Large-scale equipment impact resistance optimization design method and device based on multi-objective integration

Abstract

The invention discloses a large-scale equipment shock resistance optimization design method and device based on multi-objective integration, belongs to the technical field of large-scale equipment shock resistance design and optimization, and particularly relates to a method which comprehensively considers multiple design objectives and carries out optimization design on response of large-scale equipment in a shock environment based on a multi-objective integration method. The problem that an existing impact resistance design method only optimizes a single target and cannot deal with a complex multi-target optimization problem is solved. The method comprises the following steps: scoring the importance of each objective function to form a judgment matrix; calculating the weight of each objective function by using an analytic hierarchy process based on the judgment matrix; and S5, carrying out multi-criterion decision analysis on the optimal solution set by utilizing the weight of the objective function, and selecting an optimal design scheme. The large-scale equipment impact resistance optimization design method and device based on multi-objective integration are suitable for large-scale equipment impact resistance optimization design.

Description

Technical Field

[0001] The present invention relates to the technical field of anti - shock design and optimization of large - scale equipment, and particularly to a method for optimizing the response of large - scale equipment under shock environment based on a multi - objective integration method by comprehensively considering multiple design objectives. Background Art

[0002] During the operation of large - scale equipment, it often receives various forms of shock loads. For example, ships are subjected to wave impacts under severe sea conditions, aerospace vehicles are impacted during launch and landing, heavy machinery is impacted and vibrated during operation, and building structures are stressed during earthquakes. These shock loads can cause the dynamic response of the equipment, including acceleration, velocity, displacement, deflection, strain, pressure, etc. Excessive dynamic loads and responses may lead to equipment structural damage, performance degradation, and even safety accidents.

[0003] Existing anti - shock design methods usually optimize only for a single objective. For example, many designs only focus on improving the structural strength of the equipment to resist shock loads, but ignore the problems of increased costs and reduced performance caused by the increase in equipment weight. Some designs may reduce the structural strength in order to reduce the equipment weight and improve performance, resulting in the equipment being easily damaged under shock loads. In addition, due to equipment manufacturing costs, some designs may choose materials with lower performance or simplify the structure, unable to meet the requirements of anti - shock performance. Since the response of large - scale equipment has the characteristics of multiple variables and multiple objectives, multiple factors such as anti - shock performance, structural strength, mass, and cost need to be balanced during the design process. Traditional methods are difficult to effectively solve these complex multi - objective optimization problems.

[0004] Therefore, there is an urgent need for a method that can comprehensively consider multiple design objectives and comprehensively optimize the anti - shock performance of large - scale equipment. Summary of the Invention

[0005] The present invention proposes an anti - shock optimization design method and device for large - scale equipment based on multi - objective integration, which solves the problem that existing anti - shock design methods only optimize for a single objective and cannot handle complex multi - objective optimization problems.

[0006] The anti - shock optimization design method for large - scale equipment based on multi - objective integration according to the present invention has the following technical solutions: The method includes the following steps: Step S1: Determine the typical response indexes of the equipment and its components under external shock loads; Step S2: Establish a multi - objective optimization model with the above - mentioned typical response indexes as the objective function, and determine the design variable vector X and the constraint conditions; Step S3: Score the importance of each objective function to form a judgment matrix; based on the judgment matrix, calculate the weights of each objective function using the analytic hierarchy process; Step S4: Use the multi-objective genetic algorithm to globally optimize the design variable vector X to form an optimal solution set; Step S5: Use the weights of the objective functions to perform multi-criteria decision analysis on the optimal solution set and select the optimal design scheme; Step S6: Verify the selected optimal design scheme; according to the verification results, repeat the operations in Steps S2 to S5 to update the parameters of the multi-objective optimization model, the optimal solution set, and select a new optimal design scheme until the indicators of the optimal design scheme meet the design requirements.

[0007] Further, a preferred implementation is provided, where the typical response indicators include acceleration, velocity, displacement, deflection, strain, and pressure.

[0008] Further, a preferred implementation is provided, where the objective functions include: Minimize the acceleration response: min f1(X) = A max ; where A max is the peak acceleration; Minimize the velocity response: min f2(X) = V max ; where V max is the peak acceleration; Minimize the displacement: min f3(X) = D max ; where D max is the maximum displacement; Minimize the deflection: min f4(X) = δ max ; where δ max is the maximum deflection; Minimize the strain: min f5(X) = ε max ; where ε max is the maximum strain; Minimize the peak pressure: min f6(X) = P max ; where P max is the peak pressure; Minimize the equipment weight: min f7(X) = W; Among them, W is the weight of the device; Minimize the device cost: min f8(X) = C; Among them, C is the manufacturing cost; The design variable vector X is: X = [x1, x2,..., x n ; Among them, x i represents the i-th design parameter of the device; The constraint conditions include: Safety factor constraint: Introduce the safety factor SF to ensure that the device still has sufficient safety margin under extreme conditions; Size and space constraint: The external dimensions of the device need to meet the installation space and operation requirements; Material property constraint: The physical and chemical properties required for the material.

