Tokamak main machine assembly risk assessment method, device, platform and medium

Abstract

The application discloses a tokamak main machine assembly risk assessment method, device, platform and medium, wherein, based on the tokamak main machine assembly risk assessment method of the embodiment of the application, the corresponding finite element model is established according to the different working conditions of the tokamak main machine in the assembly process, and the multi-source uncertainty parameter description variable is established according to the multi-source uncertainty parameters in the assembly process, then the random sampling of the uncertainty parameters is carried out based on the Monte Carlo method, a plurality of groups of input variable samples are generated, and the finite element solution is executed for each sample to obtain the equivalent stress field and the safety factor corresponding to each sample, then based on the calculated equivalent stress field and safety factor, the structural failure probability and the risk degree of the assembly process are quantitatively evaluated, and the visual risk assessment report is generated. Therefore, the objective and quantitative evaluation of the complex tokamak main machine assembly process risk is realized, and the assembly safety and reliability prediction ability are improved.

Description

Technical Field

[0001] This invention relates to the field of structural safety simulation and assembly risk assessment technology for nuclear fusion devices, and particularly to an assembly risk assessment method for a tokamak main unit, a computer-readable storage medium, an assembly risk assessment device for a tokamak main unit, and an assembly risk assessment platform for a tokamak main unit. Background Technology

[0002] As research into controlled nuclear fusion deepens, the size and structural complexity of the tokamak main unit, as the core equipment for achieving magnetic confinement fusion, have increased significantly. The main unit system includes large and precision components such as vacuum chambers, superconducting magnets, support structures, and cold shielding systems. These components require multiple hoisting, positioning, docking, and pre-tightening operations during the assembly process. Any slight assembly deviation or structural abnormality may lead to stress concentration, permanent deformation, or even functional failure.

[0003] Currently, structural safety assessments during the assembly process of tokamak mainframes primarily rely on finite element simulation analysis. The finite element method can be used to calculate stress, strain, and displacement distributions under specific assembly conditions, thereby determining the structural safety margin. However, traditional finite element simulation is a deterministic analysis, only reflecting results under "nominal conditions," and cannot effectively account for the influence of multi-source uncertainties such as assembly errors, material property fluctuations, and uneven load application, leading to discrepancies between the assessment results and actual conditions. Summary of the Invention

[0004] This invention aims to at least partially solve one of the technical problems in related technologies. Therefore, the first objective of this invention is to propose an assembly risk assessment method for a tokamak main unit, combining finite element simulation results with Monte Carlo stochastic analysis to achieve quantitative, visual, and sortable analysis of risks in key components, thereby providing a scientific basis for safety control and process optimization during assembly.

[0005] A second objective of this invention is to provide a computer-readable storage medium.

[0006] The third objective of this invention is to provide an assembly risk assessment device for a tokamak main unit.

[0007] The fourth objective of this invention is to provide an assembly risk assessment platform for a tokamak main unit.

[0008] To achieve the above objectives, the assembly risk assessment method for a tokamak main unit proposed in the first aspect of the present invention includes: establishing corresponding finite element models based on different operating conditions of the tokamak main unit during the assembly process, wherein the finite element model corresponding to each operating condition includes a geometric model, material parameters, boundary conditions, and load conditions; establishing multi-source uncertainty parameter descriptive variables based on the multi-source uncertainty parameters during the assembly process, wherein the multi-source uncertainty parameters include geometric deviation, load fluctuation, material property dispersion, and contact characteristics; randomly sampling the multi-source uncertainty parameters based on the Monte Carlo method to generate N sets of multi-source uncertainty parameter descriptive variable samples; performing finite element solution on each set of multi-source uncertainty parameter descriptive variable samples to obtain the equivalent stress field and safety factor under each set of multi-source uncertainty parameter descriptive variable samples; obtaining the structural failure probability and risk level during the assembly process based on the equivalent stress field and the safety factor, and generating a visualization report of the assembly risk assessment of the tokamak main unit based on the structural failure probability and the risk level.

[0009] The assembly risk assessment method for a tokamak main unit according to an embodiment of the present invention first obtains the stress and safety factor distribution of the structure under various typical assembly conditions through finite element simulation. Then, the Monte Carlo random sampling method is introduced to transform factors such as assembly deviations, material property fluctuations, and load uncertainties into statistical variables. The impact of these factors on structural safety is repeatedly sampled and calculated to obtain the failure probability and risk distribution, thereby achieving the quantification, classification, and visualization of structural risks. Thus, by combining finite element simulation results with Monte Carlo random analysis, the risks of key components can be quantified, visualized, and ranked, providing a scientific basis for safety control and process optimization during assembly.

[0010] In addition, the assembly risk assessment method for the tokamak main unit according to the above embodiments of the present invention may also have the following additional technical features: According to one embodiment of the present invention, the geometric deviation includes the lifting point spatial error, the load fluctuation includes the clamp preload and the lifting amplification factor, the material property dispersion includes the material elastic modulus and the material yield strength, and the contact characteristics include the coefficient of friction and the support stiffness.

[0011] According to an embodiment of the present invention, obtaining the equivalent stress field and safety factor under each set of multi-source uncertainty parameter descriptor variable samples includes: importing each set of multi-source uncertainty parameter descriptor variable samples into the finite element model of the corresponding working condition, updating the corresponding parameters in the finite element model of the corresponding working condition; performing finite element solution on the updated finite element model to obtain the equivalent stress field under each set of multi-source uncertainty parameter descriptor variable samples; and obtaining the safety factor under each set of multi-source uncertainty parameter descriptor variable samples based on the equivalent stress field and material required stress under each set of multi-source uncertainty parameter descriptor variable samples.

