CPU-parallel parametric level set topology optimization method for CAD models of arbitrary shapes

Abstract

A CPU parallel parametric level set topology optimization method for arbitrarily shaped CAD models belongs to the field of high-performance design technology. It solves the problems of the split between CAD and CAE, poor and time-consuming mesh generation, and low efficiency of large-scale mesh calculations. The topology optimization parallel computing design framework of the present invention significantly improves the efficiency and accuracy of topology optimization of complex structures by introducing parametric level set methods, parallel finite element analysis technology, and seamless integration with CAD models, and has broad engineering application prospects. It not only solves many problems of traditional topology optimization methods, but also provides an efficient and practical optimization design tool for the engineering design field. It can significantly improve the efficiency and accuracy of topology optimization when processing complex CAD models, and solves the problems of insufficient communication between CAD and CAE, poor mesh generation quality, low calculation efficiency, etc. in the existing technology, and has broad engineering application prospects.

Description

Technical Field

[0001] The present invention belongs to the technical field of high-performance design of high-precision engineering structures, and relates to a CPU parallel parameterized level set topology optimization method for CAD models of arbitrary shapes. Background Art

[0002] With the rapid development of modern industry, the structural requirements in engineering design are becoming increasingly complex and diverse. Traditional structural topology optimization methods are struggling to meet these complex demands. Structural topology optimization seeks the optimal material distribution within a given design space to meet specific performance requirements. However, with increasing design demands, especially in demanding fields such as aerospace, automotive manufacturing, and construction engineering, the limitations of traditional topology optimization methods are becoming increasingly apparent, making it difficult to meet the high precision, high efficiency, and high complexity requirements of practical engineering applications.

[0003] Most emerging topology optimization methods currently focus on effectiveness within the two-dimensional design domain, while neglecting solution efficiency and engineering practicality within the three-dimensional domain. Existing methods often require significant computational resources and time costs when dealing with three-dimensional structures. Traditional methods exhibit significant performance bottlenecks, particularly when dealing with complex geometries and large-scale meshes. This lack of computational efficiency not only limits the application of topology optimization in complex engineering projects but also leads to delays in the optimization process, impacting the timeliness and cost-effectiveness of designs.

[0004] Secondly, many newly proposed topology optimization methods are mostly implemented based on advanced computing software platforms such as MATLAB. These platforms have certain advantages in algorithm verification and small-scale calculations, but due to computing power limitations, they perform poorly when processing large-scale three-dimensional meshes in actual engineering applications. Typically, these methods can only handle hundreds of thousands to millions of mesh elements, and their computational efficiency is low, which cannot meet the industrial demand for large-scale complex structural optimization. This performance shortfall significantly limits these methods in practical engineering applications, making it difficult for them to achieve their intended effect.

[0005] Furthermore, due to the differences in basis functions between CAD (Computer-Aided Design) and CAE (Computer-Aided Engineering), existing topology optimization methods often cannot directly interact with CAD models. This barrier leads to a significant gap between the optimized design and the actual engineering model, increasing the workload and time cost in the early stages of design. In traditional design processes, CAD models often require multiple manual adjustments and regeneration to meet the requirements of CAE analysis. This not only increases design complexity but can also lead to inconsistencies between the optimization results and actual engineering requirements, thus affecting the performance and quality of the final product.

[0006] The development of computer technology, particularly multi-core processors and parallel computing, has provided new approaches to addressing these challenges. Parallel computing can significantly improve computational efficiency by processing multiple tasks simultaneously. This technology holds great promise for applications in large-scale finite element analysis and topology optimization. Parallel computing can significantly reduce computational time during topology optimization and improve the accuracy and reliability of optimization results. However, effective integration of parallel computing into the topology optimization design process remains a pressing challenge in current research and applications.

