Pier rigidity optimization method based on hull docking strength

Abstract

The invention relates to the technical field of hull structure design, in particular to a pier rigidity optimization method based on hull docking strength. According to the pier timber rigidity optimization method based on the hull docking strength, on the basis of the hull docking one-dimensional beam finite element model, through the technical means of experimental design, model approximation, algorithm optimization and the like, reasonable pier timber rigidity coefficient distribution can be efficiently obtained within finite time, the time for designing the pier timber rigidity coefficient is greatly shortened, and the design efficiency is improved. And in addition, the bearing reaction level of the target pier timber can be improved, the docking safety of the ship body structure is guaranteed, and the formulating efficiency of a ship docking scheme can be effectively improved.

Description

Technical Field

[0001] The invention relates to the technical field of hull structure design, and in particular to a method for optimizing pier wood stiffness based on hull docking strength. Background Art

[0002] The docking method is a common construction method for large ships. The hull is usually located on the piers in the dock. The weight of the hull will exert a certain force on the piers. At the same time, the reaction force of the piers will also act on the hull. The docking process must not only ensure that the piers are not damaged, but also ensure that the hull structure has sufficient strength. Therefore, considering the safety of the docking state, it is necessary to calculate and verify the docking strength of the hull.

[0003] In engineering, due to different hull structure types, the ability of the hull structure at different pier positions to resist the support reaction force is also different. This ability can be obtained by comprehensive use of theoretical calculations, simulation analysis, experimental research, etc. Since the sum of the support reaction forces of each pier is equal to the weight of the hull, under the premise that the hull weight remains unchanged, it is often hoped that the weak parts of the structure will bear smaller support reaction forces, while the strong parts of the structure will bear larger support reaction forces, so as to achieve a balance between the structural capacity and the pier support reaction forces.

[0004] The stiffness coefficient of the pier affects the support reaction force of the dock pier, which in turn affects the docking strength of the hull. Changing the stiffness coefficient of a pier at a certain position will have different degrees of influence on the support reaction force at all pier positions. The large number of piers in the project will make it more difficult to evaluate the impact. Repeated inefficient iteration operations will take a lot of time, making it difficult to accurately evaluate the influence of each pier stiffness coefficient on the support reaction force at different positions in the design method of the existing technology. Therefore, how to efficiently obtain a set of reasonable pier stiffness coefficient distributions within a limited time is of great significance to the safety and economy of ship docking. Summary of the invention

[0005] The technical problem to be solved by the present invention is: to propose a pier stiffness optimization method based on the hull docking strength, which can efficiently obtain and optimize the pier stiffness coefficient distribution, greatly shorten the pier stiffness coefficient design time, help to improve the hull docking safety, and effectively improve the efficiency of formulating ship docking plans.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0007] A method for optimizing pier wood stiffness based on hull docking strength comprises the following steps:

[0008] S1, establish a one-dimensional beam finite element model of the hull and piers, and analyze the support reaction force of each pier under the deadweight of the hull;

[0009] S2, selecting sample points by using an optimized Latin hypercube design method, and establishing a neural network approximate model between the hull docking response and the pier stiffness coefficient according to the sample point data;

[0010] S3, based on the neural network approximate model, taking the minimum support reaction force of the target pier as the design goal, and taking the support reaction force of all piers less than the preset allowable value as the constraint condition, a multi-island genetic algorithm is used to perform global optimization design on the pier stiffness coefficient.

[0011] Furthermore, in step S1, the one-dimensional beam finite element model of the hull and the piers is established, specifically, based on the weight distribution of the whole ship, the profile characteristics, the pier distribution and the pier stiffness coefficient, a finite element model of the hull beam elastically supported on the dock pier is established in the finite element software, and the solver provided in the finite element software is used to calculate the support reaction data of each pier under the action of the hull's own weight.

[0012] Furthermore, in step S2, the optimized Latin hypercube design method includes:

[0013] S21, divide the whole dock into m n design space cells, and then generate sample points cell by cell;

[0014] S22, randomly generate the first sample point P1 in the first spatial cell, define the coordinates of the sample point P1 as (i1, j1, ..., g1, 1), and put the sample point P1 into the sample set P = {P1}, where i1, j1, ..., g1∈{1, 2, ..., m};

[0015] S23, generating a second sample point P2 at a position farthest from the first sample point P1 in the second spatial cell, defining the coordinates of the sample point P2 as (i2, j2, ..., g2, 2), and updating the sample set P = {P1, P2};

[0016] S24, for the kth sample point P k , where k ≥ 3, calculate the positions of each pier in the k-th spatial cell and the sample set P = {P1, P2, ..., P k-1}, and take the pier position corresponding to the maximum distance value to generate the kth sample point P k , define the sample point P k The coordinates of (i k , j k , …, g k , k), and update the sample set P = {P1, P2, ..., P k-1 , P k};

[0017] S25, repeat step S24 until m sample points are generated.

