Notch optimization method and equipment for composite structure

Abstract

The invention provides a notch optimization method of a composite structure, and relates to the technical field of data processing, and the method comprises the steps: obtaining an initial structure parameter, predefining an initial notch design domain and a target optimization parameter on the structure, dividing the initial notch design domain into a plurality of units, respectively establishing a first finite element model and a second finite element model based on the initial structure parameters; wherein the first finite element model is used for nonlinear folding analysis of an initial notch design domain, and the second finite element model is used for linear buckling analysis of the initial notch design domain; establishing a target optimization function and a constraint condition based on the sensitivity of each unit corresponding to each target optimization parameter; according to the target optimization function and the constraint condition, unit increase and decrease and iterative optimization are carried out on the initial incision design domain under the bidirectional progressive structure optimization model, a target incision design domain is generated, and the problems that existing incision optimization mostly depends on manual consideration of shape parameters of the incision surface, the optimization precision is not high enough, and the effect is not good are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and device for optimizing the incision of a composite structure. Background Art

[0002] Deployable Composite Thin-Walled Shell with Cutouts (DCTWS) structures are widely used in space applications because they can be compactly folded while maintaining sufficient stiffness when deployed. These structures usually have cutouts in thin laminates to prevent material failure, making the cutout design critical for optimizing DCTWS performance. Material safety, deployment drive capability, deformation resistance, and buckling stability are important mechanical performance indicators of the structure under folding, deployment, and load-bearing conditions, respectively, and are significantly affected by the number, size, and shape of the cutouts.

[0003] However, most existing studies focus on the parameter adjustment of the incision size or shape, and lack an effective theory for the shell element of braided composite materials, especially the lack of consideration of the sensitivity of material damage constraints and buckling stability. It is impossible to significantly improve the folding and unfolding performance, which easily increases the risk of failure and has poor optimization effect. Summary of the invention

[0004] In order to overcome the above technical defects, the purpose of the present invention is to provide a composite structure incision optimization method and equipment to solve the problem that the existing composite structure incision optimization mostly relies on manual consideration of the shape parameters of the incision surface, the optimization result is not accurate enough and the effect is poor.

[0005] The present invention discloses a composite structure incision optimization method, comprising:

[0006] Acquiring initial structural parameters, and pre-defining an initial cutout design domain and target optimization parameters on the structure, wherein the target optimization parameters include bending stiffness, critical buckling load, and failure index;

[0007] The initial cutout design domain is divided into a plurality of units, and a first finite element model and a second finite element model are respectively established based on the initial structural parameters; wherein the first finite element model is used for nonlinear folding analysis of the initial cutout design domain to determine the sensitivity of bending stiffness and failure index corresponding to each unit, and the second finite element model is used for linear buckling analysis of the initial cutout design domain to determine the sensitivity of critical buckling load corresponding to each unit;

[0008] Establishing the target optimization function and constraint conditions based on the sensitivity of each unit to each target optimization parameter;

[0009] According to the objective optimization function and the constraint conditions, the initial cutout design domain is iteratively optimized by adding or reducing units under a bidirectional progressive structural optimization model to generate a target cutout design domain.

[0010] Preferably, before establishing the target optimization function and the constraint conditions, the following steps are included:

[0011] The sensitivities of the bending stiffness, critical buckling load, and failure index corresponding to each unit are normalized and weighted to generate the total sensitivity corresponding to each unit, wherein the total sensitivity is positively correlated with the sensitivity of the bending stiffness and failure index, and negatively correlated with the sensitivity of the critical buckling load.

[0012] Preferably, a global stiffness matrix, a stress stiffness matrix and a global mechanical load vector are obtained according to the initial structural parameters;

[0013] Using the first finite element model to generate a global displacement vector corresponding to each unit obtained by nonlinear folding analysis;

[0014] Using the second finite element model to generate a global displacement vector obtained by a linear buckling analysis corresponding to each unit;

[0015] The objective optimization function and constraints include:

[0016]

[0017] Subject to: X = {x e}; x e =0 or 1;

[0018] KU=F&(K-λ 1 K σ ) m =0;

[0019] FI max (X)<1;

[0020]

[0021] Where, T(X) is the total sensitivity; w 1 、w 2 、w 3 are the weights of bending stiffness, critical buckling load and failure index, respectively; are the normalized bending stiffness, critical buckling load and sensitivity of failure index respectively; x e For any unit, x e = 0, the unit is deleted, x e = 1, the unit is retained, N is the total number of units; K is the global stiffness matrix; K σis the stress stiffness matrix; F is the global mechanical load vector; U is the global displacement vector obtained by nonlinear folding analysis; U m Global displacement vector obtained for a linear buckling analysis.

