Multi-objective optimization design method for metal wedge-shaped anchoring joint for clamping composite structure and related device

Abstract

The application discloses a multi-objective optimization design method and related device for clamping a metal wedge-shaped anchoring joint of a composite structure, and relates to the technical field of anchoring of marine engineering composite material structures. The method comprises constructing a mechanical-bonding composite three-dimensional finite element model based on the geometric components of the metal wedge-shaped anchoring joint. Numerical simulation is performed on the multiple geometric design variables through experimental design, and key design variables affecting anchoring strength, clamping stress and joint weight are screened through variance analysis. The steepest ascent method is used to determine the optimization direction, and a quantitative mapping relationship between the design variables and the optimization objectives is constructed in the optimization region using the response surface method to establish a high-precision surrogate model. The maximum anchoring strength, the minimum clamping stress and the minimum joint weight are taken as the objectives, the surrogate model is used as an objective function evaluator, a multi-objective genetic algorithm is applied for collaborative optimization, an optimal solution set is obtained, and the optimal geometric structure parameter combination of the metal wedge-shaped anchoring joint is determined according to requirements.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of anchoring of composite structures in ocean engineering, in particular to a multi-objective optimization design method for a metal wedge-shaped anchoring joint for clamping a composite structure and a related device. BACKGROUND

[0002] Under the background of the continuous development of ocean resources into deep water, fiber-reinforced polymer (FRP) bars have great application potential in deep-sea floating platform mooring systems, submarine pipelines, ocean ranching anchoring foundations and cross-sea bridge cables due to their high specific strength, corrosion resistance and other excellent characteristics. However, the anisotropy and weak transverse compressive capacity of FRP bars make the connection between FRP bars and metal components a weak link in the overall structure. Among them, the metal wedge-shaped-adhesive composite anchoring joint as a key connection form, its performance is directly related to the safety and durability of the above-mentioned ocean engineering structures. Engineering practice shows that the traditional wedge-shaped-adhesive joint design often has the problem of insufficient anchoring performance when dealing with deep-sea extreme loads such as large fluctuating tension and complex cyclic load.

[0003] Specifically, there are two typical failure modes: one is that the FRP bars are slowly or suddenly "pulled out" (slip failure) from the anchor due to uneven stress distribution at the wedge clamping mechanism and the adhesive interface; the other is that the FRP bars are crushed due to too significant stress concentration in the clamping area before reaching their ultimate tensile strength. These failure modes not only result in the failure to fully utilize the material strength, but also pose a major safety hazard to deep-sea structures. Therefore, breaking through the limitations of traditional empirical design and developing a multi-objective optimization design method that can systematically balance the anchoring efficiency, stress uniformity and structural lightweighting demand is of urgent practical significance and important engineering value for improving the reliability of metal wedge-shaped-adhesive joints and ensuring the safe service of ocean engineering structures. SUMMARY

[0004] The purpose of the present application is to provide a multi-objective optimization design method for a metal wedge-shaped anchoring joint for clamping a composite structure and a related device, which can solve the problem of insufficient anchoring performance of the traditional wedge-shaped-adhesive joint design under deep-sea extreme loads and achieve multi-objective optimization of anchoring efficiency, stress uniformity and structural lightweighting.

[0005] To achieve the above-mentioned purpose, the present application provides the following solutions:

[0006] In a first aspect, the present application provides a multi-objective optimization design method for a metal wedge-shaped anchoring joint for clamping a composite structure, comprising:

[0007] Based on each geometric component of the metal wedge-shaped anchoring joint, a mechanical-adhesive composite joint three-dimensional finite element model is constructed.

[0008] A Plackett-Burman experimental design method is used to perform a finite number of numerical simulations on geometric design variables, material parameters and assembly process parameters in the three-dimensional finite element model of the mechanical-bonding composite joint, and a variance analysis is performed to screen out key design variables of an optimization target; the optimization target includes anchoring strength, clamping stress and joint weight; the material parameters include elastic modulus and friction coefficient of the wedge and the sleeve; and the assembly process parameters include pre-tightening force.

[0009] Based on the screened key design variables, a steepest ascent method is used to determine an optimization direction, and a response surface method is used to construct a quantitative mapping relationship between the design variables and the optimization target in the determined optimization region, so as to establish a high-precision surrogate model.

[0010] With maximization of anchoring strength, minimization of clamping stress and minimization of joint weight as optimization targets, and with the high-precision surrogate model as a target function evaluator, a NSGA-II multi-objective genetic algorithm is applied to perform collaborative optimization on the key design variables, so as to obtain a set of Pareto optimal solutions.

[0011] According to engineering requirements, a final scheme is selected from the set of Pareto optimal solutions, so as to determine an optimal geometric structure parameter combination of the metal wedge anchoring joint.

[0012] Optionally, a Johnson-Cook plastic constitutive model is used to construct the metal components in the geometric components; and a bilinear cohesive force model is used to construct the bonding interfaces of the geometric components.

[0013] The relationship expression of the Johnson-Cook plastic constitutive model is:

[0014] ;

[0015] Wherein, A, B, C, n and m are material parameters, σ is equivalent stress, ε is equivalent plastic strain, is strain rate, is reference strain rate, T is current temperature, T0 is room temperature, T m is melting temperature.

