Nuclear power fuel assembly grid generation method and device and computer readable medium

Abstract

The invention provides a grid generation method and device for a nuclear power fuel assembly and a computer readable medium, and the method comprises the steps: receiving a geometric file of the nuclear power fuel assembly, and generating a hybrid grid of the nuclear power fuel assembly according to a grid generation strategy and the geometric file, the grid generation strategy comprises the steps that a structured grid is used in a boundary layer of the nuclear power fuel assembly, and an unstructured grid is used in an area outside the boundary layer; the generated hybrid grid is subjected to self-adaptive refinement processing, the self-adaptive refinement processing comprises the steps of recognizing a high-gradient area and increasing the grid density in the high-gradient area, and the high-gradient area refers to the area where the change rate of fluid parameters in space is larger than a preset threshold value when fluid flows along the wall face of the nuclear power fuel assembly. According to the method, the grid density is only increased in a high-gradient region, and relatively thick grids are kept in other regions, so that the resolution of a key region is ensured, and the waste of computing resources is effectively reduced.

Description

Technical Field

[0001] The present invention mainly relates to the technical field of nuclear power reactors, and in particular to a grid generation method, device and computer-readable medium for a nuclear power fuel assembly. Background Art

[0002] The design, optimization, and safe operation of nuclear power fuel assemblies rely heavily on computational fluid dynamics (CFD) technology. CFD simulations of the complex flow, heat transfer, and multi-physics coupling of nuclear power fuel assemblies not only improve the thermal and hydraulic performance of the assemblies but also provide critical data for safety assessments.

[0003] In computational fluid dynamics, mesh generation is a critical step. For example, both the finite volume method and the finite element method require dividing the computational domain into a grid. The quality of the grid affects the accuracy and computational efficiency of the simulation. If the grid is not divided well, it may lead to inaccurate results or a long calculation time. Therefore, mesh generation is very important for numerical simulation of fluid dynamics. In the traditional mesh generation process, the grid is usually generated uniformly in each area. The traditional uniform mesh method has difficulty in providing sufficient resolution in key areas, resulting in large errors in the simulation results. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a grid generation method, device and computer-readable medium for nuclear power fuel assemblies, so as to solve the problem that traditional grid methods are difficult to provide sufficient resolution in key areas, resulting in large errors in simulation results.

[0005] To address the above-mentioned technical problems, the present invention provides a method for generating a grid for a nuclear power fuel assembly, comprising: receiving a geometry file for a nuclear power fuel assembly; generating a hybrid grid for the nuclear power fuel assembly based on a grid generation strategy and the geometry file; the grid generation strategy comprising using a structured grid in the boundary layer of the nuclear power fuel assembly and an unstructured grid in areas outside the boundary layer, wherein the boundary layer refers to a thin layer of fluid formed on the surface of the nuclear power fuel assembly; and performing adaptive refinement on the generated hybrid grid, wherein the adaptive refinement comprises identifying high-gradient regions and increasing the grid density in the high-gradient regions; the high-gradient regions refer to regions where the spatial rate of change of fluid parameters is greater than a preset threshold when the fluid flows along the wall of the nuclear power fuel assembly. Optionally, identifying the high-gradient regions comprises: calculating the curvature of a fluid parameter curve in the hybrid grid; and determining whether the curvature is greater than or equal to a curvature threshold; if so, determining the region as the high-gradient region.

[0006] Optionally, the fluid parameter curve includes a pressure curve or a velocity curve.

[0007] Optionally, the curvature of the fluid parameter curve is calculated using the following formula:

[0008]

[0009] Wherein, K is the curvature of the fluid parameter curve, r'(s) is the first-order derivative of the fluid parameter curve, representing the tangent vector, and r"(s) is the second-order derivative of the fluid parameter curve, representing the acceleration vector.

[0010] Optionally, increasing the mesh density in the high gradient region includes: refining the high gradient region at different levels according to the curvature value according to the following formula:

[0011]

[0012] where RL is the refinement level, H, M, and L are high, medium, and low levels, respectively, and Kt and Kl are predefined curvature thresholds.

[0013] Optionally, the method further includes: training a grid parameter prediction model based on historical simulation data and a machine learning model, and optimizing the hybrid grid after adaptive refinement processing through grid parameter prediction values ​​output by the grid parameter prediction model.

