Monte Carlo-discrete longitudinal beacon coupling calculation method based on distributed source item

Abstract

The invention relates to the technical field of radiation shielding, in particular to a Monte Carlo-discrete longitudinal beacon coupling calculation method based on a distributed source item, which comprises the following steps of: acquiring Monte Carlo particle track information by utilizing Monte Carlo simulation, and converting the Monte Carlo particle track information into grid particle angular density in a discrete longitudinal beacon; performing fitting processing on the direction distribution of the angular density of the grid particles by using a spherical harmonic function to obtain a low-storage-capacity-level source item file; performing discrete longitudinal scaling method simulation based on the low-storage-magnitude source item file to obtain a discrete longitudinal scaling method simulation result; and combining a Monte Carlo simulation result with the discrete longitudinal standard method simulation result to obtain a final simulation result. Source items are arranged in a calculation area, and a space coupling calculation mode can realize source item distribution in a combined surface form, and is suitable for processing a coupling surface under a geometric complex condition. And calculation of an energy coupling mode can be carried out, and the method is suitable for processing different calculation methods and different energy conditions. Transmission and interaction of particles in a complex geometric structure can be simulated more accurately, so that the accuracy of a calculation result is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of radiation shielding, and particularly relates to a Monte Carlo-discrete ordinate coupling calculation method based on a distributed source term. BACKGROUND

[0002] With the continuous development and application of nuclear energy technology, radiation shielding design plays a crucial role in nuclear engineering. Ensuring the occupational health and safety of nuclear workers, protecting the public's right to radiation protection, and ensuring the safety of the surrounding ecological environment are the core goals of radiation shielding design. By regulating the interaction mechanism between radiation and matter, the radiation level in the target area can be effectively reduced, thereby reducing the radiation dose of personnel and the risk of material radiation damage. In nuclear engineering practice, the accuracy of shielding calculation and design has a significant impact on the operation life of nuclear facilities, the radiation safety factor of workers, and the environmental radioactivity background level. Therefore, selecting the appropriate shielding calculation method is a key link to ensure the quality of shielding system design.

[0003] Current shielding calculation mainly uses two types of numerical methods to solve the transport problem of neutrons and gamma photons in the shielding body: one is the deterministic method represented by the discrete ordinate method (SN method), which numerically solves the neutron transport equation by discretizing the spatial, energy, and directional variables; the other is the probabilistic method represented by the Monte Carlo method (MC method), which simulates the transport process of particles in the shielding body through random sampling, and can accurately describe the behavior of particles under complex geometric conditions. However, the discrete ordinate method has certain limitations in dealing with complex geometric structures, especially in shielding bodies containing large chambers or pipes, which can easily produce significant ray effects. At the same time, the Monte Carlo method can accurately simulate complex geometric models and use continuous energy cross sections, with fewer approximations, but it takes a long time to obtain reliable results in the calculation of thick shielding layers (i.e., deep penetration problems).

[0004] In summary, how to design a Monte Carlo-discrete ordinate coupling calculation method based on a distributed source term with small calculation error and fast operation speed is a problem that needs to be solved at present. SUMMARY

[0005] The present application aims to at least partially solve one of the technical problems in the related art.

[0006] To this end, the first object of the present application is to propose a Monte Carlo-discrete ordinate coupling calculation method based on a distributed source term to solve the limitations of single algorithm in complex geometric structure and thick shielding layer calculation in the prior art.

[0007] The second object of the present application is to propose a device.

[0008] A third object of the present application is to provide an electronic device.

[0009] A fourth object of the present application is to provide a computer-readable storage medium.

[0010] To achieve the above objects, the first aspect of the present application provides a Monte Carlo-discrete ordinates coupling calculation method based on a distribution source term, comprising:

[0011] Monte Carlo particle track information is obtained by Monte Carlo simulation, and the Monte Carlo particle track information is converted into grid particle angular density in discrete ordinates;

[0012] The direction distribution of the grid particle angular density is fitted by using spherical harmonics to obtain a low-storage order source term file;

[0013] Discrete ordinates method simulation is performed based on the low-storage order source term file to obtain discrete ordinates method simulation results;

[0014] The Monte Carlo simulation results and the discrete ordinates method simulation results are combined to obtain final simulation results.

