Thermal coupling simulation method and system for components

Abstract

The present invention relates to the field of thermal coupling simulation technology, and in particular to a thermal coupling simulation method and system for parts, the method comprising: performing preliminary thermal simulation based on finite element meshing to generate initial temperature distribution data and stress distribution data; combining topology optimization and microstructure feedback to adjust the geometric shape and properties of the material to generate optimized material response data; performing multimodal data fusion to simulate the fluid-solid coupling process between the cooling fluid and the material to generate damage prediction data; and finally, adjusting the material properties and geometric shape in real time through simulation iterative optimization to ensure simulation accuracy and optimize part design. Through the present invention, reliable design of parts under high temperature and high stress conditions can be achieved, fatigue damage accumulation can be reduced, and the safety and service life of parts can be improved.

Description

Technical Field

[0001] The invention relates to the technical field of thermal coupling simulation, and in particular to a thermal coupling simulation method and system for components. Background Art

[0002] Silicon materials are widely used in electronic components and high-precision mechanical applications under high temperature and high stress environments. Thermomechanical coupling simulation of components is an important method to ensure the reliability of these components under high temperature (400°C to 1100°C) and high stress (100 MPa to 200 MPa) conditions, and can help predict the thermodynamic behavior of materials and their impact on structures. The prior art (Chinese invention patent, publication number: CN117272745A, name: A thermomechanical coupling simulation method combining thermal model and high-temperature creep analysis) usually separates thermal and mechanical simulations, and ignores the interaction between thermal and mechanical effects under high temperature conditions, which leads to the lack of accuracy and reliability of simulation results, especially the inability to fully consider the grain reorganization and creep phenomena of silicon materials at high temperatures; the prior art combines thermal models with high-temperature creep analysis to perform thermomechanical coupling simulation of components, but this method cannot accurately capture the thermodynamic properties of silicon materials in complex environments, limiting the design optimization effect. Summary of the invention

[0003] In view of the many problems existing in the above-mentioned prior art, the present invention provides a thermal coupling simulation method and system for components. The present invention accurately simulates the thermodynamic behavior of silicon materials through finite element meshing, phase change micro-simulation, fluid-solid coupling simulation and multimodal data fusion, and dynamically adjusts simulation parameters and component design in combination with feedback optimization technology to improve performance and service life.

[0004] A thermal-mechanical coupling simulation method for a component, comprising:

[0005] By dividing the parts into finite element meshes, setting the thermal and mechanical properties of the parts materials, applying external heat sources and external load conditions, and performing preliminary thermal simulation, initial temperature distribution data and initial stress distribution data are generated;

[0006] Based on the initial temperature distribution data and initial stress distribution data, the topology optimization of the parts is carried out, and the linkage feedback adjustment is carried out in combination with the microstructural changes of the parts materials to generate microstructural feedback data and high-dimensional material response data;

[0007] Based on high-dimensional material response data, microscopic simulation of phase change of component materials is performed to capture the microstructural changes of grain reorganization and dislocation movement of component materials, and the simulation time step is adjusted according to the grain phase change speed and stress change rate to generate microscopic phase change data and time step control data;

[0008] Based on the microscopic phase change data, the fluid-solid coupling simulation between the cooling fluid and the component material is carried out to simulate the heat exchange process between the cooling fluid and the component material, generate preliminary fluid-solid coupling data, and predict the fatigue damage of the component material through multimodal data fusion technology to generate multimodal fusion damage prediction data;

[0009] Based on the multimodal fusion damage prediction data, global feedback analysis is performed, simulation parameters are optimized, the properties of component materials, simulation time step and external boundary conditions are adjusted, simulation iterative optimization is performed, and the final optimized simulation data is generated.

[0010] Preferably, the topology optimization includes: based on the initial temperature distribution data and the initial stress distribution data, performing local encryption and refinement of the grid in the area where the local temperature of the component is 400°C to 1100°C or the stress is 100MPa to 200MPa, generating refined grid data, and adjusting the material distribution of the component by rearranging the geometric structure of the component to optimize the heat conduction path and stress distribution of the component, and the refinement of the local grid is dynamically adjusted according to the thermal and stress change amplitude of the component.

[0011] Preferably, the microstructural feedback includes: monitoring the microstructural changes of the component material, the microstructural changes including grain reorganization, creep behavior and dislocation movement of the component material, generating microstructural feedback data by analyzing the phase change rate of the grains inside the component material and the grain boundary evolution, and the microstructural feedback data is used to adjust the geometric shape of the component material and the topological structure evolution process of the component.

[0012] Preferably, the phase change microscopic simulation is performed based on high-dimensional material response data, and the simulation time step of the component is adjusted by the following formula:

[0013]

[0014] in, is the simulation time step; is the time step adjustment factor, which represents the sensitivity adjustment coefficient of the time step; is the grain recombination rate, which indicates the rate of change of the microstructure during the grain recombination process of the material; is the stress change rate, which indicates the rate at which the stress of the material changes during the simulation.

[0015] Preferably, the high-dimensional material response data includes the thermal expansion coefficient, phase change critical temperature, elastic modulus and creep rate of the component material. The high-dimensional material response data is used to adjust the grain phase change rate of the component material in the microscopic phase change simulation of the component, and to adjust the boundary conditions and component material properties of the component simulation model in real time by accurately capturing the thermal behavior of the component material under different temperature and stress conditions.

[0016] Preferably, the fluid-solid coupling simulation is performed based on the microscopic phase change data of the component. The fluid-solid coupling simulation simulates the heat transfer process between the cooling fluid and the component material, adjusts the flow rate and flow path of the cooling fluid, and generates fluid-solid coupling simulation data. The fluid-solid coupling simulation data is used to optimize the flow path of the cooling fluid and the temperature distribution inside the component material, thereby reducing local overheating inside the component material.

[0017] Preferably, the multimodal data fusion generates multimodal fusion damage prediction data by fusing fluid-solid coupling simulation data, microstructure feedback data of components, stress distribution data of components and temperature field data of components. The multimodal fusion damage prediction data is used to predict fatigue damage accumulation of component materials under conditions of temperature of 400°C to 1100°C or stress of 100MPa to 200MPa. By analyzing the fatigue life and local stress concentration of component materials, the location where the component material fails is determined, and the data is used to optimize the design of the component.

[0018] Preferably, the simulation iterative optimization is performed based on global feedback data, and multiple adjustments are made to component material properties, simulation time steps, and component boundary conditions to ensure that the thermal and mechanical behaviors of components during the simulation process can be accurately described. The simulation iterative optimization process includes multiple rounds of simulation calculations until the simulation results reach a preset termination condition, thereby generating final component optimization simulation data.

[0019] Preferably, the simulation iterative optimization optimizes the geometric shape and material properties of components by dynamically adjusting the local grid density of components in real time and combining the fatigue damage prediction results in the global feedback data, so that the temperature and stress distribution of the components in the simulation results are uniform, and the local overheating and stress concentration of the components are effectively controlled.

[0020] A system for implementing the thermal-mechanical coupling simulation method for a component, comprising:

[0021] Meshing module, used to perform finite element meshing on components and set the thermal and mechanical properties of component materials;

[0022] A simulation calculation module is used to apply external heat source and external load conditions, perform preliminary thermal simulation, and generate initial temperature distribution data and initial stress distribution data;

[0023] Topology optimization module, which is used to perform topology optimization of components based on initial temperature distribution data and initial stress distribution data, and to make linkage feedback adjustments based on the microstructure changes of component materials to generate microstructure feedback data and high-dimensional material response data;

[0024] Phase change micro-simulation module, which is used to perform phase change micro-simulation of component materials based on high-dimensional material response data, capture the micro-structural changes of grain reorganization and dislocation movement of component materials, and adjust the simulation time step according to the grain phase change speed and stress change rate, and generate micro-phase change data and time step control data;

[0025] Fluid-solid coupling simulation module, which is used to simulate the fluid-solid coupling between the cooling fluid and the component material based on the microscopic phase change data, simulate the heat exchange process between the cooling fluid and the component material, and generate fluid-solid coupling data;

[0026] Multimodal fusion module, used to predict fatigue damage of component materials through multimodal data fusion technology and generate multimodal fusion damage prediction data;

[0027] The global feedback optimization module is used to perform global feedback analysis based on multimodal fusion damage prediction data, optimize simulation parameters, adjust the properties of component materials, simulation time step and external boundary conditions, perform simulation iterative optimization, and generate final optimized simulation data.

