General characteristic and performance flow design method for solid engine

Abstract

The invention provides a general characteristic and performance flow design method for a solid engine, and relates to the technical field of solid engine design, and the method comprises the steps: decomposing the whole design process of the solid engine into a plurality of design subtasks; constructing an initial design structure matrix; converting the initial design structure matrix into a standard design structure matrix; identifying and dividing a coupling task set and a non-coupling task set based on the standard design structure matrix; the design subtasks in the coupling task set are mutually dependent; sequencing the design sub-tasks in the uncoupled task set to obtain a first sequence; sequencing the design sub-tasks in the coupling task set to obtain a second sequence; based on the first sequence and the second sequence, recombining the overall design process of the solid engine to obtain an optimal design process sequence; graded disassembly and ordered carding of the complex nested coupling relation of the solid engine are achieved, and the overall design efficiency and design quality of the solid engine are remarkably improved.

Description

Technical Field

[0001] This application generally relates to the field of solid rocket motor design technology, and specifically to a general characteristic and performance flow design method for solid rocket motors. Background Technology

[0002] Solid rocket motors are chemical rocket propulsion devices that use solid propellants. They have many advantages, such as simple structure, ease of use, ability to maintain combat readiness for extended periods, and high mass-to-weight ratio. They are widely used in military fields such as missile weapons, launch vehicles, and spacecraft.

[0003] However, the early design of solid rocket motors is complex. Key components such as solid propellant grains, casing, nozzle, and ignition system must not only meet overall performance indicators but also require coordinated adjustments to casing wall thickness, nozzle throat diameter, and ballistic trajectory. Furthermore, the development process involves multiple disciplines—overall design, structure, manufacturing processes, and reliability—working in rapid succession and waiting for each other to complete. Any modification can trigger cross-disciplinary iterations, leading to longer development cycles, increased costs, and amplified quality risks. Existing methods for evaluating solid rocket motor process design based on explicit decoupling operations rely heavily on the characteristics of the problem and the experience of domain experts, often at the cost of partial product quality loss. This makes it difficult to systematically address complex coupled design problems, resulting in low overall engine design efficiency and quality. Summary of the Invention

[0004] In view of the above-mentioned defects or deficiencies in the prior art, it is desirable to provide a general characteristic and performance process design method for solid rocket motors that improves design efficiency and quality.

[0005] This application provides a general characteristic and performance process design method for solid rocket motors, including the following steps: The overall design process of solid rocket motors is broken down into multiple design sub-tasks; Based on the information dependencies between the design subtasks, an initial design structure matrix is ​​constructed; The initial design structure matrix is ​​transformed into a standard design structure matrix; the elements in the standard design structure matrix are used to characterize whether there are dependencies between the corresponding design subtasks. Based on the standard design structure matrix, a coupled task set and an uncoupled task set are identified and divided; there are interdependent relationships between the design subtasks within the coupled task set. The design subtasks in the uncoupled task set are sorted to obtain a first sort; and the design subtasks in the coupled task set are sorted to obtain a second sort. Based on the first and second sorting, the overall design flow of the solid rocket motor is reorganized to obtain the optimal design process sequence.

[0006] According to the technical solution provided in this application, an initial design structure matrix is ​​constructed based on the information dependencies between the design subtasks, specifically including the following steps: Based on the information dependencies between the design subtasks, a directed process graph is generated; Based on the directed graph of the process, construct the initial design structure matrix.

[0007] According to the technical solution provided in this application, based on the standard design structure matrix, the coupled task set and the uncoupled task set are identified and divided, specifically including the following steps: Calculate the adjacency matrix corresponding to the standard design structure matrix, and calculate the reachability matrix based on the adjacency matrix; Based on the reachability matrix, a judgment matrix is ​​calculated; and design subtasks that still have non-zero elements in the rows or columns of the judgment matrix (excluding themselves) are divided into coupled task sets, while design subtasks that do not have non-zero elements in the rows or columns of the judgment matrix (excluding themselves) are divided into uncoupled task sets.

[0008] According to the technical solution provided in this application, the design subtasks in the uncoupled task set are sorted to obtain a first sort, which specifically includes the following steps: Arrange the design subtasks that do not have information input in the uncoupled task set forward, or arrange the design subtasks that do not have information output backward; Repeat the above sorting operation and record the sorting level until the design subtasks of all uncoupled task sets are sorted, and the first sort is obtained.

