A Method and System for Optimizing and Analyzing the Structure of an Electrochemical Actuator Based on a Genetic Algorithm

Abstract

The present invention discloses a method and system for optimizing and analyzing the structure of an electrochemical actuator based on a genetic algorithm, which is applicable to the numerical simulation calculation of the finite element method in the process of electrochemical actuation reaction of materials in an electrolyte, and combines the genetic algorithm to achieve the purpose of optimizing the actuation performance of materials. This method is based on the eigenstress model, combines the finite element method, and realizes automatic random modeling through a program to effectively calculate the macroscopic strain of materials under different structures. Under the condition of combining the genetic algorithm, the structure of materials is optimized to increase the macroscopic strain of materials, effectively improving the actuation performance of materials. The method of the present invention can realize automatic random modeling using the finite element method, simulate electrochemical actuators of various different configurations, automatically extract the macroscopic strain of the structure by applying surface stress, and finally combine the genetic algorithm to reversely optimize the structure of materials, requiring less computing resources and computing time, with higher efficiency and lower cost.

Description

Technical Field

[0001] The present invention relates to a method and system for optimizing the analysis of an electrochemical actuator structure, which is applicable to the numerical simulation calculation of the finite element method in the process of electrochemical actuation reaction of materials in an electrolyte solution, and at the same time combines a genetic algorithm to achieve the purpose of optimizing the actuation performance of the materials. Background Art

[0002] Electrochemical devices that convert electrical energy into mechanical energy have great potential, from soft robots, autofocusing microlenses to artificial muscles. To date, achieving large strains remains challenging for electrochemical actuators.

[0003] The prominent advantages of electrochemical actuators are low operating voltage and working in an electrolyte solution. It has been found that the ligament size and configuration of electrochemical actuators can affect the macroscopic deformation of materials in the electrolyte solution, thus changing the material properties. Therefore, before conducting experiments and production on electrochemical actuator materials, if the optimal configuration can be given through screening, it can not only reduce the experimental cost, but also improve the actuation performance of the materials.

[0004] Finite element analysis (FEA) is a modern computational method that has rapidly developed for structural mechanics analysis. It is widely used to solve problems such as heat conduction, electromagnetic fields, and fluid mechanics. ABAQUS is one of the most advanced large-scale general finite element calculation and analysis software in the world, and it is widely used in almost all scientific research and engineering technology fields such as hydraulic engineering, civil engineering, bridges, machinery, mechanics, and physics. The use of finite element software (ABAQUS) can simulate electrochemical actuation phenomena, analyze the macroscopic strain of materials by adjusting the structure of the materials, and then optimize the structure of the materials in reverse. The realization of the entire process and the result data are of great reference value for industrial production and scientific research. How to combine the finite element analysis method with the research of electrochemical actuators has become a technical problem to be solved urgently. Summary of the Invention

[0005] To solve the problems of the existing technologies, the purpose of the present invention is to overcome the deficiencies of the existing technologies, and to provide a method and system for optimizing the analysis of the structure of an electrochemical actuator based on a genetic algorithm. Finite element analysis is used in the design of the material structure to achieve controllable material deformation behavior. Based on the eigenstress model, the present invention develops a method for simulating the actuation phenomenon of materials in an electrolyte by combining the finite element method. Through the program, automated random modeling is realized to effectively calculate the macroscopic strain of materials under different structures. Under the condition of combining the genetic algorithm, the structure of the materials is optimized to increase the macroscopic strain of the materials, effectively improving the actuation performance of the materials. The method of the present invention can realize automated random modeling using the finite element method, simulate electrochemical actuators of various different configurations, automatically extract the macroscopic strain of the structure by applying surface stress, and finally combine the genetic algorithm to inversely optimize the structure of the materials. Since parametric modeling and complete automatic result extraction are achieved, less computing resources and computing time are required, the efficiency is higher, the cost is lower, and the experiment can be guided using the idea and framework of process flow, which is very meaningful.

