A method and device for evaluating the optimization of a pebble bed high temperature gas cooled reactor core

Abstract

The application provides a pebble bed type high temperature gas cooled reactor core optimization evaluation method and device, the method constructs a first simulation model (multi-batch fine model) and a second simulation model (weighted low-order model), and respectively uses the first simulation model and the second simulation model for nonlinear elimination of Newton steps and Krylov steps, so that the Newton steps keep high calculation precision, and the calculation scale of the Krylov steps is reduced, the calculation scale and time can be significantly reduced on the premise of guaranteeing the accuracy of multi-dimensional physical scene representation information, and therefore the efficiency of core safety evaluation and design optimization is improved.

Description

Technical Field

[0001] This application relates to the field of high-temperature gas-cooled reactor technology, and more specifically, to a method and apparatus for optimizing and evaluating the core of a pebble bed type high-temperature gas-cooled reactor. Background Technology

[0002] The pebble bed modular high-temperature gas-cooled reactor (HTGR) boasts significant advantages such as online refueling, inherent safety, and high outlet temperatures, making it suitable for various applications including hydrogen production, steam supply, and power generation. Its fuel elements utilize TRI-structural ISOtropic (TRISO) spherical fuel particles and employ a multi-cycle refueling scheme that allows for continuous refueling through the core. Within the same control volume of the core, fuel spheres with different cycle counts and burnup histories simultaneously exist, corresponding to varying power and temperature distributions. These fuel spheres are identified as different batches based on their cycle counts. Building upon existing macroscopic power and temperature distributions within the core, further characterizing the fine-grained parameter distributions within each batch of fuel spheres and even the TRISO particles constitutes a multi-scale coupling problem of "pebble bed - multiple batches of fuel spheres - coated particles." While the multi-batch fuel sphere model can more accurately represent the physical-thermal coupling relationships within the core, efficient solutions still face new challenges. Summary of the Invention

[0003] In view of this, this application provides a pebble bed type high temperature gas-cooled reactor core optimization evaluation method and apparatus to solve the problems of large evaluation and optimization calculation volume, long calculation time and low calculation accuracy.

[0004] Specifically, this application is implemented through the following technical solution: In a first aspect, embodiments of this application provide a method for optimizing and evaluating the core of a pebble bed type high-temperature gas-cooled reactor, including: A first simulation model and a second simulation model are constructed for a pebble bed type high-temperature gas-cooled reactor core; the first simulation model includes multiple batches of fuel sphere models; the second simulation model includes a single batch of fuel sphere models; the single batch of fuel sphere models is obtained by homogenizing the multiple batches of fuel sphere models. A set of temperature nonlinear equations is constructed for the core of the pebble bed high-temperature gas-cooled reactor; the set of temperature nonlinear equations includes multiple variables to be solved; the variables to be solved include fuel sphere temperature, neutron field parameters, and thermal field parameters; The Newton-Krylov method is used to iteratively solve the temperature nonlinear equations to obtain multi-dimensional physical scene characterization information of the pebble bed high-temperature gas-cooled reactor core. The Newton-Krylov method includes a Newton step and a Krylov step. In the Newton step, the first simulation model is used for nonlinear elimination. In the Krylov step, the second simulation model is used for nonlinear elimination. The nonlinear elimination includes using the neutron field parameters and thermal field parameters to represent the temperature of the fuel sphere. Based on the multi-dimensional physical scene characterization information, the design optimization and safety assessment of the pebble bed type high-temperature gas-cooled reactor core are carried out.

[0005] Optionally, the Newton step includes: Set the initial values ​​for each variable to be solved during iteration; Based on the first simulation model, a first residual function is determined; based on the second simulation model, a second residual function is determined; based on the second residual function, a Jacobian matrix is ​​constructed; the fuel ball temperature in the first residual function and the second residual function is represented by the neutron field parameters and the thermal field parameters. Based on the first residual function and the Jacobian matrix, construct the difference equation of the variable to be solved; The difference equation of the unsolved variables is solved iteratively by Krylov step to obtain the difference of the unsolved variables; The initial values ​​of the variables to be solved are corrected based on the differences between them, and the next iteration of the Newton step is started.

[0006] Optionally, the Newton step further includes: If the first residual function satisfies the convergence condition, the iteration of the Newton step is stopped.

[0007] Optionally, determining the first residual function based on the first simulation model includes: Based on the first simulation model, a first conversion relationship is determined between the fuel ball temperature and the neutron field parameters and the thermal field parameters; based on the first conversion relationship, the first residual function is determined; The determination of the second residual function based on the second simulation model includes: Based on the second simulation model, a second conversion relationship is determined between the fuel ball temperature and the neutron field parameters and the thermal field parameters; based on the second conversion relationship, the second residual function is determined.

[0008] Optionally, the step of iteratively solving the difference equation of the unsolved variables through the Krylov step to obtain the difference of the unsolved variables includes: Based on the difference equation of the variables to be solved, construct the Krylov subspace; The basis of the Krylov subspace is iteratively updated until the convergence condition is met, and the difference of the unsolved variables after iterative update is obtained.

[0009] Optionally, the second simulation model is obtained through the following steps: Based on the volume and proportion of each fuel ball model in the first simulation model, the physical property parameters of the fuel ball models in the first simulation model are homogenized to obtain the second simulation model.

[0010] Optionally, the step of design optimization and safety assessment of the pebble bed high-temperature gas-cooled reactor core based on the multi-dimensional physical scene characterization information includes: Based on the multi-dimensional physical scene characterization information, the field distribution of the pebble bed high-temperature gas-cooled reactor core is determined; the field distribution includes temperature field distribution, neutron field and fission power distribution, and thermal field distribution. Based on the field distribution, a safety assessment is conducted on the core of the pebble bed type high-temperature gas-cooled reactor. Based on the safety assessment results, the design of the pebble bed type high-temperature gas-cooled reactor core was optimized.