[0009] According to the technical solution provided in this application, the design subtasks within the coupled task set are sorted to obtain a second sort, specifically including the following steps: The coupled task set is segmented using fuzzy clustering to obtain multiple coupled subsets; The tearing algorithm is used to calculate the information dependency strength of each design subtask in each of the coupled subsets, and the coupled subsets are sorted in descending order of information dependency strength to obtain a second sort.

[0010] According to the technical solution provided in this application, the coupled task set is segmented using a fuzzy clustering method to obtain multiple coupled subsets, specifically including the following steps: Based on the initial design structure matrix, calculate the fuzzy relationship matrix between each design subtask in the coupled task set; The transit packet of the fuzzy relation matrix is ​​calculated, and the coupled task set is divided into multiple coupled subsets of different granularities based on the transit packet and the clustering level threshold corresponding to the transit packet.

[0011] According to the technical solution provided in this application, the fuzzy relation matrix is ​​calculated using the included angle cosine method.

[0012] According to the technical solution provided in this application, the information dependency strength of each design subtask in each of the coupled subsets is calculated using the tearing algorithm, specifically including the following steps: Obtain the total information input and total information output of each design subtask within the coupled subset; The ratio of the total information input to the total information output is used as the information dependency strength of the corresponding design subtask.

[0013] According to the technical solution provided in this application, the method further includes the following steps: The optimal design process sequence is mapped to a planned design structure matrix and / or a directed process graph.

[0014] As can be seen from the above technical solution, this application has at least the following beneficial effects: This application provides a general characteristic and performance process design method for solid rocket motors, comprising: decomposing the overall design process of a solid rocket motor into multiple design sub-tasks; constructing an initial design structure matrix based on the information dependencies between the design sub-tasks; transforming the initial design structure matrix into a standard design structure matrix; using the elements in the standard design structure matrix to characterize whether there are dependencies between the corresponding design sub-tasks; identifying and dividing coupled task sets and uncoupled task sets based on the standard design structure matrix; identifying mutual dependencies between the design sub-tasks within the coupled task set; sorting the design sub-tasks in the uncoupled task set to obtain a first sort; and sorting the design sub-tasks within the coupled task set to obtain a second sort; and reorganizing the overall design process of the solid rocket motor based on the first and second sorts to obtain the optimal design process sequence.

[0015] This application decomposes the overall design process of a solid rocket motor into multiple design sub-tasks. Based on the information dependencies between these sub-tasks, a design structure matrix is ​​constructed and transformed to accurately identify and classify coupled and uncoupled task sets. Then, a scientific sorting method is applied to each of these task sets to obtain the corresponding sorting results, ultimately recombining them to form the optimal design process sequence. Compared to existing explicit separation operations that rely on dependency characteristics and expert experience and are prone to product quality loss, this solution leverages the intuitive analytical capabilities of the design structure matrix, combined with differentiated processing logic for coupled and uncoupled sets, to achieve hierarchical decomposition and orderly sorting of the complex nested coupling relationships in solid rocket motors. Furthermore, through matrix operations, fuzzy clustering, and tearing algorithms, it systematically solves the over-coupling problem caused by the functional coordination of various components and the master-slave relationship of parameters during the design process. This effectively reduces the number of cross-disciplinary iterations and unnecessary waiting time, not only reducing design complexity but also avoiding quality loss caused by human intervention, significantly improving the overall design efficiency and quality of solid rocket motors. Attached Figure Description

[0016] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings.

[0017] Figure 1 A flowchart illustrating the general characteristics and performance design process for solid rocket motors.

[0018] Figure 2 This is an example of a directed graph representing the flow of a solid rocket motor.

[0019] Figure 3 This is an example diagram of the initial design structure matrix.

[0020] Figure 4 Example diagram of a standardized design structure matrix.

[0021] Figure 5 This is an example diagram of the planned design structure matrix.

[0022] Figure 6 This is an example diagram illustrating the comprehensive design process for the general quality characteristics and performance of an engine.