[0012] According to an embodiment of the present invention, obtaining the structural failure probability and risk level during the assembly process includes: obtaining the structural failure probability and structural failure severity level during the assembly process based on the safety factor and preset safety factor threshold under each group of multi-source uncertainty parameter description variable samples; and obtaining the risk level during the assembly process based on the structural failure probability and the structural failure severity level.

[0013] According to one embodiment of the present invention, generating the assembly risk assessment visualization report of the tokamak main unit includes: statistically analyzing the probability of stress exceeding limits for each node or element of the finite element model under the corresponding working condition; obtaining the risk value of each node or element based on the probability of stress exceeding limits and the severity level of structural failure; and visually outputting the risk value in the form of a hot zone to generate a tokamak main unit assembly structure risk hot zone map in the assembly risk assessment visualization report of the tokamak main unit.

[0014] According to one embodiment of the present invention, the method further includes: establishing a multi-source uncertainty parameter template file, and using a script and the multi-source uncertainty parameter template file to drive automated uncertainty input, batch finite element analysis and risk calculation, thereby realizing automated assessment of the assembly risk of the tokamak main unit.

[0015] According to one embodiment of the present invention, the method further includes: performing structural optimization and / or process optimization on the assembly process of the tokamak main unit based on the assembly risk assessment visualization report of the tokamak main unit.

[0016] To achieve the above objectives, a computer-readable storage medium is provided in the second aspect of the present invention, which stores an assembly risk assessment program for a tokamak host. When the assembly risk assessment program for the tokamak host is executed by a processor, it implements the assembly risk assessment method for the tokamak host described in the embodiments of the present invention.

[0017] According to embodiments of the present invention, a computer-readable storage medium can combine finite element simulation results with Monte Carlo stochastic analysis by executing an assembly risk assessment program for a tokamak main unit stored thereon. This enables quantitative, visual, and sortable analysis of risks in key components, thereby providing a scientific basis for safety control and process optimization during assembly.

[0018] To achieve the above objectives, the assembly risk assessment device for a tokamak main unit proposed in the third aspect of the present invention includes: a model building module, used to build corresponding finite element models according to different working conditions of the tokamak main unit during the assembly process, wherein the finite element model corresponding to each working condition includes a geometric model, material parameters, boundary conditions, and load conditions; a parameter building module, used to build multi-source uncertainty parameter descriptive variables according to the multi-source uncertainty parameters in the assembly process, wherein the multi-source uncertainty parameters include geometric deviation, load fluctuation, material property dispersion, and contact characteristics; a Monte Carlo sampling module, used to randomly sample the multi-source uncertainty parameters based on the Monte Carlo method to generate N sets of multi-source uncertainty parameter descriptive variable samples; a finite element solution module, used to perform finite element solution on each set of multi-source uncertainty parameter descriptive variable samples to obtain the equivalent stress field and safety factor under each set of multi-source uncertainty parameter descriptive variable samples; and a risk assessment module, used to obtain the structural failure probability and risk level in the assembly process according to the equivalent stress field and the safety factor, and generate a visualization report of the assembly risk assessment of the tokamak main unit according to the structural failure probability and the risk level.

[0019] The assembly risk assessment device for a tokamak main unit according to an embodiment of the present invention first obtains the stress and safety factor distribution of the structure under various typical assembly conditions through finite element simulation. Then, it introduces the Monte Carlo random sampling method to transform factors such as assembly deviations, material property fluctuations, and load uncertainties into statistical variables, repeatedly sampling and calculating their impact on structural safety to obtain the failure probability and risk distribution, thereby achieving the quantification, classification, and visualization of structural risks. Thus, by combining finite element simulation results with Monte Carlo random analysis, it achieves the quantification, visualization, and ranking analysis of risks in key components, providing a scientific basis for safety control and process optimization during assembly.

[0020] To achieve the above objectives, the assembly risk assessment platform for a tokamak main unit proposed in the fourth aspect of the present invention includes the assembly risk assessment device for the tokamak main unit described in the above embodiment of the present invention.

[0021] The assembly risk assessment platform for the tokamak main unit according to an embodiment of the present invention, by adopting the aforementioned assembly risk assessment device for the tokamak main unit, can combine finite element simulation results with Monte Carlo stochastic analysis to achieve quantitative, visual, and sortable analysis of risks in key parts, thereby providing a scientific basis for safety control and process optimization in the assembly process.

[0022] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating the assembly risk assessment method for a tokamak main unit according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating a method for assessing the assembly risk of a tokamak main unit according to an embodiment of the present invention. Figure 3 This is a flowchart illustrating a method for assessing the assembly risk of a tokamak main unit according to another embodiment of the present invention; Figure 4 This is a block diagram of an assembly risk assessment device for a tokamak main unit according to an embodiment of the present invention. Figure 5 This is a block diagram of an assembly risk assessment platform for a tokamak main unit according to an embodiment of the present invention. Detailed Implementation

[0024] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0025] The following description, with reference to the accompanying drawings, describes an assembly risk assessment method, a computer-readable storage medium, an assembly risk assessment device, and an assembly risk assessment platform for a tokamak host according to embodiments of the present invention.

[0026] Figure 1 This is a flowchart illustrating the assembly risk assessment method for a tokamak main unit according to an embodiment of the present invention.

[0027] Specifically, in some embodiments of the present invention, such as Figure 1 As shown, the assembly risk assessment method for the tokamak main unit includes: S101, establish corresponding finite element models according to different working conditions of the tokamak main unit during the assembly process. The finite element model corresponding to each working condition includes geometric model, material parameters, boundary conditions and load conditions.

