A method and system for predicting thermal-mechanical coupling behavior of a large-size thin-walled tank during a filling process
By establishing a full-temperature-range material database and introducing a UDF real-time control mechanism, and using an adaptive time step strategy for transient thermo-mechanical iterative calculations, the problem of predicting the thermo-mechanical coupling behavior of large-size thin-walled tanks during cryogenic propellant loading was solved, and accurate prediction of structural deformation and stress distribution was achieved.
Patent Information
- Application Number
- CN202610387171.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-03-26
- Publication Date
- 2026-06-23
AI Technical Summary
Existing technologies struggle to accurately predict the complex thermo-mechanical coupling behavior of large, thin-walled tanks during cryogenic propellant loading, leading to inaccurate predictions of structural deformation and stress distribution. This makes verification particularly difficult in microgravity environments.
A full-temperature-range material database was established, fluid and solid fields were constructed, a UDF real-time control mechanism was introduced, and an adaptive time step strategy was adopted for transient thermo-mechanical iterative calculations. The structural design parameters were optimized by combining stress data.
It achieves accurate simulation of the complex thermo-mechanical coupling behavior of large-size thin-walled tanks during cryogenic propellant loading, solves the problems of large computational load and difficult convergence, and ensures accurate prediction of structural deformation and stress distribution.
Smart Images
Figure FT_1 
Figure FT_2 
Figure FT_3
Abstract
Description
Technical Field
[0001] This application relates to the field of aerospace structural simulation technology, and in particular to a method and system for predicting the thermo-mechanical coupling behavior of a large-size thin-walled tank filling process. Background Technology
[0002] The thermal and mechanical effects of cryogenic propellant injection into the tank, as well as external loads such as axial compression and bending moment, have a significant impact on the tank's structural design. After cryogenic propellant injection, the rapid temperature change from room temperature to cryogenic temperature causes the tank to contract inwards, and this contraction varies axially with temperature, resulting in uneven axial deformation. When external loads such as axial compression are applied, the deformation pattern becomes even more complex, making the tank highly susceptible to buckling and instability. Therefore, deformation analysis of cryogenic propellant injection tanks is crucial.
[0003] Currently, deformation analysis of cryogenic propellant tanks mainly focuses on the gas-liquid two-phase flow, the evolution of non-uniform temperature fields, and complex boiling heat transfer behavior during propellant injection. Limited by the large size of the tanks and the complexity of the precooling system, experiments struggle to fully capture the wall temperature field and flexibly conduct technical verification under microgravity environments and different precooling methods. Furthermore, related research primarily concentrates on temperature field changes and thermal stress characteristics generated during cryogenic propellant injection tanks, as well as tank structural design based on static strength analysis. There is a lack of research on the complex thermo-mechanical coupling behavior of large-sized, thin-walled tanks during cryogenic propellant injection, and even more so, an inability to accurately predict their structural deformation and stress distribution. Summary of the Invention
[0004] The purpose of this application is to provide a method and system for predicting the thermo-mechanical coupling behavior of large-size thin-walled tanks during the refueling process. This method can accurately simulate the complex thermo-mechanical coupling behavior of large-size thin-walled tanks during cryogenic propellant refueling, and achieve accurate prediction of structural deformation and stress distribution of large-size thin-walled tanks.
[0005] To achieve the above objectives, this application provides the following solution.