[0014] Optionally, training a grid parameter prediction model based on historical simulation data and a machine learning model includes: collecting historical simulation data, the historical simulation data including a geometric model of a nuclear power fuel assembly and its corresponding simulation results; extracting geometric features from the geometric model, extracting grid parameter features from the simulation results, and constructing a data set based on the geometric features and the grid parameter features; selecting a machine learning model, the machine learning model including a linear regression model or a random forest regression model; and using the data set to train the machine learning model to obtain the grid parameter prediction model.

[0015] Optionally, when the machine learning model is a linear regression model, the grid parameters predicted by the grid parameter prediction model include grid density and resolution, and the linear regression model is:

[0016] Y=XW+b

[0017] Where Y is the predicted value of the grid parameter;

[0018] X is the input feature matrix with dimension m×n, where m is the number of samples and n is the number of features;

[0019] W is the weight matrix with dimension n×k, where k is the number of output parameters;

[0020] B is the bias term with a dimension of 1×k.

[0021] Optionally, when the machine learning model is a random forest regression model, the grid parameters predicted by the grid parameter prediction model include non-orthogonality, distortion, and volume ratio. The random forest regression model uses multiple decision trees for prediction, and takes the average value of all trees as the final result, which is expressed as:

[0022]

[0023] in, is the predicted value of the grid parameter, Ti is the i-th decision tree, N is the number of decision trees, and X is the input feature.

[0024] Optionally, the method further includes: performing parallel computational domain decomposition on the hybrid grid after the adaptive refinement process, determining whether the number of grids on each computational domain is substantially the same, and if not, adjusting grid parameters.

[0025] To solve the above technical problems, the present invention provides a grid generation device for a nuclear power fuel assembly, comprising: a memory for storing instructions executable by a processor; and a processor for executing the instructions to implement the grid generation method for a nuclear power fuel assembly as described above.

[0026] In order to solve the above technical problems, a computer readable medium storing computer program codes is provided. When the computer program codes are executed by a processor, the grid generation method of the nuclear power fuel assembly as described above is implemented.

[0027] Compared with the prior art, the present invention has the following advantages:

[0028] The grid generation method for nuclear power fuel assemblies of the present invention increases the grid density only in high-gradient areas through an adaptive grid refinement method, and maintains a coarser grid in other areas, which not only ensures the resolution of key areas, but also effectively reduces the waste of computing resources and improves computing efficiency; secondly, the present invention maximizes parallel computing efficiency while ensuring grid quality, and can significantly improve simulation efficiency and accuracy; furthermore, the present invention quickly generates high-quality grids through automated tools and machine learning algorithms, shortening the time of the simulation preparation stage. The high-quality adaptive grid can reduce numerical errors and improve the convergence and stability of the simulation solver. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The accompanying drawings are included to provide a further understanding of the present application. They are incorporated into and constitute a part of this application. The accompanying drawings illustrate embodiments of the present application and, together with this specification, serve to explain the principles of the present application. In the accompanying drawings:

[0030] Figure 1 This is a flowchart of a method for generating a grid of a nuclear power fuel assembly according to an embodiment of the present disclosure.

[0031] Figure 2 is a flowchart of an adaptive refinement process according to an embodiment of the present disclosure.

[0032] Figure 3 yes Figure 1 Flowchart of the grid generation method for nuclear power fuel assemblies of the optimized embodiment.

[0033] Figure 4 4 is a flowchart of training a grid parameter prediction model according to an embodiment of the present disclosure.

[0034] Figure 5 This is a system block diagram of a grid generation device for a nuclear power fuel assembly according to an embodiment of the present application. DETAILED DESCRIPTION

[0035] To more clearly illustrate the technical solutions of the embodiments of this application, the following is a brief introduction to the drawings required for describing the embodiments. Obviously, the drawings described below are merely examples or embodiments of this application. Those skilled in the art can apply this application to other similar scenarios based on these drawings without inventive effort. Unless otherwise apparent from the context or otherwise noted, the same reference numerals in the figures represent the same structure or operation.

[0036] Traditional mesh generation methods use the same mesh density across the entire geometry, resulting in unnecessary computational overhead. This invention uses an adaptive mesh refinement method based on geometric features to generate a finer mesh in critical areas, such as those with high curvature and sharp edges. This ensures accurate capture of geometric features and improves the accuracy of simulation results. In particular, in high-gradient areas, the refined mesh allows for more precise interpretation of complex physical phenomena.

