfied, a first judgment result is obtained that the mean of the kth group of parameters and the mean of the k-1th group of parameters satisfy the first preset condition; if the above formula is not satisfied, a first judgment result is obtained that the mean of the kth group of parameters and the mean of the k-1th group of parameters do not satisfy the first preset condition.

[0085] S207 , the optimization device determines the kth group of first uncertainty parameters according to the kth group of parameter mean, parameter variance, target reliability index, and the k-1th round of normalized gradient vector.

[0086] In some embodiments, the optimization device may determine the kth group of first uncertainty parameters according to the following formula:

[0087]

[0088] in, represents the kth group of first uncertainty parameters in the i-th dimension, represents the mean value of the kth group of parameters, represents the parameter variance, is the target reliability index, is the k-1th normalized gradient vector in the i-th dimension. In some examples, when i=1, The kth group of first uncertainty parameters representing the working thrust dimension, represents the k-1th round of normalized gradient vector of the working thrust dimension, Represents the k-1th round of normalized gradient vector in the working time dimension. When i=2, Represents the kth group of second uncertainty parameters in the working time dimension.

[0089] S208. The optimization device determines the k-th round prediction gradient of the k-th round Kriging model for the k-th group of first uncertainty parameters.

[0090] In some examples, the k-th round prediction gradient of the k-th group of first uncertainty parameters of the k-th round kriging model in the i-th dimension can be obtained by When i=1, It represents the k-th round prediction gradient of the k-th group first uncertainty parameter of the k-th round Kriging model in the working time dimension. When i=2, Represents the k-th round prediction gradient of the k-th group of first uncertainty parameters of the k-th round Kriging model in the working thrust dimension.

[0091] S209: The optimization device determines whether the kth group of first uncertainty parameters and the kth round of prediction gradient meet the second preset condition.

[0092] If the second judgment result is obtained that the kth group of first uncertainty parameters and the kth round prediction gradient meet the second preset condition, execute S210; if the second judgment result is obtained that the kth group of first uncertainty parameters and the kth round prediction gradient do not meet the second preset condition, execute S211.

[0093] In some embodiments, the optimization device may obtain the second judgment result in the following manner:

[0094]

[0095] in, represents the kth group of first uncertainty parameters in the i-th dimension, represents the k-th round prediction gradient of the i-th dimension, Represents the second conditional threshold; if the above formula is satisfied, the second judgment result is obtained that the k-th group of first uncertainty parameters and the k-th round prediction gradient meet the second preset condition; if the above formula is not satisfied, the second judgment result is obtained that the k-th group of first uncertainty parameters and the k-th round prediction gradient do not meet the second preset condition.

[0096] S210. The optimization device determines the kth group of first uncertainty parameters as the kth group of target uncertainty parameters.

[0097] If the above second preset condition is met, the optimization device directly determines the kth group of first uncertainty parameters as the kth group of target uncertainty parameters.

[0098] S211. The optimization device determines the kth group of second uncertainty parameters based on the kth group of first uncertainty parameters, and determines the kth group of second uncertainty parameters as the kth group of target uncertainty parameters.

[0099] The accuracy of the second uncertainty parameter is higher than the accuracy of the first uncertainty parameter.

[0100] If the second preset condition is not met, the optimization device first determines the kth group of second uncertainty parameters based on the kth group of first uncertainty parameters, and then determines the kth group of second uncertainty parameters as the kth group of target uncertainty parameters. In some examples, the optimization device may use a univariate dimensionality reduction method of a kriging model to determine the second uncertainty parameters.

[0101] The following describes a process in which the optimization device determines the kth group of second uncertainty parameters of the i-th dimension based on the kth group of first uncertainty parameters of the i-th dimension:

[0102] (1) The optimization device sets the first uncertainty parameter of the kth group of the i-th dimension Converting from X space to U space, the corresponding uncertainty parameter in U space can be represented by the following formula:

[0103]

[0104] in, is the uncertainty parameter corresponding to the first uncertainty parameter of the kth group of the i-th dimension in the U space.

