A method for optimizing the configuration of a scissor-type space deployment mechanism

Abstract

This invention relates to a method for optimizing the configuration of a scissor-type spatial deployment mechanism, comprising the following steps: S1: Parametric modeling: establishing a parametric geometric model of the scissor-type deployment mechanism; S2: Automated finite element analysis: based on the parametric model in step S1, writing a script program to drive commercial finite element software to achieve automated modeling and analysis; S3: Constructing an integrated optimization system: using optimization integration software to build an automated optimization process; S4: Approximate model construction: generating hundreds of sample points using the Latin hypercube experimental design method, and running a complete automated finite element analysis to obtain performance data; S5: Implementing combinatorial optimization: to balance global search capability and local convergence efficiency, a combinatorial optimization strategy is adopted. The advantages are: it forms a complete, efficient, and reliable design process for optimizing the configuration of a scissor-type spatial deployment mechanism, solving the problems of low efficiency and insufficient optimization capability of traditional design methods.

Description

Technical Field

[0001] This invention relates to the field of aerospace structural design and optimization technology, and more specifically, to a method for optimizing the configuration of a scissor-type space deployment mechanism for large deployable antenna devices and solar panel devices for satellites. Background Technology

[0002] Scissor-type deployment mechanisms have the advantages of high storage ratio, good deployment reliability, and strong load-bearing capacity, and are widely used in large-scale deployable structures in space. Their design is a complex multivariable, multi-constraint, and highly nonlinear optimization problem. Design variables include the number of sections, deployment angle, and cross-sectional dimensions of the members. It is necessary to simultaneously meet multiple performance requirements such as light weight, high fundamental frequency, good buckling stability, and small folded size.

[0003] Traditional design methods rely on experience and iterative calculations, which are inefficient and make it difficult to find the global optimal solution. Although finite element analysis can accurately evaluate performance, the process of manually combining modeling, analysis and optimization is cumbersome and time-consuming, and it is difficult to cope with the needs of multiple rounds of iterative optimization.

[0004] Therefore, there is an urgent need for a systematic and automated configuration optimization method to efficiently and accurately obtain the optimal design scheme for the scissor-type deployment mechanism.

[0005] The preceding description is intended to provide general background information and does not necessarily constitute prior art. Summary of the Invention

[0006] The purpose of this invention is to provide a configuration optimization method for scissor-type spatial deployment mechanisms. This configuration optimization method overcomes the problems of low efficiency, poor optimization effect, and insufficient scalability of existing configuration optimization methods. It realizes the systematization and automation of the entire process of parametric design, automated simulation and multi-objective optimization of scissor-type spatial deployment mechanisms, which significantly improves design efficiency and mechanism performance.

[0007] This invention provides a method for optimizing the configuration of a scissor-type spatial deployment mechanism, comprising the following steps:

[0008] S1: Parametric Modeling: Establish a parametric geometric model of the scissor-type deployment mechanism;

[0009] S2: Automated Finite Element Analysis: Based on the parametric model in step S1, write scripts to drive commercial finite element software to achieve automated modeling and analysis;

[0010] S3: Building an integrated optimization system: Utilizing optimization integration software to build automated optimization processes;

[0011] S4: Approximate model construction: Hundreds of sample points are generated using the Latin hypercube experimental design method, and a complete automated finite element analysis is run to obtain performance data;

[0012] S5: Implement combinatorial optimization: To balance global search capability and local convergence efficiency, a combinatorial optimization strategy is adopted.

[0013] Further, step S1 includes the following steps:

[0014] S11: The core design variables are the number of sections n, the unfolded angle α, the height H, width B, wall thickness t1, t2, and shaft length jj of the rectangular hollow section of the rod;

[0015] S12: The specified unfolded length L and the pivot dimension are known or associated parameters;

[0016] S13: Based on the geometric coordination relationship of the mechanism, establish a mathematical model of the length, angle and number of links of the links, so that the geometric shape of the mechanism can be uniquely determined by inputting a set of design variables.

