Optimization method for anisotropic material, electronic equipment and computer readable medium

Abstract

The invention discloses an optimization method for an anisotropic material, electronic equipment and a computer readable medium, and relates to the field of structure optimization design, based on a dynamic penalty factor, the optimization method comprises the following steps: setting an optimization target, a constraint condition, the dynamic penalty factor and a design variable; and performing optimization iteration until an optimization target is achieved. And a more efficient and more accurate solution is provided for structural topological optimization.

Description

Technical Field

[0001] The present invention relates to the field of structural optimization design, and in particular to an optimization method for anisotropic materials, electronic equipment and computer-readable medium. Background Art

[0002] In the field of structural topology optimization, the SIMP method has always been a classic and widely used technical means. Its core principle is to construct a penalty relationship between the elastic modulus of the material and the design variable (mostly the material density is used as the design variable), so as to effectively guide the distribution of the material in the design domain, thereby achieving the purpose of optimizing the structure.

[0003] However, the traditional SIMP method has obvious defects, which uses a fixed penalty factor p. In practical applications, this fixed setting has exposed shortcomings in many cases. On the one hand, from the perspective of the convergence characteristics of the optimization results, the fixed penalty factor makes the optimization process lack adaptability. At the beginning of the optimization, the optimal form of the structure is often difficult to determine, and extensive exploration is required in a larger design space. However, the fixed p value limits the breadth and depth of this exploration, and cannot be flexibly adjusted according to the actual optimization needs of the structure, resulting in the optimization algorithm being difficult to quickly locate the potential optimal solution, making the convergence process slow. For example, in the topological optimization of complex large-scale building structures, due to the complex and diverse forces on the building structure, the fixed penalty factor may limit the exploration direction in the early stage of optimization, and fail to fully explore a variety of possible optimization paths, thereby prolonging the entire optimization process and increasing time costs.

[0004] On the other hand, the fixed penalty factor seems to be unable to cope with the details of material distribution. Actual engineering structures have fine requirements for material distribution. It is necessary to optimize the structural performance as a whole (global optimization) and pay attention to the special performance requirements of local areas (local refinement). However, it is difficult for a fixed p value to meet the requirements of both levels at the same time, which can easily cause unreasonable conditions in the stiffness distribution of the optimization results. For example, in the design of precision mechanical parts, some key parts have extremely high stiffness requirements, and the fixed penalty factor may not be able to accurately distribute enough materials to these key areas, affecting the performance of the overall structure. In addition, the fixed penalty factor often causes the "grayscale" problem of material distribution, that is, there are a large number of intermediate values ​​between the material density of 0 and 1, which contradicts the discreteness of the material in actual manufacturing (either it exists, the density is 1; or it does not exist, the density is 0), making it difficult to accurately implement the optimization results in actual production, increasing the difficulty and cost of manufacturing.

[0005] In view of these limitations of the fixed penalty factor in the traditional SIMP method, an innovative method is urgently needed to overcome these problems in order to improve the effect and value of topology optimization in practical engineering. Based on this, the present invention proposes a SIMP method with a dynamic penalty factor, which aims to provide a more efficient and accurate solution for structural topology optimization by dynamically adjusting the value of the penalty factor p during the optimization process. Summary of the invention

[0006] The present invention aims to solve one of the technical problems in the related art to a certain extent. To this end, the present invention provides an optimization method for anisotropic materials, providing a more efficient and accurate solution for structural topology optimization.

[0007] In order to achieve the above object, the present invention adopts the following technical solution: An optimization method for anisotropic materials, based on a dynamic penalty factor, comprises the following steps: Set optimization goals and constraints; Set dynamic penalty factors and design variables; Under the constraints, optimization iterations are performed based on dynamic penalty factors and design variables until the optimization goal is reached.

[0008] Optionally, the dynamic penalty factor is: in, For the The dynamic penalty factor at the iteration, is the minimum dynamic penalty factor at the initial iteration, is the maximum dynamic penalty factor at the end of the optimization process, is the current iteration number, is the maximum number of iterations, It is a parameter that controls the growth rate of the dynamic penalty factor.