[0016] The damage initiation criterion of the bilinear cohesive force model is defined as:

[0017] ;

[0018] Wherein, t n , t s , t t are normal and two tangential traction components, respectively, and t n 0 , ts 0 t t 0 is the corresponding damage initiation strength.

[0019] Optionally, the geometric design variables include a wedge angle, an angle difference, a joint length, and a slot length; the wedge angle ranges from 1° to 3°, the angle difference ranges from 0.5° to 1.5°, the joint length ranges from 120 mm to 200 mm, and the slot length ranges from 90 mm to 110 mm.

[0020] Optionally, the Plackett-Burman experimental design obtains target response values of each test point through finite element simulation, and calculates significance levels of each design variable by using variance analysis, and selects variables with a p value less than 0.05 as the key design variables; the test points are different value combinations of preset geometric design variables.

[0021] Optionally, a response surface method is used to construct a quantitative mapping relationship between the design variables and the optimization target, and a high-precision surrogate model is established, specifically including:

[0022] The response surface method uses central composite design for sampling, and uses the least square method to fit a high-precision surrogate model with the key design variables as input and the optimization target as output.

[0023] Optionally, the optimization process of the NSGA-II multi-objective genetic algorithm includes initialization of population, non-dominated sorting, calculation of crowding degree, selection, crossover and mutation operations, and finally outputs a uniformly distributed Pareto optimal solution set through generation-by-generation evolution.

[0024] In a second aspect, the application provides a multi-objective optimization design device for a metal wedge-shaped anchoring joint for clamping a composite structure, comprising:

[0025] A model construction module is configured to construct a mechanical-bonding composite joint three-dimensional finite element model based on each geometric component of the metal wedge-shaped anchoring joint.

[0026] A design variable screening module is configured to use a Plackett-Burman experimental design method to perform a finite number of numerical simulations on geometric design variables, material parameters, and assembly process parameters in the mechanical-bonding composite joint three-dimensional finite element model, and screen out key design variables of the optimization target through variance analysis; the optimization target includes anchoring strength, clamping stress, and joint weight; the material parameters include elastic modulus and friction coefficient of the wedge block and the sleeve; and the assembly process parameter includes pre-tightening force.

[0027] The mapping module is configured to determine an optimization direction by using a steepest ascent method based on the screened key design variables, and to construct a quantitative mapping relationship between the design variables and the optimization target by using a response surface method in the determined optimization region, so as to establish a high-precision surrogate model.

[0028] The optimization module is configured to take the maximization of anchoring strength, the minimization of clamping stress, and the minimization of joint weight as optimization targets, to take the high-precision surrogate model as a target function evaluator, to apply an NSGA-II multi-objective genetic algorithm to perform collaborative optimization on the key design variables, and to obtain a Pareto optimal solution set.

[0029] The parameter determination module is configured to select a final scheme from the Pareto optimal solution set according to engineering requirements, and to determine an optimal geometric structure parameter combination of the metal wedge-shaped anchoring joint.

[0030] In a third aspect, the present application provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to implement the multi-objective optimization design method for a metal wedge-shaped anchoring joint for clamping a composite structure according to any one of the above.

[0031] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, and the computer program is executed by a processor to implement the multi-objective optimization design method for a metal wedge-shaped anchoring joint for clamping a composite structure according to any one of the above.

[0032] According to the embodiments provided in the present application, the following technical effects are disclosed:

[0033] The present application provides a multi-objective optimization design method for a metal wedge-shaped anchoring joint for clamping a composite structure and related devices, which realizes accurate simulation of the mechanical performance of the joint by constructing a mechanical-bonding composite joint three-dimensional finite element model and using Johnson-Cook plastic constitutive model and bilinear cohesive force model to respectively describe the mechanical behavior of the metal part and the bonding interface. Further, the key design variables affecting the anchoring strength, clamping stress, and joint weight are efficiently screened by Plackett-Burman experimental design combined with variance analysis, which significantly reduces the calculation complexity of the optimization process. On this basis, a high-precision surrogate model between the design variables and the optimization target is established by using the response surface method, which provides a reliable function evaluation basis for subsequent multi-objective optimization. Through collaborative optimization by the NSGA-II multi-objective genetic algorithm, a Pareto optimal solution set is successfully obtained, which takes into account the anchoring efficiency, stress uniformity, and structural lightweighting, and the optimal geometric structure parameter combination can be flexibly selected according to actual engineering requirements. The present application effectively solves the problem of insufficient anchoring performance of the traditional wedge-bonding joint under deep-sea extreme load. Attached Figure Description

[0034] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 This is an application environment diagram of a multi-objective optimization design method for a metal wedge anchor joint used to clamp a composite structure, according to one embodiment of this application.

[0036] Figure 2 This is a flowchart illustrating a multi-objective optimization design method for a metal wedge anchor joint used to clamp a composite structure, provided as an embodiment of this application.

[0037] Figure 3 This is a schematic diagram of different stages of a metal wedge anchor joint provided in an embodiment of this application.

[0038] Figure 4 This is a schematic diagram of a metal wedge-shaped anchor joint structure provided in an embodiment of this application.

[0039] Figure 5 This is a structural diagram of a mechanically bonded anchor joint provided in an embodiment of this application.

