Grid mapping based multi-fuel element parallel performance analysis method, device, equipment and medium

Abstract

The application discloses a multi-fuel element parallel performance analysis method and device based on grid mapping, equipment and medium, relates to the technical field of fuel performance analysis and multi-physical field coupling analysis, and can be used for the fuel assembly or reactor core structure with symmetry and asymmetry. The corresponding grid storage field data can be divided by using the structure and non-structure grid, the scale and efficiency of performance analysis calculation can be effectively improved by combining the parallel computing strategy, and the intelligence is good. The method comprises the following steps: allocating MPI threads and setting input files for a plurality of performance analysis programs; calculating a grid translation vector parameter; controlling each MPI thread to start calling a corresponding performance analysis program for synchronous calculation under a coupling calculation time step, and obtaining a plurality of calculation result data; constructing an overall grid model of a reactor core, and obtaining a plurality of single fuel element grid data; and synchronizing the overall grid model and the plurality of single fuel element grid data to the plurality of performance analysis programs.

Description

Technical Field

[0001] This application relates to the fields of fuel performance analysis and multiphysics coupling analysis, and in particular to a method, apparatus, device and medium for parallel performance analysis of multiple fuel elements based on mesh mapping. Background Technology

[0002] As the core of a nuclear energy system, the reactor core involves extremely complex physical and chemical processes, encompassing multiple intertwined physical fields such as neutron physics, thermal hydraulics, and fuel performance variations. Accurately simulating these multi-physics coupling phenomena within the reactor core is crucial for improving the reactor's safety, economy, and operational efficiency.

[0003] In related technologies, when performing multiphysics coupling analysis of reactor cores, only nuclear thermal coupling is considered when directly coupling neutron physics with thermal-hydraulic systems. Changes in fuel performance, such as the change in pellet thermal conductivity with burnup, the influence of burnup and temperature on interstitial thermal conductivity, and complex physical phenomena occurring within the pellets, such as restructuring, densification, swelling, creep, and fission gas release, also have a profound impact on core behavior but are not considered in the analysis. Therefore, some current solutions attempt to simulate basic phenomena such as thermal strain, repositioning, and interstitial heat transfer by integrating simple fuel performance analysis modules into neutron physics programs.

[0004] In the process of developing the relevant technology, the applicant recognized that the relevant technology has at least the following problems: Limited by the single coupling method and the limitations of program development, it is usually only possible to analyze a single fuel element under the most demanding service environment. This results in the transmission of coupling parameters being limited to single values ​​or one-dimensional arrays, making it difficult to support the high-fidelity simulation requirements at the component level or core level. Although some patents have proposed the idea of ​​parallel analysis of fuel elements by reading result files, they have not effectively solved the problem of direct coupling with other programs. There are obvious deficiencies in data transmission, which limits the efficiency and accuracy of coupling analysis. Summary of the Invention

[0005] In view of this, this application provides a method, apparatus, device and medium for parallel performance analysis of multi-fuel elements based on mesh mapping. The main purpose is to solve the problem that the current methods have failed to effectively solve the problem of direct coupling with other programs, and there are obvious deficiencies in data transmission, which limit the efficiency and accuracy of coupled analysis.

[0006] According to a first aspect of this application, a method for parallel performance analysis of multiple fuel elements based on mesh mapping is provided, the method comprising: A number of performance analysis programs participating in the coupled calculation are identified, and message passing interface (MPI) threads are allocated and input files are set for the multiple performance analysis programs. The multiple performance analysis programs include a neutron physics program, a thermal hydraulic program, and a fuel performance analysis program. Based on the actual geometric layout of the reactor core to be analyzed, the grid translation vector parameters are calculated. These parameters indicate the direction and distance required to move the center grid of the reactor core to the grid where each fuel element in the reactor core is located. Each MPI thread is controlled to start calling the corresponding performance analysis program for synchronous calculation at the coupled calculation time step, and multiple calculation result data of the multiple performance analysis programs are obtained during the calculation process of each MPI thread. Using the grid translation vector parameters, an overall grid model of the reactor core is constructed, and according to the overall grid model, the multiple calculation result data are split and organized to obtain multiple individual fuel element grid data. Through the data exchange channel between the multiple performance analysis programs, the overall mesh model and the mesh data of the multiple single fuel elements are synchronized to the multiple performance analysis programs. Each MPI thread, according to its corresponding parallel computing strategy, controls the corresponding performance analysis program to analyze and calculate the corresponding single fuel element mesh data based on the overall mesh model, so as to complete the multi-fuel element parallel performance analysis under the coupled computing time step. The parallel computing strategy instructs the corresponding MPI thread to perform calculations on multiple or one fuel element.

[0007] According to a second aspect of this application, a parallel performance analysis device for multiple fuel elements based on mesh mapping is provided, the device comprising: The configuration module is used to determine multiple performance analysis programs participating in the coupled calculation, allocate message passing interface (MPI) threads to the multiple performance analysis programs, and set input files. The multiple performance analysis programs include a neutron physics program, a thermal hydraulic program, and a fuel performance analysis program. The first calculation module is used to calculate the grid translation vector parameters based on the actual geometric layout of the reactor core to be analyzed. The grid translation vector parameters are used to indicate the direction and distance required to move the grid at the center of the reactor core to the grid where each fuel element in the reactor core is located. The second calculation module is used to control each MPI thread to start calling the corresponding performance analysis program for synchronous calculation under the coupled calculation time step, and to obtain multiple calculation result data of the multiple performance analysis programs during the calculation process of each MPI thread. The construction module is used to construct the overall grid model of the reactor core using the grid translation vector parameters, and to split and organize the multiple calculation result data according to the overall grid model to obtain multiple single fuel element grid data. The analysis module is used to synchronize the overall mesh model and the mesh data of the multiple single fuel elements to the multiple performance analysis programs through the data exchange channel between the multiple performance analysis programs. Then, each MPI thread controls the corresponding performance analysis program to analyze and calculate the corresponding single fuel element mesh data according to its corresponding parallel computing strategy based on the overall mesh model, so as to complete the multi-fuel element parallel performance analysis under the coupled computing time step. The parallel computing strategy instructs the corresponding MPI thread to perform calculations on multiple or one fuel element.

[0008] According to a third aspect of this application, an apparatus is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the method described in any of the first aspects above.

[0009] According to a fourth aspect of this application, a medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any one of the first aspects above.

[0010] By utilizing the above technical solutions, this application provides a method, apparatus, device, and medium for parallel performance analysis of multiple fuel elements based on mesh mapping. This application constructs an overall mesh model of the reactor core by calculating mesh translation vector parameters, enabling the use of structured and unstructured meshes to divide corresponding mesh storage field data for both symmetric and asymmetric fuel assemblies or reactor core structures. Furthermore, the exchange and transmission of calculation results are achieved through data exchange channels between multiple performance analysis programs. This lays the foundation for subsequent multiphysics coupling calculations at the component and core levels, while effectively improving the scale and efficiency of performance analysis calculations by combining parallel computing strategies, demonstrating good intelligence.

[0011] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0012] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 This illustration shows a flowchart of a parallel performance analysis method for multiple fuel elements based on mesh mapping, provided in an embodiment of this application. Figure 2 This paper illustrates a schematic flowchart of another parallel performance analysis method for multi-fuel elements based on mesh mapping provided in an embodiment of this application. Figure 3 This illustration shows a schematic diagram of a parallel performance analysis device for multiple fuel elements based on mesh mapping, provided in an embodiment of this application. Figure 4 A schematic diagram of the device structure of a computer device provided in an embodiment of this application is shown. Detailed Implementation

[0013] Exemplary embodiments of the present application will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this application will be thorough and complete, and will fully convey the scope of the present application to those skilled in the art.