[0009] Optionally, the elastic modulus of the anisotropic material in different directions is: in, is the design variable, is the elastic modulus in the main directions, is the density design variable in the main directions, is the reference elastic modulus in the main direction, is the elastic modulus in the secondary direction, is the design variable for density in the secondary direction, is the base elastic modulus in the secondary direction.

[0010] Optionally, performing optimization iterations based on dynamic penalty factors and design variables includes the following steps: Set the minimum dynamic penalty factor for the initial iteration , and set the density design variables in the principal directions that allow the anisotropic material to be optimized and density design variables in the secondary directions Can change continuously; Gradually increase the dynamic penalty factor to make the density design variables in the main directions of the anisotropic material and density design variables in the secondary directions Approaching 0 or 1; Calculate the optimization result after each iteration. If the optimization goal is achieved, increase the dynamic penalty factor to the maximum dynamic penalty factor. , and set the density design variables in the main directions of the anisotropic material and density design variables in the secondary directions It is 0 or 1 to obtain the final optimization result.

[0011] Optionally, the optimization objectives and constraints are set in Isight, and after each iteration, Isight calls Abaqus to calculate the optimization results.

[0012] The present invention has the following beneficial effects: 1. Significantly improve the controllability of material distribution and the accuracy of optimization results The dynamic penalty factor strategy adopted by the present invention can flexibly adjust the penalty factor according to the real-time status of material distribution and the progress of optimization during the optimization process. In the early stage of optimization, a smaller penalty factor allows the material density to be continuously distributed in the design domain. This continuous distribution characteristic provides broad possibilities for global search, and the structure can explore potential optimization directions in a larger design space to avoid falling into a local optimal solution too early. For example, in the topological optimization of the automobile frame structure, the initial continuous material distribution allows the optimization algorithm to fully consider the force transmission path of different parts and find a more comprehensive optimization solution. As the iteration proceeds, the penalty factor gradually increases, and the material density is forced to approach 0 or 1. This process effectively reduces the "grayscale" area in the material distribution, making the material distribution clearer and more specific. Ultimately, the optimization result can accurately determine whether the material exists in the structure, achieve a more accurate material layout, and thus improve the accuracy of the performance prediction of the structure. For example, in the optimization of bridge structures, clear material distribution can more accurately reflect the stress and deformation of the structure under different load conditions, providing a more reliable basis for the safety design of the structure.

[0013] 2. Enhance the flexibility and applicability of structural design The present invention combines the dynamic penalty factor with anisotropic materials, further expanding the optimization space of structural design. For anisotropic materials, they have different mechanical properties in different directions. By dynamically adjusting the penalty factor, the elastic modulus changes of the material in the main direction and the secondary direction can be controlled respectively. In the design of aircraft engine blades, the stress and strain conditions of the blades in different directions vary greatly. The method of the present invention can be used to accurately adjust the distribution of materials in various directions according to the actual force characteristics, so that the blades can meet the strength requirements while reducing weight to the maximum extent and improving the efficiency of the engine. This fine control ability of the performance of anisotropic materials enables structural design to better adapt to the complex and changeable actual needs of engineering, whether in high-end manufacturing industries such as aerospace and automobile manufacturing, or in traditional industries such as building structures and mechanical equipment. It can provide more targeted and effective solutions for structural optimization, thereby significantly improving the competitiveness and performance of products.

[0014] 3. Effectively improve optimization efficiency and reduce computing costs and time consumption Since the dynamic penalty factor can guide the optimization process to converge to a reasonable material distribution state more quickly, unnecessary iterative calculations are avoided. In the topological optimization of large ship structures, the traditional fixed penalty factor method may require thousands of iterations to obtain a relatively feasible solution. However, by adopting the method of the present invention, by reasonably setting the dynamic change parameters of the penalty factor, the optimization results that meet the engineering requirements can be obtained within hundreds of iterations. This not only greatly shortens the time of optimization calculations and improves design efficiency, but also reduces the consumption of computing resources and reduces design costs. At the same time, the high quality of the optimization results also reduces subsequent design modifications and verification work, further saves the engineering development cycle, enables products to be brought to market faster, and enhances the advantages of enterprises in market competition.