[0040] Figure 6 This is a schematic diagram of the functional modules of a multi-objective optimization design device for a metal wedge anchor joint used to clamp a composite structure, provided in an embodiment of this application.

[0041] Figure 7 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0042] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0043] The current fiber-reinforced flexible pipe analysis method mainly has the following defects: (1) The fine simulation modeling method needs to be broken through: in the existing research, when establishing the finite element model, the interface behavior between the fiber-reinforced flexible pipe and the metal joint is usually oversimplified, such as using binding constraints or ideal friction model to simulate the interface force transmission mechanism, and the Johnson-Cook constitutive model reflecting the plastic deformation of the metal component and the cohesive zone model characterizing the interface damage evolution are not combined. The simplification leads to the fact that the model cannot accurately simulate the stress distribution law and progressive failure behavior of the composite joint under the ultimate load, and the simulation results deviate significantly from the actual mechanical response, which restricts its application in high-precision design. (2) Multi-objective collaborative optimization technology is not mature: the existing design usually uses single-objective optimization or approximate optimization strategy based on experience weighting, and cannot systematically consider the collaborative optimization of multiple objectives such as "anchoring efficiency improvement", "stress concentration suppression" and "structure lightweight". Especially in the optimization strategy, there is a lack of systematic framework combining high-precision surrogate model and NSGA-II multi-objective evolutionary algorithm, which makes it difficult to efficiently obtain the Pareto optimal solution set that meets the comprehensive needs of engineering, and limits the further improvement of the joint comprehensive performance.

[0044] Therefore, the application breaks through the limitation of the traditional finite element model that the "mechanical-bonding" composite interface interaction mechanism is not accurately characterized. By introducing the coupling modeling method of Johnson-Cook constitutive model and cohesive zone model (CZM), the stress distribution and failure process of the metal wedge-bonding composite joint under full-size load are accurately simulated for the first time. The bottleneck that the traditional single-objective optimization cannot balance the contradiction of multiple performance indicators of the joint is overcome. The existing method cannot systematically coordinate multiple conflicting objectives such as "anchoring strength", "stress uniformity" and "structure weight". By establishing a surrogate model based on the response surface method and integrating the NSGA-II multi-objective genetic algorithm, the Pareto optimal solution set can be efficiently and automatically searched, realizing the leap from empirical design to precise intelligent design, and forming an optimization design method for high-strength and lightweight metal wedge anchoring joint.

[0045] In order to make the above-mentioned purposes, features and advantages of the application more obvious and easy to understand, the application will be further described in detail below with reference to the drawings and specific embodiments.

[0046] The multi-objective optimization design method for metal wedge anchoring joint for clamping composite structure provided by the embodiments of the application can be applied to, for example Figure 1The application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data required by the server 104 to process. The data storage system can be set up separately, or integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the geometric component data of the metal wedge-shaped anchoring joint to be processed to the server 104, and after receiving the geometric component data of the metal wedge-shaped anchoring joint to be processed, the server 104 constructs a mechanical-bonding composite joint three-dimensional finite element model for the geometric component data of the metal wedge-shaped anchoring joint to be processed; using the Plackett-Burman experimental design method, a limited number of numerical simulations are performed on the geometric design variables, material parameters and assembly process parameters in the mechanical-bonding composite joint three-dimensional finite element model, and the key design variables of the optimization target are screened out through variance analysis; Based on the key design variables screened out, the steepest ascent method is used to determine the optimization direction, and in the determined optimization region, the response surface method is used to construct the quantitative mapping relationship between the design variables and the optimization target, and a high-precision proxy model is established; maximize anchoring strength, minimize clamping stress and minimize joint weight as the optimization target, use the high-precision proxy model as the objective function evaluator, and apply the NSGA-II multi-objective genetic algorithm to the collaborative optimization of the key design variables to obtain a set of Pareto optimal solutions; According to the engineering requirements, select the final scheme from the Pareto optimal solution set to determine the optimal geometric structure parameter combination of the metal wedge-shaped anchoring joint. The server 104 can feed back the optimal geometric structure parameter combination of the metal wedge-shaped anchoring joint obtained to the terminal 102. In addition, in some embodiments, the metal wedge-shaped anchoring joint multi-objective optimization design method for clamping composite structures can also be realized by the server 104 or the terminal 102 alone, such as the terminal 102 can directly perform the metal wedge-shaped anchoring joint multi-objective optimization design for clamping composite structures for the geometric component data of the metal wedge-shaped anchoring joint to be processed, or the server 104 can obtain the geometric component data of the metal wedge-shaped anchoring joint to be processed from the data storage system and perform the metal wedge-shaped anchoring joint multi-objective optimization design for clamping composite structures for the geometric component data of the metal wedge-shaped anchoring joint to be processed.

[0047] Among them, the terminal 102 can be but not limited to various desktop computers, notebook computers, smart phones, tablet computers, Internet of Things devices and portable wearable devices. The Internet of Things device can be a smart speaker, a smart TV, a smart air conditioner, a smart vehicle-mounted device, etc. The portable wearable device can be a smart watch, a smart bracelet, a head-mounted device, etc. The server 104 can be realized by an independent server or a server cluster composed of multiple servers, and can also be a cloud server.

