A method and system for optimizing the adjustment of a gradient-enhanced nylon energy storage bellows structure

Abstract

The application discloses a kind of based on gradient reinforced nylon energy storage bellows structure optimization adjusting method and system, it is related to energy storage bellows technical field, including: obtaining the initial structure parameter of energy storage bellows based on gradient reinforced nylon, according to initial structure parameter, the three-dimensional finite element model of energy storage bellows is constructed;Modal analysis is carried out to three-dimensional finite element model, the inherent frequency and mode shape of energy storage bellows are obtained, and key modal frequency is determined according to inherent frequency and mode shape;Based on gradient field theory, the spatial distribution function of fiber concentration and fiber direction is established, and based on key modal frequency and spatial distribution function, topological optimization model is constructed;Gradient optimization algorithm is used to calculate topological optimization model, and the distribution of fiber concentration and fiber direction is obtained;According to the fiber concentration and fiber direction distribution, the fiber configuration of gradient reinforced nylon in energy storage bellows is adjusted.The application effectively avoids the resonance problem caused by multi-modal frequency coupling in homogeneous material design.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of energy storage bellows, and in particular to an energy storage bellows structure optimization adjustment method and system based on gradient reinforced nylon. BACKGROUND

[0002] As a new advanced composite material, functional gradient materials can realize the coordination and unity of multiple functional characteristics in a single component by realizing the continuous gradient change of components or microstructure inside the material. Gradient reinforced nylon, as an important branch of functional gradient materials, exhibits excellent mechanical properties and vibration characteristics in flexible energy storage structures such as energy storage bellows by controlling the spatial distribution of the fiber reinforced phase. Energy storage bellows, as an important energy storage and conversion device, play a key role in new energy storage systems, industrial energy-saving equipment and renewable energy utilization. The structural design of the energy storage bellows directly affects the energy storage efficiency and dynamic stability. With the continuous development of structural optimization theory, traditional topology optimization, shape optimization and size optimization methods face difficulties in modal frequency coupling control and high complexity of fiber distribution optimization when dealing with gradient material structures.

[0003] The existing gradient reinforced nylon energy storage bellows structure optimization method mainly relies on traditional geometric parameter adjustment technology. By changing the geometric size of the bellows or using uniform material distribution to adjust the vibration characteristics, for example, in dealing with the dynamic optimization of energy storage bellows, the gradient reinforced nylon is regarded as an isotropic homogeneous material, and the material properties of the entire structure are described by a single elastic modulus and density parameter. Secondly, the traditional modal frequency control method directly uses mass increase or stiffness adjustment strategy when dealing with multi-modal frequency adjustment. By uniformly changing the structure parameters, all modal frequencies can be adjusted. When the energy storage bellows needs to realize independent regulation of bending modal frequency and torsional modal frequency, the traditional method has the phenomenon of design target mutual restriction and frequency coupling in multi-modal frequency separation, which leads to the inability to realize the precise separation of specific modal frequency and the directional optimization of dynamic performance. In addition, the existing fiber distribution design method lacks an optimization method based on gradient field theory. When dealing with the continuous change of fiber concentration and direction in geometric structure, there is a lack of effective spatial distribution function establishment mechanism, making it difficult to solve the resonance problem caused by the coupling of bending modal and torsional modal frequencies. SUMMARY

[0004] The application provides an energy storage bellows structure optimization adjustment method and system based on gradient reinforced nylon.

[0005] In a first aspect, the application provides a method for optimizing and adjusting the structure of a gradient-reinforced nylon energy storage bellows, comprising: obtaining initial structure parameters of the gradient-reinforced nylon energy storage bellows, and constructing a three-dimensional finite element model of the energy storage bellows according to the initial structure parameters; performing modal analysis on the three-dimensional finite element model to obtain the natural frequency and mode shape of the energy storage bellows, and determining the key modal frequency according to the natural frequency and mode shape; establishing a spatial distribution function of fiber concentration and fiber direction based on the gradient field theory, and constructing a topology optimization model based on the key modal frequency and the spatial distribution function; calculating the topology optimization model by using a gradient optimization algorithm to obtain the distribution of fiber concentration and fiber direction; and adjusting the fiber configuration of the gradient-reinforced nylon in the energy storage bellows according to the distribution of fiber concentration and fiber direction.

[0006] Optionally, in a possible implementation manner of the first aspect, the key modal frequency is determined according to the natural frequency and the mode shape, comprising: obtaining a predetermined working frequency range of the energy storage bellows, and screening target natural frequencies within the predetermined working frequency range from the natural frequencies; identifying a bending modal frequency corresponding to a bending mode and a torsional modal frequency corresponding to a torsional mode from the target natural frequencies according to the deformation mode of the mode shape; and determining the bending modal frequency and the torsional modal frequency as the key modal frequency.

[0007] Optionally, in a possible implementation manner of the first aspect, the spatial distribution function of fiber concentration and fiber direction is established based on the gradient field theory, comprising: establishing a radial distribution function of fiber concentration and fiber direction varying along the radial direction of the energy storage bellows; establishing an axial distribution function of fiber concentration and fiber direction varying along the axial direction of the energy storage bellows; and combining the radial distribution function and the axial distribution function by a gradient field coupling algorithm to form the spatial distribution function.

[0008] Optionally, in a possible implementation manner of the first aspect, the topology optimization model is constructed based on the key modal frequency and the spatial distribution function, comprising: establishing a target function with the maximum frequency difference between the bending modal frequency and the torsional modal frequency as the optimization objective; determining the value range of the optimization variable by taking the fiber concentration and fiber direction in the spatial distribution function as the optimization variable; establishing a constraint function including the total volume constraint of the material and the continuity constraint of the fiber direction; and obtaining the topology optimization model based on the target function, the optimization variable and the constraint function.

[0009] Optionally, in a possible implementation manner of the first aspect, the gradient optimization algorithm is adopted to calculate the topological optimization model to obtain the distribution of the fiber concentration and the fiber direction, including: calculating gradient information of the objective function on the optimization variable by the finite difference method; calculating a search direction and a step length based on the gradient information, and updating the optimization variable according to the search direction and the step length; repeating the steps of calculating the gradient information of the objective function on the optimization variable to the step of updating the optimization variable until the objective function converges, to obtain the final distribution of the fiber concentration and the fiber direction.