[0015] Preferably, the conversion of the Monte Carlo particle track information into grid particle angular density in discrete ordinates comprises:

[0016] Particle motion trajectories in materials are obtained by Monte Carlo simulation, including a spatial coupling method and an energy coupling method,

[0017] The spatial coupling method comprises: obtaining particle track information crossing a spatial coupling surface by simulating the results of a part of the spatial region by using the Monte Carlo method;

[0018] The energy coupling method comprises: obtaining particle track information crossing an energy boundary by simulating high-energy particle results by using the Monte Carlo method;

[0019] The size, shape and distribution of the grid system used in the discrete ordinates calculation are constructed;

[0020] The particle track information obtained by Monte Carlo simulation is positioned in the corresponding grid, and the total number of particles crossing the grid is calculated to obtain the particle number density.

[0021] Preferably, the spatial coupling method is expressed as:

[0022]

[0023] wherein, is the angular flux, W nis the weight of particle n, ΔV represents in the grid (i, j, k), ΔΩ represents in the discrete direction m, ΔE represents in the energy group g, N is the number of Monte Carlo simulation particles, λ n is the cosine value of the angle between the particle track and the normal of the boundary surface, w m is the quadrature weight coefficient.

[0024] Preferably, the energy coupling method expresses the formula as:

[0025]

[0026] wherein the upper energy limit E of the energy group g' satisfies E≤E0, the source term is distributed in all grids of SN.

[0027] Preferably, the process of fitting the direction distribution of the grid particle angular density by using the spherical harmonic function includes:

[0028] The particle angular density is converted into the form of the spherical harmonic function, the expansion coefficient of each spherical harmonic function is calculated by the numerical integration method of the quadrature group, and the low-storage-level source term file is obtained.

[0029] Preferably, the formula of the spherical harmonic fitting function is:

[0030]

[0031] wherein, is the expansion coefficient, is the spherical harmonic function is the value in the discrete direction m, and L is the expansion order.

[0032] Preferably, the combination of the Monte Carlo simulation result and the discrete ordinate method simulation result to obtain the final simulation result includes:

[0033] Based on the spatial coupling calculation mode, the global flux result is obtained by splicing the result of the Monte Carlo method calculation region and the result of the discrete ordinate method calculation;

[0034] Based on the result of the energy coupling calculation mode, the global flux result is obtained by combining the high-energy part result calculated by the Monte Carlo method and the low-energy result calculated by the discrete ordinate method.

[0035] To achieve the above purpose, the second aspect embodiment of the present application proposes a Monte Carlo-discrete ordinate coupling calculation device based on distributed source term, which comprises:

[0036] A Monte Carlo simulation module obtains Monte Carlo particle track information by using Monte Carlo simulation, and converts the Monte Carlo particle track information into grid particle angular density in the discrete ordinate method;

[0037] a conversion module, configured to obtain a low-storage source term file by fitting a direction distribution of the grid particle angular density with a spherical harmonic function;

[0038] an optimization module, configured to obtain a discrete ordinates simulation result by performing a discrete ordinates simulation based on the low-storage source term file;

[0039] a final simulation module, configured to obtain a final simulation result by combining the Monte Carlo simulation result and the discrete ordinates simulation result.

[0040] To achieve the above object, a third aspect of the present application provides an electronic device, comprising: a processor, and a memory connected with the processor in communication;

[0041] The memory stores computer-executable instructions;

[0042] The processor executes the computer-executable instructions stored in the memory to implement the method of any one of the above.

[0043] To achieve the above object, a fourth aspect of the present application provides a computer-readable storage medium, comprising computer-executable instructions stored in the computer-readable storage medium, the computer-executable instructions being executed by a processor to implement the method of any one of the above.

[0044] The Monte Carlo-discrete ordinates coupling calculation method based on a distribution source provided by the present application can realize the distribution of source terms in the form of a combination surface by placing the source terms inside the calculation region, and is suitable for processing coupling surfaces in complex geometries. The method can more accurately simulate the transmission and interaction of particles in complex geometries, thereby improving the accuracy of the calculation results. The direction distribution is fitted using a spherical harmonic function, which significantly reduces the storage size of the source term file. This reduces the storage requirement and may also speed up the processing of data. By adding the high-energy part of the Monte Carlo particle simulation result and the low-energy part of the discrete ordinates simulation result, the method provides a comprehensive simulation result of particle transmission and interaction, which can more comprehensively and efficiently simulate the behavior of particles in complex systems.