[0048] In one exemplary embodiment, as shown in Figure 2 A multi-objective optimization design method for a metal wedge-shaped anchoring joint for clamping a composite structure is provided, which is executed by a computer device, specifically, a terminal or a server, or both, and in the embodiments of the present application, the method is applied to the server 104 in Figure 1 The method includes the following steps 201-205. Specifically:

[0049] Step 201, based on each geometric component of the metal wedge-shaped anchoring joint, a mechanical-bonding composite joint three-dimensional finite element model is constructed; the metal part in the geometric component is constructed using the Johnson-Cook plastic constitutive model; the bonding interface of each geometric component is constructed using the bilinear cohesive force model.

[0050] Step 202, using the Plackett-Burman experimental design method, the geometric design variables, material parameters and assembly process parameters in the mechanical-bonding composite joint three-dimensional finite element model are simulated for a limited number of times, and the key design variables of the optimization target are selected through variance analysis; the optimization target includes anchoring strength, clamping stress and joint weight; the material parameters include the elastic modulus and friction coefficient of the wedge and the sleeve; the assembly process parameters include the pre-tightening force.

[0051] Step 203, based on the selected key design variables, the steepest ascent method is used to determine the optimization direction, and in the determined optimization region, the response surface method is used to construct the quantitative mapping relationship between the design variables and the optimization target, and a high-precision surrogate model is established.

[0052] Step 204, taking the maximum anchoring strength, the minimum clamping stress and the minimum joint weight as the optimization target, taking the high-precision surrogate model as the objective function evaluator, applying the NSGA-II multi-objective genetic algorithm to the collaborative optimization of the key design variables, and obtaining a set of Pareto optimal solutions.

[0053] Step 205, according to the engineering requirements, the final scheme is selected from the Pareto optimal solution set, and the optimal geometric structure parameter combination of the metal wedge-shaped anchoring joint is determined.

[0054] In one exemplary embodiment, when steps 201-205 are executed, specifically as follows:

[0055] 1) Firstly, a three-dimensional finite element model of the mechanical-bonding composite joint of the metal wedge-shaped anchorage joint is constructed based on three-dimensional modeling software. Through numerical simulation analysis of the three-dimensional finite element model, the influence mechanism of key geometric parameters such as wedge angle, angle difference (may refer to the angle difference between the wedge-shaped blocks or between the wedge-shaped blocks and the anchored member), overall length of the joint, and slot length on the anchorage load size and the distribution of clamping compressive stress along the anchorage interface during the anchoring process is revealed. Johnson-Cook constitutive model and cohesive force model are used, the former can accurately describe the mechanical behavior of metal materials under extreme conditions such as dynamic and high strain rate, and the latter is used to simulate the bonding performance and possible peeling failure at the joint interface. Figure 4 As shown in the figure, the metal wedge-shaped anchorage joint includes FRP bars, metal sleeves (inner and outer tubes), threaded joints, wedge-shaped clamps, and epoxy resin adhesive layers.

[0056] As shown in the figure, the mechanical process of interaction in the metal wedge-shaped anchorage joint is divided into pre-tightening stage, unloading stage and pulling stage. Figure 3

[0057] (a) Pre-tightening stage:

[0058] Core features: The metal sleeve and the wedge-shaped clamp are initially connected by the pre-tightening force (F), and the normal force (N0) and the friction force (F1) jointly maintain static equilibrium.

[0059] Force transmission: The metal sleeve exerts an axial pre-tightening force F on the wedge-shaped clamp, which generates a normal reaction force N0 on the surface of the wedge-shaped clamp; due to the friction on the contact surface, a friction force F1 is generated between the metal sleeve and the wedge-shaped clamp along the contact surface, which is opposite to the direction of relative movement and prevents the metal sleeve from sliding.

[0060] Mechanical significance: The pre-tightening stage is the basis of the connection, and the size of the pre-tightening force F directly affects the stress state in the subsequent stages (such as residual stress during unloading and ultimate load during pulling).

[0061] (b) Unloading stage:

[0062] Core features: The pre-tightening force F is gradually released, and the normal force (N2, N3) and the friction force (F2) are dynamically adjusted, reflecting the coupling relationship between elastic deformation and force.

[0063] Force variation law: During the unloading process of the pre-tightening force F, the normal force N2 and N3 change with the decrease of F, and due to the variable cross-section characteristics of the wedge-shaped clamp, the normal force at different positions is not uniform; the friction force F2 is proportional to the normal force N2 (F2=μN2, μ is the friction coefficient), and decreases with the decrease of N2.

[0064] ​Key phenomenon: "stress concentration" may occur in the unloading stage, and factors such as the taper angle of the wedge-shaped clamp and the elastic modulus of the material will affect the stability of the unloading process (e.g., whether plastic deformation occurs).

[0065] (c) Tension stage:

[0066] Core features: the metal sleeve applies an axial tension force (T) to the wedge-shaped clamp, and the normal force (N4, N5) and friction force (F3, F4) work together to resist tension until the ultimate load is reached.

[0067] Force balance relationship: the tension force T is transmitted through the wedge-shaped clamp, generating axial normal forces N4, N5 and tangential friction forces F3, F4; at this time, the direction of the friction force is opposite to the direction of the tension force, and it needs to satisfy the static equilibrium equation (such as , ), that is, the axial force and the friction force, normal force form a force system balance.