[0007] In summary, existing topology optimization methods face challenges when dealing with complex structures, including low computational efficiency, poor compatibility with CAD models, and limited computational scale. These issues not only restrict their application in practical engineering but also affect the quality and performance of design results. Summary of the Invention

[0008] The technical solution of the present invention is used to solve the problems of the separation between CAD and CAE, poor and time-consuming mesh generation, and low efficiency of large-scale mesh calculation.

[0009] The present invention solves the above technical problems through the following technical solutions:

[0010] A CPU-parallel parametric level set topology optimization method for arbitrarily shaped CAD models, including:

[0011] Implicit recognition model: Scaling and translating the geometric information of the CAD model stored in the STL file format; after completing the scaling and translation of the model, the signed distance field value is calculated for each node in the background grid; after the signed distance field is generated, the level set function is initialized;

[0012] CPU parallel finite element analysis: The structural model is divided into multiple sub-areas. By defining private and shared areas, and introducing the concepts of local domain and ghost domain, parallel finite element analysis is performed in each sub-area.

[0013] Parametric level set topology optimization: This method combines parameterization with the level set method to generate different topological configurations by adjusting parameters. The level set method is used to describe the boundaries of the structure and optimize the topology of the structure by gradually evolving these boundaries. By incorporating multiple working conditions into the topology optimization process, the structure is optimized simultaneously under multiple working conditions, thereby designing a structural solution with higher stability and reliability.

[0014] Furthermore, the scaling operation method is as follows: by traversing the coordinate information of each vertex in the STL file, the maximum and minimum values ​​of the model in the three directions of x, y, and z are found, thereby calculating the size of the model in each normal direction; the size of the background grid in each direction is divided by the size of the model in the corresponding direction to obtain three scaling factors; the smallest scaling factor is selected and applied to all coordinate values ​​of the model to complete the scaling operation of the model.

[0015] Furthermore, the translation operation method is as follows: traverse all vertices of the model again, obtain their minimum x, y, and z coordinate information, and then subtract the corresponding minimum values ​​from the x, y, and z coordinates of all vertices to complete the translation operation of the model.

[0016] Furthermore, the method for calculating the value of the signed distance field is as follows: traverse each node in the background grid and determine its position relative to the model; if the node is outside the model, the value of its signed distance field is negative; if the node is inside the model, the value is positive; the absolute value of the signed distance field is equal to the minimum distance from the node to the model surface.

[0017] Furthermore, the level set function value of each node is calculated by the value of the signed distance field and the compactly supported radial basis function; the zero isosurface of the level set function is intercepted to obtain the initial model described by the level set function.

[0018] Furthermore, when the radius of the compactly supported region is equal to one unit length, the shared area is defined as the set of all cells within one unit length from the partition boundary, and the private area is the cells that do not belong to the shared area.

[0019] Furthermore, the local domain is composed of the private area and shared area of ​​the current processor core, including all grid cells responsible for the processor core and their corresponding CSRBF tight supports and inner cells, ensuring that the processor has all necessary node information when performing CSRBF calculations.

[0020] Furthermore, the ghost domain is the shared area information obtained by the current processor core from the adjacent processor core, providing the adjacent node data required by the nodes located at the local domain boundary.

[0021] An electronic device includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the above-mentioned CPU parallel parameterized level set topology optimization method for CAD models of arbitrary shapes, and the processor is configured to execute the program stored in the memory.

[0022] A storage medium stores a computer program, which, when executed by a processor, executes the steps of the above-mentioned CPU parallel parameterized level set topology optimization method for CAD models of arbitrary shapes.

