A solid engine pressure performance-oriented adaptive weighted chaotic polynomial reliability analysis method, device, equipment and medium

Abstract

The application discloses a solid engine pressure performance-oriented adaptive weighted chaotic polynomial reliability analysis method, device, equipment and medium, and relates to the technical field of solid engines. The method comprises the following steps: obtaining training samples according to variables influencing the pressure performance of the solid engine and an interior ballistic performance calculation model; determining a weight matrix of the training samples by using a weight definition function, calculating chaotic polynomial coefficients by using a weighted least square method, and constructing a proxy model; selecting optimal sample points by using a model prediction error evaluation method and a global search learning function proposed in the application; and dynamically updating the training sample set and the weight according to an adaptive weighting strategy. It is worth noting that the update of the weight does not require additional calling of the interior ballistic calculation model. The application has better robustness to noise in data, can improve the accuracy and calculation efficiency of the reliability analysis of the pressure performance of the solid engine, and ensures that reliability risks can be predicted and avoided in the design stage.

Description

Technical Field

[0001] The present application relates to the technical field of solid motors, and in particular to an adaptive weighted chaotic polynomial reliability analysis method, device, equipment and medium for solid motor pressure performance. Background Art

[0002] Solid fuel rockets are widely used in aerospace propulsion systems. However, their manufacture, operation, and maintenance are subject to widespread uncertainty regarding material properties, geometry, and payload. This uncertainty often increases the probability of failure for fixed rockets, significantly reducing their reliability.

[0003] Currently, engineers rely on prior experience to design and optimize the parameters that influence combustion chamber pressure. However, the combustion chamber is the component primarily responsible for bearing internal pressure, and the forces acting on various parts of the combustion chamber depend on the chamber pressure. Due to uncertainties such as environmental factors, material properties, and geometric dimensions, the actual combustion chamber pressure may exceed the casing's tolerance threshold, leading to engine failure and potentially a safety incident.

[0004] Therefore, the industry needs a reliability analysis method for the pressure performance of solid rocket motors. Summary of the Invention

[0005] The present application provides an adaptive weighted chaotic polynomial reliability analysis method, device, equipment and medium for the pressure performance of solid motors, which can analyze the reliability of the pressure performance of solid motors, thereby providing effective guidance for the reliability optimization of solid motors.

[0006] To achieve the above objectives, this application adopts the following technical solutions:

[0007] In a first aspect, the present application provides an adaptive weighted chaotic polynomial reliability analysis method for solid rocket motor pressure performance, the method comprising:

[0008] Obtaining a K-th round of training sample sets and training sample pressures corresponding to each training sample group in the K-th round of training sample sets, where each training sample group in the training sample sets includes a charge reference burning rate dimension, a burning rate pressure exponent dimension, a characteristic velocity dimension, a nozzle throat ablation rate dimension, and a charge density dimension;

[0009] Estimating the density of each training sample group in the K-th round training sample set;

[0010] Determining a weight matrix of the K-th round training sample set according to the density of each training sample group in the K-th round training sample set;

[0011] Constructing a K-round chaotic polynomial model using the K-round training sample set, the training sample pressure corresponding to each training sample group in the K-round training sample set, and the weight matrix corresponding to the K-round training sample set;

[0012] Obtaining a candidate sample set, inputting the candidate sample set into the K-th round chaotic polynomial model, and obtaining a candidate predicted pressure value corresponding to each candidate sample group;

[0013] Determining the K-th round failure probability of the solid motor according to the number of candidate sample groups whose candidate predicted pressure values ​​are greater than a preset pressure threshold and the number of candidate sample groups in the candidate sample set;

[0014] Determine, based on the candidate prediction pressure value corresponding to each candidate sample group in the candidate sample set and the candidate prediction error corresponding to each candidate sample group in the candidate sample set, a K-th round learning value corresponding to each candidate sample group in the candidate sample set, and determine the candidate sample group with the smallest learning value in the candidate sample set as the K-th round target candidate sample group;

[0015] Calculating the distance between the K-th round target candidate sample group and each training sample group in the K-th round training sample set, and determining the training sample group with the smallest distance to the K-th round target candidate sample value as the K-th round target training sample group;

[0016] If the target sample distance between the K-th round target candidate sample group and the K-th round target training sample group is less than the first distance, the weight of the K-th round target training sample group is updated to obtain the K+1-th round training sample set; if the target distance is greater than or equal to the first distance, the K-th round target candidate sample group is added to the K-th round training sample set to obtain the K+1-th round training sample set;

[0017] If the failure probability from the Kkth round to the Kth round meets the condition for stopping iteration, the reliability analysis result of the pressure performance of the solid rocket engine is output, and the reliability analysis result is the failure probability of the Kth round.

[0018] Optionally, determining the K-th round learning value corresponding to each candidate sample group in the candidate sample set according to the candidate prediction pressure value corresponding to each candidate sample group in the candidate sample set and the candidate prediction error corresponding to each candidate sample group in the candidate sample set includes:

[0019] If K is equal to 1, the first round learning value corresponding to each candidate sample group in the candidate sample set is determined according to the following formula:

[0020]

[0021] Represents the i-th candidate sample group in the candidate sample set in the first iteration The corresponding first round learning value, Represents the i-th candidate sample group in the candidate sample set in the first iteration The corresponding candidate predicted pressure value, Represents the i-th candidate sample group in the candidate sample set in the first iteration the corresponding candidate prediction error;

[0022] If K is greater than 1, the K-th round learning value corresponding to each candidate sample group in the candidate sample set is determined according to the following formula:

[0023]

[0024] Represents the i-th candidate sample group in the candidate sample set in the K-th iteration The corresponding K-th round learning value, Represents the i-th candidate sample group in the candidate sample set in the K-th iteration The corresponding candidate predicted pressure value, Represents the i-th candidate sample group in the candidate sample set in the K-th iteration The corresponding candidate prediction error, represents the number of training sample groups in the K-th round training sample set, Indicates the number of training sample groups in the first round of training sample set, represents the jth training sample group in the Kth round training sample set in the Kth round iteration.

