Ceramic simulation firing design system in virtual environment

Abstract

The invention relates to the technical field of computer-aided engineering, and discloses a ceramic simulation firing design system in a virtual environment, which comprises the following steps that: the system recognizes a key region driven by a gradient or a threshold value through one-time low-cost solution based on a path finding model simplified by a physical model and a material key event threshold value; the system generates a static non-uniform grid based on the key area, and the static grid is used for executing high-cost full-physical field coupling solution; according to the method, dynamic adjustment is replaced by static prediction, the problems of numerical oscillation and convergence failure of dynamic grid solving are avoided, the system constructs the optimal static grid through path finding prediction, and the optimal path finding prediction efficiency is improved. And the prediction is checked and restarted through residual monitoring, so that the calculation efficiency and precision are ensured, and meanwhile, the engineering effectiveness facing a strong nonlinear problem is realized.

Description

TECHNICAL FIELD

[0001] The application relates to a ceramic simulation firing design system in a virtual environment and belongs to the technical field of computer-aided engineering. BACKGROUND

[0002] At present, numerical simulation methods such as finite element simulation are commonly used to virtually design and analyze specific working conditions. This method is used in the research and development stage of high-end components to evaluate product performance and predict potential physical weaknesses, so as to shorten the design cycle and reduce the cost of trial and error. When dealing with simulation tasks involving strong nonlinearity, multi-physical field coupling and long time, such as simulating the ceramic firing or material phase change process, the accuracy of simulation analysis has a direct impact on the reliability of the final result. In engineering practice, in order to ensure that the simulation result can accurately reflect the local physical details, such as high stress gradient, a common practice is to use fine calculation grids to divide the entire area of the model. When this kind of global fine grid is applied to the aforementioned multi-field coupling and long-time simulation tasks, the large number of calculation units will cause the solution time to be greatly prolonged, often reaching several days or weeks, which exceeds the acceptable iteration period of engineering design.

[0003] In order to shorten the calculation time, if a global coarse grid is used, the simulation result will be distorted due to the inability to analyze the key local physical changes, and the simulation result will lose its engineering guidance value. Another type of adaptive technology that dynamically adjusts the grid during the solution process has insufficient reliability of the calculation result when dealing with such strong nonlinearity and multi-field coupling problems, because the solver is prone to numerical oscillation or convergence failure due to real-time changes in the grid topology. Furthermore, existing technologies have limitations in simulation ideas. Some schemes focus on simulating the visualization of manual process flow rather than rigorous numerical analysis of physical processes. For example, a Chinese invention patent with the publication number CN111062149B discloses a virtual ceramic simulation firing design method and system. The core of this scheme is to provide a method for simulating and demonstrating the complete process of ceramic production, including blank pulling, glazing and kiln firing, in a VR virtual environment. The technical focus is to use Gaussian curve change functions and other means to simulate manual operations such as blank pulling and other shape modification in virtual reality, and to virtually practice the program steps of kiln firing. However, this simulation system, which focuses on process flow reproduction and realism, does not involve rigorous numerical solution problems of strong nonlinearity and multi-physical field coupling (such as thermal-chemical coupling) in the firing process, and cannot solve the engineering bottleneck of how to balance calculation accuracy, calculation efficiency and numerical stability in simulation analysis.

[0004] Therefore, how to construct a simulation design system, so that it can obtain the distribution information of the key area in a low-cost way before formally performing the high-cost multi-physical field coupling solution, and generate a static non-uniform grid with encryption optimization in the key area according to the distribution information, so as to ensure the calculation accuracy of the key area and greatly shorten the total calculation period in the subsequent solution, has become a technical problem to be solved by the present application. SUMMARY

[0005] The present application provides a ceramic simulation firing design system in a virtual environment, which mainly aims to solve the problem that in the existing computer-aided engineering simulation, the calculation accuracy, calculation efficiency and numerical stability are difficult to be considered due to the inability to predict the key area before solving.

[0006] To achieve the above-mentioned purpose, the present application provides a ceramic simulation firing design system in a virtual environment, which comprises:

[0007] The path exploration model automatic generation module is used to generate a simplified path exploration model of the physical model based on the original model, and the original model comprises a multi-physical field coupling setting;

[0008] The material key event threshold extraction module is used to extract one or more key physical event thresholds from the material attribute data of the original model;

[0009] The path exploration solving module is used to call a computer-aided engineering solver to execute the path exploration model to generate a path exploration solving result;

[0010] The key area identification module is used to identify a key area based on a composite criterion, and the composite criterion comprises a comparison relationship between a physical state value of the path exploration solving result and a key physical event threshold and a physical change gradient of the path exploration solving result;

[0011] The non-uniform grid generation module is used to generate a static non-uniform grid based on the key area, and the static non-uniform grid comprises a key area and a non-key area;

[0012] The accurate solving module is used to call the computer-aided engineering solver to execute a final simulation calculation by using the static non-uniform grid and the multi-physical field coupling setting;

[0013] The residual monitoring module is used to monitor a solver residual of the non-key area in the static non-uniform grid during the execution of the accurate solving module;

[0014] The safe restart module is used to pause the accurate solving module when the solver residual exceeds a preset failure threshold, instruct the key area identification module and the non-uniform grid generation module to update the static non-uniform grid, and resume the execution.

