A method and device for simulating local peaking factors of a ball bed of arbitrary shape

Abstract

This invention discloses a method and apparatus for simulating the local packing factor of a spherical bed of arbitrary shape. The simulation method proposed in this invention directly calculates the local line-averaged packing factor for each grid point based on the gridded coordinate data of a distributed plane. Therefore, based on the local line-averaged packing factors of all grid points, further analysis can be performed to obtain the average packing factor, radial or axial packing factor, and local packing factor distribution data of the spherical bed. This method is simple to calculate, easy to implement, and does not significantly increase the computational load with the increase of particles. It does not require surface integration and can calculate the local packing factor of spherical beds of arbitrary shape, including the local packing factor distribution of spherical beds with arbitrarily convex particle packing.

Description

Technical Field

[0001] This invention belongs to the fields of fusion reactor computational analysis technology and fusion reactor blanket spherical bed simulation technology, specifically relating to a method and apparatus for simulating the local packing factor of an arbitrary-shaped spherical bed. Background Technology

[0002] In solid-state tritium production blankets of fusion reactors, tritium multiplication is primarily achieved through the reaction of neutrons with lithium in the tritium breeder bed. The tritium multiplication rate is increased by adding neutron multiplier beds to the blanket, and tritium is purified and extracted from the bed through carrier gas purging. Obtaining an accurate and reliable tritium breeder bed packing structure is crucial for the design of solid-state tritium production blankets and the optimization of tritium multiplication performance. The packing factor is an important parameter describing the bed packing structure and is essential for neutronographic analysis and tritium multiplication rate of the fusion reactor blanket.

[0003] Currently, discrete element method (DEM) or Monte Carlo method is commonly used to simulate the random packing of particles in a ball bed to obtain particle data. Traditional ball bed packing structure analysis uses average packing factor or radial packing factor, which are applicable to regular-shaped ball bed structures, such as square, cylindrical, and toroidal ball beds. Calculating the radial packing factor requires multiple numerical surface integrations. As the number of particles in the ball bed increases, the computational resources and time required for simulation increase dramatically; furthermore, the numerical surface integration method cannot handle the packing factor analysis of irregularly shaped ball beds. Therefore, it is necessary to develop a simple and efficient technique for simulating and calculating the local packing factor and its distribution within any irregularly shaped ball bed. Summary of the Invention

[0004] To address the issues of existing technologies being complex, computationally expensive, and unable to perform packing factor analysis on irregularly shaped spherical beds, this invention provides a method and apparatus for simulating and calculating the local packing factor of spherical beds of arbitrary shapes.

[0005] This invention is achieved through the following technical solution:

[0006] A method for simulating and calculating the local packing factor of an arbitrarily shaped spherical bed includes:

[0007] Obtain data for the ball bed stacking structure model;

[0008] Set parameters and boundary conditions based on the acquired data: including computational domain boundaries, distribution plane and orientation, and grid size;

[0009] Based on the set parameters and boundary conditions, the distribution plane is divided into grids to obtain grid point coordinate data;

[0010] Based on the grid point coordinate data and the set parameters and boundary conditions, the local line average packing factor of each grid point is calculated and saved until the local line average packing factor of all grid points is obtained.

[0011] The local packing factor distribution data of the ball bed is calculated based on the local line average packing factor of all grid points.

[0012] Compared to existing numerical surface integral methods for analyzing the packing factor of sphere beds, which drastically increases the computational resources and time required for simulation as the number of spheres increases, and which cannot handle packing factor analysis for irregularly shaped sphere beds, the simulation method proposed in this invention directly calculates the local line-averaged packing factor for each grid point based on the gridded coordinate data of the distributed plane. This allows for further analysis of the average packing factor, radial or axial packing factor, and local packing factor distribution data of the sphere bed based on the local line-averaged packing factor of all grid points. This method is computationally simple, easy to implement, does not significantly increase the computational load with the number of particles, does not require surface integrals, and can calculate the local packing factor distribution of sphere beds with arbitrary shapes and even convex particle packing beds.

[0013] In a preferred embodiment, the method of the present invention further includes:

[0014] The average packing factor, radial packing factor, or axial packing factor of the ball bed are calculated based on the local line average packing factor of all grid points.