[0009] Furthermore, a preferred implementation is provided. Scoring the importance of each objective function to form a judgment matrix includes: Suppose there are m objective functions. Use expert experience to score the objective functions to obtain the average score of each objective function; according to the average score of each objective function, obtain the average score ratio of any two objective functions: Among them, a ij is the average score ratio of the i-th objective function and the j-th objective function; Si is the average score of the i-th objective function; Sj are the average scores of the j-th objective function respectively; According to the average score ratio of any two objective functions, form the judgment matrix A = [a ij :

[0010] Furthermore, a preferred implementation is provided. Based on the judgment matrix, using the analytic hierarchy process to calculate the weights of each objective function includes: Perform standardization processing on the judgment matrix A, calculate the n-th root of the product of the elements in each row, and obtain the eigenvector of the judgment matrix: The maximum eigenvalue of the judgment matrix is: Normalize the eigenvector to obtain the weights w i ;

[0011] Further, a preferred embodiment is provided. After calculating the weights of each objective function using the Analytic Hierarchy Process (AHP) based on the judgment matrix, the method further includes a step of performing consistency check: Calculate the consistency index CI and the consistency ratio CR:

[0012] where λ max is the maximum eigenvalue of the judgment matrix, and RI is the random consistency index; when CR < 0.1, the judgment matrix has satisfactory consistency.

[0013] The present invention also provides a large equipment anti - shock optimization design device based on multi - objective integration. The device includes modules: Module S1: Determine the typical response indexes of the equipment and its components under external shock loads; Module S2: Establish a multi - objective optimization model with the above - mentioned typical response indexes as objective functions, and determine the design variable vector X and the constraint conditions; Module S3: Score the importance of each objective function to form a judgment matrix; Based on the judgment matrix, calculate the weights of each objective function using the Analytic Hierarchy Process; Module S4: Use the multi - objective genetic algorithm to globally optimize the design variable vector X to form an optimal solution set; Module S5: Use the weights of the objective functions to perform multi - criterion decision - making analysis on the optimal solution set, and select the optimal design scheme; Module S6: Verify the selected optimal design scheme; According to the verification results, repeat the operations of Module S2 to S5 to update the parameters of the multi - objective optimization model, the optimal solution set, and select a new optimal design scheme until the indexes of the optimal design scheme meet the design requirements.

[0014] The present invention also provides a computer device, including: a processor and a memory. The memory is used to store the executable instructions of the processor, and the processor is configured to execute the above - mentioned large equipment anti - shock optimization design method based on multi - objective integration by executing the executable instructions.

[0015] The present invention also provides a computer storage medium, in which a computer program is stored. When the computer program runs, it executes the above - mentioned large equipment anti - shock optimization design method based on multi - objective integration.

[0016] The present invention also provides a computer program product, including computer programs / instructions. When the computer programs / instructions are executed by a processor, the steps of the above - mentioned large equipment anti - shock optimization design method based on multi - objective integration are implemented.

[0017] The present invention has the following beneficial effects: 1. The anti - shock optimization design method for large - scale equipment based on multi - objective integration analyzes the typical responses (such as acceleration, velocity, displacement, deflection, strain, pressure, etc.) of the equipment under shock loads to establish a multi - objective optimization model; determines the weights of each objective by expert experience scoring; uses a multi - objective optimization algorithm to solve, and finally realizes the optimization design of the equipment structure parameters. While improving the anti - shock performance of the equipment, it also takes into account other design objectives such as weight and cost, not only improving the anti - shock performance and safety reliability of the equipment, but also significantly reducing the weight and manufacturing cost by optimizing material selection and structure design, etc.

[0018] 2. The anti - shock optimization design method for large - scale equipment based on multi - objective integration scientifically determines the weights in multi - objective optimization by introducing expert experience scoring, comprehensively considers various response indexes of large - scale equipment under shock loads, and provides an effective optimization design method.

