Intelligent design method for stable bearing capacity adjustable and programmable active structure based on topological optimization

Abstract

The invention discloses a topological optimization-based intelligent design method for a stable bearing capacity adjustable and programmable active structure, which comprises the following steps of: determining material parameters of each material in the active structure and a finite element model corresponding to the whole structure according to design requirements in a structure design scheme; constructing a stable bearing analysis equation of the active structure based on the stress condition of the active structure in the structural design scheme; based on an active structure stable bearing analysis equation and the finite element model, constructing a target function for calculating the distribution condition of each material; and carrying out iterative solution on the target function according to a preset constraint condition so as to output the distribution condition of each material on the finite element model. According to the method provided by the invention, the stable bearing capacity can be actively adjusted through external excitation, and even an advanced active structure with a specific stable bearing capacity change rule can be shown according to preset setting.

Description

Technical Field

[0001] This invention belongs to the field of engineering structural design, and in particular relates to a programmable active structural intelligent design method based on topology optimization with adjustable stable bearing capacity. Background Technology

[0002] Structural stability is one of the core issues in structural design in fields such as civil engineering, aerospace, and mechanical manufacturing. Traditional passive structures, once manufactured, have fixed performance (including stable load-bearing capacity) and cannot adapt to changes in external loads and the environment. This can lead to excessive design redundancy under certain operating conditions, resulting in material waste; or in other unforeseen conditions, posing a risk of instability and insufficient safety.

[0003] To endow structures with adaptive capabilities, the concept of active structures has emerged. Active structures typically consist of a passive matrix material and embedded active materials (such as shape memory alloys and piezoelectric ceramics). By responding to external stimuli (such as temperature, electric fields, and magnetic fields), the active materials can change their physical properties (such as stiffness and shape), thereby adjusting the mechanical properties of the entire structure in real time, including its stable load-bearing capacity.

[0004] However, current design methods for active structures have significant bottlenecks: 1. Design relies on experience and trial and error: Existing methods largely depend on the designer's intuition and experience, manually iterating through repeated "analysis-modification" to find feasible proactive material layout solutions. This process is inefficient and makes it difficult to obtain a globally optimal or near-optimal solution.

[0005] 2. Lack of systematic automated design tools: Traditional design methods have failed to deeply integrate "active control" with "structural configuration design." How to automatically and optimally distribute active and passive materials, and enable the structure to accurately achieve the expected stable load-bearing capacity response (such as maximizing or changing according to a specific law) under active control, is an unsolved problem.

[0006] 3. Insufficient controllability and programmability: Most research focuses on using active materials to adjust the deformation or vibration characteristics of structures, while automated methods specifically designed for adjustable or even programmable stability bearing capacity—a key indicator—are still lacking. There is a lack of intelligent design tools capable of demonstrating specific stability bearing capacity variations (i.e., "programmable" behavior) under different external stimuli during the design phase.

[0007] Patent document CN120316860A provides a method for optimizing the stability of a large-span steel truss beam floating support structure, including the following steps: 1. Determining the calculation conditions; 2. Optimizing the load-bearing capacity of the floating support vessel; 3. Optimizing the lateral stability of the floating support vessel; 4. Optimizing the longitudinal stability of the floating support vessel.

[0008] Patent document CN119294205A provides a method for stability analysis of cracked and patched concrete structures based on the finite element method, including: drawing the geometric parameter conditions of the mesh model, drawing the geometric parameters of the analysis object, and the location of the preset cracks; applying boundary conditions to the finite element mesh model, performing mesh generation, defining the physical and mechanical parameters representing the concrete structure and patch paste in the finite element mesh model after mesh generation; defining the actual stress load conditions of the finite element mesh model; simulating the passivation and activation of the mesh region representing the cracks through different simulated construction steps, and carrying out finite element calculations; obtaining the internal forces of the cracked section of the concrete structure based on the calculation results, calculating the section safety factor, and evaluating its stability. Summary of the Invention

[0009] The purpose of this invention is to provide an intelligent design method for a programmable active structure with adjustable stable bearing capacity based on topology optimization. This method overcomes the shortcomings of the traditional design mode that relies on experience and trial and error, thereby efficiently and accurately designing advanced active structures that can actively adjust their stable bearing capacity through external excitation, and even exhibit specific stable bearing capacity change patterns according to predetermined settings.