[0011] Secondly, this application provides a pebble bed type high-temperature gas-cooled reactor core optimization and evaluation device, the device comprising: The first construction module is used to construct a first simulation model and a second simulation model of a pebble bed type high-temperature gas-cooled reactor core; the first simulation model includes multiple batches of fuel sphere models; the second simulation model includes a single batch of fuel sphere models; the single batch of fuel sphere models is obtained by homogenizing the multiple batches of fuel sphere models; The second construction module is used to construct the temperature nonlinear equation set of the pebble bed high-temperature gas-cooled reactor core; the temperature nonlinear equation set includes multiple variables to be solved; the variables to be solved include fuel sphere temperature, neutron field parameters and thermal field parameters. An iterative module is used to iteratively solve the temperature nonlinear equations using the Newton-Krilov method to obtain multi-dimensional physical scene characterization information of the pebble bed high-temperature gas-cooled reactor core. The Newton-Krilov method includes a Newton step and a Krilov step. In the Newton step, the first simulation model is used for nonlinear elimination. In the Krilov step, the second simulation model is used for nonlinear elimination. The nonlinear elimination includes using the neutron field parameters and thermal field parameters to represent the temperature of the fuel spheres. The evaluation module is used to perform design optimization and safety assessment of the pebble bed high-temperature gas-cooled reactor core based on the multi-dimensional physical scene characterization information.

[0012] Optionally, the iterative module is used for: Set the initial values ​​for each variable to be solved during iteration; Based on the first simulation model, a first residual function is determined; based on the second simulation model, a second residual function is determined; based on the second residual function, a Jacobian matrix is ​​constructed; the fuel ball temperature in the first residual function and the second residual function is represented by the neutron field parameters and the thermal field parameters. Based on the first residual function and the Jacobian matrix, construct the difference equation of the variable to be solved; The difference equation of the unsolved variables is solved iteratively by Krylov step to obtain the difference of the unsolved variables; The initial values ​​of the variables to be solved are corrected based on the differences between them, and the next iteration of the Newton step is started.

[0013] Optionally, the iteration module is further configured to: If the first residual function satisfies the convergence condition, the iteration of the Newton step is stopped.

[0014] Optionally, the iterative module is used for: Based on the first simulation model, a first conversion relationship is determined between the fuel ball temperature and the neutron field parameters and the thermal field parameters; based on the first conversion relationship, the first residual function is determined; Based on the second simulation model, a second conversion relationship is determined between the fuel ball temperature and the neutron field parameters and the thermal field parameters; based on the second conversion relationship, the second residual function is determined.

[0015] Optionally, the iterative module is used for: Based on the difference equation of the variables to be solved, construct the Krylov subspace; The basis of the Krylov subspace is iteratively updated until the convergence condition is met, and the difference of the unsolved variables after iterative update is obtained.

[0016] Optionally, the first building module is used for: Based on the volume and proportion of each fuel ball model in the first simulation model, the physical property parameters of the fuel ball models in the first simulation model are homogenized to obtain the second simulation model.

[0017] Optionally, the evaluation module is used for: Based on the multi-dimensional physical scene characterization information, the field distribution of the pebble bed high-temperature gas-cooled reactor core is determined; the field distribution includes temperature field distribution, neutron field and fission power distribution, and thermal field distribution. Based on the field distribution, a safety assessment is conducted on the core of the pebble bed type high-temperature gas-cooled reactor. Based on the safety assessment results, the design of the pebble bed type high-temperature gas-cooled reactor core was optimized.

[0018] Thirdly, embodiments of this application also provide a computer device, which includes a processor and a memory. The memory stores machine-readable instructions executable by the processor. The processor is used to execute the machine-readable instructions stored in the memory. When the machine-readable instructions are executed by the processor, they perform the steps of the first aspect above, or any possible implementation of the first aspect.

[0019] Fourthly, optional embodiments of this application also provide a computer-readable storage medium storing a computer program that, when run, performs the steps of the first aspect or any possible implementation of the first aspect.

[0020] The pebble bed type high-temperature gas-cooled reactor core optimization evaluation method and apparatus provided in this application construct a first simulation model (multi-batch refined model) and a second simulation model (weighted low-order model), and use them respectively for nonlinear elimination of Newton step and Krylov step. This maintains high computational accuracy of Newton step and reduces the computational scale of Krylov step. Under the premise of ensuring the accuracy of multi-dimensional physical scene representation information, it can significantly reduce the computational scale and time, thereby improving the efficiency of core safety evaluation and design optimization. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of a fuel ball shown in an exemplary embodiment of this application; Figure 2 This is a flowchart illustrating an exemplary embodiment of the core optimization and evaluation method for a pebble bed type high-temperature gas-cooled reactor according to this application; Figure 3 This is a flowchart illustrating the steps of the Newton-Kriloff method as shown in an exemplary embodiment of this application; Figure 4 This is a schematic diagram of a pebble bed type high-temperature gas-cooled reactor core optimization and evaluation device shown in an exemplary embodiment of this application; Figure 5 This is a schematic diagram of a computer device illustrated in an exemplary embodiment of this application. Detailed Implementation

[0022] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0023] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0024] It should be understood that although the terms first, second, third, etc., may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0025] Research has revealed that the core of the pebble bed high-temperature gas-cooled reactor exhibits significant multi-scale and multi-batch coupling characteristics. Optimizing and evaluating the core of the pebble bed high-temperature gas-cooled reactor requires a huge amount of computation and time, and it is difficult to guarantee computational accuracy, making it extremely challenging.

[0026] In view of this, this application provides a pebble bed type high-temperature gas-cooled reactor core optimization evaluation method and apparatus. By constructing a first simulation model (multi-batch fine model) and a second simulation model (weighted low-order model), and using them respectively for nonlinear elimination of Newton step and Krylov step, the Newton step maintains high computational accuracy and reduces the computational scale of Krylov step. Under the premise of ensuring the accuracy of multi-dimensional physical scene representation information, the computational scale and time can be significantly reduced, thereby improving the efficiency of core safety evaluation and design optimization.