[0018] Further, in step S2, the hull docking response in the neural network approximate model is specifically a sample set P = {P1, P2, ..., P m}The support reaction data of each sample point in .

[0019] Furthermore, in step S2, the stiffness coefficient of the pier wood in the neural network approximation model is specifically a sample set P = {P1, P2, ..., P m}The pier stiffness coefficient at each sample point in .

[0020] Further, in step S3, the multi-island genetic algorithm includes:

[0021] S31, the sample set P = {P1, P2, ..., P m The sample points in} are divided into several sub-populations, each of which contains a certain number of individuals at random, and the support reaction data corresponding to each individual is converted into binary coding form;

[0022] S32, evaluating the support reaction force data corresponding to the stiffness coefficient of each individual in the neural network approximate model, the smaller the support reaction force, the higher the fitness of the corresponding individual;

[0023] S33, selection is made based on the fitness of the individual. Individuals with higher fitness have a higher probability of being selected to enter the next generation.

[0024] S34, performing crossover and mutation operations on the selected individuals;

[0025] S35, repeat steps S32 to S34 until the optimal stiffness coefficient distribution is reached.

[0026] Furthermore, the crossover operation is specifically as follows: randomly selecting the stiffness coefficient of any individual from the selected individuals and taking the average value to generate a new individual.

[0027] Furthermore, the mutation operation is specifically as follows: randomly selecting a stiffness coefficient of any individual from the selected individuals, and multiplying it by a preset correction coefficient to generate a new individual.

[0028] Furthermore, the optimal stiffness coefficient distribution is specifically: after the selected individuals are subjected to crossover and mutation operations, when the rate of change of the population is less than a preset value, the corresponding stiffness coefficient distribution of each pier.

[0029] Compared with the prior art, the present invention has the following main advantages:

[0030] The present invention proposes a pier stiffness optimization method based on the hull docking strength. Based on the one-dimensional beam finite element model of the hull docking, through experimental design, approximate model, optimization algorithm and other technical means, it can efficiently obtain a more reasonable pier stiffness coefficient distribution within a limited time, greatly shorten the pier stiffness coefficient design time, and help to improve the support reaction level of the target pier, ensure the safety of the hull structure docking, and effectively improve the efficiency of formulating ship docking plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 A simplified schematic diagram of a hull and pier wood in an embodiment of the present invention;

[0032] Figure 2 A schematic diagram of sample point distribution for the test design in an embodiment of the present invention;

[0033] Figure 3 Schematic diagram of a neural network model between F1 and the change rates of K1 and K3 in an embodiment of the present invention;

[0034] Figure 4 The figure is a flow chart of the method for establishing and optimizing the approximate model in an embodiment of the present invention. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0036] It should be pointed out that, according to the needs of implementation, the various steps / components described in this application can be split into more steps / components, and two or more steps / components or partial operations of steps / components can be combined into new steps / components to achieve the purpose of the present invention.

[0037] Embodiment 1: This embodiment provides a method for optimizing the stiffness of pier wood based on the docking strength of a hull, which mainly includes the following steps:

[0038] S1, establish a one-dimensional beam finite element model of the hull and piers, and analyze the support reaction force of each pier under the deadweight of the hull;

[0039] S2, selecting sample points by using an optimized Latin hypercube design method, and establishing a neural network approximate model between the hull docking response and the pier stiffness coefficient according to the sample point data;

[0040] S3, based on the neural network approximate model, taking the minimum support reaction force of the target pier as the design goal, and taking the support reaction force of all piers less than the preset allowable value as the constraint condition, a multi-island genetic algorithm is used to perform global optimization design on the pier stiffness coefficient.

[0041] Furthermore, in step S1, the one-dimensional beam finite element model of the hull and the piers is established, specifically, based on the weight distribution of the whole ship, the profile characteristics, the pier distribution and the pier stiffness coefficient, a finite element model of the hull beam elastically supported on the dock pier is established in the finite element software, and the solver provided in the finite element software is used to calculate the support reaction data of each pier under the action of the hull's own weight.