[0010] To achieve the objectives of this invention, the following technical solution is provided: a programmable intelligent design method for stable bearing capacity based on topology optimization, comprising the following steps: Based on the design requirements in the structural design scheme, determine the material parameters of each material in the active structure and the corresponding finite element model of the overall structure; Based on the stress condition of the active structure in the structural design scheme, the stability bearing analysis equation of the active structure is constructed. Based on the active structural stability bearing analysis equation and the finite element model, an objective function is constructed to calculate the distribution of each material. The objective function is iteratively solved according to the preset constraints to output the distribution of each material on the finite element model.

[0011] This invention achieves automated and intelligent design from design requirements to final structural configuration by integrating topology optimization theory, structural stability analysis, and mathematical programming algorithms.

[0012] Specifically, the materials include active materials that can respond to external stimuli and passive materials that constitute the structural matrix.

[0013] Specifically, the active structural stability load-bearing analysis equation is obtained based on the generalized characteristic eigenvalue problem.

[0014] Specifically, the expression for the active structural stability bearing analysis equation is as follows: ; in, The overall elastic stiffness matrix of the structure. The structural geometric stiffness matrix is ​​caused by applying control to the active material. This represents the structural geometric stiffness matrix caused by external loads. This is the linear buckling load factor of the structure corresponding to external loads. This corresponds to the buckling mode.

[0015] Specifically, the objective function is constructed based on specific temperature changes and different temperature change conditions.

[0016] Specifically, the expression for the objective function is as follows: M1: ; ; ; ; ; M2: ; ; ; ; ; Where M1 represents a specific temperature change condition, and M2 represents different temperature change conditions. The overall elastic stiffness matrix of the structure. The structural geometric stiffness matrix is ​​caused by applying control to the active material. This represents the structural geometric stiffness matrix caused by external loads. This is the linear buckling load factor of the structure corresponding to external loads. For the corresponding buckling modes, To characterize the pseudo-density of the unit cell for passive material distribution, To characterize the pseudo-density of the unit cell for active material distribution, The total material volume of the structure. The compliance value of the structure. The total material volume of the structure. This represents the upper limit of the structure's compliance.

[0017] Specifically, the objective function employs the Solid Isotropic Material with Penalization method for material interpolation during the iterative solution process.

[0018] Specifically, the constraints include one or more of the following: lightweighting, stiffness, or strength of the structure.

[0019] Compared with the prior art, the beneficial effects of the present invention are as follows: Introducing topology optimization technology into active structural design enables automatic and intelligent design from performance requirements to material layout, completely changing the traditional model that relies on manual trial and error, and greatly improving design efficiency and reliability. By establishing a stable bearing capacity analysis model that accurately integrates active control effects and a customized optimization objective function; The optimization process considers the distribution of both active and passive materials and applies constraints such as volume and stiffness. This allows us to find the optimal way to utilize materials while meeting various engineering requirements, and achieve the best balance between structural performance, lightweighting, and economy. The theoretical framework and design process are universal and applicable to different types of active materials (such as shape memory alloys, piezoelectric materials, etc.) and various physical field excitations (thermal, electrical, magnetic, etc.), and have good prospects for promotion and application. Attached Figure Description

[0020] Figure 1 A flowchart illustrating the intelligent design method for a programmable active structure with adjustable stable bearing capacity provided in this embodiment; Figure 2 This embodiment provides a stable load-bearing capacity adjustable programmable active structural topology optimization intelligent design domain; Figure 3 This embodiment provides the result of active structural topology optimization intelligent design that maximizes stable load-bearing capacity. Figure 4 This embodiment provides the maximum increase in the stable bearing capacity of the intelligent design structure. Figure 5 The feature points of the stable bearing capacity programmable active structural topology optimization intelligent design curve provided in this embodiment; Figure 6 This embodiment provides the intelligent design results for programmable active structural topology optimization with stable load-bearing capacity. Figure 7 The stability bearing capacity level variation curve of the programmable intelligent design structure provided in this embodiment. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0022] like Figure 1 As shown in this embodiment, a programmable active structure intelligent design method with adjustable stable bearing capacity based on topology optimization is provided, which includes the following steps: Phase 1: Design preparation and parameter definition; First, determine the design domain of the structure and apply appropriate boundary conditions and loads according to the actual engineering application scenario. Simultaneously, clarify the performance parameters of the two key materials constituting the active structure: one is the functional active material capable of responding to external stimuli (such as shape memory alloys, piezoelectric materials, etc.), and the other is the passive material constituting the structural matrix.

[0023] Phase Two: Intelligent Optimization Design and Automatic Solution; This is the core stage of this method, which achieves automatic design by constructing a complete mathematical model for topology optimization. This stage includes the following processes: 1. Establish the analysis model: Construct an analytical mechanical model for the stability bearing capacity of the active structure. This model solves the generalized eigenvalue problem. To accurately obtain the structural stability bearing capacity index (buckling load factor) under active control. ).in, The matrix reflects the effects of controlling active materials through external stimuli (such as ΔT, ΔU), which is the basis for achieving adjustable load-bearing capacity.

