Composite material main box section parameter optimization design method

Abstract

The invention provides a composite material main box section parameter optimization design method. The method comprises the following steps that main design working conditions of strength constraint and rigidity constraint of a composite material main box section are screened out; randomly generating each optimization variable by taking the layer number as an optimization variable, and equally dividing the initial layer thickness corresponding to the optimization variable in a super layer form according to the initial layer sequence to obtain the initial layer thickness of each optimization variable; optimizing the initial layer thickness of each optimization variable to obtain a basic layer thickness; obtaining a layer laying library of the composite material main box section, wherein the layer laying library comprises a layer laying sequence corresponding to the layer laying thickness; judging whether the composite material main box section finite element model meets strength constraint and rigidity constraint requirements or not; if not, executing full-strength parameter fine adjustment until the constraint requirement is met; and performing thickness process constraint inspection on the composite material main box section to ensure that the thickness order difference of adjacent thickness partitions meets the design requirement.

Description

Technical Field

[0001] The present application belongs to the field of strength calculation, and in particular relates to a method for optimizing the design of parameters of a composite material main box section. Background Art

[0002] There are many difficulties in the optimization design of the main structure of composite materials, such as many optimization variables, complex strength design criteria, and strict process constraints. In recent years, many domestic and foreign scholars have conducted in-depth research on how to improve the efficiency and accuracy of differential and semi-analytical methods. As far as the algorithm itself is concerned, the analytical method is recognized as the most efficient and accurate of all sensitivity calculation methods, but it has not been widely studied and applied due to its complex theoretical derivation. In general, the analytical optimization algorithm needs to assemble the differential stiffness matrix of the structure through the unit differential stiffness matrix. However, the differential stiffness matrix is ​​generally difficult to analytically derive. Therefore, many commercial software avoid the analytical method and use the relatively simple theoretical differential method or semi-analytical method to solve the sensitivity.

[0003] Traditional methods have advantages such as perfect mathematical theory and fast convergence speed, but they also have significant disadvantages: the algorithm is relatively complex, has poor versatility, is only applicable to certain specific problems, and has strict requirements on the search space; the above methods are mostly local optimal methods, and the algorithm optimization results are closely related to the selection of the initial point. With the development of science and technology, in practical applications, the problems we encounter are becoming more and more complex, many of which cannot be expressed by functions at all, and the traditional methods cannot obtain satisfactory results. Therefore, in recent decades, various random algorithms have been proposed one after another, such as evolutionary algorithms, genetic algorithms, particle swarm algorithms, ant colony algorithms, immune algorithms, etc. This type of algorithm is based on group heuristic optimization algorithms, and its ideas come from the behavior of natural organisms. Compared with traditional methods, this type of method is a "black box" mechanism with good algorithm versatility, but slow convergence speed. The computational efficiency, convergence and rationality of the optimization results are far from engineering applications.

[0004] Therefore, it is desired to have a technical solution to overcome or at least alleviate at least one of the above-mentioned defects of the prior art. Summary of the invention

[0005] The purpose of the present application is to provide a method for optimizing the design of composite material main box section parameters to solve at least one problem existing in the prior art.

[0006] The technical solution of this application is:

[0007] A composite material main box section parameter optimization design method, comprising:

[0008] Step 1: Through finite element calculation, analysis and evaluation, select the main design conditions of strength constraints and stiffness constraints of the composite material main box section;

[0009] Step 2: Taking the number of plies as the optimization variable, randomly generating various optimization variables, and equally dividing the initial ply thickness corresponding to the optimization variable in the form of super layers according to the initial ply order, to obtain the initial ply thickness of each optimization variable;

[0010] Step 3: Using a two-level genetic algorithm, the initial ply thickness of each optimization variable is optimized to obtain the basic ply thickness;

[0011] Step 4: obtaining a ply library of the composite material master box section, wherein the ply library contains a ply sequence corresponding to a ply thickness;

[0012] Step 5: According to the basic ply thickness, the corresponding ply sequence is called from the ply library, a finite element model of the composite material main box section is constructed, and whether the finite element model of the composite material main box section meets the strength constraint and the stiffness constraint requirements is determined;

[0013] If not, perform full strength parameter fine-tuning until the constraint requirements are met;

[0014] Step 6: Based on the process constraints of the composite material structure design, the thickness process constraint check is performed on the composite material main box section to ensure that the thickness step difference between adjacent thickness partitions meets the design requirements.