[0022] Preferably, the initial cutout design domain is optimized based on a single objective parameter or optimized based on multiple objective parameters by adjusting the weights of the bending stiffness, the critical buckling load and the failure index.

[0023] Preferably, the initial incision design is optimized based on a single objective parameter, comprising:

[0024] Set any weight of bending stiffness, critical buckling load and failure index to 1, and the other two weights to 0;

[0025] The initial cutout design domain is iteratively optimized under a bidirectional progressive structural optimization model according to the target optimization function and constraint conditions to generate a target cutout design domain.

[0026] Preferably, the initial incision design is optimized based on multi-objective parameters, including:

[0027] The weights of the bending stiffness, the critical buckling load and the failure index are dynamically adjusted between 0 and 1 with a preset step size, and iterative optimization is performed under a bidirectional progressive structural optimization model to obtain a plurality of target cutout design domains that meet the target optimization function and constraint conditions;

[0028] And / or, obtaining a unique target cutout design domain according to preset conditions.

[0029] Preferably, a target cutout design domain satisfying the target optimization function and constraint conditions is obtained and a distribution diagram is established with bending stiffness as the X-axis and critical buckling load as the Y-axis;

[0030] In the distribution diagram, a unique target cutout design domain is determined by an ideal point method.

[0031] Preferably, the first finite element model determines the sensitivity of the bending stiffness and failure index corresponding to each unit, including:

[0032] The sensitivity of the bending stiffness is determined according to the following formula:

[0033] in is the sensitivity of compliance, which is linearly related to the sensitivity of bending stiffness; p is the preset penalty index; x e For any unit; E e is the element strain energy of the e-th element under nonlinear folding analysis;

[0034] The sensitivity of the failure index is determined according to the following formula:

[0035] in is the failure index at the centroid of the e-th unit; N e (x e ) is to determine each stress component based on the initial structural parameters; P is the failure exponent norm parameter.

[0036] Preferably, the second finite element model determines the sensitivity of the critical buckling load corresponding to each unit, including:

[0037] The sensitivity of the critical buckling load is determined according to the following formula:

[0038] Among them, E e,m is the element strain energy of the e-th element in the linear buckling analysis; x min , p are the preset values.

[0039] The present invention also provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned incision optimization method when executing the computer program.

[0040] Compared with the prior art, the above technical solution has the following beneficial effects:

[0041] The incision optimization method and device for composite structures provided in the present application are based on the BESO model and finite element modeling, and the structure is divided into multiple units. The initial incision design domain (topology) is predetermined, and the quasi-static nonlinear folding behavior and linear buckling mode of the structure are simulated by two finite element models respectively, so as to obtain the sensitivity of each unit in terms of bending stiffness and critical buckling load, and introduce the failure index to estimate the material failure. Thus, the target optimization function and constraint conditions are established to increase or decrease the units based on the initial incision design domain, so as to realize the iterative optimization of the incision, and consider the deep influence of the incision on the folding, unfolding and bearing performance of these structures, so as to solve the problem that the incision optimization of existing composite structures mostly only considers the shape parameters of the incision surface after shearing, the optimization result is not accurate enough, and the effect of improving the structural performance is not good; further, weights can be set to adjust the influence of each target optimization parameter on the optimization process, and the bending stiffness, critical buckling load and failure index are integrated through linear weighted aggregation, so as to improve the optimization effect and efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A flow chart of an embodiment of a composite structure cutting optimization method and device according to the present invention;

[0043] Figure 2A flowchart showing iterative optimization in an embodiment of a composite structure incision optimization method and device according to the present invention;

[0044] Figure 3 A schematic diagram of optimization results based on single-objective parameter optimization by adjusting the weights of bending stiffness, critical buckling load and failure index in an embodiment of a notch optimization method and device for a composite structure according to the present invention;

[0045] Figure 4 A schematic diagram of optimization results based on multi-objective parameter optimization by adjusting the weights of bending stiffness, critical buckling load and failure index in an embodiment of a composite structure notch optimization method and device described in the present invention. DETAILED DESCRIPTION

[0046] The advantages of the present invention are further described below in conjunction with the accompanying drawings and specific embodiments.

[0047] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0048] The terms used in this disclosure are for the purpose of describing specific embodiments only and are not intended to limit the disclosure. The singular forms of "a", "said" and "the" used in this disclosure and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0049] It should be understood that although the terms first, second, third, etc. may be used in the present disclosure to describe various information, such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present disclosure, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0050] In the description of the present invention, unless otherwise specified and limited, it should be noted that the terms "installed", "connected" and "connected" should be understood in a broad sense. For example, it can be a mechanical connection or an electrical connection, or it can be the internal connection between two components. It can be a direct connection or an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to the specific circumstances.

