Long-life lightweight design method for port container spreader

Abstract

The invention belongs to the technical field of harbor machinery, and particularly relates to a long-life lightweight design method for a harbor container spreader, which comprises the following steps: step 1, parametric modeling; the method comprises the following steps: step 1, constructing a parameterized model # imgabs0 #, and associating the parameterized model # imgabs0 # with a parameterized driving function to realize parameterized driving modeling, and step 2, carrying out finite element analysis on the parameterized model # imgabs1 #; the method comprises the following steps of 1, constructing a high-precision finite element model # imgabs2 #, defining attributes # imgabs4 # of material parameters in a parameterized model # imgabs3 #, and carrying out grid division on a parameterized model # imgabs5 #, and 2, calculating a fatigue life # imgabs6 #; carrying out fatigue life # imgabs7 # analysis on the structure through joint simulation; 4, carrying out lightweight calculation; and constructing a response surface agent model # imgabs8 # by utilizing simulation software. According to the long-life lightweight design method for the port container spreader, through deep fusion of multi-objective collaborative optimization and fatigue life prediction, precise lightweight design of the port spreader under long-life constraint is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of port machinery, and in particular to a long-life lightweight design method for a port container spreader. Background Art

[0002] As the core load-bearing component of the port loading and unloading system, the structural design of port container hoists directly determines the loading and unloading efficiency, energy economy and equipment service life. Traditional design methods rely too much on empirical formulas and static strength verification criteria, and adopt a conservative safety factor strategy, resulting in significant structural mass redundancy and low material utilization. Overweight design not only increases manufacturing costs, but also significantly increases crane energy consumption due to additional inertia loads, forming a structural contradiction with the green port low-carbon development goals. In addition, the traditional infinite life design theory is based on the assumption that "working stress is always lower than the fatigue limit". It neither considers the randomness and impact effects of dynamic loads (such as emergency stops and eccentric load conditions) nor ignores the behavior characteristics of very high cycle fatigue (VHCF), resulting in deviations in life prediction. Under this theoretical system, there is a lack of quantitative assessment of remaining life, and equipment maintenance relies on fixed cycles rather than actual damage status, resulting in the dual risks of premature replacement or accidental fracture.

[0003] The current fatigue life assessment system mainly relies on the simplified SN curve and linear damage accumulation theory, and its limitations are particularly prominent in the high-cycle to ultra-high-cycle fatigue range. Different industry standards (such as ASME, IIW, and DNV) have significant differences in the definition of the SN curve, especially in > In the ultra-high cycle stage, the divergence in the value of the parameter m (fatigue strength index) leads to differences in the life prediction results. When calculating fatigue life, it is necessary to consider selecting the SN curve under a specific standard. Although finite element analysis and lightweight technology have been gradually applied, existing optimization methods are mostly limited to a single goal (such as weight reduction or strength) or local structure, and lack a multidisciplinary collaborative model under the constraints of the entire life cycle. As a result, the quantitative correlation mechanism between weight reduction rate and fatigue life is missing, and the design process still relies on manual trial and error iteration, which seriously restricts the process of technological upgrading. The current industry standard system still uses static strength as the core indicator, and the requirements for quantitative control of fatigue life are weak. With the advancement of international green ports, there is an urgent need to build an innovative method that integrates lightweight design and accurate life prediction. Through multi-objective collaborative optimization and inverse solution technology, we can break through the traditional design paradigm and achieve the collaborative goals of energy efficiency improvement and life control. Summary of the Invention

[0004] Based on the existing technical problems, the present invention proposes a long-life lightweight design method for port container spreaders. By integrating parametric modeling, a modified model considering ultra-high cycle fatigue life and multi-objective inverse optimization technology, a life constraint-driven lightweight design system is constructed to achieve a dynamic balance between structural weight reduction and service life, providing core technical support for the green and intelligent upgrade of port equipment.