[0015] In at least one embodiment of the present application, in step 2, the initial layering sequence is 45° / 90° / -45° / 0° / 0° / -45° / 90° / 45°.

[0016] In at least one embodiment of the present application, in step three, a two-level genetic algorithm is used to optimize the initial ply thickness of each optimization variable to obtain a basic ply thickness, including:

[0017] 3.1. Perform the first-level genetic algorithm optimization;

[0018] 3.11. In the first-level genetic algorithm optimization, determine the fitness f of the j-th optimization variable of the i-th population ij Get value;

[0019] 3.12. The first-level genetic algorithm optimization adopts the single-point crossover method, which randomly sets a crossover point in the individual coding string, and then exchanges part of the coding operators of the two paired individuals at this point;

[0020] 3.13. The first-level genetic algorithm optimization adopts the basic position mutation method, which randomly specifies the value of a certain gene locus in the individual code string for mutation operation with a certain mutation probability;

[0021] 3.2. Perform the second-level genetic algorithm optimization;

[0022] 3.21. In the second-level genetic algorithm optimization, determine the overall fitness s of the i-th population i Get value;

[0023] 3.22. The second-level genetic algorithm optimization uses arithmetic crossover method to generate new individuals through linear combinations of two individuals;

[0024] 3.23. In the second-level genetic algorithm optimization, the crossover probability is dynamically adjusted according to the state of evolution;

[0025] 3.24. The second-level genetic algorithm optimization adopts the real number mutation method, with multiple random positions, and the unit individual parameters at the corresponding positions are randomly taken as values ​​within the parameter boundary range;

[0026] 3.3. After the two-level genetic algorithm optimization is completed, the elite samples of the selected parent generation are retained.

[0027] In at least one embodiment of the present application, in step 3.11, the j-th optimization variable fitness f of the i-th population ij The values ​​are:

[0028]

[0029] Among them, x ij represents the safety margin of the jth unit individual in the i-th population, s i is the fitness value of the global deformation constraint of the ith population, and K1 and K2 are weighted coefficients.

[0030] In at least one embodiment of the present application, in step 3.21, the overall fitness s of the i-th population i The values ​​are:

[0031]

[0032] Among them, S is the displacement extreme value of the whole machine, S max With S min are the upper and lower limits of displacement constraints, K3 and K4 are weighting coefficients.

[0033] In at least one embodiment of the present application, in step 3.22, the new individual generated by the linear combination of two individuals includes:

[0034] Randomly generate two intersection locations, and then in the two populations and Perform arithmetic crossover between them, and the new individuals generated are:

[0035]

[0036]

[0037] in, For the preferred parent, is the offspring population with poor fitness, and α is the random weight coefficient.

[0038] In at least one embodiment of the present application, in step 3.23, the crossover probability is dynamically adjusted according to the state of evolution:

[0039]

[0040] Among them, f i,max is the maximum fitness value of the population, f i,ave is the average fitness value of the population.

[0041] In at least one embodiment of the present application, in step 3.3, the following strategy is adopted to retain the preferred parent elite samples:

[0042] Given two decision variables X W With X s , represent weight and stiffness constraints respectively;

[0043] If and only if When f i (X s ) all satisfy the constraints, then according to the weight X W Sort and find the optimal population;

[0044] If and only if If there is at least one f i (X s ) does not satisfy the constraints, and there is at least one f i (X W ) satisfies the constraint condition, then in the population that satisfies the constraint, according to the weight X W Sort and find the optimal population;

[0045] If and only if If there is no f i (X s ) satisfies the constraints, then according to f i (X s ), that is, they are sorted according to the procedure that approaches the stiffness constraint target.