[0023] The advantages of the present invention are:

[0024] The parallel computing design framework for topology optimization proposed in the present invention significantly improves the efficiency and accuracy of topology optimization of complex structures by introducing a parameterized level set method, parallel finite element analysis technology, and seamless integration with CAD models, and has broad prospects for engineering application. This framework not only solves many problems of traditional topology optimization methods, but also provides an efficient and practical optimization design tool for the field of engineering design. The method proposed in the present invention can significantly improve the efficiency and accuracy of topology optimization when processing complex CAD models, and solves the problems of insufficient communication between CAD and CAE, poor mesh generation quality, and low computational efficiency existing in the prior art, and has broad prospects for engineering application. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 This is a technical solution flow chart of a CPU parallel parameterized level set topology optimization method for CAD models of arbitrary shapes according to the first embodiment of the present invention;

[0026] Figure 2 This is a flow chart of an implicit recognition model of a CPU parallel parameterized level set topology optimization method for CAD models of arbitrary shapes according to the first embodiment of the present invention;

[0027] Figure 3 This is a schematic diagram of model scaling of a CPU parallel parameterized level set topology optimization method for CAD models of arbitrary shapes according to the first embodiment of the present invention;

[0028] Figure 4 Schematic diagram of model translation of the CPU parallel parameterized level set topology optimization method for CAD models of arbitrary shapes according to the first embodiment of the present invention;

[0029] Figure 5 This is a flow chart of distance field calculation for a CPU-parameterized level set topology optimization method for CAD models of arbitrary shapes according to the first embodiment of the present invention;

[0030] Figure 6 Schematic diagram of Boolean operations of signed distance field in a CPU parallel parameterized level set topology optimization method for CAD models of arbitrary shapes according to the first embodiment of the present invention;

[0031] Figure 7 A component graph of shared and private regions of a CPU-parameterized level set topology optimization method for CAD models of arbitrary shapes according to the first embodiment of the present invention;

[0032] Figure 8 This is a definition diagram of the local domain and the "shared domain" of the CPU parallel parameterized level set topology optimization method for CAD models of arbitrary shapes according to the first embodiment of the present invention;

[0033] Figure 9 This is a graph showing the variation trend of the computational efficiency of the CPU-parameterized level set topology optimization method for CAD models of arbitrary shapes with the number of cores according to the first embodiment of the present invention;

[0034] Figure 10 This is the topology optimization result of a single working condition of a jet engine bracket using the CPU parallel parameterized level set topology optimization method for CAD models of arbitrary shapes according to the first embodiment of the present invention;

[0035] Figure 11 The results of multi-operating-condition topology optimization of a jet engine using the CPU-parameterized level set topology optimization method for CAD models of arbitrary shapes according to the first embodiment of the present invention are as follows;

[0036] Figure 12 This is the result of applying the CPU parallel parameterized level set topology optimization method for CAD models of arbitrary shapes according to the first embodiment of the present invention to the topology optimization of VR glasses parts. DETAILED DESCRIPTION

[0037] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0038] The technical solution of the present invention is further described below with reference to the accompanying drawings and specific embodiments:

[0039] Example 1

[0040] like Figure 1 The figure shows a flowchart of a CPU parallel parametric level set topology optimization method for CAD models of arbitrary shapes provided by this embodiment. By combining implicit recognition models, CPU parallel finite element analysis and parametric level set topology optimization, the present invention can achieve efficient, high-performance and high-precision topology optimization design of complex feature structures.

[0041] 1. Implicit Recognition Model

[0042] In the design process of the present invention, the implicit recognition model plays a vital role in accurately mapping the geometric shape of the CAD model to the background mesh and laying the foundation for subsequent topology optimization. Specifically, the model includes three main steps: scaling and translation of the model, generation of the signed distance field, and initialization of the level set function. Figure 2 shown.

[0043] (1) Scaling and translation of the model

[0044] During the implementation of the implicit recognition model, the geometric information of the CAD model (stored in STL file format) must first be scaled and translated to ensure that it better fits the background mesh. The purpose of this step is to enable more elements in the background mesh to participate in subsequent analysis and topology optimization, thereby improving computational efficiency and optimization accuracy.

[0045] Specifically, first, by traversing the coordinate information of each vertex in the STL file, the maximum and minimum values ​​of the model in the x, y, and z directions are found, thereby calculating the size of the model in each normal direction. Then, the size of the background mesh in each direction is divided by the size of the model in the corresponding direction to obtain three scaling factors. Select the smallest scaling factor and apply it to all coordinate values ​​of the model to complete the model scaling operation, such as Figure 3 shown.