[0015] Preferably, the comparison relationship on which the key area identification module is based is to determine whether the physical state value of the pathfinding solution enters the preset neighborhood of the key physical event threshold; the composite criterion is: the physical change gradient exceeds the preset gradient threshold or the physical state value enters the preset neighborhood.

[0016] Preferably, the automatic path exploration model generation module is used to generate the path exploration model by decoupling the physical model. The physical model decoupling method is to retain only the heat conduction physical model in the original model and separate the thermal stress physical model and the chemical reaction physical model. The accurate solution module is used to load the multi-physics coupling settings, which include the heat conduction physical model, the thermal stress physical model and the chemical reaction physical model.

[0017] Preferably, the system also includes a geometry pre-analysis module, which is used to analyze the geometry of the original model before the pathfinding solution module is executed, in order to identify geometric singularities. The pathfinding solution module is also used to perform local pre-mesh refinement in the neighborhood of the geometric singularities when generating the initial mesh of the pathfinding model based on the identification results of the geometric singularities.

[0018] Preferably, the pathfinding solution module is also used to call a computer-aided engineering solver to perform a simplified proxy mechanical solution after generating the pathfinding solution results. The temperature field in the pathfinding solution results is used as the load input to obtain the proxy stress field. The composite criterion on which the key area identification module is based also includes the analysis results of the proxy stress field.

[0019] Preferably, the pathfinding solution module is also used to analyze the physical rate of change in the time dimension based on the pathfinding solution results, and generate global time step control data. The precise solution module is also used to load the global time step control data during the final simulation calculation, and adjust its solution time step according to the global time step control data.

[0020] Preferably, the key region identification module is also used to determine whether the key region is an evolutionary key region; if it is an evolutionary key region, the non-uniform mesh generation module is used to generate a mesh library containing multiple static non-uniform meshes in advance before the precise solution module is executed, and each mesh in the mesh library corresponds to the predicted position of the evolutionary key region; the precise solution module is configured to: solve on the static non-uniform mesh, pause the solution when it is detected that the evolutionary key region has moved out of the fine region of the mesh, load the next static non-uniform mesh in the mesh library, interpolate the solution data to the next static non-uniform mesh, and then resume the solution.

[0021] Preferably, the residual monitoring module is used in the computing unit in non-critical areas. Above, obtain the solver residuals. And determine the solver residuals. Does it meet the requirements? The conditions, among which A preset failure threshold is set; the safe restart module is used to perform pause, command, update and resume execution when the conditions are met.

[0022] Preferably, the pause, command, update and resume execution performed by the safe restart module includes: pausing the precise solution module and saving the current full-field solution data; instructing the critical region identification module to update non-critical regions where the solver residual exceeds the preset failure threshold as critical regions; instructing the non-uniform mesh generation module to generate the updated static non-uniform mesh; and resuming the execution of the precise solution module based on the updated static non-uniform mesh and the full-field solution data.

[0023] Preferably, throughout the entire process of the final simulation calculation performed by the accurate solution module, and throughout the entire process of the safe restart module resuming execution of the accurate solution module, the computer-aided engineering solver performs calculations on a static non-uniform mesh with the topology unchanged or on an updated static non-uniform mesh.

[0024] Compared with the prior art, the beneficial effects of the present invention are:

[0025] 1. This invention provides a method for executing simulation tasks. By rapidly solving a pathfinding model with a simplified physical model, the spatiotemporal distribution information of regions with drastic physical changes is obtained. This information is used to guide the generation of static non-uniform meshes, enabling computing resources to be deployed to key regions all at once before the final high-cost multiphysics coupling solution begins. This method transforms the engineering problem in computer-aided engineering, where the solver performs dynamic mesh adaptation during the calculation process, resulting in numerical oscillations or convergence failures, into a static mesh optimization problem before the solution begins. This ensures the computational accuracy in key regions while achieving numerical stability in the solution process.

[0026] 2. This invention integrates the dynamic simulation results obtained from the exploration model, such as the full-time temperature field, with the applied static material constitutive data, such as the phase transition temperature threshold, to make a judgment. This expands the key region identification mechanism from relying solely on the gradient criterion of physical change rate to simultaneously covering the threshold criterion triggered by the absolute state of the material. This synergy of gradient-threshold dual criteria enables the system to use the data from the same exploration simulation to simultaneously identify regions with drastic changes and critical states, avoiding the omission of non-gradient-driven physical phenomena such as phase transitions due to model simplification, and ensuring the completeness of the final static mesh generation.