[0027] The deficiencies of the existing technical solutions are the result of the inventors' practice and careful research. Therefore, the discovery process of the above problems and the solutions proposed in this application below should be considered as the inventors' contributions to this application.

[0028] To facilitate understanding of this embodiment, the application scenario of the pebble bed high-temperature gas-cooled reactor core optimization evaluation method disclosed in this application embodiment will first be introduced. The execution entity of the pebble bed high-temperature gas-cooled reactor core optimization evaluation method provided in this application embodiment can be a computer device. In some possible implementations, the pebble bed high-temperature gas-cooled reactor core optimization evaluation method can be implemented by a processor calling computer-readable instructions stored in memory.

[0029] The active zone of a pebble bed high-temperature gas-cooled reactor core is composed of a large number of fuel spheres. For example, a single fuel sphere can have a diameter of 6 cm. The outermost layer of the fuel sphere is a 0.5 cm thick pure graphite layer, and the interior of the fuel sphere is a 5 cm diameter graphite matrix, in which coated fuel particles (triple-structured isotropic fuel particles, TRi-structural ISOtropic particles, TRISO) are randomly dispersed. Each fuel sphere contains more than 10,000 TRISO particles with a diameter of approximately 1 mm. Each TRISO particle is further divided into... The fuel sphere is composed of a core, loose pyrolytic carbon, inner dense pyrolytic carbon, dense silicon carbide, and outer dense pyrolytic carbon, layered sequentially. The structure of the fuel sphere is as follows: Figure 1 As shown. TRISO particles The nuclear fission reaction in the fuel core generates a large amount of heat, which is transferred through four cladding layers to the graphite matrix inside the fuel sphere, the outermost pure graphite layer, and finally from the surface of the fuel sphere to the surrounding helium gas. The tight coupling between different scales constitutes a three-level multi-scale structure of "TRISO particle-fuel sphere-core".

[0030] Furthermore, the pebble bed high-temperature gas-cooled reactor (HTGR) employs an "online refueling without reactor shutdown" operation mode. New fuel pellets are loaded from the top of the core and gradually descend as the HTGR operates, undergoing multiple cycles to reach the predetermined burnup. Specifically, after being unloaded from the bottom of the core, fuel pellets are sorted based on their current burnup depth: pellets that have not reached the target value are returned to the top of the core to participate in the cycle again until the target burnup value is reached before being transferred to the spent fuel storage system. This operating mechanism results in the simultaneous presence of fuel pellets at different cycle counts within the same local area of ​​the core. For ease of management and analysis, fuel pellets are typically divided into different batches based on the number of times they have passed through the core. Different batches of fuel pellets, due to their varying irradiation history within the core, exhibit significant differences in burnup depth and fission power, thus creating a non-uniform power and temperature distribution within the core.

[0031] To conduct safety assessments and design optimizations for pebble-bed high-temperature gas-cooled reactors (HTGRs), it is necessary to determine the multi-dimensional physical scene representation information inside the HTGR. Since this information cannot be directly measured, it is usually calculated using simulation models. However, in the simulation scenario of a pebble-bed HTGR, the reactor core contains a large number of fuel spheres, which are divided into multiple batches. Each fuel sphere contains a large number of fuel particles. Taking the pebble-bed modular HTGR model as an example, if each macroscopic grid contains 15 batches of fuel spheres, and each fuel sphere is divided into 10 grid layers to solve for the internal temperature distribution, the number of temperature variables for each fuel sphere in each macroscopic grid is 150, which is 150 times the number of temperature field variables in the macroscopic solid field. The required computation is extremely large.

[0032] See Figure 2 The diagram shown is a flowchart illustrating an exemplary embodiment of a pebble bed type high-temperature gas-cooled reactor core optimization and evaluation method. The method includes steps S201-S204, wherein: S201. Construct a first simulation model and a second simulation model of a pebble bed type high-temperature gas-cooled reactor core; the first simulation model includes multiple batches of fuel sphere models; the second simulation model includes a single batch of fuel sphere models; the single batch of fuel sphere models is obtained by homogenizing the multiple batches of fuel sphere models.

[0033] In this step, two types of core simulation models can be constructed based on the fuel cycle operation mode of the pebble bed high-temperature gas-cooled reactor and the power and temperature differences of different batches of fuel spheres: the first simulation model and the second simulation model.

[0034] The first simulation model consists of multiple batches of refined fuel sphere models, which can contain multiple fuel sphere models and can be divided according to batches. The fuel sphere models of each batch have significant differences in geometric parameters, burn-up depth, fission power, and thermal conductivity, which are used to accurately characterize the actual physical field changes in the reactor core.

[0035] The second simulation model is a batch-weighted average model. It homogenizes the physical properties of the fuel spheres in the first simulation model based on the volume and proportion of each fuel sphere model in the first simulation model. The second simulation model is a low-order model, a simplified version of the first simulation model, and does not distinguish between batches of fuel sphere models. This reduces the solution scale and improves computational efficiency during iteration. Its temperature control equation can be a one-dimensional thermal conductivity differential equation, and the physical properties are the parameters homogenized by volume proportion for all batches of fuel spheres within the current grid. The physical properties can be expressed by the following formula: ; ; ; in, For different batches of fuel balls, thermal conductivity This represents the percentage of quantities from different batches. The thermal conductivity of the fuel spheres is the weighted average. This represents the average value of the equivalent volumetric specific heat capacity. The equivalent volumetric specific heat capacity of fuel pellets from different batches; For the equivalent heat source density of different batches, The equivalent heat source density is the weighted average, and N is the total number of batches.