[0010] Optionally, in a possible implementation manner of the first aspect, the gradient information of the objective function on the optimization variable is calculated by the finite difference method, including: applying a disturbance to the optimization variable, and calculating the disturbed bending modal frequency and the twisted modal frequency; calculating the gradient information of the objective function on the optimization variable based on the disturbed bending modal frequency and the twisted modal frequency.

[0011] Optionally, in a possible implementation manner of the first aspect, the fiber configuration of the gradient-enhanced nylon in the energy storage bellows is adjusted according to the distribution of the fiber concentration and the fiber direction, including: determining the fiber volume fraction and the fiber orientation angle at different positions of the energy storage bellows according to the distribution of the fiber concentration and the fiber direction; and performing regional configuration adjustment on the gradient-enhanced nylon in the energy storage bellows according to the fiber volume fraction and the fiber orientation angle.

[0012] In a second aspect of the embodiments of the present application, a system for optimizing and adjusting the structure of the energy storage bellows based on the gradient-enhanced nylon is provided.

[0013] Optionally, in a possible implementation manner of the second aspect, the system includes: a structure parameter module configured to obtain initial structure parameters of the energy storage bellows based on the gradient-enhanced nylon, and to construct a three-dimensional finite element model of the energy storage bellows according to the initial structure parameters; a modal frequency module configured to perform modal analysis on the three-dimensional finite element model to obtain the natural frequency and the vibration mode of the energy storage bellows, and to determine the key modal frequency according to the natural frequency and the vibration mode; a topological optimization module configured to establish a spatial distribution function of the fiber concentration and the fiber direction based on the gradient field theory, and to construct a topological optimization model based on the key modal frequency and the spatial distribution function; a fiber distribution module configured to calculate the topological optimization model by using the gradient optimization algorithm to obtain the distribution of the fiber concentration and the fiber direction; and a structure optimization and adjustment module configured to adjust the fiber configuration of the gradient-enhanced nylon in the energy storage bellows according to the distribution of the fiber concentration and the fiber direction.

[0014] In a third aspect of the embodiments of the present application, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to perform the method for optimizing and adjusting the energy storage bellows structure based on the gradient-enhanced nylon according to the first aspect of the present application and various possible aspects related to the first aspect.

[0015] In a fourth aspect of the embodiments of the present application, a readable storage medium is provided, wherein the readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method for optimizing and adjusting the energy storage bellows structure based on the gradient-enhanced nylon according to the first aspect of the present application and various possible aspects related to the first aspect.

[0016] The method and system for optimizing and adjusting the energy storage bellows structure based on the gradient-enhanced nylon provided by the present application can not only realize the accurate separation of the bending modal frequency and the torsional modal frequency, effectively avoid the resonance problem caused by the multi-modal frequency coupling in the traditional homogeneous material design, but also realize the continuous controllable change of the fiber concentration and direction in the three-dimensional space through the coupling combination of the radial and axial distribution functions. By using the gradient optimization algorithm of the finite difference method, the calculation stability and engineering applicability are improved, so that the multi-variable optimization problem can quickly converge to the optimal solution. By converting the continuous distribution obtained by optimization into discrete manufacturing parameters and performing regional configuration adjustment, the complete conversion path from theoretical design to engineering implementation is realized, and the running stability and energy storage efficiency of the energy storage bellows under actual working conditions are effectively improved. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0018] Figure 1 is the overall flowchart of the method for optimizing and adjusting the energy storage bellows structure based on the gradient-enhanced nylon according to the present application;

[0019] Figure 2 is the application environment diagram of the method for optimizing and adjusting the energy storage bellows structure based on the gradient-enhanced nylon according to the present application. DETAILED DESCRIPTION

[0020] In order to make the above objectives, characteristics and advantages of the present application more apparent, more comprehensible, the specific embodiments of the present application are described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should belong to the protection scope of the present application.

[0021] In order to make the above objectives, characteristics and advantages of the present application more apparent, more comprehensible, the specific embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0022] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application. However, the present application can be practiced without the specific details, other than those described herein, and it is understood that the present application is not limited to the embodiments described herein and can be practiced with or in various ways wholly consistent with the spirit and scope of the present application. Accordingly, the present application is not limited to the embodiments disclosed below but can be practiced with variations that are wholly consistent with the spirit and scope of the present application.

[0023] Secondly, the "one embodiment" or "embodiment" referred to herein means that the specific features, structures or characteristics can be included in at least one implementation of the present application. "In one embodiment" appearing in different places in the specification does not mean the same embodiment, nor is it an independent or selective embodiment mutually exclusive with other embodiments.

[0024] Embodiment 1, with reference to Figure 1 and Figure 2 The first embodiment of the present application provides a gradient-reinforced nylon-based energy storage bellows structure optimization adjustment method, which comprises the following steps:

[0025] S100: Obtain the initial structure parameters of the gradient-reinforced nylon-based energy storage bellows, and construct a three-dimensional finite element model of the energy storage bellows according to the initial structure parameters.

[0026] In the embodiments of the present application, the initial structure parameters include the geometric dimensions of the energy storage bellows, the material attribute parameters of the gradient-reinforced nylon, and the boundary condition parameters; wherein the geometric dimensions include the inner diameter, the outer diameter, the corrugation depth, the corrugation spacing and the total length of the bellows, and the material attribute parameters include the elastic modulus, the Poisson's ratio and the density of the matrix material, and the elastic modulus, the Poisson's ratio and the density of the fiber material.

[0027] For example, the geometric size parameters of the energy storage bellows are obtained by: performing material property testing on the gradient reinforced nylon material to obtain physical parameters of the matrix and the fiber, determining the working boundary conditions of the energy storage bellows, and forming a complete initial structure parameter set; for example, first, the actual geometric size of the energy storage bellows is obtained by a three-dimensional scanning device, wherein the inner diameter is 20 mm to 80 mm, the outer diameter is 25 mm to 100 mm, the corrugation depth is 2 mm to 15 mm, and the corrugation pitch is 8 mm to 40 mm; secondly, the gradient reinforced nylon is subjected to tensile test and compression test by a material testing machine, and the elastic modulus of the matrix nylon is 2.5 GPa to 3.5 GPa, the Poisson's ratio is 0.35 to 0.42, the density is 1.12 g / cm³ to 1.15 g / cm³, the elastic modulus of the fiber is 230 GPa to 250 GPa, the Poisson's ratio is 0.28 to 0.33, and the density is 1.75 g / cm³ to 1.80 g / cm³.