[0017] Furthermore, the geometric compatibility formula in step S13 is:

[0018] .

[0019] Further, the constraints in step S11 are: the size of the envelope C1, C2, and C3; where C1 = (n+2) * H, C2 = L / (n * cos(α / 2)), and C3 = 2 * B + jj.

[0020] Further, step S2 includes the following steps:

[0021] S21: The script automatically creates beam element models, defines material properties, applies boundary conditions and loads, and automatically submits static analysis, linear buckling analysis, and modal analysis jobs;

[0022] S22: After the calculation is completed, the script automatically extracts the key performance indicators from the result file: total mass of the mechanism M, first natural frequency f, and buckling load factor.

[0023] Further, step S3 includes the following steps:

[0024] S31: Use the Simcode component to call batch commands, start the finite element software and run the parametric analysis script in step S2;

[0025] S32: Regarding the optimization objective and constraint settings, set the maximum fundamental frequency f as the optimization objective and the mass M and buckling factor as constraints, or set the minimum mass M as the optimization objective and the fundamental frequency f and buckling factor as constraints;

[0026] S33: The optimization software is responsible for managing the iterative updates of design variables: it runs the analysis process based on the current variable values, reads the output performance indicators, and calculates the next set of better design variables based on the built-in optimization algorithm until convergence.

[0027] Further, step S4 includes the following steps:

[0028] S41: Construct a high-precision approximate model of mass, fundamental frequency, and buckling factor using data;

[0029] S42: The optimal solution obtained by optimizing the approximate model is then verified and fine-tuned using the finite element model.

[0030] Further, step S5 includes the following steps:

[0031] S51: Employs simulated annealing or multi-island genetic algorithm for global optimization, conducting extensive searches within the design space to locate a near-global optimal region with superior performance;

[0032] S52: Using the global optimization result as the initial point, a gradient-type local optimization algorithm such as sequential quadratic programming is used to perform a fine search in the vicinity of the optimal region, and finally obtain a design scheme that satisfies all constraints and makes the objective function optimal.

[0033] The scissor-type space deployment mechanism configuration optimization method provided by this invention forms a complete, efficient, and reliable scissor-type space deployment mechanism configuration optimization design process through the deep integration of parametric modeling, automated simulation, and intelligent optimization algorithms. It solves the problems of low efficiency and insufficient optimization ability of traditional design methods, and is particularly suitable for the development of high-end equipment in the aerospace field that is extremely sensitive to performance and weight. Attached Figure Description

[0034] Figure 1 This is a flowchart illustrating the scissor-type space deployment mechanism configuration optimization method provided in Embodiment 1 of the present invention.

[0035] Figure 2 This is a schematic diagram of the planar structure of the scissor-type deployment mechanism of the present invention in its deployed state.

[0036] Figure 3 This is a schematic diagram of the cross-sectional shape of the scissor-type deployment mechanism of the present invention.

[0037] Figure 4 This is a schematic diagram of the scissor mechanism's enveloping size structure according to the present invention.

[0038] Figure 5 This is a flowchart illustrating the connection between parametric modeling, finite element software, and optimization software in this invention.

[0039] Figure 6This is a schematic diagram of the parametric driven modeling of Isight integrated with Abaqus in this invention.

[0040] Figure 7 This is a schematic diagram illustrating the integration of the approximate model with the DOE optimization process in Isight according to the present invention. Detailed Implementation

[0041] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0042] The terms "first," "second," "third," "fourth," etc., used in the specification and claims of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0043] Example 1

[0044] Figure 1 This is a flowchart illustrating the configuration optimization method for the scissor-type space deployment mechanism provided in Embodiment 1 of the present invention. Figure 2 This is a schematic diagram of the scissor-type deploying mechanism in its deployed state. Figure 3 This is a schematic diagram of the cross-sectional shape of the scissor-type deployment mechanism. Figure 4 This is a schematic diagram of the scissor mechanism's retractable envelope dimensions of the present invention. Figure 5 This is a flowchart illustrating the relationship between parametric modeling, finite element software, and optimization software in this invention. Figure 6 This is a schematic diagram illustrating the integration of Abaqus parametric-driven modeling with Isight according to the present invention. Figure 7 This diagram illustrates the integration of an approximate model with the DOE optimization process in Isight, as described in this invention. Please refer to... Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 , Figure 7 This invention provides a method for optimizing the configuration of a scissor-type spatial deployment mechanism, comprising the following steps:

[0045] S1: Parametric Modeling: Establish a parametric geometric model of the scissor-type deployment mechanism;

[0046] Specifically, step S1 of the present invention includes the following steps:

[0047] S11: The core design variables are the number of sections n (rounded to an integer, usually 5-10), the unfolded angle α (usually 20°-120°), the height H, width B, wall thickness t1, t2, and shaft length jj of the rectangular hollow section of the rod;

[0048] Among them, the constraints in step S11 are: the size of the envelope C1, C2, and C3; where C1 = (n+2)*H, C2 = L / (n*cos(α / 2)), and C3 = 2*B+jj;

[0049] S12: The specified unfolded length L and the pivot dimension are known or associated parameters;

[0050] S13: Based on the geometric coordination relationship of the mechanism, establish a mathematical model of the length, angle and number of links of the links, so that the geometric shape of the mechanism can be uniquely determined by inputting a set of design variables.

[0051] It should be noted that the geometric compatibility formula in step S13 is:

[0052] .

[0053] It should be noted that, according to geometric relationships, the scissor-type deployment mechanism consists of two sets of rods of different lengths: four short rods at the top and bottom and several sets of cross-shaped long rods connected by pivots in the middle. Here, 'a' represents the length of the four short rods at the top and bottom, and 'l' represents the length of the cross-shaped long rods connected by pivots in the middle.

[0054] S2: Automated Finite Element Analysis: Based on the parametric model in step S1, write scripts (such as Python scripts) to drive commercial finite element software (such as Abaqus) to achieve automated modeling and analysis;

[0055] Specifically, step S2 of the present invention includes the following steps:

[0056] S21: The script automatically creates beam element models, defines material properties, applies boundary conditions (such as root support) and loads (such as top surface tension), and automatically submits static analysis, linear buckling analysis, and modal analysis jobs;

[0057] S22: After the calculation is completed, the script automatically extracts the key performance indicators from the result file: total mass of the mechanism M, first natural frequency (fundamental frequency) f, and buckling load factor (buckling factor).

[0058] S3: Build an integrated optimization system: Utilize optimization integration software (such as Isight) to build automated optimization processes;

[0059] Specifically, step S3 of this invention includes the following steps:

[0060] S31: Use the Simcode component to call batch commands, start the finite element software and run the parametric analysis script in step S2;

[0061] S32: Regarding the optimization objective and constraint settings, set the maximum fundamental frequency f as the optimization objective and the mass M and buckling factor as constraints, or set the minimum mass M as the optimization objective and the fundamental frequency f and buckling factor as constraints;

[0062] S33: The optimization software is responsible for managing the iterative updates of design variables: it runs the analysis process based on the current variable values, reads the output performance indicators, and calculates the next set of better design variables based on the built-in optimization algorithm until convergence.

[0063] S4: Approximate model construction: Hundreds of sample points are generated using the Latin hypercube experimental design method, and a complete automated finite element analysis is run to obtain performance data;

[0064] Specifically, step S4 of this invention includes the following steps:

[0065] S41: Utilize data to construct high-precision approximate models (such as Kriging models and radial basis function models) regarding mass, fundamental frequency, and buckling factor; subsequent global optimization searches can be performed on the approximate models, which is extremely fast.

[0066] S42: The optimal solution obtained by optimizing the approximate model is then verified and fine-tuned through the finite element model; thereby significantly reducing the number of times time-consuming finite element analysis is directly called and improving the overall optimization efficiency.

[0067] S5: Implement combinatorial optimization: To balance global search capability and local convergence efficiency, a combinatorial optimization strategy is adopted.

