Finite element analysis method and system for gas tank base stress, electronic equipment and storage medium

Abstract

The invention discloses a gas tank base stress finite element analysis method and system, electronic equipment and a storage medium, and the method comprises the steps that for a complex geometric structure of an LNG gas tank base, a self-adaptive mesh generation technology is adopted, a stress concentration area and stress gradient changes are comprehensively considered, refined modeling is carried out on the gas tank base, a high-quality finite element mesh model is obtained, and in the mesh generation process, the stress concentration area and the stress gradient changes are comprehensively considered; the grid density of a stress concentration area under the load effect is focused, the grid quality of the area is improved, and the stress gradient change is accurately captured; in an LNG gas tank base finite element model, key load working conditions are identified, characteristic parameters such as types, sizes, directions, frequencies and durations of typical loads such as the self weight, thermal stress and wind load of a gas tank are mainly analyzed, load-time historical curves under different working conditions are obtained through load spectrum testing, and a load combination method is adopted, so that the load-time historical curves under different working conditions are obtained. And the stress distribution rule of the gas tank base under the multi-load coupling effect is researched.

Description

Technical Field

[0001] The present application relates to the field of ship safety technology, and in particular to a finite element analysis method, system, electronic equipment, and storage medium for a gas tank base stress. Background Art

[0002] Marine equipment includes a variety of equipment such as the main engine and auxiliary engine of the ship. During the operation of the ship, various marine equipment on the ship is used to complete the production operations such as navigation, berthing, loading and unloading of cargo, and ensure the safety of the ship and the personnel on board. When there are wind and waves at sea, the marine equipment will be subjected to huge loads and there is a risk of overturning. In order to ensure the safety of marine equipment, it is necessary to accurately calculate the maximum stress that the base of the marine equipment can withstand in advance.

[0003] The gas tank base is subjected to the coupling of various types of loads, such as deadweight, thermal stress, wind load, etc., and the magnitude, direction, frequency and duration of these loads are uncertain. When performing stress analysis, the prediction and optimization of the stress distribution of the LNG gas tank base under complex acceleration loads are not accurate enough due to the randomness of the loads and the nonlinearity of the composite materials. Summary of the invention

[0004] The technical problem to be solved by the present application is that when performing stress analysis, due to the randomness of loads and the nonlinearity of composite materials, the prediction and optimization of the stress distribution of the LNG tank base under complex acceleration loads is insufficiently accurate.

[0005] In order to solve the above problems, in order to solve the above technical problems or at least partially solve the above technical problems, the present application provides a gas tank base stress finite element analysis method, system, electronic equipment, and storage medium.

[0006] In a first aspect, the present invention discloses a finite element analysis method for a gas tank base stress, characterized in that it comprises the following steps:

[0007] Construct a finite element model of the LNG tank base, perform load analysis and processing, and obtain the stress distribution of the LNG tank base under the coupling of multiple loads;

[0008] According to the digital feature extraction and probability distribution fitting of random loads on the finite element model of the LNG gas tank base, a load random field model is established, and the probability analysis and processing of the gas tank base stress is carried out to obtain the random stress response of the gas tank base;

[0009] A large amount of stress sample data is obtained through sampling simulation, and a high-speed response model driven by load-stress data is established. The load parameters are input for analysis and processing to obtain the stress analysis prediction results of the gas tank base.

[0010] Preferably, before constructing the LNG tank base finite element model for the LNG tank base, the following steps are included:

[0011] According to the geometric structure of the LNG tank base, the finite element mesh model of the LNG tank base is constructed through adaptive mesh division;

[0012] The anisotropic characteristics of composite materials are added to the finite element mesh model to construct a finite element model of the LNG tank base.

[0013] Preferably, after obtaining the prediction result of the gas tank base stress analysis, the following steps are included:

[0014] A multi-objective optimization model is constructed to output a design plan for the gas tank base that meets the material type and the stress of the gas tank base according to the stress analysis results of the gas tank base and the type of material used.

[0015] Preferably, constructing a finite element model of the LNG tank base, performing load analysis processing on the LNG tank base, and obtaining the stress distribution of the LNG tank base under the coupling of multiple loads specifically includes the following steps:

[0016] By combining field tests and numerical simulations, the load-time history curves of the gas tank base under different working conditions are obtained, and the key load conditions and their characteristic parameters are determined;

[0017] Use load spectrum testing technology to conduct multi-channel and multi-point load tests on the gas tank base, obtain load data under typical working conditions, and establish a gas tank base load database;

[0018] According to the statistical characteristics of load data, the load-time history curve is processed, the load characteristic parameters are extracted, the combination coefficient and action mode of each typical load are reasonably determined, and the equivalent load of the gas tank base is obtained;

[0019] An equivalent load is applied to the finite element model of the gas tank base, and the stress distribution cloud diagram of the gas tank base under the coupling of multiple loads is calculated. The stress distribution law of the gas tank base is analyzed and the stress concentration area is identified.

[0020] Preferably, the digital feature extraction and probability distribution fitting of random loads on the finite element model of the LNG gas tank base are used to establish a load random field model, perform probability analysis processing on the gas tank base stress, and obtain the random stress response of the gas tank base, which specifically includes the following steps:

[0021] Obtain the load test data of the gas tank base under actual working conditions and extract the digital characteristics of the load;

[0022] According to the digital characteristics of the load, the probability distribution type and parameters of the load are determined by using the probability distribution fitting method;

[0023] According to the probability distribution of load, random field theory is used to generate the random field model of load;

[0024] The load random field model is used as the random boundary condition of the finite element model of the gas tank base;

[0025] The statistical samples of the stress distribution of the gas tank base are obtained by Monte Carlo simulation method;

[0026] According to the statistical samples of stress distribution, the statistical characteristics of the stress distribution of the gas tank base, such as the mean, variance and confidence interval, are calculated to determine whether the structural reliability of the gas tank base meets the design requirements.

