Nuclear system optimization method, electronic equipment and storage medium
By using general variable parameters to map optimization parameters to the initial computing model of the kernel system and optimize it, the problem that intelligent optimization algorithm can only optimize for a single problem in the kernel system design is solved, and the general intelligence of kernel system design is realized Optimization improves design efficiency and effect.
Patent Information
- Application Number
- CN202411660896.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-19
- Publication Date
- 2025-02-21
AI Technical Summary
The existing intelligent optimization algorithm can only be optimized for a single problem in the design of the kernel system, and requires targeted coding, which limits its promotion and application.
By obtaining the initial calculation model and optimization parameters, using general change parameters to map the optimization parameters into the initial calculation model, and optimizing the model based on these parameters, a new calculation model is obtained.
It realizes the general application of intelligent optimization in nuclear system design, and can intelligently map parameters according to general variable parameters, improves the efficiency and effect of nuclear system design, and avoids the need for manual programming.
Smart Images
Figure CN119494223A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of nuclear systems, and in particular to an optimization method, electronic equipment and storage medium for a nuclear system. Background Art
[0002] Nuclear design can achieve the target values of key nuclear parameters of the nuclear system by adjusting the material composition, component size, component position, etc. in the nuclear system, and is the core component of nuclear system design.
[0003] At present, nuclear design is mainly carried out by manually modifying material composition, component size, component position, etc., which is time-consuming and difficult to obtain a better design solution. Intelligent optimization algorithms can obtain better design solutions, but they can only optimize a single problem and require targeted coding, which limits the promotion and application of intelligent optimization algorithms.
[0004] Accordingly, the art needs a new nuclear system optimization solution to solve the above problems. Summary of the invention
[0005] In order to overcome the above-mentioned defects, the present application is proposed to provide a core system optimization method, electronic device and storage medium that solves or at least partially solves the technical problems that existing intelligent optimization algorithms can only optimize a single problem and require targeted coding and are difficult to expand their applications.
[0006] In a first aspect, a method for optimizing a nuclear system is provided, the method comprising:
[0007] Acquiring an initial calculation model and optimization parameters; the optimization parameters include optimization variables and optimization targets;
[0008] Acquiring a universal variation parameter, and mapping the optimization parameter to the initial calculation model based on the universal variation parameter;
[0009] The initial calculation model is optimized based on the universal change parameters to obtain a new calculation model.
[0010] In a technical solution of the above-mentioned optimization method of the core system, mapping the optimization parameter to the initial calculation model based on the universal change parameter includes:
[0011] Establishing a functional relationship between the optimization variable and the universal variation parameter;
[0012] Based on the functional relationship, modifying initial parameters in the initial calculation model;
[0013] The optimization objective is mapped into the initial calculation model.
[0014] In a technical solution of the optimization method of the above-mentioned nuclear system, the optimization variables include at least one of geometric structure related variables, material composition variables, geometric and material correspondence relationship variables and source distribution variables; the establishment of the functional relationship between the optimization variables and the universal variation parameters includes:
[0015] Establishing a functional relationship between the geometric structure related variables and the universal variation parameters based on the surface equation;
[0016] And / or, establishing a functional relationship between the material composition variable and the universal variation parameter based on the composition nuclides and the corresponding proportions;
[0017] And / or, establishing a functional relationship between the geometric body and material correspondence variable and the universal variation parameter based on the material number corresponding to the geometric grid element;
[0018] And / or, a functional relationship between the source distribution variable and the universal variation parameter is established based on a probability distribution relationship or a distribution function.
[0019] In a technical solution of the optimization method of the above-mentioned core system, the geometric structure variable includes at least one of the type, size and position of the geometric body; and the establishing of the functional relationship between the optimization variable and the universal variation parameter further includes:
[0020] Obtaining a preset range of the optimization variable;
[0021] The variation range of the universal variation parameter is obtained based on the preset range of the optimization variable.
[0022] In a technical solution of the above-mentioned method for optimizing the core system, optimizing the initial calculation model based on the universal change parameter to obtain a new calculation model includes:
[0023] Determining the general variation parameters to be used within the variation range of the general variation parameters based on a preset optimization algorithm;
[0024] The initial calculation model is optimized based on the used universal variation parameters and the functional relationship to obtain the new calculation model.