[0046] Next, the scaled model needs to be translated. Traverse all the vertices of the model again, obtain their minimum x, y, and z coordinate information, and then subtract the corresponding minimum value from the x, y, and z coordinates of all vertices to complete the translation operation of the model, such as Figure 4 This process ensures the reasonable positioning of the model in the background grid, laying the foundation for subsequent steps.

[0047] (2) Generation of signed distance field

[0048] After scaling and translating the model, the next step is to calculate the signed distance field (SDF) value for each node in the background mesh. The signed distance field (SDF) is a key data structure used to describe the model's geometry. Its value represents the distance from the background mesh node to the model surface, and its sign distinguishes the inside and outside positions of the node relative to the model.

[0049] In the specific calculation process, we first traverse each node in the background grid and determine its position relative to the model. If the node is outside the model, the value of its signed distance field is negative; if the node is inside the model, its value is positive. The absolute value of the signed distance field is equal to the minimum distance from the node to the model surface, such as Figure 5 shown.

[0050] This process generates a three-dimensional signed distance field (SDF) within the background mesh, which describes the distribution of the model within the background mesh. Accurate generation of the SDF is crucial for subsequent level set function calculations. It not only directly impacts the initialization accuracy of the level set function but also has a profound impact on the final topology optimization results.

[0051] In order to conveniently apply boundary conditions, different STL files need to be input to form different signed distance fields, and a series of Boolean calculations need to be performed for different signed distance fields. The specific implementation process is as follows Figure 6 shown.

[0052] (3) Initialization of the level set function

[0053] After the signed distance field is generated, the next step is to initialize the level set function. The level set function is an implicit function used to describe the boundaries of geometric shapes. In this invention, the level set function value of each node in the background mesh can be calculated using the signed distance field value and compactly supported radial basis functions (CSRBFs).

[0054] First, the level set function (LSF) value at each node is calculated using the signed distance field (SDF) and CSRBFs. CSRBFs, with their local support properties, improve the smoothness and accuracy of the LSF while maintaining computational efficiency. Next, the zero isosurface of the LSF is intercepted to obtain an initial model described by the LSF. This model serves as the initial design for the topology optimization process and is directly applied to subsequent optimization calculations.

[0055] Through the above steps, the implicit recognition model achieves seamless integration of the CAD model's geometric information with the background mesh, providing accurate initial data for the topology optimization process. This method improves the accuracy of topology optimization and is an indispensable component of the present invention.

[0056] Traditional topology optimization methods often require multiple data conversions and manual adjustments between the CAD model and the optimized model, increasing design complexity and time costs. To address this issue, this paper proposes a design framework that can directly interact with the CAD model, allowing the optimization results to be seamlessly applied to actual engineering designs.

[0057] This framework enables seamless two-way interaction between CAD and optimization models by directly generating parameterized level set functions corresponding to the optimization model within the CAD system. During the optimization process, designers can directly adjust the geometric parameters of the CAD model as needed, and the optimization framework automatically generates the level set function and performs the optimization calculations. The final optimization results can be exported to a CAD model format. Thanks to the smooth nature of level set modeling, the frequent smoothing and model reconstruction required in traditional methods is avoided, significantly improving design efficiency and accuracy.

[0058] 2. CPU parallel finite element analysis

[0059] This paper uses parallel finite element analysis to improve the computational efficiency of topology optimization. In parallel computing, by dividing the mesh into multiple parts and assigning them to different processor cores, the task load is balanced, avoiding queuing tasks on a single core and achieving efficient parallel processing. However, due to the characteristics of CSRBF, simple mesh partitioning can lead to some problems. Therefore, this paper proposes an improved mesh partitioning and processing strategy to ensure the accuracy and efficiency of parallel computing.