[0014] This application provides a method for parallel performance analysis of multiple fuel elements based on mesh mapping, such as... Figure 1 As shown, the method includes: S10: Identify the multiple performance analysis programs involved in the coupled computation, assign message passing interfaces (MPI) threads to the multiple performance analysis programs, and set the input files.

[0015] In this embodiment, multiple performance analysis programs that need to participate in the coupled calculations are first identified. These programs may include neutron physics programs, thermal-hydraulic programs, and fuel performance analysis programs, each responsible for simulating different physical phenomena within the reactor core. Subsequently, a corresponding thread is allocated to each performance analysis program using a Message Passing Interface (MPI). MPI is a communication protocol for parallel computing that allows data exchange between different processes (in this case, different instances of the performance analysis programs). Simultaneously, input files are set for each performance analysis program, containing information such as initial conditions, boundary conditions, and calculation parameters required for program execution.

[0016] In this way, by allocating a corresponding MPI thread to each performance analysis program through the above process, parallel execution of the programs can be achieved, improving computational efficiency. Simultaneously, the input file settings ensure that each program can start calculations under correct conditions, laying the foundation for subsequent multi-program coupled calculations. For example, assuming a multiphysics coupled analysis of a pressurized water reactor core is required, and the neutron physics program MCNP, the thermal-hydraulic program RELAP5, and the fuel performance analysis program FRAPCON are selected, then four threads are allocated to each program via MPI, and an input file containing information such as core geometry, material properties, and initial temperature distribution is set.

[0017] In step S10, which involves determining the multiple performance analysis programs participating in the coupled computation, allocating message passing interface (MPI) threads and setting input files for the multiple performance analysis programs, the process includes: S11: In response to the requirements of fuel element performance analysis, multiple performance analysis programs are determined from the preset full performance analysis programs.

[0018] In this embodiment, in response to the requirements of fuel element performance analysis, multiple performance analysis programs need to be determined from a pre-set library of performance analysis programs. Specifically, the key physical fields and analysis dimensions involved in the fuel element performance analysis can be identified first. For example, the interaction between thermal-hydraulic, structural mechanics, and neutron physics needs to be considered, or the analysis dimensions such as temperature distribution and stress-strain of the fuel element under specific operating conditions need to be considered. Then, programs capable of handling these physical fields and analysis dimensions are selected from the pre-set performance analysis program library. For example, if the neutron flux distribution of the reactor core needs to be analyzed, a program with neutron physics calculation capabilities can be selected; if the thermal-hydraulic performance of the fuel element needs to be analyzed, a program with thermal-hydraulic calculation capabilities can be selected; if the structural mechanics performance needs to be analyzed, a corresponding structural mechanics analysis program is selected. The pre-set performance analysis programs are mature software tools that have been verified and tested and can meet the performance analysis requirements of different types of fuel elements.

[0019] Through the above process, appropriate performance analysis programs can be accurately selected based on the performance analysis requirements of fuel elements, providing a foundation for subsequent multi-program coupled calculations and ensuring the accuracy and reliability of the analysis results. For example, when analyzing a new type of nuclear fuel element, by clarifying that it requires analysis of neutron physics, thermal hydraulics, and fuel performance, a neutron physics program, a thermal hydraulics program, and a fuel performance analysis program are selected from a pre-set program library. These three programs can perform precise analysis for the specific needs of this new type of fuel element.

[0020] S12: Determine the simulation data management library, and use the simulation data management library to set the coupling parameter variables for each performance analysis program according to its format requirements.

[0021] In this embodiment, a simulation data management library is determined, and the library is used to set coupling parameter variables for each performance analysis program according to its format requirements. The simulation data management library can be a MEDCoupling library, a tool for processing and managing simulation data, supporting data interaction and sharing between different programs, and possessing data format conversion and parameter setting functions. Specifically, the MEDCoupling library can be first imported into the entire computing environment. Then, according to the data input / output format requirements of each performance analysis program, a corresponding data model is created in the MEDCoupling library. Next, the parameter variables that need to be coupled, such as temperature, pressure, and displacement, are determined, and these parameter variables are uniformly defined and set in the MEDCoupling library to ensure that different performance analysis programs can recognize and read these parameters.

[0022] By using the MEDCoupling library to uniformly manage coupling parameter variables, errors caused by incompatible data formats between different programs can be avoided, improving the accuracy and efficiency of data interaction and providing a stable data foundation for multi-program coupled calculations. For example, when dealing with a problem involving the coupled analysis of neutron physics, thermal hydraulics, and fuel performance, the MEDCoupling library uniformly sets key coupling parameters such as power density, temperature and heat flux density, as well as stress and strain. This allows the neutron physics program to accurately transfer the calculated power distribution to the thermal hydraulic program, while the thermal hydraulic program can effectively transfer the results such as the temperature and heat flux of the coolant and fuel cladding to the fuel performance program. This ensures that the three programs can correctly read and use consistent boundary conditions and interactive data during coupled iterations, guaranteeing the coordination of multiphysics coupled calculations and the reliability of the calculation results.

[0023] S13: By calling the DEC parallel function, a data exchange channel is built between multiple performance analysis programs.

[0024] In this embodiment, a data exchange channel is established between multiple performance analysis programs by calling DEC parallel functions. DEC parallel functions are a set of functions used to implement data exchange and communication in parallel computing. They can utilize the multi-core or multi-node resources of a computer to improve data transmission efficiency. Specifically, the DEC parallel function library can be loaded into the computing environment first. Then, based on the running node and process information of multiple performance analysis programs, the data exchange functions in the DEC parallel functions are called. Through these functions, a data transmission channel is established between different performance analysis programs, enabling the results calculated by one program to be promptly transmitted to other related programs. For example, in a three-program coupling of neutron physics, thermal hydraulics, and fuel performance, the core power distribution data calculated by the neutron physics program is transmitted to the thermal hydraulics program through a data exchange channel as a key input for calculating the coolant and fuel temperature fields. Subsequently, the detailed temperature field and heat flux density data calculated by the thermal hydraulics program can be transmitted to the fuel performance program through the same exchange network to calculate the expansion, deformation, and thermomechanical stress of the fuel pellets and cladding. This ensures that multiphysics parameters are accurately and orderly transmitted between programs, achieving a fully coupled simulation of the entire process from power release and heat transfer to structural response.

[0025] In this way, by constructing a data exchange channel, efficient data transmission between multiple performance analysis programs can be achieved, avoiding delays and errors during data transmission, ensuring the real-time performance and accuracy of coupled calculations, and improving the performance of the entire computing system. For example, in a large-scale nuclear reactor core coupled calculation, the data exchange channel constructed through the DEC parallel function enables multiple programs such as thermal hydraulics, neutron physics, and structural mechanics to exchange data quickly and accurately, greatly shortening the calculation time.

[0026] S14: Based on the preset thread allocation strategy, allocate multiple MPI threads to multiple performance analysis programs.

[0027] In this embodiment, multiple MPI threads are allocated to multiple performance analysis programs according to a preset thread allocation strategy. The preset thread allocation strategy instructs a single MPI thread to run a single performance analysis program or to run multiple performance analysis programs in parallel. MPI (Message Passing Interface) is a programming model and message passing library for parallel computing, enabling efficient data communication and task allocation between multiple computing nodes. Specifically, a suitable thread allocation strategy can be formulated based on the hardware configuration of computing resources and the computational requirements of the performance analysis programs. For example, if a performance analysis program has a large computational load, multiple MPI threads can be allocated for it to perform parallel computation; while for programs with a smaller computational load, a single MPI thread can be allocated. Then, according to this strategy, a corresponding number of MPI threads are allocated to each performance analysis program in the computing environment, and a correspondence between threads and programs is established.