[0028] It is understood that, in this embodiment of the present invention, a corresponding finite element model is established based on different working conditions of the tokamak main unit during the assembly process (e.g., hoisting, flipping, assembling, supporting and welding positioning, etc.) to form a working condition library. The finite element model corresponding to each working condition includes: 1) a geometric model (e.g., a three-dimensional structure such as a vacuum chamber, magnet, hoisting fixture, etc.); and 2) material parameters (e.g., elastic modulus). and yield strength 3) Boundary conditions (e.g., lifting point constraints and support point stiffness); 4) Load conditions (e.g., self-weight, lifting acceleration and preload).

[0029] Optionally, in the above embodiments of the present invention, the equivalent stress field for each working condition is also calculated based on the simulation analysis results. : ; in, , , Principal stress, in MPa; , , This represents the shear stress component, with units of MPa.

[0030] Then, calculate the safety factor for each working condition: ; in, This refers to the required stress of the material, expressed in MPa. It can be determined by referring to the requirements in ASME BPVC.VIII.2-2015 "Another Rule for the Construction of Pressure Vessels" or GB_T4732.4-2024 "Analysis and Design of Pressure Vessels - Part 4: Stress Classification Methods" and using the yield strength. Obtained through calculation.

[0031] S102. Based on the multi-source uncertainty parameters in the assembly process, establish multi-source uncertainty parameter descriptive variables, including geometric deviation, load fluctuation, material property dispersion, and contact characteristics.

[0032] Furthermore, in some embodiments of the present invention, geometric deviations include lifting point spatial errors, load fluctuations include clamp preload and lifting amplification factor, material property dispersions include material elastic modulus and material yield strength, and contact characteristics include friction coefficient and support stiffness.

[0033] Specifically, in this embodiment of the invention, random errors and fluctuations in the assembly process are parameterized to form multi-source uncertainty parameter description variables: ; The meanings of each variable are shown in Table 1 below: (The specific parameters of the variables can be determined through on-site measurement data or statistical analysis based on engineering experience): Table 1. Schematic diagram of descriptive variables for multi-source uncertainty parameters

[0034] Based on the table above, all major uncertainties in the assembly process (geometric type) Assembly position error; load type External forces and dynamic load fluctuations; materials Differences in material properties; contact types Explanation of tooling support and friction changes: 1) Position deviation of lifting point (mm) Physical meaning: The three-dimensional deviation of the lifting device or lifting point from its designed position during actual assembly, including errors in the horizontal direction (x, y) and the vertical direction (z); Typical range: ±1~2mm (normal distribution); Explanation of the cause: It is caused by measurement errors, deviations in the welding position of the lifting lugs, and changes in the elongation of the rigging, etc. Engineering impact: It can cause the overall structure to tilt or the local stress to be uneven, resulting in stress concentration.

[0035] 2) Clamp preload (KN) Physical meaning: In clamps, jigs, or temporary supports, the preload or locking force applied to ensure the positioning of components; Typical range: 100–130 kN, with fluctuations of approximately ±5%; Distribution suggestion: Normal distribution ; Engineering impact: Insufficient preload may lead to gaps or slippage, while excessive preload may cause localized yielding or deformation.

[0036] 3) Material elastic modulus (GPa) Physical meaning: The stiffness index of a material in the elastic stage, reflecting the slope of the stress-strain relationship; Typical range: Approximately 190–210 GPa for steel structures; Distribution suggestion: Normal distribution ; Engineering impact: Differences in material batches or uneven heat treatment can lead to changes in stiffness, thereby affecting overall deformation and load distribution.

[0037] 4) Material yield strength (MPa) Physical meaning: The stress limit at which a material begins to undergo plastic deformation; Typical range: Q345, Q355 and other steels, approximately 300–400 MPa; Distribution suggestion: Normal distribution ; Engineering impact: Low yield strength will significantly reduce the safety factor and is one of the most critical uncertain parameters of strength.

[0038] 5) Contact or support stiffness (KN / mm) Physical meaning: Describes the equivalent elastic stiffness of the contact area of ​​the tooling support surface, lifting lug connection surface, support pad, etc. Typical range: 10–200 KN / mm; Distribution recommendation: Log-normal distribution or uniform distribution, depending on the support structure; Engineering impact: Insufficient support stiffness can cause displacement amplification, leading to increased local stress; excessive stiffness may cause stress concentration.

[0039] 6) Coefficient of friction (dimensionless) Physical meaning: The frictional performance index between contact surfaces (such as support pads, lifting lugs, clamps, etc.); Typical range: 0.10–0.18 (depending on surface finish and lubrication). Distribution recommendation: Uniform distribution ; Engineering impact: Fluctuations in the coefficient of friction can alter the load distribution on the contact surface, directly affecting the support reaction force and slippage safety.

[0040] 7) Lifting magnification factor (dimensionless) Physical meaning: It reflects the load amplification effect caused by inertia, dynamic load impact, synchronization error, etc. during the hoisting process; Typical range: 1.05–1.20; Distribution suggestion: Log-normal distribution ; Project impact: The larger the value, the higher the equivalent stress; if the suspension points are not coordinated or the braking is unstable, The fluctuations are particularly significant.

[0041] Therefore, by introducing multiple random variables in the assembly process, including geometric deviations (lifting point position errors), load fluctuations (preload, lifting acceleration), material property dispersion (elastic modulus, yield strength), and contact characteristics (friction coefficient, support stiffness), and mathematically modeling their distribution types, a multi-source uncertainty description under complex assembly conditions can be achieved.