[0046] In at least one embodiment of the present application, in step 4, obtaining a ply library of a composite material master box segment includes:

[0047] 4.1. Structural partitions are made for the composite main box section according to the composite wall panels and wing beams. In each structural partition, the ply ratio with the largest weight coefficient of ply thickness is selected according to the volume weight coefficients of different thickness partitions to ensure that each ply thickness corresponds to only one ply ratio;

[0048] 4.2. Count the ply ratios of each structural partition, and establish ply libraries corresponding to the ply ratios and thicknesses of composite wall panels and wing beams.

[0049] In at least one embodiment of the present application, in step 5, the parameter fine-tuning strategy for full-intensity parameter fine-tuning is:

[0050]

[0051] Among them, C i,j is the margin of the i-th variable under the j-th strength control criterion, M lower 、M upper are the lower and upper limits of the minimum margin of variable strength, respectively. Margin is the minimum margin under all strength constraints. Respectively represent the values ​​of the i-th variable before and after parameter adjustment.

[0052] In at least one embodiment of the present application, in step six, based on the process constraints of the composite material structure design, a thickness process constraint check is performed on the composite material main box section to ensure that the thickness step difference between adjacent thickness partitions meets the design requirements, including:

[0053] 6.1. According to the positional relationship of each structural partition, each thickness partition of the composite material main box section is coded in the plane;

[0054] 6.2. Starting from the maximum thickness of the structure, check the thickness difference of each partition step by step. If the thickness difference between the current encoded thickness partition and the adjacent thickness partition exceeds the thickness difference critical value, calculate the thickness monotonic gradient direction of the adjacent thickness partition;

[0055] 6.3. According to the monotonic gradient direction of thickness, the currently coded thickness partition is moved up to the next thickness level in the ply library until the thickness gradient meets the design requirements.

[0056] In at least one embodiment of the present application, the method further includes step seven, performing strength calculation check on the composite material main box segment, and outputting the strength margin under the current strength constraint condition.

[0057] The invention has at least the following beneficial technical effects:

[0058] The composite material main box section parameter optimization design method of the present application can meet the optimization design requirements of the composite material main box section structure ply ratio, ply thickness and other aspects under the strength constraints of the composite material strain, stability and other strength constraints. Combined with the ply library, it can realize the engineering design of the overall structure of the composite material that meets the process constraints, greatly improving the strength design efficiency of the composite material main box section structure and shortening the development cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 is a schematic diagram of a binary coding crossover according to an embodiment of the present application;

[0060] Figure 2 is a schematic diagram of binary variation of one embodiment of the present application;

[0061] Figure 3 It is a schematic diagram of the partition coding processing of the composite material main box section wall panel according to one embodiment of the present application. DETAILED DESCRIPTION

[0062] In order to make the purpose, technical scheme and advantages of the implementation of this application clearer, the technical scheme in the embodiment of this application will be described in more detail below in conjunction with the drawings in the embodiment of this application. In the drawings, the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. The described embodiments are part of the embodiments of this application, not all of them. The embodiments described below with reference to the drawings are exemplary and are intended to be used to explain this application, and should not be construed as limitations on this application. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. The embodiments of this application are described in detail below in conjunction with the drawings.

[0063] In the description of the present application, it should be understood that the terms "center", "longitudinal", "lateral", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the scope of protection of the present application.

[0064] The following is combined with Figures 1 to 3 This application is described in further detail.

[0065] The present application provides a method for optimizing the parameters of a composite material main box section, comprising the following steps:

[0066] Step 1: Through finite element calculation, analysis and evaluation, select the main design conditions of strength constraints and stiffness constraints of the composite material main box section;

[0067] Step 2: Taking the number of plies as the optimization variable, randomly generate each optimization variable, and divide the initial ply thickness corresponding to the optimization variable equally in the form of super layers according to the initial ply order to obtain the initial ply thickness of each optimization variable;

[0068] Step 3: Using a two-level genetic algorithm, the initial ply thickness of each optimization variable is optimized to obtain the basic ply thickness;

[0069] Step 4: obtaining a ply library of the composite material master box section, wherein the ply library contains a ply sequence corresponding to a ply thickness;

[0070] Step 5: According to the basic ply thickness, the corresponding ply sequence is called from the ply library, a finite element model of the composite material main box section is constructed, and it is determined whether the finite element model of the composite material main box section meets the strength constraint and the stiffness constraint requirements;

[0071] If not, perform full strength parameter fine-tuning until the constraint requirements are met;

[0072] Step 6: Based on the process constraints of the composite material structure design, the thickness process constraint check is performed on the composite material main box section to ensure that the thickness step difference between adjacent thickness partitions meets the design requirements.