[0005] The present invention proposes a long-life lightweight design method for a port container spreader, comprising the following steps: Step 1: Parametric modeling; building a parametric model and is associated with the parametric drive function to achieve parametric drive modeling.

[0006] Step 2: Parameterize the model Conduct finite element analysis; build high-precision finite element models , define the parameterized model Properties of material parameters in , for the parameterized model Perform mesh division.

[0007] Step 3: Fatigue life Calculation; fatigue life of the structure through joint simulation analyze.

[0008] Step 4: Lightweight calculation; using simulation software to build a response surface proxy model , establish structural geometric parameters and maximum equivalent stress , the quality of the overall structure and fatigue life The response relationship between them.

[0009] Step 5: Establish structural weight reduction rate and fatigue life relationship.

[0010] Step 6: Based on lifespan Weight loss rate Reverse engineering.

[0011] Preferably, in the step 1, a parameterized model is constructed Simplify, eliminate non-load-bearing attachments X and local hole features Y, define key geometric parameters, and build geometric parameter-parametric models Mapping relationship .

[0012] Preferably, in step 2, a corresponding static load is applied and dynamic loads , define the structural boundary conditions .

[0013] Preferably, the step 2 further includes obtaining structural stress by statics and transient dynamics analysis. Distribution, simulating the static and dynamic responses of the spreader during operation.

[0014] Preferably, in step 3, according to fatigue life Analysis process, combining dynamic analysis results and material SN curve, and calculating the fatigue life of the structure based on Miner linear cumulative damage theory , get the fatigue life cloud diagram of the overall structure and the location of the dangerous node and the corresponding fatigue life .

[0015] Preferably, in step 4, a sample point set is generated through experimental design , for the sample point set Perform calculations to build a response agent model , and the accuracy of the response surface was verified.

[0016] Quality , maximum equivalent stress ,life Establishing mathematical relations for multi-objective optimization for constraints , using multi-objective genetic algorithm to solve the optimal solution set .

[0017] Preferably, the multi-objective optimization mathematical relationship The expression is: Where: are the geometric design parameters ( 、 ); is the upper limit of the geometric design parameters; is the lower limit of the geometric design parameter.

[0018] Preferably, a detailed finite element analysis is performed on the optimized structure to obtain the stress of the optimized structure under working load. , deformation and fatigue life Verify to ensure the results meet the design requirements and output the optimized geometric model and geometric design parameters.

[0019] Preferably, in step 5, the generated sample points cover the design space, and the simulation is run to obtain the sample point sets The output of the data is used to construct an accurate response surface surrogate model. , by fitting multiple sets of optimization results, construct the weight loss rate and fatigue life Mathematical model between .

[0020] Through the above technical solution, it is possible to generate enough sample points to cover the design space in the DOE stage of response surface optimization, and obtain enough information to build an accurate response surface proxy model with the least number of simulations or experiments. , thus performing optimization or sensitivity analysis.

[0021] The beneficial effects of the present invention are: Through the deep integration of multi-objective collaborative optimization and fatigue life prediction, the precise lightweight design of port spreaders under long life constraints is achieved.

