Solid engine charging performance uncertainty design optimization method and related device

Abstract

The invention provides a solid engine charging performance uncertainty design optimization method and a related device, and relates to the technical field of solid engines. According to the method, the maximum impact mass ratio serves as a target function, and the working thrust and working time of the solid engine serve as constraint conditions; a solid engine charge performance initial Kriging model is established by taking a front wing length, a front wing excircle diameter, a rear wing length, a rear wing excircle diameter, a maximum pressure intensity of a combustion chamber, a front wing inclination angle, a rear wing inclination angle, a wing width, a primary charge reference burning rate and an expansion ratio as design variables; selecting a minimum performance target point according to an adaptive strategy to update the Kriging model of the charge performance of the solid engine, and carrying out optimization solution on the charge performance of the solid engine by adopting an adaptive single-cycle optimization method; through comparative analysis with a traditional optimization result, scientificity and effectiveness of the uncertainty design optimization method are verified.

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 of solid rocket motors, the level of design determines the internal ballistic performance and other technical indicators of the motor. However, due to the objective existence of multi-source uncertainties such as geometric parameters, propellant burning rate, pressure index and temperature sensitivity coefficient during the solidification, cooling, storage, transportation and flight of the rocket motor, the variation law of the combustion surface of the rocket motor is quite different from the design requirements, and the reliability of the rocket motor performance is reduced. 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] In order to achieve the above objectives, this application adopts the following technical solutions: In a first aspect, the present application provides a solid motor charge performance uncertainty design optimization method, the method comprising: According to the k-1th group of target uncertainty parameters, obtaining the k-1th round of normalized gradient vector and the kth group of parameter mean, wherein the working thrust corresponding to the kth group of parameter mean is greater than a preset thrust threshold, and the working time corresponding to the kth group of parameter mean is greater than a preset time threshold; Determine whether the kth group parameter mean and the k-1th group parameter mean 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, determine the kth group of first uncertainty parameters according to the kth group of parameters mean, parameter variance, target reliability index and the k-1th round of standardized gradient vector, and determine 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 the second preset condition, 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, combustion chamber maximum 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 working thrust dimension or working time dimension; If the first judgment result indicates that the mean values ​​of the kth group of parameters and 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.

[0006] Optionally, the method further includes: 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.

[0007] Optionally, the determining whether the kth group parameter mean and the k-1th group parameter mean meet a first preset condition to obtain a first determination result includes:

[0008] 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.

[0009] 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 standardized gradient vectors includes:

[0010] 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.

[0011] 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:

[0012] 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 kth group of first uncertainty parameters and the kth round prediction gradient satisfy the second preset condition; if the above formula is not satisfied, a second judgment result is obtained that the kth group of first uncertainty parameters and the kth round prediction gradient do not satisfy the second preset condition.

[0013] Optionally, the method further includes: Simulating the kth group of target uncertainty parameters to obtain response values ​​of multiple dimensions corresponding to the kth group of target uncertainty parameters, wherein the multiple dimensions include 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 of kriging models corresponding to each dimension respectively, so as to obtain the k+1th round of kriging models.

[0014] Optionally, the method further includes: Obtaining sample uncertainty parameters of the solid motor, 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 combustion chamber maximum 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; Simulating the sample uncertainty parameter to obtain response values ​​of multiple dimensions corresponding to the sample uncertainty parameter; 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.

[0015] In a second aspect, the present application provides a solid motor charge performance uncertainty design optimization device, the device comprising: An acquisition module, used for acquiring a k-1th round of normalized gradient vector and a kth group of parameter mean according to a k-1th group of target uncertainty parameters, wherein a working thrust corresponding to the kth group of parameter mean is greater than a preset thrust threshold, and a working time corresponding to the kth group of parameter mean 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 the kth group of first uncertainty parameters and the k-1th group of first uncertainty parameters according to the kth group of first uncertainty parameters, parameter variance, target reliability index and the k-1th round of standardized gradient vector, if the first judgment result indicates that the kth group of first uncertainty parameters mean and the k-1th group of first uncertainty parameters mean do not meet the first preset condition, and determine the 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 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, combustion chamber maximum 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 of Kriging model includes a working thrust dimension or a working time dimension; The optimization module is used to optimize the solid motor charge performance according to the kth group of parameter means if the first judgment result indicates that the kth group of parameter means and the k-1th group of parameter means meet the first preset condition; wherein k is an integer greater than 1.

