CN114722509B - Conical reinforced cabin layering sequence optimization method based on fiber continuous model - Google Patents

Conical reinforced cabin layering sequence optimization method based on fiber continuous model Download PDF

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CN114722509B
CN114722509B CN202210645363.5A CN202210645363A CN114722509B CN 114722509 B CN114722509 B CN 114722509B CN 202210645363 A CN202210645363 A CN 202210645363A CN 114722509 B CN114722509 B CN 114722509B
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周奇
林泉
金朋
黄旭丰
吴金红
胡杰翔
刘华坪
王胜一
郑建国
夏凉
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Huazhong University of Science and Technology
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Abstract

The invention provides a conical reinforced cabin layering sequence optimization method based on a fiber continuous model, which comprises the following steps of: s1: acquiring design variables, design spaces and performance indexes to be optimized of the composite material conical reinforced cabin layer to be optimized; s2: constructing a fiber continuity model based on the region sequence; s3: establishing a composite material conical reinforced cabin layer laying finite element model according to the constructed fiber continuity model based on the region sequence; s4: according to the finite element model of the layering of the composite material conical reinforced cabin, obtaining the layering quality of the composite material conical reinforced cabin through finite element analysis; s5: establishing an optimization model, and solving the optimization model by adopting a genetic algorithm; s6: and carrying out finite element analysis according to the erecting working condition and the axial pressure working condition of the composite material conical reinforced cabin, and verifying the optimization result.

Description

Conical reinforced cabin layering sequence optimization method based on fiber continuous model
Technical Field
The invention relates to the technical field of composite material conical reinforced cabin layering optimization design, in particular to a conical reinforced cabin layering sequence optimization method based on a fiber continuous model.
Background
The field of aerospace has been one of the major battlefields in fierce pursuit of countries throughout the twentieth century. The carrier rocket as an important space carrier represents the medium strength and firm foundation of national space technology. The conical reinforced cabin is widely applied to a carrier rocket, and besides various controllers and sensors can be mounted, the conical reinforced cabin is more important in structural function of bearing and transmitting load. As a thin-wall structure, the reinforced cabin can be buckled and unstable under the action of axial pressure. Therefore, stability analysis and design of the conical reinforced thin shell structure are indispensable. In addition, as an important aerospace structure, the tapered reinforced cabin has the constant theme of light weight under the condition of meeting various load constraints, and the composite material has wide application in aerospace vehicles due to the excellent performance of the composite material. In order to fully utilize the wide design space and the structure weight reduction potential of the fiber reinforced composite material, the structure of the composite material needs to be optimally designed by applying a modern structure optimization design technology. However, the high tailorability of the composite material structure brings wide design space and great difficulty to practical engineering application, and the problem of optimizing fiber continuity between areas is one of the problems. Therefore, how to ensure the continuity of the ply fibers among the design areas in the ply optimization design process is a content of important research required by the optimization design of the composite material ply structure.
Scholars at home and abroad develop a large amount of researches on the buckling analysis and the laying optimization design of the conical reinforced thin shell structure made of the composite material, but the existing researches are not complete enough on the stability research of the reinforced shell and the multi-region optimization problem under the multi-working-condition constraint condition, and the following defects mainly exist: the buckling research on the conical laminated shell and the reinforced shell mainly focuses on metal materials, and the research objects aiming at the buckling of the composite material structure are limited to reinforced straight plates and reinforced curved plates, so that the research on the conical shell of the composite material is not complete; the buckling load of the composite material conical reinforced shell is influenced by parameters such as a taper angle, a length-diameter ratio and the like, the structural buckling forms of the same reinforced shell model under different layering schemes are different, and the buckling analysis research of the reinforced shell under the condition of different rigidity matching is incomplete; a large amount of optimization design researches are conducted at home and abroad aiming at the conical reinforced shell structure made of the composite material, the buckling load is taken as an important constraint condition, multiple working conditions and multiple design variable areas are considered in the optimization process, and the research on the fiber continuity model is less in the optimization design process.
Disclosure of Invention
In view of the above, the invention provides a method for optimizing the layering sequence of a conical reinforcement cabin, which is based on a fiber continuity model, always meets the requirement on fiber continuity and minimizes the quality of the layering of the composite material conical reinforcement ribs.