[0015] As a preferred embodiment, the ball bed stacking structure model data obtained by the method of the present invention includes particle coordinates and radii, ball bed shape, and ball bed wall boundary.

[0016] In a preferred embodiment, the parameters and boundary conditions set by the method of the present invention specifically include: the maximum and minimum values ​​of the ball bed container along the x-axis and y-axis, respectively, and the maximum and minimum values ​​of the ball bed container along the z-axis;

[0017] The distribution plane is set perpendicular to the z-axis, and the grid size of the distribution plane is 0.1d to 0.25d, where d is the particle diameter.

[0018] In a preferred embodiment, the method of the present invention calculates the local line-average packing factor of the grid points using the following formula:

[0019]

[0020] In the formula, h(x,y,z) low ,z up ) represents the cutting line perpendicular to the distribution plane at the current grid point (x, y) in [z low ,zup [Inner length; n] l Indicates the number of particles intersecting the current cutting line; l i (x,y,z low ,z up ) represents the intersection line after particle i intersects the cutting line, and in [z low ,z up The length within ]; z low This represents the coordinate value of the lower boundary of the z-axis; z up This represents the coordinate value of the set upper boundary on the z-axis.

[0021] In a preferred embodiment, the intersection line of particle i and the cutting line of the present invention is in [z low ,z up ] Length l i (x,y,z low ,z up The specific calculation method for ) is as follows:

[0022] When z i >z up hour,

[0023] When z up -r i <z i ≤z up hour,

[0024] When z low +r i <z i ≤z up -r i hour,

[0025] When z low <z i ≤z low +r i hour,

[0026] When z low -r i <z i ≤z low hour,

[0027] In the formula, r i Let L be the radius of particle i. line Let L be the distance between the center of particle i and the tangent line. pz Let be the distance between the center of particle i and the lower or upper boundary of the z-axis.

[0028] In a preferred embodiment, the shape of the ball bed and the shape of the distribution plane of the present invention can be arbitrary.

[0029] The particles in the ball bed can be any convex curved surface or polyhedral particles, that is, particles that intersect the cutting line at most two points.

[0030] On the other hand, the present invention also proposes a device for simulating the local packing factor of an arbitrary-shaped spherical bed, the device comprising:

[0031] The data acquisition unit is used to acquire data from the ball bed stacking structure model.

[0032] The parameter and boundary condition setting unit sets parameters and boundary conditions based on the acquired data, including the computational domain boundary, distribution plane and orientation, and mesh size.

[0033] The grid division unit divides the distribution plane into grids according to the set parameters and boundary conditions to obtain grid point coordinate data;

[0034] The calculation cells are traversed, and the local line average packing factor of each grid point is calculated based on the grid point coordinates, set parameters, and boundary conditions, until the local line average packing factor of all grid points is obtained.

[0035] A storage unit is used to store the local line-average stacking factor of all grid points;

[0036] And the analysis unit calculates the local packing factor distribution data of the ball bed based on the local line average packing factor of all grid points.

[0037] In a preferred embodiment, the traversal calculation unit of the present invention calculates the local line average packing factor of the grid points using the following formula:

[0038]

[0039] In the formula, h(x,y,z) low ,z up ) represents the cutting line perpendicular to the distribution plane at the current grid point (x, y) in [z low ,z up [Inner length; n] l Indicates the number of particles intersecting the current cutting line; l i (x,y,z low ,z up ) represents the intersection line after particle i intersects the cutting line, and in [z low ,z up The length within ]; z low This represents the coordinate value of the lower boundary of the z-axis; z up This represents the coordinate value of the set upper boundary on the z-axis.

[0040] In a preferred embodiment, the intersection line of particle i and the cutting line of the present invention is in [z low ,z up ] Length l i (x,y,z low ,z up The specific calculation method for ) is as follows:

[0041] When z i >z up hour,

[0042] When z up -r i <z i ≤z up hour,

[0043] When z low +r i <z i ≤z up -r i hour,

[0044] When z low <z i ≤z low +r i hour,

[0045] When z low -r i <z i ≤z low hour,

[0046] In the formula, r i Let L be the radius of particle i. line Let L be the distance between the center of particle i and the tangent line. pz Let be the distance between the center of particle i and the lower or upper boundary of the z-axis.