[0105] (2) The optimization device converts the corresponding uncertainty parameters in the U space from the U space to the V space. The corresponding uncertainty parameters in the V space can be represented by the following formula:

[0106]

[0107] in, is the uncertainty parameter corresponding to the first uncertainty parameter of the kth group of the i-th dimension in the V space.

[0108] (3) The optimization device calculates the k-th round of Kriging model in the i-th dimension for the V space The predicted response value of , according to the predicted response value , calculate the corresponding failure probability:

[0109] in, is the failure probability, is the number of design variables, is the number of Gaussian integration points, is the weight of the Gaussian integration point, are the coordinates of the Gaussian integration point, , is the standard normal distribution function.

[0110] (4) Optimize the probability of equipment failure Converted into the corresponding first reliability index ,use and target reliability index Calculate the new first reliability index , the new first reliability index can also be called the first reliability index of the kth round, is the first reliability index of the k-1th round.

[0111] (5) Optimize equipment according to the new first reliability index And the uncertainty parameter corresponding to the first uncertainty parameter of the kth group of the i-th dimension in the U space , calculation and The kth group of second uncertainty parameters of the i-th dimension in the corresponding U space .

[0112] (6) The optimization device sets the kth group of second uncertainty parameters of the i-th dimension in the U space Transformed back to the kth group of second uncertainty parameters of the i-th dimension in the X space :

[0113]

[0114] in, is the kth group of second uncertainty parameters of the i-th dimension in the X space, is the kth group of second uncertainty parameters of the i-th dimension in the U space.

[0115] It should be noted that after the optimization device determines the kth group of target uncertainty parameters in S211 or S210, it can enter the next round of optimization, for example, return to S205, and then return to S205 again, and then enter k+1, that is, enter the next round of optimization.

[0116] S212. The optimization device optimizes the performance of the solid motor charge according to the mean value of the kth group of parameters.

[0117] After obtaining the kth group parameter mean, the solid motor charge performance can be optimized based on the kth group parameter mean.

[0118] like Figure 3As shown in the figure, this figure is a schematic diagram of the convergence process of the impact-to-mass ratio optimization of a solid rocket engine provided by the embodiment of the present application. As can be seen from the figure, the optimization method provided by the embodiment of the present application can optimize the impact-to-mass ratio of the solid rocket engine to 2008.36 .

[0119] Table 2 below shows the efficiency comparison between the solution of the present application and the traditional solution:

[0120]

[0121] In terms of computational efficiency, the single-loop method based on adaptive Kriging proposed in this application uses 102 sample points, while the dual-loop method based on constrained boundary sampling requires 523 sample points. Using the same computing resources and achieving the same approximate computational accuracy, the dual-loop method based on constrained boundary sampling takes approximately 150 minutes to solve the optimization model, while the single-loop method based on adaptive Kriging approximate constraint functions proposed in this application only takes 26.7 minutes. Compared with the dual-loop method based on constrained boundary sampling (the traditional solution), the computational efficiency of the single-loop method based on adaptive Kriging approximate constraint functions proposed in this application is 82.2% higher than that of the dual-loop method based on constrained boundary sampling.

[0122] Based on the above description, an embodiment of the present application provides an uncertainty design optimization method for solid motor charge performance. This method fully considers the geometric errors of the charge and the uncertainty of the propellant performance parameters, establishes a solid motor charge performance optimization model with maximum impulse-to-mass ratio as the objective function and operating thrust and operating time as constraints. A Kriging-assisted adaptive single-loop method is proposed. This method uses a single-variable dimensionality reduction method based on the Kriging model to search for the precise minimum performance target point (target uncertainty parameter). In each iteration, an approximate minimum performance target point (first uncertainty parameter) and a precise minimum performance target point (second uncertainty parameter) are selected according to an adaptive strategy to adaptively update the Kriging model of the constraint function. The Kriging-assisted adaptive single-loop method is used to solve the solid motor charge performance optimization model and obtain the optimal solution, which facilitates the high-reliability optimization design of solid motor charge performance.

