A quick prediction system for welding performance of a storage tank

Abstract

The application discloses a kind of storage tank welding performance fast prediction system, comprising: grid division module, for obtaining the three-dimensional model of storage tank, the three-dimensional model of storage tank is grid divided and grid node combination, obtain several sub-grid units;Temperature calculation module, for heat source check, welding heat source path definition, sub-grid temperature field calculation and temperature communication between sub-grid, output the temperature field of storage tank grid;Performance prediction module is used as the temperature field of storage tank grid as organizational evolution driving force, predicts the post-weld performance of storage tank grid;Post-processing module is used for visual processing to grid division result, grid node combination result, the temperature field of storage tank grid, microstructure, predicted post-weld performance of storage tank grid, and display.The present application is based on welding process numerical simulation method, auxiliary acceleration algorithm, can fastly predict the weld performance of all areas of storage tank, realize the prediction of the weld performance of all areas of storage tank, to facilitate the evaluation of storage tank strength.

Description

Technical Field

[0001] This invention belongs to the field of liquid rocket design technology, and in particular relates to a rapid prediction system for the welding performance of propellant tanks. Background Technology

[0002] The propellant tank is an important component of the liquid rocket's structure. While storing propellant, it must also withstand various loads, including complex internal and external pressures, axial pressure, and bending moments. The tank is welded from multiple parts, and the weld properties typically account for 50% to 60% of the base material's properties. The welded area is the weakest point in the tank's performance, making the weld properties crucial for evaluating the overall tank performance.

[0003] To obtain weldability, the traditional method generally involves test welding with the same material as the base material, using the sample performance to represent the tank weld performance. However, due to the influence of various factors such as welding method, process parameters, part size, and environmental factors (temperature, humidity, etc.) coupled with forces, heat, and flow, as well as the structural differences at different locations within the tank, the test piece welding method cannot obtain the true performance of the tank weld and the distribution of weld performance. Therefore, it is essential to develop a non-destructive performance evaluation method for the entire size of tank welds. Summary of the Invention

[0004] The technical problem solved by this invention is to overcome the shortcomings of the prior art and provide a rapid prediction system for the welding performance of storage tanks. Based on the numerical simulation method of the welding process and the auxiliary acceleration algorithm, it can quickly predict the weld performance of all areas of the storage tank, thereby facilitating the assessment of the tank strength.

[0005] To address the aforementioned technical problems, this invention discloses a rapid prediction system for the welding performance of storage tanks, comprising:

[0006] The mesh generation module is used to obtain the 3D model of the storage tank, perform mesh generation and mesh node combination on the 3D model of the storage tank, and obtain several sub-mesh units;

[0007] The temperature calculation module is used for heat source verification, welding heat source path definition, sub-mesh temperature field calculation and temperature communication between sub-mesh, and outputs the temperature field of the tank mesh.

[0008] The performance prediction module is used to predict the post-weld performance of the tank mesh by using the temperature field of the tank mesh as the driving force for microstructure evolution.

[0009] The post-processing module is used to visualize and display the mesh generation results, mesh node combination results, temperature field, microstructure, and predicted post-weld performance of the tank mesh.

[0010] In the aforementioned rapid prediction system for tank welding performance, the mesh generation module includes:

[0011] The box bottom spherical coordinate mesh generation submodule is used to mesh the bottom of the box in the 3D model in spherical coordinates to obtain several mesh nodes;

[0012] The cylindrical shell mesh generation submodule is used to mesh the shell wall panels in the 3D model in cylindrical coordinates to obtain several mesh nodes.

[0013] The grid node combination submodule is used to combine several grid nodes within a certain adjacent area to form a subgrid unit.

[0014] In the aforementioned rapid prediction system for tank welding performance, each individual grid node contains initial information on location coordinates, temperature, microstructure, and properties.

[0015] In the aforementioned rapid prediction system for tank welding performance, the sub-grid cell is divided into a normal grid area and a communication grid area; the normal grid area only has calculation functions, while the communication grid area has both calculation functions and the function of communicating with other sub-grid cells.