[0028] In an alternative embodiment, the material property parameters can further include anisotropy characteristic parameters of the gradient reinforced nylon, wherein the orientation angle of the fiber at different positions is determined by a fiber direction angle measuring device, and a mapping relationship between the fiber direction and the position coordinates is established.

[0029] In the embodiments of the present application, constructing a three-dimensional finite element model of the energy storage bellows according to the initial structure parameters includes creating a three-dimensional geometric model of the energy storage bellows, performing meshing, defining material properties, and setting boundary conditions.

[0030] Further, the creation of the three-dimensional geometric model adopts a parametric modeling method, which accurately describes the geometric shape of the energy storage bellows, including the circular arc transition of the corrugation, the straight section connection and the overall contour shape, by defining key control points and curve equations; wherein the manufacturing process characteristics of the energy storage bellows need to be considered in the geometric modeling process to ensure the geometric consistency of the model and the actual product, and at the same time, a parametric correlation is established to facilitate subsequent structural optimization analysis.

[0031] Further, the meshing adopts a hexahedron and tetrahedron hybrid mesh strategy to ensure the calculation accuracy and efficiency of the model, for example, structured hexahedron mesh is used for regular straight section area to improve calculation efficiency, and unstructured tetrahedron mesh is used for corrugation transition area to ensure mesh quality; secondly, the gradual mesh refinement technology is adopted in the meshing process, starting from coarse mesh and gradually refining until the convergence requirement is met; the definition of material properties is based on the spatial distribution characteristics of the gradient reinforced nylon, and the entire bellows domain is divided into several material subdomains, each subdomain is assigned with corresponding material constants according to the local fiber concentration and direction angle; the setting of boundary conditions includes displacement boundary conditions and force boundary conditions, the displacement boundary conditions simulate the constraint state of the bellows, and the force boundary conditions simulate the actual working load.

[0032] For example, when constructing a three-dimensional finite element model of the energy storage bellows, a three-dimensional model of the energy storage bellows can be established using finite element analysis software, the bellows can be divided into 3 to 8 layers in the radial direction, and 20 to 50 bellows units in the axial direction, and each bellows unit contains 100 to 500 grid units; for the gradient reinforced nylon material, the corresponding material properties are assigned according to the fiber concentration and direction at different positions to form a spatially varying material distribution model.

[0033] In an alternative embodiment, mesh refinement techniques can also be used to increase the mesh density in stress concentration areas of the bellows, such as the bellows root and transition area, by 2 to 5 times compared to the normal area, and the mesh quality control standards are: minimum angle greater than 30 degrees, maximum angle less than 150 degrees, aspect ratio less than 3, to ensure the stability and convergence of numerical calculation.

[0034] In another alternative embodiment, the boundary condition parameters can also include constraint conditions and load conditions, the constraint conditions are determined according to the actual installation method of the energy storage bellows, including fixed constraints, simply supported constraints or elastic constraints, the load conditions include internal pressure load, axial tension load and excitation load, in the present embodiment, the internal pressure load ranges from 0.5 MPa to 5 MPa, the axial tension load ranges from 100 N to 2000 N, and the excitation load ranges from 5 Hz to 500 Hz.

[0035] It should be noted that the three-dimensional finite element model established by step S100 can accurately reflect the geometric characteristics, material properties and boundary conditions of the energy storage bellows, providing a reliable calculation basis for subsequent modal analysis and structural optimization; the three-dimensional finite element model has good numerical stability and calculation efficiency, and can support gradient material distribution and optimization calculation requirements, compared to the traditional uniform material modeling method, the present application can more truly reflect the spatial variation characteristics of the gradient reinforced nylon, improving the accuracy and reliability of the model, and laying a solid foundation for realizing the precise structural optimization of the energy storage bellows.

[0036] S200: performing modal analysis on the three-dimensional finite element model to obtain the natural frequency and mode shape of the energy storage bellows, and determining the key modal frequency according to the natural frequency and mode shape.

[0037] In the present embodiment, the modal analysis of the three-dimensional finite element model is performed to obtain the natural frequency and mode shape of the energy storage bellows, which means that the natural vibration characteristics of the structure are obtained by solving the generalized eigenvalue problem, which can accurately extract the vibration characteristics of the energy storage bellows, and the specific formula is as follows:

[0038] ;

[0039] ;

[0040] wherein, is the stiffness matrix of the energy storage bellows; is the mass matrix; is the nth natural circular frequency; is the corresponding nth mode shape vector; is the nth natural frequency. is the corresponding nth mode shape vector; is the nth natural frequency. is the corresponding nth mode shape vector.

[0041] In the embodiments of the present application, the key modal frequency is determined according to the natural frequency and the mode shape, including the following steps S210-S230:

[0042] S210: Obtain a predetermined working frequency range of the energy storage bellows, and select target natural frequencies located in the predetermined working frequency range from the natural frequencies.

[0043] Specifically, the determination of the predetermined working frequency range is based on the actual operating conditions of the energy storage system, and is obtained by a combination of field testing and theoretical analysis; wherein the predetermined working frequency range usually includes mechanical vibration frequency, electromagnetic excitation frequency and fluid pulsation frequency, and the range is set to 5Hz to 500Hz; the selection process uses a frequency matching algorithm to compare the calculated natural frequencies with the predetermined working frequency range, extracts all natural frequencies located in the predetermined working frequency range as target natural frequencies.

[0044] S220: According to the deformation mode of the mode shape, identify the bending modal frequency corresponding to the bending mode and the torsional modal frequency corresponding to the torsional mode from the target natural frequencies.

[0045] Further, the identification of the deformation mode of the mode shape uses a combination of modal strain energy analysis and displacement component analysis, by comprehensively considering the spatial distribution characteristics and deformation dominant direction of the mode shape, a modal type discrimination criterion is constructed, and the relative contribution of displacement components in different directions is quantified to identify the modal type, the specific formula is as follows:

[0046] ;

[0047] ;

[0048] wherein, is the bending modal discrimination factor; is the torsional modal discrimination factor; , , are the radial, axial and circumferential displacement components of the nth node, respectively; is the total number of nodes.