[0025] Optionally, the candidate prediction error corresponding to each candidate sample group in the candidate sample set is determined by the following formula:

[0026]

[0027]

[0028] Represents the i-th candidate sample group in the candidate sample set in the K-th iteration The corresponding candidate prediction error, Indicates the prediction model of the lth type in the Kth iteration for the i-th candidate sample group in the candidate sample set The prediction results, Indicates that the prediction model of L types in the Kth iteration targets the i-th candidate sample group in the candidate sample set The average of the predicted results.

[0029] Optionally, the first distance is determined by the following formula:

[0030]

[0031] in, represents the first distance, Represents the standard deviation of the d-th dimension, where D is the number of dimensions, D=5.

[0032] Optionally, updating the weight of the K-th round target training sample group includes:

[0033]

[0034] in, is the Kth round target training sample group The updated weights, is the Kth round target training sample group The corresponding maximum pressure, Represents the target training sample group in the training sample set in the Kth iteration The corresponding target predicted pressure value, The Kth round of target training sample group Weight before update.

[0035] Optionally, the failure probability from the Kkth round to the Kth round satisfies the condition for stopping iteration, including:

[0036]

[0037]

[0038]

[0039] in, represents the standard deviation of the failure probability from round Kk to round K, represents the average failure probability from round Kk to round K, represents the failure probability in the mth iteration.

[0040] Optionally, the training sample pressure corresponding to each training sample group in the K-th round training sample set is calculated using a pre-built simulation model.

[0041] In a second aspect, the present application provides an adaptive weighted chaotic polynomial reliability analysis device for solid rocket motor pressure performance, the device comprising:

[0042] an acquisition module, configured to acquire a K-th round of training sample sets and a training sample pressure corresponding to each training sample group in the K-th round of training sample sets, wherein each training sample group in the training sample sets includes a charge reference burning rate dimension, a burning rate pressure exponent dimension, a characteristic velocity dimension, a nozzle throat ablation rate dimension, and a charge density dimension;

[0043] an estimation module, configured to estimate the density of each training sample group in the K-th round training sample set; and determine a weight matrix of the K-th round training sample set according to the density of each training sample group in the K-th round training sample set;

[0044] a training module, configured to construct a K-th round chaotic polynomial model using the K-th round training sample set, the training sample pressure corresponding to each training sample group in the K-th round training sample set, and the weight matrix corresponding to the K-th round training sample set;

[0045] The acquisition module is further used to acquire a candidate sample set;

[0046] a verification module, configured to input the candidate sample set into the K-th round chaotic polynomial model to obtain a candidate predicted pressure value corresponding to each candidate sample group; and determine the K-th round failure probability of the solid motor based on the number of candidate sample groups whose candidate predicted pressure values ​​are greater than a preset pressure threshold and the number of candidate sample groups in the candidate sample set;

[0047] a learning module, configured to determine a K-th round learning value corresponding to each candidate sample group in the candidate sample set based on a candidate predicted pressure value corresponding to each candidate sample group in the candidate sample set and a candidate prediction error corresponding to each candidate sample group in the candidate sample set, and determine the candidate sample group with the smallest learning value in the candidate sample set as the K-th round target candidate sample group;

[0048] a calculation module, configured to calculate the distance between the K-th round target candidate sample group and each training sample group in the K-th round training sample set, and determine the training sample group with the smallest distance to the K-th round target candidate sample value as the K-th round target training sample group;

[0049] an updating module, configured to update the weight of the K-th round target training sample group to obtain a K+1-th round training sample set if the target sample distance between the K-th round target candidate sample group and the K-th round target training sample group is less than a first distance; and to add the K-th round target candidate sample group to the K-th round training sample set to obtain a K+1-th round training sample set if the target distance is greater than or equal to the first distance;

[0050] The analysis module is used to output the reliability analysis result of the pressure performance of the solid motor if the failure probability from the Kkth round to the Kth round meets the stopping iteration condition, wherein the reliability analysis result is the Kth round failure probability.

[0051] In a third aspect, the present application provides a computing device, including a memory and a processor;

[0052] 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.

[0053] 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.

[0054] It can be seen from the above technical solution that this application has at least the following beneficial effects:

[0055] The application provides a solid rocket engine pressure performance adaptive weighted chaos polynomial reliability analysis method, which comprises the following steps: obtaining a Kth round training sample set and training sample pressures corresponding to each training sample group in the Kth round training sample set, each training sample group in the training sample set comprising a charge reference burning rate dimension, a burning rate pressure exponent dimension, a characteristic velocity dimension, a nozzle throat ablation rate dimension and a charge density dimension; estimating the density of each training sample group in the Kth round training sample set; determining a weight matrix of the Kth round training sample set according to the density of each training sample group; and constructing a Kth round chaos polynomial model by using the Kth round training sample set, the training sample pressures corresponding to each training sample group in the Kth round training sample set and the weight matrix corresponding to the Kth round training sample set. Then, a candidate sample set is obtained, the candidate sample set is input into the Kth round chaos polynomial model to obtain candidate predicted pressure values corresponding to each candidate sample group, the Kth round failure probability of the solid rocket engine is determined according to the number of candidate sample groups with candidate predicted pressure values greater than a preset pressure threshold and the number of candidate sample groups in the candidate sample set, the Kth round learning value corresponding to each candidate sample group in the candidate sample set is determined according to the candidate predicted pressure value corresponding to each candidate sample group in the candidate sample set and the candidate predicted error corresponding to each candidate sample group in the candidate sample set, the candidate sample group with the smallest learning value in the candidate sample set is determined as a Kth round target candidate sample group, the distance between the Kth round target candidate sample group and each training sample group in the Kth round training sample set is calculated, and the training sample group with the smallest distance to the Kth round target candidate sample value is determined as a Kth round target training sample group; if the target sample distance between the Kth round target candidate sample group and the Kth round target training sample group is less than a first distance, the weight of the Kth round target training sample group is updated to obtain a K+1th round training sample set, if the target distance is greater than or equal to the first distance, the Kth round target candidate sample group is added to the Kth round training sample set to obtain a K+1th round training sample set; if the K-kth round to Kth round failure probabilities meet a stop iteration condition, a reliability analysis result of the solid rocket engine pressure performance is output, and the reliability analysis result is the Kth round failure probability. It can be seen that the method can analyze the reliability of the solid rocket engine pressure performance, and the reliability analysis result can provide effective guidance for the reliability optimization design of the solid rocket engine, helping users to design the solid rocket engine meeting the requirements more conveniently.