The technical scheme of the invention is realized as follows: the invention provides a conical reinforced cabin layering sequence optimization method based on a fiber continuous model, which comprises the following steps of:
s1: acquiring design variables, design spaces and performance indexes to be optimized of the composite material conical reinforced cabin layer to be optimized; the design variables comprise a skin laying layer and the section size of a rib which form a conical reinforced cabin laying layer; the design space is a space formed by the variation ranges of the design variables; the performance index to be optimized is the quality of the composite material conical reinforcing rib layering;
s2: constructing a fiber continuity model based on the region sequence;
s3: establishing a composite material conical reinforced cabin layer laying finite element model according to the constructed fiber continuity model based on the region sequence;
s4: obtaining the layering quality of the conical reinforced cabin of the composite material through finite element analysis according to a finite element model of the layering of the conical reinforced cabin of the composite material;
s5: establishing an optimization model, and solving the optimization model by adopting a genetic algorithm;
s6: and carrying out finite element analysis according to the erecting working condition and the axial pressure working condition of the composite material conical reinforced cabin, and verifying the optimization result.
Based on the above technical solution, preferably, in the step S2, the step of constructing the fiber continuity model based on the region sequence is to add a ply angle sequence and a ply region length sequence on the basis of a classical guide type continuous model, and define the laminate of the composite material by the ply angle sequence and the ply region length sequence:
Figure 299542DEST_PATH_IMAGE001
(ii) a Wherein
Figure 482261DEST_PATH_IMAGE002
Representing a sequence of ply angles of the laminate;
Figure 589895DEST_PATH_IMAGE003
representing a sequence of ply region lengths; each combination corresponding to a respective single-layered laid area,
Figure 250683DEST_PATH_IMAGE004
represents the first monolayer of the laminate to
Figure 268318DEST_PATH_IMAGE005
At an angle lay
Figure 356359DEST_PATH_IMAGE006
An area; the right side of the vertical line indicates the number of paved areas corresponding to each mat.
On the basis of the above technical scheme, preferably, the step S4 of obtaining the quality of the composite material conical reinforced cabin lay-up through finite element analysis means static analysis and buckling analysis; the static analysis comprises the calculation of the quality and the maximum strain of a composite material conical reinforced cabin layer finite element model, and the buckling analysis comprises the calculation of a buckling factor.
Preferably, the step S5 of establishing an optimization model and solving the optimization model by using a genetic algorithm is to establish the following optimization model:
Figure 216868DEST_PATH_IMAGE007
；
Figure 415768DEST_PATH_IMAGE008
；
Figure 287909DEST_PATH_IMAGE009
(ii) a Wherein
Figure 546852DEST_PATH_IMAGE010
Is the model mass;
Figure 894657DEST_PATH_IMAGE011
the maximum strain of the unit is obtained after the unit simplification is carried out on the composite material conical reinforced cabin spreading finite element model;
Figure 834931DEST_PATH_IMAGE012
to the allowable strain;
Figure 623896DEST_PATH_IMAGE013
is a buckling factor;
Figure 381636DEST_PATH_IMAGE007
obtaining the condition of minimum model quality value;
the method for solving the optimization model by adopting the genetic algorithm comprises the following steps:
s501: coding the design variables;
s502: determining a fitness function;
s503: generating an initial population according to design variables and a design space, wherein individuals in the initial population represent one possible composite material conical reinforced cabin layer design;
s504: decoding operation, namely calculating the size of a performance index to be optimized corresponding to the initialized population and calculating a corresponding fitness function value;
s505: generating a new population by simultaneously selecting a selection operator, a crossover operator and a mutation operator, and calculating a corresponding fitness function value;
s506: and setting an optimization iteration termination condition, judging whether the current optimization meets the termination condition, if so, outputting the obtained optimal individual, and if not, returning to execute the step S505.
Preferably, in the step of solving the optimization model by the genetic algorithm, the design variables are coded by integer codes, the chromosome code of each individual is formed by splicing two series of integer arrays, the first array of the chromosome codes represents the laying sequence of each single layer of the laminated plate, and the second array represents the laying position of each single layer; the decoding operation is to reduce the encoded chromosome to the size of the design variable value.