[0123] Combined with the above Figures 1 to 3 The uncertainty design optimization method for solid motor charge performance provided in the embodiment of the present application is introduced in detail. The device and equipment provided in the embodiment of the present application will be introduced in conjunction with the accompanying drawings.

[0124] like Figure 4As shown in FIG, this figure is a schematic diagram of a solid motor charge performance uncertainty design optimization device provided by an embodiment of the present application, the device comprising:

[0125] An acquisition module 401 is configured to acquire, based on the k-1th group of target uncertainty parameters, a k-1th round of normalized gradient vectors and a kth group of parameter mean values, wherein the working thrust corresponding to the kth group of parameter mean values ​​is greater than a preset thrust threshold, and the working time corresponding to the kth group of parameter mean values ​​is greater than a preset time threshold;

[0126] A first judgment module 402 is configured to judge whether the mean value of the kth group of parameters and the mean value of the k-1th group of parameters meet a first preset condition, and obtain a first judgment result;

[0127] Processing module 403 is configured to determine, if the first judgment result indicates that the mean of the kth group of parameters and the mean of the k-1th group of parameters do not meet the first preset condition, the kth group of first uncertainty parameters based on the mean of the kth group of parameters, the parameter variance, the target reliability index, and the k-1th round of normalized gradient vector, and determine the kth round prediction gradient of the kth round kriging model for the kth group of first uncertainty parameters;

[0128] A second judgment module 404 is configured to judge whether the k-th group of first uncertainty parameters and the k-th round prediction gradient meet a second preset condition, and obtain a second judgment result;

[0129] The processing module 403 is further configured to determine the kth group of first uncertainty parameters as the kth group of target uncertainty parameters if the second judgment result indicates that the kth group of first uncertainty parameters and the kth round prediction gradient meet a second preset condition; wherein the uncertainty parameters include front wing length, front wing circumscribed circle diameter, rear wing length, rear wing circumscribed circle diameter, maximum combustion chamber pressure, front wing inclination angle, rear wing inclination angle, wing width, first-stage charge reference burning rate, and expansion ratio; and the prediction dimension of the kth round Kriging model includes an operating thrust dimension or an operating time dimension;

[0130] The optimization module 405 is configured to optimize the solid motor charge performance according to the mean value of the kth group of parameters if the first judgment result indicates that the mean value of the kth group of parameters and the mean value of the k-1th group of parameters meet a first preset condition; wherein k is an integer greater than 1.

[0131] Optionally, the processing module 403 is also used to determine the kth group of second uncertainty parameters based on the kth group of first uncertainty parameters if the second judgment result represents that the kth group of first uncertainty parameters and the kth round prediction gradient do not meet the second preset condition, and determine the kth group of second uncertainty parameters as the kth group of target uncertainty parameters, and the accuracy of the second uncertainty parameters is higher than the accuracy of the first uncertainty parameters.

[0132] Optionally, the first judgment module 402 is specifically configured to determine the first judgment result in the following manner:

[0133]

[0134] in, represents the mean value of the kth group of parameters, represents the mean of the k-1th group of parameters, represents the first condition threshold; if the above formula is satisfied, a first judgment result is obtained that the mean of the kth group of parameters and the mean of the k-1th group of parameters satisfy the first preset condition; if the above formula is not satisfied, a first judgment result is obtained that the mean of the kth group of parameters and the mean of the k-1th group of parameters do not satisfy the first preset condition.

[0135] Optionally, the processing module 403 is specifically configured to determine the kth group of first uncertainty parameters by:

[0136]

[0137] in, represents the kth group of first uncertainty parameters in the i-th dimension, represents the mean value of the kth group of parameters, represents the parameter variance, is the target reliability index, is the k-1th normalized gradient vector for the i-th dimension.