[0025] In a technical solution of the above-mentioned method for optimizing the nuclear system, the method further includes:
[0026] Performing kernel calculation based on the new calculation model to obtain corresponding counting results;
[0027] evaluating the new calculation model based on the counting results;
[0028] Modifying the general variation parameters based on the evaluation results, and optimizing the new calculation model based on the modified general variation parameters until the optimization goal is met;
[0029] Wherein, the nuclear calculation includes at least one of transport calculation, burnup calculation and activation calculation.
[0030] In one technical solution of the above-mentioned optimization method of the nuclear system, the initial calculation model includes a geometric structure, a material composition, a source model and a counting model;
[0031] Wherein, the geometric structure includes at least one of the type, size and position of the geometric body; the material composition includes at least one of the material nuclide composition and the corresponding relationship between the geometric body and the material; the source model includes at least one of the source position distribution, direction distribution, energy distribution and particle type; the counting model includes at least one of the counting type and counting position;
[0032] and / or,
[0033] The optimization target includes at least one of energy deposition, flux distribution and energy spectrum distribution.
[0034] In a technical solution of the above-mentioned optimization method of the nuclear system, the modifying of the initial parameters in the initial calculation model based on the functional relationship includes:
[0035] Based on the functional relationship, obtaining the initial parameters that need to be modified in the initial calculation model;
[0036] Modify the initial parameters that need to be modified.
[0037] In a second aspect, an electronic device is provided, which includes a processor and a memory, wherein the memory is suitable for storing multiple program codes, and the program codes are suitable for being loaded and run by the processor to execute the core system optimization method described in any one of the technical solutions of the above-mentioned core system optimization method.
[0038] In a third aspect, a computer-readable storage medium is provided, in which a plurality of program codes are stored, wherein the program codes are suitable for being loaded and run by a processor to execute the core system optimization method described in any one of the technical solutions of the core system optimization method described above.
[0039] The above one or more technical solutions of this application have at least one or more of the following Beneficial effects:
[0040] In the technical solution of the present application, the initial calculation model and optimization parameters are first obtained, wherein the optimization parameters include optimization variables and optimization targets; then the universal change parameters are obtained, and the optimization parameters are mapped to the initial calculation model based on the universal change parameters; finally, the initial calculation model is optimized based on the universal change parameters to obtain a new calculation model. Through the above implementation, the optimization parameters can be intelligently mapped to the nuclear system calculation model according to the universal change parameters, and the optimization design can be performed using the intelligent optimization method, which solves the problem that the current intelligent optimization can only optimize a single problem, helps to promote the application of intelligent optimization in nuclear system design, improves the efficiency and effect of nuclear system design, and does not require manual programming to encode the optimization parameters and map them to the input calculation model during the intelligent optimization process, thus realizing universal intelligent nuclear design optimization of nuclear systems. BRIEF DESCRIPTION OF THE DRAWINGS
[0041] The disclosure of the present application will become easier to understand with reference to the accompanying drawings. It is easy for those skilled in the art to understand that these drawings are only for illustrative purposes and are not intended to limit the scope of protection of the present application. Among them:
[0042] Figure 1 is a flowchart of main steps of a method for optimizing a nuclear system according to an embodiment of the present application;
[0043] Figure 2 is a flowchart of main steps of a method for optimizing a nuclear system according to another embodiment of the present application;
[0044] Figure 3 is a schematic flow chart of main steps of a method for optimizing a nuclear system according to another embodiment of the present application;
[0045] Figure 4 It is a schematic diagram of the main structure of an electronic device according to an embodiment of the present application.
[0046] List of reference numerals:
[0047] 401: processor; 402: memory. DETAILED DESCRIPTION
[0048] Some embodiments of the present application are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present application and are not intended to limit the protection scope of the present application.
[0049] In the description of the present application, "processor" may include hardware, software or a combination of the two. The processor may be a central processing unit, a microprocessor, an image processor, a digital signal processor or any other suitable processor. The processor has data and / or signal processing functions. The processor may be implemented in software, hardware or a combination of the two. Non-temporary computer-readable storage media include any suitable medium that can store program code, such as a disk, a hard disk, an optical disk, a flash memory, a read-only memory, a random access memory, etc. The term "A and / or B" means all possible combinations of A and B, such as just A, just B or A and B. The term "at least one A or B" or "at least one of A and B" has a similar meaning to "A and / or B" and may include just A, just B or A and B. The singular terms "one" and "the" may also include plural forms.