[0073] The composite material main box section parameter optimization design method of the present application firstly, in step 1, a preliminary finite element calculation analysis and evaluation is performed to screen out the main design conditions of strength constraints and stiffness constraints. In step 2, the number of plies of each structural grid unit is used as the optimization variable, each optimization variable is randomly generated, and the ply thickness corresponding to the variable is equally divided in the form of super layers according to the initial ply sequence of 45° / 90° / -45° / 0° / 0° / -45° / 90° / 45°, and the corresponding stiffness attribute file is generated.

[0074] In the composite material main box section parameter optimization design method of the present application, in step three, a two-level genetic algorithm is used to perform fitness evaluation, selection, encoding and decoding, crossover and mutation at two levels, namely, local unit individuals and global overall variable sets; and based on the fitness evaluation, a memory bank is established to retain the elite group until the optimization converges and the basic ply thickness of the composite material main box section structure is optimized.

[0075] Specifically, in this embodiment, in step 3, a two-level genetic algorithm is used to optimize the initial ply thickness of each optimization variable to obtain a basic ply thickness, including:

[0076] 3.1. Perform the first-level genetic algorithm optimization;

[0077] 3.11. In the first-level genetic algorithm optimization, determine the fitness f of the j-th optimization variable of the i-th population ij Get value;

[0078] In step 3.11, the fitness of the jth optimization variable of the i-th population is f ij The values ​​are:

[0079]

[0080] Among them, x ij represents the safety margin of the jth unit individual in the i-th population, s i is the fitness value of the global deformation constraint of the ith population, and K1 and K2 are weighted coefficients.

[0081] 3.12. The first-level genetic algorithm optimization adopts the single-point crossover method, which randomly sets a crossover point in the individual coding string, and then exchanges part of the coding operators of the two paired individuals at this point, such as Figure 1 As shown;

[0082] 3.13. The first-level genetic algorithm optimization adopts the basic position mutation method, which randomly specifies the value of a certain gene locus in the individual code string for mutation operation with a certain mutation probability, such as Figure 2 As shown;

[0083] 3.2. Perform the second-level genetic algorithm optimization;

[0084] 3.21. In the second-level genetic algorithm optimization, determine the overall fitness s of the i-th population i Get value;

[0085] In step 3.21, the overall fitness s of the i-th population i The values ​​are:

[0086]

[0087] Among them, S is the maximum value of the whole aircraft displacement (wingtip deformation, torsion angle, etc.), S max With S min are the upper and lower limits of displacement constraints, K3 and K4 are weighting coefficients.

[0088] 3.22. The second-level genetic algorithm optimization uses arithmetic crossover method to generate new individuals through linear combinations of two individuals;

[0089] Crossover under real number coding is also called real-valued recombination, which mainly includes discrete recombination, intermediate recombination and arithmetic recombination.

[0090] In step 3.22, the new individuals generated by the linear combination of two individuals include:

[0091] Randomly generate two intersection locations, and then in the two populations and Perform arithmetic crossover between them, and the new individuals generated are:

[0092]

[0093] in, For the preferred parent, is the offspring population with poor fitness, and α is the random weight coefficient.

[0094] In this way, the offspring population with poor fitness can inherit the site genes of the preferred parent population. In the actual optimization process, the weight coefficient of the preferred parent population can be appropriately increased.

[0095] 3.23. In the second-level genetic algorithm optimization, the crossover probability is dynamically adjusted according to the state of evolution;

[0096] In order to make the algorithm search for the global optimum, in step 3.23, the crossover probability is dynamically adjusted according to the state of evolution:

[0097]

[0098] Among them, f i,max is the maximum fitness value of the population, f i,ave is the average fitness value of the population.