[0006] To achieve the above object of the invention-creation, the present invention adopts the following inventive concept:

[0007] I. A method for simulating the actuation phenomenon of materials in an electrolyte, comprising the following steps:

[0008] (1) Based on the eigenstress model, write the corresponding finite element program code for automated random modeling, and combine and call the corresponding finite element program code with finite element software to generate different shape models composed of random sequences. The modeling part here will be skillfully completed through coding to achieve digital modeling.

[0009] (2) Perform finite element preprocessing, write the corresponding automated preprocessing program code, and then define the constitutive relationship of the material by writing the subroutine umat to expand the function of the program. To simulate the actuation phenomenon of materials in an electrolyte, it is also necessary to write program code to implement the application of the surface stress of the material based on the eigenstress model, the interaction between components, and the setting of boundary conditions.

[0010] (3) To quickly and intuitively analyze the results, it is necessary to write a script program to post-process the model. By extracting the node information of the model, the maximum displacement distance and strain are calculated. The method of the present invention for simulating the actuation phenomenon of materials in an electrolyte can calculate the macroscopic strain generated by materials of different configurations under the induction of surface stress.

[0011] II. Optimize the actuation effect of the material

[0012] Drawing on the theory of biological evolution, genetic algorithms simulate the problem to be solved as a biological evolution process. Through operations such as replication, crossover, and mutation, the solutions of the next generation are generated, and the solutions with low fitness function values are gradually eliminated, while the solutions with high fitness function values are increased. After evolving for N generations, it is very likely to evolve individuals with very high fitness function values. This implementation process is actually like the natural evolution process. First, find a scheme for "digitally" encoding the potential solutions to the problem. Establish the mapping relationship between the phenotype and genotype for the configuration of the model, and then initialize a population with random numbers. The individuals in the population are these digital encodings. Next, after an appropriate decoding process (obtaining the specific configuration of the model in the finite element software), use the fitness function to evaluate the fitness of each gene individual once (perform preprocessing, calculation, and result extraction on the model using the finite element method. The greater the strain, the better the actuation effect of the material, so the corresponding fitness is higher). Use the selection function to select the best according to certain regulations. Let the individual genes mutate. Then generate offspring (hoping to retain the configurations with high fitness and inherit them to the next generation).

[0013] According to the above inventive concept, the present invention adopts the following technical solutions:

[0014] An optimization analysis method for an electrochemical actuator structure based on genetic algorithms, comprising the following steps:

[0015] a. Digital modeling, the process of mapping the genotype and phenotype of the model:

[0016] First, establish a cube based on the origin of the coordinate system, and then number each face of the cube; since the structure is a symmetric structure, select to construct the symmetric part on a two-dimensional plane; on the basis of specifying the starting point and ending point of the two-dimensional plane structure, use the numbered digits to represent the path, and after obtaining the two-dimensional structure, perform rotation and mirroring to generate the complete material structure, and use the umat subroutine to define the constitutive relationship of the material; thereby map the genotype and phenotype of the model to obtain the material model;

[0017] b. Simulation of deformation occurring under the induction of surface stress of the material:

[0018] According to the model obtained in the step a, adopt the finite element method to simulate the deformation occurring under the induction of surface stress of the material, including the following steps:

[0019] (1) Write a Fortran script, that is, a.for file, to provide the Jacobian matrix of the constitutive relation of the outer shell material, that is, the change rate of stress increment corresponding to the strain increment;

[0020] (2) Write a python script, that is, a.py file, which mainly includes the following simulation parameters:

[0021] (2-1) Machine the built model into a shell to obtain a shell component; define material constants through General-UserMaterial;

[0022] (2-2) Add analysis step Step-1, set the time length to 1, the maximum number of increment steps to 100, the minimum increment step to 1e-5, and turn on geometric nonlinearity;

[0023] (2-3) After assembling the solid component and the shell component, set the interaction between the two components with the property of bonding;