[0028] In this way, by employing a reasonable thread allocation strategy, computing resources can be fully utilized, improving the scale and efficiency of performance analysis calculations. Different programs are allocated different numbers of threads according to their computational needs, avoiding resource waste and computational bottlenecks, thus enabling the entire coupled computation process to proceed more efficiently. For example, in a coupled computation involving multiple performance analysis programs of varying complexity, a pre-defined thread allocation strategy allocates four MPI threads to computationally intensive programs and one MPI thread to computationally intensive programs, allowing the entire computational task to be completed in a shorter time.

[0029] S15: Generate and input a corresponding input file for each performance analyzer so that each performance analyzer can initialize based on the input file.

[0030] In this embodiment, a corresponding input file is generated and input for each performance analysis program so that each program can initialize based on the input file. The input file defines the analysis conditions required for the corresponding performance analysis program to run, such as geometric model parameters, material properties, and boundary conditions. Specifically, the analysis conditions to be defined can be determined first according to the requirements of each performance analysis program. For example, for a neutron physics program, the geometry and material layout of the reactor core, fuel enrichment, nuclear cross-section data, and initial critical state need to be defined; for a thermal-hydraulic program, the geometry of the fluid domain, fluid properties, and inlet and outlet boundary conditions need to be defined; for a structural mechanics program, the geometric model of the structure, the elastic modulus of the material, and Poisson's ratio need to be defined. Then, using an input file generation tool or writing a script, these analysis conditions are generated into corresponding input files according to the program's input format requirements. Finally, these input files are input into the corresponding performance analysis programs, which initialize based on the information in the input files, preparing for subsequent calculations.

[0031] In this way, by generating accurate input files, the correct operating conditions can be provided for each performance analysis program, ensuring that the program can perform the analysis and calculations as expected. At the same time, the unified input file generation method also facilitates the management and modification of calculation conditions, improving the repeatability and maintainability of the calculation process. For example, when performing performance analysis on a nuclear fuel assembly, by generating detailed input files, accurate geometric models and material properties are provided for the neutron physics program, the thermal-hydraulic program, and the fuel performance analysis program, respectively, enabling the three programs to be correctly initialized and coupled for calculations.

[0032] S20: Calculate the grid translation vector parameters based on the actual geometric layout of the reactor core to be analyzed.

[0033] In this embodiment, the grid translation vector parameter indicates the direction and distance required to move the grid at the center of the reactor core to the grids located at the fuel elements within the reactor core. Specifically, it indicates the direction and distance for moving the grid at the center of the reactor core to the grids located at the fuel elements within the core. Specifically, when calculating the grid translation vector parameter, the center position of the reactor core and the positions of each fuel element can be determined based on the actual geometric layout of the reactor core. Then, the vector from the center position to the position of each fuel element is calculated; these vectors are the grid translation vector parameters.

[0034] By calculating the mesh translation vector parameters, the mesh model of each fuel element within the reactor core can be accurately constructed, providing a foundation for subsequent overall mesh model construction and calculation result processing. Furthermore, this method is applicable to both symmetric and asymmetric fuel assemblies or reactor core structures, exhibiting broad applicability. For example, for a core containing 100 fuel elements, the center position of the core is first determined. Then, for each fuel element, the vector from the center position to that element's position is calculated. For instance, if a fuel element is located 1 meter east and 0.5 meters north of the center position, its mesh translation vector parameters are (1, 0.5, 0) (assuming the z-axis direction is the core height direction and the element is at the same height as the center).

[0035] In step S20, that is, calculating the grid translation vector parameters based on the actual geometric layout of the reactor core to be analyzed, the following steps are included: S21: Determine the reactor core to be analyzed, identify the fuel elements in the reactor core, and mesh the actual geometric layout of the reactor core according to the identified fuel elements to obtain multiple single fuel element meshes.

[0036] In this embodiment, the reactor core to be analyzed is first determined. Specifically, based on actual engineering needs or research objectives, the type and specifications of the reactor core are specified, such as whether it is a pressurized water reactor core or a boiling water reactor core. After determining the reactor core, the fuel elements within it are identified. Fuel elements are key components in the reactor core that perform nuclear reactions and release energy. Different types of reactors may have different shapes, sizes, and materials for their fuel elements. The identification process can accurately determine the location, type, and other information of each fuel element based on reactor design drawings, relevant technical documents, and other materials.

[0037] Next, based on the identified fuel elements, the actual geometric layout of the reactor core is meshed, resulting in multiple single-fuel-element meshes. Mesh generation discretizes a continuous geometric space into a finite number of small mesh cells for numerical computation. Suitable meshing methods, such as structured or unstructured meshing, can be used. Structured meshing generates regularly arranged mesh cells and has high computational efficiency, but it is less adaptable to complex geometries; unstructured meshing is better suited to complex geometries, but the computational load is relatively high. Based on the geometric characteristics of the reactor core, an appropriate meshing method is selected to divide the region containing each fuel element into independent meshes, forming multiple single-fuel-element meshes.

[0038] In this way, the above process allows for accurate identification of fuel elements and targeted mesh generation, precisely describing the reactor core's geometry and providing a foundation for subsequent construction of an overall mesh model. Furthermore, this fuel element-based meshing method enables detailed processing of fuel assemblies of different types and layouts, ensuring reasonable mesh representations for both symmetrical and asymmetrical structures, thus preparing data for storage sites. For example, in the performance analysis of a novel reactor core, by accurately identifying its unique fuel element shapes and layouts and employing an unstructured meshing method, a single fuel element mesh that accurately reflects its geometric characteristics is obtained, providing a reliable data foundation for subsequent analysis.

[0039] S22: Among multiple single fuel element grids, determine the single fuel element grid located at the center of the reactor core as the center position grid, calculate the relative displacement between the center position grid and other single fuel element grids, and obtain multiple relative displacement vectors.

[0040] In this embodiment of the application, the single fuel element grid located at the center of the reactor core is determined as the center position grid among multiple single fuel element grids. Specifically, it can be based on the geometric symmetry of the reactor core. If the core has a clear geometric center, the fuel element grid closest to the center is selected as the center position grid; if there is no clear geometric center, the geometric center coordinates of all fuel element grids can be calculated, and then the grid closest to the geometric center can be selected as the center position grid.

[0041] Next, the relative displacement between the center position grid and other single fuel element grids is calculated to obtain multiple relative displacement vectors. The relative displacement refers to the change in position of one grid relative to another in space. The relative displacement vector between them can be obtained by calculating the difference between the vertex coordinates of the center position grid and the corresponding vertex coordinates of other single fuel element grids. The other single fuel element grids are the grids other than the center position grid among the multiple single fuel element grids.

[0042] Thus, through the above process, using the central grid as a reference, calculating the relative displacement vectors of other grids clearly describes the relative positional relationships between the individual fuel element grids in the entire reactor core. Accurate description of these relative positions is crucial for constructing the overall mesh model, ensuring that the grids can be correctly assembled and combined during subsequent model building. Regardless of whether the reactor core structure is symmetrical or asymmetrical, these relative displacement vectors enable precise model construction. For example, in a reactor core with a complex asymmetrical structure, accurately calculating the relative displacement vectors of each individual fuel element grid relative to the central grid provides key positional information for constructing the overall mesh model, enabling the model to accurately reflect the actual geometric layout of the core.