[0016] In the aforementioned rapid prediction system for tank welding performance, the temperature calculation module includes:

[0017] The heat source verification submodule is used to select the Gauss surface heat source and the Goldark double ellipsoid heat source, and determine the welding heat source parameters by comparing the temperature and molten pool morphology based on the actual heat source conditions.

[0018] The heat source path definition submodule is used to define the welding process path of the storage tank, including: welding speed and scanning position;

[0019] The calculation submodule is used to embed the heat source into the temperature field calculation model according to the defined tank welding process path, calculate each sub-grid cell along the path in sequence, and obtain the temperature field of each sub-grid cell; based on the obtained temperature field of each sub-grid cell, determine the temperature field of the tank grid and output it.

[0020] The communication submodule is used to update the temperature data at the same location in other subgrid cells using the calculated temperature field of the current subgrid cell.

[0021] In the aforementioned rapid prediction system for tank welding performance, the performance prediction module is used to: use the temperature field of the tank mesh as the driving force for microstructure evolution, calculate the microstructure evolution process of the tank mesh according to the microstructure evolution law of different tank materials and the quantitative mathematical model corresponding to the microstructure, and obtain the microstructure field; update the microstructure of the tank mesh using the calculated microstructure field; predict the post-weld performance of the tank mesh based on the microstructure update result and the quantitative relationship between microstructure and performance, and update the performance of the tank mesh using the calculated performance.

[0022] In the aforementioned rapid prediction system for tank welding performance, the performance prediction module includes:

[0023] The aluminum-magnesium alloy welding performance prediction submodule is used to calculate the microstructure evolution of aluminum-magnesium alloys based on the temperature field of the tank grid, and predict the post-weld performance of aluminum-magnesium alloys based on the quantitative relationship between the microstructure and properties of aluminum-magnesium alloys.

[0024] The aluminum-copper alloy welding performance prediction submodule is used to calculate the microstructure evolution of aluminum-copper alloy based on the temperature field of the tank grid, and predict the post-weld aluminum-copper alloy performance based on the quantitative relationship between the microstructure and properties of aluminum-copper alloy.

[0025] The aluminum-lithium alloy welding performance prediction submodule is used to calculate the microstructure evolution of aluminum-lithium alloy based on the temperature field of the tank grid, and predict the post-weld aluminum-magnesium alloy performance based on the quantitative relationship between the microstructure and performance of aluminum-lithium alloy.

[0026] The titanium alloy welding performance prediction submodule is used to calculate the evolution of titanium alloy microstructure based on the temperature field of the tank grid, and predict the post-weld titanium alloy performance based on the quantitative relationship between titanium alloy microstructure and performance.

[0027] The stainless steel welding performance prediction submodule is used to calculate the evolution of stainless steel microstructure based on the temperature field of the tank grid, and predict the post-weld stainless steel properties based on the quantitative relationship between stainless steel microstructure and properties.

[0028] In the aforementioned rapid prediction system for tank welding performance, the post-processing module includes:

[0029] The standard format output submodule is used to output the mesh generation results, mesh node combination results, temperature field, microstructure, and predicted post-weld performance of the tank mesh in a common standard format.

[0030] The mesh display submodule is used to output and display the mesh generation results and mesh node combination results;

[0031] The Temperature / Microstructure / Performance Display Submodule is used to display the temperature field, microstructure, and predicted post-weld performance of the tank mesh in the form of a cloud map.

[0032] The present invention has the following advantages:

[0033] (1) This invention discloses a rapid prediction system for the welding performance of storage tanks, which systematically solves the problem of in-situ prediction and non-destructive evaluation of storage tank welds, which is beneficial to evaluating the load-bearing capacity of storage tanks and fills the gap in related fields.

