Bridge crane structure optimization method based on non-probabilistic reliability

Abstract

The invention discloses a bridge crane structure optimization method based on non-probabilistic reliability. The method comprises the following steps: firstly, analyzing a mechanical model of a bridge crane girder, determining construction steps of a finite element model, and establishing the finite element model of a girder structure; secondly, a non-probability reliability index is adopted as a constraint condition of optimization analysis, a mathematical model of non-probability reliability optimization of the main beam structure is established and optimized, finally, finite element analysis is used for verifying an optimization result, and the final optimization result meets the strength and rigidity requirements. According to the non-probabilistic reliability optimization method, the weight of the main beam structure is effectively reduced, the optimization target is achieved, and the reliability constraint and the structure safety are ensured. The method provides a new thought for the structural design of the crane, and the reliability and efficiency of the design are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of lifting machinery, and specifically relates to a non-probabilistic reliability optimization of a bridge crane structure, which is applicable to reliability evaluation of a general bridge crane main beam structure, and is also applicable to reliability evaluation of other crane structure types. Background Art

[0002] As one of the basic equipment in the manufacturing industry, the innovative development of lifting machinery has an increasingly important impact on the national economy. The crane manufacturing industry usually uses traditional methods to carry out structural design, and only evaluates the safety of the structure based on the strength, stiffness, and life of the structure. It often pursues lightness or safety in the design, causing serious safety accidents or waste of resources. Taking both lightness and reliability into consideration during the crane design and manufacturing stage is an important direction that the crane industry needs to promote. Summary of the invention

[0003] In order to solve the problem that the parameters that affect the reliability of the crane main beam structure are uncertain and the experimental data are not easy to obtain, the interval model is used to analyze its non-probabilistic reliability, and the reliability index is used as the constraint condition of the optimization analysis, so that the final optimization result can not only reduce the company's product manufacturing cost, but also improve the safety of crane users. Finite element analysis is mainly used to analyze the main beam structure of the bridge crane. The construction steps of the finite element model, the static analysis of the finite element model and the result verification are specifically determined. The QG20 / 5t-31.5m bridge crane is taken as the research object, and the finite element model of its main beam structure is established. Its static strength and static stiffness are analyzed, and the results are verified.

[0004] The technical solution adopted by the present invention is: a bridge crane structure optimization method based on non-probabilistic reliability, which specifically includes the following steps:

[0005] (1) Establishing the mechanical model of the crane structure

[0006] a) The metal structure of the crane main beam has large geometric dimensions and complex structure. In order to reduce the workload of modeling and speed up the computer's solution, the model should be appropriately simplified during parametric 3D modeling;

[0007] b) Extract the geometric characteristic parameters of the crane main beam such as Figure 1As shown, by comparing the main beam structural parameters, including the main beam height H1, main beam length L1, upper and lower cover plate thickness T1, main web thickness T2, secondary web thickness T3, end beam height H2, end beam detail length L2, stiffener thickness T4, loading load P, elastic modulus E, main beam stiffener spacing S1, beam end oblique length L3, main beam width W1, rail side flange width W2, and non-rail side flange width W3, a parametric model is established according to its characteristics of symmetry about the midplane;

[0008] c) Determine the deadweight load PG. When using ANSYS software to load the crane structure model, the deadweight load of the structure can be directly obtained by setting the gravity acceleration of the structure. The gravity acceleration of the structure is set as the gravity acceleration multiplied by Take 1.1;

[0009] d) Determine the lifting load PQ. When using ANSYS software to apply the lifting load to the crane main beam structure, applying the load to a single node will cause excessive local deformation. Therefore, the load should be evenly applied to the nodes around the trolley pressure.

[0010] e) Determine the deadweight load of the main beam, the deadweight of the trolley and the lifting load. The mechanical model of the crane main beam structure is simplified as follows: Figure 2 The simply supported beam is shown.

[0011] (2) Establishing the finite element model of the bridge crane main beam structure

[0012] a) Determine material properties and unit selection

[0013] b) Use ANSYS to divide the grid

[0014] c) After establishing the main beam structure model and the track model, they need to be assembled for overall analysis, so the MPC contact technology is required.