[0068] Specifically, step S5 of this invention includes the following steps:

[0069] S51: Employs simulated annealing or multi-island genetic algorithm for global optimization, conducting extensive searches within the design space to locate a near-global optimal region with superior performance;

[0070] It should be noted that the optimization object in step S51 is the design variable S11: the number of sections n of the mechanism, the angle α of the unfolded state, the height H, width B, wall thickness t1, t2 and shaft length jj of the rectangular hollow section of the rod. The displacement of these design variables determines the geometry of the structure.

[0071] The optimization objective can be either to minimize the structural mass (with the fundamental frequency as a constraint) or to maximize the structural fundamental frequency (with mass as a constraint).

[0072] S52: Using the global optimization result as the initial point, a gradient-type local optimization algorithm such as sequential quadratic programming is used to perform a fine search in the vicinity of the optimal region, and finally obtain a design scheme that satisfies all constraints (such as the lower limit of the fundamental frequency, the lower limit of the buckling factor, and the limit of the contraction envelope size) and makes the objective function (such as the minimum mass or the maximum fundamental frequency) reach the optimum.

[0073] It should be noted that the optimization objects and optimization objectives in step S52 are the same as those in step S51: the number of sections n of the mechanism, the angle α of the unfolded state, the height H, width B, wall thickness t1, t2 and shaft length jj of the rectangular hollow section of the rod. These design variables determine the geometric form of the structure.

[0074] The optimization objective can be either to minimize the structural mass (with the fundamental frequency as a constraint) or to maximize the structural fundamental frequency (with mass as a constraint).

[0075] In summary, it is necessary to further explain that step S51 is a global optimization, which can search a large area globally and is suitable for finding regions where the optimal value may exist. However, its search direction is relatively random and may not find the optimal value in that region. Step S52 is a local optimization, which searches for the optimal value based on the gradient direction (according to the direction of increasing or decreasing directional derivative). In the entire process, if the gradient sign changes, it means that there is a stationary point where the gradient is 0 in that region, which is an extreme value (optimal value). However, finding the extreme value is affected by the initial conditions.

[0076] The following example illustrates this: If there are many mountain peaks, the global optimization in step S51 can find the highest peak, but it may not be able to find the correct route to the top. If the local optimization in step S52 is used, it can follow the gradient direction to reach the top of the initial mountain, but the initial mountain may not be the highest among all the peaks.

[0077] Therefore, this invention adopts a combination optimization of steps S51 and S52. First, through global optimization, the highest mountain is found, and then through local optimization, the summit of the highest mountain is reached.

[0078] This invention, through the deep integration of parametric modeling, automated simulation, and intelligent optimization algorithms, forms a complete, efficient, and reliable scissor-type space deployment mechanism configuration optimization design process. It solves the problems of low efficiency and insufficient optimization capability of traditional design methods, and is particularly suitable for the development of high-end equipment in the aerospace field that is extremely sensitive to performance and weight.

[0079] Example 2

[0080] Please refer to Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 , Figure 7 This embodiment takes a certain satellite scissor-type large deployable antenna mechanism as the optimization object, and the goal is to minimize the mass of the mechanism while meeting the requirements of fundamental frequency (≥0.7 Hz) and buckling stability.

[0081] 1. Determine design variables and constraints:

[0082] Design variables: number of sections n=6 (initial value, range 5-10), unfolding angle α=88.7° (initial value, range 20°-120°), section height H=15mm (range 11-25mm), section width B=40mm (range 25-40mm), wall thickness t1=1mm, t2=1mm (range 1-3mm);

[0083] Given parameters: unfolded length L = 6136 mm, pivot radius r = 8 mm, length 10 mm;

[0084] Constraints: Envelope dimensions C1, C2, C3; where C1 = (n+2)*H, C2 = L / (n*cos(α / 2)), C3 = 2*B+jj; fundamental frequency f ≥ 0.7 Hz; buckling factor > 1; variable range.

