A super-high-speed impact-resistant structure design optimization method based on an explicit topology optimization framework

Abstract

The present application belongs to the technical field of computational mechanics involving nonlinear topology optimization, and particularly relates to a super-high-speed impact-resistant structure design optimization method based on an explicit topology optimization framework. In the framework based on the moving morphable components method, upper and lower limits need to be set for the design variables of the moving morphable components method. Then, the genetic algorithm randomly generates multiple sets of candidate design variables, and an initial structure configuration is obtained therefrom. The configuration is then converted into a smoothed particle hydrodynamics model, and corresponding boundary conditions are applied. Then, the structure response of mass and displacement is obtained, and an objective function and related constraint conditions are constructed. Finally, according to the design variable update criterion of the genetic algorithm, cross and mutation genetic operations are applied to generate the next generation of candidate design variables. After the iteration ends, the optimal individual is selected as the final configuration according to the objective function and the constraint conditions. The present application has a small number of design variables, making it possible to optimize the design of super-high-speed impact-resistant structures.

Description

Technical Field

[0001] This invention belongs to the field of computational mechanics technology involving nonlinear topology optimization, specifically relating to a design optimization method for ultra-high-speed impact-resistant structures based on an explicit topology optimization framework. Background Technology

[0002] Hypervelocity Impact (HVI) protection is one of the core technologies for ensuring the survivability of aerospace vehicles. The threats primarily come from two sources: space debris and micrometeoroids in the space environment; and kinetic energy interceptors, fragments, and directed energy weapon projectiles in military applications. With the rapid development of hypersonic offensive and defensive systems, the importance of hypervelocity impact protection has risen from simply ensuring spacecraft safety to a level of strategic security and military technological superiority.

[0003] Currently, mainstream protective structure designs are still based on the Whipple shield and its numerous improvements. Its basic idea is "sacrifice for protection," using a multi-layered structure to intercept, break up, melt, vaporize, and diffuse impact debris, thereby consuming its energy and protecting the main bulkhead. The basic Whipple configuration consists of an outer "bumper shield" and an inner "backwall." The bumper shield causes the impacting object to break up, melt, and diffuse into a debris cloud, thus increasing the contact area with the main bulkhead and reducing pressure. The enhanced Whipple protective structure is an optimization based on the basic Whipple configuration, including: Stuffed Whipple – adding one or more layers of high-performance fiber fabric (such as Kevlar, Nextel) or metal mesh between the sacrificial shield and the main bulkhead. These layers can further break up and capture the debris cloud and absorb its kinetic energy, significantly improving protective performance; Multi-Shock Shield – composed of multiple spaced thin screens, which gradually consume the energy of the debris cloud through repeated impact-fragmentation processes; Metal-based composite material protective structure – using new lightweight and high-strength materials, such as aluminum-based and magnesium-based composite materials, which can significantly reduce the structural weight at the same level of protection.

[0004] A robust protective structure is the "shield" of an aircraft to cope with threats and ensure mission success. However, the significance of this shield lies in protection, not burden. True engineering wisdom lies not in endlessly stacking protection, but in forging an indestructible yet lightweight shield. For static or weakly nonlinear problems, the traditional topology optimization method is quite mature. However, for materials undergoing severe plastic deformation, melting, vaporization, or even plasmaization, the traditional topology optimization framework based on linear or weakly nonlinear mechanics is completely incapable of describing these extreme processes. In sensitivity analysis, the high discontinuity and nonlinearity of the problem make obtaining analytical sensitivity extremely difficult, while differential sensitivity requires a complete nonlinear dynamic analysis of all design variables in each iteration, making gradient-based topology optimization strategies difficult to implement. High-fidelity simulation of ultra-high-speed impacts requires meshless methods such as Smooth Particle Hydrodynamics (SPH), whose single computational cost is already extremely high. Topology optimization is a process requiring tens of thousands of iterations. Traditional SIMP methods, due to their massive design variables, are directly proportional to the number of meshes, resulting in an explosive combination of computational costs, especially in 3D problems, making them almost infeasible in practical engineering. The optimization objective for low-velocity impacts is usually to maximize stiffness or energy absorption. However, the core of ultra-high-speed impact protection is the "fragmentation-diffusion" debris cloud, and its optimization objectives (such as debris cloud morphology, maximum back-side collapse size, etc.) are difficult to express using traditional mechanical response functions.