[0027] 3. This invention also provides a mechanism for deepening exploration information. After the simplified heat conduction exploration model is executed, the system uses the output temperature field results as the load input of a simplified surrogate mechanical model, such as linear thermoelasticity. This allows for the surrogate stress field to be obtained in series at extremely low computational cost. The final key region is the union of thermal and mechanical criteria. This series connection of thermal exploration models solves the bottleneck that thermal gradient analysis alone cannot identify low thermal gradient and high stress regions caused by geometric constraints. This expands the predictive capability of the exploration stage from simple thermal insight to a comprehensive insight into the thermo-mechanical coupling effect. Attached Figure Description

[0028] Fig. 1 This is a schematic diagram of the architecture for accurate pathfinding and closed-loop verification of the system of the present invention;

[0029] Fig. 2 This is a temperature time history curve of a key measuring point in the simulation of this invention;

[0030] Fig. 3 This is a timing diagram for the verification and restart process during the accurate solution phase of this invention. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be described in further detail below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] This invention provides a ceramic simulation firing design system in a virtual environment. Within the field of Computer-Aided Engineering (CAE) technology, this system addresses the technical challenge of simultaneously achieving computational efficiency, accuracy, and numerical robustness in handling highly nonlinear, multiphysics-coupled simulation tasks through a two-stage simulation process reconstruction of exploration and precision, combined with a closed-loop fault-tolerant mechanism of verification and restart. The system architecture logically includes: an automatic exploration model generation module, a material key event threshold extraction module, an exploration solution module, a key region identification module, a non-uniform mesh generation module, a precision solution module, a residual monitoring module, and a safe restart module. The automatic exploration model generation module receives the original model submitted by the user, containing complete geometry and multiphysics coupling settings. In CAE simulation tasks such as ceramic firing, the multiphysics coupling settings of the original model typically include multiple physical models such as heat conduction, thermal stress, and chemical reactions. Direct solution would involve a massive computational load. Therefore, this module automatically generates a simplified exploration model, one specific simplification method being a simplified physical model... Decoupling involves retaining only the heat conduction physical model as the driving force, while temporarily separating the computationally expensive thermal stress and chemical reaction physical models. This allows the module to output a pathfinding model that can be solved quickly and at low cost, providing a foundation for subsequent predictive analysis. The material key event threshold extraction module addresses the challenge of missing key physical phenomena when relying solely on physical change gradients, especially events triggered by the absolute state of the material, such as phase transitions. Before the simulation begins, this module parses and extracts one or more key physical event thresholds from the material property data of the original model, such as the phase transition point of ceramic materials at a specific temperature or the activation temperature threshold of a specific chemical reaction. This module structures these threshold data for use by the key region identification module. The pathfinding solution module calls a computer-aided engineering (CAE) solver to perform a low-cost, fast solution on the pathfinding model generated by the automatic pathfinding model generation module. Taking its heat conduction model as an example, this solution process can quickly obtain the full-field temperature distribution evolution data and pathfinding solution results throughout the entire simulation period.

[0033] The critical region identification module is the core decision-making unit for optimizing the allocation of computing resources. This module receives the pathfinding solution results and critical physical event thresholds, and identifies critical regions based on a composite criterion. In CAE simulation, critical regions include not only areas with drastic physical changes but also areas where the physical state reaches the critical threshold. Therefore, the composite criterion is set to include two parallel conditions: first, the physical change gradient based on the pathfinding solution results, such as determining whether the thermal gradient value exceeds a preset gradient threshold; second, a comparison between the physical state values ​​of the pathfinding solution results and the critical physical event thresholds. Relationships, such as determining whether the temperature value obtained from the pathfinding solution falls within a preset neighborhood of the phase transition temperature threshold, are considered key regions when any region satisfies either the gradient criterion or the threshold criterion. A non-uniform mesh generation module materializes the identified abstract key region information into a computational mesh required for CAE solving. Based on the key region identification results, this module performs mesh partitioning on the geometry of the original model to generate a static non-uniform mesh. In this static non-uniform mesh, the geometric parts identified as key regions are automatically assigned a high-density, fine mesh to ensure... For high-precision calculations, a sparse, coarse mesh is maintained in non-critical areas to conserve computational resources. The precise solution module performs the final, high-cost simulation calculation. This module calls the computer-aided engineering solver, loads the static non-uniform mesh generated in the previous step, and loads the multiphysics coupling settings of the original model. These settings represent the complete physical model stripped from the exploration phase, including, for example, a heat conduction physical model, a thermal stress physical model, and a chemical reaction physical model. The solver performs the final simulation calculation with full physics coupling on this static non-uniform mesh. Throughout the entire execution of the precise solution module, the topology of the static non-uniform mesh remains unchanged, avoiding numerical oscillations and convergence failures that may be caused by dynamic mesh adjustments. The residual monitoring module provides a closed-loop verification mechanism for the predictive assumptions of the exploration model. During the multiphysics coupling calculations performed by the precise solution module, changes in the physical fields may deviate from the simplified model predictions of the exploration phase. To capture this deviation, this module runs in parallel in the background. The monitoring target is not the critical areas, but rather all computational units in the static non-uniform mesh that are divided into non-critical areas, acquiring the solver residuals of these units in real time. Solver residuals are byproducts of CAE solvers during the iteration process, representing the error or imbalance of the calculated solution. Abnormal spikes in the coarse grid region are a direct signal of path prediction failure.