[0019] 3. The anti - shock optimization design method for large - scale equipment based on multi - objective integration has wide application value, is applicable to the design of large - scale equipment that requires anti - shock optimization, provides strong technical support for the anti - shock optimization design of related equipment, and is widely applicable to the following but not limited to the following fields: ocean engineering (such as ships, offshore platforms, offshore wind power equipment, etc.), aerospace (such as spacecraft, aircraft, hypersonic vehicles, etc.), heavy machinery (such as mining machinery, construction machinery, metallurgical equipment, etc.) and civil engineering (including bridges, building structures, tunnels, etc.) The anti - shock optimization design method and device for large - scale equipment based on multi - objective integration are applicable to the anti - shock optimization design of large - scale equipment. Brief Description of the Drawings

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0021] Figure 1 It is a schematic flow chart of the anti - shock optimization design method for large - scale equipment based on multi - objective integration; Figure 2 It is a schematic diagram of the weight calculation process of expert experience scoring.

[0022] Figure 3 It is a schematic diagram of the calculation results of dynamic response analysis of the selected optimal design scheme using finite element analysis (FEA). Detailed Embodiments

[0023] To make the technical solutions and advantages of the present invention more clearly described, the following will further describe in detail and completely the specific embodiments of the present invention in conjunction with the accompanying drawings. The following described embodiments are only some preferred solutions of the present invention, rather than all implementation solutions; the following described embodiments are intended to explain the present invention and should not be construed as a limitation to the present invention; the reasonable combination of the technical features defined in each embodiment of the present invention, and all other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments of the present invention belong to the scope of protection of the present invention.

[0024] In one embodiment, there is provided an anti-shock optimization design method for large equipment based on multi-objective integration: The method includes the following steps: Step S1: Determine the typical response indexes of the equipment and its components under external shock loads; Step S2: Establish a multi-objective optimization model with the above typical response indexes as the objective function, and determine the design variable vector X and the constraint conditions; Step S3: Score the importance of each objective function to form a judgment matrix; based on the judgment matrix, use the analytic hierarchy process to calculate the weights of each objective function; Step S4: Use the multi-objective genetic algorithm to globally optimize the design variable vector X to form an optimal solution set; Step S5: Use the weights of the objective functions to perform multi-criteria decision analysis on the optimal solution set, and select the optimal design scheme; Step S6: Verify the selected optimal design scheme; according to the verification results, repeat the operations of steps S2 to S5 to update the parameters of the multi-objective optimization model, the optimal solution set, and select a new optimal design scheme until the indexes of the optimal design scheme meet the design requirements.

[0025] In this embodiment, the method can comprehensively consider multiple design objectives and scientifically determine the relative importance (weights) of each objective, thereby achieving a comprehensive optimization of the anti-shock performance of large equipment.

[0026] In this embodiment, the method introduces expert experience (database) scoring to scientifically determine the weights of each design objective, ensuring that the optimization results not only meet the anti-shock requirements, but also take into account other factors such as weight, cost, and structural strength, realizing the effective integration of multiple objectives (functions), improving the anti-shock performance and comprehensive performance of the equipment, reducing the dynamic response of the equipment under shock loads, reducing the risk of damage and failure, and improving the safety and reliability of the equipment.

[0027] In this embodiment, the method comprehensively considers various response indexes of the equipment under impact loads, scientifically and reasonably optimizes the equipment design, improves its impact resistance performance, and at the same time takes into account other factors such as weight and cost. Through the optimized design, the manufacturing and operation costs of the equipment are reduced, and the economic benefits are improved.

[0028] In this embodiment, the method can be applied to the design and optimization of various large equipment that needs to improve impact resistance performance, and is applicable to fields such as ocean engineering, aerospace, heavy machinery, and civil engineering.

[0029] In this embodiment, the method has universality and can flexibly adjust the objective function and weights according to the specific requirements and working conditions of different equipment.

[0030] In addition, in one embodiment, the typical response indexes include acceleration, velocity, displacement, deflection, strain, and pressure.

[0031] In this embodiment, the typical response indexes include, but are not limited to, the following parts: Acceleration (A): The peak value and time history of the acceleration of the equipment under impact directly affect the safety of internal components and personnel of the equipment.

[0032] Velocity (V): The change in velocity of the equipment during the impact process affects the energy transfer and impact duration.

[0033] Displacement (D): The displacement amplitude and distribution of the equipment under impact loads. Excessive displacement may cause the equipment to collide with surrounding structures.

[0034] Deflection (δ): The degree of bending deformation of structural members affects the integrity and stability of the structure.

[0035] Strain (ε): The amount of deformation of the material under impact loads reflects the elastic-plastic response and fatigue life of the material.

[0036] Pressure (p): The pressure distribution at the stressed parts of the equipment, especially the pressure change under the action of the fluid medium in the equipment pipeline, may cause structural damage or leakage.