[0043] S23: Integrate multiple relative displacement vectors to obtain the mesh translation vector parameters.

[0044] In this embodiment, multiple relative displacement vectors are integrated to obtain grid translation vector parameters. The integration process needs to consider the direction and magnitude of each relative displacement vector. Mathematical methods, such as vector addition and averaging, are used to comprehensively process these relative displacement vectors. For example, a weighted averaging method can be used. Based on factors such as the importance of each fuel element in the reactor core or its distance from the center grid, different weights are assigned to each relative displacement vector, and then a weighted average is calculated to obtain the final grid translation vector parameters.

[0045] In this way, by integrating relative displacement vectors to obtain mesh translation vector parameters, a concise set of parameters can be used to describe the translation relationships of the meshes throughout the reactor core. These mesh translation vector parameters provide crucial parameter basis for constructing the overall mesh model of the reactor core, enabling the accurate translation and combination of individual fuel element meshes during model construction to form a complete overall mesh model that accurately reflects the actual geometric layout of the reactor core. Furthermore, this vector parameter-based model construction method offers high flexibility and scalability, effectively enabling model construction for both simple symmetrical and complex asymmetrical structures. For example, in the performance analysis of a large pressurized water reactor core, the mesh translation vector parameters obtained by integrating multiple relative displacement vectors successfully constructed an overall mesh model that accurately simulates the core geometry, providing a reliable model foundation for subsequent multiphysics coupling calculations.

[0046] S30: Control each MPI thread to start calling the corresponding performance analysis program for synchronous calculation under the coupled calculation time step, and obtain multiple calculation result data from multiple performance analysis programs during the calculation process of each MPI thread.

[0047] In this embodiment, after setting the coupling calculation time step, each MPI thread is controlled to simultaneously start calling its corresponding performance analysis program for calculation. The coupling calculation time step is a time concept used in parallel computing to ensure computational synchronization between different programs. During the calculation process, each MPI thread generates a large amount of calculation result data, including but not limited to temperature distribution, neutron flux, stress, and strain. Collecting this data allows for the acquisition of multiple calculation result data from multiple performance analysis programs.

[0048] In this way, synchronous calculations are achieved through the above process, ensuring that the interactions between different physical fields can be accurately reflected on the same time scale, thus improving the accuracy of the simulation. Simultaneously, the calculation results from multiple performance analysis programs are acquired, providing a rich source of information for subsequent data integration and analysis. For example, within a set time step, the MCNP program calculates the neutron flux distribution, the RELAP5 program calculates the temperature and velocity distribution of the coolant, and the FRAPCON program calculates the temperature and stress fields of the fuel elements. Each MPI thread continuously generates these data during the calculation process, preparing for subsequent data exchange and integration.

[0049] In step S30, each MPI thread is controlled to start calling the corresponding performance analysis program for synchronous calculation within the coupled calculation time step. During the calculation process of each MPI thread, multiple calculation result data from multiple performance analysis programs are obtained, including: S31: Calculate the coupling computation time step based on the program characteristics of each performance analysis program.

[0050] In this embodiment, the coupled computation time step is calculated based on the characteristics of each performance analysis program. These characteristics encompass multiple aspects, such as the program's data processing speed, algorithm complexity, and computational resource consumption. Different performance analysis programs, due to their inherent algorithmic and functional differences, require different times when processing the same amount of data or performing the same computational task. For example, the neutron physics program solves the particle transport equations and calculates the full core power distribution, a process that is the most complex and time-consuming; the thermal-hydraulic analysis program may involve complex fluid dynamics calculations, making its calculation process relatively time-consuming; while the structural mechanics analysis program may be faster when performing simple structural stress calculations. Therefore, when calculating the time step of coupled computation, these factors should be considered comprehensively. Specifically, each performance analysis program can be tested individually first, and its running time under different scales of data and computational tasks can be recorded to analyze its time consumption patterns. Then, based on these patterns, combined with the interaction frequency and computational order of each program in the entire coupled computation, a suitable time step should be determined. This time step should ensure that each performance analysis program can complete its computational task within this time, without the computational efficiency becoming low due to excessive time, or the program failing to perform sufficient computation and causing errors due to insufficient time.

[0051] In this way, by accurately calculating the time step of the coupling calculation, the calculation rhythm of different performance analysis programs can be coordinated, enabling them to work efficiently and collaboratively in the coupling calculation. This avoids calculation delays or errors caused by unreasonable time steps, laying the foundation for accurate coupling calculations in the future. For example, in a coupling calculation involving three performance analysis programs—neutron physics, thermal hydraulics, and structural mechanics—testing revealed that the neutron physics program takes 10 seconds to complete the core power distribution calculation, the thermal hydraulics program takes 5 seconds to handle complex fluid scenarios, and the structural mechanics program takes 2 seconds to handle simple structural stress calculations. Considering the interaction frequency, a coupling calculation time step of 12 seconds was determined. This ensures that the neutron physics program completes a detailed simulation while allowing the thermal hydraulics and fuel performance programs sufficient time to execute calculations and prepare for the next round of data exchange, thereby maintaining the stability and efficiency of the coupling process.

[0052] S32: Synchronize the coupled computation time step to each MPI thread so that each MPI thread can start calling the corresponding performance analysis program for synchronous computation at the coupled computation time step.

[0053] In this embodiment, the coupled computation time step is synchronized to each MPI thread to ensure that all threads participating in the computation work at a unified time rhythm. Specifically, before the computation begins, the main control thread can send the calculated coupled computation time step to each MPI thread through the MPI message passing mechanism. After receiving the time step information, each thread stores it in a local variable so that it can control the calling and computation of the corresponding performance analysis program according to the time step in subsequent computation processes.

[0054] In this way, by synchronizing the computation time steps, the collaborative work of each MPI thread is achieved, avoiding computational chaos caused by inconsistent time steps and ensuring the accuracy and stability of the entire coupled computation process. For example, in a large-scale multiphysics coupled computation of a nuclear reactor core, multiple MPI threads are responsible for calling performance analysis programs such as thermal hydraulics, neutron physics, and structural mechanics. By synchronizing the computation time steps, these threads can perform calculations at a unified pace, enabling accurate interaction and fusion of calculation results from different physics fields.

[0055] S33: During the synchronous calculation process, continuously detect whether each performance analysis program has successfully calculated, and when multiple performance analysis programs have been detected to have successfully calculated, obtain the calculation result data output by each performance analysis program to obtain multiple calculation result data.

[0056] In this embodiment of the application, during the synchronous calculation process, it is continuously detected whether each performance analysis program has been successfully calculated, and when multiple performance analysis programs are detected to have been successfully calculated, the calculation result data output by each performance analysis program is obtained to obtain multiple calculation result data.

[0057] In practical applications, if any performance analysis program fails to perform calculations, the current performance analysis process is terminated. During synchronous computation, performance analysis programs may fail due to various reasons, such as program bugs, data errors, or insufficient computing resources. Therefore, continuous monitoring of the computation status is necessary to promptly identify these problems and take appropriate measures. Specifically, while each MPI thread is calling the performance analysis program for computation, a monitoring thread or the main thread can periodically check the running status of the performance analysis program. Successful computation can be determined by checking the program's return values ​​and output log information. If all performance analysis programs succeed, each MPI thread sends the computation results to a designated data collection node or the main thread via the MPI message passing mechanism. The main thread collects all the results, organizes and stores them for subsequent analysis and processing. Conversely, if any performance analysis program fails to perform a calculation, the main thread immediately sends a termination signal to all MPI threads, terminating the current performance analysis process and avoiding unnecessary computation and resource waste. In this way, by continuously monitoring the computation status, computational failures can be detected and the process terminated in a timely manner, ensuring the effective utilization of computing resources and the accuracy of computation results. At the same time, the computation results data of multiple performance analysis programs were successfully obtained, providing data support for subsequent multi-physics coupling computation at the component level and core level. Combined with parallel computing strategies, the scale and efficiency of performance analysis computation were effectively improved.