[0028] Compared with the prior art, the advantages and beneficial effects of the present invention are:

[0029] The present invention realizes accurate prediction of fatigue damage of silicon materials through multi-modal data fusion technology, and solves the defect that the prior art cannot fully consider the coupling effect of multiple factors;

[0030] The present invention improves material distribution and geometric shape design by combining topology optimization with microstructure feedback, and achieves effective control of local overheating and high stress concentration phenomena;

[0031] The present invention captures the heat exchange characteristics and grain evolution in the cooling process through fluid-solid coupling simulation and phase change micro-simulation, and significantly improves the simulation accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a schematic diagram of the process of the present invention;

[0033] Figure 2 It is the topology optimization flow chart of the present invention;

[0034] Figure 3 Schematic diagram of multimodal data fusion in the present invention;

[0035] Figure 4 This is a system structure block diagram of the present invention. DETAILED DESCRIPTION

[0036] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure.

[0037] like Figure 1 As shown, a thermal-mechanical coupling simulation method for a component includes:

[0038] By dividing the parts into finite element meshes, setting the thermal and mechanical properties of the parts materials, applying external heat sources and external load conditions, and performing preliminary thermal simulation, initial temperature distribution data and initial stress distribution data are generated;

[0039] In the process of thermomechanical coupling simulation, finite element meshing is to discretize the geometric structure of the parts into a series of small finite element units. The collection of these units can approximate the overall structure and physical properties of the parts. The density and division accuracy of the mesh will directly affect the accuracy and calculation efficiency of the simulation. In the present invention, the purpose of meshing is to provide a basis for subsequent thermomechanical analysis, and the nodes of each unit will be used to calculate heat transfer and mechanical stress distribution.

[0040] The setting of the thermal and mechanical properties of the material of the component usually includes but is not limited to the thermal conductivity, specific heat capacity, density, elastic modulus, Poisson's ratio, etc. These properties are used to simulate the thermal response of the component when it is subjected to heat sources (such as high temperature environment or internal heat source) and external mechanical loads (such as pressure, tension or compression). The thermal properties of the material determine how the heat propagates inside the component, while the mechanical properties determine the deformation and stress distribution of the material when it is subjected to force.

[0041] External heat sources and external loads are applied to simulate the thermal boundary conditions of components in their actual working environment. For example, the heat source may be due to the heat generated by the component during operation or the temperature change of the surrounding environment, and the load may be due to the external force applied to the component during the operation of the mechanical equipment. By applying these conditions to the component model, its thermal behavior can be accurately predicted through simulation.

[0042] Among them, for silicon materials, high temperature generally refers to a temperature range that exceeds the stability of its lattice structure and the significant change in thermal properties. The melting point of silicon is about 1414°C, and its thermal expansion coefficient and thermal conductivity will change significantly at high temperatures. Therefore, in the present invention, the high temperature range of silicon materials is defined as 400°C to 1100°C. Within this temperature range, the mechanical properties and thermal conductivity characteristics of silicon will be significantly affected, especially above 800°C, where its thermomechanical coupling effect becomes more prominent.

[0043] Regarding the mechanical properties of silicon materials, their tensile strength and fracture toughness are much lower than those of most metals. The typical fracture strength of silicon is about 200 MPa. Therefore, in the present invention, the high stress range of silicon materials is defined as: 100 MPa to 200 MPa. Within this range, silicon materials are close to the limit of their structural strength, and the risk of cracks and fractures increases significantly.

[0044] The preliminary thermal simulation is achieved by solving the heat conduction equation and the mechanical equilibrium equation. The heat conduction equation is used to describe the distribution and change of heat inside the component material, while the mechanical equilibrium equation is used to calculate the stress and deformation of the material under the action of external loads. By discretizing and solving these equations using the finite element method, the distribution results of the temperature field and stress field of the component under different working conditions can be obtained.

[0045] In the specific implementation process, the parts are first geometrically modeled to ensure that the model can accurately reflect the shape and size of the parts. Next, meshing is performed. Usually, more complex geometric shapes or local areas (such as areas with concentrated force or heat) will use higher density meshes to improve calculation accuracy. Material properties are set according to actual material parameters. Thermal properties such as thermal conductivity affect the heat conduction rate, and mechanical properties such as elastic modulus affect the deformation behavior of parts.

[0046] When setting external heat sources, temperature fields can be applied to the model by defining boundary conditions or built-in heat sources, such as setting the temperature of a surface or defining the heat source inside the material through internal heat flux. Similarly, external loads are applied through mechanical boundary conditions, such as using fixed constraints to support part of a component and applying specific forces or pressures to other parts.

[0047] When the preliminary thermal simulation is executed, the solver will gradually calculate the temperature distribution and stress distribution in each time step, and gradually iteratively update the temperature field and stress field through numerical solution, and finally generate initial temperature distribution data and initial stress distribution data, which will serve as the basis for subsequent simulation steps.

[0048] Based on the initial temperature distribution data and initial stress distribution data, the topology optimization of the parts is carried out, and the linkage feedback adjustment is carried out in combination with the microstructural changes of the parts materials to generate microstructural feedback data and high-dimensional material response data;

[0049] Topology optimization is based on the initial temperature distribution data and initial stress distribution data of the parts. By adjusting the distribution and geometry of the material, the parts can achieve maximum material utilization or optimal performance while meeting the mechanical and thermal requirements. This optimization process is based on the initial data generated by thermal simulation. By analyzing the temperature and stress concentration areas, it is determined which areas have excess or insufficient materials, and the geometry of the parts is adjusted to reduce the overall weight or improve durability while maintaining strength and thermal conductivity in the stressed or heated areas.

[0050] The linkage feedback of microstructure means that in the process of topology optimization, not only the adjustment of macrostructure is considered, but also the evolution of microstructure inside the material is introduced as feedback. Under high temperature or high stress conditions, microstructural changes such as grain structure, dislocation movement, creep behavior, etc. of the material will affect the overall performance of the parts, especially under extreme working conditions (such as high temperature or long-term operation). Through the linkage feedback mechanism, the microstructural changes of the material will directly affect the results of topology optimization, so that the optimized design is not only reflected in the adjustment of macroscopic geometric shape, but also combined with the changes in the microscopic properties of the material.

[0051] In the present invention, microstructure feedback data is generated by real-time monitoring of microscopic changes in materials (such as grain reorganization and grain boundary migration). Combined with these feedback data, the optimization algorithm can dynamically adjust the topological structure of components. For example, in areas with higher temperatures, the material may experience grain growth and strength loss. The system will feedback these changes and adjust the material distribution or mesh refinement in the area accordingly during the optimization process. High-dimensional material response data is generated by integrating multiple material properties (such as elastic modulus, thermal conductivity, creep rate, etc.), reflecting the overall performance of the material under complex working conditions. These data are used to further guide topological optimization and microstructure adjustment.

[0052] In the specific implementation process, the temperature distribution data and stress distribution data generated by the preliminary thermal simulation are first used to identify the high stress concentration areas or areas with excessively high temperatures in the parts. These areas are the focus of the optimized design, and the topology optimization algorithm determines the location of material increase or decrease by identifying these areas. For example, in high stress areas, the optimization algorithm may increase the material thickness of the area or enhance its structural support, while in low stress or stable temperature areas, the material may be subtracted to reduce weight.

[0053] The realization of microstructural feedback relies on the changes in the microscopic properties of the material. By real-time monitoring of the changes in the grain structure and dislocation movement of the material during the simulation process, the simulation system can generate microstructural feedback data. Microstructural feedback data usually includes grain size distribution, dislocation density changes, etc. These data will be passed to the topology optimization module to adjust the macrostructure. For example, if the grain structure in a certain area shows that the material has begun to creep and cause a decrease in strength, the topology optimization algorithm will consider adding additional support structures in this area to enhance its strength.

[0054] The generation of high-dimensional material response data is obtained through coupled simulation of multiple physical fields, integrating the mechanical, thermal, fatigue and other properties of the material. These data are used as the basis for multi-dimensional optimization to ensure that the design of components is not based on a single attribute, but a combination of multiple physical phenomena. For example, under high temperature and high stress environments, materials may exhibit multiple behaviors such as thermal expansion, creep and stress concentration. The system will perform fine topological optimization design based on these multi-dimensional data to ensure the overall performance of the components is optimal.

[0055] Through the topological optimization and microstructure feedback linkage of this step, the performance of components under actual working conditions can be significantly improved. Topological optimization can ensure that materials are used only where necessary, thereby reducing the weight and material consumption of components, while providing sufficient support and thermal conductivity at key locations. Combined with microstructure feedback, the optimization results are not only reflected in the geometric structure adjustment of components, but also reflect the microscopic changes inside the material, so that components still have high reliability under high temperature and high stress conditions.

[0056] Preferably, Figure 2 As shown, the topology optimization includes: based on the initial temperature distribution data and the initial stress distribution data, locally encrypting and refining the mesh in the area where the local temperature of the component is 400°C to 1100°C or the stress is 100MPa to 200MPa, generating refined mesh data, and adjusting the material distribution of the component by rearranging the geometric structure of the component to optimize the heat conduction path and stress distribution of the component. The refinement of the local mesh is dynamically adjusted according to the thermal and stress change amplitude of the component.