[0037] In addition, in one embodiment, the objective function includes: Minimize the acceleration response: min f1(X) = A max ; Wherein, A max is the peak acceleration; Minimize the velocity response: min f2(X) = V max ; Wherein, V max is the peak acceleration; Minimize displacement: min f3(X) = D max ; where D max is the maximum displacement; Minimize deflection: min f4(X) = δ max ; where δ max is the maximum deflection; Minimize strain: min f5(X) = ε max ; where ε max is the maximum strain; Minimize peak pressure: min f6(X) = P max ; where P max is the peak pressure; Minimize equipment weight: min f7(X) = W; where W is the equipment weight; Minimize equipment cost: min f8(X) = C; where C is the manufacturing cost; The design variable vector X is: X = [x1, x2,..., x n ; where x i represents the i-th design parameter of the equipment; The constraint conditions include: Safety factor constraint: Introduce a safety factor SF to ensure that the equipment still has sufficient safety margin under extreme conditions; Size and space constraints: The external dimensions of the equipment need to meet the installation space and operation requirements; Material property constraints: The physical and chemical properties required of the material.

[0038] In this embodiment, the objective functions are summarized as follows:

[0039] In this embodiment, the design variable vector is a vector composed of the design parameters of the (impact-resistant) equipment.

[0040] The design parameters of the equipment include the material parameters, structural dimension parameters, energy absorption device configuration, shock absorber parameters, connection method, boundary conditions, and mass distribution of the equipment. Specifically as follows: (1) Material parameters include the material type of the device, the physical and mechanical properties of the material; the material type includes steel, aluminum alloy, composite materials, etc.; the physical and mechanical properties of the material include density ρ, elastic modulus E, yield strength σy, energy absorption characteristics, etc.; (2) Structural dimension parameters include the cross-sectional dimensions (such as width b, height h, thickness t), length L, diameter d, etc. of components such as beams, columns, and plates; (3) Energy absorption device configuration includes the type of energy absorption element (such as honeycomb structure, metal foam, thin-walled tube, etc.), the geometric parameters of the energy absorption element (such as wall thickness, cell size), the material properties of the energy absorption element, the arrangement method of the energy absorption element (series, parallel), the installation position of the energy absorption element, etc.; (4) Shock absorber parameters include the type of shock absorber (spring shock absorber, viscoelastic damper, magnetorheological damper, etc.), the stiffness coefficient k of the shock absorber, the damping coefficient c of the shock absorber, the non-linear characteristics of the shock absorber, the installation position and direction of the shock absorber, etc.; (5) Connection methods and boundary conditions, such as bolt connection, welding, hinged connection, fixed support, elastic support, etc., have an impact on the overall stiffness and dynamic characteristics of the structure; (6) Mass distribution includes the mass m of each part of the device i and its distribution position in the structure, which affects the inertial characteristics and vibration modes of the structure.

[0041] In this embodiment, in the material property constraints, the required physical and chemical properties of the material include elastic modulus, yield strength, corrosion resistance, etc.

[0042] In addition, in one embodiment, the constraint conditions are expressed as follows: Among them, σ max (X) is the maximum stress of the material, ε max (X) is the maximum strain of the material; σ allow is the allowable stress of the material; ε allow is the allowable strain of the material; g1(X) and g2(X) belong to the elastic modulus constraint in the material property constraints, g1(X) is the material stress constraint; g2(X) is the material strain constraint; g3(X) is the safety factor constraint; g4(X) is the dimension constraint in the dimension and space constraints.

[0043] In this embodiment, since the importance of each objective function in practical applications is different, it is necessary to assign weights to them.

[0044] In this embodiment, in order to scientifically and reasonably determine the weights of each objective, an empirical method of expert scoring (or an expert experience database can be used) can be introduced to score the importance of each objective function and form a judgment matrix. Specifically: Form an expert group and invite experts with rich experience in related fields, including design engineers, material experts, structural analysis experts, cost control experts, etc.; Then, for each objective function, design a questionnaire and require experts to score the importance of each objective function according to their own experience. Usually, a 1-9 scoring system is adopted, and the higher the score, the more important the objective; After that, summarize the scores of each expert for each objective to form a judgment matrix (scoring matrix).

[0045] It is also possible to organize the own experiences of each expert into multiple expert experience databases and use the importance of each objective function in the expert experience database for scoring.