[0037] Figure 1 FIG. 1 is a flow chart of a method for generating a grid of a nuclear power fuel assembly according to an embodiment of the present disclosure. Figure 1 As shown, the grid generation method 100 of a nuclear power fuel assembly includes:

[0038] Step S1: receiving a geometry file of a nuclear power fuel assembly, and generating a hybrid mesh of the nuclear power fuel assembly according to a mesh generation strategy and the geometry file. The mesh generation strategy includes using a structured mesh in a boundary layer of the nuclear power fuel assembly and using an unstructured mesh in an area outside the boundary layer.

[0039] Nuclear power fuel assemblies consist of multiple fuel rods arranged in a grid structure with spacer grids and other components in between. Coolant (such as water or liquid metal) flows through these fuel rods, removing the heat generated by the nuclear reaction. The boundary layer of a nuclear power fuel assembly primarily refers to the thin layer of coolant formed on the surface of the fuel rods. The presence of the boundary layer affects the coolant's flow resistance and heat transfer efficiency. If the boundary layer is too thick, it can lead to poor heat transfer, local temperature increases, and even cause a boiling crisis (Departure from Nucleate Boiling, DNB), a major issue in nuclear reactor safety. Therefore, optimizing boundary layer management is crucial to ensuring the safety and efficiency of fuel assemblies. In CFD simulations of fuel assemblies, accurately capturing the boundary layer requires a high-quality mesh, especially generating a sufficiently fine boundary layer mesh near the fuel rod surface.

[0040] Optionally, generate a hybrid mesh using snappyHexMesh. snappyHexMesh is an automated hexahedral (Hex-dominant) unstructured mesh generation tool designed for complex geometries. Hybrid meshes generated using snappyHexMesh include:

[0041] 1) Geometry preprocessing, including geometry repair and geometry simplification. Geometry repair involves exporting the CAD model of the fuel assembly (fuel rods, spacer grids, etc.) as a closed STL (STereoLithography) surface mesh to ensure that there are no holes and the normals are consistent. If there are small gaps or overlaps, the geometry is repaired using Salome or Gmsh software. Salome and Gmsh are both simulation software. Geometry simplification includes but is not limited to: removing details whose impact on the flow is negligible; simplifying geometric topology, such as merging adjacent faces and simplifying the number of curve segments; and utilizing symmetry. For symmetric flow problems, only 1 / 2 or 1 / 4 of the geometry is retained to reduce the amount of meshing.

[0042] 2) Using snappyHexMesh's addLayers function, a quasi-structured prismatic mesh is generated in the boundary layer, while retaining an unstructured hexahedral / cut cell mesh in the area outside the boundary layer, forming an efficient hybrid mesh. Specifically, in the boundary layer control function (addLayerControls), a regular expression is used to match the boundary layer, define the boundary layer as a prismatic mesh, and set the mesh parameters of the prismatic mesh. Mesh parameters include but are not limited to the number of prismatic layers, the interlayer growth rate, and the total thickness. Ultimately, a uniform prismatic layer is generated in the boundary layer region - the fuel rod surface, with the interlayer thickness increasing proportionally. In the area outside the boundary layer - the grid and the gaps between the rods, an unstructured hexahedral or polyhedral mesh is generated.

[0043] Step S2: Adaptively refine the generated hybrid grid. The adaptive refinement includes identifying high-gradient areas and increasing the grid density in the high-gradient areas. The high-gradient areas refer to areas where the spatial rate of change of fluid parameters is greater than a preset threshold when the fluid flows along the wall of the nuclear power fuel assembly.

[0044] The fluid parameters include but are not limited to the velocity, pressure, and temperature of the fluid.

[0045] High-gradient regions in nuclear fuel assemblies are primarily concentrated in areas with dramatic flow variations, concentrated heat transfer, or complex structures. Physical quantities (such as velocity, temperature, and pressure) in these areas exhibit significant spatial gradients. These areas include, but are not limited to, the boundary layer on the fuel rod surface, areas near the spacer grid, and coolant inlet and outlet regions.

[0046] In the fuel rod surface boundary layer, the coolant forms a flow boundary layer due to viscosity, and the velocity rapidly increases from zero on the wall to the mainstream velocity. Near the positioning grid, the velocity and pressure of the fluid show significant gradient changes in space. The mixing wings, springs, and rigid bumps on the grid disturb the flow, generating local high-speed jets and vortices. The grid structure causes the flow to contract and expand, forming a local high-pressure difference. In the coolant inlet and outlet areas, uneven inlet distribution leads to local high speed or backflow, and the outlet section produces flow separation due to the sudden expansion structure, resulting in a sharp change in velocity.