[0057] Topology optimization is a method for optimizing structural geometry and material distribution, aiming to optimize structural performance by minimizing or maximizing certain objective functions (such as mass, stiffness, strength or thermal conductivity) while satisfying physical or design constraints. In the present invention, the basis of topology optimization is to identify local high temperature areas and stress concentration areas in parts through initial temperature distribution data and initial stress distribution data generated by preliminary thermal-mechanical coupling simulation.

[0058] Local high temperature areas are usually caused by external heat sources or insufficient internal heat dissipation. The materials in these areas may be subjected to excessively high temperatures, resulting in performance degradation. Stress concentration areas refer to the phenomenon that stress increases abnormally in certain areas due to geometric features, loading conditions or boundary constraints. Through topology optimization, these key areas can be designed with emphasis to ensure that they still have good performance in a stress and heat environment.

[0059] Mesh encryption and refinement is an important step in topology optimization. Usually, finite element simulation relies on dividing parts into several finite element meshes, and each mesh node is used to calculate local mechanical and thermal behaviors. In the high temperature and stress concentration areas of parts, the purpose of mesh encryption is to improve the simulation accuracy of these areas to better capture the response of materials under extreme conditions. The encrypted and refined mesh can perform more detailed analysis of these areas to ensure the accuracy of the simulation results.

[0060] By rearranging the geometric structure of the parts, the topology optimization algorithm will redistribute the material according to the stress and temperature distribution in different areas. For example, in high-stress areas, the algorithm may increase the material thickness or add structural support to enhance local strength; while in areas with low stress or more stable temperatures, materials may be removed to reduce the overall mass of the parts. At the same time, the topology optimization algorithm will adjust the distribution of materials to optimize the heat conduction path so that the heat in the high-temperature area can be quickly transferred to other parts to avoid local overheating.

[0061] The refinement of the local mesh is not completed statically, but is adjusted dynamically as the temperature and stress change during the thermal coupling simulation. This means that during the simulation, the system can automatically identify the stress and temperature changes of the material in different time steps, and adjust the density of the mesh in a timely manner to ensure the continuous improvement of simulation accuracy. Through dynamic mesh encryption and refinement, the simulation system can respond to complex physical phenomena more flexibly, especially for those sudden or gradually evolving stress and heat concentration areas.

[0062] In the specific implementation process, the initial temperature distribution data and the initial stress distribution data are first used to determine the key areas of the parts. These key areas may include high temperature or high stress concentration points, which are usually parts where the material is prone to deformation or failure. In these areas, the finite element mesh will be encrypted to improve the calculation accuracy. The local encrypted mesh can be generated by an adaptive mesh algorithm to dynamically adjust the mesh density according to the stress and temperature change amplitude of each mesh node. For example, in a high temperature area, if the temperature gradient is large, the mesh density will be encrypted as the temperature gradient increases, thereby more accurately capturing the changing trend of heat conduction.

[0063] At the same time, the topology optimization algorithm achieves performance improvement by rearranging the geometric structure and material distribution of components. For example, if the stress value in a certain area is close to the yield strength of the material, the optimization algorithm may recommend adding support structures or thickening the material in this area to increase the load-bearing capacity; on the contrary, in low-stress areas, materials may be removed to reduce the amount and weight of materials used. In addition, in high-temperature areas, in order to speed up heat dissipation, the distribution of materials can be redesigned along the optimal heat conduction path to achieve uniform temperature distribution.

[0064] Through this topology optimization process, the performance of the components has been significantly improved. First, the local mesh encryption and refinement enables the simulation system to capture the stress and temperature changes in key areas with higher accuracy, ensuring the reliability of the simulation results. During the design process of the components, the optimized material distribution and geometric structure can effectively alleviate the problems of local overheating or stress concentration and extend the service life of the components.

[0065] Preferably, the microstructural feedback includes: monitoring the microstructural changes of the component material, the microstructural changes including grain reorganization, creep behavior and dislocation movement of the component material, generating microstructural feedback data by analyzing the phase change rate of the grains inside the component material and the grain boundary evolution, and the microstructural feedback data is used to adjust the geometric shape of the component material and the topological structure evolution process of the component.

[0066] Under high temperature or high stress working conditions, the microstructure of the material will undergo significant changes, which mainly include phenomena such as grain recombination, creep behavior and dislocation movement. Grain recombination refers to the rearrangement or merging of grains inside the material due to high temperature or high stress, which affects the mechanical properties of the material such as strength and hardness. Creep behavior is the slow and continuous deformation of the material under long-term high temperature or high stress. Dislocation movement refers to the movement of defect lines (i.e. dislocations) inside the material under stress, which will significantly affect the plastic deformation and strength of the material.

[0067] In the present invention, microstructure feedback generates microstructure feedback data by real-time monitoring of these microscopic phenomena and analyzing the grain phase change rate and grain boundary evolution inside the material. The grain phase change rate refers to the speed at which the grains in the material change under the action of temperature and stress, and the grain boundary evolution describes the movement and change of the interface between the grains. Especially when the material is heated or subjected to high stress, the movement of the grain boundary will cause the strength and toughness of the material to change.

[0068] Through microstructural feedback data, the simulation system can dynamically adjust the geometry and topological evolution of component materials to ensure that the material maintains high structural stability during its service life. For example, when the system detects that the grain growth in a certain area is too fast or the grain boundary is unstable, the stress and heat conduction path in the area can be re-optimized by adjusting the geometry or material distribution of the area.

[0069] In the specific simulation implementation, a special microstructure simulation tool is first used to monitor the internal changes of the component material. The simulation system decomposes the material into multiple micro units, and the grain phase change rate and grain boundary evolution inside each unit can be accurately tracked through numerical simulation. Usually, the data of these microscopic changes are obtained through the coupling calculation of multiple physical fields, which can simultaneously consider the temperature field, stress field and the mechanical and thermal properties of the material.

[0070] The core of microstructural feedback lies in the modeling of grain reorganization and dislocation movement. Based on the stress and temperature field data generated by thermal simulation, the system predicts the grain evolution and dislocation density changes of the material under different working conditions. For example, in high-temperature areas, the grains of the material may grow rapidly, resulting in a decrease in local strength, while in high-stress areas, the increase in dislocation density will reduce the material's plastic deformation ability. By simulating and monitoring these phenomena, the system generates microstructural feedback data for real-time adjustment of topological structures and geometric designs.

[0071] High-dimensional material response data is an important data set in microstructure feedback, which integrates the mechanical, thermal, phase change and other behaviors of materials under complex working conditions. Through precise analysis of the material's stress change rate, thermal expansion coefficient, creep rate and other properties, the system can generate highly accurate material response data. This data can be used to further optimize the topological design, so that the components not only have good performance in the macro structure, but also have strong stability at the micro level.

[0072] By introducing microstructural feedback, the simulation system can more accurately simulate the performance changes of materials under extreme working conditions, ensuring the reliability and accuracy of the design results. One of the main effects of microstructural feedback is that it can prevent fatigue failure of materials during long-term operation. For example, by real-time monitoring of grain reorganization, the system can adjust the distribution or topology of the material in time before the grains grow excessively, avoiding the decrease in strength caused by excessive grain growth.

[0073] In addition, dislocation motion feedback can significantly improve the ability of components to resist plastic deformation under high stress conditions. Through dislocation feedback, the system can identify stress concentration points and make design adjustments to keep the material's dislocation density within a safe range during operation, reducing the risk of fatigue failure.

[0074] Based on high-dimensional material response data, microscopic simulation of phase change of component materials is performed to capture the microstructural changes of grain reorganization and dislocation movement of component materials, and the simulation time step is adjusted according to the grain phase change speed and stress change rate to generate microscopic phase change data and time step control data;

[0075] Phase change micro-simulation is mainly based on the evolution of the microstructure of materials under thermal environment, especially the phenomena of grain recombination, dislocation movement and phase change rate. Grain recombination refers to the change of grain size and shape inside the material under high temperature or stress, which has an important influence on the strength, toughness and fatigue resistance of the material. Dislocation movement is the displacement of internal defects of the material under stress, which affects the plastic deformation and hardness of the material. The phase change of a material refers to the process of transformation from one crystal structure to another, which is usually accompanied by changes in material properties.

[0076] High-dimensional material response data is the basis of the phase change simulation. It integrates the mechanical and thermal properties of the material under different conditions, including elastic modulus, thermal expansion coefficient, stress change rate, creep rate and other parameters. These data are used in the simulation to describe the response behavior of the material under different temperatures, pressures and stresses. By utilizing these high-dimensional data, the simulation can more accurately capture the microscopic phase change process of the material.