[0015] Furthermore, the present invention further provides an electronic device, comprising: one or more processors; A memory having one or more computer programs stored thereon, wherein when the one or more computer programs are executed by the one or more processors, the one or more processors implement any one of the aforementioned optimization methods.

[0016] At the same time, the present invention also provides a computer-readable medium on which a computer program is stored, and when the computer program is executed by a processor, any of the above-mentioned optimization methods is implemented.

[0017] These features and advantages of the present invention will be disclosed in detail in the following specific embodiments and drawings. The best embodiments or means of the present invention will be described in detail in conjunction with the drawings, but they are not intended to limit the technical solutions of the present invention. In addition, there are multiple features, elements, and components that appear in each of the following texts and drawings, and different symbols or numbers are marked for convenience, but they all represent components with the same or similar structures or functions. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The present invention will be further described below in conjunction with the accompanying drawings: Figure 1 It is a flow chart of the present invention.

[0019] Figure 2 A schematic diagram of an electronic device provided by the present invention; Figure 3 A schematic diagram of a computer-readable medium provided by the present invention. DETAILED DESCRIPTION

[0020] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. The embodiments in the implementation manner are intended to be used to explain the present invention and should not be construed as limiting the present invention.

[0021] References in this specification to "one embodiment" or "an example" or "an example" mean that a particular feature, structure, or characteristic described in conjunction with the embodiment itself may be included in at least one embodiment of the present patent disclosure. The appearance of the phrase "in one embodiment" in various places in the specification does not necessarily refer to the same embodiment.

[0022] As a first aspect of the present invention, the present invention provides an optimization method for anisotropic materials. The optimization method for anisotropic materials provided in this embodiment is based on a dynamic penalty factor. By adjusting the value of the dynamic penalty factor during the optimization process, the structure has higher flexibility in the initial stage of optimization. As the iteration proceeds, the dynamic penalty factor is gradually increased, and finally a more reasonable material distribution and a more accurate optimization result are achieved.

[0023] like Figure 1 As shown, the optimization method provided in this embodiment includes the following steps: Set optimization goals and constraints. Specifically, the optimization goals and constraints are set in Isight. The optimization goals and constraints can be various variables, and those skilled in the art can select appropriate optimization goals and constraints according to each different working condition. For example, the optimization goal can be set to maximize stiffness, and the constraint can be that the volume is less than 30% of the initial volume. In other embodiments, the optimization goals and constraints are flexibly set by those skilled in the art according to actual conditions, and are not limited in this implementation.

[0024] Set the dynamic penalty factor and design variables. In this embodiment, the dynamic penalty factor is: in, For the The dynamic penalty factor at the iteration, is the minimum dynamic penalty factor at the initial iteration. In this embodiment, the minimum dynamic penalty factor The preferred value is 1. is the maximum dynamic penalty factor at the end of the optimization process. In this embodiment, the maximum dynamic penalty factor The preferred value is 3. In other embodiments, those skilled in the art may also set the maximum dynamic penalty factor to Set to a higher value, no limit here. Generally speaking, when the calculation result has too many gray areas, that is, the area between 0 and 1, the maximum dynamic penalty factor Set to a higher value than 3. is the current iteration number, is the maximum number of iterations, The parameter for controlling the growth rate of the dynamic penalty factor is preferably 2 to 4. The dynamic penalty factor strategy adopted in this embodiment can flexibly adjust the penalty factor according to the real-time status of material distribution and the progress of optimization during the optimization process.