[0016] In a third aspect, the present application provides a computing device, including a memory and a 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 as described in any one of the first aspects.

[0017] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium is used to store a computer program, and the computer program is used to execute the method as described in any one of the first aspects.

[0018] It can be seen from the above technical solution that the present application has at least the following beneficial effects: The present application provides a solid motor charge performance uncertainty design optimization method, which first obtains the parameter mean of this round and the standardized gradient vector of the previous round according to the target uncertainty parameter of the previous round, and the working thrust corresponding to the parameter mean of this round is greater than the preset thrust threshold, and the corresponding working time is also greater than the preset time threshold. Then the parameter mean of this round and the parameter mean of the previous round are judged whether they meet the first preset condition. If they meet, the solid motor is optimized based on the parameter mean of this round. If it does not meet the first preset condition, the first uncertainty parameter of this round is determined based on the parameter mean, parameter variance, target reliability index and the standardized gradient vector of the previous round, and the predicted gradient of the first uncertainty parameter of this round of the Kriging model is determined. If the first uncertainty parameter of this round and the predicted gradient of this round meet the second preset condition, the first uncertainty parameter of this round is determined as the target uncertainty parameter of this round, and then the next round of circulation is entered. Among them, the uncertainty parameters include the front wing length, the front wing circumscribed circle diameter, the rear wing length, the rear wing circumscribed circle diameter, the maximum pressure of the combustion chamber, the front wing inclination angle, the rear wing inclination angle, the wing width, the reference burning rate of the first-stage charge and the expansion ratio. The prediction dimensions of the Kriging model include the working thrust or working time. It can be seen that this method can optimize the reliability of the solid motor charge performance, thereby providing effective guidance for the design of solid motors.

[0019] 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 features or beneficial effects means that specific technical features, technical solutions or beneficial effects are included in at least one embodiment. Therefore, the description of technical features, technical solutions or beneficial effects 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 realized 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 a specific embodiment that does not embody all embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A schematic diagram of a solid engine provided in an embodiment of the present application; Figure 2 A flow chart of a solid motor charge performance uncertainty design optimization method provided in an embodiment of the present application; Figure 3A schematic diagram of the optimization convergence process of the impulse-to-mass ratio of a solid rocket provided in an embodiment of the present application; Figure 4 A schematic diagram of a solid motor charge performance uncertainty design optimization device provided in an embodiment of the present application; Figure 5 A schematic diagram of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0021] The terms "first", "second", "third", etc. in the specification of this application and the accompanying drawings are used to distinguish different objects rather than to limit a specific order.

[0022] In the embodiments of the present 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 the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0023] like Figure 1 As shown in the figure, this figure is a schematic diagram of a solid engine provided by an embodiment of the present application. It can be seen from the figure that the solid engine 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 ballistic-combustion chamber maximum pressure, front wing inclination angle 15, rear wing inclination angle 16, wing width 17, first-stage charge reference burning rate and expansion ratio.

[0024] The design of the above parameters will affect the performance of solid motors, for example, the performance of solid motor charges. However, due to the objective existence of multi-source uncertainties such as geometric parameters, propellant burning rate, pressure index and temperature sensitivity coefficient in the process of solidification, cooling, storage, transportation and flight, the variation law of the charge combustion surface is quite different from the design requirements, and the reliability of charge performance is reduced. Therefore, considering the influence of uncertainty parameters on charge performance when designing solid motor charges is an effective way to improve the reliability of solid motor charge performance. By optimizing the uncertainty parameters, it can provide effective guidance for the design of the estimated engine, thereby optimizing the charge performance of the solid motor.