[0138] Optionally, the second judgment module 404 is specifically configured to determine the second judgment result in the following manner:

[0139]

[0140] in, represents the kth group of first uncertainty parameters in the i-th dimension, represents the k-th round prediction gradient of the i-th dimension, Represents the second conditional threshold; if the above formula is satisfied, a second judgment result is obtained that the k-th group of first uncertainty parameters and the k-th round prediction gradient satisfy the second preset condition; if the above formula is not satisfied, a second judgment result is obtained that the k-th group of first uncertainty parameters and the k-th round prediction gradient do not satisfy the second preset condition.

[0141] The device also includes an update module;

[0142] The updating module is used to simulate the kth group of target uncertainty parameters to obtain response values ​​of multiple dimensions corresponding to the kth group of target uncertainty parameters, where the multiple dimensions include a working time dimension and a working thrust dimension; and use the kth group of target uncertainty parameters and the corresponding response values ​​of the multiple dimensions to update the kth round kriging model corresponding to each dimension respectively to obtain the k+1th round kriging model.

[0143] The device also includes a training module;

[0144] The training module is used to obtain sample uncertainty parameters of the solid engine, wherein the sample uncertainty parameters include sample front wing length, sample front wing circumscribed circle diameter, sample rear wing length, sample rear wing circumscribed circle diameter, sample maximum combustion chamber pressure, sample front wing inclination angle, sample rear wing inclination angle, sample wing width, sample first-stage charge reference burning rate and sample expansion ratio; simulate the sample uncertainty parameters to obtain response values ​​of multiple dimensions corresponding to the sample uncertainty parameters; and construct initial round kriging models corresponding to each of the multiple dimensions based on the sample uncertainty parameters and the response values ​​of the multiple dimensions.

[0145] The device for designing and optimizing the performance uncertainty of a solid motor charge according to an embodiment of the present application may correspond to the method described in the embodiment of the present application, and the above-mentioned other operations and / or functions of each module / unit of the device for designing and optimizing the performance uncertainty of a solid motor charge are respectively to realize Figure 2 For the sake of brevity, the corresponding processes of the various methods in the illustrated embodiments are not described again here.

[0146] The present application also provides a computing device. Figure 5 As shown, this figure is a schematic diagram of a computing device provided by an embodiment of the present application, such as Figure 5 As shown, computing device 500 includes bus 501, processor 502, communication interface 503, and memory 504. Processor 502, memory 504, and communication interface 503 communicate with each other via bus 501.

[0147] The bus 501 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0148] The processor 502 may be any one or more of a central processing unit (CPU), a graphics processing unit (GPU), a microprocessor (MP), or a digital signal processor (DSP).

[0149] The communication interface 503 is used for communicating with the outside.

[0150] The memory 504 may include volatile memory, such as random access memory (RAM). The memory 504 may also include non-volatile memory, such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid state drive (SSD).

[0151] The memory 504 stores executable codes, and the processor 502 executes the executable codes to implement the aforementioned solid motor charge performance uncertainty design optimization method.

[0152] Specifically, in the implementation Figure 4 In the case of the embodiment shown, and Figure 4 When each module or unit of the solid motor charge performance uncertainty design optimization device described in the embodiment is implemented by software, the execution Figure 5 The software or program code required for the functions of each module / unit in the system may be partially or completely stored in the memory 504. The processor 502 executes the program code corresponding to each unit stored in the memory 504 to perform the aforementioned solid motor charge performance uncertainty design optimization method.

[0153] Embodiments of the present application also provide a computer-readable storage medium. The computer-readable storage medium can be any available medium capable of being stored by a computing device, or a data storage device such as a data center that contains one or more available media. The available medium can be a magnetic medium (e.g., a floppy disk, hard disk, or magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive). The computer-readable storage medium includes instructions that instruct the computing device to execute the above-described method for optimizing the performance uncertainty of a solid rocket charge.

[0154] The present application also provides a computer program product comprising one or more computer instructions that, when loaded and executed on a computing device, fully or partially generate the process or function described in the present application.

[0155] The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, or data center to another website, computer, or data center via wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means.