[0049] ​In the embodiments of the present application, the bending mode and the torsional mode can be quantitatively distinguished by using the modal type discrimination criterion. When the bending mode discrimination factor is less than the bending mode discrimination threshold value, it is determined as the bending mode; and when the torsional mode discrimination factor is greater than the torsional mode discrimination threshold value, it is determined as the torsional mode.

[0050] It should be noted that the bending mode discrimination threshold value is determined by analyzing the dominant role of the radial and axial displacement components in the bending deformation. When the proportion of the radial and axial displacement energy in the total deformation energy exceeds a certain value, it can be determined as the bending mode. The torsional mode discrimination threshold value is determined based on the contribution of the circumferential displacement component in the torsional deformation. When the circumferential displacement energy dominates in the total deformation energy, it is determined as the torsional mode. The specific value of the threshold value is obtained by combining theoretical analysis and numerical verification of typical energy storage bellows geometry, ensuring the accuracy and reliability of modal identification.

[0051] S230: Determine the bending mode frequency and the torsional mode frequency as the key modal frequency.

[0052] For example, when determining the key modal frequency according to the natural frequency and the mode shape, the first 30 orders of modes can be solved by using the Rance algorithm, and the 12 orders of modes located in the preset working frequency range are selected as the target natural frequency. Then, 6 orders of bending modes and 4 orders of torsional modes are identified by mode shape analysis, and finally the 3 orders of bending mode frequencies and the 2 orders of torsional mode frequencies are determined as the key modal frequencies.

[0053] In an alternative embodiment, the determination of the key modal frequency can also consider the multi-modal coupling effect. When two or more modal frequencies are close, modal coupling phenomenon may occur, resulting in vibration response. Through modal coupling factor analysis, the modal combination that may be coupled is identified, and the coupled modes are included in the key modal range.

[0054] It should be noted that through the modal analysis of step S200, the vibration characteristics of the energy storage bellows can be accurately obtained, providing a clear target for subsequent structure optimization. The scientific identification of the key modal frequency provides a reliable basis for avoiding resonance, and the quantitative modal analysis method improves the objectivity and accuracy of the results, ensuring the effectiveness of the modal analysis under different working conditions and the optimization potential of the dynamics performance of the energy storage bellows.

[0055] S300: Establish the spatial distribution function of the fiber concentration and the fiber direction based on the gradient field theory, and construct a topology optimization model based on the key modal frequency and the spatial distribution function.

[0056] ​​It should be noted that the gradient field theory is applied to describe the non-uniform distribution of the properties of the fiber-reinforced composite material, and the core idea is to regard the material properties (such as fiber concentration and direction) as a continuous scalar field or vector field in the spatial coordinate system, and to realize effective control and optimization of the gradient distribution of the material by establishing a mathematical function describing the change of the scalar field or vector field, so the radial distribution function and the axial distribution function are constructed in the radial and axial dimensions respectively in the application.

[0057] In the embodiment of the application, the spatial distribution functions of the fiber concentration and the fiber direction are established based on the gradient field theory, including the following steps S310-S330:

[0058] S310: Establish a radial distribution function of the fiber concentration and the fiber direction along the radial direction of the energy storage bellows.

[0059] Specifically, the radial distribution function adopts a linear gradient form to realize the continuous change of the fiber parameters from the inner wall to the outer wall. Among them, by comprehensively considering the realizability of the manufacturing process and the continuity requirement of the material distribution, the radial distribution function is constructed, and the specific formula is as follows:

[0060] ;

[0061] ;

[0062] Among them, is the radial fiber concentration distribution function; is the radial fiber direction distribution function; and are the fiber concentrations of the inner wall and the outer wall, respectively, and the value range is 0.2 to 0.5; and are the fiber direction angles of the inner wall and the outer wall, respectively, and the value range is 0 to 45 degrees; is the radial coordinate, with the unit of mm; and are the inner and outer diameters.

[0063] It should be noted that the radial gradient distribution of the fiber parameters is realized by linear interpolation, which can realize simple and effective radial gradient control, and is convenient for engineering manufacturing and parameter optimization.

[0064] S320: Establish an axial distribution function of the fiber concentration and the fiber direction along the axial direction of the energy storage bellows.

[0065] Specifically, the axial distribution function considers the periodic characteristics of the energy storage bellows and adopts the form of a basic value plus periodic modulation; by comprehensively considering the influence of the bellows geometry on the fiber distribution and the optimization requirements in the axial direction, the axial distribution function is constructed, and the specific formula is as follows:

[0066] ;

[0067] ;

[0068] wherein, is the axial fiber concentration distribution function; is the axial fiber orientation distribution function; is the base fiber concentration, taking a value of 0.3; is the base fiber orientation angle, taking a value of 0 degree; is the concentration modulation amplitude, taking a value of 0 to 0.15; is the angle modulation amplitude, taking a value of 0 degree to 20 degrees; is the corrugation period length.

[0069] It should be noted that the periodic change suitable for the corrugation geometry is realized by the sine and cosine functions, which can make the fiber distribution match the geometric characteristics of the energy storage corrugated pipe, and realize the coordinated optimization of structure and material.

[0070] S330: combining the radial distribution function and the axial distribution function by a gradient field coupling algorithm to form a spatial distribution function.

[0071] In the embodiments of the present application, the gradient field coupling algorithm realizes the fusion of radial and axial distribution by using a weighted average method, wherein by considering the interaction of radial gradient and axial periodicity, the linear combination of radial and axial distribution is realized by using a weight coefficient, and then a three-dimensional spatial distribution function is constructed, and the specific formula is as follows:

[0072] ;

[0073] ;

[0074] wherein, is the three-dimensional fiber concentration distribution function; is the three-dimensional fiber orientation distribution function; is the radial weight, taking a value of 0.6; is the axial weight, taking a value of 0.4, .

[0075] It should be noted that the selection of radial weight takes into account the main influence of radial fiber distribution on bending stiffness, and its weight contribution is determined by analyzing the sensitivity of radial fiber concentration changes to bending modal frequency; the selection of axial weight is based on the regulatory effect of the periodic characteristics of axial fiber distribution on torsional stiffness, and its weight distribution is determined by evaluating the influence of axial fiber direction changes on torsional modal frequency; the specific value of the weight coefficient is obtained by parametrically analyzing the optimization effect under different weight combinations, and selecting the weight ratio that makes the objective function converge best and has reasonable physical meaning to ensure the coordination and unity of radial and axial distribution.