[0025] 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.

[0026] In the embodiment of the present application, in view of the problem that the reliability of charge performance is reduced due to uncertainty factors such as geometric tolerances and propellant performance parameters in the design of solid motor charge, and then the uneven combustion of propellants and thrust fluctuations lead to poor design results, the present application proposes a solid motor charge performance uncertainty design optimization method. The method takes the maximum impulse-to-mass ratio (the ratio of total impulse to mass) as the objective function, and takes the solid motor working thrust and working time as constraints, and establishes an initial Kriging model; according to the adaptive strategy, the approximate minimum performance target point (first uncertainty parameter) and the precise minimum performance target point (second uncertainty parameter) obtained in each iteration are updated in an adaptive manner, and the precise minimum performance target point is searched by a single variable dimensionality reduction method based on the Kriging model, and the solid motor charge performance is optimized and solved by an adaptive single-loop method assisted by the Kriging model. Based on the solution results, the solid motor charge performance is efficiently optimized, thereby improving the reliability of the solid motor charge performance.

[0027] 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 in conjunction with the accompanying drawings. Figure 2 As shown, the 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: S201, optimizing equipment to build a sample set.

[0028] The sample set includes multiple samples, and the samples include sample features and sample responses corresponding to the sample features.

[0029] In the embodiment of the present 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, rear wing inclination angle, sample wing width, sample first-stage charge reference burning rate, and sample internal trajectory combustion chamber maximum pressure. The sample response includes sample thrust response and sample time response.

[0030] In some embodiments, the optimization device can obtain the mean, standard deviation, upper boundary, lower boundary and target reliability index of the uncertainty parameters. The mean, standard deviation, upper boundary and lower boundary of these uncertainty parameters can be specified by the user based on actual needs. Furthermore, the optimization device can obtain the feature set of the design variables of the solid engine based on the mean, standard deviation, upper boundary and lower boundary through the Latin hypercube sampling method, and then determine the response set corresponding to each group of features in the feature set through simulation, and combine each feature in the feature set with the corresponding response in the response set to obtain a sample set.

[0031] S202: The optimization device constructs an initial Kriging model based on the sample set.

[0032] In some embodiments, the optimization device obtains sample uncertainty parameters of the solid engine, including sample front wing length, sample front wing circumscribed circle diameter, sample rear wing length, sample rear wing circumscribed circle diameter, sample combustion chamber maximum 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. Then, the sample uncertainty parameters are simulated to obtain the response values ​​of multiple dimensions corresponding to the sample uncertainty parameters; based on the sample uncertainty parameters and the response values ​​of multiple dimensions, the initial round kriging models corresponding to each of the multiple dimensions are respectively constructed.

[0033] In the embodiment of the present application, the initial Kriging model includes two categories, the first category is the Kriging model for working thrust, which is used to predict the working thrust of the solid engine, and the second category is the Kriging model for working time, which is used to predict the working time of the solid 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 convenience of description, the Kriging model whose output parameters include working thrust is taken as an example for introduction. The optimization device constructs the initial Kriging model of the working thrust dimension based on the sample characteristics in the sample set and the sample responses of the working thrust dimension. It should be noted that the optimization device can also construct the initial Kriging model of the working time dimension based on the sample characteristics in the sample set and the sample responses of the working time dimension.

[0034] Table 1 shows a table of uncertainty parameters:

[0035] S203, optimizing equipment to construct a solid motor charge performance optimization model.

[0036] In some examples, the solid motor charge performance optimization model is characterized by the following formula:

[0037] 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 when represents the probability operator, Represents the target reliability probability. Represents the kriging model of the first dimension (for example, 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.

[0038] 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 represent the target reliability probability, that is, , is the target reliability index, and the above probability constraint can be transformed into: Then After the inverse transformation, we get: . For the i After the above processing, the solid motor charge performance optimization model is characterized by the following formula:

[0039] 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.

[0040] S204. The optimization device initializes the k-1th group of target uncertainty parameters.

[0041] Here, k is an integer greater than 1.