[0024] (2-4) Set the midpoints of the outermost layers in all directions of the solid component as a point set, and set a hinged boundary condition with U1 = U2 = U3 = 0 at the centroid of the model, that is, the point (0, 0), to constrain all its translational degrees of freedom. Among them, U1, U2, and U3 are the X, Y, and Z directions in the rectangular coordinate system respectively;

[0025] (2-5) Use the free mesh generation method to perform tetrahedral mesh generation on the solid component using C3D10 elements in Abaqus / Standard, with the mesh size of 2.5;

[0026] (2-6) Use the free mesh generation method and adopt an advanced algorithm in Abaqus / Standard to perform linear, reduced integration, and quadrilateral shell element (S4R) mesh generation on the shell component, with the mesh size of 2.5;

[0027] (2-7) Call the Umat subroutine and submit the task for calculation;

[0028] (3) Run the above script program using Abaqus finite element software to obtain the calculation results, so as to simulate the actuation phenomenon of the material in the electrolyte and calculate the macroscopic strain generated by the material with different configurations under surface stress induction;

[0029] In step b, use the genetic algorithm to optimize the structure of the material and automatically extract the results using parametric modeling.

[0030] Preferably, in step a, perform finite element preprocessing, write the corresponding automated preprocessing program code, and then define the constitutive relationship of the material through the umat subroutine to expand the function of the program; to simulate the actuation phenomenon of the material in the electrolyte, write program code to implement the application of the surface stress of the material based on the eigenstress model, the interaction between components, and the setting of boundary conditions.

[0031] Preferably, in the step (3), the results obtained from the simulation are analyzed through post-processing, and the coordinates of the point sets in the model are extracted and calculated in the form of a python program file, and finally output in the form of a txt file, and the actuation effect data of the structure is characterized by the results of this post-processing.

[0032] Preferably, using the mapping relationship between the genotype and phenotype of the structure, the algorithm and the finite element software Abaqus are called to each other in the form of a digital sequence, and drawing on the theory of biological evolution, the configurations with poor actuation effects are gradually eliminated, and the solutions with good actuation effects are increased. For the actuation reaction of the material in the electrolyte, finite element simulation calculations are carried out to complete the finite element simulation process.

[0033] Preferably, in the step b, the steps of optimizing the structure of the material by using the genetic algorithm are as follows:

[0034] b-1. Set the number of generations of evolution and start the loop;

[0035] b-2. Evaluate the fitness of each individual corresponding to the gene sequence;

[0036] b-3. Following the principle that the higher the fitness, the greater the selection probability, select two individuals from the population as the father and mother;

[0037] b-4. Extract the chromosomes of both parents and perform crossover to generate offspring;

[0038] b-5. Mutate the chromosomes of the offspring;

[0039] b-6. Repeat the steps of b-3, b-4, and b-5 until a new population is generated;

[0040] b-7. End the loop.

[0041] An electrochemical actuator structure optimization analysis system based on a genetic algorithm mainly includes a memory and a processor; wherein the memory is used to store a computer program; the processor is used to execute the computer program of the electrochemical actuator structure optimization analysis method based on the present invention.

[0042] Preferably, the material actuation effect optimization program is iteratively optimized by using a written python program.

[0043] Preferably, the present invention utilizes the secondary development of Abaqus and Python, combines with a genetic algorithm program for iterative optimization, and calls the finite element software Abaqus to obtain the strain value of each individual during each iteration; subsequently, the obtained strain value is used to evaluate the fitness value by substituting it into a function, and then the obtained fitness value is used for probability redistribution. The optimal configuration is inherited to the next generation by selecting, crossing, and mutating genes, and finally, when the set number of evolutionary generations is reached, the loop automatically ends.

[0044] Preferably, the present invention is used to simulate the actuation phenomenon that occurs when materials in the electrolyte are induced by surface stress. For this process, a genetic algorithm is combined to optimize the actuation effect to select the optimal configuration that can generate the maximum actuation strain.