[0099] 3.24. The second-level genetic algorithm optimization adopts the real number mutation method, with multiple random positions, and the unit individual parameters at the corresponding positions are randomly taken as values ​​within the parameter boundary range;

[0100] 3.3. After the two-level genetic algorithm optimization is completed, the elite samples of the selected parent generation are retained.

[0101] In step 3.3, the following strategy is used to retain the selected parent elite samples:

[0102] Given two decision variables X W With X s , represent weight and stiffness constraints respectively;

[0103] If and only if When f i (X s ) all satisfy the constraints, then according to the weight X W Sort and find the optimal population;

[0104] If and only if If there is at least one f i (X s ) does not satisfy the constraints, and there is at least one fi (X W ) satisfies the constraint conditions, then sort according to the weight X in the population that satisfies the constraints W to find the optimal population;

[0105] If and only if at this time, if there does not exist an f i (X s ) that satisfies the constraint conditions, then sort according to f i (X s ), that is, sort according to the procedure of approaching the stiffness constraint target.

[0106] In each optimization iteration step, select a small number of the top K "elite" populations for preservation. During the optimization process, if a small number of populations do not meet the stiffness requirements, randomly replace the populations with poor fitness from the elites retained in the previous optimization iteration step; if the number of remaining populations that satisfy the global stiffness constraint is less than the number of parent populations (P), then randomly select M (M < K) populations from the elites other than the previous optimization step to randomly replace the existing populations.

[0107] For the parameter optimization design method of the composite material main box section of the present application, in step four, obtaining the ply library of the composite material main box section includes:

[0108] 4.1. Perform structural zoning on the composite material main box section according to the composite material panel and spar. In each structural zone, according to the volume weight coefficient of different thickness zones, screen out the ply ratio with the largest weight coefficient of the ply thickness to ensure that each ply thickness corresponds to only one ply ratio;

[0109] 4.2. Statistically analyze the ply ratios of each structural zone, and establish ply libraries corresponding to the ply ratios and ply thicknesses of the composite material panel and spar respectively.

[0110] For the parameter optimization design method of the composite material main box section of the present application, in step five, according to the initial optimized thickness, call the ply sequence table corresponding to the thickness in the ply library, and use the full-strength design to measure and fine-tune the parameters until the strength convergence requirement is met or the specified optimization iteration step is reached.

[0111] According to the optimized thickness, select the ply sequence table corresponding to the thickness in the ply library. If there is no ply thickness corresponding to the thickness, select the ply sequence table corresponding to the adjacent higher-level thickness.

[0112] After updating the model, perform full-strength parameter fine-tuning until the strength constraint requirement is met. The parameter fine-tuning strategy is as follows:

[0113]

[0114] Among them, C i,jis the margin of the i-th variable under the j-th strength control criterion, M lower 、M upper are the lower and upper limits of the minimum margin of variable strength, respectively. Margin is the minimum margin under all strength constraints. Respectively represent the values ​​of the i-th variable before and after parameter adjustment.

[0115] When the optimization meets the convergence condition, the parameter adjustment is stopped.

[0116] In the composite material main box section parameter optimization design method of the present application, in step 6, the thickness process constraint check is performed on the composite material main box section according to the process constraint of the composite material structure design to ensure that the thickness step difference of adjacent thickness partitions meets the design requirements, including:

[0117] 6.1. According to the positional relationship of each structural partition, each thickness partition of the composite material main box section is coded in the plane;

[0118] 6.2. Starting from the maximum thickness of the structure, check the thickness difference of each partition step by step. If the thickness difference between the current encoded thickness partition and the adjacent thickness partition exceeds the thickness difference critical value, calculate the thickness monotonic gradient direction of the adjacent thickness partition;

[0119] 6.3. According to the monotonic gradient direction of thickness, the currently coded thickness partition is moved up to the next thickness level in the ply library until the thickness gradient meets the design requirements.

[0120] The thickness in the layer library is divided into multiple thickness steps from high to low. The thickness is carried up to a higher thickness step.