Preferably, the fitness function is determined by punishing the objective function by taking the constraint violation quantity of the intensity constraint and the stability constraint of the individuals in the population as a penalty function, and the mathematical model of the fitness function is as follows:
Figure 888841DEST_PATH_IMAGE014
(ii) a Wherein
Figure 367227DEST_PATH_IMAGE015
Is the maximum strain violation;
Figure 276277DEST_PATH_IMAGE016
the buckling violation quantity;
Figure 939339DEST_PATH_IMAGE017
；
Figure 933840DEST_PATH_IMAGE018
。
preferably, the specific steps of generating a new population through the selection operator, the crossover operator and the mutation operator are as follows:
selecting an operator: the tournament selection method is adopted, and an optimal reservation strategy is applied, and the method comprises the following specific steps: will be firstnGeneration group andntemporary populations generated after generation populations are crossed and mutated form an expanded population which is arranged from high fitness to low fitness, and a plurality of individuals with high fitness are selected to form a groupn+1 generation population;
and (3) a crossover operator: adopting single-point crossing, firstly obtaining even number of individual populations by initializing the populations, and then determining chromosomes participating in crossing according to a head-tail pairing method;
mutation operator: judging whether mutation operation occurs or not according to the occurrence probability of a mutation operator, if the mutation operation is determined to occur, determining two mutation positions in two parts of the chromosome respectively, and then completing the mutation operation; and obtaining the next generation population by simultaneously mixing and using the three operators.
Compared with the prior art, the tapered reinforced cabin layering sequence optimization method based on the fiber continuous model has the following beneficial effects:
(1) in the scheme, for the problem of fiber continuity constraint of multi-region optimization, a fiber continuity model method based on a region sequence is adopted to ensure that a layering structure always meets the requirement of fiber continuity in the optimization process; and constructing a genetic algorithm for optimizing the layering of the composite material conical reinforced cabin, wherein the objective function is that the layering quality of the composite material conical reinforced cabin is minimum, and the layering sequence of the composite material conical reinforced cabin is optimized by taking a strength condition and a stability condition as constraints.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of steps of a method for optimizing a layering sequence of a tapered reinforced cabin based on a fiber continuity model according to the invention;
FIG. 2 is a schematic diagram of a fiber continuity model based on a zone sequence in the tapered reinforced cabin layering sequence optimization method based on the fiber continuity model according to the present invention;
FIG. 3 is a finite element model of the ply of the conical reinforced cabin made of composite materials according to the method for optimizing the ply sequence of the conical reinforced cabin based on the fiber continuity model;
FIG. 4 is a cross-sectional shape of a rib in an embodiment of a method for optimizing a ply sequence of a tapered stiffened cabin based on a fiber continuous model according to the present invention;
FIG. 5 is a schematic diagram of a reinforced cabin erecting working condition and an axial pressure working condition of the tapered reinforced cabin layering sequence optimization method based on the fiber continuity model;
FIG. 6 is a schematic diagram of design variable numbering of skins and ribs by the tapered stiffened cabin layering sequence optimization method based on the fiber continuity model;
FIG. 7 is the codes of individual chromosomes in the method for optimizing the layering sequence of the tapered reinforced cabin based on the fiber continuity model;
FIG. 8 is a schematic diagram of individual cross-pairing in the tapered stiffened cabin layering sequence optimization method based on the fiber continuity model according to the present invention;
FIG. 9 is a schematic diagram of chromosome coding before and after crossing of a conical reinforced cabin layering sequence optimization method based on a fiber continuity model according to the invention;
FIG. 10 is a schematic diagram of chromosome coding before and after mutation of a tapered reinforced cabin layering sequence optimization method based on a fiber continuity model according to the present invention;
FIG. 11 is an interactive flow between an optimization program and analysis software of the tapered reinforced cabin layering sequence optimization method based on the fiber continuity model according to the present invention;