[0042] In some embodiments, when k=2, the optimization device initializes the first group of target uncertainty parameters, for example, the target uncertainty parameter can be the mean of the first group of parameters. represents the k-1th group of target uncertainty parameters in the i-th dimension, represents the mean 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.

[0043] S205. The optimization device determines the mean of the kth group of parameters and the k-1th round of standardized gradient vector according to the k-1th group of target uncertainty parameters.

[0044] 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 standardized gradient vector based on the k-1th group of target uncertainty parameters.

[0045] 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 a preset thrust threshold and the working time is greater than a preset duration threshold, the mean value of the kth group of parameters is obtained, wherein the working time refers to the time that the solid motor can work continuously.

[0046] In some embodiments, the optimization device may simulate the kth group of target uncertainty parameters to obtain response values ​​of multiple dimensions corresponding to the kth group of target uncertainty parameters, wherein the multiple dimensions include a working thrust dimension and a working time dimension. Using the kth group of target uncertainty parameters and the corresponding response values ​​of multiple dimensions, the kth round of kriging models corresponding to each dimension are updated respectively to obtain the k+1th round of kriging models.

[0047] In some examples, the optimization device may update the Kriging model of the working thrust dimension based on the kth group 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 group of target uncertainty parameters and the response value of the working time dimension.

[0048] 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.

[0049] 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, 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, execute S212.

[0050] In some embodiments, the optimization device may obtain the first judgment result in the following manner:

[0051] 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.

[0052] 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 standardized gradient vector.

[0053] In some embodiments, the optimization device may determine the kth group of first uncertainty parameters according to the following formula:

[0054] 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, represents the kth group of first uncertainty parameters of 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.

[0055] 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.

[0056] In some examples, the k-th round prediction gradient of the k-th round kriging model for the k-th group of first uncertainty parameters in the i-th dimension can be obtained by Indicates. When i=1, It represents the k-th round prediction gradient of the first uncertainty parameter of the k-th group 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.

[0057] S209: The optimization device determines whether the kth group of first uncertainty parameters and the kth round of prediction gradients meet the second preset condition.

[0058] 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.

[0059] In some embodiments, the optimization device may obtain the second judgment result in the following manner:

[0060] 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 satisfy 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 satisfy the second preset condition.

[0061] S210. The optimization device determines the kth group of first uncertainty parameters as the kth group of target uncertainty parameters.

[0062] If the above-mentioned 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.

[0063] 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.

[0064] The accuracy of the second uncertainty parameter is higher than the accuracy of the first uncertainty parameter.

[0065] 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 determine the second uncertainty parameters using a univariate dimensionality reduction method of a Kriging model.

[0066] 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: (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:

[0067] in, is the uncertainty parameter corresponding to the kth group of the first uncertainty parameter of the i-th dimension in the U space.

[0068] (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:

[0069] in, is the uncertainty parameter corresponding to the first uncertainty parameter of the kth group of the i-th dimension in the V space.

[0070] (3) The optimization device calculates the kth 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:

[0071] 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 points, , is the standard normal distribution function.

[0072] (4) Optimize the probability of equipment failure Transformed 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.

[0073] (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 .

[0074] (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 in the i-th dimension of the X space :

[0075] in, is the kth group of second uncertainty parameters in the i-th dimension of the X space, is the kth group of second uncertainty parameters of the i-th dimension in the U space.

[0076] 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 after returning to S205 again, another k+1, that is, enter the next round of optimization.

[0077] S212, the optimization device optimizes the solid motor charge performance according to the mean value of the kth group of parameters.

[0078] After obtaining the kth group parameter mean, the solid motor charge performance can be optimized based on the kth group parameter mean.

[0079] like Figure 3 As shown in FIG. 1 , the figure is a schematic diagram of the optimization convergence process of the impact-to-mass ratio of a solid rocket engine provided in an embodiment of the present application. As can be seen from the figure, the optimization method provided in the embodiment of the present application can optimize the impact-to-mass ratio of the solid rocket engine to 2008.36 .