[0034] (2) This invention discloses a rapid prediction system for the welding performance of storage tanks. In terms of mesh generation, it proposes using spherical coordinates for the tank bottom and cylindrical coordinates for the wall panels, resulting in high mesh accuracy and high calculation accuracy. Dividing the entire storage tank mesh into several sub-mesh types further enhances computational efficiency. Attached Figure Description

[0035] Figure 1 This is a block diagram of a rapid prediction system for the welding performance of a storage tank according to an embodiment of the present invention;

[0036] Figure 2 This is a schematic diagram of the structure of a storage tank according to an embodiment of the present invention;

[0037] Figure 3 This is a schematic diagram of the communication principle between sub-grid cells in an embodiment of the present invention;

[0038] Figure 4 This is a schematic diagram of a phase transformation of a titanium alloy in an embodiment of the present invention. Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments disclosed in the present invention will be described in further detail below with reference to the accompanying drawings.

[0040] like Figure 1 In this embodiment, the rapid prediction system for tank welding performance includes:

[0041] The mesh generation module is used to obtain the 3D model of the storage tank, perform mesh generation and mesh node combination on the 3D model of the storage tank, and obtain several sub-mesh units.

[0042] In this embodiment, since the tank model is large, and only the material properties around the welding torch are affected during welding, the model of the area of ​​the tank that needs to be calculated is divided into several sub-mesh units. The overlapping area between adjacent sub-mesh units is defined as the communication area. The welding performance within a single sub-mesh unit is calculated each time. The communication area enables information communication between adjacent sub-mesh units and reduces the impact of sub-mesh unit boundaries on the accuracy of the calculation results.

[0043] Preferably, the mesh generation module may specifically include:

[0044] The box bottom spherical coordinate mesh generation submodule is used to mesh the bottom of the box in the 3D model in spherical coordinates to obtain several mesh nodes.

[0045] The cylindrical shell mesh generation submodule is used to mesh the shell wall panels in the 3D model in cylindrical coordinates, resulting in several mesh nodes.

[0046] The grid node combination submodule is used to combine several grid nodes within a certain adjacent area to form a subgrid unit.

[0047] Preferably, each individual grid node contains initial information on location coordinates, temperature, microstructure, and performance.

[0048] Preferably, the sub-grid cell is divided into a normal grid area and a communication grid area. The normal grid area only has computing functions, while the communication grid area has both computing functions and the ability to communicate with other sub-grid cells.

[0049] The temperature calculation module is used for heat source verification, welding heat source path definition, sub-grid temperature field calculation and temperature communication between sub-grids, and outputs the temperature field of the tank grid.

[0050] In this embodiment, the temperature calculation module may specifically include:

[0051] The heat source verification submodule is used to select the Gauss surface heat source and the Goldark double ellipsoid heat source, and determine the welding heat source parameters by comparing the temperature and molten pool morphology based on the actual heat source conditions.

[0052] The heat source path definition submodule is used to define the welding process path of the storage tank, including welding speed and scanning position.

[0053] The calculation submodule is used to embed the heat source into the temperature field calculation model according to the defined tank welding process path. It calculates the temperature field of each sub-grid cell along the welding process path, obtaining the temperature field of each sub-grid cell. Based on the obtained temperature fields of each sub-grid cell, it determines the temperature field of the tank grid and outputs it. During calculation, the calculation submodule requires input of process parameters, environmental parameters, and material property parameters related to the welding process. This submodule includes temperature field calculation methods for Cartesian coordinates, cylindrical coordinates, and spherical coordinates, and it applies different functions to different grids based on their grid types.

[0054] The communication submodule is used to update the temperature data at the same location in other subgrid cells using the calculated temperature field of the current subgrid cell.

[0055] like Figure 3 As shown, assuming the heat source moves from sub-grid cell 1 to sub-grid cell 2, when the heat source reaches the midpoint of the communication area between sub-grid cells 1 and 2, the temperature calculation switches from sub-grid cell 1 to sub-grid cell 2. Regions in sub-grid cell 2 that are contained within sub-grid cell 1 are initialized according to the temperature of sub-grid cell 1, while regions in sub-grid cell 2 that are not contained within sub-grid cell 1 are initialized according to room temperature. Subsequently, the temperature field calculation is performed on sub-grid cell 2. The switch from heat source to heat source on sub-grid cell 2 ends, and the temperature of the communication area on sub-grid cell 1 needs to be updated according to the temperature change of the communication area on sub-grid cell 2.