[0076] In the embodiment of the present application, a topology optimization model is constructed based on key modal frequencies and spatial distribution functions, including the following steps S340-S370:

[0077] S340: Establish an objective function with maximizing the frequency difference between the bending mode frequency and the torsional mode frequency as the optimization goal.

[0078] In the embodiment of the present application, the objective function adopts the relative form of frequency difference to ensure the stability and convergence of the optimization. The specific formula is as follows:

[0079] ;

[0080] in, is the objective function value; is the bending mode frequency; is the torsional mode frequency.

[0081] S350: Taking the fiber concentration and fiber direction in the spatial distribution function as optimization variables, determining the value range of the optimization variables.

[0082] In the embodiment of the present application, the optimization variables are the boundary value parameters of the spatial distribution function, including the fiber concentration of the inner wall. , fiber concentration of outer wall , fiber direction angle of inner wall , fiber direction angle of outer wall , concentration modulation amplitude and the angle modulation amplitude The value range of the optimization variables is determined based on the physical properties of the material and the manufacturing process: and The value range is from 0.2 to 0.5. and The value range is from 0 degrees to 45 degrees. The value range is from 0 to 0.15. The value range is 0 degrees to 20 degrees.

[0083] S360: Establish a constraint function including a material total volume constraint and a fiber direction continuity constraint.

[0084] Specifically, the step of establishing a constraint function including a material total volume constraint and a fiber direction continuity constraint refers to obtaining a constraint function by controlling the total amount of material by using an average fiber concentration constraint and ensuring the moderation of the gradient by using a boundary value difference constraint, so as to ensure the physical reasonableness and engineering realizability of the optimization result, and the specific formula is as follows:

[0085]

[0086]

[0087] wherein, is the total volume of the energy storage bellows, and the unit is mm3; is the integral volume domain; 0.4 is the maximum average fiber volume fraction; 0.2 is the maximum fiber concentration gradient; and 30 degrees is the maximum fiber direction gradient.

[0088] S370: Obtain a topology optimization model based on the objective function, the optimization variable, and the constraint function.

[0089] For example, when constructing the topology optimization model based on the objective function, the optimization variable, and the constraint function, a 6-dimensional optimization problem is established, the objective function is the maximization of the relative difference between the bending modal frequency and the torsional modal frequency, the optimization variable is the 6 boundary parameters of the fiber distribution, and the constraint conditions include that the average fiber volume fraction is not more than 0.4 and the reasonable range limit of the fiber parameter gradient.

[0090] It should be noted that the step S300 solves the problem that the material distribution of the energy storage bellows is uniform and different direction modal frequencies cannot be independently controlled in the prior art by establishing a spatial distribution function based on the gradient field theory and constructing a topology optimization model; the traditional method uses homogeneous materials or simple layered design, and it is difficult to realize the frequency separation of the bending modal and the torsional modal, and multi-modal coupling resonance is easy to occur. The present application establishes radial and axial fiber distribution functions, forms a continuous three-dimensional space distribution through a gradient field coupling algorithm, and effectively controls the fiber concentration and direction. By constructing a topology optimization model with the maximization of the modal frequency difference as the target, the frequency overlap of the bending and torsional modal is effectively avoided.

[0091] S400: Calculate the topology optimization model by using a gradient optimization algorithm to obtain the distribution of the fiber concentration and the fiber direction.

[0092] In the embodiment of the present application, the topology optimization model is calculated by using a gradient optimization algorithm to obtain the distribution of the fiber concentration and the fiber direction, including the following steps S410-S430:

[0093] ​​S410: Calculate the gradient information of the objective function to the optimization variable by the finite difference method.

[0094] In the embodiment of the present application, the gradient information of the objective function to the optimization variable is calculated by the finite difference method, including the following steps S411-S412:

[0095] S411: Apply a perturbation to the optimization variable, and calculate the bending modal frequency and the torsional modal frequency after the perturbation.

[0096] Specifically, the finite difference method applies a suitable perturbation amount to each optimization variable by using a forward difference format, which ensures numerical accuracy and avoids truncation error, and ensures the accuracy of gradient calculation. The specific formula is as follows:

[0097]

[0098]

[0099] wherein, is the i-th optimization variable; is the variable value after the perturbation; is the perturbation amount. For example, when a suitable perturbation amount is applied to each optimization variable, it includes: for the six optimization variables

[0100] , , , , , ,a perturbation is applied and modal analysis is performed again to calculate the bending modal frequency and the torsional modal frequency after the perturbation, wherein each perturbation changes only one variable, and the other variables remain unchanged, and a total of six modal analysis calculations are required. It should be noted that the determination of the above perturbation amount considers the balance requirement of numerical accuracy and calculation stability, the lower limit of the perturbation amount is set based on the consideration of machine precision, to avoid the influence of rounding error on the gradient calculation accuracy due to too small perturbation; the relative form of the perturbation amount considers the magnitude difference of different optimization variables, and ensures that the gradient calculation of each variable has the same numerical accuracy level through relative perturbation; the selection of the perturbation amount size is based on the linear approximation effectiveness requirement of Taylor expansion, to ensure the linear characteristics of the objective function within the perturbation range, and avoid nonlinear error due to too large perturbation.

[0101] S412: Calculate the gradient information of the objective function to the optimization variable based on the bending modal frequency and the torsional modal frequency after the perturbation.

[0102]

[0103] ​​Specifically, the gradient information of the objective function with respect to the optimization variable is calculated based on the bending modal frequency and the torsional modal frequency after the disturbance. In order to obtain the gradient information of the objective function with respect to the optimization variable, the numerical differentiation method is used to replace the existing analytical derivation process. The partial derivative is approximated by calculating the difference of the function value, and the gradient information of any function can be directly obtained without tedious mathematical derivation. The specific formula is as follows:

[0104] ;

[0105] in, The gradient information of the objective function with respect to the optimization variable; is the objective function value after disturbance; is the objective function value before disturbance.