[0077] During the simulation process, the grain phase change rate and stress change rate are two important dynamic parameters. The grain phase change rate reflects the speed of grain reorganization during the phase change process, while the stress change rate reflects the change in stress distribution of the material under the action of external force. Through the change trends of these two parameters, the system can determine whether the microstructure of the material is in a state of drastic change, and thus adjust the simulation time step. The time step determines the time span within each calculation cycle of the simulation. A time step that is too large may not capture subtle changes in the microstructure, while a time step that is too small will lead to low calculation efficiency.

[0078] In the specific implementation process, the behavior of the component material is first characterized by high-dimensional material response data. The simulation system determines the mechanical and thermal response of the material under different temperature and stress conditions by reading the material's properties such as elastic modulus, thermal expansion coefficient and creep rate. Based on this data, the simulation can capture the grain reorganization and dislocation movement of the material in a high temperature or high stress environment.

[0079] The key to phase change micro-simulation is to dynamically adjust the simulation time step according to the material's grain phase change speed and stress change rate. The system will determine the speed of microscopic changes inside the material by monitoring the grain phase change within each time step. If the phase change speed is fast, it means that the microstructure of the material has changed dramatically in a short period of time. At this time, the time step needs to be shortened to capture these changes more finely; on the contrary, if the phase change speed is slow, the time step can be appropriately increased to improve the simulation efficiency. The calculation of the stress change rate is based on the stress field distribution of the material under different loading conditions. As the stress change intensifies, the dislocation movement inside the material will also intensify. The system will adjust the time step accordingly to ensure accurate simulation of the stress field and dislocation movement.

[0080] The system will generate microscopic phase change data and time step control data based on these data. Microscopic phase change data is used to describe the microstructural evolution of the material, including the rearrangement of the grain structure, the movement of dislocations, etc. These data will serve as the basis for subsequent thermal-mechanical coupling simulation and topology optimization to ensure that the simulation results accurately reflect the microstructural changes of the material. The time step control data is used to dynamically adjust the time step in subsequent simulation steps to ensure that the simulation can maintain sufficient accuracy when the material undergoes drastic changes, and improve the efficiency of the simulation when the material is relatively stable.

[0081] Preferably, the phase change microscopic simulation is performed based on high-dimensional material response data, and the simulation time step of the component is adjusted by the following formula:

[0082]

[0083] in, is the simulation time step; is the time step adjustment factor, which represents the sensitivity adjustment coefficient of the time step; is the grain recombination rate, which indicates the rate of change of the microstructure during the grain recombination process of the material; is the stress change rate, which indicates the rate at which the stress of the material changes during the simulation.

[0084] During the thermal-mechanical coupling simulation, the microstructure of the material (such as grains and dislocations) changes dynamically under high temperature or high stress conditions, and this change has a direct impact on the macroscopic mechanical properties of the material. Phase transformation micro-simulation is used to simulate the microstructural evolution of the material under these extreme conditions. To ensure that the simulation can accurately capture the microscopic changes, the simulation time step needs to be adjusted in time according to the dynamic change characteristics of the material.

[0085] The simulation time step of the component. The time step determines the physical time range spanned by each simulation iteration. The larger the time step, the faster the simulation time, but the accuracy may decrease; the smaller the time step, the higher the accuracy, but the calculation time increases.

[0086] It is the time step adjustment factor of the component, which represents the adjustment coefficient of the time step sensitivity. It is used to control the response speed of the simulation to the change of the time step. It is usually set by the user or the system according to actual needs to balance accuracy and efficiency.

[0087] The grain recombination rate of the component material indicates the speed of change of the material grains during the phase change process. Grain recombination usually occurs when the material is subjected to thermal stress or other external stresses. The grains affect the strength and performance of the material during the recombination process. Fast grain recombination means that the microstructure changes quickly, requiring a shorter time step.

[0088] The stress change rate of the component material indicates the rate of change of the stress field of the material during the simulation. The greater the stress change rate, the more violent the dislocation movement inside the material, the higher the risk of deformation or damage of the material, and a more detailed time step is required to capture these changes.

[0089] The core function of this formula is to dynamically adjust the simulation step size through the rate of microstructural changes (grain reorganization) and macro stress changes, so as to accurately simulate the behavior of materials in different physical time periods. For example, when the material is in a state of rapid phase change or stress mutation, the system will automatically reduce the simulation time step to capture the details of these changes; when the microstructure of the material changes slowly or the stress changes are not significant, the system can increase the time step to improve the calculation efficiency.

[0090] In the specific implementation process, the system first obtains the dynamic behavior of the material under different temperature and stress conditions through high-dimensional material response data. High-dimensional material response data includes the material's grain recombination rate, creep rate, elastic modulus, stress change rate, etc., which constitute the physical characteristics of the material in phase change and stress field. Based on this data, the system can monitor the material's phase change process and stress field evolution in real time, and calculate the grain recombination rate and stress change rate.

[0091] At the beginning of each simulation iteration, the system calculates the current grain recombination rate and stress change rate through the formula, and then substitutes these rates into the time step adjustment formula to generate a new simulation time step. For example, if the grain recombination rate At a certain moment, it increases significantly, indicating that the phase change process inside the material is accelerating, and the system will change according to the stress change rate. At the same time, calculate the severity of stress change. If the stress change rate The system will further shorten the simulation time step to ensure that the details of stress mutation and grain reorganization can be accurately captured.

[0092] On the contrary, when the microscopic changes of the material during the simulation are relatively stable, and the grain reorganization and stress change rate are low, the system will increase the simulation time step to reduce unnecessary computational overhead, thereby improving the overall efficiency of the simulation.

[0093] Through this time step adjustment mechanism based on high-dimensional material response data, the simulation system can dynamically respond to the phase change and stress change of the material, ensuring that important microstructural evolution can be captured during the simulation process and that computational efficiency can be improved during the stable phase. This dynamic adjustment mechanism makes the simulation process more accurate and flexible, especially under complex working conditions, such as high temperature and environments with drastic stress fluctuations. The system can effectively control the simulation time step and ensure the reliability of the simulation results.

[0094] By dynamically adjusting the time step, the simulation can maintain a high time resolution in the case of phase change or stress mutation inside the material, avoiding the loss of important details. For example, in the creep simulation of high-temperature materials, the shortening of the time step ensures high-precision simulation during the rapid deformation stage of the material. When the material changes are not significant, the time step is increased, which reduces unnecessary simulation iterations, thereby improving the overall calculation efficiency and reducing the waste of computing resources. For example, under relatively stable temperature and stress fields, the simulation can complete the calculation quickly and save time. By accurately simulating the microscopic phase change process of the material, the system can provide accurate data support for subsequent structural optimization and fatigue analysis, and improve the design reliability of components under extreme working conditions.

[0095] Preferably, the high-dimensional material response data includes the thermal expansion coefficient, phase change critical temperature, elastic modulus and creep rate of the component material. The high-dimensional material response data is used to adjust the grain phase change rate of the component material in the microscopic phase change simulation of the component, and to adjust the boundary conditions and component material properties of the component simulation model in real time by accurately capturing the thermal behavior of the component material under different temperature and stress conditions.

[0096] High-dimensional material response data is a quantitative description of the comprehensive behavior of materials under various physical and thermal conditions, involving various physical properties of materials, such as thermal expansion coefficient, phase transition critical temperature, elastic modulus and creep rate, etc. These data are used to accurately characterize the response of materials under different temperature, stress and time conditions, and provide a basis for adjusting the simulation model.

[0097] The coefficient of thermal expansion determines the volume change of a material under different temperature conditions. When a material is subjected to heat, the increase in temperature causes the volume of the material to expand. This property is crucial for the calculation of thermal stress.

[0098] The critical temperature of phase transition is the temperature at which a material changes from one crystal structure to another. This parameter is used to determine the starting point of the phase transition of the material and determine the reorganization behavior of the grains.

[0099] The elastic modulus determines the degree of deformation of a material under stress. A larger elastic modulus means that the material can withstand greater stress under external force, while a smaller elastic modulus reflects that the material is more flexible.

[0100] The creep rate reflects the slow deformation process of materials under high temperature or high stress conditions, which is particularly important in the simulation of long-term loads. The creep behavior directly affects the long-term service life and structural stability of the material.

[0101] In thermal-mechanical coupling simulation, high-dimensional material response data is used to adjust the grain phase change rate of component materials. The grain recombination rate affects the strength and structural stability of the material. Therefore, by monitoring these properties in real time through the simulation system, the physical properties of the material can be dynamically adjusted to ensure that the simulation model can accurately capture the microstructural changes.

[0102] In actual simulation, the material parameters of the component are first defined, and the high-dimensional material response data is input into the simulation system in this step. Each physical property is assigned a corresponding value, and as the temperature and stress change, these data are dynamically updated for real-time calculations in the simulation.