[0047] Figure 2 is a flowchart of an adaptive refinement process according to an embodiment of the present disclosure. Figure 2 As shown, the adaptive refinement process includes:

[0048] Step S21: Calculate the curvature of the fluid parameter curve in the hybrid grid.

[0049] The fluid parameter curve includes but is not limited to a pressure curve and a velocity curve.

[0050] In the two-dimensional case, for a given curve r(s), the curvature K is expressed as:

[0051]

[0052] Where K is the curvature of the fluid parameter curve, r'(s) is the first-order derivative of the fluid parameter curve, representing the tangent vector, and r"(s) is the second-order derivative of the fluid parameter curve, representing the acceleration vector.

[0053] Step S22: Determine whether the curvature is greater than or equal to the curvature threshold; if so, determine it as a high gradient area.

[0054] Step S23: Refine the high gradient region at different levels according to the curvature value.

[0055] Optionally, the high gradient region is refined at different levels according to the curvature value according to the following formula:

[0056]

[0057] where RL is the refinement level, H, M, and L are high, medium, and low levels, respectively, and Kt and Kl are predefined curvature thresholds.

[0058] The increased mesh density varies for different refinement levels: H, M, and L. Specifically, for high-level refinement H, the increased mesh density is the highest; for low-level refinement L, the increased mesh density is the lowest; and for medium-level refinement H, the increased mesh density is somewhere in between.

[0059] Traditional uniform meshing methods use the same mesh density across the entire geometry. Low mesh density makes it difficult to provide sufficient resolution in critical areas, leading to large errors in simulation results. High mesh density results in unnecessary computational overhead. The present invention's mesh generation method for nuclear power fuel assemblies uses an adaptive mesh refinement method to increase mesh density only in required areas, while maintaining a coarser mesh in other areas. This ensures resolution in critical areas while effectively reducing the waste of computational resources and improving computational efficiency.

[0060] Figure 3 yes Figure 1 Flowchart of the grid generation method of the nuclear power fuel assembly of the optimized embodiment. Figure 3 As shown, the grid generation method 300 of a nuclear power fuel assembly includes:

[0061] Step S0: converting the CAD model into a geometry file and performing geometric simplification on the geometry file;

[0062] Step S1: receiving a geometry file, and generating a hybrid mesh according to the geometry file and a mesh generation strategy, wherein the mesh generation strategy includes using a structured mesh in a boundary layer and using an unstructured mesh in other areas.

[0063] Step S2: Adaptively refine the generated hybrid grid. The adaptive refinement process includes identifying high gradient areas and increasing the grid density in the high gradient areas.

[0064] Step S3: performing parallel computational domain decomposition on the hybrid grid after the adaptive refinement process, and determining whether the number of grids on each computational domain is roughly the same; if not, adjusting the grid parameters.

[0065] For example, the hybrid grid after adaptive refinement is decomposed into parallel computational domains using the decomposePar tool and the optimized decomposition method. Parallel computation is performed on each computational domain to determine whether the number of grids on each computational domain is roughly the same. If they are the same, it indicates load balancing. If they are different, it indicates that the computational load of each core is uneven, resources are idle, and the grid parameters need to be adjusted.

[0066] Optionally, the optimized decomposition method includes but is not limited to scotch or metis.

[0067] Optionally, adjusting mesh parameters includes performing mesh quality inspection and optimization based on checkMesh, and adjusting mesh parameters based on the inspection results. Inspection quality metrics include, but are not limited to, non-orthogonal angles, aspect ratio, distortion, boundary layer thickness consistency, and negative volume elements. For example, when the inspection result includes an excessively large aspect ratio, adjusting mesh parameters includes locally refining the mesh or adjusting the refinement level.

[0068] The grid generation method for nuclear power fuel assemblies disclosed in the present invention realizes load balancing through decomposePar+scotch / metis and performs closed-loop optimization of grid quality in combination with checkMesh. While ensuring grid quality, it maximizes parallel computing efficiency and can significantly improve simulation efficiency and accuracy.

[0069] Step S4: training a grid parameter prediction model based on historical simulation data and geometric features of the machine learning model, and optimizing the hybrid grid after adaptive refinement using the grid parameter prediction values ​​output by the grid parameter prediction model.