[0045] Compared with the prior art, the present invention has the following obvious and prominent substantial features and remarkable advantages:

[0046] 1. The present invention realizes parametric modeling and establishes a mapping relationship between the phenotype and genotype of the model. This method can quickly and effectively simulate the deformation process of different configurations, providing a faster modeling method for quickly studying the actuation behavior of materials.

[0047] 2. The method of the present invention is a finite element method calculation simulation based on the eigenstress model, which further promotes the application of the finite element method in the direction of electrochemical actuators. The automated process is fully realized through a script program, which can effectively improve the preprocessing efficiency and promote the simulation research process.

[0048] 3. The macroscopic strain of the material obtained by the simulation calculation of the method of the present invention through the finite element software Abaqus has guiding significance for experiments, and can provide the optimal configuration to a certain extent for relevant research to achieve the maximum actuation strain, save materials, and optimize the configuration. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a flow chart of the model deformation induced by surface stress calculated by finite element simulation of different configurations in combination with the genetic algorithm in the preferred embodiment of the present invention.

[0050] Figure 2 It is an example diagram exported after modeling in the Abaqus 2020 finite element software in the preferred embodiment of the present invention, representing a schematic diagram of the mapping relationship between the genotype and phenotype of the model taking a single gene sequence as an example.

[0051] Figure 3 It is a stress nephogram exported after modeling and calculation in the Abaqus 2020 finite element software in the preferred embodiment of the present invention. Taking graphene material as an example, it is a result diagram of the model deformation calculated and simulated under the induction of surface stress.

[0052] Figure 4 This is a schematic diagram of the optimal configuration selected from the gene sequences of 100 sample models after 50 selections, mutations, and recombinations in the preferred embodiment of the present invention, taking graphene material as an example and combining with the genetic algorithm. Detailed implementation manners

[0053] The above scheme will be further described below in conjunction with specific implementation examples. The preferred embodiments of the present invention are described in detail as follows:

[0054] Embodiment 1:

[0055] As Figure 1 and Figure 2 shown, taking graphene material as an example, in this embodiment, a method for optimizing and analyzing the structure of an electrochemical actuator based on the genetic algorithm includes the following steps:

[0056] a. Digital modeling, the process of mapping the genotype and phenotype of the model:

[0057] First, a cube is established based on the origin of the coordinate system, and then each face of the cube is numbered; since the structure is symmetric, the symmetric part is constructed on a two-dimensional plane; on the basis of specifying the starting point and ending point of the two-dimensional plane structure, the numbers are used to represent the paths, and after obtaining the two-dimensional structure, it is rotated and mirrored to generate a complete material structure, and the umat subroutine is used to define the constitutive relationship of the material; thus, the genotype and phenotype of the model are mapped to obtain the material model;

[0058] b. Simulation of deformation under stress induction on the material surface:

[0059] According to the model obtained in the step a, the finite element method is used to simulate the deformation under stress induction on the material surface, including the following steps:

[0060] (1) Write a Fortran script, that is, a.for file, to provide the Jacobian matrix of the constitutive of the outer shell material, that is, the change rate of stress increment corresponding to strain increment;

[0061] (2) Write a python script, that is, a.py file, which mainly includes the following simulation parameters:

[0062] (2-1) Physically process the built model into a shell to obtain a shell component; define the material constants through General-UserMaterial; including the elastic modulus and Poisson's ratio, and assign them to the corresponding components;

[0063] (2-2) Add an analysis step Step-1, set the time length to 1, the maximum number of increment steps to 100, the minimum increment step to 1e-5, and turn on geometric nonlinearity;

[0064] (2-3) After assembling the solid component and the shell component, set the two components to interact with each other, with the property of binding;

[0065] (2-4) Set the midpoints within the outermost layers in all directions of the solid component as a point set, and set a hinged boundary condition with U1 = U2 = U3 = 0 at the centroid of the model, i.e., the (0, 0) point, to constrain all its translational degrees of freedom. Among them, U1, U2, and U3 are the X, Y, and Z directions in the rectangular coordinate system respectively;