[0023] Figure 7 This is a transformation diagram between the directed process graph and the design structure matrix. Detailed Implementation

[0024] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0025] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0026] like Figure 1 As shown, this application provides a general characteristic and performance process design method for solid rocket motors, including the following steps: S100 breaks down the overall design process of solid rocket motors into multiple design sub-tasks.

[0027] One approach to decomposing the overall design process of a solid rocket motor into multiple design sub-tasks is to divide it based on the motor's structural composition, functional requirements, and design flow. Each sub-task must have a clear design objective and independent input / output boundaries to avoid task overlap and ensure that no design step is omitted.

[0028] S200. Based on the information dependencies between design subtasks, construct the initial design structure matrix.

[0029] Information dependency refers to the requirement that the design output of one design subtask must serve as the design input of another design subtask. It includes unidirectional dependency and bidirectional dependency.

[0030] Specifically, based on the information dependencies between design subtasks, an initial design structure matrix is ​​constructed, including the following steps: Generate a directed process graph based on the information dependencies between the design subtasks; Based on the directed graph of the process, construct the initial design structure matrix.

[0031] It should be noted that, firstly, all design subtasks should be clearly defined, and the information flow between them should be analyzed one by one, from the information output to the information input. For example, if the output of design subtask X is the input of design subtask Y, then a directed edge from X to Y should be drawn. All directed edges should be integrated to form a complete directed process graph. Figure 2 As shown, each node represents a design subtask, identified by a number, and the direction of the arrow on the directed edge represents the direction of information flow; isolated nodes are a typical feature of uncoupled tasks, with no incoming or outgoing edges; cyclic paths are formed by multiple nodes forming a bidirectional directed edge closed loop.

[0032] The rows and columns of the matrix are arranged sequentially according to the design sub-task number. The rows represent the information output design sub-tasks, and the columns represent the information input design sub-tasks. If there is a directed edge from a row design subtask to a column design subtask in the directed graph of the process (i.e., the row design subtask provides information input to the column design subtask), then the matrix element value is the information dependency strength value. The information dependency strength value ranges from 0 to 1, where 0 indicates no direct dependency, and values ​​between 0 and 1 represent the dependency strength, with larger values ​​indicating a tighter dependency. If there is no directed edge (no direct information dependency), then the matrix element value is 0. The main diagonal elements (row number = column number) represent the autocorrelation of the design subtask itself, and their value is 0 (or ignored).

[0033] For example, if there are directed edges from index 1 to 16 in the directed graph of the process, then the element in row 1 and column 16 of the matrix has a value of 0.1 (weak dependency); if there are directed edges from index 6 to 16 in the directed graph of the process, then the element in row 6 and column 16 of the matrix has a value of 0.9 (strong dependency); if there are no directed edges from index 2 to 3 in the directed graph of the process, then the element in row 2 and column 3 of the matrix has a value of 0.

[0034] The initial design structure matrix (DSM matrix) is an n×n square matrix, where n is the number of design subtasks. The rows and columns of the matrix correspond one-to-one with the nodes (design subtasks) in the directed process graph. The values ​​of the matrix elements quantify the information dependency strength between the corresponding row and column subtasks.

[0035] S300. Transform the initial design structure matrix into a standard design structure matrix; the elements in the standard design structure matrix are used to characterize whether there are dependencies between the corresponding design subtasks.

[0036] Here, the elements in the initial matrix are dependency strength values ​​(such as 0.4, 0.7). The standard matrix converts them into binary elements of 0 or 1. 1 indicates that there is a dependency relationship between the two subtasks (regardless of the strength), and 0 indicates that there is no dependency relationship. By converting the initial design structure matrix into the standard design structure matrix, the quantitative differences in dependency strength are filtered out, focusing on the core logic of whether there is a dependency, reducing the computational complexity of subsequent coupling identification, and ensuring the clarity of the association relationship, avoiding the influence of strength value fluctuations on the judgment.

[0037] The standard design structure matrix is ​​of order n×n (where n is the number of subtasks). The main diagonal elements are usually 1 (representing the autocorrelation of the task itself), and the off-diagonal elements directly represent the correlation between tasks through 0 / 1.

[0038] S400: Based on the standard design structure matrix, identify and divide the coupled task set and the uncoupled task set; there are interdependent relationships between the design subtasks within the coupled task set.