[0046] In addition, in one embodiment, the importance of each objective function is scored to form a judgment matrix: Suppose there are m objective functions, use expert experience to score the objective functions, and obtain the average score of each objective function; according to the average score of each objective function, obtain the average score ratio of any two objective functions: where a ij is the average score ratio of the i-th objective function and the j-th objective function; Si is the average score of the i-th objective function; Sj is the average score of the j-th objective function respectively; According to the average score ratio of any two objective functions, form a judgment matrix A = [a ij :

[0047] In addition, in one embodiment, based on the judgment matrix, using the analytic hierarchy process (AHP) to calculate the weights of each objective function includes: Use the analytic hierarchy process to solve the maximum eigenvalue and the corresponding eigenvector of the judgment matrix to obtain the weight of each objective function: perform a normalization process on the judgment matrix A, calculate the n-th root of the product of the elements in each row, and obtain the eigenvector of the judgment matrix: The maximum eigenvalue of the judgment matrix is: Normalize the eigenvector to obtain the weights w i ;

[0048] In addition, in one embodiment, after calculating the weights of each objective function using the Analytic Hierarchy Process (AHP) based on the judgment matrix, the method further includes a step of performing a consistency test: Calculate the consistency index CI and the consistency ratio CR:

[0049] where λ max is the maximum eigenvalue of the judgment matrix, and RI is the random consistency index; when CR < 0.1, the judgment matrix has satisfactory consistency.

[0050] The weights obtained based on the judgment matrix with satisfactory consistency are the finally obtained weights.

[0051] In addition, in one embodiment, using a multi-objective genetic algorithm to globally optimize the design variable vector X to form an optimal solution set includes: Use a multi-objective genetic algorithm to solve, and randomly generate an initial population according to the value range of the design variable vector; Calculate the objective function values of each individual, and calculate the comprehensive fitness according to the weights of the objective functions; Generate a new population according to the operations of the genetic algorithm; After multiple generations of iteration, form an optimal solution set.

[0052] In addition, in one embodiment, the methods adopted in the multi-criteria decision analysis include the TOPSIS method, the grey relational analysis method, and the fuzzy comprehensive method.

[0053] In addition, in one embodiment, the verification of the selected optimal design scheme is as follows: Use a numerical simulation method to verify the selected optimal design scheme; In addition, in one embodiment, the verification of the selected optimal design scheme is as follows: Use finite element analysis (FEA) to perform a dynamic response analysis on the selected optimal design scheme, obtain the distributions of acceleration, displacement, and stress, and verify whether the response of the device under impact loads meets the design requirements; specifically: In addition, in one embodiment, the verification of the selected optimal design scheme is as follows: Use a prototype test method (physical test) to verify the selected optimal design scheme; specifically: Manufacture a prototype according to the optimal design scheme, conduct an impact test, measure the actual response indexes, and verify the effectiveness and reliability of the design.

[0054] In addition, in one embodiment, the verification of the selected optimal design scheme is as follows: The optimal design solution selected is verified by combining numerical simulation methods (including finite element analysis) and prototype test methods. Specifically: A prototype model is made using numerical simulation methods to simulate the response of the equipment under impact loads and obtain simulation results; a prototype is made according to the optimal design solution and an impact test is carried out to measure the actual response indicators; The actual response indicators are compared with the simulation results to verify the effectiveness and reliability of the design.

[0055] In this embodiment, according to the verification results, the parameters of the multi-objective optimization model are updated, including the update of the design variable vector X and the objective function.

[0056] In addition, in one embodiment, to verify the effect of the method, a specific embodiment is provided: Taking a marine equipment gas turbine as an example, the method is used for optimal design: S1. Determine the typical response indicators of the gas turbine under impact loads: For the working conditions of the gas turbine under impact loads, its anti-impact performance is comprehensively evaluated, and five typical response indicators of the marine equipment gas turbine under impact conditions are selected: Acceleration A: The overall acceleration peak value and time history of the gas turbine under impact directly affect the safety of internal precision components (such as rotors, bearings, blades, etc.). In addition, if the local acceleration of key parts (such as the blade root and bearing seat) is too high, it may cause component loosening, wear or damage.

[0057] Stress S: The stress distribution under impact loads is ensured not to exceed the yield strength of the material to prevent structural failure. Considering stress concentration and dynamic amplification effects, the fatigue life and fracture risk are evaluated.

[0058] Installation space I: The external dimensions of the gas turbine need to meet the space limitations of the engine room or equipment compartment to ensure that the equipment can be installed, maintained and replaced smoothly. The gas turbine and its auxiliary equipment are reasonably arranged within the limited space to avoid space waste and interference between components, and ensure the reasonable layout of pipelines and cables. In addition, considering the displacement and deformation caused by impact, sufficient space margin needs to be left to prevent the equipment from colliding with the surrounding structures under impact.