[0066] (2-5) Use the free mesh generation method to perform tetrahedral mesh generation on the solid component using C3D10 elements in Abaqus / Standard, with a mesh size of 2.5;

[0067] (2-6) Use the free mesh generation method and adopt an advanced algorithm in Abaqus / Standard to perform linear, reduced integration, and quadrilateral shell element (S4R) mesh generation on the shell component, with a mesh size of 2.5;

[0068] (2-7) Call the Umat subroutine and submit the task for calculation;

[0069] (3) Run the above script program using Abaqus finite element software to obtain the calculation results, thereby simulating the actuation phenomenon of the material in the electrolyte and calculating the macroscopic strain generated by materials with different configurations under surface stress induction;

[0070] In step b, use the genetic algorithm to optimize the structure of the material and use parametric modeling to automatically extract the results.

[0071] The simulation results are as Figure 3 shown. It can be seen from the figure that the in-plane load applied by the outer shell elements will affect the internal solid, and the stress distribution is relatively uniform and there is also appropriate strain, which is in line with the actuation reaction phenomenon of graphene in the electrolyte. This embodiment realizes parametric modeling, establishes a mapping relationship between the phenotype and genotype of the model, can quickly and effectively simulate the deformation process of different configurations, and provides a faster modeling method for quickly studying the actuation behavior of materials; the method of this embodiment is a finite element method calculation simulation based on the eigenstress model, which promotes the application of the finite element method in the direction of electrochemical actuators. The automated process is fully realized through the script program, which can effectively improve the pre-processing efficiency and promote the simulation research process; the macroscopic strain of the material obtained by the simulation calculation of this embodiment using the finite element software Abaqus has guiding significance for experiments, and to a certain extent, provides the optimal configuration for relevant research to achieve the maximum actuation strain, save materials, and optimize the configuration.

[0072] Embodiment 2:

[0073] In this embodiment, in step a, finite element preprocessing is performed, corresponding automated preprocessing program code is written, and the constitutive relationship of the material is defined through the umat subroutine to expand the functions of the program; in order to simulate the actuation phenomenon of the material in the electrolyte, program code is written to implement the application of the surface stress of the material based on the eigenstress model, the interaction between components, and the setting of boundary conditions.

[0074] In this embodiment, in step (3), the results obtained from the simulation are analyzed through postprocessing. The coordinates of the point set in the model are extracted and calculated in the form of a python program file, and finally output in the form of a txt file. The actuation effect data of the structure is characterized by the results of this postprocessing. The results obtained from the simulation are shown in Table 1.

[0075] Table 1. Maximum strain information table extracted by simulation calculation for the gene sequence of the present invention and the material configuration corresponding to the sequence

[0076]

[0077] Table 1 takes graphene material as an example. After calculating and simulating the deformation of the model under the induced surface stress, postprocessing is carried out, and its configuration and maximum strain are extracted and written into the table. This embodiment realizes parametric modeling, establishes a mapping relationship between the phenotype and genotype of the model, can quickly and effectively simulate the deformation process of different configurations, and provides a faster modeling method for quickly studying the actuation behavior of materials.

[0078] In this embodiment, by using the mapping relationship between the genotype and phenotype of the structure, the algorithm and the finite element software Abaqus are called with each other in the form of digital sequences, and by referring to biological evolution theory, the configurations with poor actuation effects are gradually eliminated, and the solutions with good actuation effects are increased. For the actuation reaction of the material in the electrolyte, finite element simulation calculation is carried out to complete the finite element simulation process. The finite element simulation calculation developed for the actuation reaction of graphene material in the electrolyte in this embodiment further promotes the development of finite element simulation for this reaction mechanism.

