Structural topology optimization method and device based on progressive nonlinear correction

Abstract

The invention relates to the technical field of computational mechanics and structure optimization design, and provides a structure topology optimization method and device based on progressive nonlinear correction.According to the method, the optimization process of structure topology is divided into an optimization initial stage, an optimization middle stage and an optimization later stage, segmented processing of the nonlinear problem of a structure is achieved, and the optimization efficiency is improved. And nonlinear factors are dynamically introduced in the middle stage of optimization and the later stage of optimization, so that the calculation efficiency and precision are balanced, the efficient solution of a complex nonlinear problem is realized, the calculation cost is remarkably reduced, and meanwhile, the accuracy of a result is ensured. The method can be widely applied to design requirements of complex structures such as large-deformation and non-linear materials and contact problems.

Description

Technical Field

[0001] The present invention relates to the technical field of computational mechanics and structural optimization design, and in particular to a structural topology optimization method and device based on progressive nonlinear correction. Background Art

[0002] As an advanced structural design method, topology optimization is widely used in aerospace, automobile manufacturing and other fields.

[0003] In general, the sources of nonlinearity in structural models include materials, structures, and boundary conditions. Existing topology optimization methods mainly assume that materials and structures are linear, and then calculate linear problems. However, this can only consider small deformation problems under the premise that nonlinearity of materials and boundary conditions does not exist, and cannot effectively handle complex working conditions such as large deformation and material nonlinearity, which limits its applicability in actual engineering.

[0004] Existing solutions often rely on high-cost nonlinear iterative calculations, which have low computational efficiency and are easily affected by initial conditions. Based on this, it is urgent to provide a new structural topology optimization method. Summary of the invention

[0005] The present invention provides a structural topology optimization method and device based on progressive nonlinear correction, which are used to solve the defects in the prior art.

[0006] The present invention provides a structural topology optimization method based on progressive nonlinear correction, comprising: In the initial stage of optimization, the structural response is simplified into a linear problem, a first objective function is determined based on the displacement vector of the structure and the external load vector, and a first material distribution density of the structure is iteratively solved when minimizing the first objective function; In the middle of the optimization, based on the progressive loading strategy, nonlinear effects are introduced to determine the displacement increment and load increment of each iterative round of loading, and based on the displacement increment and the load increment, the first objective function is corrected to obtain the second objective function, and based on the first material distribution density, the second material distribution density of the structure is iteratively solved to minimize the second objective function; In the later stage of optimization, the additional stiffness matrix caused by the material nonlinearity of the structure is determined, and based on the additional stiffness matrix, the second objective function is corrected to obtain the third objective function, and based on the second material distribution density, the third material distribution density of the structure is iteratively solved when minimizing the third objective function.

[0007] According to a structural topology optimization method based on progressive nonlinear correction provided by the present invention, the load applied in each iteration round is determined based on the following steps: Determine the step size factor for the current iteration round; Determining the load loaded in the current iteration round based on the step size factor and the target load of the current iteration round; Among them, the step factors of two adjacent iteration rounds differ by a fixed step interval.

[0008] A structural topology optimization method based on progressive nonlinear correction provided by the present invention also includes: In the middle and late stages of the optimization, based on the convergence of the optimization iterations, penalty parameters are applied to dynamically adjust the nonlinear correction strength.

[0009] A structural topology optimization method based on progressive nonlinear correction provided by the present invention also includes: Constructing a multi-objective optimization function, wherein the optimization objectives of the multi-objective optimization function include maximizing the stiffness-energy absorption ratio of the structure and the maximum allowable strain of the structure being less than or equal to a strain threshold; Solve the multi-objective optimization function and perform topology optimization on the structure.

[0010] According to a structural topology optimization method based on progressive nonlinear correction provided by the present invention, the mesh unit size of the mesh model of the structure is reduced successively in the initial stage of the optimization, the middle stage of the optimization and the late stage of the optimization.

[0011] According to a structural topology optimization method based on progressive nonlinear correction provided by the present invention, the iterative solution of minimizing the first material distribution density of the structure when the first objective function is minimized includes: The first material distribution density is determined based on a convergence rate of the first objective function.