[0085] It should be noted that the constraints C1, C2, and C3 describe the envelope of the structure in the contracted state. The contracted envelope is a given input during the design process. It is analyzed with the input variables n, α, H, and B through formulas. If the design variables n, α, H, and B generated during the optimization process do not meet the constraints of C1, C2, and C3, then this set of design data does not meet the requirements and will be excluded.

[0086] 2. Parametric modeling script development:

[0087] Write an Abaqus script named beam_creat.py in Python; this function takes the above-mentioned design variables and known parameters, and then... The system automatically calculates the coordinates of all nodes based on their geometric relationships, creates beam elements and assigns rectangular section properties, sets materials (such as aluminum alloy), adds mass points at member connections, and applies root fixed constraints, top MPC constraints, and loads.

[0088] It should be noted that the scissor-lift deployment mechanism is cantilevered, depending on the application scenario, such as... Figure 2 As shown, one end connects to the spacecraft, corresponding to Figure 2 The two x-nodes on the right side indicate root constraints, while the other end is a free end suspended externally. External loads are applied to the free end via MPC multi-point constraints. Figure 2 The two nodes on the left side of the middle.

[0089] Among them, two x-nodes are used as root constraints to fix all degrees of freedom; the inter-link nodes actually have rotational joints and have a certain rotational stiffness. To facilitate finite element parametric modeling, the inter-link nodes adopt a common node rigid connection. Although the calculation results will cause some errors, this optimization is only to find the optimal structural configuration and will not affect the configuration optimization results.

[0090] 3. Development of automated finite element analysis scripts:

[0091] Add static analysis steps (calculate bending in the Y and Z directions), buckling analysis steps (apply 18N pressure in the X direction), and frequency analysis steps in sequence to the modeling script; write a post-processing script to extract the total mass, first-order buckling factor, and first-order natural frequency from the .odb results file and write them to the text file results.txt.

[0092] The coordinate system is defined as follows: Figure 2 As shown, the X direction is the length direction of the mechanism, the Y direction is the width direction of the mechanism, and the Z direction is determined according to the right-hand rule, that is, perpendicular to the paper and outward.

[0093] It should be noted that bending in the Y direction corresponds to Figure 7 Lateral bending, Z-axis bending corresponds to Figure 7 The curve in the normal direction corresponds to the curve in the X direction. Figure 7 Central axis buckling.

[0094] 4. Optimize process integration:

[0095] Build and optimize processes in the Isight software.

[0096] Using the "Simcode" component, the command line calls `abaqus cae noGUI=model_script.py` to run the modeling and solving scripts in a headless manner;

[0097] Use the "Text Input" and "Text Output" components to read the design variable file and the results file (results.txt) respectively;

[0098] Configure the optimization components, select "Multi-Island Genetic Algorithm (MIGA)" as the first-stage global optimizer, with a population size of 50, 5 islands, and run for a certain number of generations;

[0099] Set "Non-linear Quadratic Programming (NLPQL)" as the local optimizer for the second stage, using the optimal result from the first stage as the initial value.

[0100] 5. Approximate model-assisted optimization (optional high-efficiency solution):

[0101] Using the Design of Experiments (DOE) component in Isight, 120 sets of design variable samples were generated based on the Latin hypercube method;

[0102] These 120 sets of variables were used to perform real finite element calculations through the Simcode process described above, and all input-output data were collected.

[0103] Using the "Approximate Model" component, construct a Kriging model about quality, fundamental frequency, and buckling factor based on this data;

[0104] The optimization component is connected to the approximate model to quickly complete the global search; the optimal solution obtained from the approximate model is then verified and finally optimized locally through a real finite element process.

[0105] 6. Operation Optimization and Results:

[0106] Run the Isight optimization process; the final optimization results are shown in the table below:

[0107]

[0108] Under the premise of satisfying all constraints, the mass of the mechanism is significantly reduced (or the fundamental frequency is significantly increased) compared with the initial design, and the optimal combination of number of sections, development angle and cross-sectional dimensions is obtained.