[0025] The elastic modulus of anisotropic materials in different directions are: in, is the design variable, is the elastic modulus in the main directions, is the density design variable in the main directions, is the reference elastic modulus in the main direction, is the elastic modulus in the secondary direction, is the design variable for density in the secondary direction, is the reference elastic modulus in the secondary direction. This embodiment allows the dynamic adjustment of material properties in various directions during the optimization process, thereby achieving more accurate material distribution control in different directions. This embodiment combines the dynamic penalty factor with anisotropic materials, further expanding the optimization space of structural design. For anisotropic materials, they have different mechanical properties in different directions. By dynamically adjusting the penalty factor, the elastic modulus changes of the material in the main direction and the secondary direction can be controlled respectively. For example, in the design of aerospace engine blades, the stress and strain conditions of the blades in different directions are quite different. The method provided by this embodiment can be used to accurately adjust the distribution of materials in various directions according to the actual force characteristics, so that the blades can meet the strength requirements while reducing weight to the maximum extent and improving the efficiency of the engine. This fine control ability of the properties of anisotropic materials enables structural design to better adapt to the complex and changeable actual needs of engineering, whether in high-end manufacturing industries such as aerospace and automobile manufacturing, or in traditional industries such as building structures and mechanical equipment, it can provide more targeted and effective solutions for structural optimization, thereby significantly improving the competitiveness and performance of products.

[0026] Under the constraints, optimization iterations are performed based on dynamic penalty factors and design variables, including the following sub-steps: Set the minimum dynamic penalty factor for the initial iteration , making the dynamic penalty factor smaller in the early iterations. And setting the density design variables in the main directions of the anisotropic material to be optimized and density design variables in the secondary directions It can change continuously to facilitate global search for potential optimization directions. In the early stages of optimization, a smaller penalty factor allows the material density to be continuously distributed within the design domain. This continuous distribution feature provides broad possibilities for global search, and the structure can explore potential optimization directions within a larger design space to avoid falling into a local optimal solution too early. For example, in the topology optimization of a car frame structure, the initial continuous material distribution allows the optimization algorithm to fully consider the force transmission paths of different parts and find a more comprehensive optimization solution.

[0027] As the iteration proceeds, the dynamic penalty factor is gradually increased to make the density design variables in the main directions of the anisotropic material and density design variables in the secondary directions Approaching 0 or 1, this process effectively reduces the "grayscale" area and achieves a clearer structural boundary. Specifically, when the safety margin of the calculation result is relatively large, it approaches 0, and when the safety margin is small, it approaches 1.

[0028] Calculate the optimization result after each iteration. If the optimization goal is achieved, increase the dynamic penalty factor to the maximum dynamic penalty factor. , and set the density design variables in the main directions of the anisotropic material and density design variables in the secondary directions =0 or 1 to obtain the final optimization result. Ultimately, the optimization result can accurately determine whether the material exists in the structure, achieve a more accurate material layout, and thus improve the accuracy of the performance prediction of the structure. For example, in the optimization of bridge structures, a clear material distribution can more accurately reflect the stress and deformation of the structure under different load conditions, providing a more reliable basis for the safe design of the structure.

[0029] After each iteration, Isight calls Abaqus to calculate the optimization results and updates the design variables and dynamic penalty factors according to the results until the optimization goal is achieved. In Abaqus, the dynamic penalty factor is adjusted through Python scripts or UMAT subroutines. After each iteration, the material density is read through the script, and the dynamic penalty factor is updated according to the optimization progress before the next iteration.

[0030] Since the dynamic penalty factor can guide the optimization process to converge to a reasonable material distribution state more quickly, unnecessary iterative calculations are avoided. For example, in the topology optimization of large ship structures, the traditional fixed penalty factor method may require thousands of iterations to obtain a relatively feasible solution. However, by adopting the method provided in this embodiment, by reasonably setting the dynamic change parameters of the penalty factor, the optimization results that meet the engineering requirements can be obtained within hundreds of iterations. This not only greatly shortens the time of optimization calculations and improves design efficiency, but also reduces the consumption of computing resources and reduces design costs. At the same time, the high quality of the optimization results also reduces subsequent design modifications and verification work, further saves the engineering development cycle, enables products to be brought to market faster, and enhances the company's advantages in market competition.