[0042] S103, Based on the Monte Carlo method, random sampling is performed on the multi-source uncertainty parameters to generate N sets of multi-source uncertainty parameter descriptor variable samples.

[0043] It is understood that, in this embodiment of the present invention, it is assumed that there are m uncertain parameters (e.g., lifting point deviation, clamping force, material yield strength, etc.) during the assembly process, forming the aforementioned multi-source uncertainty parameter descriptive variables: ; The distribution type and parameter of each parameter are determined experimentally or empirically to describe the "natural fluctuation range" that may occur during actual production and assembly. Based on the aforementioned distribution, N sets of multi-source uncertainty parameter descriptive variable samples are generated using the Monte Carlo method: ; Each group of samples This is one possible "assembly scenario".

[0044] S104, perform finite element analysis on each set of multi-source uncertainty parameter descriptor variable samples to obtain the equivalent force field and safety factor under each set of multi-source uncertainty parameter descriptor variable samples.

[0045] Furthermore, in some embodiments of the present invention, such as Figure 2 As shown, the equivalent force field and safety factor are obtained for each group of multi-source uncertainty parameter descriptor variable samples, including: S201, import each set of multi-source uncertainty parameter description variable samples into the finite element model of the corresponding working condition, and update the corresponding parameters in the finite element model of the corresponding working condition.

[0046] It is understood that, in this embodiment of the present invention, the parameters corresponding to each group of multi-source uncertainty parameter descriptor variable samples obtained by the aforementioned Monte Carlo sampling method are imported into the finite element model of the corresponding working condition to update the corresponding parameters in the finite element model of the corresponding working condition, thereby simulating the assembly process of the tokamak main unit under different "assembly scenarios".

[0047] It should be noted that in the above embodiments of the present invention, each working condition model in the working condition library corresponds to a typical assembly state. Therefore, when performing finite element analysis on each set of random samples, the model used is determined by the assembly working condition currently being evaluated. For example, in the risk assessment of the hoisting stage, the random sample input is the finite element model corresponding to the hoisting working condition; in the risk assessment of the closure stage, the random sample input is the finite element model corresponding to the closure working condition, and so on. For another example, if multiple assembly working conditions need to be evaluated, it is preferable to perform Monte Carlo batch analysis on each working condition in the working condition library to obtain the stress distribution, safety factor distribution, failure probability and risk level under each working condition, and then sort the working conditions or take the result with the highest risk as the comprehensive evaluation result as needed.

[0048] S202, perform finite element solution on the updated finite element model to obtain the equivalent stress field under each set of multi-source uncertainty parameter descriptor variable samples, and obtain the safety factor under each set of multi-source uncertainty parameter descriptor variable samples based on the equivalent stress field and material required stress under each set of multi-source uncertainty parameter descriptor variable samples.

[0049] It is understood that, in this embodiment of the invention, the stress distribution of the structure is recalculated by performing finite element analysis on the updated finite element model. and safety factor : ; ; Among them, if ( If the minimum value is 1.0 (which is a safety threshold), then this random assembly will lead to failure.

[0050] Therefore, by combining deterministic finite element structural simulation results with stochastic probability analysis methods, a two-layer risk assessment system of "determinism + stochasticity" is established. By performing Monte Carlo random sampling statistics on the safety factor field in the finite element calculation results, a quantitative assessment of the structural failure probability can be achieved.

[0051] S105: Based on the equivalent stress field and safety factor, obtain the structural failure probability and risk level during the assembly process, and generate a visualization report on the assembly risk assessment of the tokamak main unit based on the structural failure probability and risk level.

[0052] Furthermore, in some embodiments of the present invention, obtaining the structural failure probability and risk level during the assembly process includes: obtaining the structural failure probability and structural failure severity level during the assembly process based on the safety factor and preset safety factor threshold under each group of multi-source uncertainty parameter description variable samples; and obtaining the risk level during the assembly process based on the structural failure probability and structural failure severity level.

[0053] It is understood that, in this embodiment of the present invention, the structural failure probability is obtained through statistics: ; in, This represents the total number of samples. This is a function that evaluates to 1 if the condition is true, and 0 otherwise. The probability of structural failure (between 0 and 1).

[0054] For example, if the safety factor is calculated in 23 out of 1000 samples. Less than (For example, if the value is 1.0), then P = 23 / 1000 = 0.023, which means the probability of structural failure is 2.3%.

[0055] Furthermore, in this embodiment of the invention, the severity of the consequences of failure at different locations is taken into account. Define risk level : ; in, Risk level (dimensionless); The probability of structural failure (between 0 and 1); The severity of the failure is rated from 1 to 5.

[0056] Therefore, the failure probability And the severity of the consequences By merging these indicators, a sortable risk index can be obtained. Subsequently, the frequency of node overruns can be statistically analyzed. The severity of consequences corresponding to the safety factor is used to obtain the node risk value, generate a structural risk heat map, and realize three-dimensional visualization of the risks of the assembled structure.

[0057] Specifically, the severity of failure is classified based on the safety factor as shown in Table 2 below: Table 2 Severity Chart

[0058] Therefore, based on the safety factor As a unified evaluation indicator, a five-level severity grading system was established. ), and with threshold As a failure criterion, it enables the transformation of risk from qualitative judgment to quantitative classification.

[0059] Furthermore, in some embodiments of the present invention, such as Figure 3 As shown, a visualization report on the assembly risk assessment of the tokamak main unit is generated, including: S301 is the probability of stress exceeding the limit for each node or element in the finite element model for the corresponding working condition.