[0103] Application of thermal expansion coefficient: During the simulation process, when an external heat source is applied to a component, the system predicts the expansion behavior of the material based on the thermal expansion coefficient. The expansion of the high temperature area will cause local stress concentration, and these stresses will be fed back to the simulation model, prompting the system to adjust the grain phase transformation rate in that area. This can prevent material failure due to excessive temperature.

[0104] Application of critical temperature of phase change: When the temperature of the component reaches or exceeds the critical temperature of phase change, the simulation system will capture the change of grain structure, and the material begins to change from one phase to another. At this time, the system controls the speed of phase change by adjusting the grain phase change rate, and ensures that the phase change process can be carried out under controlled conditions without increasing the brittleness of the material or reducing its strength.

[0105] Application of elastic modulus and creep rate: For long-term operating equipment, such as turbines or pressure vessels, the creep rate determines the amount of deformation of the material under long-term high temperature or high stress conditions. The simulation system captures the stress relaxation and creep behavior of the material through high-dimensional material response data and adjusts the material properties in real time. For example, in the simulation of a pressure vessel, if a significant increase in the creep rate in a local area is detected, the system will immediately adjust the elastic modulus and stress distribution in that area to prevent excessive deformation of the local material from causing failure of the overall structure.

[0106] In this dynamic simulation process, boundary conditions (such as external temperature, pressure, etc.) and material properties are adjusted in real time. The simulation system is not only based on existing material properties, but also adjusts these properties through a feedback mechanism to ensure the accuracy of the simulation results. For example, if the thermal expansion coefficient of a certain area expands too quickly at a higher temperature, the simulation system will reduce the impact of excessive expansion on material strength by changing the material elastic modulus or phase transition critical temperature of the area in real time.

[0107] Through high-dimensional material response data, the simulation system can more accurately capture the microstructural changes of materials under extreme working conditions, and improve the accuracy and reliability of simulation by adjusting the boundary conditions and material properties of the simulation model in real time. This mechanism ensures that the component materials can maintain the best performance state under different working conditions and provides dynamic response capabilities for the performance of materials in thermal and stress changes.

[0108] Based on the microscopic phase change data, the fluid-solid coupling simulation between the cooling fluid and the component material is carried out to simulate the heat exchange process between the cooling fluid and the component material, generate preliminary fluid-solid coupling data, and predict the fatigue damage of the component material through multimodal data fusion technology to generate multimodal fusion damage prediction data;

[0109] Fluid-Solid Interaction simulation refers to modeling the interaction between solids (components) and fluids (cooling fluids) in thermodynamic and fluid dynamics simulations. This coupling includes two phenomena: the cooling fluid exchanges heat with the component material through the surface, and the temperature and velocity field of the fluid affect the thermal state of the component. Conversely, the temperature and stress state of the component also affect the flow of the fluid. Fluid-Solid Interaction simulation can more accurately reflect the thermal response of the material under cooling conditions by simulating this two-way interaction.

[0110] In the process of fluid-solid coupling, microscopic phase change data provides microstructural information of component materials, such as grain reorganization and dislocation movement, which determines the thermal conductivity, thermal expansion coefficient and stress distribution of the material. As the cooling fluid passes through the surface or inside of the component, heat is transferred from the high-temperature area to the fluid. The flow rate and temperature of the cooling fluid and the thermal conductivity of the component will jointly affect the heat exchange efficiency. The simulation system can generate accurate temperature field and stress field data by capturing the heat transfer between the cooling fluid and the material.

[0111] Multimodal data fusion technology refers to the fusion of data from different physical fields (such as temperature field, stress field and microstructure data) to generate comprehensive damage prediction data. Fatigue damage prediction is to judge the fatigue life of materials by analyzing the cumulative damage of materials under multiple thermal cycles and stress concentration. These data not only include temperature and stress data generated by fluid-solid coupling simulation, but also integrate the microscopic phase change data of materials, such as changes in grain structure, dislocation density and creep rate of materials. Through multimodal data fusion technology, simulation can generate more comprehensive and accurate multimodal fusion damage prediction data, which can be used to evaluate the fatigue life of components under complex working conditions.

[0112] In the specific implementation, the fluid-solid coupling simulation is first modeled. At the beginning of the simulation, the physical parameters of the cooling fluid and the component material are defined. The flow rate, temperature and flow path of the cooling fluid need to be set according to the specific application scenario. The thermal and mechanical properties of the component material are provided by the microscopic phase change data, which can reflect the material's grain structure, thermal conductivity and stress distribution.

[0113] During the simulation, the cooling fluid absorbs heat from the high-temperature area and transfers it to the surrounding area or the exhaust system. At the same time, the temperature of the cooling fluid will gradually increase, and the flow characteristics of the fluid (such as Reynolds number, Nusselt number, etc.) will also change accordingly. Fluid-solid coupling simulation calculates the dynamic changes of the temperature field inside the material by solving the heat conduction equation and fluid dynamics equation, and generates preliminary fluid-solid coupling data, which includes the temperature distribution and stress distribution inside the material, as well as the temperature and flow rate changes of the cooling fluid.

[0114] The efficiency of heat transfer during the cooling process depends on the thermal conductivity of the material and the heat exchange capacity of the cooling fluid. For example, a material with higher thermal conductivity can quickly transfer heat from a high temperature area to other parts during the cooling process, while the flow rate and temperature difference of the cooling fluid determine the effect of heat exchange.

[0115] The simulation system combines fluid-solid coupling data with microscopic phase change data through multimodal data fusion technology. Microscopic phase change data includes the grain recombination rate and dislocation density change of the material, which directly reflect the microstructural evolution of the material under high temperature and high stress conditions. By combining microscopic data with macroscopic temperature field and stress field data, the simulation can generate more accurate fatigue damage predictions.

[0116] Fluid-structure interaction simulation can generate accurate temperature and stress field data, especially in the heat exchange area between the cooling fluid and the material. The simulation can accurately capture the local temperature rise or stress concentration of the material. This is of great significance for optimizing the flow path of the cooling fluid and the cooling efficiency.

[0117] Through multimodal data fusion technology, the simulation system can fully integrate temperature, stress and material microstructure change data to generate accurate fatigue damage prediction results. Compared with single physical field data, fused data can more comprehensively capture the damage accumulation of materials under different working conditions and provide more reliable fatigue life assessment. For example, through fatigue damage data predicted by simulation, design engineers can optimize the geometric structure of parts and avoid designing unnecessary stress concentration points in areas with higher fatigue damage risks.

[0118] Preferably, the fluid-solid coupling simulation is performed based on the microscopic phase change data of the component. The fluid-solid coupling simulation simulates the heat transfer process between the cooling fluid and the component material, adjusts the flow rate and flow path of the cooling fluid, and generates fluid-solid coupling simulation data. The fluid-solid coupling simulation data is used to optimize the flow path of the cooling fluid and the temperature distribution inside the component material, thereby reducing local overheating inside the component material.

[0119] The basic principle of fluid-solid coupling simulation is to couple fluid dynamics (CFD) and solid mechanics (FEM) in both directions, and adjust the flow path, flow rate and heat exchange efficiency of the fluid by simulating the heat transfer between the cooling fluid and the component material. The cooling fluid flows through the surface or internal channels of the component, exchanges heat with the component material, takes away the heat on the surface of the material, prevents local overheating, and maintains the stability of the internal temperature of the component.

[0120] In this process, microscopic phase change data is key, as it reflects the material's grain reorganization, dislocation movement, and phase change speed, which directly affect the material's thermal physical properties such as thermal conductivity and thermal expansion coefficient. As the cooling fluid flows, the material's microstructure continues to evolve, and its thermal conductivity changes accordingly. Based on these microscopic data, fluid-solid coupling simulation can dynamically adjust the flow rate and path of the cooling fluid to achieve more efficient heat exchange.

[0121] The basic principle of heat transfer process can be described by heat conduction equation, while the flow of fluid is described by the governing equations of fluid dynamics (such as Navier-Stokes equations). In fluid-solid coupling simulation, the temperature field of solid material is coupled with the velocity field, pressure field and temperature field of fluid. The simulation system can obtain the heat transfer effect of fluid on the surface or inside of the material by iteratively solving these equations.

[0122] In the actual simulation implementation, the physical properties of the cooling fluid and component materials need to be accurately modeled. The flow rate, temperature, fluid density, viscosity and other properties of the cooling fluid are preset by the system or determined by the actual working conditions, and the thermal properties of the material such as thermal conductivity and thermal expansion coefficient are provided by microscopic phase change data.

[0123] The simulation starts by setting up the geometric model of the component and defining the fluid flow path. Usually, the fluid will pass through the cooling channel or surface of the component for heat exchange. The simulation system gradually adjusts the flow rate and flow path of the cooling fluid to capture the changes in cooling efficiency, thereby generating fluid-structure interaction simulation data.