[0060] Finite element analysis (FEA) is a key step in calculating structural response and evaluating structural performance during topology optimization. However, traditional FEA often struggles to meet the computational efficiency requirements of practical engineering applications when faced with large-scale three-dimensional meshes and complex geometries. To address this, the present invention utilizes parallel computing technology, enabling multi-core processors to simultaneously execute multiple computational tasks, significantly improving the efficiency of FEA.

[0061] Specifically, the present invention divides the structural model into multiple subregions and performs parallel finite element analysis within each subregion. This approach not only fully utilizes the computing power of multi-core processors but also significantly reduces overall computation time while ensuring accuracy. Furthermore, by rationally scheduling parallel computing tasks, the present invention's design framework can efficiently process large-scale three-dimensional meshes, ensuring the efficiency and reliability of the optimization process.

[0062] (1) Overview of Parallel Strategy

[0063] Traditional parallel computing strategies typically divide the entire computational domain into several subregions, then assign these subregions to different processor cores for computation. The core idea of ​​this approach is to evenly distribute computational tasks across multiple processor cores by decomposing and allocating them, thus avoiding wasted computing resources. However, the characteristics of compactly supported radial basis functions (CSRBFs) present challenges to this simple meshing approach.

[0064] A notable characteristic of CSRBF is that the level set function value at a given node depends not only on the node's own coordinate information but also on information about the surrounding nodes. This means that if the mesh is simply divided into multiple non-overlapping subdomains, nodes located at the subdomain boundaries will not be able to access relevant information belonging to other subdomains, resulting in inaccurate calculation results. Therefore, a meshing method that considers the interdependencies between nodes must be adopted to ensure that each processor core has access to the correct data required for calculation, thereby optimizing the parallel processing efficiency of CSRBF-based topology optimization.

[0065] (2) The concepts of private and shared areas in grid division

[0066] In order to meet the above challenges, Figure 7 As shown, this embodiment proposes the concepts of private area and shared area, and introduces the division strategy of local domain and shadow domain on this basis, such as Figure 8 This method aims to effectively manage the information exchange between subdomains and ensure the calculation accuracy of CSRBF.

[0067] Specifically, when meshing, a shared region is first defined as the set of elements within a certain range from the partition boundary. For example, when the radius of the compactly supported region is equal to one element length, the shared region is defined as the set of elements within one element length from the partition boundary. The private region, on the other hand, includes all other elements, i.e., those that are not part of the shared region.

[0068] In parallel computing, each processor core is responsible for computing tasks within its assigned subdomain. The private region represents the area solely responsible for that processor core, while the shared region is shared with adjacent subdomains. To ensure computational accuracy, information in the shared region must be exchanged between adjacent processor cores.

[0069] (3) Division and application of local domain and ghost domain

[0070] In the actual computing process, in order to better organize and manage the data exchange between the sub-domains, the present invention proposes the concept of dividing the local domain and the ghost domain.

[0071] Local Domain: The local domain consists of the private and shared regions of the current processor core. It includes all mesh cells that the core is responsible for and their corresponding CSRBF tight supports and inner cells, ensuring that the processor has all necessary node information when performing CSRBF calculations.

[0072] Ghost domain: The ghost domain is the shared area information obtained by the current processor core from the adjacent processor core. This information is crucial for the CSRBF calculation of the current core because it provides the neighboring node data required by the nodes at the local domain boundary.

[0073] In each iteration of the parallel computation, each processor core first calculates the CSRBF in its local domain. Next, the processor core copies information from its shared domain to neighboring cores, forming their shadow domains. This information exchange ensures that each core accurately considers the influence of surrounding nodes when calculating the CSRBF of nodes within its local domain, thereby improving computational accuracy.

[0074] (4) Optimizing parallel computing efficiency

[0075] Through the aforementioned meshing and information exchange strategies, this paper effectively addresses the challenges posed by node interdependencies in CSRBF computations. Specifically, by defining private and shared regions and introducing the concepts of local and ghost domains, each processor core can accurately access all necessary data during parallel computations, avoiding computational errors caused by a lack of boundary node information.