[0070] Figure 4 FIG. 1 is a flow chart of a training grid parameter prediction model according to an embodiment of the present disclosure. Figure 4 As shown, training the grid parameter prediction model includes:

[0071] Step S41: collecting historical simulation data, where the historical simulation data includes a geometric model of a nuclear power fuel assembly and its corresponding simulation results;

[0072] Step S42: extracting geometric features from the geometric model, extracting mesh parameter features from the simulation results, and constructing a data set based on the geometric features and mesh parameter features;

[0073] Step S43: Select a machine learning algorithm model, where the machine learning model includes a linear regression model or a random forest regression model;

[0074] Step S44: Use the training data set to train the machine learning model to obtain a grid parameter prediction model.

[0075] Optionally, the grid parameters predicted by the linear regression model include grid density and resolution, and the linear regression model is:

[0076] Y=XW+b

[0077] Where X is the input feature matrix with dimension m×n, where m is the number of samples and n is the number of features;

[0078] W is the weight matrix with dimension n×k, where k is the number of output parameters;

[0079] B is the bias term with a dimension of 1×k.

[0080] Optionally, the grid parameters of the random forest regression model include non-orthogonality, distortion, and volume ratio. The random forest regression model uses multiple decision trees for prediction, and the average of all trees is taken as the final result, which is expressed as:

[0081]

[0082] Among them, Ti is the i-th decision tree, N is the number of decision trees, and X is the input feature.

[0083] Optionally, the loss function of the grid parameter prediction model is:

[0084]

[0085] m: number of training samples;

[0086] Yi: actual grid parameters;

[0087] Prediction grid parameters;

[0088] By minimizing the MSE, the model learns to accurately predict the output grid parameters Y from the input features X.

[0089] The grid generation method for nuclear power fuel assemblies of the present invention uses automated tools and machine learning algorithms to quickly generate high-quality grids, shortening the time of the simulation preparation stage. The high-quality adaptive grid can reduce numerical errors and improve the convergence and stability of the simulation solver.

[0090] Step S5: Advanced visualization, mesh quality analysis and optimization using advanced filters in ParaView to ensure accurate capture of geometric features and uniform mesh quality.

[0091] ParaView is an open-source, cross-platform data processing and visualization program. ParaView users can quickly create visualization environments to analyze data using quantitative or qualitative methods. Its batch processing capabilities allow interactive operations within the 3D space, within the toolbar and display interface, for perfect data display and further data mining. ParaView's basic interactive visualization features include data loading, data processing, parameter adjustment, and data interaction. Functions such as cutting, cropping, contouring, and probing can all be performed within ParaView.

[0092] Flowcharts are used in this application to illustrate the operations performed by systems according to embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the various steps may be processed in reverse order or simultaneously. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0093] The present invention also provides a grid generating device for a nuclear power fuel assembly. Figure 5 This is a system block diagram of a grid generation device for a nuclear power fuel assembly according to an embodiment of the present application. Figure 5 As shown, the grid generation device 500 for a nuclear power fuel assembly may include an internal communication bus 501, a processor 502, a read-only memory (ROM) 503, a random access memory (RAM) 504, and a communication port 505. When used on a personal computer, the grid generation device 500 for a nuclear power fuel assembly may also include a hard disk 506. The internal communication bus 501 enables data communication between the components of the grid generation device 500 for a nuclear power fuel assembly. The processor 502 can make judgments and issue prompts. In some embodiments, the processor 502 can be composed of one or more processors. The communication port 505 enables data communication between the grid generation device 500 for a nuclear power fuel assembly and the outside world. In some embodiments, the grid generation device 500 for a nuclear power fuel assembly can send and receive information and data from a network via the communication port 505. The nuclear power fuel assembly grid generation device 500 may also include various forms of program storage units and data storage units, such as a hard disk 506, a read-only memory (ROM) 503, and a random access memory (RAM) 504, capable of storing various data files used for computer processing and / or communication, as well as possible program instructions executed by the processor 502. The processor executes these instructions to implement the main part of the method. The results of the processor processing are transmitted to the user device via a communication port and displayed on the user interface.

[0094] The above-mentioned operating method can be implemented as a computer program, stored in the hard disk 506, and loaded into the processor 502 for execution to implement the grid generation method of the nuclear power fuel assembly of the present application.

[0095] The present application also includes a computer-readable medium storing computer program code, which, when executed by a processor, implements the aforementioned method for generating a grid of a nuclear power fuel assembly.

