Container fatigue simulation analysis method

Abstract

The invention relates to a container fatigue simulation analysis method which comprises the following steps: establishing a three-dimensional model according to a target container, and defining material parameters; predicting the fatigue life of the target container by using a fatigue evaluation method to obtain a fatigue life prediction value; constructing a second-order response surface model by taking the wall thickness, the barrel body length and the fixed structure distance as variables; solving the second-order response surface model to obtain a Pareto optimal solution set; and screening an optimal design scheme from the Pareto optimal solution set through a fuzzy membership function. According to the method, the second-order response surface model is constructed by using the wall thickness, the barrel body length and the fixed structure distance as variables, so that the nonlinear relationship and the interaction effect among the container design variables can be more accurately captured, and a more accurate result is provided for predicting the fatigue life.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of container fatigue simulation analysis, in particular to a container fatigue simulation analysis method. BACKGROUND

[0002] The traditional container fatigue life evaluation method has the following limitations: (1) only based on static stress amplitude calculation, without considering the influence of actual asymmetric cyclic load (such as water filling and discharging process); (2) structure optimization depends on single objective parameter adjustment, which is difficult to coordinate the control of stress and life; (3) initial design often leads to insufficient life due to local stress concentration, which needs to be repeatedly tried and tested. For example, the equivalent stress (1530.8 MPa) of the original design of the inner container of the water heater under 0.45 MPa pressure is far higher than the material ultimate strength (325 MPa), and the life is not up to standard.

[0003] The prior art lacks a systematic method of coupling multi-physical fields (thermal stress, fatigue limit) and multi-objective optimization, resulting in low design efficiency and insufficient reliability. SUMMARY

[0004] To solve the above problems, the purpose of the embodiments of the present application is to provide a container fatigue simulation analysis method.

[0005] A container fatigue simulation analysis method, comprising:

[0006] Step 1: establishing a three-dimensional model according to the target container, and defining design parameters;

[0007] Step 2: predicting the fatigue life of the target container using a fatigue evaluation method to obtain a fatigue life prediction value;

[0008] Step 3: constructing a second-order response surface model with wall thickness, cylinder length, and fixed structure distance as variables;

[0009] Step 4: solving the second-order response surface model to obtain a Pareto optimal solution set;

[0010] Step 5: selecting an optimal design scheme from the Pareto optimal solution set through a fuzzy membership function.

[0011] Preferably, in step 2, the S-N curve is used to predict the fatigue life of the target container to obtain the fatigue life prediction value.

[0012] Preferably, in step 3, a second-order response surface model is constructed by nonlinear fitting with wall thickness, cylinder length, and fixed structure distance as variables, and maximum stress and fatigue life as outputs; wherein the expression of the second-order response surface model is:

[0013]

[0014] Wherein, y represents the output response to be predicted, x1 is the wall thickness, x2 is the barrel length, x3 is the fixed structure distance, β0 is a constant term, β i is a first-order coefficient, β ii is a second-order coefficient, β ij is an interaction term coefficient, and ε is a random error term.

[0015] Preferably, in step 4, the NSGA-II algorithm is used to solve the second-order response surface model to obtain a Pareto optimal solution set.

[0016] The application also provides an electronic device, including a bus, a transceiver, a memory, a processor and a computer program stored on the memory and executable on the processor, the transceiver, the memory and the processor being connected through the bus, characterized in that the computer program, when executed by the processor, implements the steps of the container fatigue simulation analysis method.

[0017] The application also provides a computer readable storage medium having a computer program stored thereon, characterized in that the computer program, when executed by a processor, implements the steps of the container fatigue simulation analysis method.

[0018] According to the specific embodiments of the application, the following technical effects are provided:

[0019] The application relates to a container fatigue simulation analysis method, and compared with the prior art, the application can more accurately capture the nonlinear relationship and interaction effect between container design variables by using the wall thickness, the barrel length and the fixed structure distance as variables to construct a second-order response surface model, so that more accurate results are provided for fatigue life prediction.