[0045] Additional aspects and advantages of the present application will be made apparent by the following description and the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0046] The above and / or additional aspects and advantages of the present application will become apparent and be readily appreciated from the following description, including the accompanying drawings, wherein:

[0047] Figure 1A flow chart of a first embodiment of the Monte Carlo-discrete ordinate coupling calculation method based on a distributed source term provided by the application;

[0048] Figure 2 A flow chart of a particle information conversion algorithm;

[0049] Figure 3 A schematic diagram of an MC-SN coupling calculation framework;

[0050] Figure 4 A schematic diagram of a geometry description of a Kobayashi 1 radiation shielding benchmark problem;

[0051] Figure 5 A schematic diagram of a single coupling surface calculation result;

[0052] Figure 6 A schematic diagram of a combined coupling surface calculation result;

[0053] Figure 7 A schematic diagram of a source term and geometry of a simple shielding example;

[0054] Figure 8 A schematic diagram of an energy coupling calculation result;

[0055] Figure 9 A schematic diagram of an energy coupling calculation result deviation;

[0056] Figure 10 A structure block diagram of a Monte Carlo-discrete ordinate coupling calculation device based on a distributed source term provided by an embodiment of the application. DETAILED DESCRIPTION

[0057] The core of the application is to provide a Monte Carlo-discrete ordinate coupling calculation method, device, electronic equipment and storage medium based on a distributed source term, which realizes the distribution of the source term in the calculation region by converting the Monte Carlo particle information into a discrete ordinate source term in the form of a distributed body source, and supports the distribution of the source term in the form of a combined surface and energy coupling calculation.

[0058] In order to enable personnel in the technical field to better understand the application scheme, the application will be further described in detail below in combination with the drawings and specific embodiments. Obviously, the described embodiments are only some of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.

[0059] Among them, the discrete ordinate method is the SN method, the Monte Carlo method is the MC method, and the subsequent description will not be repeated.

[0060] Please refer to Figure 1 ,Figure 1 A flow chart of a first embodiment of a Monte Carlo-discrete ordinates coupling calculation method based on a distributed source term provided by the application; the specific operation steps are as follows:

[0061] Step S101: Obtain Monte Carlo particle track information by using Monte Carlo simulation, and convert the Monte Carlo particle track information into grid particle angular density in the discrete ordinates.

[0062] Specifically, the motion trajectory of particles in the material is obtained by using the Monte Carlo method, including a spatial coupling method and an energy coupling method, wherein the spatial coupling method includes:

[0063] The particle track information crossing the spatial coupling surface is obtained by using the Monte Carlo method to simulate a part of the spatial region, wherein the spatial coupling method is expressed by a formula:

[0064]

[0065] wherein, is the angular flux, W n is the weight of particle n, ΔV represents in the grid (i, j, k), ΔΩ represents in the discrete direction m, ΔE represents in the energy group g, N is the number of Monte Carlo simulation particles, λ n is the cosine value of the angle between the particle track and the normal of the boundary surface, w m is the quadrature weight coefficient;

[0066] The energy coupling method uses the Monte Carlo method to simulate the high-energy particle result, and obtains the particle track information crossing the energy boundary, wherein the energy coupling method is expressed by a formula:

[0067]

[0068] wherein the upper limit of the energy of the energy group g' E satisfies E≤E0, the source term is distributed in all grids of SN;

[0069] The size, shape and distribution of the grid system used in the discrete ordinates calculation are constructed;

[0070] The particle track information obtained by the Monte Carlo simulation is located in the corresponding grid, and the total number of particles crossing the grid is calculated to obtain the particle number density.

[0071] Step S102: The direction distribution of the grid particle angular density is fitted by using the spherical harmonic function to obtain a low storage order source term file;

[0072] Specifically, the particle angular density is converted into the form of the spherical harmonic function, the expansion coefficient of each spherical harmonic function is calculated by using the numerical integration method of the quadrature group, and the low storage order source term file is obtained.

[0073] where the spherical harmonic fitting function formula is:

[0074]

[0075] where, is the expansion coefficient, is the spherical harmonic function the value in the discrete direction m, L is the expansion order.