[0080] Table 2 below shows the efficiency comparison between the solution of the present application and the traditional solution:

[0081] In terms of computational efficiency, the single-loop method based on adaptive Kriging proposed in this application uses 102 sample points, while the double-loop method based on constrained boundary sampling requires 523 sample points; under the premise of using the same computing resources and obtaining the same approximate computing accuracy, the double-loop method based on constrained boundary sampling takes about 150 minutes to solve the optimization model, while the single-loop method based on adaptive Kriging approximate constraint function proposed in this application only takes 26.7 minutes. Compared with the double-loop method based on constrained boundary sampling (traditional solution), the computational efficiency of the single-loop method based on adaptive Kriging approximate constraint function proposed in this application is 82.2% higher than that of the double-loop method based on constrained boundary sampling.

[0082] Based on the above description, the embodiment of the present application provides an uncertainty design optimization method for solid motor charge performance. The method fully considers the geometric error of the charge and the uncertainty of the propellant performance parameters, establishes a solid motor charge performance optimization model with the maximum impulse-to-mass ratio as the objective function and the working thrust and working time as constraints; proposes a Kriging-assisted adaptive single-loop method, which searches for the precise minimum performance target point (target uncertainty parameter) based on the single variable dimension reduction method of the Kriging model, and selects the approximate minimum performance target point (first uncertainty parameter) and the precise minimum performance target point (second uncertainty parameter) according to the adaptive strategy in each iteration 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 then the optimal solution is obtained, which is helpful for the high-reliability optimization design of solid motor charge performance.

[0083] Combination of 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.

[0084] like Figure 4 As shown, this figure is a schematic diagram of a solid motor charge performance uncertainty design optimization device provided in an embodiment of the present application, the device comprising: An acquisition module 401 is used to acquire a k-1th round of normalized gradient vector and a kth group of parameter mean according to a k-1th group of target uncertainty parameters, wherein the working thrust corresponding to the kth group of parameter mean is greater than a preset thrust threshold, and the working time corresponding to the kth group of parameter mean is greater than a preset time threshold; A first judgment module 402 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; Processing module 403, used for determining the kth group of first uncertainty parameters and the kth round prediction gradient of the kth round Kriging model for the kth group of first uncertainty parameters according to the kth group of parameter means, parameter variance, target reliability index and the k-1th round standardized gradient vector if the first judgment result indicates that the kth group of parameter means and the k-1th group of parameter means do not meet the first preset condition; A second judgment module 404 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 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, combustion chamber maximum 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 of Kriging model includes a working thrust dimension or a working time dimension; The optimization module 405 is used to optimize the solid motor charge performance according to the kth group of parameter means if the first judgment result indicates that the kth group of parameter means and the k-1th group of parameter means meet the first preset condition; wherein k is an integer greater than 1.

[0085] 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 characterizes 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.

[0086] Optionally, the first judgment module 402 is specifically configured to determine the first judgment result in the following manner:

[0087] 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.

[0088] Optionally, the processing module 403 is specifically configured to determine the kth group of first uncertainty parameters in the following manner:

[0089] 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.

[0090] Optionally, the second judgment module 404 is specifically configured to determine the second judgment result in the following manner:

[0091] 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 kth group of first uncertainty parameters and the kth round prediction gradient satisfy the second preset condition; if the above formula is not satisfied, a second judgment result is obtained that the kth group of first uncertainty parameters and the kth round prediction gradient do not satisfy the second preset condition.

[0092] The device also includes an update module; 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, wherein the multiple dimensions include a working time dimension and a working thrust dimension; and to update the kth round of kriging models corresponding to each dimension respectively using the kth group of target uncertainty parameters and the corresponding response values ​​of the multiple dimensions to obtain the k+1th round of kriging models.

[0093] The device also includes a training module; 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 combustion chamber maximum 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.

[0094] The solid motor charge performance uncertainty design optimization device according to the 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 solid motor charge performance uncertainty design optimization device are respectively to achieve Figure 2 For the sake of brevity, the corresponding processes of each method in the illustrated embodiment are not described in detail here.