[0076] In addition, this strategy significantly optimizes the efficiency of parallel computing. In traditional methods, because partition boundary nodes cannot access all necessary information, additional calculations or repeated processing are usually required to make up for these deficiencies, resulting in a waste of computing resources. However, through the method of the present invention, each processor core can complete all necessary CSRBF calculations in one calculation without repeated processing, thereby improving the overall computing efficiency. The specific computing efficiency changes with the number of cores as shown in the figure below. Figure 9 shown.

[0077] In summary, the CPU-based parallel finite element analysis method proposed in this paper successfully overcomes the challenges of parallel CSRBF computation by introducing the concepts of private, shared, local, and ghost domains, achieving efficient and accurate topology optimization. This method not only improves computational speed but also ensures the accuracy of the results, providing strong technical support for topology optimization of complex engineering structures.

[0078] 3. Parameterized Level Set Topology Optimization

[0079] Parametric level set topology optimization is an innovative design method proposed in this paper, primarily for topological optimization of complex structures. By combining parametric design with level set methods, this method not only improves the accuracy of topological optimization but also effectively addresses the needs of multi-condition design.

[0080] The level set method is a very effective mathematical tool for dealing with complex topological changes, such as splits and merges. It represents the structure within the design space as an implicit function, allowing the structure's boundary to be defined by the zero level set of the level set function. Compared to traditional voxel methods, the level set method offers higher accuracy when dealing with highly complex structures. However, existing level set methods, when applied to topology optimization, still suffer from low computational efficiency and difficulty in directly interacting the optimization process with CAD models.

[0081] To address these issues, this paper introduces parameterization to the traditional level set method. By parameterizing the level set function, the controllability and precision of the design variables in the optimization process are further enhanced. This parameterized level set method not only effectively represents the geometric characteristics of the structure but also allows for flexible adjustment of the optimization objectives and constraints based on design requirements, thereby achieving efficient optimization of complex structures.

[0082] (1) Parameterization: Parameterization refers to the generation of different topological configurations by adjusting parameters during the design process. This method can overcome the problems of the traditional level set method, such as the inability to generate voids, unstable iterations, and slow convergence. In this paper, parameterization is combined with the level set method to make the topology optimization process more flexible and efficient.

[0083] (2) Level set method: The level set method is a numerical technique based on implicit function representation of geometric shapes. In this paper, the level set method is used to describe the boundaries of the structure and optimize the topology of the structure by gradually evolving these boundaries.

[0084] (3) Multi-condition topology optimization: Multi-condition topology optimization is another feature of the present invention. In practical engineering applications, structures often need to maintain stability and performance under multiple conditions. By incorporating multi-condition conditions into the topology optimization process, the present invention can simultaneously optimize the structure under multiple conditions, thereby designing a structural solution with higher stability and reliability.

[0085] In complex engineering applications, structures often need to meet performance requirements simultaneously under multiple operating conditions. For example, building structures may need to consider the effects of both static and wind loads, while aerospace structures may need to cope with varying loads under different flight conditions. Therefore, optimizing structural topology under multiple operating conditions is crucial for improving overall structural performance.

[0086] The present invention first defines and analyzes different working conditions. In multi-working condition topology optimization, working conditions generally refer to the stress state of a structure under different use conditions or environments. To this end, the present invention calculates and evaluates the structural response under each working condition through finite element analysis. When performing the analysis, the present invention also takes into account the correlation and mutual influence between different working conditions to ensure that the final optimization result can show good performance under multiple working conditions. The present invention introduces a setting and adjustment mechanism for working condition weights to achieve comprehensive optimization under multiple working conditions. Specifically, each working condition is assigned a different weight value according to its importance. During the optimization process, the working condition weight will affect the calculation and optimization direction of the objective function. When the weight of a certain working condition is higher, the optimization process will be more inclined to meet the performance requirements under that working condition. Through this flexible weight setting and adjustment mechanism, the present invention can balance the contradictions between multiple working conditions and generate an optimized design that performs well under various working conditions.