[0031] As a second aspect of the present invention, there is provided an electronic device, such as Figure 2 As shown, including: One or more processors 101; The memory 102 stores one or more computer programs. When the one or more computer programs are executed by the one or more processors 101, the one or more processors 101 implement the optimization method according to the first aspect of the present invention.

[0032] The electronic device may further include one or more I / O interfaces 103 connected between the processor 101 and the memory 102 and configured to implement information interaction between the processor 101 and the memory 102 .

[0033] Among them, the processor 101 is a device with data processing capabilities, including but not limited to the central processing unit 101 (CPU), etc.; the first memory 102 is a device with data storage capabilities, including but not limited to random access memory 102 (RAM, more specifically such as SDRAM, DDR, etc.), read-only memory 102 (ROM), electrically erasable programmable read-only memory 102 (EEPROM), flash memory (FLASH); the I / O interface 103 (read-write interface) is connected between the processor 101 and the memory 102, and can realize information interaction between the processor 101 and the memory 102, including but not limited to the data bus 104 (Bus), etc.

[0034] In some embodiments, the processor 101 , the memory 102 , and the I / O interface 103 are connected to each other via a bus 104 , and further connected to other components of the computing device.

[0035] As a third aspect of the present invention, a computer readable medium is provided. Figure 3 As shown, a computer program is stored thereon, and when the computer program is executed by a processor, the optimization method provided by the first aspect of the present disclosure is implemented.

[0036] It will be understood by those skilled in the art that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. Accordingly, the computer program can be stored in a non-volatile computer-readable storage medium, and the computer program can implement the method of any of the above-mentioned embodiments when executed. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0037] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Those skilled in the art should understand that the present invention includes but is not limited to the contents described in the drawings and the above specific embodiments. Any modification that does not deviate from the functional and structural principles of the present invention will be included in the scope of the claims.

Claims

1. An optimization method for anisotropic materials, characterized in that: The optimization method is based on a dynamic penalty factor and comprises the following steps: Set optimization goals and constraints; Set dynamic penalty factors and design variables; Under the constraints, optimization iterations are performed based on dynamic penalty factors and design variables until the optimization goal is reached.

2. The optimization method according to claim 1, characterized in that: The dynamic penalty factor is: in, For the The dynamic penalty factor at the iteration, is the minimum dynamic penalty factor at the initial iteration, is the maximum dynamic penalty factor at the end of the optimization process, is the current iteration number, is the maximum number of iterations, It is a parameter that controls the growth rate of the dynamic penalty factor.

3. The optimization method according to claim 2, characterized in that: The elastic modulus of anisotropic materials in different directions are: in, is the design variable, is the elastic modulus in the main directions, is the density design variable in the main directions, is the reference elastic modulus in the main direction, is the elastic modulus in the secondary direction, is the design variable for density in the secondary direction, is the base elastic modulus in the secondary direction.

4. The optimization method according to claim 3, characterized in that: Performing optimization iterations based on dynamic penalty factors and design variables includes the following steps: Set the minimum dynamic penalty factor for the initial iteration , and set the density design variables in the principal directions that allow the anisotropic material to be optimized and density design variables in the secondary directions Can change continuously; Gradually increase the dynamic penalty factor to make the density design variables in the main directions of the anisotropic material and density design variables in the secondary directions Approaching 0 or 1; Calculate the optimization result after each iteration. If the optimization goal is achieved, increase the dynamic penalty factor to the maximum dynamic penalty factor. , and set the density design variables in the main directions of the anisotropic material and density design variables in the secondary directions It is 0 or 1 to obtain the final optimization result.

5. The optimization method according to claim 4, characterized in that: The optimization objectives and constraints are set in Isight. After each iteration, Isight calls Abaqus to calculate the optimization results.

6. An electronic device, characterized in that: include: one or more processors; A memory having one or more computer programs stored thereon, wherein when the one or more computer programs are executed by the one or more processors, the one or more processors implement the optimization method according to any one of claims 1 to 5.

7. A computer readable medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the optimization method according to any one of claims 1 to 5 is implemented.

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