[0012] According to a structural topology optimization method based on progressive nonlinear correction provided by the present invention, the iterative solution of the third material distribution density of the structure when minimizing the third objective function based on the second material distribution density includes: Based on the second material distribution density, a Newton-Raphson iterative method is used to iteratively solve the third material distribution density of the structure when minimizing the third objective function.

[0013] The present invention also provides a structural topology optimization device based on progressive nonlinear correction, comprising: A first optimization stage module is used to simplify the structural response into a linear problem at the initial stage of optimization, determine a first objective function based on the displacement vector of the structure and the external load vector, and iteratively solve the first material distribution density of the structure when minimizing the first objective function; A second optimization phase module is used to introduce nonlinear effects in the middle of optimization based on a progressive loading strategy, determine the displacement increment and load increment of each iterative round of loading, and modify the first objective function based on the displacement increment and the load increment to obtain a second objective function, and iteratively solve the second material distribution density of the structure when minimizing the second objective function based on the first material distribution density; The third optimization stage module is used to determine the additional stiffness matrix caused by the material nonlinearity of the structure in the later stage of optimization, and based on the additional stiffness matrix, correct the second objective function to obtain the third objective function, and based on the second material distribution density, iteratively solve the third material distribution density of the structure when minimizing the third objective function.

[0014] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for structural topology optimization based on progressive nonlinear correction as described above is implemented.

[0015] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for structural topology optimization based on progressive nonlinear correction as described above is implemented.

[0016] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the method for structural topology optimization based on progressive nonlinear correction as described above is implemented.

[0017] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a method and device for structural topology optimization based on progressive nonlinear correction. The method divides the optimization process of structural topology into the initial optimization stage, the middle optimization stage and the late optimization stage, realizes the segmented processing of the nonlinear problem of the structure, and dynamically introduces nonlinear factors in the middle optimization stage and the late optimization stage, thereby balancing the calculation efficiency and accuracy, realizing efficient solution of complex nonlinear problems, significantly reducing the calculation cost, and ensuring the accuracy of the results. The method can be widely applied to the design requirements of complex structures such as large deformation, nonlinear materials, and contact problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on the drawings in the following description without paying any creative work.

[0019] Figure 1 It is a schematic flow chart of a structural topology optimization method based on progressive nonlinear correction provided by the present invention; Figure 2 It is a schematic structural diagram of a structural topology optimization device based on progressive nonlinear correction provided by the present invention; Figure 3 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Since existing topology optimization methods often rely on high-cost nonlinear iterative calculations, the calculation efficiency is low and the results are easily affected by initial conditions. Based on this, an embodiment of the present invention provides a structural topology optimization method based on progressive nonlinear correction.

[0022] Figure 1 Schematic diagram of a flow chart of a structural topology optimization method based on progressive nonlinear correction provided in an embodiment of the present invention, such as Figure 1 As shown, the method includes: S1, at the initial stage of optimization, simplifying the structural response into a linear problem, determining a first objective function based on the displacement vector of the structure and the external load vector, and iteratively solving a first material distribution density of the structure when minimizing the first objective function; S2, in the middle of the optimization, based on the progressive loading strategy, introducing nonlinear effects, determining the displacement increment and load increment of each iterative round of loading, and based on the displacement increment and the load increment, correcting the first objective function to obtain a second objective function, and iteratively solving the second material distribution density of the structure when minimizing the second objective function based on the first material distribution density; S3, in the later stage of optimization, determining the additional stiffness matrix caused by the material nonlinearity of the structure, and based on the additional stiffness matrix, correcting the second objective function to obtain the third objective function, and based on the second material distribution density, iteratively solving the third material distribution density of the structure when minimizing the third objective function.

[0023] Specifically, the structural topology optimization method based on progressive nonlinear correction provided in an embodiment of the present invention is executed by a structural topology optimization device based on progressive nonlinear correction. The device can be configured in a computer, which can be a local computer or a cloud computer. The local computer can be a computer, a tablet, etc., which is not specifically limited here.

[0024] The structural topology optimization method based on progressive nonlinear correction provided in the embodiment of the present invention is divided into three stages, namely, initial optimization, mid-optimization and late optimization. After the initial optimization, the mid-optimization stage is entered, and after the mid-optimization stage is entered, the late optimization stage is entered.