[0034] The safety restart module is used to perform emergency error correction when pathfinding prediction fails, ensuring the engineering effectiveness of the system. This module works in conjunction with the residual monitoring module. When the residual monitoring module detects any calculation unit in a non-critical area... Solver residuals Exceeding the preset failure threshold (Right now When the conditions are met, the system determines that the pathfinding hypothesis has failed. At this time, the module is activated, and the safety restart module executes a deterministic error correction process: immediately pausing the calculation of the accurate solution module and saving the current full-field solution data; then, instructing the critical region identification module to forcibly update the non-critical region that caused the residual to exceed the limit as a critical region; instructing the non-uniform mesh generation module to regenerate the updated static non-uniform mesh based on the updated critical region list; finally, the accurate solution module loads the updated static non-uniform mesh, performs interpolation mapping based on the saved full-field solution data, and resumes execution. During this resumption process, the solver still performs calculations on the updated static non-uniform mesh with the topology unchanged, thus correcting prediction errors while maintaining the numerical stability of the static mesh solution; to ensure the numerical stability of the safety restart module when performing mesh updates, the mesh coordination size and element distortion rate threshold on which it depends are also determined through a calibration procedure; this procedure obtains the average element size of the static non-uniform mesh in the critical region in the non-uniform mesh generation module. Compared with the average cell size in non-critical areas The mesh coordination size is set to ,in, A constant factor greater than 1, ranging from 3 to 5, is used to force the generation of a smooth transition zone between the newly added key region and the original key region. Meanwhile, the control threshold for the element distortion rate is calibrated based on the convergence sensitivity test report of the computer-aided engineering solver on the mesh quality. The highest distortion rate value defined as good or excellent in the solver manual is selected as the upper limit of the mandatory constraint during mesh reconstruction.

[0035] To further improve the accuracy of pathfinding prediction and address the issue of missed detections in low thermal gradient, high stress areas due to geometric constraints, the pathfinding solution module can be enhanced. In this embodiment, after generating the pathfinding solution result (temperature field), the module additionally calls a computer-aided engineering solver to perform a simplified surrogate mechanical solution. This surrogate mechanical solution uses the temperature field from the pathfinding solution result as the load input, for example, performing a linear thermoelastic analysis, thereby obtaining the surrogate stress field with extremely low computational cost. Correspondingly, the composite criterion upon which the critical area identification module is based is further expanded to include the analysis results of the surrogate stress field, i.e., the thermal criterion and... The union of mechanical criteria ensures a comprehensive understanding of thermo-coupling effects. To avoid numerical instability caused by geometric singularities during the exploration phase, the system may also include a geometric pre-analysis module. This module analyzes the geometry of the original model before the exploration solution module executes, for example, by calculating geometric curvature or identifying thin-walled structures to automatically identify geometric singularities. Correspondingly, when the exploration solution module executes the exploration model and generates the initial mesh, it performs local, pre-emptive mesh refinement in the neighborhood of the geometric singularities based on the identification results, thereby ensuring the numerical stability of the exploration solution itself. The curvature radius threshold used by the geometric pre-analysis module to identify geometric singularities... The determination procedure and the element size of the initial mesh used in the pathfinding solution module Related; this procedure will Set as ,in, The scaling factor is less than 1, ranging from 0.1 to 0.5. The physical meaning of this setting is that when the radius of curvature of a geometric feature is much smaller than the initial mesh element size used in the pathfinding solution, that point is considered a geometric singularity that cannot be resolved by the initial mesh. The pathfinding solution module uses this scaling factor to generate the initial mesh for the pathfinding model. Perform preliminary local refinement in the neighborhood of geometric singularities to ensure that the size of the refined local cells is no larger than [the specified value]. This ensures the numerical convergence of the pathfinding solution itself; to maximize the value of the information obtained in the pathfinding phase, the pathfinding solution module can also be used to optimize the time step of the precise solution.

[0036] In this implementation, the pathfinding solution module is also used to analyze the physical change rate in the time dimension, such as the global maximum heating rate, based on the pathfinding solution results, and generate global time step control data accordingly. Correspondingly, during the final simulation calculation, the precise solution module, in addition to loading the static mesh and physical field settings, also loads the global time step control data and dynamically adjusts its solution time step based on this data. That is, a large step size is used during periods of gentle physical change, and a small step size is used during periods of intense change, thereby further improving computational efficiency in the time dimension. To address evolving critical regions such as crack tips or moving phase boundaries, the system can adopt a quasi-static mesh strategy. In this implementation, the critical region identification module is also used to determine whether a critical region is an evolving critical region. If it is determined to be so, the function of the non-uniform mesh generation module will change; it will no longer generate a single mesh, but will instead generate a mesh library containing multiple static non-uniform meshes before the precise solution module executes the calculation. Each grid in the algorithm corresponds to the predicted location of the evolutionary critical region. Accordingly, the execution logic of the precise solution module is modified as follows: The solution is started on a static non-uniform grid (e.g., Mesh_1), while simultaneously monitoring the location of the evolutionary critical region. When it is detected that the region is about to move out of the fine region of Mesh_1, the solution is paused, the next static non-uniform grid (Mesh_2) in the grid library is loaded, the current full-field solution data is mapped to Mesh_2 through interpolation, and the solution is resumed on Mesh_2. This process is repeated to achieve the tracking and solution of dynamic evolution problems while maintaining the numerical stability of the static grid. In an optional implementation, the critical region identification module is also used to determine whether the evolution path of the evolutionary critical region has low-order linear characteristics. If so, the non-uniform grid generation module generates the predicted location on which the grid library is based, determined by a displacement extrapolation procedure. This procedure extracts the evolutionary critical region from the pathfinding solution results at two or more consecutive historical time points. and Geometric centroid position on and Based on this, the instantaneous velocity vector is calculated. The predicted location corresponding to the next grid cell in the grid library. That is, by Given, among which To ensure that the estimated time interval matches the global time step control data loaded by the fixed and accurate solution module, the system iteratively generates a network G library covering the entire prediction time domain according to this procedure.