[0039] Furthermore, based on the standard design structure matrix, the coupled task set and the uncoupled task set are identified and divided, specifically including the following steps: Calculate the adjacency matrix corresponding to the standard design structure matrix, and calculate the reachability matrix based on the adjacency matrix; Based on the reachability matrix, calculate the judgment matrix; and divide the design subtasks in which there are non-zero elements other than themselves in the rows or columns of the judgment matrix into a coupled task set, and divide the design subtasks in which there are no non-zero elements other than themselves in the rows or columns of the judgment matrix into a non-coupled task set.

[0040] It should be noted that the adjacency matrix is ​​a square matrix that represents the direct dependencies between design subtasks. Its dimensions are the same as the standard design structure matrix (n×n, where n is the number of subtasks), and the matrix elements are either 0 or 1. It is a direct reuse of the standard design structure matrix. The rows and columns of the adjacency matrix correspond one-to-one with the design subtask numbers, with rows representing information output tasks and columns representing information input tasks.

[0041] The reachability matrix is ​​a square matrix that represents the direct and indirect dependencies between design subtasks. The elements are either 0 or 1. 1 indicates that one design subtask can pass information to another design subtask through a direct or indirect path, i.e., there is a reachability relationship. 0 indicates that there is no direct or indirect dependency.

[0042] The expression for the reachability matrix is: ; in, Let be the reachability matrix. for Unit matrix of order, It is an adjacency matrix. The number of structural design matrices and performance work items.

[0043] The judgment matrix is ​​used to filter strongly connected relationships (cyclic dependencies). Its dimension is the same as that of the reachability matrix. Its elements are obtained by the intersection operation of the reachability matrix and its own transpose, and only take the values ​​0 or 1.

[0044] The expression for the judgment matrix is: ; in, To determine the matrix, Let be the reachability matrix. This is the transpose of the matrix.

[0045] Each row and column of the design subtask in the judgment matrix is ​​checked one by one. If there are multiple independent strongly connected branches, that is, multiple unrelated cyclic dependency groups, they are divided into multiple coupled task sets. If there are no independent strongly connected branches, they are divided into uncoupled task sets.

[0046] S500. Sort the design subtasks in the uncoupled task set to obtain the first sort; and sort the design subtasks in the coupled task set to obtain the second sort.

[0047] Furthermore, the design subtasks in the uncoupled task set are sorted to obtain the first sort, which specifically includes the following steps: Arrange the design subtasks that do not have information input in the uncoupled task set forward, or arrange the design subtasks that do not have information output backward; Repeat the above sorting operation and record the sorting level until the design subtasks of all uncoupled task sets are sorted, and the first sort is obtained.

[0048] It should be noted that uncoupled tasks only have unidirectional or no dependencies, and there are no bidirectional dependencies. Therefore, they can be linearly ordered according to the input-to-output flow. Design subtasks with no information input (source tasks) are those that do not depend on any other design subtasks and can be started independently; design subtasks with no information output (sink tasks) are those that do not provide data to any other design subtasks and only serve as the final verification link in the design loop.

[0049] In the standard design structure matrix, examine the columns corresponding to design subtasks. If all elements in a column are 0 (no other task provides input), it's a design subtask with no input. These subtasks don't need to wait for other tasks' output and can be prioritized. Executing them first quickly provides basic input for subsequent tasks, reducing overall process time. Alternatively, examine the rows corresponding to design subtasks in the standard design structure matrix. If all elements in a row are 0 (no output is provided to any other task), it's a design subtask with no output. These subtasks don't affect the progress of other tasks; executing them last avoids consuming early design resources and ensures all dependent tasks are completed.

[0050] Since the remaining uncoupled tasks have unidirectional dependencies, they cannot be sorted in one go. It is necessary to gradually divide the hierarchy through an iterative logic of sorting, deleting sorted tasks, re-identifying, and re-sorting. Specifically, after each round of initial sorting, these sorted tasks are virtually deleted from the set of uncoupled tasks, that is, their impact on unsorted tasks is no longer considered, because their outputs are clear and can be used as inputs for unsorted tasks. For the remaining unsorted tasks, the rule of sorting forward when there is no information input and backward when there is no information output is reapplied to identify new hierarchical tasks. The above process is repeated until all uncoupled tasks are assigned to a clear hierarchy, forming a complete hierarchy.