[0025] First, step S1 is performed. In the initial stage of optimization, the structural response is simplified to a linear problem, the material distribution density is used as a design variable, and the optimized first material distribution density is obtained through rapid iterative convergence. It can be understood that the structure can be a mechanical structure, such as an explosion-proof reinforced structure, a high-speed light-load mechanism, a hingeless compliant mechanism, and other structures that require topological optimization, which are not specifically limited here.

[0026] The finite element equation at the initial stage of optimization can be expressed as: ; Among them, K0 is the linearized initial stiffness matrix, u0 is the displacement vector of the structure, f0 is the external load vector, which is the load of the structure, that is, the external force it is subjected to.

[0027] The first objective function can be expressed as: ; Minimizing the first objective function can be expressed as: ; The constraints are: ; in, is the dependent variable of the first objective function, ρ represents the material distribution density, as the independent variable of the first objective function, It can be a constraint condition such as displacement, stress, volume, modal frequency, etc. is the material distribution density obtained in the i-th iteration round of the initial optimization.

[0028] Then, step S2 is executed. In the middle of the optimization, based on the progressive loading strategy, nonlinear effects such as geometric nonlinearity or material nonlinearity are introduced, that is, the nonlinear problem is decomposed into multiple linear problems with small amplitude loading, and the first material distribution density is iteratively corrected.

[0029] At this point, the displacement increment and load increment of each iterative round of loading can be determined, and then the finite element equation in the middle stage of optimization, that is, the incremental finite element equation, can be determined.

[0030] The incremental finite element equation can be expressed as: ; in, The stiffness matrix corrected to take into account geometric nonlinearity for the optimization mid-term n-th iteration, To optimize the displacement increment of the nth iteration loading in the mid-term, Load increment to be applied during the nth iteration of the optimization.

[0031] And there are: ; N is the total number of iterations in the optimization phase.

[0032] Thereafter, the first objective function can be corrected using the displacement increment and load increment of each iterative round of loading to obtain the second objective function.

[0033] The second objective function can be expressed as: .

[0034] in, is the dependent variable of the second objective function.

[0035] Thereafter, the first material distribution density is used as the initial value of the material distribution density in the mid-term optimization, and the second material distribution density of the structure is iteratively solved when minimizing the second objective function.

[0036] Finally, step S3 is executed. In the later stage of optimization, material nonlinearity and complex boundary conditions are fully introduced to determine the additional stiffness matrix caused by the material nonlinearity of the structure. The finite element equation at the later stage of optimization can be expressed as: .

[0037] in, To optimize the stiffness matrix corresponding to the displacement vector u in the later stage, To optimize the displacement increment in the later stage, To optimize the later load increment.

[0038] The second objective function is modified by using the additional stiffness matrix to obtain the third objective function. The third objective function can be expressed as: ; in, is the third objective function, V is the volume of the structure, For strain and material distribution density The corresponding energy density includes nonlinear strain energy.

[0039] The final form of the optimization problem to minimize the third objective function can be: .

[0040] Thereafter, the second material distribution density is used as the initial value of the material distribution density in the later stage of optimization, and the third material distribution density of the structure is iteratively solved when minimizing the third objective function.

[0041] The structural topology optimization method based on progressive nonlinear correction provided in the embodiment of the present invention divides the optimization process of the structural topology into the initial optimization stage, the middle optimization stage and the late optimization stage, realizes the segmented processing of the nonlinear problem of the structure, and dynamically introduces nonlinear factors in the middle optimization stage and the late optimization stage, thereby balancing the computational efficiency and accuracy, realizing the efficient solution of complex nonlinear problems, significantly reducing the computational cost, and ensuring the accuracy of the results. The method can be widely applied to the design requirements of complex structures such as large deformation, nonlinear materials, and contact problems.

[0042] Based on the above embodiment, the load of each iteration round is determined based on the following steps: Determine the step size factor for the current iteration round; Determining the load loaded in the current iteration round based on the step size factor and the target load of the current iteration round; Among them, the step factors of two adjacent iteration rounds differ by a fixed step interval.

[0043] Specifically, the current iteration round is set to n, and the load loaded in the current iteration round n can be expressed as , in determining When , first determine the step size factor of the current iteration round n , the initial step size factor Can be set as needed.