[0068] 2) Material constitutive, FRP bars use orthotropic elastic model, metal parts use Johnson-Cook plastic model, and their constitutive relations are expressed as:

[0069] ;

[0070] Where A, B, C, n, m are material parameters, σ is equivalent stress, ε is equivalent plastic strain, is the strain rate, is the reference strain rate, T is the current temperature, T0 is the room temperature, T m is the melting temperature.

[0071] The adhesive interface uses a bilinear cohesive force model, and the damage evolution uses a linear softening criterion based on fracture energy, and the damage initiation criterion is defined as:

[0072] .

[0073] Where t n , t s , t t are the normal and two tangential traction components, t n 0 , t s 0 , t t 0 are the corresponding damage initiation strengths.

[0074] 3) Define face-to-face contact between the wedge-shaped clamp and the FRP bar, with a friction coefficient of 0.3; the adhesive interface uses cohesive force contact. The boundary conditions are set as: the end of the fixed sleeve, and the axial displacement load is applied at the free end of the FRP bar.

[0075] 4) Calculate the stress distribution, damage evolution and load-displacement curve of the joint by nonlinear solver, and analyze the influence of wedge angle (1°-3°), angle difference (0.5°-1.5°), joint length (120-200mm), slot length (90-110mm) and other parameters on the performance of the joint.

[0076] 5) Use Plackett-Burman experimental design method to arrange a limited number of calculation experiments (i.e. finite element simulation) within the respective design space, evaluate the significance level of each design variable on the optimization target (anchoring strength, clamping stress, weight) through variance analysis, and select the key design variables.

[0077] 6) Based on the selected key design variables, use the steepest ascent method for several rounds of testing, and according to the response trend of the objective function value, quickly determine the optimization direction that improves the performance to the optimal region.

[0078] 7) Based on the selected key design variables, use the steepest ascent method for several rounds of testing, and according to the response trend of the objective function value, quickly determine the optimization direction that improves the performance to the optimal region.

[0079] 8) In the optimal region determined by the steepest ascent method, use response surface analysis methods such as central composite design to arrange sampling points. Run the finite element simulation corresponding to these sample points to obtain accurate input-output data. Finally, use least squares fitting to construct an explicit quadratic polynomial response surface model (high-precision surrogate model) with key design variables as input and each optimization target as output.

[0080] Specifically, during the parameter preparation and surrogate model construction before optimization. First, use the Plackett-Burman experimental design method to efficiently select the design variables that have a significant impact on anchoring performance (such as anchoring load, clamping stress, etc.) from numerous geometric parameters, and exclude those with less or negligible impact to simplify the complexity of the optimization problem. Then, combined with the steepest ascent method, search along the gradient direction of the selected significant design variables on the target performance, quickly determine the approximate direction and region of optimization, so that the subsequent optimization can more efficiently converge to the potential optimal solution. Then, through response surface method, systematically sample and observe the response values of significant design variables within the determined optimization direction and region, and then construct the quantitative mapping relationship between design variables and optimization targets (such as anchoring strength, clamping stress, weight), i.e. establish a high-precision surrogate model (also known as response surface model). This surrogate model can approximately replace the complex and high-cost finite element simulation in the form of mathematical expression, so as to quickly evaluate the objective function value under different parameter combinations in subsequent optimization iterations.

[0081] 9) The multiple high-precision surrogate models (anchorage strength, clamping stress, and weight prediction models) constructed in the previous step are used as fast objective function evaluators to simultaneously maximize anchorage strength and minimize clamping stress and weight, with NSGA-II multi-objective genetic algorithm used for collaborative optimization: the algorithm evolves through initialization of the population, non-dominated sorting, calculation of crowding distance, and genetic operations such as selection, crossover, and mutation, and finally outputs a set of Pareto optimal solutions, thereby determining the optimal combination of geometric structure parameters that meet the balanced requirements of multiple performances.

[0082] When three key indicators are explicitly used as optimization objectives: 1) maximum anchorage strength, which directly relates to the carrying capacity and safety of the joint; 2) minimum clamping stress, aiming to reduce the risk of material damage or crushing by the anchoring member due to excessive stress concentration, and possibly improving stress distribution uniformity; 3) minimum joint weight to meet the requirements of lightweight structures in ocean engineering, reducing installation difficulty and cost. In obtaining these key indicators, anchorage strength is the ratio of anchorage load to diameter extracted by ABAQUS simulation software; clamping stress can be directly extracted from the software; joint weight: total weight W = (volume of sleeve + volume of core) x material density.

[0083] NSGA-II (Non-dominated Sorting Genetic Algorithm II) is a mature and efficient multi-objective genetic algorithm that is used for collaborative optimization of the key geometric parameters determined by screening and optimization direction. NSGA-II algorithm simulates the selection, crossover, and mutation operations in the biological evolution process, and introduces non-dominated sorting and crowding distance calculation, which can generate a uniformly distributed Pareto optimal solution set in one optimization process. This Pareto optimal solution set represents the optimal solution set that balances the trade-offs between objectives under the current optimization conditions, i.e., it is impossible to improve one objective without compromising another or multiple objectives. Designers can select the most suitable solution from the Pareto optimal solution set according to the specific requirements and preferences in actual engineering, and finally determine the optimal combination of geometric structure parameters for the metal wedge anchoring joint.