FIG. 12 is a curve showing the variation of the optimal individual and population average fitness with the number of iterations of the tapered reinforced cabin layering sequence optimization method based on the fiber continuity model of the present invention;
FIG. 13 is a cloud chart of maximum strain distribution under the optimal individual standing-vertical working condition of the tapered reinforced cabin layering sequence optimization method based on the fiber continuity model of the present invention;
FIG. 14 is a cloud chart of maximum strain distribution under the optimal individual axial pressure working condition of the conical reinforced cabin layering sequence optimization method based on the fiber continuity model.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
As shown in fig. 1, the invention provides a method for optimizing the layering sequence of a tapered reinforced cabin based on a fiber continuous model, which specifically comprises the following steps:
s1: obtaining design variables, design space and performance indexes to be optimized of the layering of the conical reinforced cabin made of the composite material to be optimized; the design variables comprise skin ply and rib section size for forming the conical reinforced cabin ply; the design space is a space formed by the variation range of the design variables; the performance index to be optimized is the quality of the composite material conical reinforcing rib layering;
s2: constructing a fiber continuity model based on the region sequence;
the specific content is as follows: adding a ply angle sequence and a ply area length sequence on the basis of a classical guide type continuous model, and defining a laminated plate of the composite material through the ply angle sequence and the ply area length sequence:
Figure 215917DEST_PATH_IMAGE001
(ii) a Wherein
Figure 979474DEST_PATH_IMAGE019
Representing a sequence of ply angles of the laminate;
Figure 79017DEST_PATH_IMAGE020
representing a sequence of ply region lengths; each combination corresponding to a respective single-layered laid area,
Figure 498497DEST_PATH_IMAGE004
represents the first monolayer of the laminate to
Figure 381002DEST_PATH_IMAGE005
At an angle lay
Figure 326961DEST_PATH_IMAGE006
An area; the right side of the vertical line indicates the number of paved areas corresponding to each mat. The classical guided continuous model is common knowledge in the art and will not be described in detail herein. As shown in the upper left-hand side of FIG. 2, a 6-zone composite lamination is shownThe structure of the panel; the six zones are arranged from left to right; the content of the corresponding fiber continuity model based on the region sequence is (45-30030) and 4590 | 513064); the sequences of the respective parts correspond to the following meanings: the left side of the vertical line represents the ply angle sequence of the oriented laminate; the right side of the vertical line represents the length sequence of the layering area; the 1 st single layer is paved with 5 areas at 45 degrees, namely 5 th, 4 th, 3 th, 2 th and 1 st areas; the 2 nd single layer is paved with 1 area at minus 30 degrees, which is the 5 th area; 3 areas, namely 5 th, 4 th and 3 rd areas, are paved on the 3 rd single layer at 0 degrees; the right side of the vertical line is 0, which means that the corresponding single layer is not laid, namely the 4 th single layer is deleted; 6 areas are laid on the 5 th single layer at an angle of minus 45 degrees and are the 5 th, 4 th, 3 th, 2 th, 1 th and 6 th areas; the 6 th monolayer has 4 zones laid at 90 °, which are zones 5, 4, 3, 2. The upper right and lower diagrams of fig. 2 show a specific realization of the structure of the region-sequence-based fiber continuity model of the upper left part of fig. 2. The 0 ° direction, the default initial direction, is here the direction of the prevailing stress along the axial direction of the tapered stiffened cabin lay.
S3: establishing a composite material conical reinforced cabin layer laying finite element model according to the constructed fiber continuity model based on the region sequence; specifically, a finite element model of the composite material conical reinforced cabin layer can be established by using Nastran software. As shown in FIG. 3, the large end radius R of the finite element model of the composite material conical reinforcement cabin layer 1 =600mm, radius of the small end R 2 =300mm, axial length L =600 mm. 3 axial ribs are arranged at intervals along the axial extension direction of the composite material conical reinforced cabin layer laying finite element model, 8 radial ribs are uniformly distributed along the radial direction of the composite material conical reinforced cabin layer laying finite element model, and the interval angle between every two adjacent radial ribs is 45 degrees; and the outer surfaces of the axial ribs and the radial ribs are provided with skins. The skin is divided into 32 zones by the axial and radial ribs. The axial ribs or the radial ribs are in a beam unit simplification mode and are in direct node coupling with the skin unit.