[0047] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0048] 1. This invention can calculate the local packing factor of a spherical bed of arbitrary shape, and the calculation method is simple;

[0049] 2. This invention is not limited to spherical particles; it can calculate the local packing factor distribution of any convex particle packing bed.

[0050] 3. This invention avoids surface integrals, and does not significantly increase computational resources as the number of particles increases. It can handle spherical bed systems of arbitrary shapes containing a large number of convex particles. Attached Figure Description

[0051] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings:

[0052] Figure 1 This is a schematic diagram of the method flow proposed in an embodiment of the present invention;

[0053] Figure 2 This is a schematic diagram of the device principle proposed in the embodiments of the present invention;

[0054] Figure 3 This is a schematic diagram of a cylindrical spherical bed model according to an embodiment of the present invention;

[0055] Figure 4 This is a schematic diagram of the distribution surface grid of the cylindrical spherical bed according to an embodiment of the present invention;

[0056] Figure 5 This is a schematic diagram showing the intersection of the cutting lines of the grid points and the particles of the ball bed in an embodiment of the present invention;

[0057] Figure 6 This is a radial packing factor distribution diagram of the cylindrical spherical bed according to an embodiment of the present invention;

[0058] Figure 7 This is a local packing factor distribution diagram of the cylindrical spherical bed according to an embodiment of the present invention;

[0059] Figure 8 This is a schematic diagram of a U-shaped ball bed model according to an embodiment of the present invention;

[0060] Figure 9 This is a local packing factor distribution diagram of the U-shaped spherical bed according to an embodiment of the present invention.

[0061] Figure reference numerals and corresponding component names:

[0062] 1-Grid point, 2-Particle, 3-Boundary of the ball bed wall, 4-Particle diameter, 5-Upper boundary of the z-axis, 6-Lower boundary of the z-axis, 7-Intersection line after the particle intersects with the cutting line, 8-Cutting line. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0064] Example 1:

[0065] Existing technologies mainly employ the Discrete Element Method (DEM) or Monte Carlo method to simulate the random packing of particles in a ball bed, further acquiring particle data, and then calculating the radial packing factor or local packing factor through multiple numerical surface integrals. However, this technique experiences a sharp increase in computational resources and time consumption as the number of particles in the ball bed increases, and the numerical surface integral method cannot handle the packing factor analysis of irregularly shaped ball beds. Based on this, this embodiment proposes a method for simulating and calculating the local packing factor of ball beds of arbitrary shapes. This invention obtains ball bed data to get a ball bed packing structure model; sets a calculation boundary based on the obtained ball bed packing structure model data and the desired packing factor distribution direction; meshes the distribution plane according to the set calculation boundary to obtain grid point coordinates; and calculates the local line-average packing factor of all grid points on the distribution plane based on the grid point coordinates and the set calculation boundary. Finally, it calculates the average packing factor, radial or axial packing factor, and local packing factor distribution data of the ball bed based on the local line-average packing factor of all grid points on the distribution plane. The method proposed in this embodiment can calculate the local packing factor of spherical beds of arbitrary shapes. It is not limited to spherical particles, but can calculate the local packing factor distribution of spherical beds of arbitrary convex particles. At the same time, the method proposed in this embodiment avoids surface integrals, the calculation method is simple, and it can handle spherical bed systems of arbitrary shapes containing a large number of convex particles.

[0066] Specifically, such as Figure 1 As shown, the method proposed in this embodiment includes the following steps:

[0067] Step 1: Obtain the data for the ball bed stacking structure model;

[0068] Step 2: Set parameters and boundary conditions based on the acquired data, including the computational domain boundary, distribution plane and orientation, and grid size;

[0069] Step 3: Based on the set parameters and boundary conditions, the distribution plane is divided into grids to obtain the coordinates of the grid points;

[0070] Step 4: Based on the grid point coordinates and the set parameters and boundary conditions, iterate through and calculate the local line average packing factor of each grid point until the local line average packing factor of all grid points is obtained.

[0071] Step 5: Calculate the average packing factor, radial or axial packing factor, and local packing factor distribution data of the ball bed based on the local line average packing factor of all grid points.