[0037] Example 1: This example demonstrates the application of the technical solution in a specific Computer-Aided Engineering (CAE) simulation task. The goal of this task is to perform virtual firing simulation of a special ceramic component with complex geometric features. This component includes a 0.5mm thin-walled heat dissipation fin, a 50mm thick-walled base, and an acute-angle transition zone between the two. The simulation aims to predict the stress concentration and final residual stress distribution in the thin-walled-thick-walled transition zone during a 48-hour thermo-chemical multiphysics coupled firing process. Using traditional CAE methods, if a global fine mesh of 0.1mm elements is used to analyze the thin-walled region, the total computational load will result in a simulation cycle of several weeks. On the other hand, using a global coarse mesh cannot capture the critical stress gradient, leading to distorted results. After the system of this invention is started, the pathfinder model automatic generation module receives the original model and generates a pathfinder model that retains only the thermal conductivity physical model. Simultaneously, the material critical event threshold extraction module extracts the phase transition temperature of the ceramic material from the material library, which is 1150°C. The pathfinding solution module performs a fast solution on the pathfinding model, obtaining the temperature field results in the entire time domain. The key region identification module analyzes these results based on a composite criterion. On the one hand, through thermal gradient analysis, it identifies the thin-walled and acute-angle transition zones with drastic geometric changes as gradient-driven key regions. On the other hand, it compares the temperature field with 1150... By comparing thresholds, the center of a thick-walled base with a gentle gradient but where the temperature is about to reach the phase transition point was identified as the key region driven by the threshold.

[0038] This synergistic gradient-threshold dual criterion enables the system to gain a complete understanding of key regions driven by both geometric and material constitutive factors. Based on this comprehensive list of key regions, the non-uniform mesh generation module generates a static non-uniform mesh. This mesh is only highly densely refined in approximately 8% of the identified thin-walled transition zone and thick-walled center, while remaining coarse in the remaining 92% of non-critical regions. The precise solution module loads this static non-uniform mesh and the original multiphysics coupling settings (thermochemical full coupling) to begin calculations. Because computational resources are deployed where they are truly needed, the simulation task achieves significantly improved computational efficiency while ensuring the analytical accuracy of all key physical events. At the 18th hour of operation, a localized strong exothermic chemical reaction caused by material impurities, which was unpredictable by the simplified exploration model, is activated in a coarse mesh region classified as a non-critical area. At this point, the residual monitoring module directionally monitors the solver residual in this non-critical region. The value increased rapidly, quickly exceeding the preset failure threshold. The safety restart module determines that the pathfinding hypothesis has failed and has been triggered, immediately suspends the solution, updates the region where the residual exceeds the limit to the critical region, and instructs the non-uniform mesh generation module to generate an updated static non-uniform mesh. The system loads the updated static non-uniform mesh and the saved full-field solution data, and resumes the precise solution from the breakpoint at the 18th hour. This prediction-verification-restart closed-loop working mode enables the system to automatically correct the prediction deviation of the pathfinding model when facing unexpected strong nonlinear events during the simulation process.

[0039] Example 2: This example objectively verifies the technical advantages of the proposed pathfinding-precision simulation design system in the field of computer-aided engineering (CAE) compared to existing technologies in terms of both computational efficiency and simulation accuracy. The experiment uses a special ceramic component with the same geometric model as described in Example 1 as the simulation object. This component has a 0.5mm thin wall, a 50mm thick base, and an acute-angle transition zone between them. The phase transition temperature of its material is 1150°C. The simulation task was set to simulate the thermo-chemical multiphysics coupled firing process of the component for 48 hours. The evaluation indicators were to obtain the peak stress in the key areas (based on the maximum stress values ​​at the transition zone between thin and thick walls and the center of the thick-walled base) and the total calculation time. The experiment was performed on a CAE workstation with hardware specifications including a 32-core CPU and 128GB of memory, running a standard finite element analysis (FEA) solver with thermo-chemical fully coupled solution capabilities. For objective comparison, four experimental groups were set up, all using the same CAE workstation configuration and simulation task settings: Control group A (global fine mesh): The global fine mesh method in the existing technology was used to apply a fine element size of 0.1mm to the entire area of ​​the model. The simulation results of this group were regarded as the benchmark value for subsequent accuracy comparison. Control group B (global coarse mesh): The global coarsening method in the existing technology was used. The model was subjected to a coarse element size of 2.0 mm across the entire region. Control group C (partially missing control group): This group simulated a simplified scheme with only geometric optimization capabilities. It only performed the geometric pre-analysis module function as described in the specific implementation, that is, it only refined the mesh in the geometrically acute transition area, but did not perform the pathfinding solution based on the physical field (thermal conduction). Therefore, it could not identify the key area of ​​the thick-walled base center driven by material phase change (threshold criterion). The present invention sample group: It adopted the complete pathfinding-precision design system of the present invention. This system performs automatic generation of the pathfinding model, extraction of material key event thresholds, pathfinding solution, and generates a static non-uniform mesh based on the gradient-threshold dual criterion. It then performed the precision solution module. After each group performed the simulation task, the total number of mesh elements, the total computation time, and the peak stress monitored in the two key areas (transition area and thick-walled center) were statistically analyzed. The results are summarized in Table 1.