[0022] Based on parametric modeling and high-precision finite element analysis, fatigue life is calculated by combining the SN curve modified by specific standards. This evaluation breaks through the limitations of traditional empirical design and significantly improves the reliability of life prediction and the systematic nature of lightweight design. Through the integrated application of a response surface surrogate model and a multi-objective genetic algorithm, a quantitative mapping relationship between weight reduction rate and target life was established. This supports reverse engineering to generate the optimal parameter combination, addressing the dynamic balance between lightweighting and life objectives. The optimized design effectively reduces material redundancy and energy consumption while ensuring structural safety, meeting the sustainable development needs of green port equipment and providing the industry with an efficient and intelligent design paradigm. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a schematic diagram of a long-life lightweight design method for port container spreaders proposed by the present invention; Figure 2 This is the parametric model on the ANSYS Workbench platform in step 1 of the long-life lightweight design method for port container spreaders proposed in this invention. Import graph; Figure 3 The first step of the long-life lightweight design method for port container spreaders proposed by the present invention is to import the parameterized model of the spreader. Back page image; Figure 4 The present invention proposes a method for designing a long-life lightweight port container spreader. In step 1, the spreader is constructed in a parameterized model. Schematic diagram of medium thickness parameter design; Figure 5 This is a diagram of the parameterized drive page for the spreader in step 1 of a long-life lightweight design method for a port container spreader proposed in the present invention; Figure 6Page B shows the addition of a static analysis module to construct a high-precision finite element model in step 2 of the long-life lightweight design method for port container spreaders proposed in the present invention; Figure 7 Adding a spreader material attribute page diagram in step 2 of a long-life lightweight design method for a port container spreader proposed by the present invention; Figure 8 This is a diagram of the spreader material property definition page in step 2 of the long-life lightweight design method for port container spreaders proposed by the present invention; Figure 9 The finite element model of the crane in step 2 of the long-life lightweight design method for port container cranes proposed by the present invention Divide the page graph into grids; Figure 10 The finite element model of the crane in step 2 of the long-life lightweight design method for port container cranes proposed by the present invention Define structural boundary conditions Page Map; Figure 11 The finite element model of the crane in step 2 of the long-life lightweight design method for port container cranes proposed by the present invention Static stress response cloud map page diagram; Figure 12 A transient analysis module page diagram is added to step 2 of a long-life lightweight design method for port container spreaders proposed by the present invention; Figure 13 The finite element model of the crane in step 2 of the long-life lightweight design method for port container cranes proposed by the present invention Dynamic stress response cloud map page diagram; Figure 14 This is a page diagram of adding a fatigue life analysis module in step 3 of a long-life lightweight design method for port container spreaders proposed by the present invention; Figure 15 The fatigue life of the spreader in step 3 of the long-life lightweight design method for port container spreaders proposed by the present invention is Compute page graph; Figure 16 The fatigue life of the spreader in step 3 of the long-life lightweight design method for port container spreaders proposed by the present invention is Cloud map page diagram; Figure 17 A page diagram of a lightweight design module is added in step 4 of a long-life lightweight design method for a port container spreader proposed by the present invention; Figure 18A page diagram of a sample point set S generated for the spreader lightweight calculation test design in step 4 of a long-life lightweight design method for a port container spreader proposed by the present invention; Figure 19 Establishing a response agent model for the lightweight calculation of the spreader in step 4 of the long-life lightweight design method for port container spreaders proposed in this invention Page map; Figure 20 In the fourth step of the long-life lightweight design method for port container spreaders proposed by the present invention, the lightweight calculation of the spreader adopts a multi-objective genetic algorithm to solve the Pareto optimal solution set. Page Map; Figure 21 This is a model diagram of the relationship between spreader weight reduction rate and fatigue life in step 5 of the long-life lightweight design method for port container spreaders proposed in the present invention. DETAILED DESCRIPTION

[0024] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0025] Reference Figures 1-21 ,A long-life and lightweight design method for a port container spreader, comprising steps 1, parametric modeling; like Figure 1-Figure 5 As shown, parametric models are constructed based on CAD software (such as SolidWorks, Inventor, etc.) , parameterized model Including the material parameters, shape parameters and geometric parameters of the sling related to the size of each component. And the parametric model Associated with the ANSYS parametric drive function, or through the 3D modeling module integrated in the ANSYS Workbench platform to achieve the parametric drive modeling of the above material parameters, shape parameters and geometric parameters, such as Figure 2 As shown, the parameterized model is performed on the ANSYS Workbench platform Modeling operations.

[0026] Secondly, the parameterized model Simplify, specifically eliminate non-load-bearing accessories X and local hole features Y to avoid stress concentration during finite element calculations. At the same time, avoid unnecessary parameters affecting the entire lightweight calculation process to ensure the accuracy of the calculation results.