[0072] In one optional implementation, the ball bed stacking structure model data obtained in step 1 includes particle coordinates and radii, ball bed shape, and ball bed wall boundaries.

[0073] In one optional implementation, the parameters and boundary conditions set in step 2 include: the maximum and minimum values ​​of the ball bed container along the x-axis and y-axis, respectively, and the minimum value of the ball bed container along the lower boundary of the z-axis (i.e., the minimum z-coordinate). low ) and the upper boundary of the z-axis (i.e., the maximum value of the z-coordinate z) up ), where the z-axis is the axial direction of the ball bed container, and the xy plane is a plane perpendicular to the z-axis; the set distribution plane is perpendicular to the z-axis (i.e. parallel to the xy plane), and the distribution plane grid size can be 0.1d to 0.25d, where d is the particle diameter.

[0074] An optional implementation involves calculating the local line average packing factor for each grid point based on the coordinates of grid point 1, specifically including:

[0075] Let (x, y) be the coordinates of any grid point 1. Then, the local line-average packing factor of this grid point can be calculated using the following formula:

[0076]

[0077] In the formula, h(x,y,z) low ,z up ) is the cutting line 8 perpendicular to the distribution plane at the current grid point (x,y) in [z low ,z up [Inner length; n] l Indicates the number of particles intersecting with the current cutting line 8; l i (x,y,z low ,z up ) represents the intersection line 7 after particle i intersects with cutting line 8, and in [z low ,z up The length within ]

[0078] Among them, according to the particle position (x) i ,y i ,z i ) different, in [z low ,z up The length l of the intersecting lines within the range i (x,y,z low ,z up The calculation method for ) is as follows:

[0079] When z i >z up hour,

[0080] When z up -r i <z i ≤z up hour,

[0081] When z low +r i <z i ≤z up -r i hour,

[0082] When z low <z i ≤z low +r i hour,

[0083] When z low -r i <z i ≤z low hour,

[0084] In the formula, r i Let L be the radius of particle i. line Let L be the distance between the center of particle i and the tangent line. pz Let z be the center of particle i and the lower boundary z. low or upper boundary z up The distance between them.

[0085] In one alternative implementation, the average packing factor is calculated using the following formula:

[0086]

[0087] In the formula, n is the number of local linear average packing factors within the ball bed, and γ line This is the local line-averaged packing factor.

[0088] The radial packing factor of a cylindrical spherical bed is calculated using the following formula:

[0089]

[0090] In the formula, L c Let n be the distance from inside the ball table to the central axis. c Let L be the distance from the ball bed to the central axis. c The number of local line-averaged packing factors.

[0091] The axial packing factor is calculated using the following formula:

[0092]

[0093] In the formula, L axi n is the distance from the inside of the ball-shaped bed to one end face along the axis. axi Let L be the distance from the inside of the ball table to the end face. axi The number of local line-averaged packing factors.

[0094] Based on the same technical concept, this embodiment also proposes a simulation calculation device for the local packing factor of an arbitrarily shaped spherical bed, specifically as follows: Figure 2 As shown, the device includes:

[0095] The data acquisition unit is used to acquire data from the ball bed stacking structure model.

[0096] The parameter and boundary condition setting unit sets parameters and boundary conditions based on the data acquired by the data acquisition unit, including the computational domain boundary, distribution plane and orientation, and grid size.

[0097] The grid is divided into units based on the parameters and boundary conditions set by the unit, and the distribution plane is divided into grids to obtain the coordinates of the grid points.

[0098] The calculation cells are traversed, and the local line average packing factor of each grid point is calculated based on the grid point coordinates, set parameters, and boundary conditions, until the local line average packing factor of all grid points is obtained.

[0099] Storage unit, used to store the local line average stacking factor data of all grid points;

[0100] The analysis unit calculates the average packing factor, radial or axial packing factor, and local packing factor distribution data of the ball bed based on the local line average packing factor of all grid points.

[0101] Example 2:

[0102] This embodiment uses the method and apparatus proposed in the above embodiments to calculate the local packing factor of the cylindrical spherical bed. The specific process is as follows:

[0103] (1) Obtain the data of the cylindrical spherical bed stacking structure model, including the coordinates and radii of the spherical particles, the shape of the spherical bed, and the boundary of the spherical bed wall. The spherical bed contains 9201 particles, such as... Figure 3 As shown.