[0040] Table 1: Comparison of Simulation Performance of Each Experimental Group

[0041]

[0042] The data analysis is as follows: Control group A, serving as the baseline, took 71.3 hours to compute, exceeding the engineering design cycle, but provided reliable stress baseline values ​​of 352.4 MPa (transition zone) and 281.7 MPa (thick-walled center). Control group B, although taking only 2.1 hours, suffered from severe distortion in its peak stress predictions (81.7 MPa / 55.9 MPa) in two key regions due to its inability to resolve physical details, resulting in deviations exceeding 75% from the baseline values ​​and rendering the simulation results invalid. Control group C demonstrated the limitations of the partially missing solution. It only refined the geometric sharp angles, capturing the stress in the transition zone (348.9 MPa, close to the baseline value). However, due to the lack of a physics-based pathfinding module, it failed to identify the thick-walled center driven by material phase transformation (threshold criterion) as a key region, leading to an overly coarse mesh in this region and a severely distorted stress prediction of 60.3 MPa. Distortion; This set of data objectively demonstrates that no single simplified scheme relying solely on geometric analysis or physical analysis can yield correct results; The total number of mesh elements in the sample group of this invention (4.653 million) is far lower than that of control group A (51.205 million), reducing its total computation time to 11.8 hours, saving approximately 83.4% of the time compared to the baseline value A, thus meeting the needs of engineering iteration; At the same time, due to the synergistic effect of its gradient-threshold dual criteria, the system not only identifies the transition zone (gradient-driven) but also the thick-walled center (threshold-driven), and the deviation of the peak stress prediction (349.6MPa / 278.5MPa) of the two regions from the baseline value A is within 1%; Experimental data show that the system of this invention achieves the same simulation accuracy as the global fine mesh method (control group A) with a computational cost far lower than that of the prior art, and avoids the risk of accuracy failure of other simplified methods (control groups B and C).

[0043] Example 3: This example combines Figs. 1 to 3 This describes a ceramic simulation firing design system in a virtual environment, such as... Fig. 1 As shown, the original model is input into the path exploration model automatic generation module to generate a simplified path exploration model. The path exploration solution module executes this model to generate path exploration solution results. Simultaneously, the material critical event threshold extraction module extracts critical physical event thresholds from material properties. These two inputs are sent to the critical region identification module, which identifies critical regions based on a composite criterion of gradient and threshold. The non-uniform mesh generation module generates a static non-uniform mesh based on the critical region. This mesh is loaded by the accurate solution module to perform a high-cost full-physics coupled solution, ultimately outputting the simulation calculation results. During the accurate solution execution process, the residual monitoring module monitors the solver residuals in non-critical regions in parallel, determining whether the residuals exceed the limits. If the predicted failure residual exceeds the limit, the safe restart module is activated. This module performs pause, mesh update and resumption, and sends an instruction to the critical area identification module to update, thus forming a closed loop of verification-restart.

[0044] like Fig. 2 As shown, the graph uses time (in hours) as the horizontal axis and temperature as the vertical axis. The vertical axis displays the temperature changes at three different measuring points, including: the thin-walled region exhibits the fastest heating rate and the highest peak temperature, as well as the fastest cooling rate; the thick-walled center has the slowest heating and cooling rates, and the lowest peak temperature; the temperature curve of the transition zone falls between the former two. This data can be used in the key area identification module for gradient analysis or threshold comparison; for example... Fig. 3 As shown in the figure, the timing interaction between five logical units—static non-uniform mesh, precise solution module, multiphysics solver, residual monitoring module, and safe restart module—is clearly illustrated. The process begins with the precise solution module loading the static non-uniform mesh and starting the multiphysics solver to perform a fully coupled calculation of heat conduction, thermal stress, and chemical reaction. Simultaneously, the residual monitoring module is started in parallel. During the solution iteration, the multiphysics solver calculates on the current mesh and outputs the residuals of non-critical regions to the residual monitoring module. The residual monitoring module checks the residuals. If the residuals are normal, it instructs the multiphysics solver to continue solving. If the residuals exceed the failure threshold, a safe restart is triggered. At this point, the safe restart module is activated, instructing the precise solution module to pause the solution and save the current data. Then, the restart process is started to update the mesh until the multiphysics solver finally completes the solution, and the precise solution module outputs the final result.

[0045] Example 4: This example illustrates the systematic calibration procedure for multiple key thresholds in the critical region identification module and residual monitoring module of the present invention's design system. This procedure runs before the system executes simulation tasks to eliminate reliance on the experience of engineers and ensure the reliability of identification and monitoring. For the gradient threshold in the critical region identification module, the system executes a pathfinding solution module to obtain the pathfinding solution result, taking the thermal gradient field as an example. The system then performs statistical analysis on the global values ​​of the thermal gradient field to generate a probability distribution histogram of the gradient values. The gradient threshold is deterministically set to a specific statistical quantile of this distribution, specifically the 90th percentile (P90). Thus, all regions in the top 10% of the thermal gradient are automatically identified as gradient-driven critical regions. For the preset neighborhood in the threshold criterion, the calibration is related to the phase transition dynamics of the material. For known phase transition temperatures... (e.g., 1150) The preset neighborhood is set to the time step of the pathfinding model. The relevant temperature window is set to ,in, The coefficient is based on the material's thermal conductivity to ensure that the system has enough time steps to resolve the phase transition process when the physical state enters this window.