[0006] Firstly, this application provides a method for predicting the thermo-mechanical coupling behavior of a large-size thin-walled propellant tank during refueling. The method includes: establishing a full-temperature-range material database based on the thermo-mechanical performance testing and characterization of the main material of the target tank; the target tank is a large-size thin-walled tank; constructing a fluid field and a solid field; building an initial geometric model based on the design parameters of the target tank; scaling down the initial geometric model according to the stress equivalence principle to obtain a scaled-down model; importing the scaled-down model and the material database into the fluid field to generate a mesh that meets orthogonal quality requirements; setting the initial time step and total number of calculation steps for transient calculations in the fluid field; simulating the cryogenic propellant refueling process of the target tank and performing transient fluid dynamics simulations; solving for the fluid impact load data on the tank bottom and the transient temperature field load data on the wall surface; and exporting the load data file. A real-time UDF (Unified Deformation Function) control mechanism is introduced into the dynamic fluid dynamics simulation. The fluid field is coupled with the solid field, the fluid domain is suppressed in the solid field, the scaled-down model is meshed again, the load data file is imported into the solid field, boundary conditions and external mechanical loads are determined and applied according to the service environment, and stress data is solved through transient thermo-mechanical iterative calculation. The stress data includes thermo-mechanical coupling deformation, stress distribution, and elastoplastic strain distribution data. When the stress changes abruptly or the target tank yields, the initial time step is corrected in real time according to the stress data, and the transient thermo-mechanical iterative calculation is restarted based on the corrected time step to obtain optimized stress data. The optimized stress data provides data support and engineering guidance for optimizing the structural design parameters of the target tank, formulating the forming process, actual processing and manufacturing, and flow parameters during cryogenic propellant loading.
[0007] In a second aspect, this application also provides a computer system, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the thermo-mechanical coupling behavior prediction method for the filling process of a large-size thin-walled tank as described in the first aspect of the claim.
[0008] Based on the specific embodiments provided in this application, the following technical effects are disclosed.
[0009] First, this application establishes a full-temperature-range material database to accurately describe the nonlinear thermo-mechanical behavior of the target tank's main body material at low temperatures. Second, this application introduces a real-time control mechanism using User-Defined Functions (UDFs) in transient fluid dynamics simulations. By monitoring the interface state, it automatically adjusts the refueling parameters, effectively simulating the transition from film boiling to nucleus boiling, significantly improving the accuracy of wall heat transfer calculations. Furthermore, this application proposes an adaptive time step correction strategy based on stress data, automatically refining the time step during stress abrupt changes or yielding. This ensures both the convergence and accuracy of thermo-mechanical coupling calculations while improving computational efficiency during stable phases, effectively solving the problems of high computational load and low accuracy in large-size models. Based on these features, this application accurately simulates the complex thermo-mechanical coupling behavior of large-size thin-walled tanks during cryogenic propellant refueling, effectively solving the problems of high computational load and convergence difficulties in complex fluid-structure interaction for large-size thin-walled tanks, ultimately achieving accurate prediction of structural deformation and stress distribution in large-size thin-walled tanks. Attached Figure Description
[0010] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0011] Figure 1 This is a flowchart of a method for predicting the thermo-mechanical coupling behavior of a large-size thin-walled tank filling process according to an embodiment of this application.
[0012] Figure 2 This diagram illustrates the practical application of a thermo-mechanical coupling behavior prediction method for the filling process of a large-size thin-walled tank, as described in one embodiment of this application.
[0013] Figure 3 This is a schematic diagram of the fluid simulation module in one embodiment of this application.
[0014] Figure 4 This is a schematic diagram of the structure of a computer system in another embodiment of this application. Detailed Implementation
[0015] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0016] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.
[0017] In one exemplary embodiment, a method for predicting the thermo-mechanical coupling behavior of a large-size thin-walled tank filling process is provided, such as... Figure 1 As shown, the method for predicting the thermo-mechanical coupling behavior of the filling process of this large-size thin-walled tank includes the following steps.
[0018] Step S1: Based on the thermo-mechanical property testing and characterization of the main material of the target tank, establish a material database covering the entire temperature range.
[0019] In this embodiment, the target tank is a large-size thin-walled tank. A large-size thin-walled tank usually refers to a tank with a diameter of 3m, 5m, etc., and a wall thickness of 1mm to 10mm.
[0020] Step S1 specifically includes the following steps.
[0021] The first step is to conduct thermo-mechanical property tests on the main material of the target tank to obtain raw experimental data.
[0022] Thermodynamic performance testing includes dynamic mechanical analysis experiments, laser flash method / steady-state plate method, and thermal dilatometer method. The original experimental data include Young's modulus, Poisson's ratio, thermal conductivity, coefficient of thermal expansion, and specific heat capacity. The corresponding relationship between the two is as follows: Young's modulus and Poisson's ratio are determined by dynamic mechanical analysis experiments; thermal conductivity and specific heat capacity are determined by laser flash method / steady-state plate method; and the coefficient of thermal expansion is determined by thermal dilatometer method.