[0051] In the following description, the suffixes such as "module", "component" or "unit" used to represent elements are only used to facilitate the description of the present invention, and have no specific meanings. Therefore, "module" and "component" can be used interchangeably.

[0052] Example: This embodiment discloses a method for optimizing the incision of a composite structure. It is based on a bidirectional evolutionary structural optimization model and is combined with a finite element model. It adopts a gradient-based topology optimization method. The design domain where the incision is located is iteratively modified according to the sensitivity of the objective function to changes in material distribution (bending stiffness, critical buckling load and failure index). Units are added to areas that need reinforcement, and units are moved from areas that contribute the least to the objective function to optimize the incision at the interface of the composite structure, thereby improving the optimization accuracy and efficiency, which is different from the existing modification of only the incision size / shape.

[0053] In this embodiment, the composite structure is a foldable and deployable composite material with cutouts, which can be DCTWS (Deployable Composite Thin-Walled Shell with Cutouts), or a RA-WCTH right-angle woven composite thin-walled tubular hinge structure, or other composite structures with cutouts that are deployable and foldable. The "structure" described in this embodiment is the composite structure that is deployable and foldable with cutouts, "composite" includes but is not limited to being composed of multiple parts (the two cylindrical shells mentioned below) / multiple materials (the fabrics and resins mentioned below), and the "cutout design domain" is the area where the cutout is formed (which is divided into multiple units). In this embodiment, the optimization of the cutout design domain can also be understood as the optimization of the structural topology.

[0054] As an explanation, the incision optimization method in this embodiment is based on the bidirectional evolutionary structural optimization (BESO) model, and the BESO algorithm is integrated into the incision design of DCTWS. It gradually increases or decreases material units to find the highest structural performance within a predetermined range to improve its folding and unfolding performance. Therefore, the area / volume restrictions of the incision design domain (including the initial incision design domain and the target incision design domain) are set in this embodiment.

[0055] Based on the above, for details, see Figure 1-Figure 2, the optimization method includes the following:

[0056] S1: Obtain initial structural parameters, and predefine the initial cutout design domain and target optimization parameters on the structure;

[0057] In this embodiment, the composite structure is initially designed to consist of two thin-walled cylindrical shells connected at 90 degrees (eg Figure 2 ), with a smooth transition of non-zero Gaussian curvature at the joint, the diameter of each cylindrical shell is 32 mm, the whole structure is mirror-symmetric in the xy and yz planes, and the design of the cutouts makes the structure foldable and unfoldable without damage. II plain fabric (quartz fiber) and PMT-F6 cyanate resin matrix, composite laminate stacking using fiber and resin, the initial structural parameters include the ABD matrix of the material, the ABD matrix describes the stiffness characteristics of the laminated composite plate. The load (force and moment) on the laminate is linked to the strain (including mid-plane strain and curvature). Specifically, the ABD matrix includes three 3x3 sub-matrices: A matrix, B matrix and D matrix, where the A matrix represents the in-plane stiffness of the laminate, involving the tensile and shear stiffness characteristics of the laminate under the in-plane load, the B matrix represents the coupling stiffness of the laminate, involving the bending or torsional response caused by the laminate under the in-plane load, and the tensile or shear response under the bending or torsional load, and the C matrix represents the bending stiffness of the laminate, involving the bending or torsional stiffness characteristics of the laminate under the bending or torsional load, which can be obtained or calculated from the initial structural parameters in the subsequent determination of each sensitivity and target optimization parameter.

[0058] By way of example only, the ABD matrix may be as follows:

[0059]

[0060] The initial cutout design domain is the area where the cutout may exist, that is, located at the connection between the two cylindrical shells. It is pre-set. In this embodiment, the units therein are increased or decreased based on the initial cutout design domain to achieve the optimization of the cutout design. The target optimization parameters may include but are not limited to the bending stiffness K b (X), critical buckling load λ 1 (X) and failure index FI(X). Specifically in this embodiment, it is proposed to use the failed material as a constraint while maximizing the folding strain energy, bending stiffness and critical buckling load to determine the target cutout design domain, improve the folding and unfolding performance of the structure, so as to maximize the key mechanical properties under folding, unfolding and locking conditions while avoiding any material damage.

[0061] As an illustration, a simulation model may be established to verify the optimization effect of the optimization method provided in this embodiment.