[0059] Weight M: The total weight of the gas turbine directly affects the center of gravity, stability and load distribution of the ship or platform, and needs to be controlled within the design range. The distribution of the equipment weight on the structure will affect the stress distribution and dynamic characteristics, and the weight distribution needs to be optimized to avoid local overload. On the premise of meeting the strength and stiffness requirements, lightweight high-strength materials and optimized structures are used to reduce the equipment weight.

[0060] Cost C: Select materials with high cost performance to balance performance and cost. Reduce processing difficulty and cost, design a structure that is easy to maintain, extend the maintenance cycle, reduce downtime and maintenance costs.

[0061] By determining and analyzing the above typical response indicators, the acceleration force, deformation, weight, cost, etc. of the gas turbine under impact loads can be comprehensively understood, and the existing weak links and design parameters can be identified, which provides an important basis for subsequent optimization design.

[0062] S2. Determine the anti-impact evaluation objectives (i.e., objective functions) of the gas turbine and construct a multi-objective optimization model: For the anti-impact optimization design process of the gas turbine, the following five evaluation objectives are determined.

[0063] G1: Minimize acceleration (reduce the acceleration of key components) G2: Minimize stress (reduce the maximum stress of the key component structure) G3: Installation space constraint (meet the space requirements in the engine room and avoid interference and collision) G4: Minimize weight (reduce the overall weight of the gas turbine) G5: Minimize cost (reduce the overall manufacturing and maintenance costs) S3. Use expert experience scoring to determine the weights of multiple objectives: (1) Form an expert group (or expert experience database) Invite 5 experts with rich experience in related fields, including: marine power system engineers, gas turbine design experts, vibration and shock analysis experts, materials science experts, and cost control experts.

[0064] Or integrate expert experience into an expert experience database.

[0065] (2) Design a questionnaire for investigating the anti-impact optimization objectives of the gas turbine (set scoring criteria) For the above five objectives, design an evaluation questionnaire and require experts to score the importance of each objective according to their own experience (or according to the expert experience database), using a 1-9 point system. Among them: 1 point: equally important; 3 points: slightly important; 5 points: significantly important; 7 points: very important; 9 points: extremely important; 2, 4, 6, 8 points are intermediate values.

[0066] (3) Collect expert scores The average scores after summarizing the expert scores are as follows: Target Average Evaluation Score G1 8 G2 7 G3 6 G4 4 G5 3

[0067] According to the expert scores, construct a pairwise comparison judgment matrix A = [a ij , where: Among them, Si and Sj are the average scores of the target Gi and Gj respectively.

[0068] The calculated judgment matrix A is: (4) Calculate the eigenvector and the maximum eigenvalue, and calculate the product of the elements in each row of the judgment matrix: Product of the first row: M1 = 1×1.1429×1.3333×2.0000×2.6667 = 8.1423

[0069] Product of the second row: M2 = 0.8750×1×1.1667×1.7500×2.3333 = 4.6424

[0070] Product of the third row: M3 = 0.7500×0.8571×1×1.5000×2.0000 = 1.9286

[0071] Product of the fourth row: M4 = 0.5000×0.5714×0.6667×1×1.3333 = 0.2540

[0072] Product of the fifth row: M5 = 0.3750×0.4286×0.5000×0.7500×1 = 0.0603

[0073] Calculate the nth root of the product of each row (n = 5): First row: w1 = (M1) 1 / 5 = (8.4123) 0.2 ≈1.5600

[0074] Second row: w2 = (M2) 0.2 = (4.6424) 0.2 ≈1.3831

[0075] Third row: w3 = (1.9286) 0.2 ≈1.2191

[0076] Fourth row: w4 = (0.2540) 0. 2≈0.6921

[0077] Fifth row: w5 = (0.0603) 0.2 ≈0.3968

[0078] Normalize the eigenvector and calculate the sum: Normalize to obtain the weights: Calculate the maximum eigenvalue λ max , calculate Calculate the average value: (5) Consistency check Calculate the consistency index CI: When n = 5, the random consistency index RI = 1.12. Calculate the consistency ratio CR: Since CR = 0.0287 < 0.10, the judgment matrix has satisfactory consistency and the weight calculation is effective.

[0079] (6) Obtain the weights of each objective w1′ = 0.2971 (acceleration minimization) w2′ = 0.2634 (stress minimization) w3′ = 0.2322 (installation space constraint) w4′ = 0.1318 (weight minimization) w5′ = 0.0755 (cost minimization) (7) Apply to multi-objective optimization In the multi-objective genetic algorithm, construct the comprehensive objective function: Where: f1(X) is the peak acceleration (to be minimized) f2(X) is the maximum stress (to be minimized) f3(X) is the installation space occupancy (to be minimized) f4(X) is the equipment weight (to be minimized) f5(x) is the manufacturing cost (to be minimized) Optimize the design variable vector X through the genetic algorithm, and minimize the comprehensive objective function F(X) under the condition of meeting the constraint conditions.