[0079] In this embodiment, in step b, the steps of optimizing the structure of the material by using the genetic algorithm are as follows:

[0080] b-1. Set the number of generations of evolution and start the loop;

[0081] b-2. Evaluate the fitness of each individual corresponding to the gene sequence;

[0082] b-3. Follow the principle that the higher the fitness, the greater the selection probability, and select two individuals from the population as the father and mother;

[0083] b-4. Extract the chromosomes of both parents, perform crossover, and generate offspring;

[0084] b-5. Mutate the chromosomes of the offspring;

[0085] b-6. Repeat steps b-3, b-4, and b-5 until a new population is generated;

[0086] b-7. End the loop.

[0087] Figure 4 This is an optimal configuration schematic diagram selected after 50 selections, mutations, and recombinations of the gene sequences of 100 sample models by combining the genetic algorithm with graphene materials as an example in this embodiment. The method of this embodiment is applicable to the numerical simulation calculation of the finite element method in the process of the electrochemical actuation reaction of materials in the electrolyte, and combines the genetic algorithm to achieve the purpose of optimizing the actuation performance of the materials. Based on the intrinsic stress model, this method combines the finite element method to develop a method that can be used to simulate the actuation phenomenon of materials in the electrolyte. Through the program, automatic random modeling is realized to effectively calculate the macroscopic strain of materials under different structures. Under the condition of combining the genetic algorithm, the structure of the materials is optimized to increase the macroscopic strain of the materials, effectively improving the actuation performance of the materials. The method of this embodiment can realize automatic random modeling using the finite element method, simulate electrochemical actuators of various different configurations, automatically extract the macroscopic strain of the structure by applying surface stress, and finally combine the genetic algorithm to inversely optimize the structure of the materials. Since parametric modeling and complete automatic result extraction are achieved, less computing resources and computing time are required, the efficiency is higher, the cost is lower, and the experiment can be guided using the idea and framework of the process flow, which is very meaningful.

[0088] Embodiment 3:

[0089] This embodiment is basically the same as the above embodiment, with the special feature that:

[0090] In this embodiment, an electrochemical actuator structure optimization analysis system based on the genetic algorithm includes a memory and a processor; wherein the memory is used to store computer programs; the processor is used to execute the computer programs of the electrochemical actuator structure optimization analysis method based on the genetic algorithm in the above embodiment.

[0091] The electrochemical actuator structure optimization analysis system based on the genetic algorithm in this embodiment can realize automatic random modeling using the finite element method, simulate electrochemical actuators of various different configurations, automatically extract the macroscopic strain of the structure by applying surface stress, and finally combine the genetic algorithm to inversely optimize the structure of the materials. Since parametric modeling and complete automatic result extraction are achieved, less computing resources and computing time are required, the efficiency is higher, and the cost is lower.

[0092] The above has described the embodiments of the present invention in conjunction with the accompanying drawings. However, the present invention is not limited to the above embodiments, and various changes can be made according to the purpose of the invention of the present invention. Any changes, modifications, substitutions, combinations or simplifications made based on the spirit and principle of the technical solution of the present invention shall be equivalent replacement methods. As long as they meet the invention purpose of the present invention and do not deviate from the technical principle and inventive concept of the present invention, they all fall within the protection scope of the present invention.