[0027] Preferably, the sampling simulation obtains a large amount of stress sample data, establishes a high-speed response model driven by load-stress data, inputs load parameters for analysis and processing, and obtains the prediction results of the gas tank base stress analysis, which specifically includes the following steps:

[0028] According to the structural characteristics and material properties of the gas tank base, a random stress response analysis is performed on the gas tank base to obtain the stress response data of the gas tank base under different load conditions;

[0029] The Monte Carlo method is used to perform sampling simulation for the obtained random stress response data of the gas tank base. By randomly extracting load parameters and inputting them into the finite element model for calculation, a large amount of gas tank base stress sample data is obtained.

[0030] Based on the obtained gas tank base stress sample data, a high-speed response model is established with load parameters as input and the predicted results of the gas tank base stress distribution as output;

[0031] When establishing the high-speed response model, the characteristic parameters of the load are used as the input characteristics of the model, and the stress values ​​at various positions of the gas tank base are used as the output targets of the model;

[0032] The established high-speed response model is trained and tested to optimize the model's hyperparameters and structure. The trained high-speed response model is deployed to the gas tank base stress distribution rapid assessment system. When new load condition parameters are input, the model can quickly predict the stress distribution of the gas tank base.

[0033] Preferably, the method of constructing a finite element mesh model of the LNG tank base by adaptive meshing according to the geometric structure of the LNG tank base specifically includes the following steps:

[0034] Obtain the 3D geometric model data of the LNG tank base and preprocess the model;

[0035] According to the stress characteristics of the LNG tank base, potential stress concentration areas are identified, and a local encrypted adaptive meshing strategy is used for these areas.

[0036] The LNG tank base is meshed using eight-node hexahedral elements, and the mesh density is increased in stress concentration areas and areas with large stress gradient changes.

[0037] Apply loads and boundary conditions to the finite element mesh model to simulate the stress state of the LNG tank base under actual working conditions;

[0038] Stress analysis is performed on the LNG tank base to obtain the stress distribution cloud map and stress values ​​in the stress concentration area. The calculation results are post-processed to extract the stress gradient change curve in the stress concentration area and evaluate the stress performance and structural integrity of the LNG tank base.

[0039] In a second aspect, the present invention discloses a finite element analysis and calculation system for stress of an LNG tank base, which comprises:

[0040] Stress distribution analysis unit: construct a finite element model of the LNG tank base, perform load analysis and processing, and obtain the stress distribution of the LNG tank base under the coupling of multiple loads;

[0041] Random stress response unit: Based on the digital feature extraction and probability distribution fitting of random loads on the finite element model of the LNG tank base, a load random field model is established to perform probability analysis and processing of the tank base stress to obtain the random stress response of the tank base;

[0042] Stress analysis and prediction unit: Sampling simulation obtains a large amount of stress sample data, establishes a high-speed response model driven by load-stress data, inputs load parameters for analysis and processing, and obtains the stress analysis prediction results of the gas tank base.

[0043] In a third aspect, the present invention discloses an electronic device, which includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

[0044] Memory, used to store computer programs;

[0045] The processor is used to implement the steps of the gas tank base stress finite element analysis method when executing the program stored in the memory.

[0046] In a fourth aspect, the present invention discloses a storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the gas tank base stress finite element analysis method are implemented.

[0047] The above technical solution provided by this application has the following advantages compared with the prior art:

[0048] The present application provides a finite element analysis method, system, electronic device, and storage medium for the stress of the gas tank base. In the method, for the complex geometric structure of the LNG gas tank base, an adaptive meshing technology is used, and the stress concentration area and stress gradient changes are comprehensively considered to refine the modeling of the gas tank base to obtain a high-quality finite element mesh model. In the meshing process, the mesh density of the stress concentration area under the load is focused on to improve the mesh quality of the area to accurately capture the stress gradient changes; in the finite element model of the LNG gas tank base, the key load conditions are identified, and the characteristic parameters such as the type, size, direction, frequency, and duration of typical loads such as the dead weight of the gas tank, thermal stress, and wind load are analyzed. Through the load spectrum test, the load-time history curve under different working conditions is obtained, and the load combination method is used to study the stress distribution law of the gas tank base under the coupling of multiple loads. The stress analysis accuracy and efficiency of the LNG gas tank base are improved, and an effective method is provided for structural optimization design. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0050] 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, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0051] Figure 1 Schematic diagram of a finite element analysis method for gas tank base stress provided in this application Figure 1 ;

[0052] Figure 2 Schematic diagram of a finite element analysis method for gas tank base stress provided in this application Figure 2 ;

[0053] Figure 3 A model diagram of a gas tank base stress finite element analysis method provided in this application Figure 1 ;

[0054] Figure 4 A model diagram of a gas tank base stress finite element analysis method provided in this application Figure 2 ;

[0055] Figure 5 A model diagram of a gas tank base stress finite element analysis method provided in this application Figure 3. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in this application will be clearly and completely described below in conjunction with the drawings in this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0057] First, see Figure 1-2 The present invention discloses a finite element analysis method for a gas tank base stress, which is characterized by comprising the following steps:

[0058] Step S1: constructing a finite element mesh model of the LNG tank base through adaptive meshing according to the geometric structure of the LNG tank base;

[0059] Step S2: adding the anisotropic characteristics of the composite material to the finite element mesh model to construct a finite element model of the LNG tank base;

[0060] Step S3: constructing a finite element model of the LNG tank base, performing load analysis and processing on the LNG tank base, and obtaining the stress distribution of the LNG tank base under the coupling of multiple loads;

[0061] Step S4: According to the digital feature extraction and probability distribution fitting of the random load on the finite element model of the LNG gas tank base, a load random field model is established, and a probability analysis and processing of the gas tank base stress is performed to obtain the random stress response of the gas tank base;

[0062] Step S5: sampling simulation to obtain a large amount of stress sample data, establish a high-speed response model driven by load-stress data, input load parameters for analysis and processing, and obtain the prediction results of the gas tank base stress analysis;

[0063] Step S6: construct a multi-objective optimization model, and output a design scheme for the gas tank base that meets the material type and the stress of the gas tank base according to the stress analysis results of the gas tank base and the type of material used.