[0044] After that, the step size factor according to the current iteration round n is used And the target load, determine the load of the current iteration round n The target load can be the maximum load ,but It can be expressed as: .

[0045] It can be understood that the step factors of two adjacent iterations differ by a fixed step interval. , the fixed step interval The product of the target load is the load increment of the current iteration round n, that is, .

[0046] The relationship between the step factors of two adjacent iteration rounds can be expressed as: .

[0047] In the embodiment of the present invention, the loading load is determined by the relationship between the step factors of each iteration round, and the loading amplitude can be gradually increased to ensure the stability of the optimization process.

[0048] Based on the above embodiment, it also includes: In the middle and late stages of the optimization, based on the convergence of the optimization iterations, penalty parameters are applied to dynamically adjust the nonlinear correction strength.

[0049] Specifically, in the embodiment of the present invention, a penalty parameter β, i.e., a nonlinear correction parameter, can be introduced to gradually adjust the optimization sensitivity analysis formula to achieve dynamic adjustment of the nonlinear correction strength. The penalty parameter β can be equivalent to 0 in the initial optimization stage, and gradually increase in the middle and late optimization stages to fully consider the nonlinear effect.

[0050] Among them, the sensitivity analysis formula can be expressed as: ; in, is the composite numerical gradient of the i-th iteration round in the middle or late optimization period, which can indicate the convergence of the optimization iteration in the middle or late optimization period. is the linear numerical gradient of the i-th iteration round in the middle or late optimization period, which can represent the linear convergence of the middle or late optimization period. It is the nonlinear numerical gradient of the i-th iteration round in the middle or late stage of optimization, which can represent the nonlinear convergence of the middle or late stage of optimization.

[0051] High-precision gradient information is calculated through composite numerical gradient and finite difference technology, which can realize dynamic adjustment of nonlinear correction intensity, improve the stability and convergence speed of the optimization process, and avoid divergence problems caused by over-correction.

[0052] Based on the above embodiment, it also includes: Constructing a multi-objective optimization function, wherein the optimization objectives of the multi-objective optimization function include maximizing the stiffness-energy absorption ratio of the structure and the maximum allowable strain of the structure being less than or equal to a strain threshold; Solve the multi-objective optimization function and perform topology optimization on the structure.

[0053] Specifically, in an embodiment of the present invention, a multi-objective optimization function can be constructed for nonlinear problems, and its optimization objectives may include maximizing the stiffness-energy absorption ratio of the structure and the maximum allowable strain of the structure being less than or equal to the strain threshold. By comprehensively considering multiple performance indicators such as structural stiffness, energy absorption, and material nonlinearity, effective optimization of nonlinear effects can be achieved.

[0054] On the basis of the above-mentioned embodiment, the grid unit size of the grid model of the structure is reduced successively in the initial stage of the optimization, the middle stage of the optimization and the late stage of the optimization.

[0055] Specifically, in the embodiment of the present invention, a multi-resolution model can be designed, and a grid model with a coarse grid can be used to converge quickly in the early stage of optimization, and the grid can be gradually refined in the middle and late stages of optimization to improve accuracy.

[0056] Based on the above embodiment, the iterative solution to minimize the first material distribution density of the structure when the first objective function is minimized includes: The first material distribution density is determined based on a convergence rate of the first objective function.

[0057] Specifically, in an embodiment of the present invention, when iteratively solving the first objective function to minimize the first material distribution density of the structure, the convergence rate of the first objective function can be used to determine the first material distribution density. That is, the convergence rate of the first objective function is used to determine the end condition of the initial optimization. Since the main goal of the initial optimization is to quickly converge to obtain the first material distribution density, when the convergence rate of the first objective function reaches a certain level, such as a number of consecutive iterations (for example, 3-5 times), the change in the first objective function is less than a set threshold (such as 1%-5% of the initial value of the first objective function), it is considered that the linear stage of the initial optimization is over and the nonlinear stage of the mid-term optimization is entered.

[0058] On the basis of the above embodiment, the iterative solution of the third material distribution density of the structure when minimizing the third objective function based on the second material distribution density includes: Based on the second material distribution density, a Newton-Raphson iterative method is used to iteratively solve the third material distribution density of the structure when minimizing the third objective function.