[0056] 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

[0057] Figure 1 A flowchart of an adaptive weighted chaotic polynomial reliability analysis method for solid rocket motor pressure performance provided in an embodiment of the present application;

[0058] Figure 2 A schematic diagram of a solid rocket engine provided in an embodiment of the present application;

[0059] Figure 3 A schematic diagram of an adaptive weighted chaotic polynomial reliability analysis device for solid rocket motor pressure performance provided in an embodiment of the present application;

[0060] Figure 4 A schematic diagram of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0061] 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.

[0062] 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.

[0063] To make the description of the following embodiments clear and concise, a brief introduction to the related technologies is first given:

[0064] Solid-state engines use solid propellant as a power source for launch vehicles and other equipment. The propellant burns in the combustion chamber, producing high-temperature, high-pressure combustion gases. These gases are ejected through a nozzle, generating thrust to propel the vehicle. This thrust is directly dependent on changes in combustion chamber pressure, making it a crucial parameter for evaluating engine performance.

[0065] Furthermore, the combustion chamber is the component primarily responsible for bearing internal pressure, and the magnitude of the forces acting on various parts of the combustion chamber depends on the chamber pressure. Due to uncertainties such as environmental factors, material properties, and geometric dimensions, the actual combustion chamber pressure may exceed the casing's tolerance threshold, leading to engine failure or even a major safety incident.

[0066] Therefore, evaluating the reliability of solid motor combustion chamber pressure while considering uncertain variables is of great significance for improving the working safety of solid motors.

[0067] In view of this, an embodiment of the present application provides an adaptive weighted chaotic polynomial reliability analysis method for the pressure performance of solid motors. The method can be applied to a processing device, which can be a terminal or a server. Terminals include but are not limited to smartphones, tablet computers, laptops, personal digital assistants, or smart wearable devices. The server can be a cloud server, such as a central server in a central cloud computing cluster, or an edge server in an edge cloud computing cluster. Of course, the server can also be a server in a local data center. A local data center refers to a data center directly controlled by the user. This method can analyze the reliability of the pressure performance of a solid motor, and the reliability analysis results can provide effective guidance for the reliability optimization design of a solid motor, helping users to more conveniently design a solid motor that meets their needs.

[0068] In order to make the technical solution of this application clearer and easier to understand, the following describes the adaptive weighted chaotic polynomial reliability analysis method for solid rocket motor pressure performance provided by the embodiment of this application in conjunction with the accompanying drawings. Figure 1 As shown in FIG, this figure is a flow chart of an adaptive weighted chaotic polynomial reliability analysis method for solid rocket motor pressure performance provided by an embodiment of the present application, the method comprising:

[0069] S101: A processing device obtains a K-th round training sample set and the training sample pressure corresponding to each training sample group in the K-th round training sample set.

[0070] Each training sample group of the training sample set includes a charge reference burning rate dimension, a burning rate pressure exponent dimension, a characteristic velocity dimension, a nozzle throat ablation rate dimension, and a charge density dimension.

[0071] In some embodiments, the processing device may first obtain information on uncertain variables such as the given charge reference burning rate, burning rate pressure index, characteristic velocity, nozzle throat ablation rate, charge density, and deterministic variable information such as the combustion chamber cylinder diameter and nozzle throat diameter. The mean value of the uncertain variables is , the standard deviation is , where d=1,2,3…D, D=5, represents the mean of the uncertainty variable in the dth dimension, Represents the standard deviation of the uncertainty variable in the dth dimension. For example, the first dimension can be the charge reference burning rate dimension, the second dimension can be the burning rate pressure exponent dimension, the third dimension can be the characteristic velocity dimension, the fourth dimension can be the nozzle throat ablation rate dimension, and the fifth dimension can be the charge density dimension.

[0072] like Figure 2 , which is a schematic diagram of a solid motor provided by an embodiment of the present application. The solid motor primarily comprises a motor housing 201, a charge 202, a thermal insulation layer 203, and a nozzle 204. The distribution of factors influencing combustion chamber pressure is shown in Table 1 below.

[0073] Table 1:

[0074]

[0075] After initializing the above uncertainty variables, the processing device can use the Latin hypercube sampling method to obtain the first round of training sample sets.

[0076] Next, the processing device may also calculate the training sample pressure corresponding to each training sample group in the first round of training sample set through a pre-built simulation model.

[0077] Among them, the training sample set of the Kth round is obtained based on the training sample set of the K-1th round, which is introduced below.

[0078] S102: The processing device estimates the density of each training sample group in the K-th round training sample set.