[0062] S2: dividing the initial cutout design domain into a plurality of units, and establishing a first finite element model and a second finite element model based on initial structural parameters; wherein the first finite element model is used for nonlinear folding analysis of the initial cutout design domain to determine the sensitivity of the bending stiffness and failure index corresponding to each unit, and the second finite element model is used for linear buckling analysis of the initial cutout design domain to determine the sensitivity of the critical buckling load corresponding to each unit;

[0063] In this embodiment, the finite element model is constructed using the commercial finite element software ABAQUS2023 and the general shell element (element type S4R in ABAQUS). The two finite element models are used for nonlinear folding analysis and linear buckling analysis, respectively. The specific parameters of the model are based on the above initial structural parameters. The structural optimization objective function values ​​(C(X) (compliance), K b (X) (bending stiffness) and λ 1 (X), constraint conditions FI(X) and information at the centroid of each unit. Specifically, it includes: the failure index, strain energy and cross-sectional stress of each unit when RA-WCTH is folded 45°; the strain energy of each unit in the first-order buckling mode of RA-WCTH, etc.

[0064] The above division of the initial cutout design domain can be discretized based on the BESO model so that each unit is assigned an independent design variable x in the following iterative optimization. e , which is constrained to a value of 0 or 1, where 0 indicates the removal of the element and 1 indicates the retention of the element. The effective stiffness matrix of each element is defined as D e =x e D 0 ; where D 0 It represents the stiffness matrix of the unit in the initial state, and the D matrix is ​​determined corresponding to the above ABD matrix.

[0065] Specifically, in this embodiment, the sensitivity of the bending stiffness and the failure index is used as the variables of the target optimization function, and the sensitivity of the failure index is used as the constraint condition. Therefore, specifically, the first finite element model determines the sensitivity of the bending stiffness and the failure index corresponding to each unit, including:

[0066] First, for the failure index, in this embodiment, the failure index adopts a multi-dimensional failure criterion for failure analysis. Specifically, the criterion defines three failure indexes based on the resultant force of six forces and moments: N x 、N y 、Nxy 、M x 、M y and M xy These indices correspond to in-plane tension, out-of-plane bending, and in-plane tension and out-of-plane bending coupled failure modes, so the three failure indices are expressed as:

[0067]

[0068] in

[0069]

[0070] f i represents the experimentally measured strength of the laminate in the yarn direction, with the subscripts t and c representing tension and compression, respectively, given by the initial structural parameters (ABD matrix).

[0071] The failure index FI(X) is used to establish its constraint function according to the P-norm generalized norm:

[0072] in is the failure index at the centroid of the e-th unit, obtained based on the first finite element model, P is the failure index norm parameter, when P = 1, the P norm gives the average failure index, when P → ∞, the P norm orientation maximum failure index FI max (X), P is generally 3-6.

[0073] Using the market derivative rule, the sensitivity of the failure index is determined according to the following formula:

[0074] in is the failure index at the centroid of the e-th unit; N e (x e ) is to determine each stress component based on the initial structural parameters, that is, N e (x e )={N x ,N y ,N xy}; P is the failure index norm parameter, FI e (x e ) Refer to the above formula.

[0075] Based on the above, FI can be determined e (x e ) with respect to each stress component is

[0076]

[0077] To simplify the calculation, we set This greatly improves computational efficiency.

[0078] Based on the above, the sensitivity of the failure index in the optimization method of this embodiment is determined for the following constraints.

[0079] Second, for the bending stiffness:

[0080] The sensitivity of the bending stiffness is determined according to the following formula:

[0081] in is the sensitivity of compliance, which is linearly related to the sensitivity of bending stiffness; p is the preset penalty index; x e For any unit; E e is the element strain energy of the e-th element under nonlinear folding analysis, Size equal to E e ;

[0082] As an illustration, in this embodiment, the equivalent use compliance (equal to the sum of the elastic strain energies of all units in the structure, expressed as the strain energy stored after deformation E b (X)) represents the bending stiffness. For structures with different design cutouts, K b (X) and C(X) have a clear linear relationship and can be converted using preset linear relationship coefficients.

[0083] Furthermore, for the buckling load:

[0084] The second finite element model determines the sensitivity of the critical buckling load corresponding to each unit, including:

[0085] Differentiate and combine the linear buckling finite element equilibrium equations

[0086] That is, the sensitivity of the critical buckling load is determined according to the following formula:

[0087] Among them, E e,m is the element strain energy of the e-th element in the linear buckling analysis; x min , p are preset values, where K and K are determined using the material interpolation model. σ The differential expression of , the node displacement vector of the e-th unit in the buckling mode, the element stiffness matrix of the e-th unit extracted from the global stiffness matrix, etc. are calculated.