[0005] Traditional implicit topology optimization methods, such as the SIMP method, suffer from inherent drawbacks such as numerous variables, high computational cost, and unclear boundaries, which are amplified in this extreme problem. Therefore, an explicit topology optimization framework with fewer variables and more direct geometric expression is particularly important. Existing technologies have proposed the Moving Morphable Components (MMC) method, which characterizes the structural topology through a set of explicitly described geometric components, such as beams and plates, and their motion variables. These components interlock and merge to form different topological structures. Due to its clear geometric description, this method allows for easy addition of manufacturing constraints during the optimization process, and its optimization results can be directly imported into CAD for modeling, reducing the burden of post-processing. Simultaneously, this method yields clear and smooth geometric boundaries, enabling it to be well integrated with meshless methods such as SPH, generating SPH particle models with high precision, and thus more accurately simulating ultra-high-speed impact processes. Furthermore, its fewer design variables allow it to obtain topological structures with tens of times the design space required by pixel-based optimization methods, making the Moving Morphable Components method a perfect complement to non-gradient algorithms.

[0006] Therefore, this invention proposes a design optimization method for ultra-high-speed impact-resistant structures based on an explicit topology optimization framework. Summary of the Invention

[0007] The purpose of this invention is to provide a design optimization method for ultra-high-speed impact-resistant structures based on an explicit topology optimization framework. This invention utilizes the moving deformable component method framework, employs existing industrial software to accurately analyze its complex nonlinear dynamic processes, and uses a non-gradient genetic algorithm (GA) to update the design variables. The moving deformable component method has a very small number of design variables, typically only a few hundred or even a few dozen, which makes the optimized design of ultra-high-speed impact-resistant structures possible compared to traditional pixel-based methods that often involve thousands of design variables.

[0008] The specific technical solution adopted by this invention is as follows: A design optimization method for ultra-high-speed impact-resistant structures based on an explicit topology optimization framework first requires setting upper and lower limits for the design variables of the Moving Morphable Components (MMC) method within the framework of the Moving Morphable Components (MMC) method to ensure that they only vary within a predetermined range. Subsequently, the genetic algorithm randomly generates a set of candidate design variables, thereby obtaining the initial structural configuration; the configuration is then converted into a Smoothed Particle Hydrodynamics (SPH) model, and corresponding boundary conditions are applied. Subsequently, an explicit dynamic method was used for analysis to obtain the structural response in terms of mass and displacement. Based on these output structural responses, the objective function and its related constraints were constructed. Finally, based on the design variable update criteria of the genetic algorithm, crossover and mutation genetic operations are applied to generate the next generation of candidate design variables; after the iteration is completed, the optimal individual is selected as the final configuration according to the objective function and constraints.

[0009] This invention includes the following detailed steps: Step 1: Parametrically model and output mesh files based on the design variables of the Moving Deformable Component (MMC) method; Step 2: Convert the mesh model file into an SPH particle model using commercial software; Step 3: Perform explicit kinetic analysis and extract the results; Step 4: Based on the information of the objective function, constraint function, and design variables, update the design variables of the next generation using the criteria of the genetic algorithm.