[0076] Step S103: based on the low storage order source term file, a discrete ordinate method simulation is performed to obtain a discrete ordinate method simulation result;

[0077] Step S104: the Monte Carlo simulation result and the discrete ordinate method simulation result are combined to obtain a final simulation result.

[0078] Based on the spatial coupling calculation mode, the global flux result is obtained by splicing the results of the Monte Carlo method calculation region and the results of the discrete ordinate method calculation.

[0079] Based on the energy coupling calculation mode, the global flux result is obtained by combining the high-energy part result calculated by the Monte Carlo method and the low-energy result calculated by the discrete ordinate method.

[0080] The embodiment provides a Monte Carlo-discrete ordinate coupling calculation method based on a distributed source term. By placing the source term inside the calculation region, the source term distribution in the form of a combined surface is realized, which is suitable for processing coupled surfaces in complex geometries. The method can more accurately simulate the transport and interaction of particles in complex geometries, thereby improving the accuracy of the calculation result. The spherical harmonic function is used to fit the direction distribution, which significantly reduces the storage size of the source term file. The storage requirement is reduced, and the data processing speed can also be accelerated. The method provides a comprehensive simulation result of particle transport and interaction, which can more comprehensively and efficiently simulate the behavior of particles in complex systems.

[0081] Based on the above embodiment, the Monte Carlo-discrete ordinate coupling calculation method based on a distributed source term is described as follows: Figure 2 as shown in the specific implementation, the specific implementation is as follows:

[0082] By converting the MC particle track information into the particle angular density of the grid in the SN, instead of the angular flux on the boundary surface, the source term of the method can be distributed in the entire calculation space, and is not limited to the boundary surface of the calculation region. Therefore, the source term can be placed inside the calculation region to realize the source term distribution in the form of a combined surface, and energy coupling can be performed. In addition, by fitting the direction distribution with a spherical harmonic function, the size of the source term file can be reduced by more than one order of magnitude. The specific content is as follows

[0083] Theoretical derivation of MC-SN coupling calculation method based on distributed source term

[0084] The whole idea is to locate MC particles on the coupling surface to the grid they belong to, and then convert the track information into the source term of SN. The source term of SN is composed of the particle number density of each grid through which the coupling surface passes.

[0085] The particle number density in energy group g and discrete direction m in grid (i, j, k) is:

[0086]

[0087] Where w m is the quadrature weight coefficient, is the particle number angular density.

[0088] The weight of MC particles on the coupling surface located in grid (i, j, k) and in the range of energy group g and discrete direction m is represented as:

[0089]

[0090] The weight of MC particles can be regarded as the actual particle number. And in SN The spatial coordinates of

[0091]

[0092] It can be obtained:

[0093]

[0094] The distribution of particle number density in the direction is fitted using spherical harmonics, which is represented as:

[0095]

[0096] Where is the expansion coefficient, is the spherical harmonic in discrete direction m, and L is the expansion order.

[0097] The spherical harmonic is defined as:

[0098]

[0099] Where μ is the cosine value of azimuth angle θ, is the polar angle, is the associated Legendre polynomial. When K = 0 Otherwise, 0. and K are defined as follows:

[0100]

[0101] The spherical harmonics satisfy orthogonality, i.e.,

[0102]

[0103] When l = r and k' = s, δ lr,k′s = 1, otherwise 0. Using discrete quadrature sets for integration, we can write:

[0104]

[0105] Using orthogonality, we can obtain the spherical harmonics corresponding coefficients in a straightforward way:

[0106]

[0107] With the spherical harmonics fitting, the data stored in the SN source term file, which is the particle number angular density in each grid for each energy group and each discrete direction, is changed to store the expansion coefficients of the spherical harmonics in each grid for each energy group. For example, when the expansion order is taken as 4 and the discrete quadrature set is taken as S16, the particle number density in the grid (i, j, k) for energy group g in the direction m needs to store 25 expansion coefficients, instead of 320 values corresponding to the discrete directions.

[0108] Similar ideas are taken in the energy coupling calculation. For example, set the energy boundary, the low-energy particle number density produced by the MC simulation of high-energy particles, and take the same method of converting MC particle information into SN particle number density as the source term of SN calculation. The following is the derivation of the source term conversion algorithm in the energy coupling calculation.