[0036] The boundary conditions for the second simulation model can be the same as those for the first simulation model. A first-type boundary condition with a temperature gradient of 0 is used at the center of the fuel sphere, while a third-type boundary condition is used on the outer surface of the fuel sphere. The energy exchange between the second simulation model and the external environment can be considered only for the thermal conduction between the fuel sphere and the macroscopic solid, and the convective heat transfer with helium, excluding radiative heat transfer between batches of fuel spheres. The sum of the radiative heat transfer between all batches of fuel spheres within the current grid is 0.

[0037] For example, the expression for the first simulation model can be The functional expression of the second simulation model can be Where x is the parameter to be solved. The temperature of the fuel ball. For neutron field parameters, These are the parameters for the thermal field.

[0038] Thus, the second simulation model has far fewer variables to be solved than the first simulation model. Assuming there are 15 batches of fuel balls in each computational grid, and each fuel ball is divided into 10 grid layers to solve for the internal temperature distribution, then within each grid, the first simulation model has 150 variables to be solved, while the second simulation model has only 10 variables to be solved.

[0039] Solving the second simulation model is also much simpler than solving the first one. First, the second simulation model doesn't involve multiple batches, thus eliminating the need for iterations between batches of fuel spheres. Second, the properties in the second simulation model are obtained by homogenizing the properties of the first simulation model, and these properties remain constant in each solution, saving the need for iterations between fuel sphere temperature and properties. Therefore, the computational cost of the second simulation model is far less than that of the first. The second simulation model retains the coupling effect between the fuel sphere and other physical fields; therefore, the calculation results of the second simulation model will change as the macroscopic temperature field and nuclear fission power change.

[0040] S202. Construct a set of temperature nonlinear equations for the core of the pebble bed high-temperature gas-cooled reactor; the set of temperature nonlinear equations includes multiple variables to be solved; the variables to be solved include fuel sphere temperature, neutron field parameters, and thermal field parameters.

[0041] In this step, after establishing the simulation model, the temperature field, fission source terms, and thermal boundary conditions within the reactor core can be coupled to form a multi-dimensional, strongly nonlinear set of temperature equations. The set of equations contains multiple variables to be solved, including but not limited to: fuel sphere temperature distribution, neutron field parameters (such as group constant, core power density, fission source terms), and thermal field parameters (such as solid temperature, helium convection heat transfer, and thermal property parameters).

[0042] S203. The Newton-Krilov method is used to iteratively solve the temperature nonlinear equations to obtain multi-dimensional physical scene characterization information of the pebble bed high-temperature gas-cooled reactor core; the Newton-Krilov method includes the Newton step and the Krilov step; the Newton step uses the first simulation model for nonlinear elimination; the Krilov step uses the second simulation model for nonlinear elimination; the nonlinear elimination includes using the neutron field parameters and thermal field parameters to represent the temperature of the fuel sphere.

[0043] In this step, the Newton-Krylov (NK) method can be used to iteratively solve the temperature nonlinear equations. The Newton step is used to handle the nonlinear part, and the Krylov step is used to solve the linearized equations. This embodiment employs two types of nonlinear elimination strategies in the NK method, achieving a balance between solution efficiency and accuracy.

[0044] In the nonlinear elimination of the Newton step, the first simulation model of multiple batches of fuel spheres can be called to calculate the higher-order fuel sphere temperature, thereby accurately reflecting the real-time coupling relationship between the fuel sphere and the macroscopic temperature field and neutron field, ensuring that the final convergence result is consistent with the real physical field. The first simulation model can be used to update the Jacobian matrix and the direction of subsequent iterations.

[0045] In the nonlinear elimination of the Krylov step, the fuel ball temperature variable can be replaced with the result determined based on the second simulation model, thereby significantly reducing the computational load of the fuel ball, greatly reducing the size of the variables to be solved, and improving the solution efficiency of the Krylov subspace.

[0046] The aforementioned nonlinear elimination can treat the fuel ball temperature as an implicit function that can be calculated from the neutron field and thermal field parameters, thereby eliminating the fuel ball temperature variable and ensuring that the solution scale retains only the core variables of the macroscopic physical field.

[0047] In some possible implementations, the Newton step includes: Set the initial values ​​for each variable to be solved during iteration; Based on the first simulation model, a first residual function is determined; based on the second simulation model, a second residual function is determined; based on the second residual function, a Jacobian matrix is ​​constructed; the fuel ball temperature in the first residual function and the second residual function is represented by the neutron field parameters and the thermal field parameters. Based on the first residual function and the Jacobian matrix, construct the difference equation of the variable to be solved; The difference equation of the unsolved variables is solved iteratively by Krylov step to obtain the difference of the unsolved variables; The initial values ​​of the variables to be solved are corrected based on the differences between them, and the next iteration of the Newton step is started.

[0048] In this step, at the start of the solution process, initial iteration values ​​can be set for the neutron field parameters, thermal field parameters, and fuel ball temperature, which are waiting to be solved, based on the core steady-state field distribution or historical transient results. These initial values ​​are used to initiate the outer Newton iteration of the NK method.

[0049] In the Newton step, the first residual function and the second residual function of the nonlinear equation system can be constructed based on the current iteration value of the variable to be solved, and the Jacobian matrix corresponding to the second residual function can be constructed through the difference quotient or other numerical linearization methods.

[0050] For example, based on the first simulation model, a first conversion relationship between the fuel ball temperature and the neutron field parameters and the thermal field parameters can be determined; based on the first conversion relationship, a first residual function can be determined; based on the second simulation model, a second conversion relationship between the fuel ball temperature and the neutron field parameters and the thermal field parameters can be determined; based on the second conversion relationship, a second residual function can be determined.

[0051] In the first residual function, the residual and its partial derivative of the fuel ball temperature are not treated as independent variables. Instead, based on the dependency (i.e., transformation relationship) between the fuel ball temperature and the neutron field and thermal field calculated by the first simulation model (i.e., the refined model of multiple batches of fuel balls), the fuel ball temperature is expressed as a function of the neutron field parameters and the thermal field parameters, thus achieving nonlinear elimination of the fuel ball temperature. Similarly, the residual and its partial derivative of the fuel ball temperature in the second residual function are also determined based on the dependency relationship between the fuel ball temperature and the neutron field and thermal field in the second simulation model.