[0010] The technical effects achieved by this invention are as follows: This invention is based on the framework of the mobile deformable component method. It uses existing industrial software to accurately analyze the complex nonlinear dynamic process and employs a non-gradient genetic algorithm (GA) to update the design variables. Compared to pixel-based topology optimization, this method has a very small number of design variables, typically only a few hundred or even a few dozen. Compared to traditional pixel-based methods that often have tens of thousands of design variables, this makes the optimized design of ultra-high-speed impact-resistant structures possible. Attached Figure Description

[0011] Figure 1 These are different parameters of the present invention Corresponding component shape diagram; Figure 2 The different in this invention The described first Root 2D component shape diagram; In the diagram, (a): uniform width; (b): linearly variable width; (c): quadratic variable width; Figure 3 This is a schematic diagram of the two-dimensional topology optimization evolution based on the mobile deformable component method of the present invention; Figure 4 This is a flowchart of the ultra-high-speed impact-resistant structure optimization design based on the moving deformable component method of this invention; Figure 5 It is the unit cell configuration within the design space of the optimization method of this invention. Detailed Implementation

[0012] To make the objectives and advantages of this invention clearer, the invention will be specifically described below with reference to embodiments. It should be understood that the following text is merely used to describe one or more specific embodiments of the invention and does not strictly limit the scope of protection specifically claimed by the invention.

[0013] like Figure 1 As shown, a design optimization method for ultra-high-speed impact-resistant structures based on an explicit topology optimization framework is first proposed. In the framework based on Moving Morphable Components (MMC), upper and lower limits need to be set for the design variables of the Moving Morphable Components method to ensure that they only change within a predetermined range. Subsequently, the genetic algorithm randomly generates a set of candidate design variables, thereby obtaining the initial structural configuration; the configuration is then converted into a Smoothed Particle Hydrodynamics (SPH) model, and corresponding boundary conditions are applied. Subsequently, an explicit dynamic method was used for analysis to obtain the structural response in terms of mass and displacement. Based on these output structural responses, the objective function and its related constraints were constructed. Finally, based on the design variable update criteria of the genetic algorithm, crossover and mutation genetic operations are applied to generate the next generation of candidate design variables; after the iteration is completed, the optimal individual is selected as the final configuration according to the objective function and constraints.

[0014] This invention includes the following detailed steps: Step 1: Parametrically model and output mesh files based on the design variables of the Moving Deformable Component (MMC) method; Step 2: Convert the mesh model file into an SPH particle model using commercial software; Step 3: Perform explicit kinetic analysis and extract the results; Step 4: Based on the objective function, constraint function, and design variable information, update the design variables of the next generation using the criteria of the genetic algorithm.

[0015] In step 1, structural components with hyperelliptical shapes are used as basic elements in the optimization process, and the topology of the structure is represented by a Topology Description Function (TDF). The mathematical form of the topology description function is as follows: in, Represents the given structural design domain. Indicated in the structural design domain The area containing all components; Indicates the interior of the structure. Indicates the boundary of the structure. Then it represents the exterior of the structure; for a structure composed of The topology description function of the structure composed of root components Depend on The calculation yielded, where Indicates the first The TDF value of the root component.

[0016] In two dimensions The TDF mathematical expression for the root component is written as: in Here, It is a relatively large even number used to adjust the contour features of the hyperellipse shape of a 2D component (typically set). ),and Indicates the half-length of a two-dimensional component. Represents the coordinates of the center point of the two-dimensional component. Representing the local coordinate system Relative to global coordinate system The tilt angle, This represents the half-width of a two-dimensional component.

[0017] parameter Controlling the roundness and sharpness of the hyperelliptical component outline, when When the value is taken to infinity, the shape of the component approaches a rectangle; in three dimensions, when... When the value is infinity, the component is a cuboid. Its topological description function can be found in relevant papers, and will not be elaborated here.