[0064] Specifically, for the complex geometric structure of the LNG tank base, the adaptive meshing technology is used to comprehensively consider the stress concentration area and stress gradient changes, and the tank base is finely modeled to obtain a high-quality finite element mesh model. In the meshing process, the mesh density of the stress concentration area under the load is focused on to improve the mesh quality in this area to accurately capture the stress gradient changes; in the finite element model of the LNG tank base, the key load conditions are identified, and the characteristic parameters such as the type, size, direction, frequency and duration of typical loads such as the dead weight of the gas tank, thermal stress, and wind load are analyzed. Through the load spectrum test, the load-time history curve under different working conditions is obtained, and the load combination method is used to study the stress distribution law of the tank base under the coupling of multiple loads. The stress analysis accuracy and efficiency of the LNG tank base are improved, providing an effective method for structural optimization design.

[0065] In step S1, adaptive meshing technology is used to model the complex geometric structure of the LNG tank base, and the stress concentration area and stress gradient changes are comprehensively considered to obtain a high-quality finite element mesh model. During the meshing process, the mesh density of the stress concentration area under the load is focused on to improve the mesh quality in this area so as to accurately capture the stress gradient changes.

[0066] In one embodiment, the overall coordinate system of the model has the positive direction of the X axis pointing to the bow, the positive direction of the Y axis pointing to the port side, and the positive direction of the Z axis pointing vertically upward. The model range is from FR2 to FR10 in the X direction, the full width of the superstructure in the Y direction, and all non-main hull structures under the LNG tank deck in the Z direction, including the LNG tank deck and the crew deck.

[0067] This project uses a finite element model of a three-dimensional plate-beam combined unit for calculation, and the model unit is mainly a 2D shell unit. The division of the grid unit refers to the modeling principle of the plate-beam model in 1.9.4 of the "Steel Inland Ship Construction Code 2016". The strong frame has no less than 2 units in the web height direction, the aspect ratio of the plate unit does not exceed 3, and the use of triangular plate units in the model is minimized as much as possible. The finite element grid size is 100mm×100mm, and the grid shape is as close to a square as possible.

[0068] The scope and boundary conditions of the model are shown in the following table:

[0069]

[0070] In step S2, according to the anisotropic characteristics of the LNG tank base composite material, a composite material constitutive model that takes into account material nonlinearity and damage evolution is established. Through material parameter identification tests, the stress-strain curve, strength, stiffness and other mechanical performance parameters of the composite material under different acceleration loads are obtained. The established constitutive model is verified using the obtained material parameters in combination with numerical simulation methods to improve the accuracy of prediction of the stress level and stress direction of the composite material.

[0071] In step S3, in the finite element model of the LNG gas tank base, key load conditions are identified, and characteristic parameters such as type, size, direction, frequency and duration of typical loads such as the gas tank deadweight, thermal stress, and wind load are analyzed in detail. Through load spectrum testing, load-time history curves under different working conditions are obtained, and the load combination method is used to study the stress distribution law of the gas tank base under the coupling of multiple loads.

[0072] In some embodiments, the calculated load should cover various loads that the LNG fuel tank base may be subjected to during its entire life cycle, which is a combined load. To ensure that the fuel tank base has sufficient capacity to withstand all relevant loads and sufficient safety margin, all relevant load scenarios and design conditions must be included. This includes the mass of the ship, the maximum value of the LNG fuel tank gravity load, and some environmental loads and unexpected loads that may be encountered during the service life, as shown below:

[0073] (1) Structural deadweight: Considering the gravity acceleration (9810 mm / s2), the whole model is applied.

[0074] (2) LNG tank weight: total mass of the equipment after filling (25600kg) x gravitational acceleration (9.81m / s2) = 251136N, applied at the center of gravity position G2 of the storage tank + cold box when fully loaded with LNG.

[0075] (3) Environmental loads: Refer to the environmental load requirements of 4.2.7.4 of the "2021 Code for the Use of Natural Gas Fuel in Ships", and consider the long-term distribution of ship motion in irregular waves during its service life. Ship motion should include surge, sway, heave, roll, pitch and yaw. When estimating the acceleration acting on the fuel tank, this method adopts the method in the code:

[0076] a) Acceleration along the length of the ship x =2g;

[0077] b) Acceleration along the width of the ship a y =1g;

[0078] c) Vertical upward acceleration a zu =1g;

[0079] d) Vertical downward acceleration azd =2g (including gravity effect);

[0080] The calculation conditions are divided into four conditions according to the classification of environmental loads (such as Figure 3-5 ).

[0081] In step S4, in view of the influence of load uncertainty on the stress analysis of the LNG tank base, the random field theory is used to describe the random distribution characteristics of the load. Through the digital feature extraction and probability distribution fitting of the random load, a load random field model is established. It is used as the random boundary condition of the finite element model of the tank base to carry out the probabilistic analysis of the tank base stress, focusing on the influence of the spatial distribution characteristics and time variation law of the load on the statistical characteristic quantities such as the mean, variance, and confidence interval of the tank base stress distribution.

[0082] In step S5, based on the random stress response of the gas tank base, the Monte Carlo method is used to obtain a large amount of stress sample data through sampling simulation, and a machine learning algorithm, such as a support vector machine, is used to establish a load-stress data-driven high-speed response model. The model uses parameters such as load type, size, direction, frequency, and duration as input, and the predicted result of the stress distribution of the gas tank base as output, so as to achieve rapid evaluation of the stress distribution of the gas tank base.