[0058] For example, in a complex coupled calculation for the performance analysis of nuclear fuel assemblies, the thermal-hydraulic program failed during the calculation process due to data anomalies. The detection thread promptly detected this situation and notified the main thread, which then terminated the entire calculation process, preventing other programs from continuing invalid calculations. At the same time, the failure information was recorded for subsequent troubleshooting.

[0059] S40: Using the grid translation vector parameters, construct the overall grid model of the reactor core, and according to the overall grid model, split and organize multiple calculation result data to obtain multiple individual fuel element grid data.

[0060] In this embodiment, a global mesh model of the reactor core is first constructed using the previously calculated mesh translation vector parameters. This global mesh model contains the mesh information of all fuel elements within the core. Then, based on this global mesh model, the calculation results data obtained from each MPI thread are split and organized. Specifically, the calculation results data corresponding to each fuel element mesh in the global mesh model are extracted to form multiple single fuel element mesh data sets.

[0061] In this way, by constructing an overall mesh model and splitting and organizing the calculation results data, accurate analysis from the whole to the part is achieved. This not only improves the efficiency of data processing but also facilitates subsequent performance analysis of individual fuel elements. For example, assuming the overall mesh model contains mesh information for 100 fuel elements, datasets containing calculation results for all fuel elements are obtained from MCNP, RELAP5, and FRAPCON programs. Based on the overall mesh model, these datasets are split into 100 individual fuel element datasets, each containing calculation results for the corresponding fuel element's neutron flux, temperature, stress, etc.

[0062] In step S40, the overall mesh model of the reactor core is constructed using the mesh translation vector parameters. Then, based on this overall mesh model, multiple calculation results are split and organized to obtain multiple individual fuel element mesh data, including: S41: Using the reference mesh translation vector, operate on the mesh at the center position to obtain the overall mesh model.

[0063] In this embodiment, a series of operations are performed on the central location grid, referencing the grid translation vector, to construct the overall grid model. The central location grid is the single fuel element grid at the center of the reactor core, determined in previous steps; it serves as the fundamental reference for constructing the overall grid. The grid translation vector contains information such as the displacement and rotation of each single fuel element grid relative to the central location grid. The operations performed on the central location grid include copying, translation, rotation, and merging. The specific process for generating the overall grid is as follows: First, the central location mesh is copied to quickly obtain multiple mesh copies with the same structure as the central location mesh, providing basic material for subsequent construction of the overall mesh. Next, the central location mesh is set, and using it as a reference, the multiple mesh copies are translated according to the mesh translation vector. This translation operation allows these mesh copies to be moved to appropriate positions based on the actual layout of the reactor core. Simultaneously, based on the actual geometry of the reactor core, specific mesh copies requiring rotation are identified and rotated. For example, some fuel elements may be arranged at specific angles in the core; rotation allows the corresponding mesh copy to align with the orientation of the actual fuel elements. Finally, based on the current positions and angles of the multiple mesh copies, they are merged. This merging operation is similar to stitching together components. Figure 1 Similarly, each mesh copy is combined into a complete mesh model to obtain the complete mesh model as the overall mesh model.

[0064] In this way, through a series of operations such as copying, translating, rotating, and merging, a precise overall mesh model that matches the actual structure of the reactor core can be constructed. Whether it's a symmetrical fuel assembly or a complex asymmetrical core structure, this flexible approach allows for the creation of appropriate meshes using both structured and unstructured grids to store field data. This provides an accurate geometric model foundation for subsequent coupled calculations, enabling the calculation results to more realistically reflect the physical characteristics of the reactor core. For example, in a nuclear reactor core with a complex geometry, some fuel elements are arranged in a spiral pattern. Through the above operations, the meshes corresponding to these fuel elements can be accurately constructed, ensuring that the overall mesh model is highly consistent with the actual core structure.

[0065] S42: Query the fuel element for each calculation result data, and map multiple calculation result data onto the overall mesh model according to the fuel element for each calculation result data, and split the mapped overall mesh model into multiple single fuel element meshes to obtain multiple single fuel element mesh data.

[0066] In this embodiment, the corresponding fuel element for each calculation result is queried, and then multiple calculation result data are mapped onto the overall mesh model and split into multiple individual fuel element mesh data. The calculation result data is obtained from multiple performance analysis programs in the preceding steps. This data contains calculation information of different physical fields in the reactor core, but it is dispersed and needs to be mapped to specific fuel elements.

[0067] Specifically, the process begins by querying the identifier associated with each calculation result to determine which fuel element the data pertains to. For example, each fuel element has a unique identifier, which is included in the calculation result data; querying this identifier clarifies the correspondence. Next, based on the fuel element each calculation result corresponds to, multiple calculation result data are mapped onto the overall mesh model. This assigns corresponding calculation result attributes to each fuel element mesh in the overall mesh model, ensuring that the overall mesh model contains not only geometric structure information but also physics calculation information. Finally, the mapped overall mesh model is split into multiple individual fuel element meshes, each containing the geometric information and calculation result data of its corresponding fuel element, thus obtaining multiple individual fuel element mesh data sets.

[0068] In this way, by accurately mapping the computational results to the overall mesh model and breaking them down into individual fuel element mesh data, a tight integration of computational results and specific fuel elements is achieved. This allows for convenient access to detailed information about each fuel element during subsequent multiphysics coupling calculations at the component and core levels, providing precise data support for the coupling calculations. Simultaneously, this data organization method, combined with parallel computing strategies, effectively improves the scale and efficiency of performance analysis calculations. For example, in the performance analysis of a large nuclear reactor core, this method allows massive amounts of computational results to be organized systematically, with each computing node responsible for processing a portion of the individual fuel element mesh data, achieving parallel computing and significantly reducing computation time.

[0069] S50: Through the data exchange channel between multiple performance analysis programs, the overall mesh model and the mesh data of multiple single fuel elements are synchronized to multiple performance analysis programs. Each MPI thread controls the corresponding performance analysis program to analyze and calculate the corresponding single fuel element mesh data according to its corresponding parallel computing strategy based on the overall mesh model, so as to complete the parallel performance analysis of multiple fuel elements under the coupled computing time step.

[0070] In this embodiment, a data exchange channel is pre-established between multiple performance analysis programs, which can be implemented through the communication function of MPI or a dedicated coupling library (such as MEDCoupling). Then, the overall mesh model and multiple individual fuel element mesh data are synchronized to each performance analysis program through these channels. Each MPI thread, according to its corresponding parallel computing strategy (such as one-to-one or one-to-many), uses the overall mesh model to control the corresponding performance analysis program to analyze and calculate the individual fuel element mesh data. The parallel computing strategy instructs the corresponding MPI thread to perform calculations on multiple or one fuel element. Finally, the parallel performance analysis of multiple fuel elements is completed within the set coupling calculation time step.