[0109] If the energy boundary is set to E0, the relationship between the particle number angular density of SN and the low-energy particle number density produced by the MC simulation in the grid (i, j, k) and energy group g' and in the discrete direction m is:

[0110]

[0111] where the upper bound of energy group g' E satisfies E≤E0. The source term is distributed in all the grids of SN. Similarly, using the spherical harmonics to fit the source term in the direction m, we have:

[0112]

[0113] The final result is obtained by adding the result of the MC simulation of the high-energy part and the result of the SN simulation of the low-energy part.

[0114] The MC calculation program used in the method is MCShield, and the SN calculation program is TORT. The MC track information conversion program MC2SN is developed. The MC input file automatic processing program ModGdml and the SN input file automatic processing program TORTIW are also developed. The description of each file is shown in Table 1.

[0115] Table 1 Description of calculation files in MC-SN coupling calculation framework

[0116]

[0117]

[0118] The calculation flow is described as follows.

[0119] First, the original MC calculation files mc.config, mc.gdml and the SN original calculation file sn.i are provided, and the coupling surface is determined according to the characteristics of the problem.

[0120] Second, the geometry of the original MC calculation file is cut and processed by ModGdml, and the particle track information statistical parameters are set. The geometry of the original SN file is processed by TORTIW, and the source term parameters are set, and the SN parameter file param used in MC2SN is generated. The default way of geometry processing is as follows. In the MC-SN calculation based on the boundary source term, the geometry calculated by MC is the whole geometry, and the geometry after the coupling surface is divided is calculated by SN. In the MC-SN calculation based on the distributed source term, the geometry calculated by MC is the geometry after the coupling surface is divided, and the whole geometry is calculated by SN.

[0121] Third, MC calculation is performed, and the particle track information file parinfo on the coupling surface is generated.

[0122] Fourth, the conversion of MC particle track information to SN source term is performed. The input files are the particle track information file ParInfo and the SN parameter file param, and the output file is the SN source term file sns.

[0123] Fifth, SN calculation is performed.

[0124] In one embodiment, the calculation flow of the coupling MC and SN method includes the following steps: first, the calculation domain is divided into MC calculation region and SN calculation region. Then, MC simulation is performed, and the particle information calculated by MC is converted into the source term for SN calculation. Finally, SN calculation is performed. The flow of the coupling calculation is shown in Figure 3 .

[0125] where the combination surface form coupling calculation

[0126] To demonstrate the applicability and advantage of using combination surface in MC-SN calculation based on distributed volume source, Kobayashi 1 radiation shielding benchmark problem (scattering case) is calculated in this section. The geometry is illustrated in Fig. 1. Figure 4 where the surface at x=0, y=0, z=0 is reflective boundary and the rest surfaces are vacuum boundary. The material cross section is shown in Table 2.

[0127] Table 2 Material specification of Kobayashi 1 example

[0128]

[0129] In SN calculation, the calculation region is divided into 50x50x50 meshes with mesh size of 20mmx20mmx20mm, and S 16 Full symmetry quadrature set. The number of particles in MC direct calculation is 1e9, and MESH tally is set with the same mesh division as SN calculation. In MC-SN coupling calculation, the number of particles in MC simulation is 1e7, and the single coupling surface is located at x=110mm. The combination coupling surface is composed of three planes of the cube containing source term, which are: plane 1: x=110mm, y:0~110mm, z:0~110mm; plane 2: y=110mm, z:0~110mm, x:0~110mm; plane 3: z=110mm, x:0~110mm, y:0~110mm. The order of spherical harmonics expansion is 3. To consider the reflection effect, the SN calculation region is the whole geometry.

[0130] To exclude the influence of the difference between SN and MC calculation results on the comparison of the advantage of different MC-SN coupling calculation methods, only the deviation of different MC-SN coupling calculation methods relative to SN calculation results is compared. The average value of flux in each layer along Y direction in the range of y:-110~1000mm is calculated, and the relative deviation of different methods is compared in Figure 5 and Figure 6 The results of two calculation methods are given and compared. The average absolute relative deviation of combination coupling surface is less than 5%, while the average absolute relative deviation of single coupling surface is in the range of 5%~45%. It can be seen that the combination coupling surface has obvious improvement in calculation accuracy compared with single coupling surface.