[0050] As described in the background technology, the current nuclear design is mainly carried out by manually modifying the material composition, component size, component position, etc., which has the problem of time-consuming design and difficulty in obtaining a better design solution. Intelligent optimization algorithms can obtain better design solutions, but they can only optimize a single problem and require targeted coding, which limits the promotion and application of intelligent optimization algorithms.
[0051] In order to solve the above problems, the present application provides a core system optimization method, electronic device and storage medium.
[0052] See attached Figure 1 , Figure 1 FIG. 1 is a flow chart of the main steps of a method for optimizing a nuclear system according to an embodiment of the present application, which is applied to a nuclear system. Figure 1 As shown, the optimization method of the core system in the embodiment of the present application mainly includes the following steps S101 to S103.
[0053] Step S101: obtaining an initial calculation model and optimization parameters;
[0054] Among them, the optimization parameters include optimization variables and optimization targets.
[0055] Step S102: obtaining common variation parameters, and mapping optimization parameters to an initial calculation model based on the common variation parameters;
[0056] Step S103: Optimize the initial calculation model based on the common change parameters to obtain a new calculation model.
[0057] Based on the method described in steps S101 to S103 above, the optimization parameters can be intelligently mapped to the nuclear system calculation model according to the universal change parameters, and the optimization design can be performed using the intelligent optimization method, thereby solving the problem that the current intelligent optimization can only optimize a single problem. This helps to promote the application of intelligent optimization in nuclear system design, improves the efficiency and effectiveness of nuclear system design, and during the intelligent optimization process, there is no need for manual programming to encode the optimization parameters and map them to the input calculation model, thereby realizing universal intelligent nuclear design optimization of nuclear systems.
[0058] The above steps S101 to S103 are further explained below.
[0059] In some implementations of step S101, the calculation model of the initial design solution may be first imported into the core system so that the core system acquires the initial calculation model.
[0060] Specifically, the initial computational model includes geometric structure, material composition, source model, and counting model, etc.
[0061] Among them, the geometric structure can include the type of geometric body (such as geometric bodies of different shapes such as spheres and cylinders), size (the size of the geometric body will affect various parameters of the nuclear reaction, such as the reaction cross section, etc.), position (the relative position of the components in the nuclear system has an important influence on the interaction of nuclear reactions, neutron transport, etc.), etc.
[0062] Material composition may include material nuclide composition (nuclide is an atom with a specific number of protons and neutrons. Different nuclides behave differently in nuclear reactions, such as uranium-235 and uranium-238 have different reaction characteristics in nuclear fission), the correspondence between geometric bodies and materials (i.e., which geometric bodies are made of which materials), etc.
[0063] The source model can include source position distribution (the position distribution of nuclear reaction sources in the nuclear system affects the initial position of neutron generation, etc.), direction distribution (the directional characteristics of source-emitted particles affect the transport path of particles in the nuclear system), energy distribution (that is, the energy distribution range of source-emitted particles, and particles of different energies interact differently in the nuclear system), particle types (such as different types of particles such as neutrons and protons), etc.
[0064] The counting model includes counting types (such as counting of different physical quantities such as neutron flux and energy deposition, which are important indicators for evaluating the performance of nuclear systems), counting locations (that is, at which locations in the nuclear system the counting is performed, and the counting results at different locations can reflect the performance of different areas of the nuclear system), etc.
[0065] After obtaining the initial calculation model, the user can specify the optimization parameters (including the optimization variable V and the optimization target M), that is, the user can select one or more factors as the optimization variables according to the specific needs of the core system and the direction to be optimized.
[0066] The optimization variable V may include one or more of geometric structure related variables (such as the type, size and position of the geometric body), material composition variables, geometric body and material correspondence variables and source distribution variables.
[0067] For example, if a user wants to improve the energy output efficiency of a nuclear system, they may choose to optimize variables such as source distribution or material composition.