[0106] It should be noted that compared with the traditional analytical gradient method, the use of the finite difference method avoids the derivation of sensitivity equations and the calculation of the adjoint method, simplifying the difficulty of algorithm implementation; secondly, for composite functions, the finite difference method does not require layer-by-layer differentiation, and accurate gradient information can be obtained directly by calculating the function value; in addition, the finite difference method has low requirements on the smoothness of the function, and even if there is slight numerical noise in the objective function, a stable gradient result can still be obtained.

[0107] For example, when the optimization variables are disturbed, the fiber concentration of the inner wall is first A disturbance of 0.001 is applied, and the modal frequencies are recalculated to obtain the new objective function value. The gradient component is calculated using the difference formula. This process is then repeated for all six optimization variables to obtain the complete gradient vector.

[0108] S420: Calculate the search direction and step size based on the gradient information, and update the optimization variables according to the search direction and step size.

[0109] In the embodiment of the present application, in order to determine the search direction of the optimization iteration, the steepest descent method is adopted, and the gradient information is used to guide the update direction of the optimization variable. The negative gradient direction is the direction in which the function value decreases fastest, which can ensure that the objective function decreases monotonically in each iteration and ensure the convergence of the algorithm. The calculation formula of the search direction is as follows:

[0110] ;

[0111] in, For the The search direction of the iteration, is the gradient vector.

[0112] Secondly, the step length determination adopts the backtracking line search method, the initial step length is set to 1.0, if the updated objective function value fails to decrease, the step length is halved, and the process is repeated until the new function value is less than the original function value plus one tenth of the predicted decrease amount in the gradient direction, wherein the minimum step length is set to 0.001 to prevent slow convergence caused by too small step length.

[0113] Further, the optimization variable is updated according to the search direction and the step length, and the specific formula is as follows:

[0114] ;

[0115] wherein, is the current optimization variable vector; is the step length of the i-th iteration; is the updated optimization variable vector.

[0116] It should be noted that, by using the backtracking line search method, the method has stronger adaptability and reliability compared with the fixed step length method, wherein the backtracking mechanism can automatically adjust the step length according to the local characteristics of the objective function, a smaller step length is used in the region with sharp function value change to ensure stability, a larger step length is used in the region with gentle function value change to improve efficiency, and substantial progress is ensured in each iteration to avoid slow convergence caused by too small step length or numerical instability caused by too large step length; in addition, the setting of the minimum step length prevents invalid oscillation of the algorithm in the local flat region, and provides a step length control strategy for the fiber distribution optimization of the energy storage bellow.

[0117] S430: repeat the steps of calculating the gradient information of the objective function on the optimization variable to the step of updating the optimization variable until the objective function converges, to obtain the final distribution of the fiber concentration and the fiber direction.

[0118] It should be noted that, in order to determine whether the optimization algorithm reaches the convergence state, the double standards of the relative change of the objective function and the gradient norm are adopted, both the change of the function value and the change of the gradient information are considered, which can accurately identify the convergence state of the algorithm and avoid premature termination or excessive iteration.

[0119] In the embodiment of the present application, the convergence condition of the objective function is:

[0120] ;

[0121] wherein, is the objective function value of the i-th iteration; is the 2-norm of the gradient vector, and when the two conditions are met at the same time, it is determined that the objective function converges.

[0122] ​​It should be noted that by using the relative change criterion of the objective function to ensure that the function value reaches a steady state, the gradient norm criterion ensures that the search direction tends to a zero vector, and the combination of the two conditions can accurately identify the true convergence state; wherein the use of the relative change criterion makes the criterion applicable to objective function values of different magnitudes, improving the universality of the algorithm.

[0123] Secondly, the selection of the above-mentioned relative change criterion of the objective function takes into account the engineering significance of the modal frequency of the energy storage bellows. When the relative change of the frequency difference reaches an engineering acceptable accuracy level, it is determined that the optimization effect has met the actual demand. The setting of the gradient norm tolerance is based on the convergence characteristics of the optimization algorithm. When the gradient information tends to zero, it indicates that the optimal solution has been approached. The use of the double criterion improves the reliability of the convergence judgment, prevents misjudgment that may be caused by a single criterion, and ensures the quality of the optimization result.

[0124] In an alternative embodiment, for the convergence condition of the objective function, the maximum number of iterations can be set to 50 to prevent infinite loops of the algorithm. If convergence is not achieved after the maximum number of iterations is reached, the current optimal result is output and a warning message is given.

[0125] In another alternative embodiment, for the convergence condition of the objective function, the amount of change in the optimization variable in consecutive iterations can also be considered. When the variable change amount is less than a set threshold, it is also considered that the objective function converges, avoiding oscillation around the optimal solution.

[0126] It should be noted that step S400 solves the implementation difficulty and calculation instability problem caused by gradient calculation in the prior art by using a gradient optimization algorithm based on finite difference method. The present application uses finite difference method to calculate the gradient, which is simple and reliable and easy to implement in engineering. Secondly, by using reasonable convergence conditions and iteration control, the accuracy of the optimization result is ensured.

[0127] S500: Adjust the fiber configuration of the gradient-enhanced nylon in the energy storage bellows according to the distribution of the fiber concentration and the fiber direction.

[0128] In the embodiments of the present application, adjusting the fiber configuration of the gradient-enhanced nylon in the energy storage bellows according to the distribution of the fiber concentration and the fiber direction includes the following steps S510-S520:

[0129] S510: Determine the fiber volume fraction and fiber orientation angle at different positions of the energy storage bellows according to the distribution of the fiber concentration and the fiber direction.

[0130] In the embodiments of the present application, the determination of the fiber volume fraction and the fiber orientation angle at different positions of the energy storage bellows requires the discretization of the continuous spatial distribution function into specific manufacturing parameters, the conversion from the theoretical distribution to the actual configuration is achieved through spatial sampling and parameter mapping, and then the continuous distribution obtained by optimization can be converted into discrete parameters executable by the manufacturing equipment, ensuring the consistency of the theoretical design and the actual manufacturing.

[0131] For example, the energy storage bellows is divided into 3 to 5 layers in the radial direction, and is divided into several segments in the axial direction according to the corrugation period. The fiber parameters in each region take the distribution function value of the center point of the region. For radial layering, the fiber volume fractions of the inner layer, the middle layer and the outer layer are respectively taken as the numerical values of the radial distribution function at the corresponding radii, and the fiber orientation angle is also determined in this way.