[0052] Then, based on the Jacobian matrix J(x) and the first residual matrix F1, a standard Newton linearized difference equation can be constructed. (That is, the equation of the variable to be solved), used to find the variable correction amount of the current Newton step iteration. (That is, the difference between the variables to be solved).

[0053] Because the difference equations are high-dimensional and have sparse and numerous coefficient matrices, the Krzylov subspace iterative method can be used to solve the difference equations of the unsolved variables through iteration over Krzylov subspaces. During the Krzylov iteration process, the second residual function can be called in each iteration to calculate the Jacobian matrix-vector multiplication for equation updates, further reducing the solution cost.

[0054] Then, the difference is calculated. Then, it can be superimposed onto the corresponding variable to be solved to form the input value for the next Newton step iteration. Repeat the above process until the residual is less than a preset threshold, at which point the Newton step converges.

[0055] In some embodiments, the difference equation of the unsolved variables is solved iteratively using the Krylov step to obtain the difference of the unsolved variables, including: Based on the difference equation of the unsolved variables, a Krylov subspace is constructed; the basis of the Krylov subspace is iteratively updated until the convergence condition is met, and the iteratively updated difference of the unsolved variables is obtained.

[0056] In this step, instead of using the computationally intensive multi-batch fuel ball fine model (i.e., the first simulation model), the Krylov subspace is constructed based on the second simulation model (i.e., the low-order model of fuel ball weighted average).

[0057] In one possible implementation, the second residual function F2 can be expressed as: ; Where x is the variable to be solved. The temperature of the fuel ball. For neutron field parameters, For thermal field parameters, Let be the neutron field residual function. This is the residual function of the thermal field. , This is the mapping function corresponding to the second simulation model. Let the difference between the second simulation model and the first simulation model be a constant within each Newton step, then: ; in, This is the mapping function corresponding to the first simulation model.

[0058] For example, the calculation of the first simulation model can be performed first. Update the difference values ​​between the first simulation model and the second simulation model. The first residual function F1(x) is calculated and used as the right-hand side term of the difference equation for the unsolved variables. Then, the second residual function F2(x) can be determined, and the Jacobian matrix J(x) is calculated using F2(x). Taking the DJNK method as an example, the second residual function F2(x) is called once each time the Jacobian matrix is ​​calculated using the difference quotient. This residual function calculates the second simulation model once and uses the second simulation model to update the fuel ball parameters. ,parameter It is updated before the start of each Newton step and set to a constant value within the current Newton step. In the JFNK method, the matrix-vector multiplication is calculated using the second residual function F2(x) within each linear step.

[0059] In some implementations, the above convergence condition may refer to the ratio between the first residual function of the (k+1)th iteration and the first residual function of the initial iteration being less than a preset value.

[0060] The Jacobian matrix above can be: .

[0061] in, The second residual function is the one that has not undergone linear elimination. The second residual function after eliminating the fuel ball temperature, x is the part of the second residual function that is determined only by the temperature of the fuel ball, and x is the variable to be solved (explicit variable). The temperature of the fuel ball.

[0062] By solving the temperature nonlinear equations, multi-dimensional physical scene characterization information can be obtained. For example, multi-dimensional physical scene characterization information can include the actual temperature distribution of each batch of fuel pellets, the core thermal field distribution (such as solid temperature, gas temperature, and hot spot location), neutron field power distribution, reactivity changes, and steady-state or transient behavior under multi-field coupling states.

[0063] See Figure 3 The diagram shown is a flowchart illustrating the steps of the Newton-Krylov method according to an exemplary embodiment of this application. Taking the JFNK method as an example, the steps include Newton iterations and Krylov iterations (GMRES). The Newton iterations include N1 to N6, and the GMRES iterations include G1 to G8.

[0064] The Newtion iteration includes: N1. Set the initial values ​​for the iteration; N2, Set iteration conditions For Until convergence; Where k represents the kth Newton step iteration.

[0065] N3. Determine the first residual function using the first simulation model. (Residuals under higher-order models), using the second simulation model to determine the second residual function. (Residuals under the low-order model), and construct the Jacobian matrix using the second residual function. .

[0066] N4. Solving the difference equation of the variables using GMRES iteration Please provide a solution.

[0067] in, This is the variable correction amount for the current iteration step.

[0068] N5. Calculate the initial variables for the next iteration. ; N6, End iteration.

[0069] GMRES iterations include: G1. Use the right-hand side of the difference equation of the unsolved variables as the initial residual vector of the linear subproblem. And calculate its norm. , used for initializing the Krylov subspace; G2. Initialization of the inner layer solution of the Krylov subspace, and determination of the orthogonal basis of the subspace. ; G3, Set iteration conditions For Until convergence; Where j represents the j-th iteration.

[0070] G4, Perform Jacobian matrix-vector multiplication. ,definition , represents mapping the orthogonal basis of the subspace of the j-th iteration of the Krylov subspace to the original variable. In the space, we have: ; in, This represents the precondition matrix constructed in the k-th Newton iteration, used to solve the difference equation of the variables. Preprocessing is performed. The step size is the finite difference perturbation step.

[0071] G5. Constructing orthogonal bases using the Arnoldi procedure. sum matrix ; Where m represents the total number of orthogonal bases, This represents the Hessenberg matrix generated by the Arnoldi process.

[0072] G6. Constructing a linear least squares problem ; in, Let represent the coefficient vector in the mVikrilov subspace that determines the approximate solution of the linear subproblem by the minimum residual criterion; Let be the orthonormal basis (unit basis) vectors, and y be the linear combination weights used to characterize the orthonormal basis vectors in the Vikrilov subspace.