[0087] In summary, the CAD parallel computing design framework proposed in the present invention significantly improves the efficiency and accuracy of topological optimization of complex structures by introducing parameterized level set methods, parallel finite element analysis technology and seamless integration with CAD models, and has broad prospects for engineering applications. This framework not only solves many problems of traditional topological optimization methods, but also provides an efficient and practical optimization design tool for the field of engineering design. Through the above-mentioned technical means, the method proposed in the present invention can significantly improve the efficiency and accuracy of topological optimization when processing complex CAD models, solve the problems existing in the prior art such as insufficient communication between CAD and CAE, poor mesh generation quality, and low computational efficiency, and has broad prospects for engineering applications.

[0088] The following is the specific application of the method of the present invention:

[0089] Application 1: Jet engine bracket

[0090] Taking the engineering structure of the jet engine bracket as an example, the single working condition topology optimization and multi-working condition topology optimization effects of the present invention are tested respectively. For the single working condition, four single working conditions are used for topology optimization. In these four numerical design cases, the volume fraction of the material in the optimization is defined as 20%. The optimization design results of the four design cases are all Figure 10 The first design case is presented in the upper left area, and the lower left area shows the design results of the second design case. The optimized solution of the third design case is presented in Figure 10 The upper right part of Figure 10 The lower right part of the figure provides the design topology of the fourth design case. In addition, in order to more clearly show the optimization solutions in four different cases, Figure 10 The optimized topology is provided in six different perspectives, including isometric view, front view, back view, left view, top view and bottom view.

[0091] For multiple working conditions, the multiple load conditions of the jet engine bracket, namely the four different load conditions mentioned above, are all considered in the optimization. The corresponding optimization design results are shown in Figure 2. Figure 11 The figure also includes six different views of the topology. It can be seen that the current design successfully eliminates the jagged boundary design issue of the density method, which is due to the use of the level set function to construct an implicit identification model. Overall, the optimization results of the jet engine bracket under several different boundary conditions strongly demonstrate the effectiveness of this invention.

[0092] Application 2: VR glasses parts

[0093] In order to further demonstrate the powerful ability of the proposed CAD-oriented parallel computing design framework on extremely complex structures, the engineering design case of VR glasses is taken as an example. Figure 2 (a). It can be easily seen that the corresponding structural geometry is much more complex than the above design case, where a large number of thin and special structural features exist within the design domain. The parallel design of such a structure is relatively difficult to solve.

[0094] The topology of the project structure after optimization is as follows Figure 12 As shown in the figure, even though the VR glasses' components have complex geometries, several thin stripes and geometric features are generated in the final optimized topology. The proposed CAD-oriented parallel computational design framework demonstrates its superior capabilities in optimizing engineering structures using implicit recognition models. Furthermore, the final design features smooth boundaries and clear interfaces within the solid cavity, providing further benefits for later manufacturing. In summary, the optimization results for the VR glasses clearly demonstrate the powerful capabilities of the proposed CAD-oriented parallel computational design framework in solving optimization problems in complex geometric structures for engineering applications. Compared to existing engineering or industrial software, the proposed framework combines computationally efficient parallel optimization design with the effectiveness of accurate implicit recognition models, demonstrating considerable engineering potential. Therefore, the proposed CAD-oriented parallel computational design framework has considerable potential for future engineering applications. It can not only promote the development and expansion of parametric level set topology optimization methods in practical engineering fields, but also effectively and fully integrate modern industrial CAD modeling software into topology optimization through the developed implicit representation model.

[0095] An electronic device includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the CPU parallel parameterized level set topology optimization method for arbitrarily shaped CAD models in Example 1, and the processor is configured to execute the program stored in the memory.