[0018] According to Different mathematical expressions result in different shapes for the two-dimensional components. These shapes include: 1) Uniform width, its The mathematical expression is: in, Describing the uniform half-width of a two-dimensional component, then the first... The design variable vector of the root two-dimensional component is written as: 2) Linear variable width, its The mathematical expression is: in, Let represent the half-widths at both ends of the two-dimensional component, then the... The design variable vector of the root two-dimensional component is written as: 3) Secondary variable width, its The mathematical expression is: in, Let represent the half-widths at both ends and the middle of the two-dimensional component, respectively. Then the... The design variable vector of the root two-dimensional component is written as: Figure 2 The different in this invention The described first Root 2D component shape diagram.

[0019] In the two-dimensional topology optimization process based on the mobile deformable component method, components within the design domain form the optimized overall structural topology through deformation, movement, overlap, and coverage. (Refer to...) Figure 3 .

[0020] Within the framework of Movable Deformable Component (MMC) topology optimization, the optimization formula for a general problem can be written as: in, Describe the objective function. This indicates the conditions that need to be satisfied in the optimization problem. One constraint condition. Represent design variables The allowed set. Unlike traditional topology optimization methods based on mesh cells, each component has very few design parameters, which can significantly reduce the number of design variables during the optimization process. In large-scale computational problems, the Moving Deformable Component (MMC) method can effectively and significantly reduce the computation time required for optimization solutions and improve optimization design efficiency.

[0021] Based on the geometric component description described above, in structural topology optimization problems... The design variable vector for each component can be written as: The MMC design variables mentioned above have clear geometric meanings, which are then converted into geometric parameters. These geometric parameters are then transformed into geometric descriptions recognizable by geometric software (such as SpaceClaim, SolidWorks, and CATIA). By creating parametric modeling scripts within the geometric modeling environment, the corresponding geometric models are automatically updated or reconstructed based on the MMC variables, thus achieving a seamless transition from numerical optimization results to 3D geometric entities. This step eliminates the need for manual redrawing; simply updating the MMC parameters quickly generates the corresponding geometric configuration.

[0022] After obtaining the complete 3D geometric model based on the Moving Deformable Component (MMC) method, the mesh generation stage is required. At this stage, mesh parameters must be pre-defined, such as the desired element type (e.g., tetrahedral, hexahedral, or hybrid elements), average element size, and constraints on maximum and minimum element sizes. Subsequently, the automatic mesh generation algorithm built into the geometry software or the accompanying finite element preprocessing module is used to automatically mesh the model. It is particularly important to emphasize that this process does not rely on manual intervention; instead, the software automatically generates the mesh by setting reasonable mesh generation criteria. To improve the accuracy of subsequent calculations based on the SPH method, the regularity and element quality of the mesh should be ensured as much as possible. For example, the aspect ratio, twist, and size transition of adjacent elements should be controlled to avoid numerical instability during subsequent particleization.

[0023] In step 2, after completing the MMC-based structural topology optimization and obtaining the final mesh model, the mesh model needs to be further converted into an SPH particle model for subsequent explicit dynamic analysis. This process can be implemented in mature commercial finite element / explicit dynamics software (such as LS-DYNA, ABAQUS / Explicit, etc.) and can be automated by calling or customizing scripts.

[0024] First, an appropriate particleization strategy should be selected based on the geometric characteristics of the obtained MMC mesh. For structures with complex topologies or irregular details, the "element centroid method" can be used, which generates an SPH particle at the geometric centroid of each finite element, thus better preserving the overall mass distribution and geometric characteristics of the original configuration. For regular geometries (such as regular cuboids, cylinders, or symmetrical structures), the "pointseeding" function built into commercial software can be used to automatically arrange particles within the geometric domain according to a given particle spacing, thereby effectively reducing modeling complexity and computational cost while ensuring computational accuracy. If necessary, a local refinement strategy can also be applied to key areas, using higher-density particle partitioning in areas of stress concentration or impact response sensitivity to improve local simulation accuracy.