[0015] e) Based on the performance parameters of the bridge crane provided, the APDL language is used to perform finite element modeling on the main beam structure of the bridge crane, and the following is obtained: Figure 3 Finite element model of the main beam shown.

[0016] (3) Establishment of crane structure optimization model

[0017] a) Determine the objective function of the main beam structure optimization as the weight function function.

[0018] b) The objective function of structural optimization is the quality of the main beam structure. The biggest factor affecting the structural quality is the structural size, so the combination of design variables is: x = [T1, T2, T3, T4, H].

[0019] c) Constraints on the main beam structure size, strength and rigidity

[0020] (4) Based on the above summary of the three optimization elements of objective function, design variables and constraints, let the strength function be M1, the stiffness function be M2, and the mass function be M3. The mathematical expression of the model is as follows:

[0021] Solving for η y The inner optimization mathematical model is:

[0022] (5) Based on the mathematical model constructed in step (4), the outer model is solved using the whale algorithm, and the inner non-probabilistic reliability index is solved using the fmincon function. The iterative curve of the optimized solution is as follows: Figure 4 shown.

[0023] (6) The main beam was optimized using ANSYS optimization method, response surface optimization method, and non-probabilistic reliability optimization method. The specific information of the optimization methods is shown in Table 1.

[0024] Table 1 Optimization method information table

[0025] (7) The results of the non-probabilistic reliability optimization design of the main beam finite element model were verified. The equivalent stress cloud diagram and the vertical displacement cloud diagram are shown in the figure. Figure 5 , Figure 6 shown.

[0026] (8) Compared with the prior art, the present invention firstly establishes the function function of the main beam through the response surface model, and uses the optimization method to check the reliability of the original size; secondly, establishes the mathematical model of non-probabilistic reliability optimization of the main beam structure and optimizes it, and compares the results of the three optimization methods. It is found that the non-probabilistic reliability optimization result is more robust, and the weight of the main beam structure is reduced after optimization; finally, the optimization result is verified by finite element analysis, and the final optimization result meets the strength and stiffness requirements. The present invention takes into account both the fit of the response surface model and the influence of uncertain variables. Its design result is more robust than deterministic optimization, and is verified by engineering examples. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0028] Figure 1 It is a schematic diagram of the main beam structure of the bridge crane in the present invention.

[0029] Figure 2 It is a simplified mechanical model of the main beam structure in the present invention.

[0030] Figure 3 It is the finite element model of the main beam in the present invention.

[0031] Figure 4 It is the optimized iteration curve graph in the present invention.

[0032] Figure 5 It is the equivalent stress cloud diagram of the mid-span part of the optimized main beam structure in the present invention.

[0033] Figure 6 It is a vertical displacement cloud diagram of the optimized main beam structure in the present invention.

[0034] Figure 7 It is the equivalent stress cloud diagram of the main beam structure in the present invention.

[0035] Figure 8 It is the equivalent Von Mises stress cloud diagram of the mid-span part of the main beam structure in the present invention.

[0036] Fig. 9 It is a vertical displacement cloud diagram of the main beam structure in the present invention. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] In order to make the above-mentioned objects, features, technical solutions and advantages of the present invention clearer, the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings:

[0039] Example

[0040] 1) The rated lifting load of the bridge crane is 20 tons, the trolley weight is 5 tons, and the trolley gauge is 31.5m. This type of crane has the advantages of light weight, simple structure and low energy consumption, and is widely used in factories.

[0041] Table 4 Main performance parameters of QG20 / 5t-31.5m bridge crane

[0042] 2) Use APDL language to perform finite element modeling on the main beam structure of the bridge crane and obtain the following Figure 3 Finite element model of the main beam shown.

[0043] 3) The strength analysis of the main beam structure was carried out using ANSYS software, and its equivalent Von Mises stress diagram is shown in Figure 7.

[0044] 4) Conduct local stress analysis on the mid-span of the main beam, such as Figure 8 shown.