[0079] In some embodiments, the processing device may use a kernel density estimation algorithm to calculate the density of each training sample group in the training sample set. Specifically, the processing device may determine the density of the training sample set using the following formula:

[0080]

[0081] in, represents the nth training sample group in the training sample set, Represents the nth training sample group in the Kth round training sample set in the Kth round iteration The density, Represents the nth training sample group in the Kth round of training sample set The variable of the dth dimension in , Indicates the first K-th round of training sample set The variable of the dth dimension of the training sample group, It represents the bandwidth of the d-th dimension in the K-th round training sample set, D=5, and D represents the total number of dimensions of the training sample set. represents the Gaussian kernel function.

[0082] Among them, another ,but: .

[0083] bandwidth It can be obtained through the empirical formula:

[0084]

[0085] in, Represents the sample standard deviation of the d-th dimension in the K-th round training sample set: . Indicates the first K round of training sample set The average value of the dimensions.

[0086] S103: The processing device determines a weight matrix of the K-th round training sample set according to the density of each training sample group in the K-th round training sample set.

[0087] In some embodiments, the processing device determines the weight of the K-th round training sample set using the following formula:

[0088]

[0089] in, represents the weight matrix of the training sample group in the K-th round training sample set, Indicates the number of training samples in the K-th round of iteration training sample groups density.

[0090] S104: The processing device constructs a K-th round chaotic polynomial model using the K-th round training sample set, the training sample pressure corresponding to each training sample group in the K-th round training sample set, and the weight matrix corresponding to the K-th round training sample set.

[0091] In some embodiments, the K-th round chaotic polynomial model can be expressed by the following formula:

[0092]

[0093] In some embodiments, the processing device may be based on the truncation degree By truncating the above formula, we can obtain:

[0094]

[0095] in, , is the total number of chaotic polynomials, represents the cutoff degree, =5.

[0096]

[0097]

[0098] in, Represents the input variable in the Kth iteration The predicted pressure value, represents the coefficient of the p-th polynomial, Represents input variables The multivariate orthogonal polynomials of Represents the multidimensional index of the p-th polynomial conforming vector, Represents a vector The first dimension scalar in , Represents a vector The second-dimensional scalar in , Represents a vector The third scalar in , Represents a vector The fourth-dimensional scalar in , Represents a vector The fifth-dimensional scalar in . Indicates the d-th dimension random variable corresponding to A one-variable orthogonal polynomial of order, Represents input variables The d-th dimension scalar of .

[0099] In some embodiments, the orthogonal polynomial follows the Askey scheme, which selects a matching orthogonal polynomial based on the parameter distribution type. For example, if the charge reference burning rate is a normal distribution, the basis function is a Hermite polynomial. Table 2 shows the correspondence between parameter distribution types and orthogonal polynomials.

[0100] Table 2:

[0101]

[0102] Among them, the coefficients of the chaotic polynomial model are solved using the weighted least squares method:

[0103]

[0104]

[0105] wherein, denotes a coefficient vector of the chaotic polynomial model, denotes a weight matrix of the training sample group in the Kth round of training sample set, denotes a vector composed of the training sample pressures corresponding to all training sample groups in the Kth round of training sample set. denotes the 0th orthogonal polynomial when the input variable is denotes the 1st orthogonal polynomial when the input variable is denotes the Pth orthogonal polynomial when the input variable is denotes the Pth orthogonal polynomial when the input variable is denotes the Pth orthogonal polynomial when the input variable is denotes the Pth orthogonal polynomial when the input variable is Similarly, details are not repeated here.

[0106] After constructing the chaotic polynomial model, the processing device can train the chaotic polynomial model by using the Kth round of training sample set, the training sample pressure corresponding to each training sample group in the Kth round of training sample set, and the weight matrix corresponding to the Kth round of training sample set.

[0107] S105, the processing device acquires the candidate sample set.

[0108] In some embodiments, the processing device acquires the candidate sample set in a similar manner to acquiring the training sample set, and details are not repeated here.

[0109] S106, the processing device inputs the candidate sample set into the Kth round of chaotic polynomial model to obtain the candidate predicted pressure value corresponding to each candidate sample group.

[0110] In some embodiments, after the processing device acquires the candidate sample set, the processing device can input the candidate sample set into the Kth round of chaotic polynomial model to obtain the candidate predicted pressure value corresponding to each candidate sample group.

[0111] S107, the processing device determines the Kth round failure probability of the solid rocket engine according to the number of candidate sample groups whose candidate predicted pressure values are greater than the preset pressure threshold, and the number of candidate sample groups in the candidate sample set.

[0112] After the processing device obtains the candidate predicted pressure value corresponding to each candidate sample group, the processing device compares the candidate predicted pressure value with the preset pressure threshold to obtain the number of candidate samples whose candidate predicted pressure values are greater than the preset pressure threshold, and then determines the Kth round failure probability of the solid rocket engine according to the number of candidate samples in the candidate sample set and the number of candidate samples whose candidate predicted pressure values are greater than the preset pressure threshold.

[0113] Specifically, the processing device can determine the failure probability of the Kth round using the following formula:

[0114]

[0115] in, represents the failure probability of the Kth round, Indicates the number of candidate samples whose candidate prediction pressure values ​​are greater than the preset pressure threshold. Indicates the number of candidate samples in the candidate sample set.

[0116] S108. The processing device determines the K-th round learning value corresponding to each candidate sample group in the candidate sample set based on the candidate prediction pressure value corresponding to each candidate sample group in the candidate sample set and the candidate prediction error corresponding to each candidate sample group in the candidate sample set, and determines the candidate sample group with the smallest learning value in the candidate sample set as the K-th round target candidate sample group.