[0023] The second step involves characterizing the original experimental data using a fitting method to address the nonlinear characteristics of the thermodynamic properties of the main material as a function of temperature. Constitutive equations for anisotropic yielding and plastic hardening criteria are then constructed to obtain a material database covering the entire temperature range.
[0024] Among them, piecewise polynomial fitting method was used to characterize the original experimental data such as Young's modulus, Poisson's ratio, thermal conductivity, coefficient of thermal expansion and specific heat capacity, and a high-precision material database covering the entire temperature range from 20K (Kelvin) to 500K was established. Through tensile, compression and bulging tests, constitutive equations for anisotropic yield criterion and plastic hardening criterion were constructed.
[0025] Step S2: Construct the fluid field and solid field (structural field).
[0026] In this embodiment, the fluid field and solid field are built on the Workbench platform (a platform in the ANSYS simulation software), which can be directly dragged and dropped. The solution of the fluid field is used as the boundary condition of the structural field, and corresponding boundary conditions can also be applied to the structural field.
[0027] Step S3: Construct an initial geometric model based on the design parameters of the target tank. Scale down the initial geometric model according to the stress equivalence principle to obtain a scaled-down model. Import the scaled-down model and material database into the fluid field to generate a mesh that meets the orthogonal quality requirements.
[0028] In this embodiment, considering the difficulties in meshing large-size thin-walled tanks—large overall size (meters / tens of meters), extremely thin wall thickness (millimeters), and a significant difference in diameter-to-thickness ratio—while requiring at least 3-5 layers of elements in the wall thickness direction, if the entire model is meshed according to the fine dimensions of the thin-walled region, the number of meshes can easily reach tens of millions or even hundreds of millions. Furthermore, the mesh matching degree at the fluid-solid interface is highly demanding, and mesh mismatches easily occur at the interface between the solid mesh of the thin-walled component and the fluid domain mesh. Based on these considerations, this embodiment generates a mesh that meets the orthogonal quality requirements mainly through the following steps.
[0029] The first step is to use ANSYS Mesh to generate a mesh, distinguishing between the solid domain and the fluid domain.
[0030] The storage tank is 6mm thick and 13m long.
[0031] The second step is to perform multizone meshing on the solid domain.
[0032] Specifically, linear dimension control is applied to the tank thickness direction, with a value of 2mm; surface dimension control is applied to the tank cross-section, with a value of 6mm; volume dimension control is applied to the tank, with a value of 6mm; when dividing the grid at the chamfer, the curvature snapping function is enabled, and 10 layers of grid are forcibly divided.
[0033] The third step is to perform automatic mesh generation on the fluid domain.
[0034] Specifically, an expansion layer is added to the interface between the fluid domain and the solid domain, with 5 expansion layers and a growth rate of 1.2; surface size control is applied to the interface with a value of 18 mm; volume size control is applied to the fluid domain as a whole with a value of 54 mm; when meshing at the chamfer, the curvature snapping function is enabled and 10 mesh layers are forcibly created.
[0035] Step S4: Set the initial time step and total number of calculation steps for transient calculation in the fluid field, simulate the cryogenic propellant loading process of the target tank and perform transient fluid dynamics simulation, solve for the fluid impact load data on the tank bottom and the transient temperature field load data on the wall, and export the load data file.
[0036] In this embodiment, the cryogenic propellant can be liquid hydrogen, liquid oxygen, etc.; the load data file refers to the file corresponding to the fluid impact load data on the tank bottom and the transient temperature field load data on the wall. This embodiment also introduces a UDF real-time control mechanism in the transient fluid dynamics simulation, which includes the following steps.
[0037] The first step is to simulate the cryogenic propellant loading conditions of the target tank and configure the initial parameters.
[0038] The initial parameters include: environmental parameters, initial parameters of solids and fluids in the model, cryogenic propellant parameters, fluid inlet and outlet types, and conjugate heat transfer boundaries of the walls.
[0039] The second step is to define a phase change model to capture the fluctuations at the gas-liquid interface and use the phase change heat transfer equation to calculate the wall boiling heat transfer.