[0059] Specifically, in the later stage of optimization, material and geometric nonlinear factors are fully introduced, and the Newton-Raphson iterative method is adopted. The second material distribution density is used as the initial value of the material distribution density to solve the minimization of the third objective function. The third material distribution density can be determined quickly and accurately to achieve the optimization of the structural topology.

[0060] On the basis of the above-mentioned embodiments, in the embodiments of the present invention, parallel computing technology can be combined to distribute nonlinear solutions to multiple computing nodes, thereby improving the efficiency of solving large-scale problems.

[0061] like Figure 2 As shown, based on the above embodiment, an embodiment of the present invention provides a structural topology optimization device based on progressive nonlinear correction, including: The first optimization stage module 21 is used to simplify the structural response into a linear problem at the initial stage of optimization, determine a first objective function based on the displacement vector of the structure and the external load vector, and iteratively solve the first material distribution density of the structure when minimizing the first objective function; The second optimization stage module 22 is used to introduce nonlinear effects based on the progressive loading strategy in the middle stage of optimization, determine the displacement increment and load increment of each iterative round of loading, and modify the first objective function based on the displacement increment and the load increment to obtain a second objective function, and iteratively solve the second material distribution density of the structure when minimizing the second objective function based on the first material distribution density; The third optimization stage module 23 is used to determine the additional stiffness matrix caused by the material nonlinearity of the structure in the later stage of optimization, and based on the additional stiffness matrix, correct the second objective function to obtain the third objective function, and based on the second material distribution density, iteratively solve the third material distribution density of the structure when minimizing the third objective function.

[0062] On the basis of the above-mentioned embodiment, in the structural topology optimization device based on progressive nonlinear correction provided in the embodiment of the present invention, the load applied in each iteration round is determined based on the following steps: Determine the step size factor for the current iteration round; Determining the load loaded in the current iteration round based on the step size factor and the target load of the current iteration round; Among them, the step factors of two adjacent iteration rounds differ by a fixed step interval.

[0063] On the basis of the above-mentioned embodiment, the structural topology optimization device based on progressive nonlinear correction provided in the embodiment of the present invention further includes a dynamic adjustment module for: In the middle and late stages of the optimization, based on the convergence of the optimization iterations, penalty parameters are applied to dynamically adjust the nonlinear correction strength.

[0064] On the basis of the above-mentioned embodiment, the structural topology optimization device based on progressive nonlinear correction provided in the embodiment of the present invention further includes a multi-objective optimization module for: Constructing a multi-objective optimization function, wherein the optimization objectives of the multi-objective optimization function include maximizing the stiffness-energy absorption ratio of the structure and the maximum allowable strain of the structure being less than or equal to a strain threshold; Solve the multi-objective optimization function and perform topology optimization on the structure.

[0065] On the basis of the above-mentioned embodiment, in the structural topology optimization device based on progressive nonlinear correction provided in the embodiment of the present invention, the mesh unit size of the mesh model of the structure is reduced successively in the initial stage of the optimization, the middle stage of the optimization and the late stage of the optimization.

[0066] On the basis of the above-mentioned embodiment, in the structural topology optimization device based on progressive nonlinear correction provided in the embodiment of the present invention, the first optimization stage module is specifically used for: The first material distribution density is determined based on a convergence rate of the first objective function.

[0067] On the basis of the above-mentioned embodiment, in the structural topology optimization device based on progressive nonlinear correction provided in the embodiment of the present invention, the third optimization stage module is specifically used for: Based on the second material distribution density, a Newton-Raphson iterative method is used to iteratively solve the third material distribution density of the structure when minimizing the third objective function.

[0068] Specifically, the functions of each module in the structural topology optimization device based on progressive nonlinear correction provided in the embodiment of the present invention correspond one-to-one to the operating procedures of each step in the above-mentioned method embodiment, and the effects achieved are also consistent. Please refer to the above-mentioned embodiment for details, and no further description will be given in the embodiment of the present invention.

[0069] Figure 3 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 3 As shown, the electronic device may include: a processor (Processor) 310, a communication interface (Communications Interface) 320, a memory (Memory) 330 and a communication bus 340, wherein the processor 310, the communication interface 320, and the memory 330 communicate with each other through the communication bus 340. The processor 310 may call the logic instructions in the memory 330 to execute the structural topology optimization method based on progressive nonlinear correction provided in the above embodiments.