[0117] In some embodiments, the processing device may first determine the size of K, specifically:

[0118] If K is equal to 1, the first round learning value corresponding to each candidate sample group in the candidate sample set is determined according to the following formula:

[0119]

[0120] Represents the i-th candidate sample group in the candidate sample set in the first iteration The corresponding first round learning value, Represents the i-th candidate sample group in the candidate sample set in the first iteration The corresponding candidate predicted pressure value, Represents the i-th candidate sample group in the candidate sample set in the first iteration the corresponding candidate prediction error;

[0121] If K is greater than 1, the K-th round learning value corresponding to each candidate sample group in the candidate sample set is determined according to the following formula:

[0122]

[0123] Represents the i-th candidate sample group in the candidate sample set in the K-th iteration The corresponding K-th round learning value, Represents the i-th candidate sample group in the candidate sample set in the K-th iteration The corresponding candidate predicted pressure value, Represents the i-th candidate sample group in the candidate sample set in the K-th iteration The corresponding candidate prediction error, represents the number of training sample groups in the K-th round training sample set, Indicates the number of training sample groups in the first round of training sample set, represents the jth training sample group in the Kth round training sample set in the Kth round iteration.

[0124] It should be noted that in the embodiment of the present application, the variable K representing the round and other variables representing the number (such as d, j, i, etc.) are all positive integers.

[0125] In some embodiments, the candidate prediction error corresponding to each candidate sample group in the candidate sample set is determined by the following formula:

[0126]

[0127]

[0128] Represents the i-th candidate sample group in the candidate sample set in the K-th iteration The corresponding candidate prediction error, Indicates the prediction model of the lth type in the Kth iteration for the i-th candidate sample group in the candidate sample set The prediction results, Indicates that the prediction model of L types in the Kth iteration targets the i-th candidate sample group in the candidate sample set The average of the predicted results.

[0129] In some embodiments, L=4, the processing device can construct four chaotic polynomial verification models using Hermite, Laguerre, Jacobi and Legendre orthogonal polynomials as basis functions respectively. . Specifically:

[0130]

[0131]

[0132]

[0133]

[0134] in, Indicates the chaotic polynomial verification model with Hermite as the basis function for the input variables in the Kth round of iteration The predicted pressure value, Represents input variables The multivariate Hermite orthogonal polynomials of , represents the coefficient of the pth Hermite polynomial;

[0135] Indicates the chaotic polynomial verification model with Laguerre as the basis function for the input variable in the Kth iteration The predicted pressure value, Represents input variables The multivariate Laguerre orthogonal polynomials of , represents the coefficient of the pth Laguerre polynomial;

[0136] Indicates the chaotic polynomial verification model with Jacobi as the basis function for the input variable in the Kth iteration The predicted pressure value, Represents input variables The multivariate Jacobi orthogonal polynomials of , represents the coefficient of the p-th Jacobi polynomial;

[0137] Indicates the chaotic polynomial verification model with Legendre as the basis function for the input variable in the Kth iteration The predicted pressure value, Represents input variables The multivariate Legendre orthogonal polynomials of , represents the coefficient of the p-th Legendre polynomial.

[0138] S109: The processing device calculates the distance between the K-th round target candidate sample group and each training sample group in the K-th round training sample set, and determines the training sample group with the smallest distance to the K-th round target candidate sample value as the K-th round target training sample group.

[0139] In some embodiments, the processing device may calculate the distance between the K-th round target candidate sample group and each training sample group in the K-th round training sample set, and then determine the training sample group with the smallest distance to the K-th round target candidate sample value as the K-th round target training sample group. The above distance is a Euclidean distance.

[0140] S110: The processing device determines whether the target sample distance is less than a first distance.

[0141] The target sample distance is the distance between the target candidate sample group of round K and the target training sample group of round K, such as the Euclidean distance. If the target sample distance is less than the first distance, S111 is executed. If the target sample distance is not less than the first distance, S112 is executed.

[0142] The first distance is determined by the following formula:

[0143]

[0144] in, represents the first distance, Represents the standard deviation of the d-th dimension, where D is the number of dimensions, D=5.

[0145] S111: The processing device updates the weight of the K-th round target training sample group to obtain the K+1-th round training sample set.

[0146] In some embodiments, the target sample distance is less than the first distance, and the processing device may update the weight of the target training sample group in the Kth round using the following formula:

[0147]

[0148] in, is the Kth round target training sample group The updated weights, is the Kth round target training sample group The corresponding maximum pressure, Represents the target training sample group in the training sample set in the Kth iteration The corresponding target predicted pressure value, The Kth round of target training sample group Weight before update.

[0149] It should be noted that the processing device updates the weight of the K-1th round target training sample group. After obtaining the weight of the Kth round target training sample group, the Kth round training sample set is obtained. The specific updating process is similar to the above embodiment and will not be repeated here.

[0150] S112: The processing device adds the K-th round target candidate sample group to the K-th round training sample set to obtain the K+1-th round training sample set.

[0151] If the target sample distance is not less than the first distance, the processing device may add the K-th round target candidate sample group to the K-th round training sample set to obtain the K+1-th round training sample set. It should be noted that the process of the processing device processing the K-1-th round training sample set to obtain the K-th round training sample set is similar and will not be repeated here.

[0152] S113. If the failure probability from the Kkth round to the Kth round meets the conditions for stopping iteration, output the reliability analysis results of the solid rocket motor pressure performance.

[0153] The reliability analysis result is the K-th round failure probability.

[0154] In some embodiments, the failure probability from the Kkth round to the Kth round satisfies the conditions for stopping iterations, including:

[0155]

[0156]

[0157]

[0158] in, represents the standard deviation of the failure probability from round Kk to round K, represents the average failure probability from round Kk to round K, represents the failure probability in the mth iteration.

[0159] Table 3 is a schematic table of the maximum pressure reliability analysis results of the solid rocket motor provided in the embodiments of the present application.

[0160] Table 3:

[0161]

[0162] From the reliability analysis results shown in Table 3, we can see that due to the introduction of the adaptive weighting strategy and active learning algorithm, the interior ballistic calculation model was only called 28 times, and the final calculated failure probability is , with an error of only 0.59% compared with the Monte Carlo simulation.