[0088] As an option, in this embodiment, in order to achieve the balance of the three objective optimization parameters and facilitate the following optimization process and improve the processing efficiency, before establishing the objective optimization function and the constraint conditions, it includes:

[0089] The sensitivities of the bending stiffness, critical buckling load, and failure index corresponding to each unit are normalized and weighted to generate the total sensitivity corresponding to each unit, wherein the total sensitivity is positively correlated with the sensitivity of the bending stiffness and failure index, and negatively correlated with the sensitivity of the critical buckling load.

[0090] That is, the three independent sensitivities are aggregated using linear weighted summation. Specifically, they are first normalized to 0-1, as shown in the following formula:

[0091]

[0092] The subscripts max and min represent the maximum and minimum values ​​of all unit sensitivities, respectively;

[0093] The sensitivity expression after aggregation is as follows:

[0094] w 1 、w 2 、w 3 are the weights of the sensitivity of bending stiffness, critical buckling load and failure index, which are consistent with the objective optimization function below. The positive and negative signs determine the evolution direction of the unit dominated by retention or deletion, that is, if RA-WCTH is to have higher stiffness and critical buckling load, the units in the highlighted area need to be retained. The unit evolution strategy based on the failure index is the opposite.

[0095] Optionally, in order to reduce the grid independence and checkerboard phenomenon caused by unit division, sensitivity filtering and other methods can be used. Specifically, the applicable filter is iteratively executed based on the weight function of the original sensitivity and the filter radius, weight factor, etc.

[0096] S3: Establishing the target optimization function and constraint conditions based on the sensitivity of each unit to each target optimization parameter;

[0097] Specifically, based on the above-mentioned initial structural parameters, the first finite element model, and the second finite element model, various parameters for optimization can be obtained, including but not limited to sensitivity: obtaining a global stiffness matrix, a stress stiffness matrix, and a global mechanical load vector according to the initial structural parameters; using the first finite element model to generate a global displacement vector obtained by a nonlinear folding analysis corresponding to each unit; using the second finite element model to generate a global displacement vector obtained by a linear buckling analysis corresponding to each unit, etc.

[0098] Therefore, in order to solve the multi-objective topology optimization problem of finding the optimal shape, number and position of the cutouts to optimize the bending stiffness and critical buckling load of the RA-WCTH and satisfy the failure constraints of the structural materials, the objective optimization function and constraints established include:

[0099]

[0100] Subject to: X = {x e}; x e =0 or 1;

[0101] KU=F&(K-λ 1 K σ ) m =0;

[0102] FI max (X)<1;

[0103]

[0104] Where, T(X) is the total sensitivity; w 1 、w 2 、w 3 are the weights of the sensitivity of bending stiffness, critical buckling load and failure index, respectively; are the normalized bending stiffness, critical buckling load and sensitivity of failure index respectively; x e For any unit, x e = 0, the unit is deleted, x e = 1, the unit is retained, N is the total number of units; K is the global stiffness matrix; K σ is the stress stiffness matrix; F is the global mechanical load vector; U is the global displacement vector obtained by nonlinear folding analysis; U m Global displacement vector obtained for a linear buckling analysis.

[0105] S4: According to the objective optimization function and the constraint conditions, the initial cutout design domain is iteratively optimized by adding or reducing units under the bidirectional progressive structural optimization model to generate a target cutout design domain.

[0106] In this embodiment, the information of each unit (serial number and corresponding sensitivity) is transmitted to the BESO optimizer, which is similar to the existing iterative optimization method. As an example, the binary method is used to update the design variables in this embodiment. In the iterth iteration, the maximum unit sensitivity α is first determined. max and the minimum unit sensitivity α min The middle threshold Traverse all cells and find the cells with a sensitivity value less than Unit design variables Update to 0, and the remaining units will update the design variables to 1. This process will cycle until the current target volume (1-ert)V is met. iter(X), according to the current distribution of design variables, the units of RA-WCTH are deleted to update the geometric model.

[0107] Specifically, as an option, a very small target volume and evolution rate (such as N = 5) are set to allow BESO to slowly delete the units in the initial cutout design domain. When the structure satisfies the constraint FI(X) < 1, the target volume is reset to the volume of the current structure. This can stop BESO from further deleting units in the initial cutout design domain in time, which can maximize the prevention of loss of stiffness and critical buckling load.