[0040] The phase change model is a multiphase flow mixture model + Lee model. The Lee phase change model is used to define the interphase mass transfer between liquid hydrogen and hydrogen gas, and its mass transfer rate is controlled by the following equation.
[0041] when T＞ Time (evaporation process): .
[0042] when T＜ Time (condensation process): .
[0043] In the formula, and For phase transition mass transfer source terms, subscript l and v They are liquid phase and gas phase, respectively; r The mass transfer coefficient is adjusted based on the actual experimental phase transition time. and It is the volume fraction; and Density; T The temperature during the cryogenic propellant loading process; This is the saturation temperature set based on operating conditions.
[0044] The third step is for UDF to control the injection flow and venting in real time.
[0045] The monitoring point is set to read the temperature gradient of the fluid-solid interface and the internal gas pressure of the target tank in real time. When the gas phase coverage at the fluid-solid interface exceeds the set threshold and exhibits film boiling characteristics, the UDF automatically adjusts the inlet pressure or increases the instantaneous mass flow rate to break the vapor layer attached to the wall and induce a change in the fluid flow pattern, thereby converting film boiling into nucleus boiling.
[0046] Define a pressure outlet boundary that changes over time, capture the pressure changes inside the target tank during cryogenic propellant refueling, and open the outlet when a specified pressure threshold is reached.
[0047] Step S5: Couple the fluid field with the solid field (structural field), suppress the fluid domain in the solid field (structural field), mesh the scaled-down model again, import the load data file into the solid field, determine and apply boundary conditions and external mechanical loads according to the service environment, and solve the stress data through transient thermo-mechanical iterative calculation.
[0048] In this embodiment, when meshing the scaled-down model again, it is necessary to maintain consistency with the meshing method in step S3. The final stress data obtained includes: thermo-mechanical coupling deformation, stress distribution, and elastoplastic strain distribution data.
[0049] Boundary conditions and external mechanical loads: Apply a microgravity field, set the contact type of tank components, add displacement constraints to limit the tank's degrees of freedom; set the external load history that varies over time to simulate the structural stress state during refueling.
[0050] In transient thermo-mechanical iterative calculations: temperature field data or fluid impact load data at a specified time step of the fluid calculation can be read and mapped onto solid mesh nodes. Any time-varying external mechanical loads can also be added to the solid domain.
[0051] Step S6: When the stress changes abruptly or the target tank yields, the initial time step is corrected in real time based on the stress data, and the transient thermo-mechanical iterative calculation is restarted based on the corrected time step to obtain the optimized stress data.
[0052] In this embodiment, the solution setup employs a second-order implicit time discretization scheme. The initial time step must be very small, and as the stress stabilizes, the time step is gradually increased to ensure accurate calculations while improving efficiency. Simultaneously, the time step control must adapt to both thermal and mechanical stresses to enhance the computational accuracy of the thermo-mechanical coupled finite element model. Specifically, based on the initial time step, when a stress abrupt change or the tank material enters a yielding state is detected, the time step is automatically refined (e.g., reduced to 1 / 2 to 1 / 5 of the original time step). When the stress change stabilizes, the time step is automatically increased to improve computational efficiency. The transient thermo-mechanical iterative calculation is restarted based on the corrected time step to accurately capture the detailed changes in key states such as stress abrupt changes and yielding.
[0053] Step S7: Use the optimized stress data to provide data support and engineering guidance for optimizing the structural design parameters of the target tank, formulating the forming process, actual processing and manufacturing, and flow parameters during the cryogenic propellant loading process.
[0054] In this embodiment, the Mises stress of key parts of the tank (such as the Y-ring connection at the bottom of the tank and the weld area of the cylinder section) is extracted and combined with the yield strength at the corresponding temperature in the material database established in step S1 to calculate the dynamic safety factor. If the local equivalent elastic strain exceeds the material yield strength, it is determined that the area needs topology optimization or wall thickness adjustment. According to the analysis results, in areas such as where the safety factor is >2.0, the wall thickness is gradually reduced by 0.02 mm in each iteration until the process limit or the critical value of the safety factor is reached, thereby achieving structural lightweighting. For fluid impact zones and thermal stress concentration zones, if stress concentration is found, the wall thickness is locally increased or the weld is locally thickened in the geometric model. If the structural optimization space is limited, the filling process is optimized in reverse. By adjusting the inlet flow curve in the UDF, the peak value of the fluid impact load is reduced, or the opening of the exhaust valve is controlled to regulate the internal pressure, thereby eliminating the possibility of plastic deformation of the tank without changing the structure.