[0042] Furthermore, in step S2, the optimized Latin hypercube design method includes:

[0043] 1) Divide the design space evenly into m n Space units, and then generate sample points grid by grid. For the first sample point P1, it can be randomly generated in the first grid. Define the position of point P1 (i1, j1, ..., g1, 1) (i1, j1, ..., g1∈{1, 2, ..., m}), and put point P1 into the sample set P = {P1};

[0044] 2) For the second sample point P2, it will be generated in the second grid. The position with coordinates (i1, j1, ..., g1, 1) is already occupied by P1, so the sample point P2 can only be placed in the remaining space unit of the second grid. The coordinates of the position of point P2 are defined as (i2, j2, ..., g2, 2). The maximum distance between the position of the remaining space unit of the second grid and the existing sample point P1 can be expressed as max(d((i2, j2, ..., g2, 2), P1))(i2∈{r i |r i =1, 2, ..., m; r i ≠i1}, j2∈{r j |r j =1, 2, ..., m; r j ≠j1},…,g2∈{r g |r g =1, 2, ..., m; r g ≠g1}). Calculate the distance between the remaining spatial unit position of the second grid and the sample point P1, and the sample point P2 will be placed in the spatial unit farthest from point P1. Update the sample set P = {P1, P2};

[0045] 3) For the kth sample point P k , {k|k=3,4,…,m-1}, indicating that there are sample points {P1,P2,…,P k-1} has occupied k-1 grids. Therefore, the kth sample point can only appear in the remaining space unit of the kth grid, that is, Pk (i k , j k , …, g k , k)(i k ∈{r i |r i =1, 2, ..., m; r i ≠i1,i2,…,i k-1},j k ∈{r j |r j =1, 2, ..., m; r j ≠j1,j2,…,j k-1}, …, g k ∈{r g |r g =1, 2, ..., m; r g ≠g1,g2,…,g k-1}). In the remaining space unit of the kth grid, calculate the positions of each pier and the existing sample points {P1, P2, ..., P k-1}, and the maximum distance value is taken as the eigenvalue. The position of the log corresponding to the maximum eigenvalue is the coordinate position of the kth sample point (i k , j k , …, g k , k), the kth sample point P k The maximum and minimum distance between the existing sample point P can be expressed as max(d((i k , j k , …, g k Update the sample set P = {P1, P2, ..., P k-1 , P k};

[0046] 4) Repeat step 3) until m-1 sample points are generated;

[0047] 5) The mth sample point is placed in the last remaining spatial unit.

[0048] Further, in step S2, the hull docking response in the neural network approximate model is specifically a sample set P = {P1, P2, ..., P m}; the stiffness coefficient of the pier wood in the neural network approximate model, specifically the sample set P = {P1, P2, ..., P m}The pier stiffness coefficient at each sample point in .

[0049] Further, in step S3, the multi-island genetic algorithm includes:

[0050] S31, the sample set P = {P1, P2, ..., Pm The sample points in} are divided into several sub-populations, each of which contains a certain number of individuals at random, and the support reaction data corresponding to each individual is converted into binary coding form;

[0051] S32, evaluating the support reaction force data corresponding to the stiffness coefficient of each individual in the neural network approximate model, the smaller the support reaction force, the higher the fitness of the corresponding individual;

[0052] S33, selection is made based on the fitness of the individual. Individuals with higher fitness have a higher probability of being selected to enter the next generation.

[0053] S34, performing crossover and mutation operations on the selected individuals;

[0054] S35, repeat steps S32 to S34 until the optimal stiffness coefficient distribution is reached.

[0055] The crossover operation is specifically: randomly selecting the stiffness coefficient of any individual from the selected individuals and taking the average value to generate a new individual.

[0056] The mutation operation is specifically as follows: randomly selecting a stiffness coefficient of any individual from the selected individuals, and multiplying it by a preset correction coefficient to generate a new individual.

[0057] Furthermore, the optimal stiffness coefficient distribution is specifically: after the selected individuals are subjected to crossover and mutation operations, when the rate of change of the population is less than a preset value, the corresponding stiffness coefficient distribution of each pier.

[0058] Embodiment 2: This embodiment provides a method for optimizing the stiffness of pier wood based on the docking strength of the hull, which mainly includes:

[0059] 1) A one-dimensional beam finite element model of the hull and piers is established to analyze the reaction force of the piers under the deadweight of the hull.