[0108] In this embodiment, based on the above, each objective optimization parameter realizes the influence on the objective optimization function and constraints through weights. Each weight can be pre-configured or dynamically adjusted autonomously during the optimization process. In order to further improve the accuracy of the optimization results (substantially improve the structural performance after optimization) and the optimization efficiency, the initial incision design domain can be optimized based on single objective parameters or based on multi-objective parameters by adjusting the weights of the bending stiffness, critical buckling load and sensitivity of the failure index.

[0109] Based on the above, the initial incision design is optimized based on single objective parameters, including: setting any weight of bending stiffness, critical buckling load and sensitivity of failure index to 1, and the other two weights to 0; according to the objective optimization function and constraints, the initial incision design domain is iteratively optimized under the bidirectional progressive structural optimization model to generate the target incision design domain.

[0110] Specifically, as an example Figure 3 shown. Figure 3 (a) to Figure 3 (c) shows the failure index sensitivity FI(X)(0, 0, 1), bending stiffness sensitivity K b (X)(1,0,0), and the critical buckling load sensitivity λ 1 The optimization results of RA-WCTH topology (initial cut design domain) under the action of (X)(0, 1, 0) alone. Figure 3 As shown in (a), the unit deletion of RA-WCTH starts from the concentration of FI(X), and the formation of the cut continues to spread to the periphery with this place as the center. max After the constraint condition of (X)<1 is met, the volume of the initial incision design domain / overall structure no longer decreases, the incision shape is basically finalized, and the final incision is approximately elliptical. Figure 3 In (b), the element deletion starts with the element that contributes the least to the bending stiffness. Since the failure index sensitivity is not considered, the failure index of the structure is relatively Figure 3(a) Always maintain a very high level. In this case, a large number of units need to be deleted from the structure until the cut spreads from top to bottom to the failure index concentration area, so that the structure meets the constraints. Too low volume fraction leads to the optimization result of pure stiffness orientation showing the worst bending stiffness anyway. Figure 3 In (c), the deletion of units also starts from the unit with the lowest contribution to the critical buckling load. Finally, under the condition of satisfying the failure constraint, the structure obtains a discontinuous long strip cut. Although this optimization result retains the 1 The central area (X) contributes the most, but has the worst critical buckling load. Figure 3 (b) and Figure 3 (c) however, has a higher critical buckling load.

[0111] Based on the above, the initial cutout design is optimized based on multi-objective parameters, including: dynamically adjusting the weights of the bending stiffness, critical buckling load and sensitivity of the failure index between 0 and 1 with a preset step, and iteratively optimizing under a bidirectional progressive structural optimization model to obtain a number of target cutout design domains that meet the objective optimization function and constraint conditions;

[0112] In the above, each weight is dynamically adjusted between 0 and 1 with a preset step size, that is, the influence of each target optimization parameter on the optimization process is dynamically adjusted.

[0113] Specifically, as an example, Figure 4 As shown, Figure 4 (a) for Under the constraint of , each weight is sampled at 0.125 intervals in the range of 0 to 1 to construct the distribution space. The weight combination represented by each point is brought into the BESO model to optimize the incision design domain of the structure. According to the above simulation results, Figure 4 (a) shows 45 examples of optimization results that meet the bundle conditions (target cutout design domain, each point represents a structure with a target cutout design domain). The optimization results can be divided into 7 cutout types (clusters) according to the similarity of the cutout position, number and shape. It can be seen that, if Figure 4 (b) The distribution diagram with bending stiffness as the X-axis and critical buckling load as the Y-axis is located at the Pareto boundary (such as Figure 4 (b) The curve connecting the top points in the middle and upper part of each target cut design domain has better bending stiffness and critical buckling load, and meets the constraint of failure index.

[0114] Based on the above, since the BESO model is based on each unit x eThe constraint is a value of 0 or 1, where 0 represents the removal of the unit and 1 represents the retention of the unit. Therefore, the above optimization based on single-objective parameters can output a unique unit set, which is a set of values ​​0 and 1. The above optimization based on multi-objective parameters can output multiple unit sets, each of which is a set of values ​​0 and 1. Therefore, the difference between the target incision design domain and the initial incision design domain is that the states of the units therein are different, that is, a incision is formed on the structure after optimization.

[0115] It should be noted that the above sampling intervals are only used as examples, and it is understandable that adjusting the sampling intervals of the weights can further improve the accuracy and comprehensiveness of the optimization results. Each parameter required in the above optimization process can be obtained from the initial structural parameters, the first finite element model and / or the second finite element model.