[0071] In this way, the synchronization and transmission of calculation results are achieved through data exchange channels, ensuring data consistency and computational synchronization between different programs. Simultaneously, combined with parallel computing strategies, the scale and efficiency of performance analysis calculations are effectively improved, laying a solid foundation for multi-physics coupled calculations at the component and core levels. For example, after data splitting and organization, the overall mesh model and the mesh data of 100 individual fuel elements are synchronized to the MCNP, RELAP5, and FRAPCON programs via MPI's communication function. Each MPI thread, according to a one-to-one parallel computing strategy, controls these programs to analyze and calculate the mesh data of individual fuel elements, and completes parallel performance analysis of all fuel elements within a set time step, obtaining detailed performance parameters of each fuel element within the core.

[0072] In practical applications, after completing the parallel performance analysis of multiple fuel elements within a coupled computation time step, to ensure the accuracy and reliability of the calculation results, it is necessary to perform a convergence check on the analysis results output by each performance analysis program according to a preset error standard. The error standard is an important indicator for measuring how close the calculated results are to the true values. It can be set according to the specific needs and accuracy requirements of the reactor core performance analysis; for example, it could be a relative error less than a certain percentage or an absolute error less than a fixed value. By performing a convergence check on the results of each performance analysis program, it is possible to determine whether the current calculation has met the expected accuracy requirements, thus providing a basis for decision-making in subsequent calculation steps.

[0073] When the analysis results output by each performance analysis program meet the error criteria, it indicates that the calculation at the current time step has converged and the calculation process can be advanced. Therefore, the time step of the coupled calculation is advanced to determine the next time step. In this process, the calculation efficiency and analysis accuracy need to be considered comprehensively. If the step size is too large, the calculation results may be inaccurate, while if the step size is too small, the calculation time and resource consumption will increase. After determining the next time step, each MPI thread is controlled to start calling the corresponding performance analysis program to continue synchronous calculations. During the calculation process of each MPI thread, multiple new calculation result data from multiple performance analysis programs are acquired. The overall mesh model of the reactor core is reconstructed using the mesh translation vector parameters. According to the overall mesh model, the multiple new calculation result data are split and organized to obtain multiple new single fuel element mesh data. Through the data exchange channel between multiple performance analysis programs, the overall mesh model and multiple new single fuel element mesh data are synchronized to multiple performance analysis programs. Each MPI thread then controls the corresponding performance analysis program to analyze and calculate the corresponding new single fuel element mesh data according to its corresponding parallel computing strategy and the overall mesh model, so as to complete the multi-fuel element parallel performance analysis in the next time step.

[0074] When the analysis results output by any performance analysis program do not meet the error standard, it indicates that the calculation at the current time step has not yet converged and needs to be recalculated. Therefore, each MPI thread is recontrolled to start calling the corresponding performance analysis program for synchronous calculation at the coupled calculation time step. During the calculation process of each MPI thread, multiple calculation result data from multiple performance analysis programs are acquired. Using the grid translation vector parameters, an overall grid model of the reactor core is constructed. According to the overall grid model, the multiple calculation result data from the recalculation are split and reorganized to obtain multiple redefined single fuel element grid data. Through the data exchange channel between multiple performance analysis programs, the overall grid model and the redefined single fuel element grid data are synchronized to multiple performance analysis programs. Each MPI thread then controls the corresponding performance analysis program to re-analyze the redefined single fuel element grid data according to its corresponding parallel computing strategy and the overall grid model, thereby completing the multi-fuel element parallel performance analysis at the coupled calculation time step.

[0075] In summary, the logical process of the parallel performance analysis method for multi-fuel elements based on mesh mapping proposed in this application is summarized as follows: See Figure 2 By establishing inter-program data exchange channels (DECs) and setting input files and MPI parameters, and initializing the collaborative environment of each program, the first step is to calculate the mesh translation vector to construct the overall mesh model of the reactor core, providing a geometric basis for subsequent data mapping. After initializing the computation time step, the main loop is entered: continuously checking whether each performance analysis program has terminated; if not, it dynamically acquires the input field template and output data, and achieves cross-fuel element mesh mapping through operations such as mesh translation, rotation, and merging, and uses DECs to complete inter-program data interaction; then, the overall mesh is split, the processed data is distributed to the program input fields corresponding to each MPI thread, and parallel solving of the current time step is started. After the solution is completed, the convergence of the results is verified by a preset error standard: if the condition is met, the formula is used. Proceeding to the next time step while maintaining solution time continuity, wherein, Indicates the current time step. Indicates the time increment. This indicates the next time step obtained from the calculation; if convergence is not achieved, the current time step remains unchanged, and the multi-program synchronous calculation process is re-triggered to ensure local calculation accuracy. Throughout this process, the mesh translation vector serves as the core parameter for spatial mapping, supporting the storage and transfer of mesh data for symmetric / asymmetric core structures, and improving the efficiency of multiphysics coupling analysis by combining parallel computing strategies.

[0076] The method provided in this application constructs an overall grid model of the reactor core by calculating grid translation vector parameters. This allows for the division of corresponding grid storage field data using both structured and unstructured grids for both symmetric and asymmetric fuel assemblies or reactor core structures. Furthermore, the method facilitates the exchange and transmission of calculation results through data exchange channels between multiple performance analysis programs. This lays the foundation for subsequent multiphysics coupling calculations at the component and core levels, while effectively improving the scale and efficiency of performance analysis calculations by combining parallel computing strategies, demonstrating good intelligence.

[0077] Furthermore, as Figure 1 In a specific implementation of the method, this application provides a parallel performance analysis device for multiple fuel elements based on mesh mapping, such as... Figure 3 As shown, the device includes: a setting module 301, a first calculation module 302, a second calculation module 303, a construction module 304, and an analysis module 305.

[0078] The configuration module 301 is used to determine multiple performance analysis programs participating in the coupled calculation, allocate message passing interface (MPI) threads to the multiple performance analysis programs and set input files. The multiple performance analysis programs include a neutron physics program, a thermal hydraulic program and a fuel performance analysis program. The first calculation module 302 is used to calculate the grid translation vector parameters based on the actual geometric layout of the reactor core to be analyzed. The grid translation vector parameters are used to indicate the direction and distance required to move the grid at the center of the reactor core to the grid where each fuel element in the reactor core is located. The second calculation module 303 is used to control each MPI thread to start calling the corresponding performance analysis program for synchronous calculation under the coupled calculation time step, and to obtain multiple calculation result data of the multiple performance analysis programs during the calculation process of each MPI thread. The construction module 304 is used to construct the overall grid model of the reactor core using the grid translation vector parameters, and to split and organize the multiple calculation result data according to the overall grid model to obtain multiple single fuel element grid data. Analysis module 305 is used to synchronize the overall mesh model and the mesh data of the multiple single fuel elements to the multiple performance analysis programs through the data exchange channel between the multiple performance analysis programs. Each MPI thread, according to its corresponding parallel computing strategy, controls the corresponding performance analysis program to analyze and calculate the corresponding single fuel element mesh data based on the overall mesh model, so as to complete the multi-fuel element parallel performance analysis under the coupled computing time step. The parallel computing strategy instructs the corresponding MPI thread to perform calculations on multiple or one fuel element.

[0079] In a specific application scenario, module 301 is configured to respond to the performance analysis requirements of fuel elements by determining the plurality of performance analysis programs from a set of preset performance analysis programs; determining a simulation data management library, and using the simulation data management library to set coupling parameter variables for each performance analysis program according to its format requirements, wherein the simulation data management library is a MEDCoupling library; constructing a data exchange channel between the plurality of performance analysis programs by calling the DEC parallel function; allocating multiple MPI threads to the plurality of performance analysis programs according to a preset thread allocation strategy, wherein the preset thread allocation strategy is used to instruct a single MPI thread to run a single performance analysis program or to run multiple performance analysis programs in parallel; generating and inputting a corresponding input file for each performance analysis program, so that each performance analysis program is initialized according to the input file, wherein the input file is used to define the analysis conditions required for the corresponding performance analysis program to run.