[0121] In a preferred embodiment of the present application, the thickness partitions of each composite integral structure are encoded within the plane according to the positional relationship of each structural partition, as shown in the following formula: Figure 3 shown.

[0122]

[0123] Start from the maximum thickness of the structure and check the thickness difference of each partition step by step. If A i,j The thickness of the adjacent structure exceeds the critical value of step difference △T, such as A in the span direction i-1,j -A i,j ≥△T, first calculate the gradient change direction of the thickness of the adjacent structural units, such as Figure 3 As shown; according to the calculated thickness monotonic gradient direction, let A i,j In the layer library, move up one level of thickness until A is met. i-1,j -A i,j ≤△T、A i,j-1 -A i,j ≤△T、Ai-1,j-1 -A i,j ≤△T.

[0124] The composite material main box section parameter optimization design method of the present application further includes step seven, after the processability check of each variable thickness is completed, of performing strength calculation check on the composite material main box section and outputting the strength margin under the current strength constraint condition.

[0125] The composite material main box section parameter optimization design method of this application can solve the optimization problem of composite material main box section structure with many variables, complex constraints and strict process requirements. It can meet the optimization design requirements of composite material main box section structure ply ratio, ply thickness and other strength constraints under composite material strain, stability and other strength constraints, and combined with the ply library, it can realize the engineering design of the overall structure of composite materials that meets process constraints, greatly improving the strength design efficiency of composite material main box section structure and shortening the development cycle.

[0126] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A composite material main box section parameter optimization design method, characterized in that: include: Step 1: Screen out the main design conditions of strength constraints and stiffness constraints of the composite main box section through finite element calculation analysis and evaluation; Step 2: Taking the number of plies as the optimization variable, randomly generating various optimization variables, and equally dividing the initial ply thickness corresponding to the optimization variable in the form of super layers according to the initial ply order, to obtain the initial ply thickness of each optimization variable; Step 3: Using a two-level genetic algorithm, the initial ply thickness of each optimization variable is optimized to obtain the basic ply thickness; Step 4: obtaining a ply library of the composite material master box section, wherein the ply library contains a ply sequence corresponding to a ply thickness; Step 5: According to the basic ply thickness, the corresponding ply sequence is called from the ply library, a finite element model of the composite material main box section is constructed, and whether the finite element model of the composite material main box section meets the strength constraint and the stiffness constraint requirements is determined; If not, perform full strength parameter fine-tuning until the constraint requirements are met; Step 6: Based on the process constraints of the composite material structure design, the thickness process constraint check is performed on the composite material main box section to ensure that the thickness step difference between adjacent thickness partitions meets the design requirements.

2. The composite material main box segment parameter optimization design method according to claim 1 is characterized in that: In step 2, the initial ply sequence is 45° / 90° / -45° / 0° / 0° / -45° / 90° / 45°.

3. The composite material main box section parameter optimization design method according to claim 1 is characterized in that: In step 3, a two-level genetic algorithm is used to optimize the initial ply thickness of each optimization variable to obtain the basic ply thickness, including: 3.

1. Perform the first-level genetic algorithm optimization; 3.

11. In the first-level genetic algorithm optimization, determine the fitness f of the j-th optimization variable of the i-th population ij Get value; 3.

12. The first-level genetic algorithm optimization adopts the single-point crossover method, which randomly sets a crossover point in the individual coding string, and then exchanges part of the coding operators of the two paired individuals at this point; 3.

13. The first-level genetic algorithm optimization adopts the basic position mutation method, which randomly specifies the value of a certain gene locus in the individual code string for mutation operation with a certain mutation probability; 3.

2. Perform the second-level genetic algorithm optimization; 3.

21. In the second-level genetic algorithm optimization, determine the overall fitness s of the i-th population i Get value; 3.

22. The second-level genetic algorithm optimization uses arithmetic crossover method to generate new individuals through linear combinations of two individuals; 3.

23. In the second-level genetic algorithm optimization, the crossover probability is dynamically adjusted according to the state of evolution; 3.

24. The second-level genetic algorithm optimization adopts the real number mutation method, with multiple random positions, and the unit individual parameters at the corresponding positions are randomly taken as values ​​within the parameter boundary range; 3.