[0055] To verify the effectiveness of the aforementioned method for predicting the thermo-mechanical coupling behavior of the filling process in large-size thin-walled tanks, such as... Figure 2 As shown, this embodiment also provides a practical application scenario.
[0056] The thermodynamic properties of the 2219-T87 aluminum alloy, the main material of the storage tank, were tested across the entire temperature range (20K to 500K). Considering that the thermodynamic properties of the material change nonlinearly with temperature, a piecewise polynomial fitting method was used to characterize the original experimental data such as Young's modulus, Poisson's ratio, thermal conductivity, coefficient of thermal expansion, and specific heat capacity.
[0057] Tensile tests were conducted in seven different rolling directions from 0° to 90° to obtain the engineering stress-strain curves and anisotropy coefficients of the material. r (Value). The anisotropic yield characteristics of the material are accurately characterized using three yield models: Hill48, Barlat89, and Hill90.
[0058] The plastic hardening behavior of the material was studied by selecting stress-strain curves along the rolling direction. Three hardening models—Swift (unsaturated), Ludwig (strong hardening), and Voce (saturated hardening)—were compared. The Voce model was ultimately chosen to describe the hardening behavior of the 2219-T87 aluminum alloy.
[0059] Use three directions: 0°, 45°, and 90°. rThe Hill48 and Barlat89 yield models were calibrated to obtain the anisotropic parameters of the models, which are used to describe the plastic flow behavior of materials in low-temperature environments.
[0060] Based on the design parameters of the hydrogen-oxygen storage tank, a geometric model was constructed in UG software. Given the large size of the hydrogen-oxygen storage tank, the geometric model was scaled down according to the principle of stress equivalence to obtain a scaled-down model, thereby improving computational efficiency.
[0061] Import the scaled-down model into SpaceClaim, define the fluid domain and the solid domain, and establish the fluid-solid interface.
[0062] The model was meshed: an expansion layer mesh was applied to the fluid domain near the solid domain, with an expansion layer ratio of 1.2 and 5 layers; local sizing was also performed on the thin-walled solid domain. The mesh quality was checked to ensure that the mesh orthogonality quality was greater than 0.2 to meet the accuracy requirements for capturing the boundary layer thermal gradient. Finally, pressure parameters were selected for mesh independence verification.
[0063] Configure a multiphase flow model in Fluent, with liquid hydrogen as the primary phase and hydrogen gas as the secondary phase. Enable the Lee phase change model to define the interphase mass transfer between liquid hydrogen and hydrogen gas, set the evaporation and condensation coefficients, and calibrate the mass transfer rate through actual physical experiments.
[0064] The temperature gradient at the fluid-solid interface and the internal gas pressure of the tank are read in real time at the monitoring points. The boundary conditions are dynamically controlled by writing and loading a UDF: when the gas phase coverage at the interface exceeds the set threshold and exhibits film boiling characteristics, the UDF automatically adjusts the inlet pressure or increases the instantaneous mass flow rate to break the vapor layer attached to the wall, induce a change in the fluid flow pattern, and thus convert film boiling to nucleation boiling.
[0065] A virtual coating can be applied to the inner wall of the tank to prevent the vapor film generated during direct contact between the fluid and the solid wall. By changing the interfacial heat transfer coefficient, it reduces the accumulation of gas phase at the wall surface and prevents a decrease in heat transfer rate due to film boiling. The principle of the fluid simulation module can be found in [link to relevant documentation]. Figure 3 .
[0066] Define a time-varying pressure outlet boundary, capture pressure changes inside the tank during filling, and open the outlet when a specified pressure threshold is reached (e.g., in stages such as 0.12 MPa, 0.18 MPa, etc.). This ultimately generates fluid impact load data on the tank bottom and transient temperature field load data on the tank walls.
[0067] For the service environment of the storage tank, a structural mechanical boundary model is established in the structural simulation module ANSYS Mechanical.