[0020] In order to make the above objectives, characteristics and advantages of the application more apparent, clear and easy to understand, the following preferred embodiments are specifically described below, and the accompanying drawings are referred to for detailed description. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0022] Figure 1 The application provides a container fatigue simulation analysis method flowchart. DETAILED DESCRIPTION

[0023] In the description of the present application, it needs to be understood that the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, which is only for the convenience of describing the present application and simplifying the description, and does not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application.

[0024] In addition, the terms "first", "second" are only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the technical features indicated. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of "multiple" is two or more, unless otherwise specifically limited.

[0025] In the present application, unless otherwise specifically defined and limited, the terms "mounting", "connection", "connection", "fixing" and the like should be broadly understood, for example, it can be fixedly connected, or it can be detachably connected, or integrally connected; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium; it can be the communication inside two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0026] Please refer to Figure 1 A container fatigue simulation analysis method, comprising:

[0027] 1. Modeling and load setting: establish a three-dimensional model of the container, define the material design parameters (enamel steel elastic modulus 200GPa, Poisson's ratio 0.3, S-N curve fatigue limit 180MPa@10 7 times)

[0028] 2. Fatigue life prediction: calculate the equivalent stress amplitude based on the Goodman correction formula:

[0029]

[0030] Where, σ a is the stress amplitude, σ m is the average stress, σ b is the tensile strength of the material 325MPa, combined with the S-N curve to predict the life.

[0031] 3. Multi-objective optimization: a second-order response surface model is constructed with wall thickness, cylinder length, and fixed structure distance as variables.

[0032] • Design variables: wall thickness (5-12 mm), cylinder length (800-1200 mm), fixed structure distance (150-300 mm);

[0033] • Objective functions: minimize maximum stress (f1 = min σmax), maximize fatigue life (f2 = max N);

[0034] • Constraint conditions: life ≥ 160,000 cycles (upper limit of national standard), wall thickness ≤ 12 mm (cost control).

[0035] Algorithm implementation process:

[0036] (1) Initialization of population: 100 initial design schemes (variable combinations) are generated using Latin hypercube sampling, and σmax and N are obtained through finite element simulation.

[0037] (2) Construction of second-order response surface model (RSM): with variables as input and σmax and N as output, the nonlinear relationship is fitted (such as ), replacing time-consuming finite element calculation.

[0038] ① Specific expression of second-order response surface model

[0039] For any target response y (such as maximum equivalent stress σmax or fatigue life N), the expression of the second-order response surface model is:

[0040]

[0041] After expansion, the form is as follows:

[0042]

[0043] Where y: the output response to be predicted (such as σmax or N), x1, x2, x3: design variables, β0: constant term, β i : first-order coefficient, β ii : second-order coefficient, β ij : interaction term coefficient, ε: random error term, assumed to follow a normal distribution with mean 0.

[0044] This model contains 10 undetermined coefficients (1 constant term + 3 first-order terms + 3 second-order terms + 3 interaction terms), which need to be solved by sample data regression.

[0045] Solution process: four-step method to construct high-precision response surface model

[0046] Step one: Design of Experiments (DoE)

[0047] Central Composite Design (CCD) is used to arrange sample points in a three-dimensional design space, including 8 cube corner points (232^323 full factorial design), 6 axial points (star points, used to estimate curvature), and 6 center points (used to estimate experimental error), totaling 20 sample points, evenly covering the design space and ensuring model robustness.

[0048] Step 2: Finite element simulation to obtain training data

[0049] For each sample point, construct a finite element model and apply a standard load spectrum.

[0050] Run thermal-mechanical coupling simulation and extract two key responses:

[0051] y1 = σ max (maximum equivalent stress)

[0052] y2 = N (predicted fatigue life)

[0053] Form a training data set

[0054] Step 3: Regression analysis to solve model coefficients (least squares method)

[0055] Write the model in matrix form:

[0056] Where:

[0057] Y: 20x1 response vector (such as σmax for all samples)

[0058] X: 20x10 design matrix, each row corresponding to 10 function values of a sample point (such as 1, x1, x2, x3, x12, …, x2x31, x_1, x_2, x_3, x_1^2, …, x_2x_31, x1, x2, x3, x12, …, x2x3)

[0059] β: 10x1 regression coefficient vector

[0060] ε: 20x1 error vector

[0061] Use ordinary least squares (OLS) to solve the coefficients:

[0062] If X T If X is close to singular (e.g., strong correlation between variables), use ridge regression (Ridge Regression) for improvement.