[0080] In a specific application scenario, the first calculation module 302 is used to determine the reactor core to be analyzed, identify the fuel elements in the reactor core, and divide the actual geometric layout of the reactor core into multiple single fuel element grids according to the identified fuel elements; determine the single fuel element grid located at the center of the reactor core as the center position grid among the multiple single fuel element grids, calculate the relative displacement between the center position grid and other single fuel element grids to obtain multiple relative displacement vectors, wherein the other single fuel element grids are the grids other than the center position grid among the multiple single fuel element grids; and integrate the multiple relative displacement vectors to obtain the grid translation vector parameters.

[0081] In a specific application scenario, the second calculation module 303 is used to calculate the coupled calculation time step based on the program characteristics of each performance analysis program; synchronize the coupled calculation time step to each MPI thread so that each MPI thread starts calling the corresponding performance analysis program for synchronous calculation under the coupled calculation time step; continuously detect whether each performance analysis program has calculated successfully during the synchronous calculation process, and when it is detected that multiple performance analysis programs have calculated successfully, obtain the calculation result data output by each performance analysis program to obtain the multiple calculation result data; wherein, if it is detected that there is a performance analysis program that has failed to calculate among the multiple performance analysis programs, the current performance analysis process is terminated.

[0082] In a specific application scenario, the construction module 304 is used to operate on the central position grid with reference to the grid translation vector to obtain the overall grid model. The operations performed on the central position grid include copying, translation, rotation, and merging. The module also queries the fuel element corresponding to each of the calculation result data, maps the multiple calculation result data to the overall grid model according to the fuel element corresponding to each of the calculation result data, and splits the mapped overall grid model into multiple single fuel element grids to obtain the multiple single fuel element grid data.

[0083] In a specific application scenario, the construction module 304 is used to copy the central position grid to obtain multiple grid copies; set the central position grid, and translate the multiple grid copies according to the grid translation vector based on the central position grid; and determine the specified grid copies with rotation requirements among the multiple grid copies according to the actual geometric layout of the reactor core and rotate them; and merge the multiple grid copies according to their current positions and angles to obtain a complete grid model as the overall grid model.

[0084] In specific application scenarios, the device further includes: The verification module is used to perform convergence verification on the analysis and calculation results output by each performance analysis program according to a preset error standard after completing the parallel performance analysis of the multi-fuel element under the coupled calculation time step. The second calculation module 303 is further configured to advance the coupled calculation time step when the analysis and calculation results output by each of the performance analysis programs meet the error standard, determine the next time step, control each MPI thread to start calling the corresponding performance analysis program to continue synchronous calculation in the next time step, and acquire multiple new calculation result data of the multiple performance analysis programs during the calculation process of each MPI thread, reuse the grid translation vector parameters to construct the overall grid model of the reactor core, and split and organize the multiple new calculation result data according to the overall grid model to obtain multiple new single fuel element grid data, and synchronize the overall grid model and the multiple new single fuel element grid data to the multiple performance analysis programs through the data exchange channel between the multiple performance analysis programs, so that each MPI thread controls the corresponding performance analysis program to analyze and calculate the corresponding new single fuel element grid data according to its corresponding parallel computing strategy and the overall grid model, so as to complete the multi-fuel element parallel performance analysis in the next time step; The second calculation module 303 is further configured to, when the analysis and calculation results output by any performance analysis program do not meet the error standard, re-control each MPI thread to start calling the corresponding performance analysis program for synchronous calculation at the coupled calculation time step, and during the calculation process of each MPI thread, acquire multiple calculation result data from the recalculated multiple performance analysis programs, construct the overall mesh model of the reactor core using the mesh translation vector parameters, and, according to the overall mesh model, split and organize the multiple recalculated calculation result data to obtain multiple redefined single fuel element mesh data. Through the data exchange channel between the multiple performance analysis programs, synchronize the overall mesh model and the redefined multiple single fuel element mesh data to the multiple performance analysis programs, so that each MPI thread, according to its corresponding parallel calculation strategy, controls the corresponding performance analysis program to re-analyze and calculate the corresponding redefined single fuel element mesh data based on the overall mesh model, thereby completing the multi-fuel element parallel performance analysis at the coupled calculation time step.

[0085] The apparatus provided in this application constructs an overall grid model of the reactor core by calculating grid translation vector parameters. This allows for the division of corresponding grid storage field data using both structured and unstructured grids for both symmetric and asymmetric fuel assemblies or reactor core structures. Furthermore, the apparatus facilitates the exchange and transmission of calculation results through data exchange channels between multiple performance analysis programs. This lays the foundation for subsequent multiphysics coupling calculations at the component and core levels, while effectively improving the scale and efficiency of performance analysis calculations by combining parallel computing strategies, demonstrating good intelligence.

[0086] It should be noted that other corresponding descriptions of the functional units involved in the multi-fuel element parallel performance analysis device based on mesh mapping provided in this application embodiment can be found in the following references. Figure 1 and Figure 2 The corresponding descriptions in [the document] will not be repeated here.

[0087] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0088] The above embodiments and the technical features in the embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0089] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

[0090] In an exemplary embodiment, see Figure 4 The invention also provides a computer device including a bus, a processor, a memory, and a communication interface. It may also include an input / output interface and a display device, wherein the various functional units can communicate with each other via the bus. The memory stores a computer program, and the processor executes the program stored in the memory to perform the grid-mapping-based parallel performance analysis method for multi-fuel elements described in the above embodiments.

[0091] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the grid-mapped multi-fuel element parallel performance analysis method.

[0092] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented in hardware or by using software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) and includes several instructions to cause a computer device (such as a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0093] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of a preferred embodiment, and the modules or processes shown in the drawings are not necessarily essential for implementing this application.

[0094] Those skilled in the art will understand that the modules in the apparatus of the implementation scenario can be distributed within the apparatus of the implementation scenario as described, or they can be located in one or more apparatuses different from this implementation scenario, with corresponding changes. The modules of the above-described implementation scenario can be combined into one module, or they can be further divided into multiple sub-modules.

[0095] The serial numbers in this application are for descriptive purposes only and do not represent the superiority or inferiority of the implementation scenario.

[0096] The above disclosures are only a few specific implementation scenarios of this application. However, this application is not limited to these. Any variations that can be conceived by those skilled in the art should fall within the protection scope of this application.

Claims

1. A parallel performance analysis method for multiple fuel elements based on mesh mapping, characterized in that, include: A number of performance analysis programs participating in the coupled calculation are identified, and message passing interface (MPI) threads are allocated and input files are set for the multiple performance analysis programs. The multiple performance analysis programs include a neutron physics program, a thermal hydraulic program, and a fuel performance analysis program. Based on the actual geometric layout of the reactor core to be analyzed, the grid translation vector parameters are calculated. These parameters indicate the direction and distance required to move the center grid of the reactor core to the grid where each fuel element in the reactor core is located. Each MPI thread is controlled to start calling the corresponding performance analysis program for synchronous calculation at the coupled calculation time step, and multiple calculation result data of the multiple performance analysis programs are obtained during the calculation process of each MPI thread. Using the grid translation vector parameters, an overall grid model of the reactor core is constructed, and according to the overall grid model, the multiple calculation result data are split and organized to obtain multiple individual fuel element grid data. Through the data exchange channel between the multiple performance analysis programs, the overall mesh model and the mesh data of the multiple single fuel elements are synchronized to the multiple performance analysis programs. Each MPI thread, according to its corresponding parallel computing strategy, controls the corresponding performance analysis program to analyze and calculate the corresponding single fuel element mesh data based on the overall mesh model, so as to complete the multi-fuel element parallel performance analysis under the coupled computing time step. The parallel computing strategy instructs the corresponding MPI thread to perform calculations on multiple or one fuel element.