[0156] When the computer program product is executed by a computer, the computer performs any of the aforementioned methods for designing and optimizing solid motor charge performance with uncertainty. The computer program product may be a software installation package that can be downloaded and executed on a computer when any of the aforementioned methods for designing and optimizing solid motor charge performance with uncertainty is desired.

[0157] The descriptions of the processes or structures corresponding to the above figures have different emphases. For parts that are not described in detail in a certain process or structure, please refer to the relevant descriptions of other processes or structures.

[0158] The above description is only a specific implementation method of the present application, but the protection scope of the present application is not limited thereto. Any changes or replacements within the technical scope disclosed in the present application should be included in the protection scope of the present application.

Claims

1. A solid rocket charge performance uncertainty design optimization method, characterized in that: The method comprises: According to the k-1th group of target uncertainty parameters, obtaining the k-1th round of normalized gradient vectors and the kth group of parameter mean values, wherein the working thrust corresponding to the kth group of parameter mean values ​​is greater than a preset thrust threshold, and the working time corresponding to the kth group of parameter mean values ​​is greater than a preset time threshold; Determine whether the mean value of the kth group of parameters and the mean value of the k-1th group of parameters meet a first preset condition, and obtain a first determination result; If the first judgment result indicates that the mean of the kth group of parameters and the mean of the k-1th group of parameters do not meet the first preset condition, determining the kth group of first uncertainty parameters based on the mean of the kth group of parameters, the parameter variance, the target reliability index, and the k-1th round of standardized gradient vector, and determining the kth round prediction gradient of the kth round kriging model for the kth group of first uncertainty parameters; Determine whether the k-th group of first uncertainty parameters and the k-th round prediction gradient meet a second preset condition, and obtain a second determination result; If the second judgment result indicates that the kth group of first uncertainty parameters and the kth round prediction gradient meet a second preset condition, then the kth group of first uncertainty parameters is determined as the kth group of target uncertainty parameters; wherein the uncertainty parameters include front wing length, front wing circumscribed circle diameter, rear wing length, rear wing circumscribed circle diameter, maximum combustion chamber pressure, front wing inclination angle, rear wing inclination angle, wing width, first-stage charge reference burning rate, and expansion ratio; and the prediction dimension of the kth round Kriging model includes a working thrust dimension or a working time dimension; If the first judgment result indicates that the mean value of the kth group of parameters and the mean value of the k-1th group of parameters meet the first preset condition, the solid motor charge performance is optimized according to the mean value of the kth group of parameters; wherein k is an integer greater than 1.

2. The method according to claim 1, characterized in that The method further comprises: If the second judgment result represents that the kth group of first uncertainty parameters and the kth round prediction gradient do not meet the second preset condition, then the kth group of second uncertainty parameters is determined based on the kth group of first uncertainty parameters, and the kth group of second uncertainty parameters is determined as the kth group of target uncertainty parameters, and the accuracy of the second uncertainty parameters is higher than the accuracy of the first uncertainty parameters.

3. The method according to claim 1, characterized in that The determining whether the mean value of the kth group of parameters and the mean value of the k-1th group of parameters meet a first preset condition to obtain a first determination result includes: in, represents the mean value of the kth group of parameters, represents the mean of the k-1th group of parameters, represents the first condition threshold; if the above formula is satisfied, a first judgment result is obtained that the mean of the kth group of parameters and the mean of the k-1th group of parameters satisfy the first preset condition; if the above formula is not satisfied, a first judgment result is obtained that the mean of the kth group of parameters and the mean of the k-1th group of parameters do not satisfy the first preset condition.

4. The method according to claim 1, wherein The step of determining the kth group of first uncertainty parameters according to the kth group of parameter means, parameter variances, target reliability indicators, and the k-1th round of normalized gradient vectors includes: in, represents the kth group of first uncertainty parameters in the i-th dimension, represents the mean value of the kth group of parameters, represents the parameter variance, is the target reliability index, is the k-1th normalized gradient vector for the i-th dimension.