[0046] Preset failure threshold in residual monitoring module The calibration adopts a baseline failure simulation procedure. This procedure loads a known representative CAE model, and then the system artificially applies a coarse mesh to a non-critical region within the known high-stress region (which should be the critical region) of the model, thereby constructing a baseline failure case that will inevitably produce high errors. The system executes the accurate solver module to run the baseline failure case and instructs the residual monitoring module to measure and record the peak value of the solver residuals generated in the artificially coarse mesh region. Preset failure threshold This is then definitively set as the reduction value of the peak value, specifically the set value. By executing the above calibration procedure, the gradient threshold, preset neighborhood, and preset failure threshold in the system of the present invention are determined. All of them have been transformed from experience-based preset values ​​to repeatable calibrated engineering parameters.

[0047] Example 5: This example further illustrates the specific execution procedure of the safety restart module after it is triggered at the 18th hour as in Example 1, for generating an updated static non-uniform mesh. This procedure aims to ensure the numerical stability of the solution after restarting. When the residual monitoring module determines the solver residual of a non-critical region... Exceeding the preset failure threshold Afterwards, the safe restart module marks the area as a newly added critical region; the non-uniform mesh generation module is called again, and at this time, a specific algorithm for mesh fusion and transition is executed: this module obtains the original list of critical regions and the list of newly added critical regions, and calculates the spatial union of the two; then, the system does not simply densify the newly added critical regions, but automatically analyzes their spatial proximity between the new and old critical regions. If the distance between the two is less than a certain preset mesh coordination size, the system will automatically generate a transition mesh densification zone between them to ensure a smooth connection between the fine meshes; finally, the mesh generator re-subdivides the static non-uniform mesh topology based on this merged transition mesh densification zone. This process focuses on the mesh quality control of the transition region, such as controlling the distortion rate of the control unit to be less than 0.8, to prevent new numerical instabilities caused by sudden changes in mesh quality during the restart solution.

[0048] The reliability of this procedure depends on the engineering trade-offs of the calibration parameters; among them, the preset failure threshold... Set as The threshold is the balance point between monitoring sensitivity and system stability. This value ensures that the system can be triggered when physical mutations are still in their infancy (the residuals have just begun to grow abnormally, but are far from reaching the level that will cause the solution to diverge). This provides sufficient response time for the above-mentioned mesh reconstruction procedure, while avoiding frequent triggering of the system by conventional numerical noise due to an excessively low threshold, thus ensuring the overall stability of the accurate solution stage. The initial gradient threshold uses the P90 percentile (a relatively conservative setting), which has pre-classified most potential physical change areas into the critical region, making the safe restart itself a fault-tolerant mechanism for dealing with low-probability extreme unforeseen events, rather than a routine operation.

[0049] Example 6: This example illustrates the specific working procedure of the system designed in this invention when handling the simulation task of a ceramic turbine blade with an internal cooling channel. This task involves both geometric singularities and thermo-mechanical coupling effects. The geometric features of this blade model include multiple sharp corners with a curvature radius of less than 0.1 mm at the root of the cooling channel. At the same time, its thick central hub is subjected to strong mechanical constraints from the outer cold end structure during the heating process. Before starting the automatic generation module of the exploration model, the system calls the geometric pre-analysis module. This module performs a computational geometric scan on the geometry of the original model, automatically identifying and marking all regions with a curvature radius of less than 0.2 mm as geometric singularities. When the exploration solution module generates the initial mesh for the simplified exploration model, i.e., the pure heat conduction model, based on the identification results of these geometric singularities, it maintains a coarse mesh globally (element size 3.0 mm) while performing local pre-mesh refinement only in the neighborhood of the marked sharp corners.

[0050] The pathfinding module quickly completes the pure heat conduction calculation, obtaining the pathfinding solution result, i.e., the temperature field, in the full time domain. At this point, the system activates a proxy mechanics solver in series. This solver uses the temperature field from the pathfinding solution result as the load input and performs a linear thermoelastic analysis with low computational cost to quickly calculate the proxy stress field. The result of this proxy stress field shows that in the central hub region where the thermal gradient is gentle, there is a stress concentration exceeding 150 MPa caused by mechanical constraints. This region is not shown in the gradient field of the pure heat conduction analysis. Finally, the composite criterion used by the key region identification module is expanded. The identified key region is the union of the gradient criterion (from the heat conduction analysis, covering the cooling channels), the threshold criterion (from the material properties), and the proxy stress criterion (from the proxy mechanics analysis, covering the central hub). Based on this union result, the non-uniform mesh generation module generates a final static non-uniform mesh that simultaneously covers geometric singularities, material phase transition regions, and mechanical constraint stress regions. This mesh is delivered to the accurate solution module to perform the final simulation calculation containing complete multiphysics coupling settings.