[0116] Furthermore, based on the optimization results of the multi-objective parameters, a target incision design domain suitable for the scenario can be selected. Therefore, in a preferred embodiment, when multiple target incision design domains are generated based on the optimization of the multi-objective parameters, a unique target incision design domain is obtained according to the preset conditions. Specifically, in this embodiment, if Figure 4 As shown in (b), a target cutout design domain that satisfies the target optimization function and constraint conditions is obtained, and a distribution diagram of each target cutout design domain is established with bending stiffness as the X-axis and critical buckling load as the Y-axis; in the distribution diagram, a unique target cutout design domain is determined by the ideal point method.

[0117] The above-mentioned ideal point method specifically constructs a hypothetical point in an isomorphic manner. The point that is closest to the point in Euclidean distance and located on the Pareto frontier is considered to be the optimal design of the multi-objective topology optimization (target cutout design domain) of the structure. It should be noted that the ideal point method is only used as an example. Other existing algorithms that can filter out a unique result from multiple results can also be used here. Specifically, conditional judgment and other methods can be set to determine the unique target cutout design domain.

[0118] It can be understood that, through the above-mentioned simulation based on the initial structural parameters and the optimization method provided in this embodiment, the structure after multi-objective BESO optimization has the largest bending stiffness, and stores the largest elastic strain energy when folded 45°, and has the strongest self-expanding ability. The places with the highest risk of material damage are still concentrated at the edges of the incisions, but after optimization, the failure index has been reduced below the safety threshold. In addition, the multi-objective optimization results also have the largest critical buckling load. Therefore, according to the actual application scenario, optimization based on single-objective parameters or optimization based on multi-objective parameters can be selected to improve the optimized material performance, that is, to improve the optimization effect and optimization efficiency.

[0119] The incision optimization method provided in this embodiment is based on the BESO model and finite element modeling. The structure is divided into multiple units. Two finite element models are used to simulate the quasi-static symmetrical folding behavior and linear buckling mode of the structure respectively, and the sensitivity of each unit to each target optimization parameter is obtained. The failure index is introduced to estimate the material failure. The target optimization function and constraint conditions are established to increase or decrease the units based on the initial incision design domain, and the target incision design domain is obtained to achieve the optimization of the incision. Furthermore, it is also set to determine the sensitivity of each unit through linear weighted aggregation of multiple target optimization parameters such as bending stiffness, critical buckling load and failure index, and the influence of each target optimization parameter on the optimization process can be adjusted by controlling the weight, so as to achieve single-objective optimization and multi-objective optimization, so as to improve the applicability of the optimization method according to the actual application scenario.

[0120] In addition, the optimization method provided in this embodiment is applicable to isotropic and anisotropic thin shell structures, and can also be easily extended to the solution of other multi-condition and multi-objective topological optimization problems (structures with incision design domains). That is, the optimization method for the incision is not only applicable to the above-mentioned structure (DCTWS), but can also be used for the optimization of incision structures of other composite materials with expandable and foldable incisions.

[0121] Each step of the above method can be implemented by software, hardware or a combination thereof. Software-based implementations can be uploaded, downloaded or accessed remotely through communication means, including but not limited to the Internet, electronic communication, etc.

[0122] To achieve the above object, the present invention also provides a computer device, which can be set to multiple, and its components can be dispersed in different computer devices, and the computer device can be a smart phone, tablet computer, laptop computer, desktop computer, etc. that executes a program. The computer device of this embodiment includes at least but is not limited to: a memory and a processor that can be interconnected through a system bus.

[0123] In this embodiment, the memory may include a program storage area and a data storage area, wherein the program storage area may store an application required by the operating system and at least one function; the data storage area may store user information on the computer device. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, etc.

[0124] The processor may be a central processing unit, a controller, a microcontroller, a microprocessor, or other data processing chip in some embodiments. The processor is generally used to control the overall operation of a computer device. In this embodiment, the processor is used to run the program code or data stored in the memory to implement the above-mentioned optimization method.

[0125] To achieve the above object, the present invention also provides a computer-readable storage medium, which includes multiple storage media, such as flash memory, hard disk, multimedia card, etc., on which a computer program is stored, and when the program is executed by a processor, the corresponding functions are realized to implement the method of the above embodiment. The memory can be coupled to the processor so that the processor can read and write information from / to the storage medium, and execute the corresponding program module to perform the above steps.