5. The method according to claim 2, characterized in that The determining whether the k-th group of first uncertainty parameters and the k-th round prediction gradient meet a second preset condition to obtain a second determination result includes: in, represents the kth group of first uncertainty parameters in the i-th dimension, represents the k-th round prediction gradient of the i-th dimension, Represents the second conditional threshold; if the above formula is satisfied, a second judgment result is obtained that the k-th group of first uncertainty parameters and the k-th round prediction gradient satisfy the second preset condition; if the above formula is not satisfied, a second judgment result is obtained that the k-th group of first uncertainty parameters and the k-th round prediction gradient do not satisfy the second preset condition.

6. The method according to claim 1, characterized in that The method further comprises: Simulating the kth group of target uncertainty parameters to obtain response values ​​of multiple dimensions corresponding to the kth group of target uncertainty parameters, the multiple dimensions including a working thrust dimension and a working time dimension; The kth group of target uncertainty parameters and the corresponding response values ​​of multiple dimensions are used to update the kth round Kriging model corresponding to each dimension respectively to obtain the k+1th round Kriging model.

7. The method according to any one of claims 1 to 6, characterized in that The method further comprises: Obtaining sample uncertainty parameters of the solid rocket motor, wherein the sample uncertainty parameters include a sample front wing length, a sample front wing circumscribed circle diameter, a sample rear wing length, a sample rear wing circumscribed circle diameter, a sample maximum combustion chamber pressure, a sample front wing inclination angle, a sample rear wing inclination angle, a sample wing width, a sample first-stage charge reference burning rate, and a sample expansion ratio; Simulating the sample uncertainty parameters to obtain response values ​​of multiple dimensions corresponding to the sample uncertainty parameters; Based on the sample uncertainty parameter and the response values ​​of the multiple dimensions, initial round kriging models corresponding to the multiple dimensions are respectively constructed.

8. A solid rocket charge performance uncertainty design optimization device, characterized in that: The device comprises: an acquisition module, configured to acquire, based on the k-1th group of target uncertainty parameters, a k-1th round of normalized gradient vectors and a kth group of parameter mean values, wherein the working thrust corresponding to the kth group of parameter mean values ​​is greater than a preset thrust threshold, and the working time corresponding to the kth group of parameter mean values ​​is greater than a preset time threshold; A first judgment module is used to judge whether the mean value of the kth group of parameters and the mean value of the k-1th group of parameters meet a first preset condition, and obtain a first judgment result; a processing module, configured to determine, if the first judgment result indicates that the mean of the kth group of parameters and the mean of the k-1th group of parameters do not meet a first preset condition, a kth group of first uncertainty parameters based on the mean of the kth group of parameters, the parameter variance, the target reliability index, and the k-1th round of standardized gradient vector, and determine a kth round prediction gradient of the kth round kriging model for the kth group of first uncertainty parameters; A second judgment module is used to judge whether the k-th group of first uncertainty parameters and the k-th round prediction gradient meet a second preset condition, and obtain a second judgment result; The processing module is further configured to, if the second judgment result indicates that the kth group of first uncertainty parameters and the kth round prediction gradient satisfy a second preset condition, determine the kth group of first uncertainty parameters as the kth group of target uncertainty parameters; wherein the uncertainty parameters include front wing length, front wing circumscribed circle diameter, rear wing length, rear wing circumscribed circle diameter, maximum combustion chamber pressure, front wing inclination angle, rear wing inclination angle, wing width, first-stage charge reference burning rate, and expansion ratio; and the prediction dimension of the kth round Kriging model includes a working thrust dimension or a working time dimension; An optimization module is used to optimize the solid motor charge performance according to the kth group parameter mean if the first judgment result indicates that the kth group parameter mean and the k-1th group parameter mean meet the first preset condition; wherein k is an integer greater than 1.

9. A computing device, characterized in that including memory and processor; One or more computer programs are stored in the memory, and the one or more computer programs include instructions; when the instructions are executed by the processor, the computing device executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium is used to store a computer program, and the computer program is used to execute the method according to any one of claims 1 to 7.

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