3. After the two-level genetic algorithm optimization is completed, the elite samples of the selected parent generation are retained.

4. The composite material main box section parameter optimization design method according to claim 3 is characterized in that: In step 3.11, the fitness of the jth optimization variable of the i-th population is f ij The values ​​are: Among them, x ij represents the safety margin of the jth unit individual in the ith population, s i is the fitness value of the global deformation constraint of the ith population, and K1 and K2 are weighted coefficients.

5. The composite material main box section parameter optimization design method according to claim 4 is characterized in that: In step 3.21, the overall fitness s of the i-th population i The values ​​are: Among them, S is the maximum displacement of the whole machine, S max With S min are the upper and lower limits of displacement constraints, K3 and K4 are weighting coefficients.

6. The composite material main box section parameter optimization design method according to claim 5 is characterized in that: In step 3.22, the new individuals generated by the linear combination of two individuals include: Randomly generate two intersection locations, and then in the two populations and Perform arithmetic crossover between them, and the new individuals generated are: in, To be the preferred parent, is the offspring population with poor fitness, and α is the random weight coefficient.

7. The composite material main box section parameter optimization design method according to claim 6 is characterized in that: In step 3.23, the crossover probability is dynamically adjusted according to the state of evolution: Among them, f i,max is the maximum fitness value of the population, f i,ave is the average fitness value of the population.

8. The composite material main box section parameter optimization design method according to claim 7 is characterized in that: In step 3.3, the following strategy is used to retain the selected parent elite samples: Given two decision variables X W With X s , represent weight and stiffness constraints respectively; If and only if When f i (X s ) all satisfy the constraints, then according to the weight X W Sort and find the optimal population; If and only if If there is at least one f i (X s ) does not satisfy the constraints, and there is at least one f i (X W ) satisfies the constraint condition, then in the population that satisfies the constraint, according to the weight X W Sort and find the optimal population; If and only if If there is no f i (X s ) satisfies the constraints, then according to f i (X s ), that is, they are sorted according to the procedure that approaches the stiffness constraint target.

9. The composite material main box section parameter optimization design method according to claim 8, characterized in that: In step 4, the ply library of the composite material master box section is obtained, including: 4.

1. Structural partitions are made for the composite main box section according to the composite wall panels and wing beams. In each structural partition, the ply ratio with the largest weight coefficient of ply thickness is selected according to the volume weight coefficients of different thickness partitions to ensure that each ply thickness corresponds to only one ply ratio; 4.

2. Count the ply ratios of each structural partition, and establish ply libraries corresponding to the ply ratios and thicknesses of composite wall panels and wing beams.

10. The composite material main box section parameter optimization design method according to claim 9, characterized in that: In step 5, the parameter fine-tuning strategy for full-intensity parameter fine-tuning is: Among them, C i,j is the margin of the i-th variable under the j-th strength control criterion, M lower 、M upper are the lower and upper limits of the minimum margin of variable strength, respectively. Margin is the minimum margin under all strength constraints. Respectively represent the values ​​of the i-th variable before and after parameter adjustment.

11. The composite material main box section parameter optimization design method according to claim 10, characterized in that: In step 6, based on the process constraints of the composite material structure design, the thickness process constraint check is performed on the composite material main box section to ensure that the thickness step difference between adjacent thickness partitions meets the design requirements, including: 6.

1. According to the positional relationship of each structural partition, each thickness partition of the composite material main box section is coded in the plane; 6.

2. Starting from the maximum thickness of the structure, check the thickness difference of each partition step by step. If the thickness difference between the current encoded thickness partition and the adjacent thickness partition exceeds the thickness difference critical value, calculate the thickness monotonic gradient direction of the adjacent thickness partition; 6.

3. According to the monotonic gradient direction of thickness, the currently coded thickness partition is moved up to the next thickness level in the ply library until the thickness gradient meets the design requirements.

12. The composite material main box section parameter optimization design method according to claim 11, characterized in that: The method also includes step seven, performing strength calculation and checking on the composite material main box section, and outputting the strength margin under the current strength constraint condition.

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