All the axial ribs and the radial ribs can adopt T-shaped sections, the T-shaped section parameters are shown in figure 4, the axial ribs and the radial ribs comprise flanges and webs, and T is 1 、t 2 The thickness of the flanges and webs, respectively, is determined by the number of plies of composite material of each rib.L 1 The width of the edge strip is the width of the edge strip,L 2 web height in the figure, web lengthL 2 =5 mm. The flanges are connected in abutment with the skin in the width direction, so that the actual T-section has the same curvature as the skin in the region of the flanges. Due to the cone angle of the composite structure of the skin and the ribs, the cross-sectional radius of curvature of the skin at the large end is 60 mm and the cross-sectional radius of curvature at the small end is 30 mm, whereas in the non-end position the cross-sectional radius of curvature of the skin varies with the cross-sectional position, i.e. the radius of curvature of the skin in the non-end position is between 30 mm and 60 mm. The right part of the figure 4 is a space occupation indication of a certain T-shaped axial rib or a certain T-shaped radial rib bead on the circumference of the skin, and a space occupation angle
Figure 535089DEST_PATH_IMAGE021
。
In the scheme, the skin, the axial ribs and the radial ribs are all made of T700 carbon fiber composite materials. The material properties are given in the table below.
Figure 176286DEST_PATH_IMAGE023
In table E 1 、E 2 And G 12 Respectively, the modulus of elasticity along the fiber direction, the modulus of elasticity perpendicular to the fiber direction, and the shear modulus; x T Is the tensile strength in the fibre direction, Y T Is the tensile strength perpendicular to the fiber direction; x C Is the compressive strength along the fibre direction, Y C Is the compressive strength perpendicular to the fiber direction;
Figure 862482DEST_PATH_IMAGE024
represents the poisson's ratio; s 12 The shear strength is indicated.
The carrying working conditions of the composite material conical reinforced cabin layer finite element model are divided into two types: erecting and axial compression working conditions. As shown in figure 5, for the vertical working condition, the large end of the composite material conical reinforced cabin layer finite element model is fixedly supported, and the circle center of the small end is subjected to M application through MPC multi-point constraint Z =110000N·M bending moment and F =70000N shearing force, wherein the force and the bending moment act on the small end node through a circle center control point of the multipoint constraint RBE 2; and for the axial compression working condition, the large end of the composite material conical reinforced cabin layering finite element model is fixedly supported, and the small end face is applied with an axial compression load of F = 80000N. The multi-point constraint RBE2 is common knowledge in the art and will not be described in detail herein.
S4: obtaining the layering quality of the conical reinforced cabin of the composite material through finite element analysis according to a finite element model of the layering of the conical reinforced cabin of the composite material; finite element analysis can be performed by those skilled in the art using the Nastran software, and the finite element analysis method can be equally understood by referring to the field, and the description is not detailed.
Obtaining the layering quality of the conical reinforced cabin of the composite material through finite element analysis, namely through static analysis and buckling analysis; the static analysis comprises the step of calculating the quality and the maximum strain of a finite element model of the composite material conical reinforced cabin layer, and the buckling analysis comprises the step of calculating a buckling factor.
S5: establishing an optimization model, and solving the optimization model by adopting a genetic algorithm; the specific process is as follows: the following optimization model is established:
Figure 662948DEST_PATH_IMAGE025
；
Figure 41976DEST_PATH_IMAGE026
；
Figure 170469DEST_PATH_IMAGE027
(ii) a Wherein
Figure 660357DEST_PATH_IMAGE028
Is the model mass;
Figure 49750DEST_PATH_IMAGE011
the maximum strain of the unit is obtained after the unit simplification is carried out on the composite material conical reinforced cabin spreading finite element model;
Figure 599680DEST_PATH_IMAGE012
(ii) is an allowable strain;
Figure 215469DEST_PATH_IMAGE029
is a buckling factor;
Figure 509047DEST_PATH_IMAGE007
in order to obtain the condition of minimum model quality value, the finite element analysis result file can be directly read according to Nastran software;
the design variables of the optimization model include the skin ply and the section size of the ribs, and as shown in FIG. 6, the skin is divided into 32 areas; the number of axial ribs is 3, the number of radial ribs is 8, and the total design variables is 48.
The constraint conditions of the optimization model comprise two aspects of strength constraint and stability constraint. For the strength constraint of the composite material, the engineering generally adopts a method of constraining strain, so that the maximum strain of the material when carrying the load does not exceed the allowable strain value. Therefore, the strain design constraint in this example is that the operating strain in the three directions of the outermost two layers does not exceed the design allowable strain. The specific magnitude of the allowable strain is designed as follows: allowable compressive strain
Figure 18526DEST_PATH_IMAGE030
Allowable tensile strain
Figure 473778DEST_PATH_IMAGE031
Allowable shear strain
Figure 576863DEST_PATH_IMAGE032
。
On the other hand, buckling is one of the main failure modes of the composite material conical reinforcement cabin layering finite element model and must be considered in the optimization process. Considering that the linear buckling operation is simple and the analysis efficiency is high, the finite element linear buckling load is taken as a constraint condition in the optimization process.