[0096] When the grid generation method for nuclear power fuel assemblies is implemented as a computer program, it can also be stored in a computer-readable storage medium as an article of manufacture. For example, computer-readable storage media can include, but are not limited to, magnetic storage devices (e.g., hard disks, floppy disks, magnetic strips), optical disks (e.g., compact disks (CDs), digital versatile disks (DVDs)), smart cards, and flash memory devices (e.g., electrically erasable programmable read-only memories (EPROMs), cards, sticks, key drives). In addition, the various storage media described herein can represent one or more devices and / or other machine-readable media for storing information. The term "machine-readable medium" can include, but is not limited to, wireless channels and various other media (and / or storage media) that can store, contain, and / or carry code and / or instructions and / or data.

[0097] It should be understood that the embodiments described above are merely illustrative. The embodiments described herein may be implemented in hardware, software, firmware, middleware, microcode, or any combination thereof. For hardware implementation, the processor may be implemented within one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, and / or other electronic units designed to perform the functions described herein, or a combination thereof.

[0098] The basic concepts have been described above. It will be apparent to those skilled in the art that the above disclosures are merely illustrative and do not constitute limitations on this application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and amendments to this application. Such modifications, improvements, and amendments are suggested in this application and remain within the spirit and scope of the exemplary embodiments of this application.

[0099] At the same time, this application uses specific terms to describe the embodiments of this application. For example, "one embodiment," "an embodiment," and / or "some embodiments" refer to a certain feature, structure, or characteristic related to at least one embodiment of this application. Therefore, it should be emphasized and noted that "one embodiment," "an embodiment," or "an alternative embodiment" mentioned twice or multiple times in different locations in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of this application may be appropriately combined.

[0100] A computer-readable medium may include a propagated data signal embodying computer program code, for example, in baseband or as part of a carrier wave. The propagated signal may be in a variety of forms, including electromagnetic, optical, etc., or a suitable combination thereof. A computer-readable medium may be any computer-readable medium other than a computer-readable storage medium that can be connected to an instruction execution system, apparatus, or device to communicate, propagate, or transmit the program for use. The program code on the computer-readable medium may be transmitted via any suitable medium, including radio, cable, fiber optic cable, radio frequency signal, or similar medium, or any combination of the above.

[0101] Similarly, it should be noted that, in order to simplify the presentation of this disclosure and thereby facilitate understanding of one or more embodiments of the invention, the foregoing descriptions of the embodiments of this application sometimes combine multiple features into a single embodiment, figure, or description thereof. However, this disclosure method does not imply that the subject matter of this application requires more features than those mentioned. In fact, an embodiment may have fewer features than all of the features of a single embodiment disclosed above.

[0102] As used herein, unless the context clearly indicates otherwise, the terms "a," "an," "an," and / or "the" are not intended to refer to the singular but may include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements specifically identified, and these steps and elements do not constitute an exclusive list. A method or apparatus may also include other steps or elements.

[0103] Unless otherwise specified, the relative arrangement of the parts and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present application. Meanwhile, it should be understood that, for ease of description, the sizes of the various parts shown in the accompanying drawings are not drawn according to actual proportional relationships. Technology, methods and equipment known to those of ordinary skill in the relevant art may not be discussed in detail, but in appropriate cases, the technology, methods and equipment should be considered as a part of the specification. In all examples shown and discussed here, any specific value should be interpreted as being merely exemplary, rather than as a limitation. Therefore, other examples of exemplary embodiments can have different values. It should be noted that similar numbers and letters represent similar items in the following drawings, and therefore, once an item is defined in an accompanying drawing, it does not need to be further discussed in subsequent drawings.

[0104] In some embodiments, numbers are used to describe the quantity of components and attributes. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise stated, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification are approximate values, which may change according to the required characteristics of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining digits. Although the numerical domains and parameters used to confirm the breadth of their range in some embodiments of the present application are approximate values, in specific embodiments, the settings of such numerical values ​​are as accurate as possible within the feasible range.

[0105] Although the present application has been described with reference to the current specific embodiments, ordinary technicians in this technical field should recognize that the above embodiments are only used to illustrate the present application, and various equivalent changes or substitutions can be made without departing from the spirit of the present application. Therefore, as long as the changes and modifications to the above embodiments are within the scope of the essential spirit of the present application, they will fall within the scope of the present application.