[0083] As an example, the stress is calculated according to the working conditions, and the specific evaluation data is shown in the table:

[0084]

[0085]

[0086]

[0087] In step S6, the established stress analysis model of the gas tank base is combined with the structural optimization algorithm. The stress level and stress peak of the gas tank base are used as constraints, and the composite material layup scheme and geometric dimensions are used as design variables. A multi-objective optimization model is constructed, and a genetic algorithm is used to search the optimization design space to obtain an optimized design scheme for the gas tank base that meets the strength requirements and has lightweight characteristics. During the optimization process, emphasis is placed on the influence of parameters such as the composite material layup angle and thickness on the acceleration impact resistance of the gas tank base.

[0088] Step S1 specifically includes the following steps:

[0089] Step S11: obtaining the three-dimensional geometric model data of the LNG tank base and preprocessing the model;

[0090] Step S12: According to the stress characteristics of the LNG tank base, potential stress concentration areas are identified, and a local encrypted adaptive meshing strategy is adopted for these areas;

[0091] Step S13: meshing the LNG tank base using eight-node hexahedral units, and increasing mesh density in stress concentration areas and areas with large stress gradient changes;

[0092] Step S14: applying loads and boundary conditions in the finite element mesh model to simulate the stress state of the LNG tank base under actual working conditions;

[0093] Step S15: Perform stress analysis on the LNG tank base to obtain stress distribution cloud diagram and stress value in stress concentration area, post-process the calculation results, extract stress gradient change curve in stress concentration area, and evaluate the stress performance and structural integrity of the LNG tank base.

[0094] Specifically, first, the 3D geometric model data of the LNG tank base is obtained from CAD software or 3D scanner, and the model is preprocessed by software such as Geomagic Studio. The geometric structure is simplified by using functions such as removing small faces and filling holes, and defects and discontinuous surfaces in the model are repaired. Then, according to the structural characteristics and stress conditions of the LNG tank base, stress analysis software such as ANSYS is used to determine the stress concentration area and the area with large stress gradient changes, such as welded joints, sharp corners, etc., and the mesh size of 5mm to 2mm is set in these areas. Next, the Delaunay algorithm is used to mesh the LNG tank base model, and dense meshes with a size of 5mm to 2mm are generated in the stress concentration area and the area with large stress gradient changes, and sparse meshes with a size of 5mm to 10mm are generated in other areas. The quality of the generated mesh model is evaluated by the mesh quality factor method, and the mesh quality factor range is set to 5 to 1. When the quality factor is less than 5, the meshing parameters are returned to adjust, the mesh density is increased or the unit type is changed, and the meshing is re-performed until a mesh model that meets the quality requirements is generated. The properties of the generated high-quality mesh model are set, and the Q345B steel material commonly used in the LNG tank base is selected. The elastic modulus of the material is defined as 210GPa, the Poisson's ratio is 3, and the yield strength is 345MPa. The bottom surface of the LNG tank base is set as a fixed constraint, and a uniformly distributed pressure load of 5MPa is applied to the top surface. Finally, the generated high-quality LNG tank base finite element mesh model is exported to INP format, and the mesh model is imported into ANSYS Workbench for static analysis. The stress and strain distribution cloud map of the LNG tank base under the design condition is obtained, providing data support for the design optimization of the LNG tank base.

[0095] Step S2 specifically includes the following steps:

[0096] Step S21: according to the anisotropic characteristics of the composite material of the LNG tank base, a composite material constitutive model considering the material nonlinearity and damage evolution characteristics is established;

[0097] Step S22: Designing a material parameter identification test plan for the established composite material constitutive model, and obtaining mechanical performance parameters such as stress-strain curve, strength and stiffness of the composite material under different acceleration loads through the test;

[0098] Step S23: establishing a composite material finite element numerical model according to the obtained mechanical property parameters of the composite material, and analyzing the mechanical response characteristics of the composite material under different acceleration loads through numerical simulation;

[0099] Step S24: Compare and analyze the numerical simulation results with the material test results to evaluate the accuracy and applicability of the established composite material constitutive model;

[0100] Step S25: If the numerical simulation results are consistent with the test results, it is considered that the established composite material constitutive model can accurately describe the mechanical behavior of the LNG tank base composite material and can be used for subsequent structural analysis and optimization design;

[0101] Step S26: If the numerical simulation results are significantly different from the test results, the composite material constitutive model needs to be corrected and improved until the model can accurately predict the mechanical properties of the composite material;

[0102] Step S27: Genetic algorithm and neural network algorithm are used to optimize the material parameters in the established composite material constitutive model to improve the accuracy and robustness of the model, and provide a reliable mechanical property prediction tool for the design and application of LNG tank base composite materials.

[0103] Specifically, when designing the constitutive model of the composite material for the LNG tank base, the anisotropic properties of the material are first taken into account, and the nonlinear damage mechanics theory is used to describe the stress-strain relationship and damage evolution of the material under different loads. For example, the continuous damage mechanics (CDM) model can be used in combination with Hill's anisotropic yield criterion to simulate the nonlinear behavior and damage development of the composite material. In terms of material parameter identification, tensile, compression and shear tests are designed, and data are collected through high-precision strain gauges and force sensors, such as stress-strain curves measured under compression loads of 5, 0, and 5 GPa, as well as the corresponding strength and stiffness parameters. Using these test data, the constitutive parameters of the composite material, such as the yield strength, hardening model and damage parameters of the material, are optimized through genetic algorithms to ensure that the model can accurately predict the behavior observed in the experiment. In finite element analysis, software such as ANSYS or ABAQUS is used to establish a numerical model of the composite material to simulate the mechanical response under different acceleration loads. For example, the stress distribution and deformation of the composite material under a dynamic load of 0 GPa are simulated to analyze the safety and reliability of the material. The accuracy of the constitutive model is evaluated by comparing the numerical simulation results with the experimental data, such as the fit of the stress-strain curve and the consistency of damage evolution. If differences are found, the neural network algorithm can be used to further adjust and optimize the model parameters, such as adjusting the rate parameter of damage evolution or the anisotropic strength factor, until the simulation results match the experimental data. This iterative process ensures the high accuracy and robustness of the model, providing a reliable mechanical property prediction tool for the design and application of LNG tank base composites.