The step of solving the optimization model by adopting the genetic algorithm comprises the following steps:
s501: coding the design variables;
the encoding and decoding of the genetic algorithm is a process of mapping the feasible solution space and the genetic algorithm search space. For the problem of layering optimization of the composite material conical reinforced cabin, the integer coding method can enable layering angle variables to correspond to integer coding values one by one, conversion between binary numbers and decimal numbers is avoided, and efficiency of optimization design is improved to a great extent. As shown in figure 7, the coding operation of the invention adopts integer coding, the chromosome coding of each individual of the genetic algorithm is formed by splicing two strings of integer arrays, and the first array of the chromosome coding represents the layering sequence of each single layer of the laminated plate, namely the layering sequence of the skin; the second array represents the position of laying of each single layer; the decoding operation is to reduce the encoded chromosome to the size of the design variable value. Considering process constraints, the ply angle discrete values are defined as 0 °, ± 45 °, 90 °, so the coding scheme is 0 for 0 °, 1 for 45 °, 2 for-45 °, 3 for 90 °. For the layer-wise tile area array (35251104) in FIG. 7, 3 indicates that the first monolayer is tiled at 45 in the 3 rd zone, 5 indicates that the second monolayer is tiled at 0 in the 5 th zone, and so on, item 70 of the second array indicates that the corresponding monolayer is unpainted, i.e., the layer is deleted.
S502: determining a fitness function; the method specifically comprises the steps of punishing an objective function by taking the intensity constraint and stability constraint violation quantity of an individual in a population as a penalty function, so as to obtain a fitness function of the individual, wherein a mathematical model of the fitness function is as follows:
Figure 408553DEST_PATH_IMAGE014
(ii) a Wherein
Figure 38117DEST_PATH_IMAGE015
Is the maximum strain violation;
Figure 664271DEST_PATH_IMAGE016
the amount of buckling violation;
Figure 254652DEST_PATH_IMAGE017
；
Figure 890033DEST_PATH_IMAGE018
。
s503: generating an initial population according to design variables and a design space, wherein individuals in the initial population represent one possible composite material conical reinforced cabin layer design; in one embodiment, the initial population for the genetic algorithm is set to 50.
S504: decoding operation, namely calculating the size of a performance index to be optimized corresponding to the initialized population and calculating a corresponding fitness function value;
s505: generating a new population by simultaneously selecting a selection operator, a crossover operator and a mutation operator, and calculating a corresponding fitness function value; the concrete contents are as follows:
selecting an operator: the tournament selection method is adopted, and an optimal reservation strategy is applied, and the method comprises the following specific steps: will be firstnGeneration group andntemporary populations generated after generation populations are crossed and mutated form an expanded population which is arranged from high to low in fitness, and a plurality of individuals with high fitness values are selected to form the first groupn+1 generation population;
and (3) a crossover operator: adopting single-point crossing, firstly obtaining a population of an even number of individuals by initializing the population, and then determining chromosomes participating in crossing according to a head-tail pairing method; as shown in fig. 8, assuming that the population size is 100, when the crossover operation is performed, the population is subjected to individual pairing, an individual 1 is paired with an individual 51, an individual 2 is paired with an individual 52, and so on; assuming that the individual 1 is determined to cross the individual 51, two crossing points, such as the crossing points 1 and 2 in fig. 9, need to be determined, and the crossing operation can be completed after the crossing points are determined. In one embodiment, the crossover probability of the genetic algorithm is set to 0.95.
Mutation operator: as shown in fig. 10, first, whether mutation operation occurs is determined according to the occurrence probability of a mutation operator, if mutation is determined, two mutation positions are determined at two parts of a chromosome, and then mutation operation is completed; mutation operation can cause the local gene of the parent chromosome to be mutated, thereby improving the local searching capability. In one embodiment, the mutation probability of the genetic algorithm is set to 0.05.
And obtaining the next generation population by simultaneously mixing and using the three operators.