[0124] The key steps of the simulation process include:

[0125] Adjustment of flow rate and flow path: Through the calculation of fluid-structure coupling simulation, the system can identify the areas with local overtemperature in the parts based on the temperature distribution results, and adjust the flow rate and flow path of the cooling fluid in real time. For example, if overheating is detected in a certain area, the system will increase the fluid flow rate in that area or change the flow direction to remove the heat faster.

[0126] Simulation of heat transfer process: The simulation system simulates the heat exchange process by calculating the heat transfer coefficient between the cooling fluid and the material surface. The efficiency of heat transfer depends on the thermal conductivity of the material, the flow rate of the cooling fluid, and the contact area between the fluid and the material. By optimizing the flow path of the fluid, the simulation can evenly distribute the heat to a larger area, thereby reducing local overheating.

[0127] Fluid-solid coupling feedback: In each simulation iteration cycle, the system adjusts the fluid parameters based on the real-time temperature field and velocity field feedback, and gradually optimizes the cooling scheme. This feedback mechanism ensures the dynamics and accuracy of the simulation results, and can quickly respond to changes in the temperature distribution inside the material and make effective adjustments.

[0128] Through fluid-solid coupling simulation, the simulation system can effectively reduce local overheating inside components and optimize the flow path of the cooling fluid.

[0129] Preferably, Figure 3As shown, the multimodal data fusion generates multimodal fusion damage prediction data by fusing fluid-solid coupling simulation data, microstructure feedback data of components, stress distribution data of components and temperature field data of components. The multimodal fusion damage prediction data is used to predict fatigue damage accumulation of component materials under conditions of temperatures of 400°C to 1100°C or stresses of 100MPa to 200MPa. By analyzing the fatigue life and local stress concentration of component materials, the location where component materials fail is determined and used to optimize the design of components.

[0130] The basic principle of multimodal data fusion is to generate comprehensive prediction information by combining simulation data from different physical fields to accurately reflect the behavior of materials under complex working conditions. Each data type has a unique role in the multimodal fusion process:

[0131] Fluid-Solid Interaction Simulation Data: Fluid-Solid Interaction Simulation provides the temperature distribution inside the material and the heat exchange between the fluid and the material. The efficiency of heat transfer, changes in flow rate, and adjustments to the cooling path will affect the temperature field of the material, thereby determining the stress state inside the material.

[0132] Microstructure feedback data: The evolution of the material's microstructure (such as grain reorganization and dislocation movement) directly affects the material's macroscopic mechanical properties. Through the microstructure feedback in the simulation, we can understand the structural changes of the material under high temperature or high stress, and thus make a more accurate judgment on the material's fatigue performance.

[0133] Stress distribution data: Stress concentration is often one of the main causes of fatigue damage accumulation. Stress distribution data reflects the stress changes in different areas inside the material and identifies high stress areas that may cause fatigue failure.

[0134] Temperature field data: Temperature field data shows the temperature distribution inside the material. High temperature areas are usually prone to material deformation, creep or grain phase change. Temperature field data combined with stress distribution data can more comprehensively analyze the weak areas of the material.

[0135] By fusing these different types of data, multimodal fusion damage prediction can capture the global performance of materials under complex working conditions. Especially in high temperature and high stress environments, the fatigue damage accumulation process of materials is very complex, and a single data source often cannot fully reflect the working state of the material. Multimodal data fusion integrates all relevant data to provide more accurate damage prediction results, helping engineers identify material weaknesses and optimize designs.

[0136] In the specific implementation process, first, the simulation system obtains the corresponding data sources from different physical fields:

[0137] Fluid-structure coupling simulation data is generated by coupling simulation of cooling fluid and component materials. The data includes the local temperature of the material, the heat transfer of the cooling fluid, and the cooling efficiency.

[0138] Microstructure feedback data is generated by the microstructure simulation of the material. These data reflect the grain recombination rate, dislocation density and phase change process, and are directly used to evaluate the microscopic damage of the material.

[0139] Stress distribution data is generated through finite element mechanics simulation, indicating the stress concentration areas of components under the action of external forces and temperature.

[0140] The temperature field data is generated through thermal simulation, reflecting the heat distribution state inside the material, especially the changes in the high-temperature area.

[0141] In the process of multimodal data fusion, the system first aligns the data in space and time to ensure that each data point has a consistent time step and spatial resolution. For example, stress distribution data and temperature field data need to be synchronized through the same time step to ensure that the simulation results reflect the overall state of the parts at the same time point. Spatial alignment maps data from different physical fields to a unified grid by overlapping the grids, ensuring that each data point can reflect the same spatial area.

[0142] After completing the data alignment, the system uses a fusion algorithm to comprehensively analyze different types of data and generate multimodal fusion damage prediction data. This data not only includes the overall fatigue life assessment of the material, but also can identify local areas where failure may occur. For example, when the system detects that a certain area has both high stress concentration and is in a high temperature environment and the microstructure of the material has undergone significant grain phase transformation, the simulation results will indicate that fatigue failure may occur in this area, further indicating the need for optimized design.

[0143] In order to improve the accuracy of damage prediction, the system will also model fatigue damage accumulation, usually using Miner's law or other more complex fatigue damage models. These models can calculate the fatigue accumulation of materials through historical loads and current stress levels, and adjust future design strategies based on the degree of material damage. For example, through multimodal data fusion, the system may find that certain areas of a component are close to the fatigue limit, prompting the designer to add reinforcement materials or change the cooling path in this area to delay material failure.

[0144] Based on the multimodal fusion damage prediction data, global feedback analysis is performed, simulation parameters are optimized, the properties of component materials, simulation time step and external boundary conditions are adjusted, simulation iterative optimization is performed, and the final optimized simulation data is generated.

[0145] Preferably, the simulation iterative optimization is performed based on global feedback data, and multiple adjustments are made to component material properties, simulation time steps, and component boundary conditions to ensure that the thermal and mechanical behaviors of components during the simulation process can be accurately described. The simulation iterative optimization process includes multiple rounds of simulation calculations until the simulation results reach a preset termination condition, thereby generating final component optimization simulation data.

[0146] The principle of simulation iterative optimization is to adjust and optimize based on the continuous feedback of simulation data, gradually approaching the actual working state of components. The core of this process is to use the temperature field, stress field, material microstructure change and other data generated by each round of simulation as input through the global feedback mechanism to optimize the key parameters in the next round of simulation, including material properties, simulation time step and component boundary conditions.

[0147] Adjustment of material properties: As temperature and stress conditions change during the simulation, the thermal conductivity, elastic modulus and other properties of the material may change accordingly. These changes will be reflected in the thermal-mechanical coupling behavior of the material, so the simulation needs to be adjusted in real time according to the evolution of material properties to ensure that the thermodynamic properties of the material can be accurately simulated in subsequent simulations.

[0148] Optimization of simulation time step: The time step determines the computational time span of each simulation iteration. In the stage of high temperature or high stress changes, a smaller time step can ensure that the simulation captures fine dynamic changes, while in the relatively stable stage, a larger time step can improve computational efficiency. Therefore, a balance can be achieved between accuracy and efficiency by adjusting the time step through feedback.

[0149] Adjustment of boundary conditions: Boundary conditions directly determine how heat and stress are transferred in the simulation. During the simulation iteration process, as the temperature distribution and stress concentration of the components change, the boundary conditions also need to be updated. For example, when the flow rate or path of the cooling fluid changes, the simulation system needs to dynamically adjust the corresponding boundary conditions to ensure that the simulation model can accurately reflect the actual working environment of the components.

[0150] The goal of simulation iterative optimization is to make the system gradually approach the preset accuracy and stability requirements through multiple rounds of simulation adjustments until the simulation results reach the termination conditions.

[0151] In the actual simulation iterative optimization process, the system first performs the first round of simulation calculations through initial settings. In this round of calculations, the simulation system generates preliminary temperature field and stress field data based on the initial material properties, time step and boundary conditions. As the simulation proceeds, global feedback data of components is gradually generated, including the following aspects:

[0152] Temperature and stress field distribution: The system records the temperature and stress distribution inside the component at each time step, paying special attention to changes in high temperature areas and stress concentration areas.

[0153] Microstructure feedback data: Through microscopic phase transformation simulation, the system records the grain phase transformation rate and dislocation movement of the material under high temperature or high stress conditions.

[0154] Fatigue damage prediction data: Based on multimodal data fusion, the system predicts the fatigue accumulation of materials under high temperature and high stress conditions and identifies potential failure areas.

[0155] Based on these feedback data, the simulation system enters the next round of iterative optimization. Each round of simulation adjusts parameters based on the feedback data generated in the previous round:

[0156] Adjustment of material properties: The system dynamically adjusts material parameters such as elastic modulus and thermal conductivity based on the material's microstructural feedback data. For example, if it detects that the material grain phase change rate in a high-temperature area has increased significantly, the system will reduce the thermal conductivity of that area, thereby slowing down heat transfer and preventing local overheating from causing material failure.