[0131] Energy coupling calculation

[0132] A simple shielding body example is calculated in this section. The geometry and material distribution are shown in Fig. 2. Figure 7Where the source strength is uniformly distributed in the range of x:-100~ -50mm, y:-750~750mm, z:-750~750mm, and the source energy is uniformly distributed in the range of 1.94~2.23MeV. The length and width of the three layers of shielding materials are both 1500mm, and the thicknesses are 5cm, 10cm, and 5cm in sequence. The calculation adopts the vacuum boundary condition. The statistical region is located at x:200~250mm, y:-250~250mm, z:-250~250mm, and the statistical quantity is the body flux spectrum.

[0133] In the SN calculation, the calculation region is divided into 48x40x40 grids with a grid size of 25mmx50mmx50mm, and the S16 full-symmetry quadrature group is used. MC directly calculates 1e8 particles, and sets the MESH statistics consistent with the SN grid division. In the MC-SN coupled calculation, the number of MC simulated particles is 1e7. The energy boundary for energy coupling calculation is set to 8.76E-7MeV, which is the upper limit of the 3rd energy group. The calculation process is to calculate the part with energy greater than 8.76E-7MeV by MC. The part with energy lower than 8.76E-7MeV is calculated by SN. The number of MC simulated particles is 1e7, and the SN calculation region is divided into 50x50x50 grids.

[0134] The calculation results and result deviations are shown in Figure 8 and Figure 9 Due to the differences in cross-section and radiation effects, etc., there are differences between the results of MC and SN, and the differences are more significant at low energy. By setting the energy boundary to 8.76E-7MeV, the results of energy coupling calculation show different characteristics at low and high energy. Above the energy boundary, the results of energy coupling calculation are basically consistent with the results of MC, and below the energy boundary, the results of energy coupling calculation are basically consistent with the results of SN. The above results verify the correctness of the energy coupling method, and provide a calculation approach in the case of using different calculation methods in different energy ranges.

[0135] Comparison of source file sizes

[0136] The source sizes of the MC-SN coupling method based on distributed source proposed in this method and the traditional MC-SN coupling method based on boundary source are compared. The example model is consistent with that in 2. Energy coupling calculation. The calculation conditions of the two coupling methods are as follows: the coupling surface of the MC-SN method based on distributed body source is located at x=112.5mm, and the coupling surface of the MC-SN method based on boundary surface source is located at x=10cm. The source file size comparison is shown in Table 3.

[0137] Table 3 Comparison of source file sizes

[0138]

[0139] The size of the source term file of the MC-SN calculation method based on the distributed source term is 6.5 MB, and the size of the source term file of the MC-SN calculation method based on the boundary source term is 360.0 MB. If the grid in the Y direction and the Z direction is refined to 200x200, the size of the source term file of the MC-SN calculation method based on the distributed source term is 163.6 MB, and the size of the source term file of the MC-SN calculation method based on the boundary source term is 10.8 GB.

[0140] Reference is made to Figure 10 , Figure 10 A structure block diagram of a Monte Carlo-discrete ordinates coupling calculation device based on a distributed source term is provided for an embodiment of the application; a specific device can include:

[0141] A Monte Carlo simulation module 100 obtains Monte Carlo particle track information by Monte Carlo simulation, and converts the Monte Carlo particle track information into a grid particle angular density in a discrete ordinate;

[0142] A conversion module 200 obtains a low-storage-order source term file by fitting processing of the directional distribution of the grid particle angular density by using spherical harmonics;

[0143] An optimization module 300 performs discrete ordinate method simulation based on the low-storage-order source term file, and obtains a discrete ordinate method simulation result;

[0144] A final simulation module 400 combines the Monte Carlo simulation result and the discrete ordinate method simulation result, and obtains a final simulation result.

[0145] The Monte Carlo-discrete ordinate coupling calculation device based on a distributed source term of the embodiment is used to realize the Monte Carlo-discrete ordinate coupling calculation method based on a distributed source term described above, and therefore the specific embodiments of the Monte Carlo-discrete ordinate coupling calculation device based on a distributed source term can be seen from the embodiment part of the Monte Carlo-discrete ordinate coupling calculation method based on a distributed source term described above, for example, the Monte Carlo simulation module 100, the conversion module 200, the optimization module 300, and the final simulation module 400 are respectively used to realize steps S101, S102, S103, and S104 in the Monte Carlo-discrete ordinate coupling calculation method based on a distributed source term described above, and therefore the specific embodiments can refer to the description of the corresponding embodiment part, and will not be described here.