[0104] Step S3 specifically includes the following steps:

[0105] Step S31: by combining field testing and numerical simulation, the load-time history curve of the gas tank base under different working conditions is obtained, and the key load conditions and their characteristic parameters are determined;

[0106] Step S32: using load spectrum testing technology to perform multi-channel and multi-point load testing on the gas tank base, obtaining load data under typical working conditions, and establishing a gas tank base load database;

[0107] Step S33: According to the statistical characteristics of the load data, the load-time history curve is processed to extract the load characteristic parameters, and the combination coefficient and action mode of each typical load are reasonably determined to obtain the equivalent load of the gas tank base;

[0108] Step S34: applying an equivalent load to the finite element model of the gas tank base, calculating the stress distribution cloud map of the gas tank base under the multi-load coupling, analyzing the stress distribution law of the gas tank base, and identifying the stress concentration area.

[0109] Specifically, according to the structural characteristics and stress conditions of the LNG tank base, a finite element model of the LNG tank base is established. The model comprehensively considers factors such as material properties and geometric dimensions to accurately simulate the mechanical behavior of the tank base. By analyzing the actual working state of the LNG tank base, the key load conditions that have a significant impact on the structural strength and fatigue life of the tank base are identified, and the corresponding load time history curve data is obtained for each key condition. The load combination method is adopted to superimpose and combine the load time history curves under multiple load conditions to simulate the complex loads on the LNG tank base during actual use. The combined load is used as the input condition, and the load is applied to the finite element model of the LNG tank base. The stress response results of the tank base under the coupling of multiple loads are obtained through finite element solution. The stress distribution cloud map of the LNG tank base is analyzed to identify the stress concentration area and evaluate the force characteristics and potential weak points of the tank base under the combined load. For the high stress area of ​​the LNG tank base, local mesh encryption is performed to improve the calculation accuracy of the area and obtain more accurate and reliable stress distribution results. Based on the stress distribution law of LNG tank base, the structural form and dimensional parameters of the tank base are optimized, and improvement measures are proposed, such as adding reinforcing ribs and reasonably arranging supports, so as to reduce the stress concentration and improve the bearing capacity and reliability of the tank base.

[0110] It can be understood that in order to establish the finite element model of the LNG tank base, it is first necessary to collect the geometric dimension data of the tank base, such as the length, width, height, etc. of the base, and select appropriate material property parameters, such as the elastic modulus of the base material is 210GPa and the Poisson's ratio is 3. Then use finite element software such as ANSYS to establish a three-dimensional solid model of the tank base and divide the grid, and the grid unit size is set to 10mm. Through the force analysis of the tank base, it is identified that the tank base is subjected to a large load under key conditions such as driving conditions, braking conditions, and steering conditions. For these conditions, the load time history data in all directions of the tank base is measured and recorded, the time step is set to 01s, and the measurement time is 10s. The load superposition combination method is adopted, and the load combination program is written using MATLAB software to superimpose the load time history data under different conditions to simulate the complex loads in the actual use of the tank base. The combined load data was imported into ANSYS, the load was applied to the finite element model of the gas tank base, and the finite element solution analysis was performed to obtain the stress distribution cloud map of the gas tank base. Through the analysis of the stress cloud map, it was found that the gas tank base had stress concentration at the connection between the bottom plate and the side plate, and the maximum stress value reached 120MPa. For this high stress area, the local grid size was refined to 2mm, and the stress calculation was re-performed to obtain a more accurate stress result, and the maximum stress value was corrected to 150MPa. According to the stress distribution law of the gas tank base, a 10mm thick reinforcement rib was added in the stress concentration area, and the support layout was optimized so that the position of the support point matched the load transfer path. After structural optimization, the maximum stress value of the gas tank base was reduced to 100MPa, which improved the strength and reliability of the gas tank base.

[0111] Step S4 specifically includes the following steps:

[0112] Step S41: Obtaining load test data of the gas cylinder base under actual working conditions and extracting digital features of the load;

[0113] Step S42: Determine the probability distribution type and parameters of the load using a probability distribution fitting method according to the digital characteristics of the load;

[0114] Step S43: Generate a random field model of the load using random field theory according to the probability distribution of the load;

[0115] Step S44: using the load random field model as a random boundary condition of the gas tank base finite element model;

[0116] Step S45: obtaining a statistical sample of the stress distribution of the gas tank base by using a Monte Carlo simulation method;

[0117] Step S46: Calculate the statistical characteristic quantities such as the mean, variance and confidence interval of the stress distribution of the gas tank base according to the statistical samples of the stress distribution, and judge whether the structural reliability of the gas tank base meets the design requirements.

[0118] Specifically, the randomness of the load is described by random field theory, and the statistical distribution parameters of the load data are obtained. Using these parameters, a random field model is constructed through advanced statistical methods. The model can reflect the actual distribution of the load on the LNG tank base. The random field model is used as input, and the model is digitized through finite element analysis software. The random field model is applied to the boundary conditions of the finite element model to perform stress analysis. The stress response of the base is quantified through the probability analysis method to obtain the probabilistic characteristics of the stress distribution. According to the obtained probability analysis results, the model parameters are adjusted to optimize the model performance and ensure the accuracy of the analysis results. The analysis results are further processed and interpreted through data processing technology so that engineers can better understand and apply these results. Finally, the supervised learning in the machine learning algorithm is used to train and verify the model to ensure that the model still maintains high accuracy and reliability under new load conditions.