[0157] Time step optimization: The time step is optimized based on the stress change rate and temperature change rate in the simulation. In areas where stress and temperature change dramatically, the system will shorten the time step to ensure that these subtle changes are captured; while in stable areas, the time step can be increased to improve computational efficiency.

[0158] Update of boundary conditions: Based on the heat transfer efficiency of the cooling fluid, the system adjusts the flow rate and flow pattern of the cooling path. If it detects that the cooling effect in a certain area is insufficient, the system will increase the fluid flow rate in that area or reset the flow path of the cooling fluid to prevent the local temperature from being too high.

[0159] This iterative optimization process continues until the simulation results reach the preset termination conditions. The termination conditions usually include the following aspects:

[0160] The temperature field and stress field inside the material reach a stable state and no longer change significantly.

[0161] The fatigue damage prediction results show that the fatigue life of the material has met the design requirements and the failure risk is controlled within an acceptable range.

[0162] The global error after multiple rounds of simulation meets the set tolerance requirements and the simulation accuracy is up to standard.

[0163] Through simulation iterative optimization, the system can significantly improve the simulation accuracy and computational efficiency, and ensure that the thermal-mechanical coupling behavior of components under complex working conditions can be accurately simulated. Specific effects include:

[0164] Accurate prediction of temperature and stress fields: Iterative simulation optimization enables the system to gradually and accurately predict the temperature and stress distribution of components. Especially in high temperature and high stress environments, the simulation can dynamically adjust material properties and boundary conditions to ensure that the simulation results are consistent with the actual situation.

[0165] Accurate assessment of fatigue damage: Through global feedback and iterative optimization, the simulation system can accurately predict the accumulation of fatigue damage in components. By adjusting the time step and material properties, the simulation can capture the evolution of fatigue damage in materials under high temperature and high stress conditions, providing an accurate basis for design optimization.

[0166] Decision support for optimized design: Simulation iterative optimization provides reliable decision support for engineering design. Through multiple rounds of feedback and adjustment of simulation results, engineers can find potential problems in the design and improve the reliability of parts by further optimizing material distribution, cooling paths and boundary conditions. For example, through multiple rounds of simulation, the system can recommend strengthening cooling or adjusting material combinations in key stress areas to extend the fatigue life of parts.

[0167] Preferably, the simulation iterative optimization optimizes the geometric shape and material properties of components by dynamically adjusting the local grid density of components in real time and combining the fatigue damage prediction results in the global feedback data, so that the temperature and stress distribution of the components in the simulation results are uniform, and the local overheating and stress concentration of the components are effectively controlled.

[0168] The basic principle of simulation iterative optimization is to adjust the local mesh density, material properties and geometric shape of the parts based on the feedback data that is continuously updated during the simulation process. The dynamic adjustment of the mesh density helps to refine the areas with significant stress concentration and temperature changes during the simulation process, while the optimization of material properties and geometric shapes can effectively improve the thermal conductivity and mechanical properties of the material, reduce high temperature areas and stress concentration areas, and prevent overheating and material fatigue damage.

[0169] Dynamic adjustment of local mesh density: During the simulation process, as the temperature field and stress field change, the system analyzes the stress concentration and temperature gradient in the local area of ​​the component in real time. In the stress concentration area or the local area with drastic temperature changes, the system will automatically increase the mesh density of the area. Through more detailed mesh division, the simulation can capture more subtle stress and temperature changes, and realize accurate simulation of complex stress and temperature fields.

[0170] Application of global feedback data: Global feedback data includes temperature distribution, stress concentration, and fatigue damage prediction results in multimodal data fusion. Through comprehensive analysis of these feedback data, the simulation system can identify weak areas inside the component, such as local stress concentration points or high temperature areas. Based on this data, the system optimizes the geometry and material properties of the component in real time. For example, by adjusting the geometry to reduce stress concentration, or by changing the material properties to improve thermal conductivity, so as to disperse heat more evenly and reduce overheating.

[0171] Feedback on fatigue damage prediction results: Through fatigue damage prediction, the system can accurately identify the areas where parts are most likely to fail due to fatigue, and dynamically adjust the design based on these prediction results. For example, if fatigue accumulation is fast in a certain local area, the simulation system may increase the material thickness in that area, or select a material with better fatigue resistance to slow down the development of damage.

[0172] In the specific implementation process, the simulation first performs initial meshing on the entire geometric model of the component and sets the initial material properties and geometry. In the first round of simulation, the system calculates the global thermal-mechanical coupling behavior of the component based on the initial temperature and stress fields. At this stage, the local mesh density is usually relatively uniform and is mainly used to determine the global temperature and stress distribution.

[0173] As the simulation enters multiple rounds of iterative optimization, the system begins to dynamically adjust local areas through global feedback data:

[0174] Local mesh density adjustment: During the simulation process, the system increases the mesh density in areas with large temperature gradients or stress concentrations. For example, in high-temperature areas or edge parts where stress changes significantly, the system refines the mesh so that the simulation can capture more microscopic stress changes and thermal-mechanical coupling behaviors. The basis for mesh refinement includes data such as the temperature change rate and stress change rate. Through real-time analysis of these data, the areas that need refinement the most are determined.

[0175] Geometry optimization: The simulation system will adjust the geometry of the component based on the feedback data. For example, in the stress concentration area, the system will optimize the geometry to reduce the stress concentration effect, perhaps by adjusting the local curvature or increasing the material thickness to evenly distribute the stress.

[0176] Material property optimization: Based on the temperature and stress field data of the local area in the simulation, the system will also dynamically adjust the thermal and mechanical properties of the material. For example, if the thermal conductivity of a certain area is poor, resulting in heat accumulation, the system may optimize the material properties of that area and select a material with better thermal conductivity to ensure that the heat can be dispersed faster and prevent local overheating.

[0177] Global feedback and fatigue damage control: Through multimodal data fusion, the simulation system can generate fatigue damage prediction results. Based on the fatigue accumulation, the system identifies the location most likely to fail and optimizes the material and geometric design of the area in real time. For example, by increasing the thickness of the local material or adjusting the geometry of the area to reduce the accumulation of fatigue damage. Through this feedback mechanism, the system can gradually optimize the material and structural design in each round of simulation.

[0178] Simulation iterative optimization can significantly improve the accuracy and reliability of simulation by adjusting local mesh density, optimizing geometric shapes and material properties in real time. The specific effects are reflected in the following aspects:

[0179] By dynamically adjusting the mesh density, the simulation system can more accurately capture the local thermal behavior of components, especially in high temperature and stress concentration areas. Mesh refinement greatly improves the resolution of simulation results. Combined with the optimization of geometry and materials, the temperature and stress fields are more evenly distributed, reducing local overheating and stress concentration. For example, in the simulation of nuclear reactor pressure vessels, through geometry optimization and material adjustment, the stress distribution in the vessel wall can be effectively balanced to avoid material failure caused by local stress concentration.

[0180] Through fatigue damage prediction and feedback, the system can identify the areas where the material is most susceptible to fatigue failure and optimize the design accordingly. Through geometric optimization and material property adjustment, simulation can effectively extend the fatigue life of parts.

[0181] Simulation iterative optimization not only improves the accuracy of simulation, but also makes the design optimization process more efficient. Through simulation, engineers can quickly identify potential problems in the design and gradually optimize the design through multiple rounds of iterations. Real-time feedback and optimization mechanisms make the design adjustment process more intelligent and automated, reducing manual intervention and improving design efficiency.

[0182] like Figure 4 As shown, a system for implementing the thermal-mechanical coupling simulation method for a component comprises:

[0183] The meshing module is used to perform finite element meshing on components and set the thermal and mechanical properties of component materials; the geometric structure of the components is discretized through finite element meshing to establish a computational grid. This module sets the thermal and mechanical properties of components so that subsequent simulations can accurately capture the thermal conduction and mechanical behavior of the material. The finite element method is a mature numerical analysis method, and its core lies in dividing complex geometric bodies into small units, making the calculation of physical fields controllable and detailed.

[0184] The simulation calculation module is used to apply external heat source and external load conditions, perform preliminary thermal simulation, and generate initial temperature distribution data and initial stress distribution data; apply external heat source and load conditions, perform thermal simulation, and generate initial temperature distribution data and stress distribution data of parts after being heated and stressed. This simulation captures the initial response of parts by solving the state evolution of materials under thermal-mechanical coupling.