[0146] In order to realize the above-mentioned embodiments, the application further provides an electronic device, including a processor and a memory in communication connection with the processor; the memory stores computer execution instructions; and the processor executes the computer execution instructions stored in the memory to realize the method provided by the above-mentioned embodiments.

[0147] To achieve the above-mentioned embodiments, the present application further provides a computer readable storage medium, wherein the computer readable storage medium stores computer execution instructions, and the computer execution instructions are executed by a processor to implement the method provided by the foregoing embodiments.

[0148] To achieve the above-mentioned embodiments, the present application further provides a computer program product, comprising a computer program, and the computer program is executed by a processor to implement the method provided by the foregoing embodiments.

[0149] The collection, storage, use, processing, transmission, provision and disclosure of user personal information involved in the present application comply with relevant laws and regulations and do not violate public order and good customs.

[0150] It should be noted that the personal information from the user should be collected for legal and reasonable purposes, and should not be shared or sold outside these legal uses. In addition, such collection / sharing should be carried out after the user's informed consent is received, including but not limited to informing the user to read the user agreement / user notice before the user uses the function, and signing the agreement / authorization including authorization of relevant user information. In addition, any necessary steps should be taken to protect and ensure access to such personal information data, and to ensure that other people with access to personal information data comply with their privacy policy and processes.

[0151] The present application is expected to provide embodiments in which the user can selectively prevent the use or access of personal information data. That is, the present disclosure is expected to provide hardware and / or software to prevent or block access to such personal information data. Once the personal information data is no longer needed, the risk is minimized by limiting data collection and deleting data. In addition, such personal information is de-identified, if applicable, to protect the privacy of the user.

[0152] In the foregoing embodiment description, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples. In addition, the person skilled in the art can combine and combine the different embodiments or examples described in the present specification and the features of the different embodiments or examples, without contradiction.

[0153] Moreover, the terms "first", "second", "third", etc. are used herein only to describe different steps or categories of steps in a claim for patent purposes, and are not to be construed as indicating or implying relative importance of one step to another or a quantity of steps. Thus, features defined with "first", "second" or "third" can explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of "plurality" is at least two, for example, two, three, etc., unless otherwise explicitly and specifically limited.

[0154] Any process or method descriptions or blocks in flow charts herein and elsewhere can be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps in the process, and alternate implementations are possible. In some embodiments, the processes or methods described in flow charts herein and elsewhere can be tailored by reordering steps and / or adding or omitting one or more of the described steps, and the order of the steps can be changed.

[0155] Logic and / or steps represented in flow charts herein and elsewhere can, for example, be embodied in computer executable code, which can be implemented in any computer readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer based system, processor containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. In the context of this specification, a "computer readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The computer readable medium can specifically include a memory, a magnetic or optical disk storage device, and / or a

[0156] It should be understood that parts of the present application can be realized in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be realized as software or firmware stored in a memory and executed by a suitable instruction execution system. As such, if realized in hardware, and in another embodiment, any one or a combination of the following technologies known in the art can be used: discrete logic circuitry having logic gates for implementing logic functions on data signals, application specific integrated circuits having appropriate combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), and the like.

[0157] Those skilled in the art of the present technology can understand that all or part of the steps carried out by the above-mentioned embodiments can be completed by a program instructing the relevant hardware, and the program can be stored in a computer readable storage medium. When the program is executed, it includes one of the steps of the method embodiments or a combination thereof.

[0158] In addition, each functional unit in each embodiment of the present application can be integrated into one processing module, or each unit can exist physically alone, or two or more units can be integrated into one module. The above-mentioned integrated module can be realized in the form of hardware or in the form of a software functional module. When the integrated module is realized in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer readable storage medium.

[0159] The above-mentioned storage medium can be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it should be understood that the above-mentioned embodiments are exemplary and cannot be understood as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above-mentioned embodiments within the scope of the present application.