[0060] It is understood that, in this embodiment of the present invention, the probability of stress exceeding the limit at each node or element of the finite element model under the corresponding working condition is obtained in the following way: ; in, This represents the probability that a "stress exceedance" will occur at this location in multiple samplings.

[0061] S302, based on the probability of stress exceeding the limit and the severity level of structural failure, obtain the risk value of each node or unit.

[0062] It is understood that, in this embodiment of the present invention, the risk value of each node or unit is calculated based on the probability of stress exceeding the limit and the corresponding severity level of structural failure.

[0063] S303 visualizes the risk level as a hot zone, generating a hot zone map of the tokamak main unit assembly structure risk in the visualization report of the tokamak main unit assembly risk assessment.

[0064] It is understood that, in this embodiment of the present invention, the risk level value is visualized and output in the form of hot zones (e.g., the higher the risk level value, the redder the hot zone color), thereby generating a visualization report of the assembly risk assessment of the tokamak main unit.

[0065] It should be noted that, in the above embodiments of the present invention, in order to improve the intuitiveness of the risk results, after the finite element simulation analysis is completed, an automated post-processing script can be used to realize the visual mapping between risk information and model geometry. The specific process is as follows: Step A1: Export simulation result data Exporting nodal stresses and equivalent stresses from finite element analysis software (e.g., ABAQUS, ANSYS, etc.) and safety factor The resulting files (e.g., .odb, .rst, or...) wait).

[0066] Step A2: Calculate the out-of-limit frequency of nodes or elements. According to the security threshold Count the number of failures for each node (or unit) across all samples:

[0067] in, For nodes The probability of exceeding the limit; This represents the Monte Carlo sample size.

[0068] Subsequently, based on the probability of "stress exceeding the limit" and the failure severity corresponding to the safety factor at that location, the risk level value at that location is calculated.

[0069] Step A3: Generate the risk level field and write it into the model. The risk level values ​​are divided into five risk ranges (e.g., 0–0.01, 0.01–0.05, 0.05–0.1, 0.1–0.2, >0.2), and the corresponding risk level identifier field is generated in the post-processing software. Then, the field is written into the model database through a Python script (ABAQUS) or APDL macro (ANSYS).

[0070] Step A4: Automatic coloring display and result linkage The post-processing interface automatically assigns different colors to different risk levels (e.g., blue - safe, yellow - medium risk, red - high risk). Then, a 3D risk distribution map is generated in the visualization window, simultaneously labeling the highest-risk areas with their corresponding probability values. This map can be rotated and scaled synchronously with the original geometric model and can be exported as an image or a 3D interactive format.

[0071] Step A5: Output a traceable risk report Automatically generate risk identification reports, including: volume or area percentage of each risk level area; coordinate range and location name of high-risk areas; corresponding working condition number and sample size; safety threshold and statistical method description.

[0072] This enables automated identification and geometrically linked display of simulation result data and structural risk hotspots, significantly improving the intuitiveness and engineering guidance of the results analysis.

[0073] Furthermore, in some embodiments of the present invention, the method further includes: establishing a multi-source uncertainty parameter template file, and using scripts and the multi-source uncertainty parameter template file to drive automated uncertainty input, batch finite element analysis and risk calculation, thereby realizing automated assessment of the assembly risk of the tokamak main unit.

[0074] It is understood that in this embodiment of the present invention, by establishing a multi-source uncertainty parameter template file, the standardization and repeatability of uncertainty modeling are achieved. Furthermore, by utilizing scripts and the multi-source uncertainty parameter template file to drive automated uncertainty input, batch finite element analysis, and risk calculation, consistency and repeatability of risk analysis for different working conditions and different batches of assembly are achieved. Thus, through parameterized input templates and batch solution scripts, a streamlined management process is realized, enabling automatic generation of random samples, automatic updating of model parameters, and automatic statistical analysis of results, ensuring the standardization, traceability, and reproducibility of the analysis process.

[0075] It should be noted that, in the above embodiments of the present invention, in order to simplify the random parameter input and batch analysis process, a standardized multi-source uncertainty parameter template file is established to realize parameter-driven simulation automation. The specific process is as follows: Step B1: Create a parameter template file Define an uncertainty parameter template in an Excel file or a text file (.csv, .txt), including the following fields: parameter name, distribution type, mean, standard deviation, unit, and value range. An example is shown in Table 3 below: Table 3. Parameter Template File Illustration

[0076] This template can be expanded into a shared parameter table for multiple operating conditions to ensure that all analysis processes use a unified input definition.

[0077] Step B2: Random Sample Generation and Automatic Input File Writing Write a Python script to read template files and automatically generate distributions according to the distribution type. Group random samples And write the parameter values ​​of each group into the finite element input file (such as ABAQUS). (File or ANSYS command stream file).

[0078] Step B3: Batch Calculation and Result Collection Batch calculations can be performed by calling the finite element solver using scripts, and execution can be accelerated through multi-threading or cluster scheduling. Subsequently, stress and safety factor results are automatically extracted after each solution and stored in a unified database.

[0079] Step B4: Result Consistency Verification After the analysis is completed, the input and output data for each sample are compared to confirm the consistency of parameter distribution, sample size, and calculation results. If any anomalies are found (e.g., input range exceeding limits or sample duplication), a warning is automatically recorded and resampling is initiated.

[0080] Step B5: Template Reuse and Update Mechanism When the engineering design is updated or the assembly scheme is adjusted, only the mean and standard deviation of the relevant parameters in the template file need to be modified, without rewriting the script or model.

[0081] Therefore, the aforementioned template system can be reused for different working conditions and different tokamak assembly stages, thereby achieving standardization, automation and traceability of the analysis process.