[0163] Table 4 compares the maximum pressure reliability analysis results of the solid rocket motor considering noise provided in the embodiments of the present application.

[0164] Table 4:

[0165]

[0166] The results show that the proposed method still has high computational efficiency when noisy data is introduced. The zero-dimensional interior ballistic calculation model is called 28 and 35 times, respectively, and the prediction errors of the failure probability are 0.29% and 0.59%, respectively.

[0167] Based on the above description, the adaptive weighted chaotic polynomial reliability analysis method judges the prediction error of the chaotic polynomial model by the difference of basis functions, proposes an adaptive weighting strategy, updates the training sample set and sample weight of the chaotic polynomial model according to the model prediction error and the mutual correlation information of the training samples, and improves the solution efficiency and noise resistance of the maximum pressure reliability analysis of the solid motor. (1) Compared with the reliability analysis method based on sampling, the present invention uses a small number of samples to construct a high-precision proxy model, and uses the proxy model to replace the solid motor interior ballistic calculation model for reliability analysis, thereby improving the computational efficiency; (2) Compared with the existing reliability analysis method based on the proxy model, the present invention proposes a prediction error evaluation method for the chaotic polynomial model, and combines the adaptive weighting strategy to select sample points and update sample weights, so as to achieve a rapid prediction of the maximum pressure failure probability of the solid motor at a lower computational cost.

[0168] Combined with the above Figures 1 to 2 The adaptive weighted chaotic polynomial reliability analysis method for the pressure performance of a solid rocket motor provided in an embodiment of the present application is introduced in detail. The apparatus and device provided in the embodiment of the present application will be introduced below in conjunction with the accompanying drawings.

[0169] like Figure 3 As shown in FIG, this figure is a schematic diagram of an adaptive weighted chaotic polynomial reliability analysis device for solid rocket motor pressure performance provided by an embodiment of the present application, the device comprising:

[0170] An acquisition module 301 is configured to acquire a K-th round of training sample sets and training sample pressures corresponding to each training sample group in the K-th round of training sample sets, wherein each training sample group in the training sample sets includes a charge reference burning rate dimension, a burning rate pressure exponent dimension, a characteristic velocity dimension, a nozzle throat ablation rate dimension, and a charge density dimension;

[0171] An estimation module 302 is configured to estimate the density of each training sample group in the K-th round training sample set; and determine a weight matrix of the K-th round training sample set based on the density of each training sample group in the K-th round training sample set;

[0172] A training module 303 is configured to construct a K-th round chaotic polynomial model using the K-th round training sample set, the training sample pressure corresponding to each training sample group in the K-th round training sample set, and the weight matrix corresponding to the K-th round training sample set;

[0173] The acquisition module 301 is further used to acquire a candidate sample set;

[0174] A verification module 304 is configured to input the candidate sample set into the K-th round chaotic polynomial model to obtain a candidate predicted pressure value corresponding to each candidate sample group; and determine the K-th round failure probability of the solid motor based on the number of candidate sample groups whose candidate predicted pressure values ​​are greater than a preset pressure threshold and the number of candidate sample groups in the candidate sample set;

[0175] A learning module 305 is configured to determine a K-th round learning value corresponding to each candidate sample group in the candidate sample set based on the candidate predicted pressure value corresponding to each candidate sample group in the candidate sample set and the candidate prediction error corresponding to each candidate sample group in the candidate sample set, and determine the candidate sample group with the smallest learning value in the candidate sample set as the K-th round target candidate sample group;

[0176] A calculation module 306 is configured to calculate the distance between the K-th round target candidate sample group and each training sample group in the K-th round training sample set, and determine the training sample group with the smallest distance to the K-th round target candidate sample value as the K-th round target training sample group;

[0177] An updating module 307 is configured to update the weight of the K-th round target training sample group to obtain a K+1-th round training sample set if the target sample distance between the K-th round target candidate sample group and the K-th round target training sample group is less than a first distance; and if the target distance is greater than or equal to the first distance, add the K-th round target candidate sample group to the K-th round training sample set to obtain a K+1-th round training sample set.

[0178] The analysis module 308 is configured to output a reliability analysis result of the solid rocket motor pressure performance if the failure probability from the Kkth round to the Kth round meets the condition for stopping iteration, where the reliability analysis result is the Kth round failure probability.

[0179] Optionally, the learning module 305 is specifically used to:

[0180] If K is equal to 1, the first round learning value corresponding to each candidate sample group in the candidate sample set is determined according to the following formula:

[0181]

[0182] Represents the i-th candidate sample group in the candidate sample set in the first iteration The corresponding first round learning value, Represents the i-th candidate sample group in the candidate sample set in the first iteration The corresponding candidate predicted pressure value, Represents the i-th candidate sample group in the candidate sample set in the first iteration the corresponding candidate prediction error;

[0183] If K is greater than 1, the K-th round learning value corresponding to each candidate sample group in the candidate sample set is determined according to the following formula:

[0184]

[0185] Represents the i-th candidate sample group in the candidate sample set in the K-th iteration The corresponding K-th round learning value, Represents the i-th candidate sample group in the candidate sample set in the K-th iteration The corresponding candidate predicted pressure value, Represents the i-th candidate sample group in the candidate sample set in the K-th iteration The corresponding candidate prediction error, represents the number of training sample groups in the K-th round training sample set, Indicates the number of training sample groups in the first round of training sample set, represents the jth training sample group in the Kth round training sample set in the Kth round iteration.

[0186] Optionally, the candidate prediction error corresponding to each candidate sample group in the candidate sample set is determined by the following formula:

[0187]

[0188]

[0189] Represents the i-th candidate sample group in the candidate sample set in the K-th iteration The corresponding candidate prediction error, Indicates the prediction model of the lth type in the Kth iteration for the i-th candidate sample group in the candidate sample set The prediction results, Indicates that the prediction model of L types in the Kth iteration targets the i-th candidate sample group in the candidate sample set The average of the predicted results.