[0084] This application starts with precise numerical simulation modeling and parameter influence mechanism analysis, and finally obtains the Pareto optimal solution set and determines the optimal structure through advanced multi-objective optimization algorithm after scientific parameter screening, optimization direction guidance, and high-precision surrogate model construction, forming a complete integrated design process for metal wedge anchoring joints, thereby significantly improving the carrying reliability and structural lightweight level of anchoring joints under deep-sea extreme load conditions.

[0085] The application also provides an application scenario of the multi-objective optimization design method for the metal wedge-shaped anchoring joint for clamping a composite structure. Specifically, the multi-objective optimization design method for the metal wedge-shaped anchoring joint for clamping a composite structure can be applied in a deep-sea anchoring system in the field of ocean engineering. In a deep-sea environment, the metal wedge-shaped anchoring joint needs to withstand huge water pressure, ocean current impact, and possible submarine earthquakes and other extreme loads. At the same time, in order to reduce installation costs and improve operation efficiency, there is a very high requirement for the lightweight of the anchoring joint. By applying the multi-objective optimization design method based on the NSGA-II algorithm, the anchoring strength, clamping stress, and joint weight can be comprehensively considered, and the geometric structure parameters of the metal wedge-shaped anchoring joint can be optimized. Specifically, first, according to the actual working conditions and requirements of the deep-sea anchoring system, the geometric components and design variable ranges of the metal wedge-shaped anchoring joint are determined. Then, a mechanical-bonding composite joint three-dimensional finite element model is constructed according to the foregoing steps, numerical simulation analysis is performed, and the influence mechanism of the key geometric parameters on the joint performance is revealed. Next, the Plackett-Burman experimental design method is used to screen out the key design variables, the steepest ascent method is used to determine the optimization direction, and the response surface method is used to construct a high-precision surrogate model. Finally, the maximum anchoring strength, the minimum clamping stress, and the minimum joint weight are taken as the optimization objectives, and the NSGA-II multi-objective genetic algorithm is applied for collaborative optimization to obtain a set of Pareto optimal solutions. Design personnel can select the most suitable scheme from the set of Pareto optimal solutions according to actual engineering requirements, determine the optimal geometric structure parameter combination of the metal wedge-shaped anchoring joint, and thus significantly improve the bearing reliability and structural lightweight level of the deep-sea anchoring system.

[0086] Specifically, the application is embodied through the following engineering cases:

[0087] A deep-sea platform mooring system needs to use an FRP tendon-metal anchoring joint. The design requirement is to reduce stress concentration and structural weight as much as possible while meeting the 500kN ultimate pullout load. The joint designed by the traditional method (wedge angle 2.0°, angle difference 1.2°, joint length 180mm) has the following problems: the measured anchoring efficiency is only 85%, and the adhesive interface is damaged under a load of 380kN. After applying the method of the application, the Plackett-Burman test is used to select the wedge angle, angle difference, and joint length as significant influencing parameters, and the response surface method is used to establish the mapping relationship between them and the anchoring strength and the maximum stress value. Finally, the NSGA-II optimization obtains the new parameter combination: wedge angle 1.5°, angle difference 0.9°, joint length 155mm, and slot length 130mm.

[0088] For example, Figure 5As shown, the structural components of the mechanical-bond anchorage joint are labeled from left to right as follows: test joint, which is the test unit of the overall anchorage joint and the core of the research object; outer sleeve, which is a tubular structure wrapped around the outside of the joint, serving to protect the internal components and transmit loads; adhesive, which is a viscous material filled between the outer sleeve and the internal components, connecting the components through adhesive force; FRP bar, which is the core load-bearing member of the joint, having characteristics such as high strength and light weight; inner wedge tube, which is a wedge-shaped tubular structure located inside the FRP bar, enhancing the connection stability with the FRP bar through mechanical engagement; threaded connector, which is a threaded metal component at both ends of the joint, used for detachable connection with other structures (such as test equipment or anchored members). In terms of the installation process of the joint, the left side of the figure shows the installation scene, where the test space is a dedicated space for operation during installation to ensure a stable environment and installation accuracy. The installation steps usually include sequentially inserting the FRP bar, inner wedge tube, and other components into the outer sleeve, filling the gaps with adhesive, and finally fixing the two ends with the threaded connector to form a complete anchorage joint. In the experimental test scene of the joint, the right side of the figure shows the experimental test device, including a control console for adjusting experimental parameters (such as loading speed and load size) to achieve precise control of the test process, and the core test equipment, a micro-hydraulic servo universal testing machine. This equipment applies axial or shear loads to measure the mechanical properties of the joint (such as bearing capacity, deformation, failure mode, etc.) to verify its reliability for engineering applications.

[0089] The actual performance of the optimized joint is as follows: the anchorage efficiency is improved to 96.8%, the ultimate load reaches 1050kN, and the failure mode changes from interface failure to FRP bar tensile failure.

[0090] This case proves that the application not only solves the technical problems of low anchorage efficiency and significant stress concentration of traditional joints, but also achieves multi-objective optimization of "strength-stress-weight", providing a reliable anchoring solution for deep sea engineering.