[0082] Furthermore, in some embodiments of the present invention, the method further includes: performing structural optimization and / or process optimization on the assembly process of the tokamak main unit based on the assembly risk assessment visualization report of the tokamak main unit.

[0083] It is understood that, in this embodiment of the present invention, key high-risk parts can also be identified based on the assembly risk assessment visualization report of the tokamak main unit, thereby optimizing the structure and / or process of the assembly process of the tokamak main unit. For example, prioritizing the monitoring points (strain gauges, displacement gauges), optimizing assembly process parameters such as lifting point positions, fixture design, and welding sequence, and reinforcing or adjusting the process of parts exceeding the threshold.

[0084] Therefore, a feedback mechanism of "risk assessment - process adjustment - reanalysis" is established in the result output stage. When the structural risk exceeds the threshold, it can automatically return to the uncertainty input stage (resetting parameters) to achieve closed-loop control of assembly process risk.

[0085] The assembly risk assessment process of the tokamak main unit assembly risk assessment method of the present invention will be described below with reference to specific embodiments of the present invention: Step C1: Establish the finite element model Based on the different working conditions of the tokamak main unit during the assembly process (e.g., hoisting, flipping, assembling, support and welding positioning, etc.), a three-dimensional structural geometric model is established in finite element analysis software. The geometric model includes the main unit vacuum chamber section, support fixtures, hoisting tools and clamps, as well as the material parameters, boundary conditions and load conditions applied to each component. Material parameters: Elastic modulus Poisson's ratio Yield strength ; Boundary conditions: Displacement constraints at lifting points, support stiffness Coefficient of contact friction ; Loading conditions: Gravity load Preload hoisting acceleration coefficient .

[0086] Subsequently, the stress field of the structure under nominal working conditions was obtained through finite element analysis. With safety factor distribution .

[0087] Step C2: Establish multi-source uncertainty parameter description variables Considering geometric deviations, load fluctuations, material property dispersion, and contact characteristics present during the assembly process, the variable describing the multi-source uncertainty parameter is defined as follows: ; in, The spatial deviation of the suspension point (mm) follows a normal distribution; The clamping preload (KN) follows a normal distribution. The elastic modulus of the material (GPa) follows a normal distribution. The yield strength of the material (MPa) follows a normal distribution; The support stiffness (KN / mm) follows a log-normal distribution; The coefficient of friction (dimensionless) follows a uniform distribution; The hoisting amplification factor (dimensionless) follows a log-normal distribution.

[0088] Step C3: Monte Carlo Sampling and Finite Element Analysis Calculation The Monte Carlo method is used to randomly sample the above parameters to generate... Group sample: ; For each set of parameter samples: 1) Import them into the finite element model; 2) Update the corresponding parameters in the model; 3) Solve to obtain the equivalent stress field under that set of parameters. .

[0089] Calculate the minimum safety factor for each simulation output under this random assembly state: ; Subsequently, the minimum safety factor for each critical component was extracted. And determine whether the safety threshold is met. .

[0090] Step C4: Structural Failure Probability Estimation Set security threshold Define the indicator function: ; Structural failure probability The estimate is: ; in, This represents the total number of samples. The probability of structural failure (between 0 and 1).

[0091] Step C5: Risk Calculation and Classification The severity grading criteria, combined with the safety factor, are shown in Table 4 below: Table 4. Severity Grading Standards (Illustrated)

[0092] Define risk level: ; Then, calculate the values ​​for each part. And sort them by risk level to identify high-risk areas.

[0093] Step C6: Risk Hotspot Identification and Output The frequency of exceeding limits was statistically analyzed for each node or element of the structure: ; in, The probability of "stress exceeding limit" occurring at this location in multiple samplings is given. Based on the probability of "stress exceeding limit" and the failure severity corresponding to the safety factor at this location, the risk level value of this location is calculated.

[0094] Optionally, in some embodiments of the present invention, the risk level results can be projected onto a three-dimensional geometric model to generate a risk heat map, which is then visualized as a hot zone. The redder the color, the higher the risk level, thereby generating a risk heat map of the tokamak assembly structure and realizing an intuitive display of the structural risk distribution.

[0095] In summary, the assembly risk assessment method for the tokamak main unit according to embodiments of the present invention first obtains the stress and safety factor distribution of the structure under various typical assembly conditions through finite element simulation. Then, the Monte Carlo random sampling method is introduced to transform factors such as assembly deviations, material property fluctuations, and load uncertainties into statistical variables. The impact of these factors on structural safety is repeatedly sampled and calculated to obtain the failure probability and risk distribution, thus achieving the quantification, classification, and visualization of structural risks. Therefore, by combining finite element simulation results with Monte Carlo random analysis, the risks of key components can be quantified, visualized, and ranked, providing a scientific basis for safety control and process optimization during assembly.

[0096] Based on the assembly risk assessment method for the tokamak host in the foregoing embodiments of the present invention, the present invention also proposes a computer-readable storage medium storing an assembly risk assessment program for the tokamak host, which, when executed by a processor, implements the assembly risk assessment method for the tokamak host described in the foregoing embodiments of the present invention.

[0097] It should be understood that the specific implementation of the computer-readable storage medium in the embodiments of the present invention can be found in the specific implementation of the assembly risk assessment method of the tokamak host in the foregoing embodiments of the present invention, and will not be repeated here to reduce redundancy.

[0098] In summary, the computer-readable storage medium according to embodiments of the present invention, by executing the assembly risk assessment program of the tokamak main unit stored thereon, can combine finite element simulation results with Monte Carlo stochastic analysis to achieve quantitative, visual, and sortable analysis of risks in key parts, thereby providing a scientific basis for safety control and process optimization in the assembly process.