[0056] The performance prediction module is used to predict the post-weld performance of the tank mesh by using the temperature field of the tank mesh as the driving force for microstructure evolution.

[0057] In this embodiment, the performance prediction module is specifically used to: use the temperature field of the tank mesh as the driving force for microstructure evolution, calculate the microstructure evolution process of the tank mesh according to the microstructure evolution law of different tank materials and the quantitative mathematical model corresponding to the microstructure, and obtain the microstructure field; update the microstructure of the tank mesh using the calculated microstructure field; predict the post-weld performance of the tank mesh based on the microstructure update result and the quantitative relationship between microstructure and performance, and update the performance of the tank mesh using the calculated performance.

[0058] Preferably, the performance prediction module may specifically include:

[0059] The aluminum-magnesium alloy welding performance prediction submodule is used to calculate the microstructure evolution of aluminum-magnesium alloys based on the temperature field of the tank grid, and predict the post-weld performance of aluminum-magnesium alloys based on the quantitative relationship between the microstructure and properties of aluminum-magnesium alloys.

[0060] The aluminum-copper alloy welding performance prediction submodule is used to calculate the microstructure evolution of aluminum-copper alloys based on the temperature field of the tank grid, and predict the post-weld performance of aluminum-copper alloys based on the quantitative relationship between the microstructure and properties of aluminum-copper alloys.

[0061] The aluminum-lithium alloy welding performance prediction submodule is used to calculate the microstructure evolution of aluminum-lithium alloy based on the temperature field of the tank grid, and predict the post-weld aluminum-magnesium alloy performance based on the quantitative relationship between the microstructure and performance of aluminum-lithium alloy.

[0062] The titanium alloy welding performance prediction submodule is used to calculate the evolution of titanium alloy microstructure based on the temperature field of the tank grid, and predict the post-weld titanium alloy properties based on the quantitative relationship between the titanium alloy microstructure and properties.

[0063] The stainless steel welding performance prediction submodule is used to calculate the evolution of stainless steel microstructure based on the temperature field of the tank grid, and predict the post-weld stainless steel properties based on the quantitative relationship between stainless steel microstructure and properties.

[0064] The post-processing module is used to visualize and display the mesh generation results, mesh node combination results, temperature field, microstructure, and predicted post-weld performance of the tank mesh.

[0065] In this embodiment, the post-processing module may specifically include:

[0066] The standard format output submodule is used to output the mesh generation results, mesh node combination results, temperature field, microstructure, and predicted post-weld performance of the tank mesh in a common standard format.

[0067] The mesh display submodule is used to output and display the mesh generation results and mesh node combination results.

[0068] The Temperature / Microstructure / Performance Display Submodule is used to display the temperature field, microstructure, and predicted post-weld performance of the tank mesh in the form of a cloud map.

[0069] Based on the above embodiments, the following specific example will be used to introduce the four aspects of mesh generation, temperature calculation, performance prediction, and post-processing.

[0070] A schematic diagram of the storage tank structure is shown below. Figure 2 As shown, the main welds of the tank body have the greatest impact on the load-bearing capacity of the storage tank. The main welds of the tank body can be divided into two types: circumferential welds and longitudinal welds. Figure 2 The winning bid included a box-shaped weld seam. The bottom of the box was meshed using spherical coordinates, while the wall panels were meshed using cylindrical coordinates. To reduce the mesh size, a certain length of area around the weld was used as the computational domain, which was further divided into several sub-mesh elements. Adjacent sub-mesh elements overlapped to some extent; this overlap area was called the communication zone. Figure 3 As shown, sub-grid cell 1 and sub-grid cell 2 share a communication area.

[0071] Temperature is fundamental for predicting microstructure and properties. Heat source verification is crucial in welding temperature simulation. Users can verify the heat source of a welding device based on specimen-level test results, specifically adjusting heat source parameters until the experimental and simulated temperatures and weld pool morphology show high consistency. Next, the user inputs the welding path (including welding speed and scanning path) into the system, which then embeds the heat source into the temperature field calculation model based on the welding path. The user inputs welding-related process parameters (such as power and efficiency), environmental parameters (such as temperature, heat flux exchange coefficient, and emissivity), and physical property parameters (such as specific heat capacity, thermal conductivity, and density). This submodule applies different temperature field calculation functions to different mesh types based on their mesh types. Communication between different subgrids also needs to be handled in this module. When the heat source moves to a certain position in the communication area between two adjacent meshes (usually the middle position), the calculated subgrid needs to be switched.