[0045] 5) The main beam was optimized using ANSYS optimization method, response surface optimization method, and non-probabilistic reliability optimization method. The specific information of the optimization method is shown in Table 1, and the optimization results are shown in Table 2.

[0046] Table 2 Comparison of crane main beam structure optimization results

[0047] 6) According to step 5), the results of the non-probabilistic reliability optimization are rounded, and the rounded results are shown in Table 3.

[0048] Table 3 Optimization results rounded

[0049] 7) The local stress analysis of the main beam span shows that the maximum stress is near the lower web in the mid-span, and its magnitude is 75.5Mpa. According to the above strength verification formula, the allowable stress is calculated. The crane metal structure is Q235 steel, and the yield limit is 235Mpa. According to the "Crane Design Manual", when the load condition is A1, n = 1.48, then:

[0050] 8) ANSYS software is used to analyze the stiffness of the crane main beam structure model. The vertical displacement cloud diagram is shown in the figure below: Fig. 9 shown.

[0051] 9) According to the stiffness verification formula and the Crane Design Manual, the calculation formula for the allowable deflection of the main beam of a bridge crane at the mid-span position is expressed as:

[0052] It should be noted that the above-mentioned real-time solution should be understood as illustrative rather than limiting the protection scope of the present invention, and the protection scope of the present invention shall be subject to the specification of the claims. For those skilled in the art, some non-essential improvements and adjustments made to the present invention still fall within the protection scope of the present invention without departing from the essence and scope of the present invention.

Claims

1. A bridge crane structure optimization method based on non-probabilistic reliability, characterized by: First, the mechanical model of the crane structure is established. According to the performance parameters of the bridge crane provided, the load combination A1 is selected from the Crane Design Manual. The working condition is that the trolley is located in the middle of the main beam span, fully loaded and hoisted, and the wind load is not considered. Under this working condition and load combination, the APDL language is used to perform finite element modeling on the main beam structure of the bridge crane to obtain the finite element model of the main beam; secondly, the non-probabilistic reliability analysis method is used to analyze the non-probabilistic reliability of the crane structure with strength, stiffness and stability as constraints. The three optimization elements of objective function, design variables and constraints are summarized to build an optimization model suitable for the crane structure and determine the non-probabilistic reliability optimization model of the main beam structure.

2. The non-probabilistic reliability optimization method for crane structure according to claim 1 is characterized in that This is accomplished through the following steps: (1) Establishing the mechanical model of the crane main beam structure a) The metal structure of the crane main beam has large geometric dimensions and complex structure. In order to reduce the workload of modeling and speed up the computer's solution, the model should be appropriately simplified during parametric 3D modeling; b) Select the material properties and unit selection of the crane main beam metal structure; c) Determine the load model under the action of lateral force and vertical force; d) Use ANSYS to divide the unit grid. Based on the regular shapes of the crane main beam structure and the square track, free grid division is adopted; e) After the main beam structure model and track model are established, they need to be assembled for overall analysis, so MPC contact technology is needed; f) Determine loads and boundary constraints. (2) Establish a finite element model of the crane main beam structure. According to the performance parameters of the bridge crane provided, the load combination A1 is selected from the Crane Design Manual. The working condition is that the trolley is located in the middle of the main beam span, fully loaded and hoisted, and wind load is not considered. Under this working condition and load combination, the APDL language is used to perform finite element modeling on the bridge crane main beam structure to obtain the finite element model of the main beam. (3) Based on the summary of the three optimization elements: objective function, design variables, and constraints, an optimization model of the crane structure is built. (4) According to the initial design parameters of the crane main beam structure and relevant engineering requirements, a mathematical model for structural optimization is built. The outer model is solved using the whale algorithm, and the inner non-probabilistic reliability index is solved using the fmincon function to optimize the iterative curve of the solution.

3. The non-probabilistic reliability optimization method for the main beam of a bridge crane according to claim 1 is characterized in that In the step 2, the crane main beam structure is solved, strength and stiffness calculations and analysis are performed, and verification and checking are performed.

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