[0024] 2. Construct an optimization model: Based on the specific design objectives (such as maximizing stable bearing capacity, or requiring the structure to exhibit a specific bearing capacity variation sequence under different excitations), customize the corresponding objective function. .

[0025] 3. Apply design constraints: Introduce necessary constraints into the optimization model. For example, limiting the total volume of active and passive materials. The maximum flexibility (i.e., minimum stiffness) of a constrained structure. The maximum stress of the structure is limited to not exceeding the strength limit. These measures are taken to ensure that the final designed structure simultaneously meets the requirements for lightweighting, stiffness, and strength.

[0026] 4. Automatic Iterative Solution: The above optimization model is automatically solved using efficient mathematical programming algorithms (such as the moving asymptote method / sequential quadratic programming method, etc.) without manual intervention, and the optimal active / passive material distribution design scheme is obtained directly.

[0027] Phase 3: Design verification and performance validation; Further detailed mechanical analysis and verification are conducted on the optimal structural configuration obtained through topology optimization to verify its performance under design requirements and ensure the feasibility and reliability of the design scheme.

[0028] More specifically, in this embodiment, the objective function of the optimization model refers to maximizing the stable bearing capacity under a specific temperature change regulation ΔT, through active structural topology optimization, intelligent design, and regulation of ΔT under different temperature changes. i The objective functions M1 and M2 of the active structural topology optimization intelligent design based on the stability bearing capacity programming under action are expressed as follows: M1: ; ; ; ; ; M2: ; ; ; ; ; The embodiment solves its structural response using the finite element method, where the design variables depend on the finite element mesh, wherein... The pseudo-density of the unit cell, used to characterize the distribution of passive and active materials, is employed as an optimization design variable. and These represent the total material volume and compliance value of the structure, respectively. and These represent the total material volume and the upper limit of compliance of the structure, respectively. The material interpolation method used for topology optimization in the model is the dual-material Solid Isotropic Material with Penalization (SIMP) method. ; in, The elastic modulus of the element material where the design variable is located. For passive material elastic modulus, For active material elastic modulus, As the lower limit of the elastic modulus, it is taken as 10 in this embodiment. min( , ), p It is the penalty coefficient.

[0029] Objective functions M1 and M2 can be directly derived or obtained directly using automatic differentiation to obtain the sensitivity to the optimization variables, and used for gradient-based optimization iterative solutions.

[0030] The mathematical programming method used in this embodiment is the Moving Asymptote Method (MMA).

[0031] For example, the following presents the intelligent design of active structural topology optimization for maximizing stable bearing capacity under a specific temperature change ΔT, and the different temperature changes ΔT. i The present invention provides a detailed description of the method through embodiments of active structural topology optimization intelligent design based on stable bearing capacity programming under load. Without loss of generality, all parameters in the following examples are dimensionless.

[0032] by Figure 2 As shown, this is the rectangular design area selected in this embodiment, with a width of [missing information]. L x = 0.5, height is L y =1.0, bottom boundary fixed, top center subjected to a vertically downward external load. F Assume that the elastic modulus of both the active and passive materials is the same. E Poisson's ratio is υ The coefficient of thermal expansion of active materials α = 0.001. The maximum temperature change under active control is ΔT = 50. 30% of the entire rectangular design area It is 2.5 times the initial value.

[0033] like Figure 3 The diagram shows the optimized design structure obtained from the objective function M1 provided in this embodiment. Figure 3 It was found that in the optimized structure, the active material is located at the top and inside, while the passive material is mainly distributed on both sides. Furthermore, the structure maintains a stable load-bearing capacity level under active control (denoted as [insert value here] in this embodiment). ) and the stable bearing capacity level without active regulation (denoted as in this embodiment) The ratio of ).

[0034] That is, under active control, the structural stability bearing capacity increased by 2.48 times, which is far higher than the stability bearing capacity level without active control.

[0035] Based on the objective function M2, an active structure intelligent design with programmable stability bearing capacity is performed. In this embodiment, the design aims to obtain three active structures, denoted as S1, S2, and S3, which satisfy the following conditions: The S1 stable bearing capacity level increases with the increase of the active control level; The S2 stable bearing capacity level first increases and then remains unchanged as the level of active regulation increases; The S3 stable bearing capacity level first increases and then decreases as the level of active regulation increases.