[0185] The topology optimization module is used to optimize the topology of components based on the initial temperature distribution data and initial stress distribution data, and to make linkage feedback adjustments based on the microstructural changes of the component materials to generate microstructural feedback data and high-dimensional material response data; based on the initial temperature distribution and stress distribution data generated by the simulation, the component topology is optimized, and the material distribution is adjusted based on the microstructural changes. The purpose of this module is to optimize the geometry and material distribution of components to make the heat conduction and mechanical behavior more uniform, thereby improving the performance of the components.

[0186] The phase change micro-simulation module is used to simulate the phase change of component materials based on high-dimensional material response data, capture the microstructural changes of grain reorganization and dislocation movement of component materials, and adjust the simulation time step according to the grain phase change speed and stress change rate to generate microscopic phase change data and time step control data; based on the high-dimensional material response data generated after topology optimization, capture the microstructural changes of grain reorganization and dislocation movement of component materials. During the simulation process, the time step is adjusted in combination with the phase change speed and stress change rate to accurately simulate the evolution of the material microstructure under thermal effects.

[0187] The fluid-solid coupling simulation module is used to simulate the fluid-solid coupling between the cooling fluid and the component material based on the microscopic phase change data, simulate the heat exchange process between the cooling fluid and the component material, and generate fluid-solid coupling data; use the microscopic phase change data to simulate the heat exchange process between the cooling fluid and the component material, and generate fluid-solid coupling simulation data. By simulating the flow rate, temperature of the cooling fluid and the heat transfer behavior of the component, the system can more accurately predict the thermal behavior of the component, especially in a dynamic environment.

[0188] The multimodal fusion module is used to predict fatigue damage of component materials through multimodal data fusion technology, generate multimodal fusion damage prediction data, and fuse fluid-solid coupling simulation data with other key data (such as stress distribution and microstructure feedback) to predict fatigue damage. Through multimodal data fusion technology, the system can more comprehensively evaluate the fatigue life and failure risk of materials.

[0189] The global feedback optimization module is used to perform global feedback analysis based on multimodal fusion damage prediction data, optimize simulation parameters, adjust the properties of component materials, simulation time steps and external boundary conditions, perform simulation iteration optimization, and generate the final optimized simulation data. Based on fatigue damage prediction data, the material properties, simulation time steps and external boundary conditions of components are optimized in real time. Through multiple simulation iterations, the module continuously adjusts simulation parameters and finally generates optimized simulation data to achieve adaptive optimization of component design.

[0190] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A thermal-mechanical coupling simulation method for components, characterized in that: include: By dividing the parts into finite element meshes, setting the thermal and mechanical properties of the parts materials, applying external heat sources and external load conditions, and performing preliminary thermal simulation, initial temperature distribution data and initial stress distribution data are generated; Based on the initial temperature distribution data and initial stress distribution data, the topology optimization of the parts is carried out, and the linkage feedback adjustment is carried out in combination with the microstructural changes of the parts materials to generate microstructural feedback data and high-dimensional material response data; Based on high-dimensional material response data, microscopic simulation of phase change of component materials is performed to capture the microstructural changes of grain reorganization and dislocation movement of component materials, and the simulation time step is adjusted according to the grain phase change speed and stress change rate to generate microscopic phase change data and time step control data; Based on the microscopic phase change data, the fluid-solid coupling simulation between the cooling fluid and the component material is carried out to simulate the heat exchange process between the cooling fluid and the component material, generate preliminary fluid-solid coupling data, and predict the fatigue damage of the component material through multimodal data fusion technology to generate multimodal fusion damage prediction data; Based on the multimodal fusion damage prediction data, global feedback analysis is performed, simulation parameters are optimized, the properties of component materials, simulation time step and external boundary conditions are adjusted, simulation iterative optimization is performed, and the final optimized simulation data is generated.

2. The thermal-mechanical coupling simulation method for components according to claim 1, characterized in that: The topology optimization includes: based on the initial temperature distribution data and the initial stress distribution data, locally encrypting and refining the mesh in the area where the local temperature of the component is 400°C to 1100°C or the stress is 100MPa to 200MPa, generating refined mesh data, and adjusting the material distribution of the component by rearranging the geometric structure of the component to optimize the heat conduction path and stress distribution of the component. The refinement of the local mesh is dynamically adjusted according to the thermal and stress change amplitude of the component.

3. The thermal-mechanical coupling simulation method for components according to claim 1, characterized in that: Microstructural feedback includes: monitoring the microstructural changes of component materials, including grain reorganization, creep behavior and dislocation movement of component materials, generating microstructural feedback data by analyzing the phase change rate and grain boundary evolution of grains inside the component materials, and the microstructural feedback data is used to adjust the geometric shape of the component materials and the topological structure evolution process of the component.

4. The thermal-mechanical coupling simulation method for components according to claim 1, characterized in that: The phase change micro-simulation is performed based on high-dimensional material response data, and the simulation time step of the component is adjusted by the following formula: in, is the simulation time step; is the time step adjustment factor, which represents the sensitivity adjustment coefficient of the time step; is the grain recombination rate, which indicates the rate of change of the microstructure during the grain recombination process of the material; is the stress change rate, which indicates the rate at which the stress of the material changes during the simulation.

5. The thermal-mechanical coupling simulation method for components according to claim 1, characterized in that: The high-dimensional material response data includes the thermal expansion coefficient, phase change critical temperature, elastic modulus and creep rate of the component material. The high-dimensional material response data is used to adjust the grain phase change rate of the component material in the microscopic phase change simulation of the component, and to adjust the boundary conditions and component material properties of the component simulation model in real time by accurately capturing the thermal behavior of the component material under different temperature and stress conditions.

6. The thermal-mechanical coupling simulation method for components according to claim 1, characterized in that: The fluid-solid coupling simulation is performed based on the microscopic phase change data of the component. The fluid-solid coupling simulation simulates the heat transfer process between the cooling fluid and the component material, adjusts the flow rate and flow path of the cooling fluid, and generates fluid-solid coupling simulation data. The fluid-solid coupling simulation data is used to optimize the flow path of the cooling fluid and the temperature distribution inside the component material, thereby reducing local overheating inside the component material.

7. The thermal-mechanical coupling simulation method for components according to claim 6, characterized in that: The multimodal data fusion generates multimodal fusion damage prediction data by fusing fluid-solid coupling simulation data, microstructure feedback data of components, stress distribution data of components and temperature field data of components. The multimodal fusion damage prediction data is used to predict fatigue damage accumulation of component materials under conditions of temperature of 400°C to 1100°C or stress of 100MPa to 200MPa. By analyzing the fatigue life and local stress concentration of component materials, the location where component materials fail is determined, and the data is used to optimize the design of components.

8. The thermal-mechanical coupling simulation method for components according to claim 1, characterized in that: The simulation iterative optimization is performed based on global feedback data. By adjusting the material properties of the components, the simulation time step and the boundary conditions of the components multiple times, it ensures that the thermal and mechanical behaviors of the components during the simulation process can be accurately described. The simulation iterative optimization process includes multiple rounds of simulation calculations until the simulation results reach the preset termination conditions, thereby generating the final component optimization simulation data.

9. The thermal-mechanical coupling simulation method for parts according to claim 8, characterized in that: The simulation iterative optimization optimizes the geometric shape and material properties of components by dynamically adjusting the local mesh density of components in real time and combining the fatigue damage prediction results in the global feedback data, so that the temperature and stress distribution of the components in the simulation results are uniform, and the local overheating and stress concentration of the components are effectively controlled.

10. A system for implementing the thermal-mechanical coupling simulation method for a component according to any one of claims 1 to 9, characterized in that: include: Meshing module, used to perform finite element meshing on components and set the thermal and mechanical properties of component materials; A simulation calculation module is used to apply external heat source and external load conditions, perform preliminary thermal simulation, and generate initial temperature distribution data and initial stress distribution data; Topology optimization module, which is used to perform topology optimization of components based on initial temperature distribution data and initial stress distribution data, and to make linkage feedback adjustments based on the microstructure changes of component materials to generate microstructure feedback data and high-dimensional material response data; Phase change micro-simulation module, which is used to perform phase change micro-simulation of component materials based on high-dimensional material response data, capture the micro-structural changes of grain reorganization and dislocation movement of component materials, and adjust the simulation time step according to the grain phase change speed and stress change rate, and generate micro-phase change data and time step control data; Fluid-solid coupling simulation module, which is used to simulate the fluid-solid coupling between the cooling fluid and the component material based on the microscopic phase change data, simulate the heat exchange process between the cooling fluid and the component material, and generate fluid-solid coupling data; Multimodal fusion module, used to predict fatigue damage of component materials through multimodal data fusion technology and generate multimodal fusion damage prediction data; The global feedback optimization module is used to perform global feedback analysis based on multimodal fusion damage prediction data, optimize simulation parameters, adjust the properties of component materials, simulation time step and external boundary conditions, perform simulation iterative optimization, and generate final optimized simulation data.

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