[0119] It can be understood that when describing the randomness of loads through random field theory, a Gaussian random field model can be used. First, by statistically analyzing 1000 sets of load data, the mean of the load is 50kN and the standard deviation is 10kN. Then, the probability density function of the load data is fitted using the maximum likelihood estimation method, and the normal distribution N(50,10^2) is obtained. Next, the random field is discretized into 10 independent random variables using the Karhunen-Loeve expansion to construct a random field model of the load. In the ANSYS finite element software, the random field model is used as a boundary condition to perform stress analysis on the LNG tank base. The Monte Carlo method is used to perform 1000 random sampling simulations to obtain the statistical characteristics of the stress distribution: the stress mean is 200MPa and the standard deviation is 20MPa. According to the results of the probability analysis, the parameters such as the correlation length of the random field model are adjusted to optimize the model performance so that the relative error of the stress mean and standard deviation is controlled within 5%. MATLAB is used to visualize stress distribution, draw stress cloud diagrams and probability density curves, which are convenient for engineers to analyze and make decisions. Finally, the support vector machine algorithm is used to establish a load-stress mapping model with random field model parameters as features, stress response as labels, and a training sample capacity of 500. The accuracy of 5-fold cross validation reaches more than 95%, ensuring the generalization ability of the model and providing a reliable basis for engineering applications.

[0120] Step S5 specifically includes the following steps:

[0121] Step S51: performing random stress response analysis on the gas tank base according to the structural characteristics and material properties of the gas tank base to obtain stress response data of the gas tank base under different load conditions;

[0122] Step S52: for the obtained random stress response data of the gas tank base, a Monte Carlo method is used to perform sampling simulation, by randomly extracting load parameters and inputting them into the finite element model for calculation, so as to obtain a large amount of gas tank base stress sample data;

[0123] Step S53: establishing a high-speed response model with load parameters as input and a predicted result of the stress distribution of the gas tank base as output according to the obtained gas tank base stress sample data;

[0124] Step S54: when establishing the high-speed response model, the characteristic parameters of the load are used as the input characteristics of the model, and the stress values ​​at various positions of the gas tank base are used as the output targets of the model;

[0125] Step S55: Train and test the established high-speed response model, optimize the model's hyperparameters and structure, and deploy the trained high-speed response model to the gas tank base stress distribution rapid assessment system. When new load condition parameters are input, the model can quickly predict the stress distribution of the gas tank base.

[0126] Specifically, the stress data generated by the Monte Carlo method will be used as the input of the support vector machine model to train and verify the accuracy of the model. After the model is built, the support vector machine model can quickly predict the stress distribution by inputting the actual load data. In order to improve the response speed and accuracy of the model, the algorithm parameters will be further optimized to ensure that the data-driven model can effectively handle large-scale data sets. In addition, the data relationship between stress response and stress distribution will be deeply analyzed to optimize the evaluation method and improve the accuracy and efficiency of the evaluation. Through these steps, a rapid and accurate evaluation of the stress distribution of the LNG tank base can be achieved.

[0127] It can be understood that before using the stress data generated by the Monte Carlo method as the input of the support vector machine (SVM) model, it is first necessary to generate a large number of possible load conditions through random sampling technology, and calculate the corresponding stress response in each case. For example, 10,000 different load conditions can be simulated and the corresponding stress values ​​can be calculated. These data will be used to train the SVM model to ensure that the model can learn the mapping relationship from load to stress. During the model training process, the radial basis function (RBF) is used as the kernel function, and the performance of the model is optimized by adjusting parameters such as the penalty coefficient C (for example, set to 10) and the γ parameter of the kernel function (for example, set to 1). The performance of the model on unseen data is evaluated by cross-validation methods, such as five-fold cross-validation, to ensure that the model has good generalization ability. After the model optimization is completed, the actual load data is input into the SVM model, and the model will quickly predict the stress distribution based on the learned data pattern. In order to handle large-scale data sets and improve the response speed of the model, a distributed computing framework such as Apache Spark can be used to process data in parallel, which can significantly reduce the time for data processing and model prediction. For example, the data is distributed to 10 nodes, and each node processes 1,000 data points. In addition, in order to further improve the accuracy and efficiency of the evaluation, the data of the stress response predicted by the model and the actual stress distribution will be deeply analyzed. Statistical analysis methods such as deviation analysis and variance analysis are used to identify the possible prediction errors of the model under specific types of loads, so as to fine-tune the SVM model and optimize its prediction performance. Through these comprehensive steps, a rapid and accurate evaluation of the stress distribution of the LNG tank base can be achieved, providing a scientific basis for structural safety.

[0128] Step S6 specifically includes the following steps:

[0129] Step S61: establishing a finite element stress analysis model according to the geometric dimensions of the LNG tank base and the composite material layup scheme, and obtaining the stress distribution of the LNG tank base under different working conditions;

[0130] Step S62: determining the constraint conditions of the stress analysis of the LNG tank base, including the allowable range of the stress level and the stress peak value, as the constraint conditions of the structural optimization design;

[0131] Step S63: Using the composite material layup scheme and geometric dimensions as design variables for structural optimization design, a multi-objective optimization mathematical model with minimization of the mass of the LNG tank base as the objective function is established;

[0132] Step S64: using a genetic algorithm to solve the multi-objective optimization mathematical model, and searching for the optimal composite material layup scheme and geometric dimension parameter combination under the premise of satisfying stress constraints;

[0133] Step S65: updating the composite material layup scheme and geometric dimensions of the LNG tank base according to the optimal design parameter combination obtained by searching with the genetic algorithm, and obtaining a lightweight design scheme that meets the strength requirements;

[0134] Step S66: inputting the composite material layup scheme and geometric dimension parameters corresponding to the lightweight design scheme into the finite element stress analysis model, performing stress verification analysis, and determining whether the optimized design scheme meets the strength requirements;

[0135] Step S67: If the stress verification analysis result meets the strength requirement, the optimized design scheme is output as the final design scheme of the LNG tank base; if it does not meet the strength requirement, the genetic algorithm is returned to search and optimize again.