[0036] To meet the above conditions, such as Figure 5 The figure shows the key points used in the stable bearing capacity programming in the intelligent design example for the objective function M2 in this embodiment.

[0037] The horizontal axis of the curve represents the active control level ΔT, and the vertical axis represents the stable bearing capacity level under active control (denoted as ΔT in this embodiment). ) and the stable bearing capacity level without active regulation (denoted as in this embodiment) The ratio of ).

[0038] like Figure 6 The figure shows the optimized design structure obtained by the objective function M2 provided in this embodiment. It can be seen that the overall macroscopic distribution of active and passive materials in the three structures is similar, but there are significant differences in the local distribution, which results in the three structures having significantly different levels of stable load-bearing capacity.

[0039] like Figure 7 As shown, the stable load-bearing capacity levels of the three structures under active control (denoted as ) ) and the stable bearing capacity level without active regulation (denoted as The curves showing the ratio of the three structures' stability bearing capacity levels to the active control level ΔT are presented. It can be seen that the stability bearing capacity level change curves of all three structures passed the corresponding key points as required by the design, and demonstrated the pre-set trend requirements. This embodiment verifies the effectiveness of the objective function M2 for automatically and intelligently designing active structures with programmable stability bearing capacity performance.

[0040] Furthermore, the terms "upper," "lower," "inner," "outer," "front," and "rear" are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Unless otherwise specifically stated, the relative steps, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the invention.

[0041] Of course, the above description is only a specific embodiment of the present invention and is not intended to limit the scope of the present invention. All equivalent changes or modifications made to the structure, features and principles described in the claims of the present invention should be included in the scope of the claims of the present invention.

[0042] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A programmable active structural intelligent design method based on topology optimization with adjustable stable bearing capacity, characterized in that, Includes the following steps: Based on the design requirements in the structural design scheme, determine the material parameters of each material in the active structure and the corresponding finite element model of the overall structure; Based on the stress condition of the active structure in the structural design scheme, the stability bearing analysis equation of the active structure is constructed. Based on the active structural stability bearing analysis equation and the finite element model, an objective function is constructed to calculate the distribution of each material. The objective function is iteratively solved according to the preset constraints to output the distribution of each material on the finite element model.

2. The intelligent design method for stable bearing capacity-adjustable programmable active structures based on topology optimization according to claim 1, characterized in that, The materials include active materials that can respond to external stimuli and passive materials that constitute the structural matrix.

3. The intelligent design method for stable bearing capacity-adjustable programmable active structures based on topology optimization according to claim 1, characterized in that, The active structural stability load-bearing analysis equation is obtained based on the generalized characteristic eigenvalue problem.

4. The intelligent design method for stable bearing capacity-adjustable programmable active structures based on topology optimization according to claim 1 or 3, characterized in that, The expression for the active structural stability load-bearing analysis equation is as follows: ； in, The overall elastic stiffness matrix of the structure. The structural geometric stiffness matrix is ​​caused by applying control to the active material. This represents the structural geometric stiffness matrix caused by external loads. This is the linear buckling load factor of the structure corresponding to external loads. This corresponds to the buckling mode.

5. The intelligent design method for stable bearing capacity-adjustable programmable active structures based on topology optimization according to claim 1, characterized in that, The objective function is constructed based on specific temperature changes and under different temperature change conditions.

6. The intelligent design method for stable bearing capacity-adjustable programmable active structures based on topology optimization according to claim 5, characterized in that, The expression for the objective function is as follows: M1: ； ； ； ； ； M2: ； ； ； ； ； Where M1 represents a specific temperature change condition, and M2 represents different temperature change conditions. The overall elastic stiffness matrix of the structure. The structural geometric stiffness matrix is ​​caused by applying control to the active material. This represents the structural geometric stiffness matrix caused by external loads. This is the linear buckling load factor of the structure corresponding to external loads. For the corresponding buckling modes, To characterize the pseudo-density of the unit cell for passive material distribution, To characterize the pseudo-density of the unit cell for active material distribution, The total material volume of the structure. The compliance value of the structure. The total material volume of the structure. This represents the upper limit of the structure's compliance.

7. The intelligent design method for stable bearing capacity-adjustable programmable active structures based on topology optimization according to claim 6, characterized in that, The objective function is solved iteratively using the Solid Isotropic Material with Penalization (SIMP) method for material interpolation.

8. The intelligent design method for stable bearing capacity-adjustable programmable active structures based on topology optimization according to claim 1, characterized in that, The constraints include one or more of the following: lightweighting, stiffness, or strength of the structure.

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