[0068] The optimization target M may include energy deposition (i.e., the deposition of energy in the nuclear system during the nuclear reaction), flux distribution (such as neutron flux distribution, which is very important for the control of nuclear reactions and the performance evaluation of nuclear systems), energy spectrum distribution, etc. (i.e., the distribution of particles with different energies, which reflects the energy characteristics of nuclear reactions in the nuclear system).
[0069] It should be pointed out that the above examples of the initial calculation model and optimization parameters are only for illustrative purposes. In practical applications, those skilled in the art can make settings according to specific scenarios and requirements, and no limitation is made here.
[0070] In the above step S101, by importing the initial calculation model and defining the optimization parameters, the problem that the traditional intelligent optimization algorithm needs to define the optimization parameters through programming can be solved, thereby improving the versatility of intelligent optimization.
[0071] The above is a further description of step S101. The following is a further description of step S102.
[0072] In some implementations of the above step S102, a general change parameter may be obtained first. The general change parameter is a set of predefined parameters X i (i=1, 2, ..., n), these universal variation parameters are an abstract and universal representation method that can be used to construct a relationship with the optimization variables.
[0073] Further, the optimization parameters may be mapped to the initial calculation model based on the universal variation parameters. Specifically, the following steps S1021 to S1023 may be included:
[0074] Step S1021: establishing a functional relationship between the optimization variables and the general variation parameters;
[0075] That is, define the optimization variable as a function expression of the changing parameter, such as V i =f(X1, X2…X n ).
[0076] Specifically, in terms of geometric structure-related variables, such as the type, size, and position of the geometric body, a functional relationship between the geometric structure-related variables and the universal variation parameters can be established based on the surface equation.
[0077] Surface equations can describe geometric properties such as the boundary and shape of a geometric body. Suppose we consider a simple cylindrical nuclear reactor core, where the radius r and height h in the geometric dimensions are the variables to be optimized. Then we can define universal variable parameters X1 and X2, and establish a functional relationship in the form of surface equations such as: r = X1sin(X2), h = X1cos(X2). Here, the optimization variables r and h are expressed as functions of universal variable parameters X1 and X2 through surface equations in the form of trigonometric functions. If we want to consider the position of the geometric body, such as the coordinates (x, y, z) of the core in three-dimensional space, we can further define a simple functional relationship such as x = X3, y = X4, z = X5, where X3, X4, and X5 are also universal variable parameters.
[0078] In terms of material composition, the functional relationship between material composition variables and universal variation parameters can be established based on the constituent nuclides and their corresponding proportions.
[0079] The properties of nuclear materials are determined by the nuclides they contain and their proportions. For example, a nuclear fuel mixture consists of uranium-235 (U-235) and uranium-238 (U-238). Let the proportion of U-235 p be the material composition variable. We can define a universal variable parameter X6 and establish a functional relationship Here, the proportion p of U-235 is expressed as a function of the universal variable parameter X6 through a simple logical function form. Furthermore, if there are other nuclides involved in the composition, a functional relationship can also be constructed similarly using multiple universal variable parameters.
[0080] In terms of the variables of the correspondence between geometry and materials, a functional relationship between the variables of the correspondence between geometry and materials and the universal variation parameters can be established based on the material number corresponding to the geometric grid element.
[0081] In the geometric model of the nuclear system, many geometric cells are usually divided, and each cell corresponds to a specific material. Assuming that the nuclear system is divided into 100 geometric cells, each cell can be filled with different materials, and is identified by the material number m (m = 1, 2, ... represents different materials), then a universal variable parameter X7 can be defined to establish a functional relationship (in Indicates rounding down), for example, when X7=3.5, m=4, indicating that the fourth material will be allocated to the corresponding geometric grid element, thereby adjusting the corresponding relationship between the geometry and the material according to the universal change parameter.
[0082] In terms of source distribution, a functional relationship between source distribution variables and common variation parameters can be established based on a probability distribution relationship or a distribution function.
[0083] The distribution of nuclear reaction sources in nuclear systems has certain probabilistic characteristics, such as the emission probability distribution of neutron sources in space. Consider a neutron source distribution on a two-dimensional plane. Assuming that the emission probability P(x, y) of the neutron source (x, y) on the plane follows the form of Gaussian distribution, and we want to optimize this distribution, we can set the universal variable parameters X8 and X9 to control the mean and standard deviation of the Gaussian distribution respectively, then the probability distribution function can be expressed as: Here, the functional relationship between source distribution (neutron emission probability distribution) and universal variable parameters is constructed through universal variable parameters X8 and X9.