[0136] It can be understood that according to the geometric dimensions of the LNG tank base (such as 2 meters long, 1 meter wide, and 5 meters high) and the composite material laying scheme (such as using carbon fiber reinforced epoxy resin-based composite materials, with a fiber volume fraction of 60% and a laying angle of [0° / 45° / 90° / -45°]s), a three-dimensional solid model and a finite element model of the LNG tank base are established in the ANSYS finite element analysis software, and material parameters, mesh division, load conditions, and boundary conditions are set. Stress analysis is performed under 5 times the working pressure, drop impact, thermal expansion and contraction, and the Mises equivalent stress distribution cloud map of different areas of the LNG tank base is obtained. According to the composite material strength theory and the LNG tank design specification, the constraint conditions for the stress analysis of the LNG tank base are determined, requiring that the Mises stress peak value does not exceed the safety factor of the material allowable strength (such as 5), and the interlaminar stress does not exceed the allowable value of the interlaminar strength (such as 20MPa). A multi-objective optimization mathematical model was established with the composite material ply angle and thickness as design variables (e.g., the angle was taken at intervals of 15° in the range of 0° to 90°, and the thickness was taken at intervals of 5mm in the range of 10mm to 50mm), and the total mass minimization of the LNG tank base was taken as the objective function. The model was solved using the MATLAB genetic algorithm toolbox, with the population size set to 50, the evolutionary generation to 100, the crossover probability to 8, and the mutation probability to 1. A set of optimal design parameter combinations that met the stress constraints were searched (e.g., the ply angle was [0° / 60° / -60°]s, and the single layer thickness was 15mm). This set of design parameters was input into the finite element model, the composite material ply scheme and geometric dimensions of the LNG tank base were updated, and the stress analysis verification was re-performed. The results showed that the stress level of the optimized design scheme met the strength requirements under all working conditions. Compared with the initial scheme, the total mass of the LNG tank base was reduced by 30%, achieving a lightweight design. Finally, the three-dimensional digital model file and engineering drawing of the optimized design scheme were generated to provide guidance for the production and manufacturing of the LNG tank base.

[0137] In a second aspect, the present invention discloses a finite element analysis and calculation system for stress of an LNG tank base, which comprises:

[0138] Stress distribution analysis unit: construct a finite element model of the LNG tank base, perform load analysis and processing, and obtain the stress distribution of the LNG tank base under the coupling of multiple loads;

[0139] Random stress response unit: Based on the digital feature extraction and probability distribution fitting of random loads on the finite element model of the LNG tank base, a load random field model is established to perform probability analysis and processing of the tank base stress to obtain the random stress response of the tank base;

[0140] Stress analysis and prediction unit: Sampling simulation obtains a large amount of stress sample data, establishes a high-speed response model driven by load-stress data, inputs load parameters for analysis and processing, and obtains the stress analysis prediction results of the gas tank base.

[0141] It can be understood that the stress distribution analysis unit studies the stress distribution law under the coupling of multiple loads. In view of the uncertainty of loads, the random field theory is used to describe the random distribution characteristics of loads and conduct probability analysis. The random stress response unit extracts the digital characteristics of random loads on the finite element model of the LNG gas tank base and fits the probability distribution to obtain the random stress response of the gas tank base. The stress analysis prediction unit establishes a high-speed response model driven by load stress data to realize the rapid evaluation of the stress distribution of the LNG gas tank base. The system can realize the stress analysis of the base and can output the optimal gas tank base design according to the actual load parameters and composite materials.

[0142] In a third aspect, the present invention discloses an electronic device, which includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

[0143] Memory, used to store computer programs;

[0144] The processor is used to implement the steps of the gas tank base stress finite element analysis method when executing the program stored in the memory.

[0145] It can be understood that the electronic equipment is installed with a gas tank base stress finite element analysis system, which implements the steps of the gas tank base stress finite element analysis method, can realize the stress analysis of the base, and can output the optimal gas tank base design according to the actual load parameters and composite materials.

[0146] In a fourth aspect, the present invention discloses a storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the gas tank base stress finite element analysis method are implemented.

[0147] It can be understood that the storage medium stores the steps of the gas tank base stress finite element analysis method, which can realize the stress analysis of the base and output the optimal gas tank base design according to the actual load parameters and composite materials.

[0148] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0149] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0150] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0151] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be connected, detachably connected, or integrated; it can be mechanically connected or electrically connected; it can be directly connected or indirectly connected through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0152] In the present invention, unless otherwise clearly specified and limited, a first feature being "above" or "below" a second feature may include that the first and second features are in direct contact, or may include that the first and second features are not in direct contact but are in contact through another feature between them. Moreover, a first feature being "above", "above" and "above" a second feature includes that the first feature is directly above and obliquely above the second feature, or simply indicates that the first feature is higher in level than the second feature. A first feature being "below", "below" and "below" a second feature includes that the first feature is directly below and obliquely below the second feature, or simply indicates that the first feature is lower in level than the second feature.

[0153] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms should not be understood as necessarily referring to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification.

[0154] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

[0155] The above is a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A finite element analysis method for gas tank base stress, characterized in that: The following steps are involved: Construct a finite element model of the LNG tank base, perform load analysis and processing, and obtain the stress distribution of the LNG tank base under the coupling of multiple loads; According to the digital feature extraction and probability distribution fitting of random loads on the finite element model of the LNG tank base, a load random field model is established, and the probability analysis and processing of the tank base stress is carried out to obtain the random stress response of the tank base. A large amount of stress sample data is obtained through sampling simulation, and a high-speed response model driven by load-stress data is established. The load parameters are input for analysis and processing to obtain the stress analysis prediction results of the gas tank base.