[0104] (2) The parameters and boundary conditions for calculating the local packing factor of the cylindrical spherical bed are set as follows: the maximum and minimum values ​​of the spherical bed container along the x-axis and y-axis, respectively, and the minimum value of the spherical bed container along the z-axis. low and maximum value z up The distribution plane is set to be perpendicular to the z-axis, circular in shape, and the grid size of the distribution plane is 0.25d, where d is the particle diameter.

[0105] (3) Divide the circular distribution plane into a grid according to the set parameters and boundary conditions, such as... Figure 4 As shown.

[0106] (4) Based on the coordinates of the grid points, calculate the local line average packing factor for each grid point, such as... Figure 5 As shown.

[0107] (5) Based on the local line-averaged packing factor of all grid points, the radial packing factor of the cylindrical spherical bed is further analyzed and obtained, such as... Figure 6 As shown, and the local stacking factor distribution, such as Figure 7 As shown.

[0108] Example 3:

[0109] This embodiment uses the method and apparatus proposed in Embodiment 1 to calculate the local packing factor of the U-shaped spherical bed. The specific process is as follows:

[0110] (1) Obtain the U-shaped spherical bed packing structure model data, including the coordinates and radii of the spherical particles, the shape of the U-shaped spherical bed, and the boundary of the bed wall. This U-shaped spherical bed contains 76,193 particles, such as... Figure 8 As shown.

[0111] (2) The parameters and boundary conditions for calculating the local packing factor of the U-shaped ball bed are set as follows: the maximum and minimum values ​​of the ball bed container along the x-axis and y-axis, and the minimum and maximum values ​​of the ball bed container along the z-axis; the distribution plane is set to be perpendicular to the z-axis, with a U-shape, and the distribution plane grid size is 0.25d, where d is the particle diameter.

[0112] (3) The U-shaped distribution plane is divided into grids according to the set parameters and boundary conditions.

[0113] (4) Based on the coordinates of the grid points, calculate the local line average packing factor for each grid point.

[0114] (5) Based on the local line average packing factor of all grid points, the local packing factor distribution of the U-shaped spherical bed can be further analyzed, such as... Figure 9 As shown.

[0115] It should be noted that Embodiments 2 and 3 are merely illustrative examples and are not intended to limit the scope of the invention. The method proposed in this embodiment can calculate the local packing factor of regular and irregular spherical beds, and can further obtain the radial packing factor based on the local packing factor. In other words, the method proposed in this embodiment is applicable to the calculation of the local packing factor of spherical beds with arbitrary shapes and distribution plane shapes, and can calculate the local packing factor distribution of spherical beds with arbitrary convex curved surfaces or polyhedral particles (i.e., particles that intersect the cutting line at most two points). It can handle spherical bed systems of arbitrary shapes containing a large number of convex particles, and does not require surface integration, making the calculation method simple.

[0116] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0117] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0118] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0119] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0120] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for simulating and calculating the local packing factor of an arbitrarily shaped spherical bed, characterized in that, include: Obtain data on the pebble bed stacking structure model; the pebble bed stacking structure is a collection of tritium breeder and neutron multiplier pebble bed particles in the solid tritium production blanket of a fusion reactor; Based on the acquired pellet data of the ball bed, set the analysis parameters and boundary conditions, including calculating the boundary of the ball bed region, the plane and direction of the packing factor distribution, and the grid size; Based on the set parameters and boundary conditions, the stacking factor distribution plane is divided into grids to obtain grid point coordinate data; Based on the grid point coordinate data and the set analysis parameters and boundary conditions, the local average packing factor of the ball bed at each grid point is calculated and saved until the local average packing factor of the ball bed at all grid points is obtained. The local packing factor distribution data of the ball bed is calculated based on the local average packing factor of all grid points. The local average packing factor of the ball bed at the grid points is calculated using the following formula: ； In the formula, h(x, y, z) low , z up ) represents the cutting line perpendicular to the distribution plane at the current grid point (x, y) in [z low ,z up [Inner length; n] l Indicates the number of particles intersecting the current cutting line; l i (x, y, z low , z up ) represents the intersection line after particle i intersects the cutting line, and in [z low , z up The length within ]; z low This represents the coordinate value of the lower boundary of the z-axis; z up This represents the coordinate value of the set upper boundary on the z-axis; The intersection line after particle i intersects the cutting line and is in [z low , z up ] Length l i (x, y, z low , z up The specific calculation method for ) is as follows: When z i >z up hour, ; When z up -r i <z i ≤ z up hour, ; When z low +r i <z i ≤ z up -r i hour, ; When z low <z i ≤ z low +r i hour, ; When z low -r i <z i ≤ z low hour, ; In the formula, r i Let L be the radius of particle i. line Let L be the distance between the center of particle i and the tangent line. pz Let be the distance between the center of particle i and the lower or upper boundary of the z-axis.