S506: and setting an optimization iteration termination condition, judging whether the current optimization meets the termination condition, if so, outputting the obtained optimal individual, and if not, returning to execute the step S505. For example, the termination condition of the genetic algorithm may be set to the maximum number of iterations 50.
And in the optimization iteration process, the optimization program needs to perform information interaction with Nastran, and each iteration calculation optimization program needs to perform file interaction with Nastran twice, wherein one time is an analysis file of modified Nastran bdf, and the other time is a reading calculation result file f 06. The specific interaction process is as follows: the interface program modifies bdf the value on the unit attribute card corresponding to the design variable, then calls Nastran to submit the modified bdf file, after the finite element calculation, generates the result file f06, the interface program reads the result data to do the constraint processing, and carries on the optimization search, modifies the design variable, forms the initial variable value of the next iteration. The specific process is shown in FIG. 11.
In one embodiment, the obtained curve of the optimal individual and the population average fitness along with the change of the iteration times is shown in fig. 12, and it can be seen that the fitness function value of the population optimal individual is rapidly increased before 10 generations. At 35 generations, the optimization algorithm converged to an optimal solution with a fitness function value of 0.177 and an objective function of 5.664 kg. The population mean fitness rises to a higher level rapidly before the 15 th generation, and gradually slows down and tends to converge during the 15 th generation to the 50 th generation.
S6: and carrying out finite element analysis according to the erecting working condition and the axial pressure working condition of the composite material conical reinforced cabin, and verifying the optimization result.
The maximum strain distribution cloud chart under the optimal individual erecting working condition is shown in figure 13, and the maximum strain is 1460
Figure 108524DEST_PATH_IMAGE033
If the allowable strain is smaller than the allowable strain, the strain constraint condition is met; first orderThe buckling factor is 1.051, and the buckling mode is local buckling of the skin. As shown in FIG. 14, the maximum strain at the axle load condition is 460
Figure DEST_PATH_IMAGE034
Far smaller than allowable strain, and meeting the strain constraint condition; the first-order buckling factor is 2.30, and the buckling mode is local buckling of the skin. When the first-order linear buckling mode is local buckling of the skin, the critical buckling bearing capacity of the composite structure of the skin and the ribs is larger than the linear buckling load, so that the composite structure of the skin, the axial ribs and the radial ribs under two working conditions meets buckling constraint conditions.
The composite structure of the skin and the ribs meeting the strength constraint and the stability constraint is obtained through optimization, and the mass of the composite structure is 5.664 kg. The optimized individual ply optimization results show that the obtained multi-regional ply results satisfy that the thinnest region ply sequence is a subset of each thicker region ply sequence, namely, the ply optimized structure obtained through the fiber continuity model based on the regional sequences satisfies the fiber continuity condition.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and should not be taken as limiting the scope of the present invention, which is intended to cover any modifications, equivalents, improvements, etc. within the spirit and scope of the present invention.

Claims (2)

1. The tapered reinforcement cabin layering sequence optimization method based on the fiber continuous model is characterized by comprising the following steps of: the method comprises the following steps:
s1: acquiring design variables, design spaces and performance indexes to be optimized of the composite material conical reinforced cabin layer to be optimized; the design variables comprise skin ply and rib section size for forming the conical reinforced cabin ply; the design space is a space formed by the variation range of the design variables; the performance index to be optimized is the quality of the composite material conical reinforcing rib layering;
s2: constructing a fiber continuity model based on the region sequence;
s3: establishing a finite element model of the spreading of the conical reinforced cabin made of the composite material according to the constructed fiber continuity model based on the area sequence;
s4: obtaining the layering quality of the conical reinforced cabin of the composite material through finite element analysis according to a finite element model of the layering of the conical reinforced cabin of the composite material;
s5: establishing an optimization model, and solving the optimization model by adopting a genetic algorithm;
s6: carrying out finite element analysis according to the erecting working condition and the axial compression working condition of the composite material conical reinforced cabin, and verifying the optimization result;