[0126] It should be noted that the embodiments of the present invention have better practicability and do not impose any form of limitation on the present invention. Any technician familiar with the field may use the technical content disclosed above to change or modify it into an equivalent effective embodiment. However, any modification or equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A method for optimizing the incision of a composite structure, characterized in that: include: Acquire initial structural parameters, and predefine an initial cutout design domain and target optimization parameters on the structure, wherein the target optimization parameters include bending stiffness, critical buckling load, and failure index; The initial cutout design domain is divided into a plurality of units, and a first finite element model and a second finite element model are respectively established based on the initial structural parameters; wherein the first finite element model is used for nonlinear folding analysis of the initial cutout design domain to determine the sensitivity of bending stiffness and failure index corresponding to each unit, and the second finite element model is used for linear buckling analysis of the initial cutout design domain to determine the sensitivity of critical buckling load corresponding to each unit; Establishing the target optimization function and constraint conditions based on the sensitivity of each unit to each target optimization parameter; According to the objective optimization function and the constraint conditions, the initial cutout design domain is iteratively optimized by adding or reducing units under a bidirectional progressive structural optimization model to generate a target cutout design domain.

2. The incision optimization method according to claim 1, characterized in that: Before establishing the target optimization function and constraints, include: The sensitivities of the bending stiffness, critical buckling load, and failure index corresponding to each unit are normalized and weighted to generate the total sensitivity corresponding to each unit, wherein the total sensitivity is positively correlated with the sensitivity of the bending stiffness and failure index, and negatively correlated with the sensitivity of the critical buckling load.

3. The incision optimization method according to claim 1, characterized in that: Obtaining a global stiffness matrix, a stress stiffness matrix and a global mechanical load vector according to the initial structural parameters; Using the first finite element model to generate a global displacement vector corresponding to each unit obtained by nonlinear folding analysis; Using the second finite element model to generate a global displacement vector obtained by a linear buckling analysis corresponding to each unit; The objective optimization function and constraints include: Subject to: X = {x e }; x e =0 or 1; KU=F&(K-λ1K σ )U m =0; BE max (X)<1; Where T(X) is the total sensitivity; w1, w2, and w3 are the weights of bending stiffness, critical buckling load, and failure index, respectively; are the normalized bending stiffness, critical buckling load and sensitivity of failure index respectively; x e For any unit, x e = 0, the unit is deleted, x e = 1, the unit is retained, N is the total number of units; K is the global stiffness matrix; K σ is the stress stiffness matrix; F is the global mechanical load vector; U is the global displacement vector obtained by nonlinear folding analysis; U m Global displacement vector obtained for a linear buckling analysis.

4. The incision optimization method according to claim 2 or 3, characterized in that: By adjusting the weights of bending stiffness, critical buckling load and failure index, the initial cutout design domain is optimized based on single objective parameters or based on multi-objective parameters.

5. The incision optimization method according to claim 4, characterized in that: The initial incision design is optimized based on single objective parameters, including: Set any weight of bending stiffness, critical buckling load and failure index to 1, and the other two weights to 0; The initial cutout design domain is iteratively optimized according to the target optimization function and constraint conditions under a bidirectional progressive structural optimization model to generate a target cutout design domain.

6. The incision optimization method according to claim 4, characterized in that: The initial incision design is optimized based on multi-objective parameters, including: Dynamically adjusting the weights of the bending stiffness, critical buckling load and failure index between 0 and 1 with a preset step size, and iteratively optimizing under a bidirectional progressive structural optimization model to obtain a number of target cutout design domains that meet the target optimization function and constraint conditions; And / or, obtaining a unique target cutout design domain according to preset conditions.

7. The incision optimization method according to claim 6, characterized in that: Obtaining a target cutout design domain that satisfies the target optimization function and constraint conditions and establishing a distribution diagram with bending stiffness as the X-axis and critical buckling load as the Y-axis; In the distribution diagram, a unique target cutout design domain is determined by an ideal point method.

8. The incision optimization method according to claim 1, characterized in that: The first finite element model determines the sensitivity of the bending stiffness and failure index corresponding to each unit, including: The sensitivity of the bending stiffness is determined according to the following formula: in is the sensitivity of compliance, which is linearly related to the sensitivity of bending stiffness; p is the preset penalty index; x e For any unit; E e is the element strain energy of the e-th element under nonlinear folding analysis; The sensitivity of the failure index is determined according to the following formula: in is the failure index at the centroid of the e-th unit; N e (x e ) is to determine each stress component based on the initial structural parameters; P is the failure exponent norm parameter.

9. The incision optimization method according to claim 1, characterized in that: The second finite element model determines the sensitivity of the critical buckling load corresponding to each unit, including: The sensitivity of the critical buckling load is determined according to the following formula: Among them, E e,m is the element strain energy of the e-th element in the linear buckling analysis; x m i n , p are the preset values.

10. A computer device, characterized in that: The computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the incision optimization method according to any one of claims 1 to 9 when executing the computer program.

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