[0025] After particle generation, the corresponding boundary conditions need to be determined based on the actual position of the structure in space and the boundary environment. For example, fixed or displacement boundaries should be applied in the constrained region, and corresponding external forces, velocities, or impact conditions should be applied in the loaded region to realistically reflect the experimental or engineering scenario. Simultaneously, appropriate material parameters should be defined for the model to ensure the stability and physical accuracy of the explicit dynamic solution.

[0026] Subsequently, key solution parameters for explicit dynamic analysis need to be configured, including analysis step size, time integration control, SPH particle interaction criteria (such as kernel function type, smoothing length, and particle spacing control), and contact algorithms suitable for specific problems. Based on this, convergence and stability criteria should be defined, such as total energy balance, time step stability conditions, or the magnitude of change in key response quantities, to ensure the reliability of numerical results. Finally, the physical quantities to be output (such as particle displacement, stress, strain, etc.) and their output frequencies should be selected, and appropriate file formats and storage strategies should be set for subsequent data analysis and visualization.

[0027] All of the above processes can be automated through secondary development interfaces or scripting languages ​​(such as Python, key file macros, etc.) provided by commercial software.

[0028] In step 3, after completing the preprocessing based on the SPH particle model, calculations can be initiated in commercial explicit dynamics software (such as LS-DYNA, ABAQUS / Explicit, etc.). After obtaining the complete numerical response, a reasonable mathematical description needs to be constructed according to the research objectives or optimization requirements, i.e., the objective function and constraint functions need to be formulated. For example, the degree of damage or energy absorption of a key plate can be used as the objective function to characterize the protective performance of the structure under extreme conditions; at the same time, the amount of material used, the overall mass, the maximum stress, or the displacement limit can be used as constraints to ensure that the structure meets both the strength and stiffness requirements and the constraints of material and manufacturing costs. If multi-objective optimization is involved, weighting coefficients can be introduced or a hierarchical strategy can be adopted to integrate multiple objectives into a single evaluation index.

[0029] In step 4, the genetic algorithm uses multi-objective optimization or weighted single-objective optimization to represent the objective function and constraint function.

[0030] After constructing the objective function and constraints and extracting the design variables and response data obtained from the previous round of calculations, the core stage of the genetic algorithm is entered, namely the updating and evolution of the next generation of design variables. This process follows the standard evolutionary principles of genetic algorithms, including the basic operations of selection, crossover, and mutation.

[0031] First, selection is based on the combined fitness of the previous generation of individuals under the objective function and constraints. Common strategies include roulette wheel selection, tournament selection, or rank-based selection to ensure that individuals with higher fitness have a greater probability of entering the next generation, while preserving a certain degree of diversity.

[0032] Subsequently, in the crossover phase, the design variable information of the selected parent individuals is reorganized through single-point, two-point, or uniform crossover methods to generate offspring with new characteristics, thereby enabling a wider range of exploration in the solution space. The mutation operation randomly alters certain design variables with a low probability, preventing the algorithm from getting trapped in local optima and maintaining population diversity. For MMC design variables, mutation can manifest as perturbations of geometric parameters such as component position, size, or angle.

[0033] Optimization typically involves a trade-off between structural performance and material usage, i.e., a trade-off between the objective function and constraint functions. In genetic algorithms, this manifests as a multi-objective optimization. In multi-objective optimization scenarios, genetic algorithms employ the Pareto front strategy, comparing the non-dominant relationships of individuals across multiple objectives to select a set of mutually balancing solutions, providing designers with diverse optimization schemes. If a single comprehensive objective is preferred, a weighted summation method or hierarchical optimization strategy can be used to weight and combine multiple performance indicators according to importance coefficients into a single objective function for evolution, thereby achieving a simplified optimization process.

[0034] The following strategies can be used during the iteration process to reduce computational load and improve the stability of the analysis: Algorithm Flow Figure 4 As shown: a. Identify non-convergence cases and automatically stop them once the expected computation time is exceeded, while setting the objective function to a very large value.

[0035] b. For design variables that severely fail to meet volume constraints, set their objective function to a very large value.