[0099] Figure 4 This is a block diagram of an assembly risk assessment device for a tokamak main unit according to an embodiment of the present invention.

[0100] Specifically, in some embodiments of the present invention, such as Figure 4 As shown, the assembly risk assessment device 100 of the tokamak main unit includes: a model building module 10, a parameter building module 20, a Monte Carlo sampling module 30, a finite element solution module 40, and a risk assessment module 50.

[0101] The model building module 10 is used to build corresponding finite element models for different working conditions of the tokamak main unit during the assembly process. The finite element model for each working condition includes a geometric model, material parameters, boundary conditions, and load conditions. The parameter building module 20 is used to build multi-source uncertainty parameter descriptor variables based on the multi-source uncertainty parameters during the assembly process. The multi-source uncertainty parameters include geometric deviation, load fluctuation, material property dispersion, and contact characteristics. The Monte Carlo sampling module 30 is used to randomly sample the multi-source uncertainty parameters based on the Monte Carlo method to generate N sets of multi-source uncertainty parameter descriptor variable samples. The finite element solution module 40 is used to perform finite element solution on each set of multi-source uncertainty parameter descriptor variable samples to obtain the equivalent stress field and safety factor under each set of multi-source uncertainty parameter descriptor variable samples. The risk assessment module 50 is used to obtain the structural failure probability and risk level during the assembly process based on the equivalent stress field and safety factor, and generate a visualization report of the assembly risk assessment of the tokamak main unit based on the structural failure probability and risk level.

[0102] Furthermore, in some embodiments of the present invention, geometric deviations include lifting point spatial errors, load fluctuations include clamp preload and lifting amplification factor, material property dispersions include material elastic modulus and material yield strength, and contact characteristics include friction coefficient and support stiffness.

[0103] Furthermore, in some embodiments of the present invention, the finite element solution module 40 is also used to: import each set of multi-source uncertainty parameter descriptor variable samples into the finite element model of the corresponding working condition, update the corresponding parameters in the finite element model of the corresponding working condition; perform finite element solution on the updated finite element model to obtain the equivalent stress field under each set of multi-source uncertainty parameter descriptor variable samples; and obtain the safety factor under each set of multi-source uncertainty parameter descriptor variable samples based on the equivalent stress field and material required stress under each set of multi-source uncertainty parameter descriptor variable samples.

[0104] Furthermore, in some embodiments of the present invention, the risk assessment module 50 is also used to obtain the structural failure probability and structural failure severity level during the assembly process based on the safety factor and preset safety factor threshold under each group of multi-source uncertainty parameter description variable samples, and to obtain the risk level during the assembly process based on the structural failure probability and structural failure severity level.

[0105] Furthermore, in some embodiments of the present invention, the risk assessment module 50 is also used to: statistically analyze the probability of stress exceeding limits at each node or element of the finite element model under the corresponding working condition; obtain the risk value of each node or element based on the probability of stress exceeding limits and the severity level of structural failure; and visualize the risk value in the form of hot zones to generate a risk hot zone map of the tokamak main unit assembly structure in the assembly risk assessment visualization report of the tokamak main unit.

[0106] Furthermore, in some embodiments of the present invention, the parameter establishment module 20 is also used to establish a multi-source uncertainty parameter template file, and to realize the automated assessment of the assembly risk of the tokamak main unit by using scripts and the multi-source uncertainty parameter template file to drive automated uncertainty input, batch finite element analysis and risk calculation.

[0107] Furthermore, in some embodiments of the present invention, the risk assessment module 50 is also used to perform structural optimization and / or process optimization of the assembly process of the tokamak main unit based on the assembly risk assessment visualization report of the tokamak main unit.

[0108] It should be understood that the specific implementation of the assembly risk assessment device 100 for the tokamak host in this embodiment of the invention corresponds one-to-one with the specific implementation of the assembly risk assessment method for the tokamak host in the aforementioned embodiment of the invention. To reduce redundancy, it will not be described again here.

[0109] In summary, the assembly risk assessment device for the tokamak main unit according to an embodiment of the present invention first obtains the stress and safety factor distribution of the structure under various typical assembly conditions through finite element simulation. Then, it introduces the Monte Carlo random sampling method to transform factors such as assembly deviations, material property fluctuations, and load uncertainties into statistical variables. It repeatedly samples and calculates their impact on structural safety, obtaining the failure probability and risk distribution, thus achieving the quantification, classification, and visualization of structural risks. Therefore, by combining finite element simulation results with Monte Carlo random analysis, it achieves the quantification, visualization, and ranking analysis of risks in key components, thereby providing a scientific basis for safety control and process optimization during assembly.

[0110] Figure 5 This is a block diagram of an assembly risk assessment platform for a tokamak main unit according to an embodiment of the present invention.

[0111] Specifically, in some embodiments of the present invention, such as Figure 5 As shown, the assembly risk assessment platform 1000 for the tokamak host includes the assembly risk assessment device 100 for the tokamak host described in the above embodiment of the present invention.

[0112] It should be understood that the specific implementation of the assembly risk assessment platform 1000 for the tokamak host in the embodiments of the present invention can refer to the specific implementation of the assembly risk assessment method for the tokamak host in the foregoing embodiments of the present invention. To reduce redundancy, it will not be described again here.

[0113] In summary, the assembly risk assessment platform for the tokamak main unit according to the embodiments of the present invention, by adopting the aforementioned assembly risk assessment device for the tokamak main unit, can combine finite element simulation results with Monte Carlo stochastic analysis to achieve quantitative, visual, and sortable analysis of risks in key parts, thereby providing a scientific basis for safety control and process optimization in the assembly process.