[0080] In the above steps, the weights are calculated by combining the Analytic Hierarchy Process (AHP). According to the expert scores, a pairwise comparison judgment matrix between the objectives is constructed, and the maximum eigenvalue and the corresponding eigenvector of the judgment matrix are solved. Finally, the weights of each objective are obtained, which scientifically reflects the relative importance of each objective in the anti-shock optimization design of gas turbines. This process ensures the objectivity and rationality of the weight distribution in multi-objective optimization and provides a guarantee for the reliability of the optimization results.

[0081] S4. Introduce a multi-objective optimization algorithm to achieve parameter optimization and obtain the optimal solution set: For the anti-shock optimization design of a gas turbine, the design variable (vector) X includes: Material type: such as high-strength alloy, composite material, etc.

[0082] Structural dimensions (parameters): casing thickness t, support structure dimension L b and shaft diameter d, etc.

[0083] Vibration absorber parameters: stiffness k, damping coefficient c. Each design variable has a reasonable value range.

[0084] According to the value range of the design variables, randomly generate an initial population. Assume the population size is 100 groups of individuals to ensure the diversity of solutions. For each individual, calculate the values of each objective function f i (X) according to its design variable X. Determine the weight w i based on expert experience, and calculate the comprehensive fitness F(X) of each individual: where the weight w i comes from the calculation result of the Analytic Hierarchy Process (AHP) in step S3.

[0085] According to the comprehensive fitness F(X), select the individuals with higher fitness to enter the next generation. With a certain crossover probability (such as 0.8), perform a crossover operation on the selected individuals to exchange some genes (design variables) to generate new individuals. With a certain mutation probability (such as 0.05), perform a mutation operation on the crossed individuals to randomly change the values of some genes to increase the diversity of the population.

[0086] Repeat the above calculations and genetic operations iteratively, for example, 100 generations. In each generation, identify the non-dominated solutions and update the (Pareto) optimal solution set.

[0087] S5. Use the weights of the objective functions to perform multi-criteria decision analysis on the optimal solution set and select the optimal design scheme.

[0088] S6. Verify whether the optimal design scheme is effective until the indicators of the optimal design scheme meet the design requirements. Use FEA software to establish a three-dimensional finite element model of the gas turbine, including key components and structures. Input the material properties (such as elastic modulus, density, Poisson's ratio, etc.) in the optimal design scheme. Set the impact load conditions to simulate the impact waveforms under actual working conditions (such as half-sine wave, triangular wave). Perform transient dynamic analysis to calculate the response of the equipment under the impact load. Obtain results such as acceleration, stress, displacement, and deflection. Check whether each response index meets the design requirements, such as whether the peak acceleration and stress level are within the allowable range.

[0089] According to the optimal design scheme, a scaled-down model of the gas turbine or a physical prototype of the key components is fabricated. Ensure that the materials and structures of the prototype are consistent with those of the actual equipment. Use an impact test bench or a drop hammer test device to apply simulated impact loads. The test conditions should be consistent with the boundary conditions in the numerical simulation. Install acceleration sensors, strain gauges, etc. at key positions. Record the dynamic response data during the test, compare the test data with the numerical simulation results, and verify the accuracy of the model.

[0090] Repeat the optimization process, return to the multi-objective optimization step, re-perform the genetic algorithm solution, and update the Pareto optimal solution set and the optimal design scheme. Conduct numerical simulation and experimental verification on the new optimal design scheme until all indicators meet the design requirements, and determine the final optimization scheme.

[0091] The above further describes the technical solutions provided by the present invention through several specific embodiments to highlight the advantages and beneficial effects of the technical solutions provided by the present invention. However, the above several specific embodiments are not used as limitations on the present invention. Any reasonable modifications and improvements to the present invention, reasonable combinations of implementation manners, equivalent replacements, etc. within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A large-scale equipment anti-shock optimization design method based on multi-objective integration, characterized by: The method comprises the following steps: Step S1: Determine typical response indicators of the equipment and its components under external impact loads; Step S2: Establish a multi-objective optimization model with the above typical response index as the objective function, and determine the design variable vector X and constraint conditions; Step S3: Score the importance of each objective function to form a judgment matrix; based on the judgment matrix, use the hierarchical analysis method to calculate the weight of each objective function; Step S4: using a multi-objective genetic algorithm to perform global optimization on the design variable vector X to form an optimal solution set; Step S5: Using the weight of the objective function, perform multi-criteria decision analysis on the optimal solution set and select the optimal design solution; Step S6: verify the selected optimal design solution; according to the verification result, repeat the operations of steps S2 to S5, update the parameters of the multi-objective optimization model, the optimal solution set, and select a new optimal design solution until the various indicators of the optimal design solution meet the design requirements.