[0073] G7. Calculate the initial variables for the next iteration. ; in, Let represent the basis matrix consisting of orthogonal basis vectors of m Krylov subspaces.

[0074] G8, End iteration.

[0075] S204. Based on the multi-dimensional physical scene characterization information, perform design optimization and safety assessment on the pebble bed type high-temperature gas-cooled reactor core.

[0076] In this step, the field distribution of the pebble bed high-temperature gas-cooled reactor core can be determined based on the multi-dimensional physical scene characterization information. The field distribution includes the temperature field distribution, neutron field and fission power distribution, and thermal field distribution. Then, a safety assessment of the pebble bed high-temperature gas-cooled reactor core can be performed based on the field distribution. Based on the safety assessment results, the design of the pebble bed high-temperature gas-cooled reactor core can be optimized.

[0077] The temperature field distribution can include the radial temperature distribution of each batch of fuel pellets, the highest temperature at the center of the fuel pellets, the temperature field of solid graphite in the pellet bed region, the temperature field of the helium coolant, the location of core hot spots and their temperature rise characteristics, etc. This information can be used to determine the thermal safety margin of the TRISO cladding and the risk of local overheating.

[0078] Neutron field and fission power distribution can include the spatial distribution of neutron flux across multiple groups, fission power density distribution, power differences between batches of fuel pellets due to varying burnup, and the overall core multiplication factor trend. This information can be used to assess core power uniformity and local power peaks.

[0079] The thermal field distribution can include the temperature gradient along the solid heat conduction path, the convective heat transfer coefficient distribution in the pebble bed region, the thermal flow parameters of the helium channels, and the heat transport paths and distribution patterns within the reactor core. This information can be used to determine whether the core's heat removal capacity meets design requirements.

[0080] After obtaining the field distribution, a multi-dimensional safety assessment can be performed to determine the safety characteristics of the reactor core under steady-state or transient conditions. For example, fuel temperature safety margin checks, local power peak and hot spot analysis, thermal safety index assessments, and neutron safety checks can be conducted.

[0081] Once the safety assessment results are obtained, the core design can be optimized based on these results to achieve higher thermal performance and a greater safety margin. For example, optimization may include optimization of multi-batch fuel ball arrangement, core geometry and fuel distribution strategy, thermal channels and heat exchange structures, and power control strategy.

[0082] The pebble bed type high-temperature gas-cooled reactor core optimization and evaluation method provided in this application constructs a first simulation model (multi-batch refined model) and a second simulation model (weighted low-order model), and uses them respectively for nonlinear elimination of Newton step and Krylov step. This maintains high computational accuracy of Newton step and reduces the computational scale of Krylov step. Under the premise of ensuring the accuracy of multi-dimensional physical scene representation information, it can significantly reduce the computational scale and time, thereby improving the efficiency of core safety assessment and design optimization.

[0083] See Figure 4 The diagram shown is a schematic representation of an exemplary embodiment of a pebble bed type high-temperature gas-cooled reactor core optimization and evaluation device. The device includes: The first construction module 410 is used to construct a first simulation model and a second simulation model of a pebble bed type high-temperature gas-cooled reactor core; the first simulation model includes multiple batches of fuel sphere models; the second simulation model includes a single batch of fuel sphere models; the single batch of fuel sphere models is obtained by homogenizing the multiple batches of fuel sphere models; The second construction module 420 is used to construct the temperature nonlinear equation set of the pebble bed type high-temperature gas-cooled reactor core; the temperature nonlinear equation set includes multiple variables to be solved; the variables to be solved include fuel sphere temperature, neutron field parameters and thermal field parameters. Iteration module 430 is used to iteratively solve the temperature nonlinear equations using the Newton-Krilov method to obtain multi-dimensional physical scene characterization information of the pebble bed high-temperature gas-cooled reactor core; the Newton-Krilov method includes a Newton step and a Krilov step; the Newton step uses the first simulation model for nonlinear elimination; the Krilov step uses the second simulation model for nonlinear elimination; the nonlinear elimination includes using the neutron field parameters and thermal field parameters to represent the temperature of the fuel spheres; The evaluation module 440 is used to perform design optimization and safety assessment of the pebble bed type high-temperature gas-cooled reactor core based on the multi-dimensional physical scene characterization information.

[0084] Optionally, the iteration module 430 is used for: Set the initial values ​​for each variable to be solved during iteration; Based on the first simulation model, a first residual function is determined; based on the second simulation model, a second residual function is determined; based on the second residual function, a Jacobian matrix is ​​constructed; the fuel ball temperature in the first residual function and the second residual function is represented by the neutron field parameters and the thermal field parameters. Based on the first residual function and the Jacobian matrix, construct the difference equation of the variable to be solved; The difference equation of the unsolved variables is solved iteratively by Krylov step to obtain the difference of the unsolved variables; The initial values ​​of the variables to be solved are corrected based on the differences between them, and the next iteration of the Newton step is started.

[0085] Optionally, the iteration module 430 is used for: If the first residual function satisfies the convergence condition, the iteration of the Newton step is stopped.

[0086] Optionally, the iteration module 430 is used for: Based on the first simulation model, a first conversion relationship is determined between the fuel ball temperature and the neutron field parameters and the thermal field parameters; based on the first conversion relationship, the first residual function is determined; Based on the second simulation model, a second conversion relationship is determined between the fuel ball temperature and the neutron field parameters and the thermal field parameters; based on the second conversion relationship, the second residual function is determined.

[0087] Optionally, the iteration module 430 is used for: Based on the difference equation of the variables to be solved, construct the Krylov subspace; The basis of the Krylov subspace is iteratively updated until the convergence condition is met, and the difference of the unsolved variables after iterative update is obtained.

[0088] Optionally, the first building module 410 is used for: Based on the volume and proportion of each fuel ball model in the first simulation model, the physical property parameters of the fuel ball models in the first simulation model are homogenized to obtain the second simulation model.