[0084] It should be pointed out that the above examples of optimizing variables and establishing functional relationships between optimization variables and general variation parameters are only for illustrative purposes. In practical applications, those skilled in the art may make settings according to specific scenarios and requirements, which are not limited here.
[0085] Furthermore, in the process of establishing the functional relationship between the optimization variable and the universal variation parameter, it is also necessary to obtain the preset range of the optimization variable, and obtain the variation range of the universal variation parameter based on the preset range of the optimization variable.
[0086] Specifically, it is necessary to automatically map and generate the universal variation parameter X according to the preset range of the user-defined optimization variable V i For example, in the example of the cylindrical core radius r mentioned above, if the user defines the preset range of r as [1m, 5m], then when r = X1sin(X2), it is necessary to determine the value range of X1 and X2 based on the value range of X1 and X2 and the characteristics of trigonometric functions, so that r is within the range of [1m, 5m]. This ensures that the values of the common variation parameters are reasonable throughout the optimization process and are consistent with the user's preset requirements for the optimization variables.
[0087] The above is a further explanation of step S1021.
[0088] Step S1022: modifying the initial parameters in the initial calculation model based on the functional relationship;
[0089] In some implementations of step S1022, initial parameters that need to be modified in the initial calculation model may be obtained based on the functional relationship, and the initial parameters that need to be modified may be modified.
[0090] Specifically, in the above step S1021, the optimization variable V and the general change parameter X have been established. i(i=1,2,...,n) i =f(X1, X2…X n ), in step S1022, this functional relationship can be used to reversely map the values of the universal variation parameters back to the initial calculation model to determine and modify the relevant initial parameters in the initial calculation model, including surface equations, geometric body positions, material composition, geometric body corresponding material numbers and source distribution, etc.
[0091] Taking the functional relationship between the above geometric structure related variables and the universal variable parameters as an example, for the above geometric cylindrical core, there is a functional relationship such as radius r = X1sin(X2), height h = X1cos(X2). After the values of X1 and X2 are determined (for example, X1 = 3, ), we can calculate the and Then, in the initial calculation model, the parameters representing the core radius and height can be modified to the calculated values.
[0092] The above is a further explanation of step S1022.
[0093] Step S1023: Map the optimization target to the initial counting model.
[0094] Optimization targets, such as energy deposition, flux distribution, and energy spectrum distribution, are indicators for measuring whether the performance of the nuclear system has reached an ideal state. The counting model in the initial calculation model can use a specific calculation method to count the physical quantities related to the optimization target in the nuclear system, so the optimization target can be mapped to the counting model to complete the mapping of the initial calculation model. This mapping directly corresponds the physical quantity concerned by the optimization target to the counting type in the counting model.
[0095] Specifically, if the optimization goal is energy deposition, then the counting model needs to focus on the type and location of counting related to energy deposition. For example, the energy deposition is counted at different components of a nuclear reactor (such as fuel rods, coolant channels, etc.).
[0096] If the optimization objective is flux distribution, then the counting model will focus on counting neutron flux at different spatial locations. For example, counting points are set at different radial and axial locations in the reactor core to obtain the value of neutron flux.
[0097] If the optimization target is the energy spectrum distribution, then the counting model will have corresponding energy spectrum counting settings, which may involve counting the number of particles or energies in different energy intervals. For example, it will adjust the counting parameters such as energy range and resolution according to the requirements of the optimization target.
[0098] In the above step S102, by defining a set of universal change parameters and mapping the initial computing model changes into a function of the universal change parameters, the problem of manual encoding and modeling of computing model changes in the core system during traditional intelligent optimization can be solved, which fundamentally solves the problem that traditional intelligent optimization can only solve a single problem.
[0099] The above is a further description of step S102. The following is a further description of step S103.