[0109] As can be seen from the above description, the advantages of this invention are:

[0110] The scissor-type space deployment mechanism configuration optimization method provided by this invention forms a complete, efficient, and reliable scissor-type space deployment mechanism configuration optimization design process through the deep integration of parametric modeling, automated simulation, and intelligent optimization algorithms. It solves the problems of low efficiency and insufficient optimization ability of traditional design methods, and is particularly suitable for the development of high-end equipment in the aerospace field that is extremely sensitive to performance and weight.

[0111] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included 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 method for optimizing the configuration of a scissor-type spatial deployment mechanism, characterized in that, Includes the following steps: S1: Parametric Modeling: Establish a parametric geometric model of the scissor-type deployment mechanism; S2: Automated Finite Element Analysis: Based on the parametric model in step S1, write scripts to drive commercial finite element software to achieve automated modeling and analysis; S3: Building an integrated optimization system: Utilizing optimization integration software to build automated optimization processes; S4: Approximate model construction: Hundreds of sample points are generated using the Latin hypercube experimental design method, and a complete automated finite element analysis is run to obtain performance data; S5: Implement combinatorial optimization: To balance global search capability and local convergence efficiency, a combinatorial optimization strategy is adopted.

2. The scissor-type spatial deployment mechanism configuration optimization method according to claim 1, characterized in that, Step S1 includes the following steps: S11: The core design variables are the number of sections n, the unfolded angle α, the height H, width B, wall thickness t1, t2, and shaft length jj of the rectangular hollow section of the rod; S12: The specified unfolded length L and the pivot dimension are known or associated parameters; S13: Based on the geometric coordination relationship of the mechanism, establish a mathematical model of the length, angle and number of links of the links, so that the geometric shape of the mechanism can be uniquely determined by inputting a set of design variables.

3. The scissor-type spatial deployment mechanism configuration optimization method according to claim 2, characterized in that, The geometric compatibility formula in step S13 is: 。 4. The scissor-type spatial deployment mechanism configuration optimization method according to claim 2, characterized in that, The constraints in step S11 are: the envelope dimensions C1, C2, and C3; where C1 = (n+2) * H, C2 = L / (n * cos(α / 2)), and C3 = 2 * B + jj.

5. The scissor-type spatial deployment mechanism configuration optimization method according to claim 1, characterized in that, Step S2 includes the following steps: S21: The script automatically creates beam element models, defines material properties, applies boundary conditions and loads, and automatically submits static analysis, linear buckling analysis, and modal analysis jobs; S22: After the calculation is completed, the script automatically extracts the key performance indicators from the result file: total mass of the mechanism M, first natural frequency f, and buckling load factor.

6. The scissor-type spatial deployment mechanism configuration optimization method according to claim 1, characterized in that, Step S3 includes the following steps: S31: Use the Simcode component to call batch commands, start the finite element software and run the parametric analysis script in step S2; S32: Regarding the optimization objective and constraint settings, set the maximum fundamental frequency f as the optimization objective and the mass M and buckling factor as constraints, or set the minimum mass M as the optimization objective and the fundamental frequency f and buckling factor as constraints; S33: The optimization software is responsible for managing the iterative updates of design variables: it runs the analysis process based on the current variable values, reads the output performance indicators, and calculates the next set of better design variables based on the built-in optimization algorithm until convergence.

7. The scissor-type spatial deployment mechanism configuration optimization method according to claim 1, characterized in that, Step S4 includes the following steps: S41: Construct a high-precision approximate model of mass, fundamental frequency, and buckling factor using data; S42: The optimal solution obtained by optimizing the approximate model is then verified and fine-tuned using the finite element model.

8. The scissor-type spatial deployment mechanism configuration optimization method according to claim 1, characterized in that, Step S5 includes the following steps: S51: Employs simulated annealing or multi-island genetic algorithm for global optimization, conducting extensive searches within the design space to locate a near-global optimal region with superior performance; S52: Using the global optimization result as the initial point, a gradient-type local optimization algorithm such as sequential quadratic programming is used to perform a fine search in the vicinity of the optimal region, and finally obtain a design scheme that satisfies all constraints and makes the objective function optimal.

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