[0063] Step 4: Model validation and accuracy assessment

[0064] Calculate the coefficient of determination R 2 and the adjusted

[0065] Where n = 20 is the sample size and p = 9 is the number of independent variables.

[0066] Requirement R 2 >0.92, This indicates that the model fits well.

[0067] Leave-one-out cross-validation (LOOCV) can be used to calculate the mean squared error of prediction (PMSE) to ensure generalization ability.

[0068] (c) Integration into NSGA-II Optimization Engine

[0069] The two trained response surface models y1(x) and y2(x) are embedded into the NSGA-II algorithm as objective functions;

[0070] Encoding method: Real number encoding, each individual is (x1, x2, x3);

[0071] Fitness function:

[0072] Minimize: f1 = y1(x) = σ max (x)

[0073] Maximize: f² = y²(x) = N(x)

[0074] Constraint handling: Individuals with N < 160,000 are set as infeasible solutions, and the constraint dominance principle is used for ranking.

[0075] After 100 generations of evolution, the Pareto optimal solution set was obtained, providing multiple compromise solutions for engineering selection.

[0076] 4. Verification and iteration: Rebuild the model according to the optimized parameters and re-simulate to verify whether the lifespan meets the standard (e.g., national standard of 80,000-160,000 cycles).

[0077] Decision selection: The optimal solution is selected from the Pareto solution set by using the "fuzzy membership function". For example, the parameter combination of σmax = 306.81MPa and N = 162,000 times (wall thickness 8mm, cylinder length 1000mm, fixed distance 220mm) is selected.

[0078] 5. Verification Iteration

[0079] The model was reconstructed based on the optimized parameters and re-simulated using software: the equivalent stress was 306.81 MPa (reduction of 80%), the lifespan was 162,000 cycles (exceeding the national standard limit), and the error rate was <5%. The verification was successful, achieving an integrated closed loop of "simulation-optimization-verification".

[0080] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for fatigue simulation analysis of containers, characterized in that, include: Step 1: Create a 3D model of the target container and define the design parameters; Step 2: Use fatigue assessment methods to predict the fatigue life of the target container and obtain the predicted fatigue life value; Step 3: Construct a second-order response surface model using wall thickness, cylinder length, and fixed structural distance as variables; Step 4: Solve the second-order response surface model to obtain the Pareto optimal solution set; Step 5: Select the optimal design scheme from the Pareto optimal solution set using the fuzzy membership function.

2. The container fatigue simulation analysis method according to claim 1, characterized in that, In step 2, the fatigue life of the target container is predicted using the SN curve to obtain the predicted fatigue life value.

3. The container fatigue simulation analysis method according to claim 1, characterized in that, In step 3, a second-order response surface model is constructed by nonlinear fitting using wall thickness, cylinder length, and fixed structure distance as variables, and maximum stress and fatigue life as outputs; the expression for the second-order response surface model is: Where y represents the output response to be predicted, x1 is the wall thickness, x2 is the cylinder length, x3 is the fixed structural distance, β0 is a constant term, and β i β is the coefficient of the linear term. ii β is the coefficient of the quadratic term. ij ε is the coefficient of the interaction term, and ε is the random error term.

4. The container fatigue simulation analysis method according to claim 3, characterized in that, In step 4, the NSGA-II algorithm is used to solve the second-order response surface model to obtain the Pareto optimal solution set.

5. An electronic device comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, characterized in that, When the computer program is executed by the processor, it implements the steps in the container fatigue simulation analysis method as described in any one of claims 1-4.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps in the container fatigue simulation analysis method as described in any one of claims 1-4.

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