2. The method for simulating and calculating the local packing factor of an arbitrary-shaped spherical bed according to claim 1, characterized in that, Also includes: Based on the local line average packing factor of all grid points, the average packing factor, radial packing factor, axial packing factor, and local packing factor distribution data of the ball bed are calculated.

3. The method for simulating and calculating the local packing factor of an arbitrary-shaped spherical bed according to claim 1, characterized in that, The acquired data for the ball bed packing structure model includes the three-dimensional coordinates and radii of the particles, the shape of the ball bed, and the boundary of the ball bed wall.

4. The method for simulating and calculating the local packing factor of an arbitrary-shaped spherical bed according to claim 1, characterized in that, The specific analysis parameters and boundary conditions set include: the maximum and minimum values ​​of the ball bed container along the x-axis and y-axis of the packing factor distribution plane, respectively, and the maximum and minimum values ​​of the ball bed container along the z-axis perpendicular to the packing factor distribution plane; The distribution plane is set to be perpendicular to the z-axis, and the grid size of the packing factor distribution plane is 0.1d~0.25d, where d is the particle diameter.

5. The method for simulating and calculating the local packing factor of an arbitrary-shaped spherical bed according to claim 1, characterized in that, The shape of the ball bed and the shape of the distribution plane can be arbitrary. The particles in the ball bed are arbitrary convex curved surfaces or polyhedral particles, that is, particles that intersect the cutting line at most two points.

6. A device for simulating and calculating the local packing factor of an arbitrary-shaped spherical bed, characterized in that, The device includes: The data acquisition unit is used to acquire data on the pebble bed stacking structure model; the pebble bed stacking structure is a collection of tritium breeder and neutron multiplier pebble bed particles in the solid tritium production blanket of the fusion reactor; The parameter and boundary condition setting unit sets the analysis parameters and boundary conditions based on the acquired ball bed particle data, including the computational domain boundary, the packing factor distribution plane and direction, and the grid size. The grid is divided into units based on the set analysis parameters and boundary conditions to obtain grid point coordinate data. The calculation cells are traversed, and the local average packing factor of the ball bed is calculated for each grid point based on the grid point coordinates and the set analysis parameters and boundary conditions, until the local average packing factor of the ball bed for all grid points is obtained. A storage unit is used to store the average packing factor of the local line of the ball bed for all grid points; The analysis unit calculates the local packing factor distribution data of the ball bed based on the local average packing factor of all grid points; the traversal calculation unit calculates the local average packing factor of the ball bed at the grid points using the following formula: ； In the formula, h(x, y, z) low , z up ) represents the cutting line perpendicular to the distribution plane at the current grid point (x, y) in [z low ,z up [Inner length; n] l Indicates the number of particles intersecting the current cutting line; l i (x, y, z low , z up ) represents the intersection line after particle i intersects the cutting line, and in [z low , z up The length within ]; z low This represents the coordinate value of the lower boundary of the z-axis; z up This represents the coordinate value of the upper boundary on the z-axis; the intersection line after particle i intersects the cutting line and lies within [z]. low , z up ] Length l i (x,y, z low , z up The specific calculation method for ) is as follows: When z i >z up hour, ; When z up -r i <z i ≤ z up hour, ; When z low +r i <z i ≤ z up -r i hour, ; When z low <z i ≤ z low +r i hour, ; When z low -r i <z i ≤ z low hour, ; In the formula, r i Let L be the radius of particle i. line Let L be the distance between the center of particle i and the tangent line. pz Let be the distance between the center of particle i and the lower or upper boundary of the z-axis.

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