[0132] In an alternative embodiment, the determination of the fiber volume fraction and the fiber orientation angle can also take into account the constraints of the manufacturing process, in which the fiber volume fraction needs to meet the manufacturability requirements, usually limited to 0.2 to 0.5, too low to affect the reinforcement effect, too high to cause the matrix material to be difficult to fully infiltrate the fibers. Secondly, the variation gradient of the fiber orientation angle should also be controlled within a reasonable range, and the angle difference between adjacent regions should not exceed 15 degrees, to ensure the continuity of fiber laying and the feasibility of the process.

[0133] S520: Regionally configuring and adjusting the gradient reinforced nylon in the energy storage bellows according to the fiber volume fraction and the fiber orientation angle.

[0134] In the embodiments of the present application, the regionally configuring and adjusting the gradient reinforced nylon in the energy storage bellows according to the fiber volume fraction and the fiber orientation angle means using a combination of layered laying and local adjustment to achieve the overall gradient distribution by controlling the density and direction of each layer of fibers, and then effectively controlling the fiber configuration at different positions to achieve the spatial distribution characteristics required by the design.

[0135] Specifically, the regional configuration includes: first, determining the amount of fiber in each region according to the fiber volume fraction, calculating the required fiber weight and matrix material weight; then setting the direction and path of fiber laying according to the fiber orientation angle, and using computer-controlled fiber laying equipment to lay automatically according to the predetermined trajectory; finally, injecting and curing the matrix material to ensure the full combination of fibers and matrix through pressure and temperature control.

[0136] In an alternative embodiment, the sub-regional configuration adjustment can also adopt a multi-step laying process, first laying a base fiber layer that bears the main load, with the fiber direction arranged along the principal stress direction and a relatively high fiber volume fraction; then laying a functional fiber layer that adjusts the vibration characteristics, with the fiber direction determined according to the modal frequency optimization results and a relatively low fiber volume fraction; and finally laying a protective and connecting fiber layer to ensure the integrity and continuity of the overall structure.

[0137] In another alternative embodiment, the sub-regional configuration adjustment can also include a quality control and verification link, using online detection technology to monitor the position, direction and density of the fibers in real time during the fiber laying process, to ensure that the actual configuration deviates within the allowable range from the design requirements; and after laying is completed, sample detection is performed, the accuracy of the fiber distribution is verified through microscope observation and image analysis, the actual values of the fiber volume fraction and the orientation angle are measured, and the deviation reasons are compared and analyzed with the design values.

[0138] For example, for an energy storage bellows with an inner diameter of 50 mm and an outer diameter of 70 mm, the radial direction is divided into three layers, and the axial direction is divided into several segments with a period of 20 mm; the fiber volume fraction of the inner layer is 0.25, and the orientation angle is 5 degrees; the fiber volume fraction of the middle layer is 0.35, and the orientation angle is 20 degrees; the fiber volume fraction of the outer layer is 0.45, and the orientation angle is 35 degrees; then the bending modal frequency of the energy storage bellows after configuration adjustment is 182 Hz, the torsional modal frequency is 245 Hz, and the frequency separation effect meets the design requirements.

[0139] It should be noted that step S500 solves the problem that the optimization results in the prior art are difficult to implement in engineering by converting the theoretical distribution obtained by optimization into actual fiber configuration; traditional methods often stop at the theoretical analysis stage and lack a complete conversion path from design to manufacturing, while the present application establishes a mapping relationship from continuous distribution function to discrete manufacturing parameters, realizes accurate manufacturing of gradient distribution through sub-regional configuration, not only realizes the optimization design goal of the energy storage bellows based on gradient reinforced nylon, but also provides a feasible implementation path for the engineering application of gradient material structure.

[0140] In summary, the application provides a gradient-enhanced nylon-based energy storage bellows structure optimization adjustment method and system. By establishing a fiber spatial distribution function based on the gradient field theory and a topology optimization model for maximizing the frequency difference, the bending modal frequency and the torsional modal frequency can be accurately separated, effectively avoiding the resonance problem caused by multi-modal frequency coupling in traditional homogeneous material design. The coupling combination of the radial and axial distribution functions enables continuous and controllable changes in fiber concentration and direction in three-dimensional space. The use of the finite difference method for gradient optimization improves the calculation stability and engineering applicability, enabling the multi-variable optimization problem to quickly converge to the optimal solution. The continuous distribution obtained through optimization is converted into discrete manufacturing parameters and adjusted in different regions, realizing the complete conversion path from theoretical design to engineering implementation, and effectively improving the operational stability and energy storage efficiency of the energy storage bellows under actual working conditions.

[0141] In one embodiment of the application, the application provides a gradient-enhanced nylon-based energy storage bellows structure optimization adjustment system, which includes: a structure parameter module for obtaining the initial structure parameters of the gradient-enhanced nylon-based energy storage bellows, and constructing a three-dimensional finite element model of the energy storage bellows based on the initial structure parameters; a modal frequency module for performing modal analysis on the three-dimensional finite element model to obtain the natural frequency and mode shape of the energy storage bellows, and determining the key modal frequency based on the natural frequency and mode shape; a topology optimization module for establishing the spatial distribution function of fiber concentration and fiber direction based on the gradient field theory, and constructing a topology optimization model based on the key modal frequency and the spatial distribution function; a fiber distribution module for calculating the topology optimization model using a gradient optimization algorithm to obtain the distribution of fiber concentration and fiber direction; and a structure optimization adjustment module for adjusting the fiber configuration of the gradient-enhanced nylon in the energy storage bellows based on the distribution of fiber concentration and fiber direction.

[0142] The embodiment also provides an electronic device suitable for a gradient-enhanced nylon-based energy storage bellows structure optimization adjustment method, which includes a memory and a processor. The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions to implement a gradient-enhanced nylon-based energy storage bellows structure optimization adjustment method as described in the above embodiment.

[0143] The embodiment also provides a storage medium having a computer program stored thereon, which is executed by a processor to implement a gradient-enhanced nylon-based energy storage bellows structure optimization adjustment method as described in the above embodiment.