[0089] Optionally, the evaluation module 440 is used for: Based on the multi-dimensional physical scene characterization information, the field distribution of the pebble bed high-temperature gas-cooled reactor core is determined; the field distribution includes temperature field distribution, neutron field and fission power distribution, and thermal field distribution. Based on the field distribution, a safety assessment is conducted on the core of the pebble bed type high-temperature gas-cooled reactor. Based on the safety assessment results, the design of the pebble bed type high-temperature gas-cooled reactor core was optimized.

[0090] The pebble bed type high-temperature gas-cooled reactor core optimization and evaluation device provided in this application constructs a first simulation model (multi-batch refined model) and a second simulation model (weighted low-order model), and uses them respectively for nonlinear elimination of Newton step and Krylov step. This maintains high computational accuracy of Newton step and reduces the computational scale of Krylov step. Under the premise of ensuring the accuracy of multi-dimensional physical scene representation information, it can significantly reduce the computational scale and time, thereby improving the efficiency of core safety assessment and design optimization.

[0091] The processing flow of each module in the device and the interaction flow between each module can be referred to the relevant descriptions in the above method embodiments, and will not be detailed here.

[0092] This application also provides a computer device, such as... Figure 5 The diagram shown is a schematic representation of a computer device structure according to an exemplary embodiment of this application. The computer device includes: A processor 51 and a memory 52; the memory 52 stores machine-readable instructions executable by the processor 51, and the processor 51 executes the machine-readable instructions stored in the memory 52. ​​When the machine-readable instructions are executed by the processor 51, the processor 51 performs the following steps: A first simulation model and a second simulation model are constructed for a pebble bed type high-temperature gas-cooled reactor core; the first simulation model includes multiple batches of fuel sphere models; the second simulation model includes a single batch of fuel sphere models; the single batch of fuel sphere models is obtained by homogenizing the multiple batches of fuel sphere models. A set of temperature nonlinear equations is constructed for the core of the pebble bed high-temperature gas-cooled reactor; the set of temperature nonlinear equations includes multiple variables to be solved; the variables to be solved include fuel sphere temperature, neutron field parameters, and thermal field parameters; The Newton-Krylov method is used to iteratively solve the temperature nonlinear equations to obtain multi-dimensional physical scene characterization information of the pebble bed high-temperature gas-cooled reactor core. The Newton-Krylov method includes a Newton step and a Krylov step. In the Newton step, the first simulation model is used for nonlinear elimination. In the Krylov step, the second simulation model is used for nonlinear elimination. The nonlinear elimination includes using the neutron field parameters and thermal field parameters to represent the temperature of the fuel sphere. Based on the multi-dimensional physical scene characterization information, the design optimization and safety assessment of the pebble bed type high-temperature gas-cooled reactor core are carried out.

[0093] Optionally, the Newton step includes: Set the initial values ​​for each variable to be solved during iteration; Based on the first simulation model, a first residual function is determined; based on the second simulation model, a second residual function is determined; based on the second residual function, a Jacobian matrix is ​​constructed; the fuel ball temperature in the first residual function and the second residual function is represented by the neutron field parameters and the thermal field parameters. Based on the first residual function and the Jacobian matrix, construct the difference equation of the variable to be solved; The difference equation of the unsolved variables is solved iteratively by Krylov step to obtain the difference of the unsolved variables; The initial values ​​of the variables to be solved are corrected based on the differences between them, and the next iteration of the Newton step is started.

[0094] Optionally, the Newton step further includes: If the first residual function satisfies the convergence condition, the iteration of the Newton step is stopped.

[0095] Optionally, determining the first residual function based on the first simulation model includes: Based on the first simulation model, a first conversion relationship is determined between the fuel ball temperature and the neutron field parameters and the thermal field parameters; based on the first conversion relationship, the first residual function is determined; The determination of the second residual function based on the second simulation model includes: Based on the second simulation model, a second conversion relationship is determined between the fuel ball temperature and the neutron field parameters and the thermal field parameters; based on the second conversion relationship, the second residual function is determined.

[0096] Optionally, the step of iteratively solving the difference equation of the unsolved variables through the Krylov step to obtain the difference of the unsolved variables includes: Based on the difference equation of the variables to be solved, construct the Krylov subspace; The basis of the Krylov subspace is iteratively updated until the convergence condition is met, and the difference of the unsolved variables after iterative update is obtained.

[0097] Optionally, constructing the second simulation model includes: Based on the volume and proportion of each fuel ball model in the first simulation model, the physical property parameters of the fuel ball models in the first simulation model are homogenized to obtain the second simulation model.

[0098] Optionally, the step of design optimization and safety assessment of the pebble bed high-temperature gas-cooled reactor core based on the multi-dimensional physical scene characterization information includes: Based on the multi-dimensional physical scene characterization information, the field distribution of the pebble bed high-temperature gas-cooled reactor core is determined; the field distribution includes temperature field distribution, neutron field and fission power distribution, and thermal field distribution. Based on the field distribution, a safety assessment is conducted on the core of the pebble bed type high-temperature gas-cooled reactor. Based on the safety assessment results, the design of the pebble bed type high-temperature gas-cooled reactor core was optimized.

[0099] The aforementioned memory 52 includes a main memory 521 and an external memory 522; the main memory 521, also known as internal memory, is used to temporarily store the computational data in the processor 51, as well as the data exchanged with external memory 522 such as a hard disk. The processor 51 exchanges data with the external memory 522 through the main memory 521.

[0100] The specific execution process of the above instructions can be referred to the steps of the pebble bed type high temperature gas-cooled reactor core optimization evaluation method described in the embodiments of this application, and will not be repeated here.

[0101] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this application according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0102] This application also provides a computer-readable storage medium storing a computer program. When executed by a processor, this computer program performs the steps of the pebble bed high-temperature gas-cooled reactor core optimization and evaluation method described in the above-described method embodiments. The storage medium can be volatile or non-volatile computer-readable storage.