[0070] In addition, the logic instructions in the above-mentioned memory 330 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0071] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the structural topology optimization method based on progressive nonlinear correction provided in the above-mentioned embodiments.

[0072] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the structural topology optimization method based on progressive nonlinear correction provided in the above-mentioned embodiments.

[0073] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0074] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0075] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. 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 embodiments of the present invention.

Claims

1. A structural topology optimization method based on progressive nonlinear correction, characterized in that: include: In the initial stage of optimization, the structural response is simplified into a linear problem, a first objective function is determined based on the displacement vector of the structure and the external load vector, and a first material distribution density of the structure is iteratively solved when minimizing the first objective function; In the middle of the optimization, based on the progressive loading strategy, nonlinear effects are introduced to determine the displacement increment and load increment of each iterative round of loading, and based on the displacement increment and the load increment, the first objective function is corrected to obtain the second objective function, and based on the first material distribution density, the second material distribution density of the structure is iteratively solved to minimize the second objective function; In the later stage of optimization, the additional stiffness matrix caused by the material nonlinearity of the structure is determined, and based on the additional stiffness matrix, the second objective function is corrected to obtain the third objective function, and based on the second material distribution density, the third material distribution density of the structure is iteratively solved when minimizing the third objective function.

2. The structural topology optimization method based on progressive nonlinear correction according to claim 1, characterized in that: The load applied in each iteration is determined based on the following steps: Determine the step size factor for the current iteration round; Determining the load loaded in the current iteration round based on the step size factor and the target load of the current iteration round; Among them, the step factors of two adjacent iteration rounds differ by a fixed step interval.

3. The structural topology optimization method based on progressive nonlinear correction according to claim 1, characterized in that: Also includes: In the middle and late stages of the optimization, based on the convergence of the optimization iterations, penalty parameters are applied to dynamically adjust the nonlinear correction strength.

4. The structural topology optimization method based on progressive nonlinear correction according to claim 1, characterized in that: Also includes: Constructing a multi-objective optimization function, wherein the optimization objectives of the multi-objective optimization function include maximizing the stiffness-energy absorption ratio of the structure and the maximum allowable strain of the structure being less than or equal to a strain threshold; Solve the multi-objective optimization function and perform topology optimization on the structure.

5. The structural topology optimization method based on progressive nonlinear correction according to any one of claims 1 to 4, characterized in that: The mesh unit size of the mesh model of the structure decreases in sequence in the initial stage of the optimization, the middle stage of the optimization and the late stage of the optimization.

6. The structural topology optimization method based on progressive nonlinear correction according to any one of claims 1 to 4, characterized in that: The iterative solution for minimizing the first material distribution density of the structure when the first objective function is minimized includes: The first material distribution density is determined based on a convergence rate of the first objective function.

7. The structural topology optimization method based on progressive nonlinear correction according to any one of claims 1 to 4, characterized in that: The iteratively solving the third material distribution density of the structure when minimizing the third objective function based on the second material distribution density includes: Based on the second material distribution density, a Newton-Raphson iterative method is used to iteratively solve the third material distribution density of the structure when minimizing the third objective function.

8. A structural topology optimization device based on progressive nonlinear correction, characterized in that: include: A first optimization stage module is used to simplify the structural response into a linear problem at the initial stage of optimization, determine a first objective function based on the displacement vector of the structure and the external load vector, and iteratively solve the first material distribution density of the structure when minimizing the first objective function; A second optimization phase module is used to introduce nonlinear effects in the middle of optimization based on a progressive loading strategy, determine the displacement increment and load increment of each iterative round of loading, and modify the first objective function based on the displacement increment and the load increment to obtain a second objective function, and iteratively solve the second material distribution density of the structure when minimizing the second objective function based on the first material distribution density; The third optimization stage module is used to determine the additional stiffness matrix caused by the material nonlinearity of the structure in the later stage of optimization, and based on the additional stiffness matrix, correct the second objective function to obtain the third objective function, and based on the second material distribution density, iteratively solve the third material distribution density of the structure when minimizing the third objective function.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the structural topology optimization method based on progressive nonlinear correction as described in any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the structural topology optimization method based on progressive nonlinear correction as described in any one of claims 1 to 7 is implemented.

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