[0100] In some implementations of the above step S103, optimizing the initial calculation model based on the universal change parameter to obtain a new calculation model may include the following steps S1031 to S1032:
[0101] Step S1031: determining the general variation parameters to be used within the variation range of the general variation parameters based on a preset optimization algorithm;
[0102] Among them, the preset optimization algorithm (i.e., the pre-set intelligent optimization algorithm) is a tool for finding the optimal solution. Common intelligent optimization algorithms include genetic algorithm, particle swarm algorithm, etc. These algorithms have their own characteristics and working mechanisms. For example, the genetic algorithm simulates the inheritance, mutation and selection operations in the biological evolution process, and the particle swarm algorithm simulates the group intelligent behavior. In practical applications, those skilled in the art can select a suitable intelligent optimization algorithm according to the characteristics of the nuclear system design problem (such as the complexity of the problem, the relationship between variables, etc.), which is not limited here.
[0103] General variable parameter X i (i=1, 2, ..., n) is to convert the user-defined optimization variable V into a function expression V i =f(X1, X2…X n ) is mapped to the initial calculation model in the form of, and these universal variation parameters each have their own variation range, which can be obtained according to the preset range of the user-defined optimization variable, and will not be repeated here.
[0104] The range of the general variable parameter defines the value limit in the subsequent optimization process and is the effective interval for searching the optimal solution. i Within the range of changes, the selected intelligent optimization algorithm is used to determine the appropriate X i value.
[0105] For example, if the preset optimization algorithm is a genetic algorithm, the algorithm will generate i A set of individuals is randomly generated within the range of variation (each individual contains a set of X iThen, through the iterative process of genetic algorithm, including selection, crossover and mutation operations, X is continuously adjusted within this range. i The values of are used to find the optimal combination that achieves the optimization objectives (such as energy deposition, flux distribution, etc.). These selected values will be further used to construct new computational models to gradually approach the optimal nuclear system design.
[0106] The above is the description of step S1031.
[0107] Step S1032: Optimize the initial calculation model based on the used common variation parameters and functional relationships to obtain a new calculation model.
[0108] Specifically, the general variation parameters and the function V can be selected according to i =f(X1, X2…X n ) Obtain the parameters that need to be modified in the initial calculation model, and modify them in the initial calculation model to optimize the initial calculation model and generate a new calculation model. This new calculation model is obtained based on the parameter adjustment during the optimization process.
[0109] In the above step S103, by combining the intelligent mapping of optimization parameters and calculation models, no manual programming is required for encoding the optimization parameters and mapping them with the input calculation model during the intelligent optimization process, thereby realizing general intelligent core design optimization of the core system.
[0110] The above is the description of step S103.
[0111] Furthermore, in some implementations, after step S103, core calculation, evaluation, and modification of common variation parameters may be performed according to the new calculation model until the new calculation model gradually approaches the optimal calculation model.
[0112] See attached Figure 2 , Figure 2 FIG. 1 is a flow chart of the main steps of a method for optimizing a nuclear system according to another embodiment of the present application. Figure 2 As shown, the optimization method of the core system in the embodiment of the present application also includes the following steps S201 to S203.
[0113] Step S201: performing kernel calculation based on the new calculation model to obtain corresponding counting results;
[0114] Among them, nuclear calculations include at least one of transport calculations, burnup calculations, and activation calculations. Transport calculations are used to study the transport process of particles in a nuclear system, burnup calculations focus on the consumption of nuclear fuel, and activation calculations involve the activation of materials during nuclear reactions. These calculations can obtain various physical properties of a nuclear system under new parameter configurations.
[0115] Step S202: Evaluate the new calculation model based on the counting results;
[0116] After obtaining the corresponding counting results, the current new calculation model can be evaluated according to the counting results. For example, if the optimization goal is the uniformity of energy deposition, then the counting results of energy deposition are used to determine whether the current calculation model meets the requirements.
[0117] Step S203: modify the general variation parameters based on the evaluation results, and optimize the new calculation model based on the modified general variation parameters until the optimization target is met.
[0118] Specifically, the general change parameters can be changed and adjusted according to the evaluation results and in combination with the pre-set intelligent optimization algorithm, and the steps of generating a new calculation model and performing core calculation (i.e., step S103, steps S201-203) can be repeated until the optimization target is met. This iterative process continuously adjusts the parameters of the calculation model to gradually approach the optimal design solution.
[0119] The above is a further description of step S201 to step S203.
[0120] In the optimization method of the nuclear system provided in this application, refer to the attached Figure 3 , Figure 3 FIG. 1 is a flow chart of the main steps of a method for optimizing a nuclear system according to another embodiment of the present application. Figure 3 As shown, the following steps S301 to S308 are included.