[0027] Finally define key geometric parameters such as wall thickness , Section height , web spacing etc. are continuous design variables, and a geometric parameter ( , )-parameterized model mapping relationship is constructed. The parameterized model is compatible with subsequent finite element analysis. After variable adjustment, the mesh division and boundary conditions are automatically adapted, which can significantly improve the design iteration efficiency.

[0028] Step 2: Finite element analysis; As Figure 1 and Figure 6-Figure 13 shown, a high-precision finite element model is constructed in ANSYS , the properties of the material parameters in the parameterized model are defined, the parameterized model is meshed, especially the welding area and the stress concentration area need to be finely meshed to ensure calculation accuracy and efficiency. According to the actual working conditions, the corresponding static load and dynamic load are applied, the structural boundary conditions are defined, and it is ensured that the force and deformation of the parameterized model meet the actual working conditions. The structural stress distribution is obtained through static and transient dynamic analyses to simulate the static and dynamic responses of the spreader during operation.

[0029] Step 3: Fatigue life calculation; As Figure 1 and Figure 14-16 shown, the fatigue life analysis of the structure is carried out through the co-simulation of ANSYS Workbench and nCode. According to the fatigue life analysis process, combining the dynamic analysis results and the material S-N curve, the fatigue life of the structure is calculated based on the Miner linear cumulative damage theory, and the fatigue life contour map of the overall structure, the positions of dangerous nodes and the corresponding fatigue life are obtained.

[0030] Among them, when importing the material S-N curve, the differences in different standards in the ultra-high cycle fatigue stage should be considered. For example, based on the BS7608 standard, the fatigue strength index m of class C welds is 3.5, but in the IIW standard, in the high cycle fatigue stage (E+04 < N < 1×E+07, HCF), m = 3.5, and in the ultra-high cycle fatigue stage (N > 1×E+07, VHCF), m = 22.

[0031] Step 4: Lightweight calculation;​ like Figure 1 as well as Figures 17-20 As shown in the figure, the response surface surrogate model is constructed using the Design Xplorer module in ANSYS , establish the structural geometric parameters ( 、 ) and maximum equivalent stress , the quality of the overall structure and fatigue life The response relationship between them. The sample point set is generated through experimental design (DOE) , for the sample point set Perform calculations to build a response agent model , and verify the accuracy of the response surface. , maximum equivalent stress ,life Establishing mathematical relations for multi-objective optimization for constraints , using the multi-objective genetic algorithm (MOGA) to solve the Pareto optimal solution set . Multi-objective optimization mathematical relationship expression: Where: are the geometric design parameters ( 、 wait); is the upper limit of the geometric design parameters; is the lower limit of the geometric design parameter.

[0032] Conduct detailed finite element analysis and verification on the optimized structure to obtain the stress of the optimized structure under working load , deformation and fatigue life Verify to ensure the results meet the design requirements and output the optimized geometric model and geometric design parameters ( 、 wait).

[0033] Step 5: Establish structural weight reduction rate and fatigue life relationship.

[0034] like Figure 1 as well as Figure 21 As shown, in order to ensure the accuracy of the response surface, in the DOE stage of response surface optimization, sufficient sample points will be generated to cover the design space, and the simulation will be run to obtain the sample point sets. The output results of the response surface model are used to obtain sufficient information to build an accurate response surface surrogate model with the least number of simulations or experiments. , so as to perform optimization or sensitivity analysis. By fitting multiple sets of optimization results, the weight loss rate is constructed. and fatigue life Mathematical model between .

[0035] Step 6: Based on lifespan Weight loss rate Reverse engineering; Target lifespan is the input, output weight loss rate The range of values ​​and corresponding geometric design parameters ( 、 etc.) combination.