[0095] The present application also provides a computing device. Figure 5 As shown, this figure is a schematic diagram of a computing device provided in 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.

[0096] 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 only one type of bus.

[0097] 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).

[0098] The communication interface 503 is used for communicating with the outside.

[0099] The memory 504 may include a volatile memory, such as a random access memory (RAM). The memory 504 may also include a non-volatile memory, such as a read-only memory (ROM), a flash memory, a hard disk drive (HDD), or a solid state drive (SSD).

[0100] The memory 504 stores executable codes, and the processor 502 executes the executable codes to perform the aforementioned solid motor charge performance uncertainty design optimization method.

[0101] Specifically, in implementing 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 execute the above-mentioned solid motor charge performance uncertainty design optimization method.

[0102] The embodiment of the present application also provides a computer-readable storage medium. The computer-readable storage medium can be any available medium that can be stored by a computing device or a data storage device such as a data center containing one or more available media. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a tape), an optical medium (such as a DVD), or a semiconductor medium (such as a solid-state hard disk). The computer-readable storage medium includes instructions that instruct the computing device to execute the above-mentioned solid motor charge performance uncertainty design optimization method.

[0103] The embodiment of the present application further provides a computer program product, which includes one or more computer instructions. When the computer instructions are loaded and executed on a computing device, the process or function described in the embodiment of the present application is generated in whole or in part.

[0104] 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.

[0105] When the computer program product is executed by a computer, the computer executes any of the aforementioned methods for designing and optimizing the performance uncertainty of a solid motor charge. The computer program product may be a software installation package, and when any of the aforementioned methods for designing and optimizing the performance uncertainty of a solid motor charge is needed, the computer program product may be downloaded and executed on a computer.

[0106] The descriptions of the processes or structures corresponding to the above-mentioned 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.

[0107] The above description is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions 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 vector and the kth group of parameter mean, wherein the working thrust corresponding to the kth group of parameter mean is greater than a preset thrust threshold, and the working time corresponding to the kth group of parameter mean is greater than a preset time threshold; Determine whether the kth group parameter mean and the k-1th group parameter mean 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, determine the kth group of first uncertainty parameters according to the kth group of parameters mean, parameter variance, target reliability index and the k-1th round of standardized gradient vector, and determine 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 the second preset condition, 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, combustion chamber maximum 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 working thrust dimension or working time dimension; If the first judgment result indicates that the mean values ​​of the kth group of parameters and 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 kth group parameter mean and the k-1th group parameter mean 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, characterized in that: 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 standardized 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 kth group of first uncertainty parameters and the kth round prediction gradient satisfy the second preset condition; if the above formula is not satisfied, a second judgment result is obtained that the kth group of first uncertainty parameters and the kth 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, wherein the multiple dimensions include 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 of kriging models corresponding to each dimension respectively, so as to obtain the k+1th round of kriging models.

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 motor, 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 combustion chamber maximum 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; Simulating the sample uncertainty parameter to obtain response values ​​of multiple dimensions corresponding to the sample uncertainty parameter; 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, used for acquiring a k-1th round of normalized gradient vector and a kth group of parameter mean according to a k-1th group of target uncertainty parameters, wherein a working thrust corresponding to the kth group of parameter mean is greater than a preset thrust threshold, and a working time corresponding to the kth group of parameter mean 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 the kth group of first uncertainty parameters and the k-1th group of first uncertainty parameters according to the kth group of first uncertainty parameters, parameter variance, target reliability index and the k-1th round of standardized gradient vector, if the first judgment result indicates that the kth group of first uncertainty parameters mean and the k-1th group of first uncertainty parameters mean do not meet the first preset condition, and determine the 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 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, combustion chamber maximum 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 of Kriging model includes a working thrust dimension or a working time dimension; The optimization module is used to optimize the solid motor charge performance according to the kth group of parameter means if the first judgment result indicates that the kth group of parameter means and the k-1th group of parameter means 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 as claimed in 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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