[0051] That is, by first locating the source task and sink task to lock the beginning and end of the process, and then gradually filling the intermediate levels through repeated iterations, the unidirectional dependency relationship of uncoupled tasks is transformed into a linear ordered sequence. It basically gets rid of the dependence of traditional sorting on expert experience, and is based entirely on the objective data of the design structure matrix, ensuring the scientificity and repeatability of the sorting results. At the same time, it provides a clear and conflict-free uncoupled task execution order for the reorganization of the overall design process.

[0052] Furthermore, the design subtasks within the coupled task set are sorted to obtain a second sort, which specifically includes the following steps: The coupled task set is segmented using fuzzy clustering to obtain multiple coupled subsets; The tearing algorithm is used to calculate the information dependency strength of each design subtask in each coupled subset, and the coupled subsets are sorted in descending order of information dependency strength to obtain the second sort.

[0053] It should be noted that the core purpose of fuzzy clustering is to split a large coupled set into multiple smaller coupled subsets with strong internal correlations and weak external correlations based on the degree of association, thereby reducing the cyclic dependency range of each coupled subset and facilitating subsequent sorting. In other words, it quantifies the association similarity between tasks using mathematical methods and then groups them according to a similarity threshold.

[0054] Here, the fuzzy relation matrix can be calculated using the cosine of the included angle method. The coupled task set is then segmented using fuzzy clustering to obtain multiple coupled subsets. Specifically, the steps include: Based on the initial design structure matrix, calculate the fuzzy relationship matrix between each design subtask in the coupled task set; Calculate the transit packet of the fuzzy relation matrix, and based on the transit packet and the clustering level threshold corresponding to the transit packet, divide the coupled task set into multiple coupled subsets of different granularities.

[0055] Specifically, based on the initial design structure matrix, the correlation similarity between any two tasks i and j is calculated to form a fuzzy relation matrix. The elements of the fuzzy relation matrix are calculated according to the following formula: ; in, For the task i and tasks j The elements of the corresponding fuzzy relation matrix take values ​​in the range [0,1]. For the task k and tasks i Interdependence For the task k and tasks j Interdependence n This indicates the number of coupled, centralized design subtasks.

[0056] The fuzzy relation matrix only reflects the direct similarity between tasks. It needs to be expanded to include both direct and indirect similarities using a transitive packet function to ensure the transitivity of the associations. The transitive packet function is as follows: ; in, For fuzzy relation matrix k The power, for everything greater than k A natural number l that satisfies .

[0057] Using packet passing functions The collection Coupled sets can be partitioned and sorted, and truncated. Only the lower triangular matrix is ​​meaningful. The clustering level is defined as the number of clustering levels, with values ​​ranging from (0, 1] and its elements... Calculate using the following formula: ; When passing tasks in the packet function i The similarity with task j is greater than At that time, the task i and tasks j They are grouped into the same subset; λ The larger the subset size, the finer the subset division, and the higher the task similarity requirement. λ The smaller the subset, the coarser the subset division, and the lower the task similarity requirement.

[0058] The coupled set can be segmented by selecting a certain clustering level in the table above based on factors such as information loss, independence, and engineering experience.

[0059] Furthermore, the tearing algorithm is used to calculate the information dependency strength of each design subtask in each coupled subset, specifically including the following steps: Obtain the total information input and total information output for each design subtask within the coupled subset; The ratio of total information input to total information output is used as the information dependency strength of the corresponding design subtask.

[0060] It should be noted that the core of the tearing algorithm is to quantify the strength of information dependencies and break circular dependencies. For each coupled subset, the execution priority of the task is determined by calculating the information dependency strength (input / output ratio) of the task, so that the circularly dependent tasks form an orderly iteration.

[0061] Total information input It is all other task-to-task pairs within the coupling subset. i Total information input, including direct input and indirect input (such as task input). i(Parameters and data from other tasks required for the design process), the calculation is performed by summing the non-zero elements (including dependency strength) of the corresponding columns in the initial design matrix; total information output. It is a task i The total information output for all other tasks within the coupled subset, including direct and indirect outputs (such as task...). i Once completed, it can be provided as parameters and data for other tasks. During calculation, the sum of non-zero elements in the corresponding row of the initial design matrix (including dependency strength) is taken.