Claims

1. A Monte Carlo-discrete ordinates coupled calculation method based on distribution source terms, characterized in that, The method comprises the following steps: Monte Carlo particle track information is obtained by Monte Carlo simulation, and the Monte Carlo particle track information is converted into grid particle angular density in discrete longitudinal scale; A low storage order source item file is obtained by fitting the direction distribution of the grid particle angular density with spherical harmonics; Discrete longitudinal scale method simulation is performed based on the low storage order source item file to obtain discrete longitudinal scale method simulation results; The Monte Carlo simulation results and the discrete longitudinal scale method simulation results are combined to obtain final simulation results.

2. The distribution-based source term Monte Carlo-discrete ordinates coupled calculation method of claim 1, wherein, The conversion of the Monte Carlo particle track information into grid particle angular density in discrete longitudinal scale comprises the following steps: Particle motion trajectories in materials are obtained by Monte Carlo simulation, including a space coupling method and an energy coupling method, The space coupling method comprises the following steps: the results of simulating a part of the space region by the Monte Carlo method are used to obtain particle track information crossing the space coupling surface; The energy coupling method comprises the following steps: the results of simulating high-energy particles by the Monte Carlo method are used to obtain particle track information crossing the energy boundary; The size, shape and distribution of the grid system used in the discrete longitudinal scale calculation are constructed; The particle track information obtained by Monte Carlo simulation is positioned in the corresponding grid, and the total number of particles crossing the grid is calculated to obtain the particle number density.

3. The Monte Carlo-discrete ordinates coupled method based on distribution source terms according to claim 2, wherein, The expression formula of the space coupling method is as follows: where, is the angular flux, W n is the weight of particle n, ΔV represents within the grid (i, j, k), ΔΩ represents within the discrete direction m, ΔE represents within the energy group g, N is the number of Monte Carlo simulation particles, λ n is the cosine value of the angle between the particle track and the normal of the boundary surface, w m is the quadrature weight coefficient.

4. The Monte Carlo-discrete ordinates coupled method based on distribution source terms according to claim 2, wherein, The expression formula of the energy coupling method is as follows: where the upper bound of the energy of the group g' E satisfies E≤E0, the source term Distributed in all the grids of the SN.

5. The distribution-based source term Monte Carlo-discrete ordinates coupled calculation method of claim 2, wherein, The fitting of the direction distribution of the grid particle angular density with spherical harmonics to obtain the low storage order source item file comprises the following steps: The particle angular density is converted into the form of spherical harmonics, and the expansion coefficients of each spherical harmonic are calculated by numerical integration method through integral groups to obtain the low storage order source item file.

6. The distribution-based source term Monte Carlo-discrete ordinates coupled calculation method of claim 3, wherein, The formula of the spherical harmonic fitting function is as follows: where are the expansion coefficients, are the spherical harmonics are the values in the discrete direction m, and L is the expansion order.

7. The distribution-based source term Monte Carlo-discrete ordinates coupled calculation method of claim 2, wherein, The combination of the Monte Carlo simulation results and the discrete longitudinal scale method simulation results to obtain the final simulation results comprises the following steps: Based on the space coupling calculation method, the global flux results are obtained by splicing the results of the Monte Carlo method calculation region and the results of the discrete longitudinal scale method calculation; Based on the results of the energy coupling calculation method, the global flux results are obtained by combining the high-energy part results calculated by the Monte Carlo method and the low-energy results calculated by the discrete longitudinal scale method.

8. A device for Monte Carlo-discrete ordinates coupled calculation based on distributed source terms, characterized in that, The method comprises the following steps: A Monte Carlo simulation module is used to obtain Monte Carlo particle track information by Monte Carlo simulation, and the Monte Carlo particle track information is converted into grid particle angular density in discrete longitudinal scale; A conversion module is used to fit the direction distribution of the grid particle angular density with spherical harmonics to obtain a low storage order source item file; An optimization module is used to perform discrete longitudinal scale method simulation based on the low storage order source item file to obtain discrete longitudinal scale method simulation results; A final simulation module is used to combine the Monte Carlo simulation results and the discrete longitudinal scale method simulation results to obtain final simulation results.

9. An electronic device, comprising: The method comprises the following steps: A processor and a memory connected in communication with the processor; The memory stores computer execution instructions; The processor executes the computer execution instructions stored in the memory to realize the method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer-executable instructions, which, when executed by a processor, implement the method according to any one of claims 1-7.

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