[0072] When predicting performance, the temperature field in the temperature calculation module is used as the driving force for phase transformation. The microstructure of the weld and heat-affected zone after welding is calculated. Based on the quantitative relationship between microstructure and performance, the post-weld performance is predicted. This embodiment provides sub-modules for predicting the welding performance of aluminum-magnesium alloys (5XXX series), aluminum-copper alloys (2XXX series), aluminum-lithium alloys (2195, 2198, etc.), titanium alloys (TA, TB and TC) and stainless steel.

[0073] The following section uses TC series titanium alloys as an example to illustrate the temperature-driven microstructure prediction process. At room temperature, TC series titanium alloys consist of β phase and lamellar α phase. C Phase, basket-shaped α B The phase is composed of martensite α′ phase, and its phase transformation diagram is shown in Figure 1. Figure 4 As shown, the content of various phases is related to temperature and time. Specifically, the β phase is directed towards α (including lamellar α). C Phase, basket-shaped α B When a phase transitions from a diffusing phase to a diffusion phase, under isothermal conditions, the model describing the diffusion phase transition is the Johnson-Mehl-Avrami-Kolmogorov (JMAK) model, which follows the following rules:

[0074] X α (t)=1-exp(-bt)=1-bt n )···(1)

[0075] Among them, X α (t) represents the proportion of the α phase at time t, and b and n are material properties and temperature-dependent parameters, which can be obtained from the isothermal transformation curve. The transformation from the β phase to the α′ phase is a shear-type phase transition. Shear-type phase transitions are only temperature-dependent and occur instantaneously. The model describing shear-type phase transitions is the Koistinen-Marburger (KM) model. Taking the transformation of the α′ phase as an example:

[0076] X α′ (t)=1-exp(-b KM (T Ms -T))···(2)

[0077] Among them, X α′ (t) represents the proportion of the α′ phase at temperature T, where T Ms b is the temperature at which martensite begins to transform. KM These are thermophysical parameters related to the material. The thickness of the α-phase lamellars has a significant impact on the mechanical properties of Ti-6Al-4V components. The α-phase lamellar width at equilibrium at different temperatures is also considered. α can be calculated by weighting. Lath The weight is the content of the new growth phase at the corresponding temperature, i.e.:

[0078]

[0079] The content of various phases and the width α of the α phase lamellar can be calculated using formulas (1)-(3) after welding. Lath Based on the relationship between the yield stress of Ti alloy and the thickness of the lamellar layers, the yield strength of the weld can be predicted, i.e.:

[0080]

[0081] Finally, the calculation results are output to a standard format, and the post-processing module is used to display the results, including a cloud map display containing mesh, temperature, microstructure and performance, or a slice display of a specific cross section.

[0082] Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make possible changes and modifications to the technical solutions of the present invention by utilizing the methods and techniques disclosed above without departing from the spirit and scope of the present invention. Therefore, any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solutions of the present invention shall fall within the protection scope of the technical solutions of the present invention.

[0083] The contents not described in detail in this specification are common knowledge to those skilled in the art.