[0096] A storage medium stores a computer program, which, when executed by a processor, executes the steps of the CPU parallel parameterized level set topology optimization method for CAD models of arbitrary shapes in embodiment 1.

[0097] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A CPU parallel parameterized level set topology optimization method for arbitrarily shaped CAD models, characterized by: include: Implicit recognition model: scaling and translation operations on the geometric information of CAD models stored in STL file format; After completing the scaling and translation of the model, the value of the signed distance field is calculated for each node in the background grid; after the signed distance field is generated, the level set function is initialized; CPU parallel finite element analysis: The structural model is divided into multiple sub-areas. By defining private and shared areas, and introducing the concepts of local domain and ghost domain, parallel finite element analysis is performed in each sub-area. Parametric level set topology optimization: This method combines parameterization with the level set method to generate different topological configurations by adjusting parameters. The level set method is used to describe the boundaries of the structure and optimize the topology of the structure by gradually evolving these boundaries. By incorporating multiple working conditions into the topology optimization process, the structure is optimized simultaneously under multiple working conditions, thereby designing a structural solution with higher stability and reliability.

2. The CPU parallel parameterized level set topology optimization method for CAD models of arbitrary shapes according to claim 1, characterized in that: The scaling operation method is as follows: by traversing the coordinate information of each vertex in the STL file, the maximum and minimum values ​​of the model in the three directions of x, y, and z are found, thereby calculating the size of the model in each normal direction; the size of the background grid in each direction is divided by the size of the model in the corresponding direction to obtain three scaling factors; the smallest scaling factor is selected and applied to all coordinate values ​​of the model to complete the scaling operation of the model.

3. The CPU parallel parameterized level set topology optimization method for CAD models of arbitrary shapes according to claim 2, characterized in that: The translation operation method is as follows: traverse all vertices of the model again, obtain their minimum x, y, z coordinate information, and then subtract the corresponding minimum value from the x, y, z coordinates of all vertices to complete the translation operation of the model.

4. The CPU parallel parameterized level set topology optimization method for CAD models of arbitrary shapes according to claim 1, characterized in that: The method for calculating the value of the signed distance field is as follows: traverse each node in the background grid and determine its position relative to the model; if the node is outside the model, the value of its signed distance field is negative; if the node is inside the model, the value is positive; the absolute value of the signed distance field is equal to the minimum distance from the node to the model surface.

5. The CPU parallel parameterized level set topology optimization method for CAD models of arbitrary shapes according to claim 4, characterized in that: The level set function value of each node is calculated by the value of the signed distance field and the compactly supported radial basis function; the zero isosurface of the level set function is intercepted to obtain the initial model described by the level set function.

6. The CPU parallel parameterized level set topology optimization method for CAD models of arbitrary shapes according to claim 1, characterized in that: When the radius of the compactly supported domain is equal to one unit length, the shared area is defined as the set of all cells within one unit length from the partition boundary, and the private area is the cell that does not belong to the shared area.

7. The CPU parallel parameterized level set topology optimization method for CAD models of arbitrary shapes according to claim 1, characterized in that: The local domain is composed of the private area and shared area of ​​the current processor core, including all grid cells responsible for the processor core and their corresponding CSRBF tight supports and inner cells, ensuring that the processor has all necessary node information when performing CSRBF calculations.

8. The CPU parallel parameterized level set topology optimization method for CAD models of arbitrary shapes according to claim 1, characterized in that: The ghost domain is the shared area information obtained by the current processor core from the adjacent processor core, which provides the adjacent node data required by the nodes located at the local domain boundary.

9. An electronic device comprising a memory and a processor, characterized in that: The memory is used to store a program that supports the processor to execute the CPU parallel parameterized level set topology optimization method for arbitrary shape CAD models as described in any one of claims 1 to 8, and the processor is configured to execute the program stored in the memory.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the CPU parallel parameterized level set topology optimization method for CAD models of arbitrary shapes according to any one of claims 1 to 8 are executed.

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