[0051] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0052] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A ceramic simulation firing design system in a virtual environment, characterized by, The system comprises: a pathfinding model automatic generation module configured to generate a simplified pathfinding model of a physical model based on an original model, the original model comprising a multi-physics coupling setting; a material key event threshold extraction module configured to extract one or more key physical event thresholds from material attribute data of the original model; a pathfinding solution module configured to call a computer-aided engineering solver to execute the pathfinding model to generate a pathfinding solution result; a key region identification module configured to identify a key region based on a composite criterion, the composite criterion comprising a comparison relationship between a physical change gradient of the pathfinding solution result and a physical state value of the pathfinding solution result and the key physical event threshold; a non-uniform mesh generation module configured to generate a static non-uniform mesh based on the key region, the static non-uniform mesh comprising the key region and a non-key region; a precise solution module configured to call the computer-aided engineering solver to perform a final simulation calculation using the static non-uniform mesh and the multi-physics coupling setting; a residual error monitoring module configured to monitor a solver residual error of the non-key region in the static non-uniform mesh during execution of the precise solution module; and a safe restart module configured to pause the precise solution module when the solver residual error exceeds a preset failure threshold, instruct the key region identification module and the non-uniform mesh generation module to update the static non-uniform mesh, and resume execution.

2. The ceramic simulation firing design system in a virtual environment according to claim 1, wherein, The comparison relationship on which the key region identification module is based is a judgment of whether the physical state value of the pathfinding solution result enters a preset neighborhood of the key physical event threshold; and the composite criterion is that the physical change gradient exceeds a preset gradient threshold or the physical state value enters the preset neighborhood.

3. The ceramic simulation firing design system in a virtual environment according to claim 1, wherein, The pathfinding model automatic generation module is configured to generate the pathfinding model in a manner of decoupling the physical model, the manner of decoupling the physical model being to retain only a heat conduction physical model in the original model and strip a thermal stress physical model and a chemical reaction physical model; and the precise solution module is configured to load a multi-physics coupling setting, the multi-physics coupling setting comprising the heat conduction physical model, the thermal stress physical model, and the chemical reaction physical model.

4. The ceramic simulation firing design system in a virtual environment according to claim 1, wherein, The system further comprises a geometry pre-analysis module configured to analyze a geometry of the original model before execution of the pathfinding solution module to identify a geometry singularity point, and the pathfinding solution module is further configured to, when executing the pathfinding model, perform local pre-encryption of a mesh in a neighborhood of the geometry singularity point when generating an initial mesh of the pathfinding model according to the identification result of the geometry singularity point.

5. The ceramic simulation firing design system in a virtual environment according to claim 1, wherein, The pathfinding solution module is further configured to, after generating the pathfinding solution result, call the computer-aided engineering solver to execute a simplified agent mechanics solution once to obtain an agent stress field by taking a temperature field in the pathfinding solution result as a load input; and the composite criterion on which the key region identification module is based further comprises an analysis result of the agent stress field.

6. The ceramic simulation firing design system in a virtual environment according to claim 1, wherein, The pathfinding solution module is further configured to analyze a physical change rate of the pathfinding solution result in a time dimension to generate global time step control data; and the precise solution module is further configured to load the global time step control data during execution of the final simulation calculation and adjust a solution time step thereof according to the global time step control data.

7. The system of claim 1, wherein the system is configured to: receive a user input of a desired firing schedule; and display a simulated firing of the ceramic object based on the desired firing schedule. The key region identification module is further configured to determine whether the key region is an evolving key region; if the key region is an evolving key region, the non-uniform mesh generation module is configured to generate a mesh library containing a plurality of static non-uniform meshes in advance before the precise solving module is executed, each mesh in the mesh library corresponding to a predicted position of the evolving key region; the precise solving module is configured to solve on the static non-uniform mesh, pause the solving when it is monitored that the evolving key region moves out of the fine area of the mesh, load the next static non-uniform mesh in the mesh library, and resume the solving after the solution data is interpolated to the next static non-uniform mesh.

8. The ceramic simulation firing design system in a virtual environment according to claim 1, wherein, The residual monitoring module is used in the computing units of non-critical areas. Above, obtain the solver residuals. And determine the solver residuals. Does it meet the requirements? The conditions, among which The preset failure threshold; The safety restart module is configured to execute the pausing, the instruction, the updating, and the resuming execution when the condition is met.

9. The ceramic simulation firing design system in a virtual environment according to claim 1, wherein, The pausing, the instruction, the updating, and the resuming execution executed by the safety restart module include pausing the precise solving module and saving the full-field solution data at the current time, instructing the key region identification module to update a non-key region with a solver residual exceeding a preset failure threshold to a key region, instructing the non-uniform mesh generation module to generate an updated static non-uniform mesh, and resuming the execution of the precise solving module based on the updated static non-uniform mesh and the full-field solution data.

10. The ceramic simulation firing design system in a virtual environment according to claim 1, wherein, In the whole process of executing the final simulation calculation of the precise solving module and in the whole process of resuming the execution of the precise solving module by the safety restart module, the computer-aided engineering solver performs calculation on the static non-uniform mesh or the updated static non-uniform mesh with an unchanged topological structure.

Images