[0062] The formula for calculating the information dependency strength of the design subtask is: ; in, For the task i The strength of information dependence.

[0063] Information dependency strength reflects the independence of a task. The smaller the information dependency strength, the fewer the input requirements and the greater the output contribution, indicating stronger independence, and thus it should be executed first. Conversely, the larger the information dependency strength, the more the task depends on the input of other tasks, and thus it should be executed later.

[0064] If tasks within the coupled subset have a clear logical order, they are prioritized for sorting according to this logical order, followed by verification of dependency strength. If there is no clear logical order, they are strictly sorted according to information dependency strength from smallest to largest, with stronger independence taking precedence, thus obtaining a second sort. By using mathematical tools to transform implicit circular dependencies into explicit ordered iterations, the necessary connections between coupled tasks are preserved while breaking the deadlock of unordered waiting, providing an efficient and conflict-free execution order for coupled tasks for subsequent overall design process reorganization.

[0065] S600, based on the first and second sorting, reorganizes the overall design process of the solid rocket motor to obtain the optimal design process sequence.

[0066] First, the first and second sorting are integrated in terms of information flow compatibility to ensure that the input of the coupled subset can connect with the output of the preceding uncoupled task, and that the output of the coupled subset can provide support for the subsequent uncoupled task, thereby eliminating process conflicts, such as preventing a task from starting execution before its input has been generated, optimizing the task connection order, and reducing waiting time.

[0067] Uncoupled tasks flow unidirectionally from input to output without reverse iteration; coupled tasks are executed in an orderly manner with iterations within subsets and between subsets, with necessary iterations only occurring within subsets, avoiding large-scale rework across subsets and disciplines; the execution order of all tasks is perfectly matched with information dependencies, forming an optimal design process sequence that is free of redundancy, has low iteration, and is highly efficient.

[0068] Furthermore, this method also includes the following steps: mapping the optimal design process sequence to a planned design structure matrix and / or a directed process graph.

[0069] The purpose of this step is to transform the abstract sorting results into an intuitive and actionable engineering implementation guide through structured matrices and graphical models.

[0070] Here, the planned design structure matrix is ​​a DSM matrix with its rows and columns rearranged based on the optimal sequence. The matrix elements still represent the dependencies between tasks, but the rows and columns are arranged according to the optimal sequence, making the dependencies exhibit lower triangular or ordered characteristics, intuitively reflecting the sequential logic of task execution. The planned DSM matrix and the directed process graph are different representations of the same optimal design process sequence, and the two are completely equivalent.

[0071] For example, the overall design process of a solid rocket motor is decomposed into 16 design sub-tasks, namely, propellant charge design, mass ratio design, working pressure design, throat diameter and expansion ratio design, shell design, propellant structure design, insulation layer design, internal ballistic performance simulation, ignition process structural integrity analysis, temperature environment structural integrity analysis, engine overload ablation analysis, tail nozzle heat transfer analysis, engine structural strength analysis, engine unstable combustion analysis, drop safety analysis, and propellant stability analysis under thermal load conditions, as shown in Table 1.

[0072] Table 1 Design Subtasks

[0073] Based on the above 16 design sub-tasks, the information dependencies between each sub-task are analyzed, and the related logic is presented intuitively in the form of a directed graph, such as... Figure 2 As shown, each node corresponds to a design subtask, identified by numbers 1 to 16, and the direction of the directed edges represents the direction of information flow. For example, node 6 pointing to node 9 indicates that the output information of the charge structure design serves as the input basis for the structural integrity analysis of the ignition process; nodes 1 and 8 are bidirectional directed edges, reflecting their interdependence.

[0074] Then, combining engineering practice, the information dependency strength among the 16 design sub-tasks was quantitatively analyzed from three dimensions: reliability, safety, and environmental adaptability. Figure 2 The directed graph shown is transformed into the initial design structure matrix (N), as follows: Figure 3As shown, the matrix is ​​a 16×16 square matrix, with rows and columns corresponding to the design sub-task numbers in Table 1. The matrix elements range from [0,1], with larger values ​​indicating stronger information dependencies between tasks, and 0 indicating no direct information dependency. For example, the element in row 6 and column 16 is 0.9, representing a very high dependency of the charge structure design on charge stability analysis; the element in row 1 and column 16 is 0.1, representing a relatively weak dependency of the charge propellant quantity on charge stability analysis.