[0121] Step S301: obtaining an initial calculation model and optimization parameters;
[0122] Step S302: obtaining a common variation parameter, and establishing a functional relationship between the optimization variable and the common variation parameter;
[0123] Step S303: Mapping the optimization parameters to the initial calculation model based on the general variation parameters;
[0124] Step S304: determining the general variation parameters to be used within the variation range of the general variation parameters based on a preset optimization algorithm;
[0125] Step S305: Optimizing the initial calculation model based on the common change parameters to obtain a new calculation model;
[0126] Step S306: performing kernel calculation based on the new calculation model and evaluating the counting result;
[0127] Step S307: Determine whether the optimization target is met;
[0128] Further, if satisfied, the process ends; otherwise, step S308 is executed;
[0129] Step S308: modifying the general change parameters based on the evaluation results;
[0130] Further, after executing step S308, return to step S305 until the optimization target is met.
[0131] The above is an explanation of the optimization method of the nuclear system provided in this application.
[0132] Through the above-mentioned implementation mode, the optimization parameters can be intelligently mapped to the nuclear system calculation model according to the universal change parameters, and the intelligent optimization method can be used for optimization design, which solves the problem that the current intelligent optimization can only optimize a single problem. It helps to promote the application of intelligent optimization in nuclear system design, improves the efficiency and effectiveness of nuclear system design, and in the intelligent optimization process, there is no need for manual programming to encode the optimization parameters and map them to the input calculation model, thus realizing universal intelligent nuclear design optimization of nuclear systems.
[0133] It should be pointed out that although the various steps in the above embodiments are described in a specific order, those skilled in the art can understand that in order to achieve the effect of the present application, different steps do not have to be executed in such an order. They can be executed simultaneously (in parallel) or in other orders. These changes are within the scope of protection of the present application.
[0134] It is understood by those skilled in the art that all or part of the processes in the method for implementing the above-mentioned embodiment of the present application can also be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable storage medium may include: any entity or device, medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal and software distribution medium, etc. that can carry the computer program code.
[0135] Furthermore, the present application also provides an electronic device. Figure 4 , Figure 4 FIG. 1 is a schematic diagram of the main structure of an electronic device according to an embodiment of the present application. Figure 4As shown, the electronic device in the embodiment of the present application mainly includes a processor 401 and a memory 402. The memory 402 can be configured to store a program for executing the optimization method of the core system of the above method embodiment, and the processor 401 can be configured to execute the program in the memory 402, which includes but is not limited to the program for executing the optimization method of the core system of the above method embodiment. For ease of explanation, only the part related to the embodiment of the present application is shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present application.
[0136] In some possible implementations of the present application, the electronic device may include multiple processors 401 and multiple memories 402. The program for executing the optimization method for the core system of the above-mentioned method embodiment may be divided into multiple subprograms, and each subprogram may be loaded and run by the processor 401 to execute different steps of the optimization method for the core system of the above-mentioned method embodiment. Specifically, each subprogram may be stored in different memories 402, respectively, and each processor 401 may be configured to execute the program in one or more memories 402 to jointly implement the optimization method for the core system of the above-mentioned method embodiment, that is, each processor 401 executes different steps of the optimization method for the core system of the above-mentioned method embodiment, respectively, to jointly implement the optimization method for the core system of the above-mentioned method embodiment.
[0137] The above-mentioned multiple processors 401 may be processors deployed on the same device. For example, the above-mentioned electronic device may be a high-performance device composed of multiple processors, and the above-mentioned multiple processors 401 may be processors configured on the high-performance device. In addition, the above-mentioned multiple processors 401 may also be processors deployed on different devices. For example, the above-mentioned electronic device may be a server cluster, and the above-mentioned multiple processors 401 may be processors on different servers in the server cluster.
[0138] Furthermore, the present application also provides a computer-readable storage medium. In a computer-readable storage medium embodiment according to the present application, the computer-readable storage medium can be configured to store a program for executing the optimization method of the core system of the above-mentioned method embodiment, and the program can be loaded and run by the processor to implement the above-mentioned optimization method of the core system. For ease of explanation, only the parts related to the embodiment of the present application are shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present application. The computer-readable storage medium can be a memory device formed by various electronic devices. Optionally, the computer-readable storage medium in the embodiment of the present application is a non-temporary computer-readable storage medium.