2. The large-scale equipment anti-shock optimization design method based on multi-objective integration according to claim 1 is characterized in that: The typical response indicators include acceleration, velocity, displacement, deflection, strain and pressure.

3. The large-scale equipment anti-shock optimization design method based on multi-objective integration according to claim 1 is characterized in that: The objective function includes: Minimize acceleration response: min f1(X)=A max ; Among them, A max is the peak acceleration; Minimize velocity response: min f2(X)=V max ; Among them, V max is the peak acceleration; Minimize displacement: min f3(X)=D max ; Among them, D max is the maximum displacement; Minimize deflection: min f4(X)=δ max ; Among them, δ max is the maximum deflection; Minimize strain: min f5(X)=e max ; Among them, ε max is the maximum strain; Minimize pressure peaks: min f6(X)=P max ; Among them, P max is the peak pressure; Minimize equipment weight: min f7(X)=W; Where W is the weight of the equipment; Minimize equipment costs: min f8(X) = C; Where C is the manufacturing cost; The design variable vector X is: X=[x1,x2…,x n ]; Among them, x i represents the i-th design parameter of the equipment; The constraints include: Safety factor constraint: Introduce safety factor SF to ensure that the equipment still has sufficient safety margin under extreme conditions; Size and space constraints: The equipment's dimensions must meet installation space and operation requirements; Material property constraints: the physical and chemical properties that the material is required to possess.

4. The large-scale equipment anti-shock optimization design method based on multi-objective integration according to claim 1 is characterized in that: The importance of each objective function is scored to form a judgment matrix including: Assuming there are m objective functions, expert experience is used to score the objective functions and the average score of each objective function is obtained; based on the average score of each objective function, the average score ratio of any two objective functions is obtained: Among them, a ij is the average score ratio of the i-th objective function and the j-th objective function; Si is the average score of the i-th objective function; Sj is the average score of the j-th objective function; According to the average score ratio of any two objective functions, a judgment matrix A = [a ij ]:

5. The large-scale equipment anti-shock optimization design method based on multi-objective integration according to claim 4 is characterized in that: The method of calculating the weights of each objective function based on the judgment matrix by using the hierarchical analysis method includes: Standardize the judgment matrix A, calculate the nth root of the product of each row element, and obtain the eigenvector of the judgment matrix: The maximum eigenvalue of the judgment matrix is: Normalize the feature vector to obtain the weight w of each objective function; 6. The large-scale equipment anti-shock optimization design method based on multi-objective integration according to claim 5 is characterized in that: After calculating the weights of each objective function using the analytic hierarchy process (AHP) based on the judgment matrix, the method further includes the step of performing a consistency check: Calculate the consistency index CI and consistency ratio CR: Among them, λ max is the maximum eigenvalue of the judgment matrix, RI is the random consistency index; when CR<0.1, the judgment matrix has satisfactory consistency.

7. A large-scale equipment anti-shock optimization design device based on multi-objective integration, characterized in that: The device comprises modules: Module S1: Determine the typical response indicators of the equipment and its components under external impact loads; Module S2: Establish a multi-objective optimization model with the above typical response index as the objective function, and determine the design variable vector X and constraint conditions; Module S3: Score the importance of each objective function to form a judgment matrix; based on the judgment matrix, use the hierarchical analysis method to calculate the weight of each objective function; Module S4: Use a multi-objective genetic algorithm to globally optimize the design variable vector X and form an optimal solution set; Module S5: Using the weight of the objective function, conduct multi-criteria decision analysis on the optimal solution set and select the optimal design solution; Module S6: Verify the selected optimal design scheme; according to the verification results, repeat the operations of modules S2 to S5, update the parameters of the multi-objective optimization model, the optimal solution set, and select a new optimal design scheme until the various indicators of the optimal design scheme meet the design requirements.

8. A computer device comprising: A processor and a memory, characterized in that the memory is used to store executable instructions of the processor, and the processor is configured to execute the large-scale equipment impact resistance optimization design method based on multi-objective integration as described in any one of claims 1 to 6 by executing the executable instructions.

9. A computer storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is run, the method for optimizing the impact resistance of large equipment based on multi-objective integration as described in any one of claims 1 to 6 is executed.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the large-scale equipment impact resistance optimization design method based on multi-objective integration as described in any one of claims 1 to 6 are implemented.

Images