Claims

1. A method for generating a grid of a nuclear power fuel assembly, characterized in that: include: receiving a geometry file of a nuclear power fuel assembly, and generating a hybrid mesh of the nuclear power fuel assembly according to a mesh generation strategy and the geometry file, wherein the mesh generation strategy includes using a structured mesh in a boundary layer of the nuclear power fuel assembly and using an unstructured mesh in an area outside the boundary layer, wherein the boundary layer refers to a thin layer area formed by fluid on the surface of the nuclear power fuel assembly; The generated hybrid grid is adaptively refined, and the adaptive refinement includes identifying high-gradient areas and increasing the grid density in the high-gradient areas. The high-gradient areas refer to areas where the spatial rate of change of fluid parameters is greater than a preset threshold when the fluid flows along the wall of the nuclear power fuel assembly.

2. The grid generation method for a nuclear power fuel assembly according to claim 1, wherein: Identifying high gradient areas includes: calculating the curvature of a fluid parameter curve in the hybrid grid; It is determined whether the curvature is greater than or equal to a curvature threshold; if so, it is determined to be the high gradient area.

3. The grid generation method for a nuclear power fuel assembly according to claim 2, wherein: The fluid parameter curve includes a pressure curve or a velocity curve.

4. The grid generation method for a nuclear power fuel assembly according to claim 2, wherein: The curvature of the fluid parameter curve is calculated using the following formula: Wherein, K is the curvature of the fluid parameter curve, r'(s) is the first-order derivative of the fluid parameter curve, representing the tangent vector, and r"(s) is the second-order derivative of the fluid parameter curve, representing the acceleration vector.

5. The grid generation method for a nuclear power fuel assembly according to claim 4, characterized in that: Increasing the mesh density in the high gradient region includes: performing different levels of refinement on the high gradient region according to the curvature value according to the following formula: where RL is the refinement level, H, M, and L are high, medium, and low levels, respectively, and Kt and Kl are predefined curvature thresholds.

6. The grid generation method for a nuclear power fuel assembly according to claim 1, wherein: Also includes: A grid parameter prediction model is trained based on historical simulation data and a machine learning model, and the hybrid grid after adaptive refinement is optimized using the grid parameter prediction values ​​output by the grid parameter prediction model.

7. The method for generating a grid of a nuclear power fuel assembly according to claim 6, wherein: Training a grid parameter prediction model based on historical simulation data and machine learning models includes: Collecting historical simulation data, wherein the historical simulation data includes a geometric model of a nuclear power fuel assembly and corresponding simulation results; Extracting geometric features from the geometric model, extracting mesh parameter features from the simulation results, and constructing a data set based on the geometric features and the mesh parameter features; Select a machine learning model, wherein the machine learning model includes a linear regression model or a random forest regression model; The data set is used to train the machine learning model to obtain the grid parameter prediction model.

8. The method for generating a grid of a nuclear power fuel assembly according to claim 7, wherein: When the machine learning model is a linear regression model, the grid parameters predicted by the grid parameter prediction model include grid density and resolution. The linear regression model is: Y=XW+b Where Y is the predicted value of the grid parameter; X is the input feature matrix with dimension m×n, where m is the number of samples and n is the number of features; W is the weight matrix with dimension n×k, where k is the number of output parameters; B is the bias term with a dimension of 1×k.

9. The grid generation method for a nuclear power fuel assembly according to claim 7, wherein: When the machine learning model is a random forest regression model, the grid parameters predicted by the grid parameter prediction model include non-orthogonality, distortion, and volume ratio. The random forest regression model uses multiple decision trees for prediction, and the average value of all trees is taken as the final result, which is expressed as: in, is the predicted value of the grid parameter, Ti is the i-th decision tree, N is the number of decision trees, and X is the input feature.

10. The grid generation method for nuclear power fuel assembly according to claim 1, characterized in that: The method further includes: performing parallel computational domain decomposition on the hybrid grid after the adaptive refinement process, judging whether the number of grids on each computational domain is substantially the same, and if not, adjusting the grid parameters.

11. A grid generation device for a nuclear power fuel assembly, characterized in that: include: a memory for storing instructions executable by the processor; A processor is configured to execute the instructions to implement the grid generation method for a nuclear power fuel assembly according to any one of claims 1 to 10.

12. A computer-readable medium storing computer program code, wherein the computer program code, when executed by a processor, implements the grid generation method for a nuclear power fuel assembly according to any one of claims 1 to 10.

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