[0103] This application also provides a computer program product, including a computer program / instruction, which, when executed by the computer program / instruction processor, implements the pebble bed type high-temperature gas-cooled reactor core optimization and evaluation method provided in the various embodiments of this application.

[0104] The aforementioned computer program product can be implemented through hardware, software, or a combination thereof. In one optional embodiment, the computer program product is specifically embodied in a computer storage medium; in another optional embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0105] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and devices described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division; in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection may be through some communication interfaces; the indirect coupling or communication connection of devices or units may be electrical, mechanical, or other forms.

[0106] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0107] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0108] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0109] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The scope of protection of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0110] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for optimizing and evaluating the core of a pebble bed type high-temperature gas-cooled reactor, characterized in that, The method includes: A first simulation model and a second simulation model are constructed for a pebble bed type high-temperature gas-cooled reactor core; the first simulation model includes multiple batches of fuel sphere models; the second simulation model includes a single batch of fuel sphere models; the single batch of fuel sphere models is obtained by homogenizing the multiple batches of fuel sphere models. A set of temperature nonlinear equations is constructed for the core of the pebble bed high-temperature gas-cooled reactor; the set of temperature nonlinear equations includes multiple variables to be solved; the variables to be solved include fuel sphere temperature, neutron field parameters, and thermal field parameters; The Newton-Krylov method is used to iteratively solve the temperature nonlinear equations to obtain multi-dimensional physical scene characterization information of the pebble bed high-temperature gas-cooled reactor core. The Newton-Krylov method includes a Newton step and a Krylov step. In the Newton step, the first simulation model is used for nonlinear elimination. In the Krylov step, the second simulation model is used for nonlinear elimination. The nonlinear elimination includes using the neutron field parameters and thermal field parameters to represent the temperature of the fuel sphere. Based on the multi-dimensional physical scene characterization information, the design optimization and safety assessment of the pebble bed type high-temperature gas-cooled reactor core are carried out.

2. The method according to claim 1, characterized in that, The Newton step includes: Set the initial values ​​for each variable to be solved during iteration; Based on the first simulation model, a first residual function is determined; based on the second simulation model, a second residual function is determined; based on the second residual function, a Jacobian matrix is ​​constructed; the fuel ball temperature in the first residual function and the second residual function is represented by the neutron field parameters and the thermal field parameters. Based on the first residual function and the Jacobian matrix, construct the difference equation of the variable to be solved; The difference equation of the unsolved variables is solved iteratively by Krylov step to obtain the difference of the unsolved variables; The initial values ​​of the variables to be solved are corrected based on the differences between them, and the next iteration of the Newton step is started.

3. The method according to claim 2, characterized in that, The Newton step also includes: If the first residual function satisfies the convergence condition, the iteration of the Newton step is stopped.

4. The method according to claim 2, characterized in that, The step of determining the first residual function based on the first simulation model includes: Based on the first simulation model, a first conversion relationship is determined between the fuel ball temperature and the neutron field parameters and the thermal field parameters; based on the first conversion relationship, the first residual function is determined; The determination of the second residual function based on the second simulation model includes: Based on the second simulation model, a second conversion relationship is determined between the fuel ball temperature and the neutron field parameters and the thermal field parameters; based on the second conversion relationship, the second residual function is determined.

5. The method according to claim 2, characterized in that, The iterative solution of the difference equation of the unsolved variables through the Krylov step to obtain the difference of the unsolved variables includes: Based on the difference equation of the variables to be solved, construct the Krylov subspace; The basis of the Krylov subspace is iteratively updated until the convergence condition is met, and the difference of the unsolved variables after iterative update is obtained.

6. The method according to claim 1, characterized in that, The second simulation model is obtained through the following steps: Based on the volume and proportion of each fuel ball model in the first simulation model, the physical property parameters of the fuel ball models in the first simulation model are homogenized to obtain the second simulation model.

7. The method according to claim 1, characterized in that, The design optimization and safety assessment of the pebble bed high-temperature gas-cooled reactor core based on the multi-dimensional physical scene characterization information includes: Based on the multi-dimensional physical scene characterization information, the field distribution of the pebble bed high-temperature gas-cooled reactor core is determined; the field distribution includes temperature field distribution, neutron field and fission power distribution, and thermal field distribution. Based on the field distribution, a safety assessment is conducted on the core of the pebble bed type high-temperature gas-cooled reactor. Based on the safety assessment results, the design of the pebble bed type high-temperature gas-cooled reactor core was optimized.

8. A pebble bed type high-temperature gas-cooled reactor core optimization and evaluation device, characterized in that, The device includes: The first construction module is used to construct a first simulation model and a second simulation model of a pebble bed type high-temperature gas-cooled reactor core; the first simulation model includes multiple batches of fuel sphere models; the second simulation model includes a single batch of fuel sphere models; the single batch of fuel sphere models is obtained by homogenizing the multiple batches of fuel sphere models; The second construction module is used to construct the temperature nonlinear equation set of the pebble bed high-temperature gas-cooled reactor core; the temperature nonlinear equation set includes multiple variables to be solved; the variables to be solved include fuel sphere temperature, neutron field parameters and thermal field parameters. An iterative module is used to iteratively solve the temperature nonlinear equations using the Newton-Krilov method to obtain multi-dimensional physical scene characterization information of the pebble bed high-temperature gas-cooled reactor core. The Newton-Krilov method includes a Newton step and a Krilov step. In the Newton step, the first simulation model is used for nonlinear elimination. In the Krilov step, the second simulation model is used for nonlinear elimination. The nonlinear elimination includes using the neutron field parameters and thermal field parameters to represent the temperature of the fuel spheres. The evaluation module is used to perform design optimization and safety assessment of the pebble bed high-temperature gas-cooled reactor core based on the multi-dimensional physical scene characterization information.

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

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

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