[0075] To facilitate calculation, the initial design matrix is ​​transformed into a standardized design structure matrix. A matrix element with a value of 1 indicates a direct information dependency between tasks in the corresponding row and column (regardless of the initial dependency strength), while a value of 0 indicates no direct information dependency. For example, if the element in row 5 and column 6 of the initial design structure matrix is ​​0.7 (the shell structure design depends on the charge structure design), the corresponding element in the standardized design structure matrix is ​​converted to 1; if the element in row 2 and column 3 of the initial design structure matrix is ​​0, the corresponding element in the standardized design structure matrix is ​​retained as 0. The specific standardized design structure matrix is ​​as follows: Figure 4 As shown.

[0076] Will Figure 4 The standardized design structure matrix in the model is represented as an adjacency matrix. A : ; Recalculate the reachability matrix R , c Take 16: ; Then, calculate the judgment matrix. L : ; Based on the above judgment matrix L It can be seen that design subtasks 1, 3, 4, 5, 6, 7, and 8 form a coupled set, while design subtasks 2, 9, 10, 11, 12, 13, 14, 15, and 16 form a non-coupled set, i.e., independent tasks.

[0077] The first sorting, based on the decomposition algorithm and combined with the actual production process, is shown in Table 2.

[0078] Table 2 First Ranking

[0079] The fuzzy relationship matrix between the design subtasks is calculated using the cosine mean angle method, which is widely used in fuzzy clustering techniques. M : ; Initial design matrixN Arithmetic mean matrix of bidirectional information dependencies between subtasks in the design : ; when When the condition is met, the packet passing function in this example is: ; by For example, to illustrate Calculation and analysis results: ; Therefore, the coupled set is divided into , The results of dynamic clustering analysis are shown in Table 3.

[0080] Table 3 Results of dynamic cluster analysis

[0081] Based on practical engineering considerations, two stages were selected where the propellant dosage and internal ballistic performance simulations were relatively separate. Therefore, according to the clustering level... It is divided into 4 coupling sets , , , To avoid repetition, the above four coupling sets are designated as coupling set 4, coupling set 5, coupling set 6, and coupling set 7, respectively.

[0082] The tearing algorithm is used to calculate the information dependency strength of each task within four coupling sets and determine their ranking. Taking coupling set 4 as an example, its first-round calculation results are shown in Table 4. Therefore, task 5 is first torn from the matrix, and the reduction matrix is ​​repeated to determine the ranking of coupling set 4. Similarly, the internal sorting of coupling set 5 is calculated as follows: Considering coupling sets 4 through 7 as tasks, they still constitute coupling sets. The execution order can be determined by calculating the information dependency strength. The final planned DSM is as follows: Figure 5 As shown.

[0083] Table 4 Calculation results of information dependence strength

[0084] Based on the mapping relationship between the DSM and the directed graph, after adding data items, the final comprehensive design flow for the engine's general quality characteristics and performance can be obtained, such as... Figure 6 As shown in Table 5, the engine design process sequence is based on the actual production process.

[0085] Table 5. Sequencing of Engine Design Process

[0086] The transformation diagram between the directed graph of the flow and the design structure matrix in this example is as follows: Figure 7 As shown.

[0087] This application decomposes the overall design process of a solid rocket motor into multiple design sub-tasks. Based on the information dependencies between these sub-tasks, a design structure matrix is ​​constructed and transformed to accurately identify and classify coupled and uncoupled task sets. Then, a scientific sorting method is applied to each of these task sets to obtain the corresponding sorting results, ultimately recombining them to form the optimal design process sequence. Compared to existing explicit separation operations that rely on dependency characteristics and expert experience and are prone to product quality loss, this solution leverages the intuitive analytical capabilities of the design structure matrix, combined with differentiated processing logic for coupled and uncoupled sets, to achieve hierarchical decomposition and orderly sorting of the complex nested coupling relationships in solid rocket motors. Furthermore, through matrix operations, fuzzy clustering, and tearing algorithms, it systematically solves the over-coupling problem caused by the functional coordination of various components and the master-slave relationship of parameters during the design process. This effectively reduces the number of cross-disciplinary iterations and unnecessary waiting time, not only reducing design complexity but also avoiding quality loss caused by human intervention, significantly improving the overall design efficiency and quality of solid rocket motors.