[0190] Optionally, the first distance is determined by the following formula:

[0191]

[0192] in, represents the first distance, Represents the standard deviation of the d-th dimension, where D is the number of dimensions, D=5.

[0193] Optionally, the updating module 307 is specifically configured to update the weight by the following formula:

[0194]

[0195] wherein, the Kth target training sample group the updated weight, the Kth target training sample group the corresponding target training sample maximum pressure, the target training sample group in the Kth iteration, the corresponding target prediction maximum pressure value, the Kth target training sample group the weight before updating.

[0196] Optionally, the Kth-kth to Kth failure probability satisfies the stop iteration condition, and the stop iteration condition comprises:

[0197]

[0198]

[0199]

[0200] wherein, the standard deviation of the Kth-kth to Kth failure probability, the average value of the Kth-kth to Kth failure probability, the failure probability in the mth iteration.

[0201] The solid engine pressure performance adaptive weighted chaotic polynomial reliability analysis device according to the embodiments of the present application can correspond to the method described in the embodiments of the present application, and the above-mentioned other operations and / or functions of each module / unit of the solid engine pressure performance reliability analysis device are respectively realized Figure 1 The corresponding flow of each method in the embodiments is shown, and for the sake of brevity, it will not be repeated here.

[0202] The embodiments of the present application also provide a computing device. As Figure 4 shown, the figure is a schematic diagram of a computing device provided by the embodiments of the present application. The computing device 400 comprises a bus 401, a processor 402, a communication interface 403 and a memory 404. The processor 402, the memory 404 and the communication interface 403 communicate through the bus 401.

[0203] The bus 401 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 4 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.

[0204] The processor 402 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).

[0205] The communication interface 403 is used for communicating with the outside.

[0206] The memory 404 may include volatile memory, such as random access memory (RAM). The memory 404 may also include non-volatile memory, such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid state drive (SSD).

[0207] The memory 404 stores executable codes, and the processor 402 executes the executable codes to perform the aforementioned reliability analysis method for the pressure performance of the solid rocket motor.

[0208] Specifically, in the implementation Figure 3 In the case of the embodiment shown, and Figure 3 When each module or unit of the reliability analysis device for the pressure performance of the solid rocket engine described in the embodiment is implemented by software, Figure 3 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 404. The processor 402 executes the program code corresponding to each unit stored in the memory 404 to perform the reliability analysis method of the solid rocket motor pressure performance.

[0209] 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 analyzing the reliability of the solid rocket motor's pressure performance.

[0210] 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.

[0211] 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) or wireless (e.g., infrared, wireless, microwave, etc.) means.

[0212] When the computer program product is executed by a computer, the computer performs any of the aforementioned methods for analyzing the reliability of pressure performance of a solid rocket motor. The computer program product may be a software installation package, which can be downloaded and executed on a computer when any of the aforementioned methods for analyzing the reliability of pressure performance of a solid rocket motor is required.

[0213] 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.

[0214] 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. An adaptive weighted chaotic polynomial reliability analysis method for solid rocket motor pressure performance, characterized by: The method comprises: Obtaining a K-th round of training sample sets and training sample pressures corresponding to each training sample group in the K-th round of training sample sets, where each training sample group in the training sample sets includes a charge reference burning rate dimension, a burning rate pressure exponent dimension, a characteristic velocity dimension, a nozzle throat ablation rate dimension, and a charge density dimension; Estimating the density of each training sample group in the K-th round training sample set; Determining a weight matrix of the K-th round training sample set according to the density of each training sample group in the K-th round training sample set; Constructing a K-round chaotic polynomial model using the K-round training sample set, the training sample pressure corresponding to each training sample group in the K-round training sample set, and the weight matrix corresponding to the K-round training sample set; Obtain a candidate sample set; input the candidate sample set into the K-th round chaotic polynomial model to obtain a candidate predicted pressure value corresponding to each candidate sample group; Determining the K-th round failure probability of the solid motor according to the number of candidate sample groups whose candidate predicted pressure values ​​are greater than a preset pressure threshold and the number of candidate sample groups in the candidate sample set; Determine, based on the candidate prediction pressure value corresponding to each candidate sample group in the candidate sample set and the candidate prediction error corresponding to each candidate sample group in the candidate sample set, a K-th round learning value corresponding to each candidate sample group in the candidate sample set, and determine the candidate sample group with the smallest learning value in the candidate sample set as the K-th round target candidate sample group; The determining, according to the candidate prediction pressure value corresponding to each candidate sample group in the candidate sample set and the candidate prediction error corresponding to each candidate sample group in the candidate sample set, a K-th round learning value corresponding to each candidate sample group in the candidate sample set includes: If K is equal to 1, the first round learning value corresponding to each candidate sample group in the candidate sample set is determined according to the following formula: Represents the i-th candidate sample group in the candidate sample set in the first iteration The corresponding first round learning value, Represents the i-th candidate sample group in the candidate sample set in the first iteration The corresponding candidate predicted pressure value, Represents the i-th candidate sample group in the candidate sample set in the first iteration the corresponding candidate prediction error; If K is greater than 1, the K-th round learning value corresponding to each candidate sample group in the candidate sample set is determined according to the following formula: Represents the i-th candidate sample group in the candidate sample set in the K-th iteration The corresponding K-th round learning value, Represents the i-th candidate sample group in the candidate sample set in the K-th iteration The corresponding candidate predicted pressure value, represents the candidate prediction error corresponding to the i-th candidate sample group in the candidate sample set in the K-th iteration, represents the number of training sample groups in the K-th round training sample set, Indicates the number of training sample groups in the first round of training sample set, represents the jth training sample group in the Kth round training sample set in the Kth round iteration; Calculating the distance between the K-th round target candidate sample group and each training sample group in the K-th round training sample set, and determining the training sample group with the smallest distance to the K-th round target candidate sample value as the K-th round target training sample group; If the target sample distance between the K-th round target candidate sample group and the K-th round target training sample group is less than the first distance, the weight of the K-th round target training sample group is updated to obtain the K+1-th round training sample set; if the target sample distance is greater than or equal to the first distance, the K-th round target candidate sample group is added to the K-th round training sample set to obtain the K+1-th round training sample set; If the failure probability from the Kkth round to the Kth round meets the condition for stopping iteration, the reliability analysis result of the pressure performance of the solid rocket engine is output, and the reliability analysis result is the failure probability of the Kth round.