Claims

1. An optimization analysis method for the structure of an electrochemically actuated device based on a genetic algorithm, characterized in that, It includes the following steps: a. Digital modeling, the process of mapping the genotype and phenotype of the model: First, a cube is established based on the origin of the coordinate system, and then each face of the cube is numbered; since the structure is symmetric, the symmetric part is constructed on the two-dimensional plane; On the basis of defining the starting point and ending point of the two-dimensional plane structure, the numbers are used to represent the paths, and after obtaining the two-dimensional structure, it is rotated and mirrored to generate the complete material structure, and the umat subroutine is used to define the constitutive relationship of the material; thus, the genotype and phenotype of the model are mapped to obtain the material model; b. Simulation of deformation under the induction of surface stress of the material: According to the model obtained in step a, the finite element method is used to simulate the deformation under the induction of surface stress of the material, including the following steps: (1) Write a Fortran script, that is, a.for file, to provide the Jacobian matrix of the constitutive of the outer shell material, that is, the change rate of stress increment corresponding to strain increment; (2) Write a python script, that is, a.py file, which mainly includes the following simulation parameters: (2-1) The built model is processed into a shell to obtain a shell component; the material constants are defined through General-User Material; (2-2) Add an analysis step Step-1, set the time length to 1, the maximum number of increment steps to 100, the minimum increment step to 1e-5, and turn on geometric nonlinearity; (2-3) After assembling the solid component and the shell component, set the interaction between the two components, and the attribute is bonding; (2-4) Set the midpoint of the outermost layer in each direction of the solid component as a point set, and set a hinged boundary condition with U1 = U2 = U3 = 0, that is, the body center of the model, that is, the point (0, 0), to constrain all its translational degrees of freedom; where U1, U2, and U3 are the X, Y, and Z directions in the rectangular coordinate system respectively; (2-5) Use the free mesh generation method to perform tetrahedral mesh generation on the solid component using C3D10 elements in Abaqus / Standard, and the mesh size is 2.5; (2-6) Use the free mesh generation method to perform linear, reduced integration, and quadrilateral shell element (S4R) mesh generation on the shell component using an advanced algorithm in Abaqus / Standard, and the mesh size is 2.5; (2-7) Call the Umat subroutine and submit the task for calculation; (3) Run the above script program using Abaqus finite element software to obtain the calculation results, so as to simulate the actuation phenomenon of the material in the electrolyte and calculate the macroscopic strain generated by materials with different configurations under the induction of surface stress; In step b, the genetic algorithm is used to optimize the structure of the material, and parametric modeling is used to automatically extract the results.

2. The method for optimizing and analyzing the structure of an electrochemical actuator based on a genetic algorithm according to claim 1, wherein: In the step a, finite element preprocessing is carried out, corresponding automated preprocessing program codes are written, and then the constitutive relations of materials are defined through umat subroutines to expand the functions of the program; in order to simulate the actuation phenomenon of materials in the electrolyte, program codes are written to implement the application of the surface stress of materials based on the eigenstress model, the interaction between components, and the setting of boundary conditions.

3. The optimization analysis method of the electrochemical actuator structure based on the genetic algorithm according to claim 1, wherein: In the step (3), the results obtained from the simulation are analyzed through postprocessing. The coordinates of the point sets in the model are extracted and calculated in the form of a python program file, and finally output in the form of a txt file. The actuation effect data of the structure are characterized by the results of this postprocessing.

4. The method for optimizing and analyzing the structure of an electrochemical actuator based on a genetic algorithm according to claim 1, wherein: Using the mapping relationship between the genotype and phenotype of the structure, the algorithm and the finite element software Abaqus are called with each other in the form of digital sequences. And drawing on the theory of biological evolution, the configurations with poor actuation effects are gradually eliminated, and the solutions with good actuation effects are increased. For the actuation reaction of materials in the electrolyte, finite element simulation calculations are carried out to complete the finite element simulation process.

5. The method for optimizing and analyzing the structure of an electrochemical actuator based on a genetic algorithm according to claim 1, characterized in that: In the step b, the steps of optimizing the structure of the material by using the genetic algorithm are as follows: b-1. Set the number of generations of evolution and start the loop; b-2. Evaluate the fitness of each individual corresponding to the gene sequence; b-3. In accordance with the principle that the higher the fitness, the greater the selection probability, select two individuals from the population as the father and mother; b-4. Extract the chromosomes of both parents and perform crossover to produce offspring; b-5. Mutate the chromosomes of the offspring; b-6. Repeat the steps of b-3, b-4, and b-5 until a new population is generated; b-7. End the loop.

6. An electrochemical actuator structure optimization analysis system based on a genetic algorithm, characterized in that: It mainly includes a memory and a processor; wherein the memory is used to store computer programs; the processor is used to execute the computer program of the electrochemical actuator structure optimization analysis method according to any one of claims 1-5 based on the genetic algorithm.

Images