[0060] Based on the information of the whole ship weight distribution, profile characteristics, dock pier distribution, dock pier stiffness coefficient and so on, a finite element model of the hull beam elastically supported on the dock pier is established in the finite element software, and the support reaction force value of each dock pier under the action of the hull's deadweight is calculated by the solver provided by the finite element software.

[0061] 2) The optimized Latin hypercube design method is used to select sample points, and a neural network approximate model between the hull docking response and the pier stiffness coefficient is established based on the sample data.

[0062] The optimized Latin hypercube design method generates more uniform sampling points than the traditional Latin hypercube method, and obtains more reasonable test data under limited time cost. Based on the test data, a neural network approximate model between the hull docking response and the pier stiffness coefficient is established, and the credibility of the approximate model is verified by combining error analysis and other means. The neural network model has the ability to approximate complex nonlinear functions and has strong fault tolerance.

[0063] 3) Based on the neural network approximate model, the design objective is to minimize the support reaction of the target pier, and the constraint condition is that the support reaction of all piers is less than the allowable value. The multi-island genetic algorithm is used to perform global optimization design of the pier stiffness coefficient.

[0064] The use of the neural network approximate model reduces the number of calls to the finite element software and shortens the optimization time. The multi-island genetic algorithm can better achieve global optimization and prevent falling into the local optimal solution.

[0065] 4) Comprehensively apply the above technologies to form an intelligent optimization design method for pier stiffness coefficient based on the hull docking strength.

[0066] Embodiment 3: The total length of the hull of this embodiment is 60m, the total weight is 600t, the weight is evenly distributed, the hull is divided into 6 theoretical stations along the length direction, the length of each theoretical station is 10m, the piers are set between two adjacent stations, evenly distributed along the length direction, the hull is located on the piers, the stiffness coefficients of each pier are K1, K2, ... K6, and the support reaction forces of each pier are F1, F2, ... F6, respectively. The simplified schematic diagram of the hull docking is as follows: Figure 1 shown.

[0067] In this embodiment, the stiffness coefficients of K1, K2, ... K6 are all 10000 kN / mm, and the range of the change rate is -30% -30%. Taking K1 and K3 as design variables (the design variables can be selected according to the actual project), taking the minimum response of F1 and F3 as the optimization target (the optimization target can be selected according to the actual project), the ability of the hull structure at different pier positions to resist the support reaction force is different. The acquisition of this ability does not belong to the content of this patent and is not described in detail here. Assuming that the support reaction force at all pier positions does not exceed 20% of the original level as a constraint condition, a set of optimal stiffness coefficient distributions is given. The optimization mathematical expression is as follows:

[0068] Find K1 and K3

[0069] Min F1 and F3

[0070] stF2,F4,F5,F6≤[F]

[0071] 1) Through the experimental design process, the optimized Latin hypercube design method is used to simulate the numerical combination of the change rates of K1 and K3, as shown in the schematic diagram Figure 2 shown.

[0072] 2) The dock pier support reaction force under the above input is calculated by the one-dimensional beam finite element model. A neural network approximate model is established based on the sample data, such as Figure 3 The credibility of the approximate model is verified by error analysis.

[0073] 3) Based on this approximate model, a multi-island genetic algorithm is used to carry out optimization analysis, such as Figure 4 shown.

[0074] After optimization, when K1 = 7030579.08 N / mm, K3 = 7075532.158 N / mm, the structure satisfies the above mathematical expression. Table 1 shows the comparison between the optimized approximate model and the actual model, and the maximum error value is 0.31%.

[0075] Table 1 Comparison of approximate model and actual model response

[0076]

[0077] Table 2 shows the comparison of the support reaction forces of each pier before and after optimization. It can be found that the support reaction force of the target pier is reduced by 17.02% at most after optimization, and the support reaction force levels of the piers at other positions are within the allowable range, meeting the preset design requirements of this embodiment.

[0078] Table 2 Comparison of support reaction force response before and after optimization

[0079]

[0080]

[0081] Furthermore, all parts involved in this application that are not described in detail are the same as the prior art or are implemented using the prior art.

[0082] In summary:

[0083] The present invention proposes a pier stiffness optimization method based on the hull docking strength. Based on the one-dimensional beam finite element model of the hull docking, through experimental design, approximate model, optimization algorithm and other technical means, it can efficiently obtain a more reasonable pier stiffness coefficient distribution within a limited time, greatly shorten the pier stiffness coefficient design time, and help to improve the support reaction level of the target pier, ensure the safety of the hull structure docking, and effectively improve the efficiency of formulating ship docking plans.