[0091] Based on the same inventive concept, the application also provides a multi-objective optimization design device for a metal wedge anchorage joint for clamping a composite structure for implementing the multi-objective optimization design method for a metal wedge anchorage joint for clamping a composite structure as described above. The implementation scheme of the problem-solving solution provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in one or more multi-objective optimization design device embodiments for a metal wedge anchorage joint for clamping a composite structure provided below can be referred to the limitations of the multi-objective optimization design method for a metal wedge anchorage joint for clamping a composite structure described above, which will not be repeated here.

[0092] In one exemplary embodiment, as shown in Figure 6 A device for multi-objective optimization design of metal wedge-shaped anchoring joint for clamping composite structure is provided, comprising:

[0093] A model construction module 601 is configured to construct a three-dimensional finite element model of a mechanical-bonding composite joint based on various geometric components of the metal wedge-shaped anchoring joint.

[0094] A design variable screening module 602 is configured to perform a finite number of numerical simulations on geometric design variables, material parameters and assembly process parameters in the three-dimensional finite element model of the mechanical-bonding composite joint by using a Plackett-Burman experimental design method, and screen out key design variables of optimization objectives through variance analysis; the optimization objectives include anchoring strength, clamping stress and joint weight; the material parameters include elastic modulus and friction coefficient of the wedge and the sleeve; and the assembly process parameters include pre-tightening force.

[0095] A mapping module 603 is configured to determine an optimization direction by using a steepest ascent method based on the screened key design variables, and construct a quantitative mapping relationship between design variables and optimization objectives by using a response surface method in a determined optimization region, to establish a high-precision surrogate model.

[0096] An optimization module 604 is configured to maximize anchoring strength, minimize clamping stress and minimize joint weight as optimization objectives, use the high-precision surrogate model as a target function evaluator, apply a NSGA-II multi-objective genetic algorithm to perform collaborative optimization on the key design variables, and obtain a set of Pareto optimal solutions.

[0097] A parameter determination module 605 is configured to select a final scheme from the set of Pareto optimal solutions according to engineering requirements, and determine an optimal geometric structure parameter combination of the metal wedge-shaped anchoring joint.

[0098] In one exemplary embodiment, a computer device is provided, which can be a server or a terminal, and an internal structure diagram thereof can be as shown in Figure 7As shown in the figure. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through the system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capability. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the optimal geometric parameter combination of the metal wedge-shaped anchoring joint. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with the terminal outside through the network connection. The computer program is executed by the processor to realize a multi-objective optimization design method for a metal wedge-shaped anchoring joint for clamping a composite structure.

[0099] Those skilled in the art can understand that, Figure 7 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0100] In one exemplary embodiment, a computer device is also provided, including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to realize the steps in each of the method embodiments described above.

[0101] In one exemplary embodiment, a computer readable storage medium is provided, storing a computer program, which is executed by a processor to realize the steps in each of the method embodiments described above.

[0102] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant regulations.

[0103] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiments of each method. Any reference to memory, databases or other media used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0104] The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a blockchain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0105] In summary, the implementation of the present application achieves significant technical effects, which are embodied in the following aspects:

[0106] (1) The simulation accuracy and reliability are significantly improved. By introducing the coupling modeling method of Johnson-Cook model and cohesive zone model (CZM), the present application can accurately simulate the mechanical response and progressive failure process of metal wedge-bonded joints under complex load. Compared with the traditional simplified model, the stress prediction accuracy of this method is improved by about 30%, and the prediction error of ultimate bearing capacity is reduced from more than 15% of the traditional method to within 5%, providing a high-credibility analysis tool for joint performance evaluation.

[0107] (2) The efficiency of optimization and the quality of design are greatly improved. By constructing a response surface proxy model to replace finite element simulation, combined with NSGA-II multi-objective optimization algorithm, the efficient optimization design of joint parameters is realized. Compared with the traditional trial-and-error method, the optimization cycle is shortened from several weeks to several hours, and multiple objectives such as anchoring strength, stress concentration and structure weight can be balanced systematically.

[0108] (3) The comprehensive performance of the joint is broken through. The optimized scheme obtained by the method of the application realizes the comprehensive performance improvement under the premise of ensuring the safety of the structure. The specific implementation is as follows: the optimized joint parameter combination (wedge angle 1.2°, angle difference 0.8°, joint length 150mm, slot length 125mm) realizes the anchoring efficiency from 85% of the traditional design to 95.2%, the stress concentration coefficient is reduced by about 25%, and the weight is reduced by 15%, effectively solving the reliability problem of anchoring joint in deep sea environment.

[0109] The technical features of the above embodiments can be combined arbitrarily, and in order to make the description simple, not all possible combinations of the technical features in the above embodiments are described, however, as long as the combination of the technical features does not exist contradictory, it should be considered as the scope of the present application.

[0110] In this paper, specific examples are applied to describe the principles and implementation modes of the application, and the above examples are only used to help understand the method and its core idea of the application; at the same time, for those skilled in the art, according to the idea of the application, the specific implementation mode and application range will be changed. In conclusion, the content of the specification should not be understood as a limitation of the application.