Claims

1. A rapid prediction system for the welding performance of storage tanks, characterized in that, include: The mesh generation module is used to acquire the 3D model of the storage tank, perform mesh generation and mesh node combination on the 3D model of the storage tank, and obtain several sub-mesh units. The mesh generation module includes: a tank bottom spherical coordinate mesh generation sub-module, used to mesh the tank bottom in the 3D model using spherical coordinates to obtain several mesh nodes; a shell cylindrical coordinate mesh generation sub-module, used to mesh the shell wall panels in the 3D model using cylindrical coordinates to obtain several mesh nodes; and a mesh node combination sub-module, used to combine several mesh nodes in adjacent areas to form sub-mesh units. Each individual mesh node contains position coordinate information, temperature, microstructure, and initial performance information. Each sub-mesh unit is divided into a normal mesh area and a communication mesh area. The normal mesh area only has calculation functions, while the communication mesh area has calculation functions and the ability to communicate with other sub-mesh units. The temperature calculation module is used for heat source verification, welding heat source path definition, sub-mesh temperature field calculation and temperature communication between sub-mesh, and outputs the temperature field of the tank mesh. The performance prediction module is used to predict the post-weld performance of the tank mesh by using the temperature field of the tank mesh as the driving force for microstructure evolution. The post-processing module is used to visualize and display the mesh generation results, mesh node combination results, temperature field, microstructure, and predicted post-weld performance of the tank mesh.

2. The rapid prediction system for tank welding performance according to claim 1, characterized in that, The temperature calculation module includes: The heat source verification submodule is used to select the Gauss surface heat source and the Goldark double ellipsoid heat source, and determine the welding heat source parameters by comparing the temperature and molten pool morphology based on the actual heat source conditions. The heat source path definition submodule is used to define the welding process path of the storage tank, including: welding speed and scanning position; The calculation submodule is used to embed the heat source into the temperature field calculation model according to the defined tank welding process path, calculate each sub-grid cell along the path in sequence, and obtain the temperature field of each sub-grid cell; based on the obtained temperature field of each sub-grid cell, determine the temperature field of the tank grid and output it. The communication submodule is used to update the temperature data at the same location in other subgrid cells using the calculated temperature field of the current subgrid cell.

3. The rapid prediction system for tank welding performance according to claim 2, characterized in that, The performance prediction module is used to: use the temperature field of the tank grid as the driving force for microstructure evolution, calculate the microstructure evolution process of the tank grid according to the microstructure evolution law of different tank materials and the quantitative mathematical model corresponding to the microstructure, and obtain the microstructure field. The microstructure of the tank grid is updated using the calculated microstructure field; Based on the microstructure update results, the post-weld performance of the tank mesh is predicted based on the quantitative relationship between microstructure and performance, and the performance of the tank mesh is updated using the calculated performance.

4. The rapid prediction system for tank welding performance according to claim 2, characterized in that, The performance prediction module includes: The aluminum-magnesium alloy welding performance prediction submodule is used to calculate the microstructure evolution of aluminum-magnesium alloys based on the temperature field of the tank grid, and predict the post-weld performance of aluminum-magnesium alloys based on the quantitative relationship between the microstructure and properties of aluminum-magnesium alloys. The aluminum-copper alloy welding performance prediction submodule is used to calculate the microstructure evolution of aluminum-copper alloy based on the temperature field of the tank grid, and predict the post-weld aluminum-copper alloy performance based on the quantitative relationship between the microstructure and properties of aluminum-copper alloy. The aluminum-lithium alloy welding performance prediction submodule is used to calculate the microstructure evolution of aluminum-lithium alloy based on the temperature field of the tank grid, and predict the post-weld aluminum-magnesium alloy performance based on the quantitative relationship between the microstructure and performance of aluminum-lithium alloy. The titanium alloy welding performance prediction submodule is used to calculate the evolution of titanium alloy microstructure based on the temperature field of the tank grid, and predict the post-weld titanium alloy performance based on the quantitative relationship between titanium alloy microstructure and performance. The stainless steel welding performance prediction submodule is used to calculate the evolution of stainless steel microstructure based on the temperature field of the tank grid, and predict the post-weld stainless steel properties based on the quantitative relationship between stainless steel microstructure and properties.

5. The rapid prediction system for tank welding performance according to claim 3, characterized in that, The post-processing module includes: The standard format output submodule is used to output the mesh generation results, mesh node combination results, temperature field, microstructure, and predicted post-weld performance of the tank mesh in a common standard format. The mesh display submodule is used to output and display the mesh generation results and mesh node combination results; The Temperature / Microstructure / Performance Display Submodule is used to display the temperature field, microstructure, and predicted post-weld performance of the tank mesh in the form of a cloud map.

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