[0084] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for optimizing pier stiffness based on hull docking strength, characterized in that: The steps include: S1, establish a one-dimensional beam finite element model of the hull and piers, and analyze the support reaction force of each pier under the deadweight of the hull; S2, selecting sample points by using an optimized Latin hypercube design method, and establishing a neural network approximate model between the hull docking response and the pier stiffness coefficient according to the sample point data; S3, based on the neural network approximate model, taking the minimum support reaction force of the target pier as the design goal, and taking the support reaction force of all piers less than the preset allowable value as the constraint condition, a multi-island genetic algorithm is used to perform global optimization design on the pier stiffness coefficient.

2. The method for optimizing the stiffness of pier timbers based on the docking strength of a hull according to claim 1, characterized in that In step S1, the one-dimensional beam finite element model of the hull and the pier is established, specifically, based on the weight distribution of the whole ship, the profile characteristics, the pier distribution and the pier stiffness coefficient, a finite element model of the hull beam elastically supported on the dock pier is established in the finite element software, and the solver provided by the finite element software is used to calculate the support reaction force data of each pier under the action of the hull's own weight.

3. The method for optimizing the stiffness of pier timbers based on the docking strength of a hull according to claim 1, characterized in that In step S2, the optimized Latin hypercube design method includes: S21, divide the whole dock into m n design space cells, and then generate sample points cell by cell; S22, randomly generate the first sample point P1 in the first spatial cell, define the coordinates of the sample point P1 as (i1, j1, ..., g1, 1), and put the sample point P1 into the sample set P = {P1}, where i1, j1, ..., g1∈{1, 2, ..., m}; S23, generating a second sample point P2 at a position farthest from the first sample point P1 in the second spatial cell, defining the coordinates of the sample point P2 as (i2, j2, ..., g2, 2), and updating the sample set P = {P1, P2}; S24, for the kth sample point P k , where k ≥ 3, calculate the positions of each pier in the k-th spatial cell and the sample set P = {P1, P2, ..., P k-1 }, and take the pier position corresponding to the maximum distance value to generate the kth sample point P k , define the sample point P k The coordinates of (i k , j k , …, g k , k), and update the sample set P = {P1, P2, ..., P k-1 , P k }; S25, repeat step S24 until m sample points are generated.

4. The method for optimizing the stiffness of pier timbers based on the docking strength of a hull according to claim 3, characterized in that In step S2, the hull docking response in the neural network approximate model is specifically a sample set P = {P1, P2, ..., P m }The support reaction data of each sample point in .

5. The method for optimizing the stiffness of pier timbers based on the docking strength of a hull according to claim 4, characterized in that In step S2, the stiffness coefficient of the pier wood in the neural network approximation model is specifically a sample set P = {P1, P2, ..., P m }The pier stiffness coefficient at each sample point in .

6. The method for optimizing the stiffness of pier timbers based on the docking strength of a hull according to claim 5, characterized in that In step S3, the multi-island genetic algorithm includes: S31, the sample set P = {P1, P2, ..., P m The sample points in} are divided into several sub-populations, each of which contains a certain number of individuals at random, and the support reaction data corresponding to each individual is converted into binary coding form; S32, evaluating the support reaction force data corresponding to the stiffness coefficient of each individual in the neural network approximate model, the smaller the support reaction force, the higher the fitness of the corresponding individual; S33, selection is made based on the fitness of the individual. Individuals with higher fitness have a higher probability of being selected to enter the next generation. S34, performing crossover and mutation operations on the selected individuals; S35, repeat steps S32 to S34 until the optimal stiffness coefficient distribution is reached.

7. The method for optimizing pier stiffness based on hull docking strength according to claim 6, characterized in that: The crossover operation is specifically: randomly selecting the stiffness coefficient of any individual from the selected individuals and taking the average value to generate a new individual.

8. The method for optimizing pier wood stiffness based on hull docking strength according to claim 6, characterized in that: The mutation operation is specifically as follows: randomly selecting the stiffness coefficient of any individual from the selected individuals, and multiplying it by a preset correction coefficient to generate a new individual.

9. The method for optimizing pier stiffness based on hull docking strength according to claim 6, characterized in that: The optimal stiffness coefficient distribution is specifically: after the selected individuals are subjected to crossover and mutation operations, when the population change rate is less than a preset value, the corresponding stiffness coefficient distribution of each pier.

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