in step S2, the constructing of the fiber continuity model based on the region sequence is to add a ply angle sequence and a ply region length sequence on the basis of the classical guide-type continuity model, and define the laminate of the composite material by the ply angle sequence and the ply region length sequence:
Figure 757277DEST_PATH_IMAGE001
(ii) a Wherein
Figure 733323DEST_PATH_IMAGE002
Representing a sequence of ply angles of the laminate;
Figure 742867DEST_PATH_IMAGE003
representing a sequence of ply region lengths; each combination corresponding to a respective single-layered laid area,
Figure 120759DEST_PATH_IMAGE004
represents the first monolayer of the laminate to
Figure 924767DEST_PATH_IMAGE005
At an angle lay
Figure 388110DEST_PATH_IMAGE006
An area; the right side of the vertical line shows the number of the corresponding laying areas of each layer;
obtaining the layering quality of the conical reinforced cabin of the composite material through finite element analysis in the step S4, wherein static analysis and buckling analysis are carried out; the static analysis comprises the steps of calculating the quality and the maximum strain of a composite material conical reinforced cabin layer finite element model, and the buckling analysis comprises the steps of calculating a buckling factor;
in step S5, the optimization model is established, and the genetic algorithm is used to solve the optimization model, and the following optimization model is established:
Figure 201345DEST_PATH_IMAGE007
；
Figure 433743DEST_PATH_IMAGE008
；
Figure 939811DEST_PATH_IMAGE009
(ii) a Wherein
Figure 624870DEST_PATH_IMAGE010
The model mass is taken as the model mass;
Figure 241796DEST_PATH_IMAGE011
the maximum strain of the unit is obtained after the unit simplification is carried out on the composite material conical reinforced cabin spreading finite element model;
Figure 63121DEST_PATH_IMAGE012
(ii) is an allowable strain;
Figure 740090DEST_PATH_IMAGE013
is a buckling factor;
Figure 912446DEST_PATH_IMAGE014
obtaining the condition of minimum model quality value;
the step of solving the optimization model by adopting a genetic algorithm comprises the following steps:
s501: coding the design variables;
s502: determining a fitness function;
s503: generating an initial population according to design variables and a design space, wherein individuals in the initial population represent one possible composite material conical reinforced cabin layer design;
s504: decoding operation, namely calculating the size of a performance index to be optimized corresponding to the initialized population and calculating a corresponding fitness function value;
s505: generating a new population by simultaneously selecting a selection operator, a crossover operator and a mutation operator, and calculating a corresponding fitness function value;
s506: setting an optimization iteration termination condition, judging whether the current optimization meets the termination condition, if so, outputting the obtained optimal individual, and if not, returning to execute the step S505;
in the step of solving the optimization model by the genetic algorithm, the design variables are coded by adopting integer codes, the chromosome code of each individual is formed by splicing two strings of integer arrays, the first array of the chromosome codes represents the laying sequence of each single layer of the laminated board, and the second array represents the laying position of each single layer; the decoding operation is to restore the coded chromosome to the size of the design variable value;
the fitness function is determined by taking the constraint violation quantity of the intensity constraint and the stability constraint of the individuals in the population as a penalty function, and punishing an objective function to obtain the fitness function of the individual, wherein the mathematical model is as follows:
Figure 67484DEST_PATH_IMAGE015
(ii) a Wherein
Figure 274474DEST_PATH_IMAGE016
Is the maximum strain violation;
Figure 122344DEST_PATH_IMAGE017
the buckling violation quantity;
Figure 781996DEST_PATH_IMAGE018
；
Figure 469286DEST_PATH_IMAGE019
。
2. the tapered stiffened cabin layering sequence optimization method based on the fiber continuity model of claim 1, wherein: the specific steps of generating a new population through the selection operator, the crossover operator and the mutation operator are as follows:
selecting an operator: the tournament selection method is adopted, and an optimal reservation strategy is applied, and the method comprises the following specific steps: will be firstnGeneration group andntemporary populations generated after generation populations are crossed and mutated form an expanded population which is arranged from high to low in fitness, and a plurality of individuals with high fitness values are selected to form the first groupn+1 generation population;
and (3) a crossover operator: adopting single-point crossing, firstly obtaining even number of individual populations by initializing the populations, and then determining chromosomes participating in crossing according to a head-tail pairing method;
mutation operator: judging whether mutation operation occurs or not according to the occurrence probability of a mutation operator, if the mutation operation is determined to occur, determining two mutation positions in two parts of the chromosome respectively, and then completing the mutation operation; and obtaining the next generation population by simultaneously mixing and using the three operators.
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