[0144] The storage medium proposed in the embodiment belongs to the same inventive concept as the method for optimizing and adjusting the energy storage corrugated pipe structure based on gradient enhanced nylon proposed in the above embodiment. Technical details not described in the embodiment can be seen from the above embodiment, and the embodiment has the same beneficial effects as the above embodiment.

[0145] Through the above description of the embodiments, those skilled in the art can clearly understand that the present application can be realized by means of software and necessary general hardware, and of course can also be realized by hardware, but in many cases the former is a better embodiment. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, and the computer software product can be stored in a computer readable storage medium, such as a floppy disk, a read-only memory (ROM), a random access memory (RAM), a FLASH, a hard disk or an optical disk, etc., including a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of various embodiments of the present application.

[0146] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the above embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for optimizing the adjustment of a gradient-enhanced nylon-based energy storage bellows structure, characterized in that, The method comprises the following steps: obtaining initial structural parameters of the gradient-reinforced nylon energy storage bellows, and constructing a three-dimensional finite element model of the energy storage bellows according to the initial structural parameters; performing modal analysis on the three-dimensional finite element model to obtain natural frequencies and vibration modes of the energy storage bellows, and determining key modal frequencies according to the natural frequencies and the vibration modes; establishing a spatial distribution function of fiber concentration and fiber direction based on the gradient field theory, and constructing a topology optimization model based on the key modal frequencies and the spatial distribution function; calculating the topology optimization model by using a gradient optimization algorithm to obtain the distribution of fiber concentration and fiber direction; adjusting the fiber configuration of the gradient-reinforced nylon in the energy storage bellows according to the distribution of fiber concentration and fiber direction; the step of establishing the spatial distribution function of fiber concentration and fiber direction based on the gradient field theory comprises the following steps: establishing a radial distribution function of fiber concentration and fiber direction along the radial direction of the energy storage bellows; establishing an axial distribution function of fiber concentration and fiber direction along the axial direction of the energy storage bellows; combining the radial distribution function and the axial distribution function by using a gradient field coupling algorithm to form the spatial distribution function; the step of constructing the topology optimization model based on the key modal frequencies and the spatial distribution function comprises the following steps: establishing an objective function with the maximum frequency difference between the bending modal frequency and the torsional modal frequency as the optimization target; determining the value range of the optimization variables by taking the fiber concentration and fiber direction in the spatial distribution function as the optimization variables; establishing a constraint function including a total material volume constraint and a fiber direction continuity constraint; obtaining the topology optimization model based on the objective function, the optimization variables and the constraint function.

2. A method of optimizing the adjustment of a gradient-enhanced nylon-based energy storage bellows structure according to claim 1, characterized in that, the step of determining the key modal frequencies according to the natural frequencies and the vibration modes comprises the following steps: obtaining a predetermined working frequency range of the energy storage bellows, and screening target natural frequencies located in the predetermined working frequency range from the natural frequencies; identifying the bending modal frequency corresponding to the bending mode and the torsional modal frequency corresponding to the torsional mode from the target natural frequencies according to the deformation mode of the vibration mode; determining the bending modal frequency and the torsional modal frequency as the key modal frequencies.

3. A method of optimizing the adjustment of a gradient-enhanced nylon-based energy- storage bellows structure according to claim 2, characterized in that, the step of calculating the topology optimization model by using a gradient optimization algorithm to obtain the distribution of fiber concentration and fiber direction comprises the following steps: calculating the gradient information of the objective function with respect to the optimization variables by using a finite difference method; calculating a search direction and a step length based on the gradient information, and updating the optimization variables according to the search direction and the step length; repeating the steps of calculating the gradient information of the objective function with respect to the optimization variables and updating the optimization variables until the objective function converges, to obtain the final distribution of fiber concentration and fiber direction.

4. A method of optimizing the adjustment of a gradient-enhanced nylon-based energy- storage bellows structure according to claim 3, characterized in that, the step of calculating the gradient information of the objective function with respect to the optimization variables by using a finite difference method comprises the following steps: applying a perturbation to the optimization variables, and calculating the perturbed bending modal frequency and the perturbed torsional modal frequency; calculating the gradient information of the objective function with respect to the optimization variables based on the perturbed bending modal frequency and the perturbed torsional modal frequency.

5. A method of optimizing the adjustment of a gradient-enhanced nylon-based energy- storage bellows structure according to claim 4, characterized in that, According to the fiber concentration and fiber direction distribution, the fiber configuration of the gradient reinforced nylon in the energy storage bellows is adjusted, comprising: According to the fiber concentration and fiber direction distribution, the fiber volume fraction and fiber orientation angle at different positions of the energy storage bellows are determined; According to the fiber volume fraction and the fiber orientation angle, the fiber configuration of the gradient reinforced nylon in the energy storage bellows is adjusted.

6. A gradient reinforced nylon based energy storage bellows structure optimized regulation system, using a gradient reinforced nylon based energy storage bellows structure optimized regulation method as claimed in any one of claims 1-5, characterized in that, Comprising: A structure parameter module is configured to obtain initial structure parameters of the energy storage bellows based on the gradient reinforced nylon, and construct a three-dimensional finite element model of the energy storage bellows according to the initial structure parameters; A modal frequency module is configured to perform modal analysis on the three-dimensional finite element model to obtain the natural frequency and mode shape of the energy storage bellows, and determine the key modal frequency according to the natural frequency and mode shape; A topology optimization module is configured to establish a spatial distribution function of the fiber concentration and fiber direction based on the gradient field theory, and construct a topology optimization model based on the key modal frequency and the spatial distribution function; A fiber distribution module is configured to calculate the topology optimization model by using a gradient optimization algorithm to obtain the distribution of the fiber concentration and fiber direction; A structure optimization adjustment module is configured to adjust the fiber configuration of the gradient reinforced nylon in the energy storage bellows according to the fiber concentration and fiber direction distribution.

7. A computer device, comprising: Comprising: A memory, a processor and a computer program, the computer program is stored in the memory, the processor runs the computer program to execute the structure optimization adjustment method of the energy storage bellows based on the gradient reinforced nylon according to any one of claims 1 to 5.

8. A readable storage medium, characterized by, The computer program is stored in the readable storage medium, and the computer program is executed by the processor to realize the structure optimization adjustment method of the energy storage bellows based on the gradient reinforced nylon according to any one of claims 1 to 5.

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