2. The finite element analysis method for gas tank base stress according to claim 1, characterized in that: Before constructing the LNG tank base finite element model for the LNG tank base, the following steps are included: According to the geometric structure of the LNG tank base, the finite element mesh model of the LNG tank base is constructed through adaptive mesh division; The anisotropic characteristics of composite materials are added to the finite element mesh model to construct a finite element model of the LNG tank base.

3. The finite element analysis method for gas tank base stress according to claim 1 is characterized in that: After obtaining the prediction result of the gas tank base stress analysis, the following steps are included: A multi-objective optimization model is constructed to output a design plan for the gas tank base that meets the material type and the stress of the gas tank base according to the stress analysis results of the gas tank base and the type of material used.

4. The finite element analysis method for gas tank base stress according to claim 1, characterized in that: The method of constructing a finite element model of the LNG tank base, performing load analysis and processing, and obtaining the stress distribution of the LNG tank base under the coupling of multiple loads specifically includes the following steps: By combining field tests and numerical simulations, the load-time history curves of the gas tank base under different working conditions are obtained, and the key load conditions and their characteristic parameters are determined; Use load spectrum testing technology to conduct multi-channel and multi-point load tests on the gas tank base, obtain load data under typical working conditions, and establish a gas tank base load database; According to the statistical characteristics of load data, the load-time history curve is processed, the load characteristic parameters are extracted, the combination coefficient and action mode of each typical load are reasonably determined, and the equivalent load of the gas tank base is obtained; An equivalent load is applied to the finite element model of the gas tank base, and the stress distribution cloud map of the gas tank base under the action of multi-load coupling is calculated. The stress distribution law of the gas tank base is analyzed and the stress concentration area is identified.

5. The gas tank base stress finite element analysis method according to claim 1, characterized in that: The method extracts digital features and fits probability distribution of random loads on the finite element model of the LNG gas tank base, establishes a load random field model, performs probability analysis on the gas tank base stress, and obtains the random stress response of the gas tank base, specifically including the following steps: Obtain the load test data of the gas tank base under actual working conditions and extract the digital characteristics of the load; According to the digital characteristics of the load, the probability distribution type and parameters of the load are determined by using the probability distribution fitting method; According to the probability distribution of load, random field theory is used to generate the random field model of load; The load random field model is used as the random boundary condition of the finite element model of the gas tank base; The statistical samples of the stress distribution of the gas tank base are obtained by Monte Carlo simulation method; According to the statistical samples of stress distribution, the statistical characteristics of the stress distribution of the gas tank base, such as the mean, variance and confidence interval, are calculated to determine whether the structural reliability of the gas tank base meets the design requirements.

6. The gas tank base stress finite element analysis method according to claim 1, characterized in that: The sampling simulation obtains a large amount of stress sample data, establishes a high-speed response model driven by load-stress data, inputs load parameters for analysis and processing, and obtains the prediction results of the gas tank base stress analysis, which specifically includes the following steps: According to the structural characteristics and material properties of the gas tank base, a random stress response analysis is performed on the gas tank base to obtain the stress response data of the gas tank base under different load conditions; The Monte Carlo method is used to perform sampling simulation for the obtained random stress response data of the gas tank base. By randomly extracting load parameters and inputting them into the finite element model for calculation, a large amount of gas tank base stress sample data is obtained. Based on the obtained gas tank base stress sample data, a high-speed response model is established with load parameters as input and the predicted results of the gas tank base stress distribution as output; When establishing the high-speed response model, the characteristic parameters of the load are used as the input characteristics of the model, and the stress values ​​at various positions of the gas tank base are used as the output targets of the model; The established high-speed response model is trained and tested to optimize the model's hyperparameters and structure. The trained high-speed response model is deployed to the gas tank base stress distribution rapid assessment system. When new load condition parameters are input, the model can quickly predict the stress distribution of the gas tank base.

7. The gas tank base stress finite element analysis method according to claim 2, characterized in that: According to the geometric structure of the LNG tank base, the finite element mesh model of the LNG tank base is constructed by adaptive mesh division, which specifically includes the following steps: Obtain the 3D geometric model data of the LNG tank base and preprocess the model; According to the stress characteristics of the LNG tank base, potential stress concentration areas are identified, and a local encrypted adaptive meshing strategy is used for these areas. The LNG tank base is meshed using eight-node hexahedral elements, and the mesh density is increased in stress concentration areas and areas with large stress gradient changes. Apply loads and boundary conditions to the finite element mesh model to simulate the stress state of the LNG tank base under actual working conditions; Stress analysis is performed on the LNG tank base to obtain the stress distribution cloud map and stress values ​​in the stress concentration area. The calculation results are post-processed to extract the stress gradient change curve in the stress concentration area and evaluate the stress performance and structural integrity of the LNG tank base.

8. A gas tank base stress finite element analysis system, characterized in that: include: Stress distribution analysis unit: construct a finite element model of the LNG tank base, perform load analysis and processing, and obtain the stress distribution of the LNG tank base under the coupling of multiple loads; Random stress response unit: Based on the digital feature extraction and probability distribution fitting of random loads on the finite element model of the LNG tank base, a load random field model is established to perform probability analysis and processing of the tank base stress to obtain the random stress response of the tank base; Stress analysis and prediction unit: Sampling simulation obtains a large amount of stress sample data, establishes a high-speed response model driven by load-stress data, inputs load parameters for analysis and processing, and obtains the stress analysis prediction results of the gas tank base.

9. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; Memory, used to store computer programs; The processor is used to implement the steps of the gas tank base stress finite element analysis method described in any one of claims 1-7 when executing the program stored in the memory.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the gas tank base stress finite element analysis method as described in any one of claims 1 to 7 are implemented.

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