[0068] To suppress the fluid domain in the structural field, the scaled-down model needs to be meshed again, and the meshing method must be consistent with that of the fluid domain.
[0069] Apply a microgravity field. Set the contact type of the tank components and apply remote displacement constraints to the connection between the tank bottom and the cylindrical section, restricting its six degrees of freedom, but setting it to a deformable state to allow coordinated deformation of the connection area and avoid false stress concentration.
[0070] The external load history is set according to the typical operating conditions of the tank to simulate the structural stress state during refueling.
[0071] The theory of large structural deformation is activated to accommodate the geometric nonlinear deformation that may occur in thin-walled structures.
[0072] By using a unidirectional coupling connection between the fluid field and the structural field, temperature field data or fluid impact load data at a specified time step of the fluid calculation are read and mapped onto solid mesh nodes.
[0073] In conjunction with external mechanical loads, transient thermo-mechanical iterative calculations are performed on a scaled-down model. The solver comprehensively calculates the thermal stress caused by temperature difference, fluid impact, and mechanical stress caused by external loads, generating thermo-mechanical coupled deformation and stress distribution data. To balance computational accuracy and efficiency, a second-order implicit time discretization scheme is used in the solution setup.
[0074] An adaptive time step strategy is introduced to adjust the time step in real time based on stress data: the time step is set to 0.01s during the initial stage of refueling.
[0075] When a sudden change in stress is detected, such as when the tank reaches a yielding state, the time step is further refined to 0.01 s to accurately capture the stress change. As the stress tends to stabilize later, the time step is gradually increased. The time step control is adapted to both thermal and mechanical stress, thereby improving the calculation accuracy of the thermo-mechanical coupled finite element model.
[0076] In summary, this application can accurately simulate the complex thermo-mechanical coupling behavior of large-size thin-walled tanks during cryogenic refueling, effectively solving the problems of large computational load and convergence difficulties in complex fluid-structure interaction of large-size tanks, and ultimately achieving accurate prediction of tank structural deformation and stress distribution under microgravity refueling conditions.
[0077] In another exemplary embodiment, a computer system is provided, which may be a server or a terminal, and its internal structure diagram may be as follows: Figure 4As shown, the computer system includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores stress data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements a method for predicting the thermo-mechanical coupling behavior of a large-size thin-walled tank filling process.
[0078] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer system to which the present application is applied. A specific computer system may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.
[0079] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.
[0080] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).
[0081] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.
[0082] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.
[0083] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.
Claims
1. A method for predicting the thermo-mechanical coupling behavior of a large-size thin-walled tank filling process, characterized in that, The method for predicting the thermo-mechanical coupling behavior of the filling process in large-size thin-walled tanks includes: Based on the thermo-mechanical property testing and characterization of the main material of the target tank, a material database covering the entire temperature range is established; the target tank is a large-size thin-walled tank. Construct fluid and solid fields; An initial geometric model is constructed based on the design parameters of the target tank. The initial geometric model is scaled down according to the stress equivalence principle to obtain a scaled-down model. The scaled-down model and the material database are imported into the fluid field to generate a mesh that meets the orthogonal quality requirements. In the fluid field, the initial time step and total number of calculation steps for transient calculation are set to simulate the cryogenic propellant loading process of the target tank and perform transient fluid dynamics simulation. The fluid impact load data on the tank bottom and the transient temperature field load data on the wall are obtained, and the load data file is exported. A UDF real-time control mechanism is introduced in the transient fluid dynamics simulation. The fluid field is coupled with the solid field, the fluid domain is suppressed in the solid field, the scaled-down model is meshed again, the load data file is imported into the solid field, boundary conditions and external mechanical loads are determined and applied according to the service environment, and stress data is solved by transient thermo-mechanical iterative calculation; the stress data includes: thermo-mechanical coupling deformation, stress distribution and elastoplastic strain distribution data; When the stress changes abruptly or the target tank yields, the initial time step is corrected in real time based on the stress data, and the transient thermo-mechanical iterative calculation is restarted based on the corrected time step to obtain the optimized stress data. The optimized stress data provides data support and engineering guidance for optimizing the structural design parameters of the target tank, formulating the forming process, actual processing and manufacturing, and flow parameters during the cryogenic propellant loading process.