[0139] So far, the technical solution of the present application has been described in conjunction with an embodiment shown in the accompanying drawings, but it is easy for those skilled in the art to understand that the protection scope of the present application is obviously not limited to these specific embodiments. Without departing from the principles of the present application, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present application.
Claims
1. A method for optimizing a nuclear system, characterized in that: The method comprises: Acquiring an initial calculation model and optimization parameters; the optimization parameters include optimization variables and optimization targets; Acquiring a universal variation parameter, and mapping the optimization parameter to the initial calculation model based on the universal variation parameter; The initial calculation model is optimized based on the universal change parameters to obtain a new calculation model.
2. The method for optimizing a nuclear system according to claim 1, characterized in that: Mapping the optimization parameter to the initial calculation model based on the universal variation parameter comprises: Establishing a functional relationship between the optimization variable and the universal variation parameter; Based on the functional relationship, modifying initial parameters in the initial calculation model; The optimization objective is mapped into the initial calculation model.
3. The method for optimizing a nuclear system according to claim 2, characterized in that: The optimization variables include at least one of geometric structure related variables, material composition variables, geometric and material correspondence variables, and source distribution variables; the establishment of the functional relationship between the optimization variables and the universal variation parameters includes: Establishing a functional relationship between the geometric structure related variables and the universal variation parameters based on the surface equation; And / or, establishing a functional relationship between the material composition variable and the universal variation parameter based on the composition nuclides and the corresponding proportions; And / or, establishing a functional relationship between the geometric body and material correspondence variable and the universal variation parameter based on the material number corresponding to the geometric grid element; And / or, a functional relationship between the source distribution variable and the universal variation parameter is established based on a probability distribution relationship or a distribution function.
4. The method for optimizing a nuclear system according to claim 3, characterized in that: The geometric structure variables include at least one of the type, size and position of the geometric body; the establishing of the functional relationship between the optimization variables and the universal variation parameters also includes: Obtaining a preset range of the optimization variable; The variation range of the universal variation parameter is obtained based on the preset range of the optimization variable.
5. The method for optimizing a nuclear system according to claim 4, characterized in that: The optimizing the initial calculation model based on the universal change parameter to obtain a new calculation model comprises: Determining the general variation parameters to be used within the variation range of the general variation parameters based on a preset optimization algorithm; The initial calculation model is optimized based on the used universal variation parameters and the functional relationship to obtain the new calculation model.
6. The method for optimizing a nuclear system according to claim 1, characterized in that: The method further comprises: Performing kernel calculation based on the new calculation model to obtain corresponding counting results; evaluating the new calculation model based on the counting results; Modifying the general variation parameters based on the evaluation results, and optimizing the new calculation model based on the modified general variation parameters until the optimization goal is met; Wherein, the nuclear calculation includes at least one of transport calculation, burnup calculation and activation calculation.
7. The method for optimizing a nuclear system according to claim 1, characterized in that: The initial computational model includes a geometric structure, a material composition, a source model, and a counting model; Wherein, the geometric structure includes at least one of the type, size and position of the geometric body; the material composition includes at least one of the material nuclide composition and the corresponding relationship between the geometric body and the material; the source model includes at least one of the source position distribution, direction distribution, energy distribution and particle type; the counting model includes at least one of the counting type and counting position; and / or, The optimization target includes at least one of energy deposition, flux distribution and energy spectrum distribution.
8. The method for optimizing a nuclear system according to claim 2, characterized in that: The modifying of the initial parameters in the initial calculation model based on the functional relationship includes: Based on the functional relationship, obtaining the initial parameters that need to be modified in the initial calculation model; Modify the initial parameters that need to be modified.
9. An electronic device, comprising a processor and a memory, wherein the memory is suitable for storing a plurality of program codes, characterized in that: The program code is suitable for being loaded and run by the processor to execute the optimization method of the core system according to any one of claims 1 to 8.
10. A computer-readable storage medium storing a plurality of program codes, characterized in that: The program code is suitable for being loaded and run by a processor to execute the optimization method of the core system according to any one of claims 1 to 8.