[0088] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the inventive concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A general characteristic and performance flow design method for solid rocket motors, characterized in that, Includes the following steps: The overall design process of solid rocket motors is broken down into multiple design sub-tasks; Based on the information dependencies between the design subtasks, an initial design structure matrix is ​​constructed; Transform the initial design structure matrix into a standard design structure matrix; The elements in the standard design structure matrix are used to characterize whether there are dependencies between the corresponding design subtasks; Based on the standard design structure matrix, the coupled task set and the uncoupled task set are identified and divided. The design subtasks within the coupled task set are interdependent; The design subtasks in the uncoupled task set are sorted to obtain a first sort; The design subtasks within the coupled task set are then sorted to obtain a second sort. Based on the first and second sorting, the overall design flow of the solid rocket motor is reorganized to obtain the optimal design process sequence.

2. The general characteristic and performance process design method for solid rocket motors according to claim 1, characterized in that, Based on the information dependencies between the design subtasks, an initial design structure matrix is ​​constructed, which includes the following steps: Based on the information dependencies between the design subtasks, a directed process graph is generated; Based on the directed graph of the process, construct the initial design structure matrix.

3. The general characteristic and performance process design method for solid rocket motors according to claim 1, characterized in that, Based on the standard design structure matrix, the coupled task set and the uncoupled task set are identified and divided, specifically including the following steps: Calculate the adjacency matrix corresponding to the standard design structure matrix, and calculate the reachability matrix based on the adjacency matrix; Based on the reachability matrix, a judgment matrix is ​​calculated; and design subtasks that still have non-zero elements in the rows or columns of the judgment matrix (excluding themselves) are divided into coupled task sets, while design subtasks that do not have non-zero elements in the rows or columns of the judgment matrix (excluding themselves) are divided into uncoupled task sets.

4. The general characteristic and performance process design method for solid rocket motors according to claim 1, characterized in that, The design subtasks in the uncoupled task set are sorted to obtain a first sort, which specifically includes the following steps: Arrange the design subtasks that do not have information input in the uncoupled task set forward, or arrange the design subtasks that do not have information output backward; Repeat the above sorting operation and record the sorting level until the design subtasks of all uncoupled task sets are sorted, and the first sort is obtained.

5. The general characteristic and performance process design method for solid rocket motors according to claim 1, characterized in that, The design subtasks within the coupled task set are sorted to obtain a second sort, which specifically includes the following steps: The coupled task set is segmented using fuzzy clustering to obtain multiple coupled subsets; The tearing algorithm is used to calculate the information dependency strength of each design subtask in each of the coupled subsets, and the coupled subsets are sorted in descending order of information dependency strength to obtain a second sort.

6. The general characteristic and performance process design method for solid rocket motors according to claim 5, characterized in that, The coupled task set is segmented using fuzzy clustering to obtain multiple coupled subsets, specifically including the following steps: Based on the initial design structure matrix, calculate the fuzzy relationship matrix between each design subtask in the coupled task set; The transit packet of the fuzzy relation matrix is ​​calculated, and the coupled task set is divided into multiple coupled subsets of different granularities based on the transit packet and the clustering level threshold corresponding to the transit packet.

7. The general characteristic and performance process design method for solid rocket motors according to claim 6, characterized in that, The fuzzy relation matrix is ​​calculated using the cosine of the included angle method.

8. The general characteristic and performance process design method for solid rocket motors according to claim 5, characterized in that, The tearing algorithm is used to calculate the information dependency strength of each design subtask in each of the coupled subsets, specifically including the following steps: Obtain the total information input and total information output of each design subtask within the coupled subset; The ratio of the total information input to the total information output is used as the information dependency strength of the corresponding design subtask.

9. The general characteristic and performance process design method for solid rocket motors according to claim 1, characterized in that, The method further includes the following steps: The optimal design process sequence is mapped to a planned design structure matrix and / or a directed process graph.

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