[0036] Through the deep integration of multi-objective collaborative optimization and fatigue life prediction, the precise lightweight design of port spreaders under the long life constraint is achieved. Based on parametric modeling and high-precision finite element analysis, the fatigue life is predicted by combining the SN curve modified by specific standards. This evaluation breaks through the limitations of traditional empirical design and significantly improves the reliability of life prediction and the systematic nature of lightweight design. Through the integrated application of a response surface surrogate model and a multi-objective genetic algorithm, a quantitative mapping relationship between weight reduction rate and target life was established. This supports reverse engineering to generate the optimal parameter combination, addressing the dynamic balance between lightweighting and life objectives. The optimized design effectively reduces material redundancy and energy consumption while ensuring structural safety, meeting the sustainable development needs of green port equipment and providing the industry with an efficient and intelligent design paradigm.

[0037] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A long-life lightweight design method for port container spreaders, characterized by: The following steps are included: Step 1: Parametric modeling; building a parametric model and is associated with the parametric drive function to achieve parametric drive modeling; Step 2: Parameterize the model Conduct finite element analysis; build high-precision finite element models , define the parameterized model Properties of material parameters in , for the parameterized model Perform mesh division; Step 3: Fatigue life Calculation; fatigue life of the structure through joint simulation analyze; Step 4: Lightweight calculation; using simulation software to build a response surface proxy model , establish structural geometric parameters and maximum equivalent stress , the quality of the overall structure and fatigue life The response relationship between Step 5: Establish structural weight reduction rate and fatigue life relationship; Step 6: Based on lifespan Weight loss rate Reverse engineering.

2. The method for designing a long-life lightweight port container spreader according to claim 1, characterized in that: In the step 1, the parameterized model is constructed Simplify, eliminate non-load-bearing attachments X and local hole features Y, define key geometric parameters, and build geometric parameter-parametric models Mapping relationship .

3. The method for designing a long-life lightweight port container spreader according to claim 1, characterized in that: In the second step, a corresponding static load is applied. and dynamic loads , define the structural boundary conditions .

4. The method for designing a long-life lightweight port container spreader according to claim 3, characterized in that: The second step also includes obtaining structural stress through static and transient dynamic analysis. Distribution, simulating the static and dynamic responses of the spreader during operation.

5. The method for designing a long-life lightweight port container spreader according to claim 1, characterized in that: In step 3, according to the fatigue life Analysis process, combining dynamic analysis results and material SN curve, and calculating the fatigue life of the structure based on Miner linear cumulative damage theory , get the fatigue life cloud diagram of the overall structure and the location of the dangerous node and the corresponding fatigue life .

6. The method for designing a long-life lightweight port container spreader according to claim 1, characterized in that: In step 4, a sample point set is generated through the Design of Experiments (DOE) , for the sample point set Perform calculations to build a response agent model , and verify the accuracy of the response surface; Quality , maximum equivalent stress ,life Establishing mathematical relations for multi-objective optimization for constraints , using multi-objective genetic algorithm to solve the optimal solution set .

7. The method for designing a long-life lightweight port container spreader according to claim 6, characterized in that: The multi-objective optimization mathematical relationship The expression is: ; Where: are geometric design parameters ( 、 ); is the upper limit of the geometric design parameters; is the lower limit of the geometric design parameter.

8. The method for designing a long-life lightweight port container spreader according to claim 7, characterized in that: Conduct detailed finite element analysis and verification on the optimized structure to obtain the stress of the optimized structure under working load , deformation and fatigue life Verify to ensure the results meet the design requirements and output the optimized geometric model and geometric design parameters.

9. The method for designing a long-life lightweight port container spreader according to claim 1, characterized in that: In step 5, the generated sample points cover the design space, and the simulation is run to obtain the sample point sets. The output of the data is used to construct an accurate response surface surrogate model. , by fitting multiple sets of optimization results, construct the weight loss rate and fatigue life Mathematical model between .

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