[0036] c. Establish a database of design variables. For the same design variable, directly use the previous objective function and constraint function to avoid redundant analysis.

[0037] d. Selectively adjust small-sized elements (low-mass SPH particles) that do not affect the main structural path to increase the analysis step size and reduce the total analysis time.

[0038] In previous classic algorithms for describing topology based on pixels, the number of design variables using non-gradient algorithms was enormous, making computationally inefficient. However, this method requires only a very small number of design variables to accurately describe the topology of the structure. Furthermore, in nonlinear analysis, because the configuration is explicit, SPH particles more accurately describe the structure boundaries when converted to an SPH particle model. Based on the method in this invention, structures such as... Figure 5 The complex structure shown requires tens or even hundreds of times more design variables if designed using traditional methods. This would take tens or even hundreds of times more time for computationally expensive nonlinear dynamics, which is almost unacceptable.

[0039] This invention is based on the framework of the mobile deformable component method, using existing industrial software to accurately analyze its complex nonlinear dynamic process, and employing a non-gradient genetic algorithm (GA) to update the design variables. The mobile deformable component method has very few design variables, typically only a few hundred or even a few dozen, which makes the optimized design of ultra-high-speed impact-resistant structures possible compared to traditional pixel-based methods that often have tens of thousands of design variables.

[0040] The above description is merely a preferred embodiment of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention. Structures, devices, and operating methods not specifically described or explained in this invention are implemented according to conventional methods in the art unless otherwise specified or limited.

Claims

1. A design optimization method for ultra-high-speed impact-resistant structures based on an explicit topology optimization framework, characterized in that: First, within the framework of the movable deformable component method, it is necessary to set upper and lower limits for the design variables of the movable deformable component method. Subsequently, the genetic algorithm randomly generates a set of candidate design variables, thereby obtaining an initial structural configuration; the configuration is then converted into a smooth particle hydrodynamic model, and corresponding boundary conditions are applied. Subsequently, an explicit dynamic method was used for analysis to obtain the structural response in terms of mass and displacement. Based on these output structural responses, the objective function and its related constraints were constructed. Finally, based on the design variable update criteria of the genetic algorithm, crossover and mutation genetic operations are applied to generate the next generation of candidate design variables; after the iteration is completed, the optimal individual is selected as the final configuration according to the objective function and constraints. The design optimization method includes the following detailed steps: Step 1: Parametrically model and output mesh files based on the design variables of the Moving Deformable Component (MMC) method; Step 2: Convert the mesh model file into an SPH particle model in the software; Step 3: Perform explicit kinetic analysis and extract the results; Step 4: Based on the objective function, constraint function, and design variable information, update the design variables of the next generation using the criteria of a genetic algorithm; In step 1, structural components with hyperelliptical shapes are used as basic elements in the optimization process, and the topological configuration of the structure is represented using a topological description function. The mathematical form of the topological description function is as follows: ； in, Represents a given structural design domain. Indicated in the structural design domain The area containing all components; Indicates the interior of the structure. Indicates the boundary of the structure. Then it represents the exterior of the structure; for a given structure... The topology description function of the structure composed of root components Depend on The calculation yielded, where Indicates the first The TDF value of the root component; In two dimensions The TDF mathematical expression for the root component is written as: ； in ； Here, It is a relatively large even number used to adjust the contour features of the hyperellipse shape of a 2D component (typically set). ),and Indicates the half-length of a two-dimensional component. Represents the coordinates of the center point of the two-dimensional component. Representing the local coordinate system Relative to global coordinate system The tilt angle, This represents the half-width of a two-dimensional component; parameter Controlling the roundness and sharpness of the hyperelliptical component outline, when When the value is taken to infinity, the shape of the component approaches a rectangle; in three dimensions, when... When the value is taken as infinity, the component is a cuboid. According to Different mathematical expressions result in different shapes for the two-dimensional components. These shapes include: 1) Uniform width, its The mathematical expression is: ； in, Describing the uniform half-width of a two-dimensional component, then the first... The design variable vector of the root two-dimensional component is written as: ； 2) Linear variable width, its The mathematical expression is: ； in, Let represent the half-widths at both ends of the two-dimensional component, then the... The design variable vector of the root two-dimensional component is written as: ； 3) Secondary variable width, its The mathematical expression is: ； in, Let represent the half-widths at both ends and the middle of the two-dimensional component, respectively. Then the... The design variable vector of the root two-dimensional component is written as: ； In the two-dimensional topology optimization process based on the mobile deformable component method, the components in the design domain form the optimized overall topology of the structure through deformation, movement, overlap, and coverage. Within the framework of topology optimization using the moving deformable component method, the optimization formula for the general problem can be written as follows: ； in, Describe the objective function. This indicates the conditions that need to be satisfied in the optimization problem. One constraint condition. Represent design variables The allowed set; based on the geometric component description of the above form, in the structural topology optimization problem, The design variable vector of the root component is written as: ； The MMC design variables in the above-mentioned Movable Deformable Component method have clear geometric meanings, which are then converted into geometric parameters. These geometric parameters are then converted into geometric descriptions that can be recognized in geometric software. By establishing parametric modeling scripts in the geometric modeling environment, the corresponding geometric models are automatically updated or reconstructed based on the MMC variables, thereby achieving a seamless connection from numerical optimization results to three-dimensional geometric entities. This step does not require manual redrawing; simply updating the MMC parameters is sufficient to quickly generate the corresponding geometric configuration. After obtaining the complete 3D geometric model based on the MMC variable of the moving deformable component method, the mesh generation stage is required. At this time, the mesh parameters need to be given in advance. Then, the automatic mesh generation algorithm built into the geometry software or the matching finite element preprocessing module is called to automatically mesh the model. In step 2, after completing the structural topology optimization based on the Movable Deformable Component (MMC) method and obtaining the final mesh model, the mesh model needs to be further converted into an SPH particle model. The specific process is implemented in mature commercial finite element / explicit dynamics software and can be automated by calling or customizing scripts. All of the above processes can be automated through the secondary development interfaces or scripting languages ​​provided by the software.