[0114] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0115] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0116] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0117] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this invention and simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0118] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0119] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components, unless otherwise explicitly limited. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0120] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can mean that the first feature is in direct contact with the second feature, or that the first feature is in indirect contact with the second feature through an intermediate medium. Furthermore, "above," "over," and "on top" of the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply that the first feature is at a lower horizontal level than the second feature.

[0121] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for assessing assembly risk of a tokamak main unit, characterized in that, The method includes: The corresponding finite element models are established according to the different working conditions of the tokamak main unit during the assembly process. The finite element model corresponding to each working condition includes geometric model, material parameters, boundary conditions and load conditions. Based on the multi-source uncertainty parameters in the assembly process, multi-source uncertainty parameter descriptive variables are established, wherein the multi-source uncertainty parameters include geometric deviation, load fluctuation, material property dispersion, and contact characteristics. The geometric deviation includes lifting point space error, the load fluctuation includes clamp preload and lifting amplification factor, the material property dispersion includes material elastic modulus and material yield strength, and the contact characteristics include friction coefficient and support stiffness. Based on the Monte Carlo method, random sampling is performed on the multi-source uncertainty parameters to generate N sets of multi-source uncertainty parameter descriptive variable samples; Perform finite element analysis on each group of multi-source uncertainty parameter descriptor variable samples to obtain the equivalent force field and safety factor under each group of multi-source uncertainty parameter descriptor variable samples; Based on the equivalent stress field and the safety factor, the structural failure probability and risk level during the assembly process are obtained, and a visualization report on the assembly risk assessment of the tokamak main unit is generated based on the structural failure probability and the risk level.

2. The assembly risk assessment method for the tokamak main unit according to claim 1, characterized in that, The process of obtaining the equivalent force field and safety factor under each group of multi-source uncertainty parameter descriptor variable samples includes: Each set of multi-source uncertainty parameter descriptor variable samples is imported into the finite element model of the corresponding working condition, and the corresponding parameters in the finite element model of the corresponding working condition are updated. The updated finite element model is solved by finite element analysis to obtain the equivalent stress field under each set of multi-source uncertainty parameter descriptor variable samples. Based on the equivalent stress field and material required stress under each set of multi-source uncertainty parameter descriptor variable samples, the safety factor under each set of multi-source uncertainty parameter descriptor variable samples is obtained.

3. The assembly risk assessment method for the tokamak main unit according to claim 2, characterized in that, The acquisition of the structural failure probability and risk level during the assembly process includes: Based on the safety factor and preset safety factor threshold of each group of multi-source uncertainty parameter descriptor variable samples, the structural failure probability and structural failure severity level in the assembly process are obtained, and the risk level in the assembly process is obtained based on the structural failure probability and the structural failure severity level.

4. The assembly risk assessment method for the tokamak main unit according to claim 3, characterized in that, The generation of the assembly risk assessment visualization report for the tokamak main unit includes: The probability of stress exceeding the limit is statistically analyzed for each node or element in the finite element model of the corresponding working condition; Based on the probability of stress exceeding the limit and the severity level of structural failure, obtain the risk value of each node or unit; The risk level is visualized as a hot zone, generating a hot zone map of the tokamak main unit assembly structure risk in the assembly risk assessment visualization report of the tokamak main unit.

5. The assembly risk assessment method for the tokamak main unit according to claim 1, characterized in that, The method further includes: A template file for multi-source uncertainty parameters is established, and an automated assessment of the assembly risk of the tokamak host is achieved by using scripts to drive automated uncertainty input, batch finite element analysis, and risk calculation.

6. The assembly risk assessment method for the tokamak main unit according to claim 1, characterized in that, The method further includes: Based on the assembly risk assessment visualization report of the tokamak main unit, the assembly process of the tokamak main unit is structurally optimized and / or process optimized.

7. A computer-readable storage medium, characterized in that, It stores an assembly risk assessment program for the tokamak host, which, when executed by the processor, implements the assembly risk assessment method for the tokamak host as described in any one of claims 1-6.

8. An assembly risk assessment device for a tokamak main unit, characterized in that, The device includes: The model building module is used to build corresponding finite element models based on different working conditions of the tokamak main unit during the assembly process. The finite element model corresponding to each working condition includes geometric model, material parameters, boundary conditions and load conditions. The parameter establishment module is used to establish multi-source uncertainty parameter descriptive variables based on the multi-source uncertainty parameters in the assembly process. The multi-source uncertainty parameters include geometric deviation, load fluctuation, material property dispersion, and contact characteristics. The geometric deviation includes lifting point space error, the load fluctuation includes clamp preload and lifting amplification factor, the material property dispersion includes material elastic modulus and material yield strength, and the contact characteristics include friction coefficient and support stiffness. The Monte Carlo sampling module is used to randomly sample the multi-source uncertainty parameters based on the Monte Carlo method to generate N sets of multi-source uncertainty parameter descriptor variable samples; The finite element solution module is used to perform finite element solution on each group of multi-source uncertainty parameter descriptor variable samples to obtain the equivalent force field and safety factor under each group of multi-source uncertainty parameter descriptor variable samples. The risk assessment module is used to obtain the structural failure probability and risk level during the assembly process based on the equivalent stress field and the safety factor, and to generate a visualization report of the assembly risk assessment of the tokamak main unit based on the structural failure probability and the risk level.

9. An assembly risk assessment platform for a tokamak main unit, characterized in that, The assembly risk assessment platform includes the assembly risk assessment device for the tokamak main unit as described in claim 8.

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