2. The method according to claim 1, characterized in that The candidate prediction error corresponding to each candidate sample group in the candidate sample set is determined by the following formula: Represents the i-th candidate sample group in the candidate sample set in the K-th iteration The corresponding candidate prediction error, Indicates the prediction model of the lth type in the Kth iteration for the i-th candidate sample group in the candidate sample set The prediction results, Indicates that the prediction model of L types in the Kth iteration targets the i-th candidate sample group in the candidate sample set The average of the predicted results.

3. The method according to claim 1, characterized in that The first distance is determined by the following formula: in, represents the first distance, Represents the standard deviation of the d-th dimension, where D is the number of dimensions, D=5.

4. The method according to claim 1, wherein The updating of the weight of the K-th round target training sample group includes: in, is the Kth round target training sample group The updated weights, is the Kth round target training sample group The corresponding maximum pressure, Represents the target training sample group in the training sample set in the Kth iteration The corresponding target predicted pressure value, The Kth round of target training sample group Weight before update.

5. The method according to claim 1, wherein The failure probability from the Kkth round to the Kth round satisfies the conditions for stopping iteration, including: in, represents the standard deviation of the failure probability from round Kk to round K, represents the average failure probability from round Kk to round K, represents the failure probability in the mth iteration.

6. The method according to claim 1, characterized in that The training sample pressure corresponding to each training sample group in the K-th round training sample set is calculated by a pre-built simulation model.

7. An adaptive weighted chaotic polynomial reliability analysis device for solid rocket motor pressure performance, characterized in that: The device comprises: an acquisition module, configured to acquire a K-th round of training sample sets and a training sample pressure corresponding to each training sample group in the K-th round of training sample sets, wherein each training sample group in the training sample sets includes a charge reference burning rate dimension, a burning rate pressure exponent dimension, a characteristic velocity dimension, a nozzle throat ablation rate dimension, and a charge density dimension; an estimation module, configured to estimate the density of each training sample group in the K-th round training sample set; and determine a weight matrix of the K-th round training sample set according to the density of each training sample group in the K-th round training sample set; a training module, configured to construct a K-th round chaotic polynomial model using the K-th round training sample set, the training sample pressure corresponding to each training sample group in the K-th round training sample set, and the weight matrix corresponding to the K-th round training sample set; The acquisition module is further used to acquire a candidate sample set; a verification module, configured to input the candidate sample set into the K-th round chaotic polynomial model to obtain a candidate predicted pressure value corresponding to each candidate sample group; and determine the K-th round failure probability of the solid motor based on the number of candidate sample groups whose candidate predicted pressure values ​​are greater than a preset pressure threshold and the number of candidate sample groups in the candidate sample set; a learning module, configured to determine a K-th round learning value corresponding to each candidate sample group in the candidate sample set based on a candidate predicted pressure value corresponding to each candidate sample group in the candidate sample set and a candidate prediction error corresponding to each candidate sample group in the candidate sample set, and determine the candidate sample group with the smallest learning value in the candidate sample set as the K-th round target candidate sample group; Specifically, if K is equal to 1, the first round learning value corresponding to each candidate sample group in the candidate sample set is determined according to the following formula: Represents the i-th candidate sample group in the candidate sample set in the first iteration The corresponding first round learning value, Represents the i-th candidate sample group in the candidate sample set in the first iteration The corresponding candidate predicted pressure value, Represents the i-th candidate sample group in the candidate sample set in the first iteration the corresponding candidate prediction error; If K is greater than 1, the K-th round learning value corresponding to each candidate sample group in the candidate sample set is determined according to the following formula: Represents the i-th candidate sample group in the candidate sample set in the K-th iteration The corresponding K-th round learning value, Represents the i-th candidate sample group in the candidate sample set in the K-th iteration The corresponding candidate predicted pressure value, represents the candidate prediction error corresponding to the i-th candidate sample group in the candidate sample set in the K-th iteration, represents the number of training sample groups in the K-th round training sample set, Indicates the number of training sample groups in the first round of training sample set, represents the jth training sample group in the Kth round training sample set in the Kth round iteration; a calculation module, configured to calculate the distance between the K-th round target candidate sample group and each training sample group in the K-th round training sample set, and determine the training sample group with the smallest distance to the K-th round target candidate sample value as the K-th round target training sample group; an updating module, configured to update the weight of the K-th round target training sample group to obtain a K+1-th round training sample set if the target sample distance between the K-th round target candidate sample group and the K-th round target training sample group is less than a first distance; and to add the K-th round target candidate sample group to the K-th round training sample set to obtain a K+1-th round training sample set if the target sample distance between the K-th round target candidate sample group and the K-th round target training sample set is greater than or equal to the first distance; The analysis module is used to output the reliability analysis result of the pressure performance of the solid motor if the failure probability from the Kkth round to the Kth round meets the stopping iteration condition, and the reliability analysis result is the Kth round failure probability.

8. 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 6.

9. 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 6.

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