2. The ultra-high-speed shock-resistant structure design optimization method based on an explicit topology optimization framework according to claim 1, characterized in that: In step 3, after completing the preprocessing based on the SPH particle model, the calculation can be started in commercial explicit dynamics software. After obtaining the complete numerical response, a reasonable mathematical description needs to be constructed according to the research objectives or optimization requirements, that is, the objective function and constraint function are formulated.

3. The ultra-high-speed shock-resistant structure design optimization method based on an explicit topology optimization framework according to claim 1, characterized in that: In step 4, the genetic algorithm uses multi-objective optimization or weighted single-objective optimization to represent the objective function and constraint function. After constructing the objective function and constraints and extracting the design variables and response data obtained from the previous round of calculation, the core stage of the genetic algorithm is entered, namely the updating and evolution of the next generation of design variables. This process follows the standard evolutionary principles of the genetic algorithm, including basic operations such as selection, crossover, and mutation. First, selection is based on the combined fitness of the previous generation of individuals under the objective function and constraints. Common strategies include roulette wheel selection, tournament selection, or ranking-based selection methods to ensure that individuals with higher fitness have a greater probability of entering the next generation, while preserving a certain degree of diversity. Subsequently, in the crossover phase, the design variable information of the selected parent individuals is reorganized through single-point, two-point, or uniform crossover methods to generate offspring with new characteristics; the mutation operation randomly changes certain design variables with a small probability to avoid the algorithm getting trapped in local optima and to maintain the diversity of the population; for the design variables of the Moving Deformable Component (MMC) method, the mutation is manifested as the perturbation of the geometric parameters of the component position, size, or angle.

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