2. The method according to claim 1, characterized in that, The process of determining multiple performance analysis programs participating in the coupled computation, allocating message passing interface (MPI) threads to these multiple performance analysis programs, and setting input files includes: In response to the requirements for fuel element performance analysis, the plurality of performance analysis programs are determined from a set of preset performance analysis programs; A simulation data management library is determined, and coupling parameter variables are set for each performance analysis program according to its format requirements using the simulation data management library, which is the MEDCoupling library; By calling the DEC parallel function, a data exchange channel is established between the multiple performance analysis programs; According to a preset thread allocation strategy, multiple MPI threads are allocated to the multiple performance analysis programs, wherein the preset thread allocation strategy is used to instruct a single MPI thread to run a single performance analysis program or to run multiple performance analysis programs in parallel. A corresponding input file is generated for each performance analysis program and input into the program so that each performance analysis program is initialized according to the input file, wherein the input file is used to define the analysis conditions required for the corresponding performance analysis program to run.

3. The method according to claim 1, characterized in that, The calculation of the grid translation vector parameters based on the actual geometric layout of the reactor core to be analyzed includes: The reactor core to be analyzed is determined, the fuel elements in the reactor core are identified, and the actual geometric layout of the reactor core is meshed according to the identified fuel elements to obtain multiple single fuel element meshes. Among the plurality of single fuel element grids, the single fuel element grid located at the center of the reactor core is determined as the center position grid. The relative displacement between the center position grid and the other single fuel element grids is calculated to obtain a plurality of relative displacement vectors. The other single fuel element grids are the grids among the plurality of single fuel element grids excluding the center position grid. The multiple relative displacement vectors are integrated to obtain the grid translation vector parameters.

4. The method according to claim 1, characterized in that, The control mechanism ensures that each MPI thread initiates synchronous computation by calling the corresponding performance analysis program within the coupled computation time step, and acquires multiple computation result data from the multiple performance analysis programs during the computation process of each MPI thread, including: The time step of the coupled computation is calculated based on the program characteristics of each of the performance analysis programs. The coupled computation time step is synchronized to each MPI thread so that each MPI thread can start calling the corresponding performance analysis program to perform synchronous computation at the coupled computation time step. During the synchronous calculation process, the system continuously monitors whether each performance analysis program has successfully completed the calculation. If all performance analysis programs have successfully completed the calculation, the system obtains the calculation result data output by each performance analysis program to obtain the multiple calculation result data. If a performance analysis program fails to complete the calculation, the current performance analysis process is terminated.

5. The method according to claim 1, characterized in that, The process involves constructing an overall mesh model of the reactor core using the mesh translation vector parameters, and then splitting and organizing the multiple calculation result data according to the overall mesh model to obtain multiple individual fuel element mesh data, including: Referring to the mesh translation vector, operations are performed on the mesh at the center position to obtain the overall mesh model. The operations performed on the mesh at the center position include copying, translation, rotation, and merging. The calculation result data is queried for each fuel element, and the multiple calculation result data are mapped onto the overall mesh model according to each fuel element. The mapped overall mesh model is then split into multiple single fuel element meshes to obtain the multiple single fuel element mesh data.

6. The method according to claim 5, characterized in that, The step of operating on the central mesh position based on the mesh translation vector to obtain the overall mesh model includes: The central grid is copied to obtain multiple grid copies; The center position grid is set, and the multiple grid replicas are translated according to the grid translation vector based on the center position grid. The specified grid replicas with rotation requirements are determined among the multiple grid replicas and rotated according to the actual geometric layout of the reactor core. Based on the current position and angle of the multiple grid copies, the multiple grid copies are merged to obtain a complete grid model, which is then used as the overall grid model.

7. The method according to claim 1, characterized in that, The method further includes: After completing the parallel performance analysis of the multi-fuel element under the coupled calculation time step, the convergence test is performed on the analysis calculation results output by each performance analysis program according to the preset error standard. When the analysis and calculation results output by each of the performance analysis programs meet the error criteria, the coupled calculation time step is advanced to determine the next time step. Each MPI thread is then controlled to start calling the corresponding performance analysis program to continue synchronous calculation in the next time step. During the calculation process of each MPI thread, multiple new calculation result data from the multiple performance analysis programs are acquired. The grid translation vector parameters are reused to construct the overall grid model of the reactor core. According to the overall grid model, the multiple new calculation result data are split and organized to obtain multiple new single-fuel element grid data. The overall grid model and the multiple new single-fuel element grid data are synchronized to the multiple performance analysis programs through the data exchange channel between the multiple performance analysis programs. Each MPI thread, according to its corresponding parallel computing strategy, controls the corresponding performance analysis program to analyze and calculate the corresponding new single-fuel element grid data based on the overall grid model, thereby completing the multi-fuel element parallel performance analysis in the next time step. When the analysis and calculation results output by any performance analysis program do not meet the error standard, the MPI threads are re-controlled to start calling the corresponding performance analysis program for synchronous calculation at the coupled calculation time step. During the calculation process of each MPI thread, multiple calculation result data from the recalculated performance analysis programs are acquired. Using the grid translation vector parameters, an overall grid model of the reactor core is constructed. According to the overall grid model, the recalculated multiple calculation result data are split and reorganized to obtain multiple redefined single fuel element grid data. Through the data exchange channel between the multiple performance analysis programs, the overall grid model and the redefined multiple single fuel element grid data are synchronized to the multiple performance analysis programs. Then, each MPI thread, according to its corresponding parallel computing strategy, controls the corresponding performance analysis program to re-analyze and calculate the corresponding redefined single fuel element grid data based on the overall grid model, so as to complete the multi-fuel element parallel performance analysis at the coupled calculation time step.

8. A parallel performance analysis device for multiple fuel elements based on mesh mapping, characterized in that, include: The configuration module is used to determine multiple performance analysis programs participating in the coupled calculation, allocate message passing interface (MPI) threads to the multiple performance analysis programs, and set input files. The multiple performance analysis programs include a neutron physics program, a thermal hydraulic program, and a fuel performance analysis program. The first calculation module is used to calculate the grid translation vector parameters based on the actual geometric layout of the reactor core to be analyzed. The grid translation vector parameters are used to indicate the direction and distance required to move the grid at the center of the reactor core to the grid where each fuel element in the reactor core is located. The second calculation module is used to control each MPI thread to start calling the corresponding performance analysis program for synchronous calculation under the coupled calculation time step, and to obtain multiple calculation result data of the multiple performance analysis programs during the calculation process of each MPI thread. The construction module is used to construct the overall grid model of the reactor core using the grid translation vector parameters, and to split and organize the multiple calculation result data according to the overall grid model to obtain multiple single fuel element grid data. The analysis module is used to synchronize the overall mesh model and the mesh data of the multiple single fuel elements to the multiple performance analysis programs through the data exchange channel between the multiple performance analysis programs. Then, each MPI thread controls the corresponding performance analysis program to analyze and calculate the corresponding single fuel element mesh data according to its corresponding parallel computing strategy based on the overall mesh model, so as to complete the multi-fuel element parallel performance analysis under the coupled computing time step. The parallel computing strategy instructs the corresponding MPI thread to perform calculations on multiple or one fuel element.

9. A device comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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