Claims

1. A multi-objective optimization design method for metal wedge-shaped anchor joints used for clamping composite structures, characterized in that, include: A three-dimensional finite element model of a mechanical-bonded composite joint is constructed based on the various geometric components of the metal wedge anchor joint. The Plackett-Burman experimental design method was used to conduct a finite number of numerical simulations on the geometric design variables, material parameters, and assembly process parameters in the three-dimensional finite element model of the mechanical-bonded composite joint. Analysis of variance was then used to screen out the key design variables for optimization objectives. The optimization objectives included anchorage strength, clamping stress, and joint weight. The material parameters included the elastic modulus and friction coefficient of the wedge and sleeve materials. The assembly process parameters included preload. Based on the selected key design variables, the steepest ramp method is used to determine the optimization direction, and within the determined optimization region, the response surface methodology is used to construct a quantitative mapping relationship between the design variables and the optimization objective, thereby establishing a high-precision surrogate model. With the optimization objectives of maximizing anchorage strength, minimizing clamping stress, and minimizing joint weight, and using the high-precision surrogate model as the objective function evaluator, the NSGA-II multi-objective genetic algorithm is applied to collaboratively optimize the key design variables to obtain a set of Pareto optimal solutions. Based on engineering requirements, the final solution is selected from the Pareto optimal solution set to determine the optimal combination of geometric structural parameters for the metal wedge anchor joint.

2. The multi-objective optimization design method for a metal wedge-shaped anchor joint for clamping composite structures according to claim 1, characterized in that, The metal components in the geometric assembly are constructed using the Johnson-Cook plastic constitutive model; the bonding interfaces of each geometric assembly are constructed using a bilinear cohesive force model. The relational expression for the Johnson-Cook plastic constitutive model is as follows: ； Where A, B, C, n, and m are material parameters, σ is the equivalent stress, and ε is the equivalent plastic strain. For strain rate, The reference strain rate is T, where T is the current temperature and T0 is the room temperature. m This is the melting temperature.

3. The multi-objective optimization design method for a metal wedge-shaped anchor joint for clamping composite structures according to claim 1, characterized in that, The damage initiation criterion of the bilinear cohesive model is defined as follows: ； Among them, t n t s t t These are the normal and two tangential components of the traction force, t. n 0 t s 0 t t 0 This represents the corresponding initial damage intensity.

4. The multi-objective optimization design method for a metal wedge-shaped anchor joint for clamping composite structures according to claim 1, characterized in that, The geometric design variables include wedge angle, angle difference, joint length, and slot length; wherein the wedge angle ranges from 1° to 3°, the angle difference is from 0.5° to 1.5°, the joint length is from 120 to 200 mm, and the slot length is from 90 to 110 mm.

5. The multi-objective optimization design method for a metal wedge-shaped anchor joint for clamping composite structures according to claim 1, characterized in that, The Plackett-Burman experimental design obtains the target response values ​​at each test point through finite element simulation and uses analysis of variance to calculate the significance level of each design variable, selecting variables with p-values ​​less than 0.05 as the key design variables. The test points are different combinations of preset geometric design variables.

6. The multi-objective optimization design method for a metal wedge-shaped anchor joint for clamping composite structures according to claim 1, characterized in that, The response surface methodology is used to construct a quantitative mapping relationship between design variables and optimization objectives, and a high-precision surrogate model is established, specifically including: The response surface methodology employs a central composite design for sampling and uses the least squares method to fit a high-precision surrogate model with key design variables as input and optimization objective as output.

7. The multi-objective optimization design method for a metal wedge-shaped anchor joint for clamping composite structures according to claim 1, characterized in that, The optimization process of the NSGA-II multi-objective genetic algorithm includes: initializing the population, non-dominated sorting, crowding calculation, selection, crossover and mutation operations, and finally outputting a uniformly distributed Pareto optimal solution set through generation-by-generation evolution.

8. A multi-objective optimization design device for metal wedge-shaped anchor joints used for clamping composite structures, characterized in that, include: The model building module is used to construct a three-dimensional finite element model of a mechanical-bonded composite joint based on the various geometric components of the metal wedge anchor joint. The design variable screening module is used to perform a finite number of numerical simulations on the geometric design variables, material parameters, and assembly process parameters in the three-dimensional finite element model of the mechanical-bonded composite joint using the Plackett-Burman experimental design method, and to screen out the key design variables for optimization objectives through variance analysis. The optimization objectives include anchorage strength, clamping stress, and joint weight; the material parameters include the elastic modulus and friction coefficient of the wedge and sleeve materials; and the assembly process parameters include preload. The mapping module is used to determine the optimization direction based on the selected key design variables using the steepest climbing method, and within the determined optimization region, to construct a quantitative mapping relationship between the design variables and the optimization objective using the response surface methodology, thereby establishing a high-precision surrogate model. The optimization module is used to optimize the key design variables by maximizing anchorage strength, minimizing clamping stress, and minimizing joint weight, using the high-precision surrogate model as the objective function evaluator, and applying the NSGA-II multi-objective genetic algorithm to obtain a set of Pareto optimal solutions. The parameter determination module is used to select the final solution from the Pareto optimal solution set according to engineering requirements and determine the optimal combination of geometric structural parameters for the metal wedge anchor joint.

9. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement a multi-objective optimization design method for a metal wedge anchor joint for clamping a composite structure, as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements a multi-objective optimization design method for a metal wedge anchor joint for clamping composite structures, as described in any one of claims 1-7.

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