2. The method for predicting the thermo-mechanical coupling behavior of the filling process of a large-size thin-walled tank according to claim 1, characterized in that, The thermo-mechanical property testing and characterization of the main material of the target tank, and the establishment of a material database covering the entire temperature range, specifically include: The thermo-mechanical properties of the main material of the target tank are tested to obtain raw experimental data; the raw experimental data include at least: Young's modulus, Poisson's ratio, thermal conductivity, coefficient of thermal expansion and specific heat capacity; To address the nonlinear characteristics of the thermodynamic properties of the main material as a function of temperature, a fitting method was used to characterize the original experimental data, and constitutive equations for anisotropic yielding criteria and plastic hardening criteria were constructed to obtain a material database covering the entire temperature range.
3. The method for predicting the thermo-mechanical coupling behavior of the filling process of a large-size thin-walled tank according to claim 1, characterized in that, The generation of a mesh that meets the orthogonal quality requirements specifically includes: Use ANSYS Mesh to generate a mesh, distinguishing between solid and fluid domains; The solid domain is divided into multizone meshes; The fluid domain is automatically meshed.
4. The method for predicting the thermo-mechanical coupling behavior of the filling process of a large-size thin-walled tank according to claim 1, characterized in that, The transient fluid dynamics simulation specifically includes: Simulate the cryogenic propellant loading conditions of the target tank and configure the initial parameters; A phase change model is defined to capture gas-liquid interface fluctuations, and the phase change heat transfer equation is used to calculate wall boiling heat transfer; the phase change model includes a multiphase flow mixture model and a Lee model; UDF provides real-time control over the injection flow rate and outlet venting.
5. The method for predicting the thermo-mechanical coupling behavior of the filling process of a large-size thin-walled tank according to claim 4, characterized in that, The expression for the Lee model is: ， T ＞ ； ， T ＜ ； In the formula, and For phase transition mass transfer source terms, subscript l and v They are liquid phase and gas phase, respectively; r The mass transfer coefficient; and It is the volume fraction; and Density; T The temperature during the cryogenic propellant loading process; This is the saturation temperature set based on operating conditions.
6. The method for predicting the thermo-mechanical coupling behavior of the filling process of a large-size thin-walled tank according to claim 4, characterized in that, The UDF provides real-time control of the injection flow rate and outlet venting, specifically including: The monitoring point is set to read the temperature gradient of the fluid-solid interface and the internal gas pressure of the target tank in real time. When the gas phase coverage at the fluid-solid interface exceeds the set threshold and exhibits film boiling characteristics, the UDF automatically adjusts the inlet pressure or increases the instantaneous mass flow rate. Define a pressure outlet boundary that changes over time, capture the pressure changes inside the target tank during cryogenic propellant refueling, and open the outlet when a specified pressure threshold is reached.
7. The method for predicting the thermo-mechanical coupling behavior of the filling process of a large-size thin-walled tank according to claim 1, characterized in that, The determination and application of boundary conditions and external mechanical loads based on the service environment specifically includes: Apply a microgravity field, set the contact type of the target tank's components, and add displacement constraints to limit the target tank's degrees of freedom; The external load history was set to vary over time to simulate the structural stress state during cryogenic propellant loading.
8. The method for predicting the thermo-mechanical coupling behavior of the filling process of a large-size thin-walled tank according to claim 1, characterized in that, In the transient thermo-mechanical iterative calculation, temperature field data or fluid impact load data at a specified time step of the fluid calculation can be read arbitrarily and mapped onto solid mesh nodes. Arbitrary external mechanical loads that vary with time can also be added to the solid domain.
9. The method for predicting the thermo-mechanical coupling behavior of the filling process of a large-size thin-walled tank according to claim 1, characterized in that, The initial time step is corrected in real time, specifically including: Based on the initial time step, the time step is automatically refined when a sudden change in stress or the target tank yields is detected. When the stress change tends to stabilize, the time step is automatically increased.
10. A computer system, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the method for predicting the thermo-mechanical coupling behavior of the filling process of a large-size thin-walled tank as described in any one of claims 1-9.