Fast simulation method and system for multi-physical field coupling

Abstract

The present application relates to the field of nuclear reactor simulation. Disclosed are a fast simulation method and system for multi-physical field coupling and a storage medium. The method comprises: at intervals of a first preset interval time, collecting state information of each physical field of a nuclear reactor in a transient process, so as to obtain physical field snapshots of a plurality of physical fields, the transient process being simulated by a reactor simulation model corresponding to the nuclear reactor; stitching the physical field snapshots of the plurality of physical fields, so as to obtain a fully-concatenated physical field; constructing a matrix on the basis of the fully-concatenated physical field; performing order reduction processing on the matrix, so as to construct a timing model corresponding to the fully-concatenated physical field; and decomposing the timing model corresponding to the fully-concatenated physical field, so as to obtain timing models corresponding to the plurality of physical fields. The fast simulation method for multi-physical field coupling of the present application can reduce the inconsistency between different physical fields while increasing the computation speed.

Description

Multi-physics coupling fast simulation method and system TECHNICAL FIELD

[0001] The present application relates to the field of nuclear reactor simulation, in particular to a multi-physics coupling fast simulation method and system and a storage medium. BACKGROUND

[0002] A nuclear reactor is a highly complex system, in which multiple physical processes occur and multiple physics exist in a coupled relationship, i.e., their states and behaviors affect each other. In order to accurately describe and predict the behavior of the reactor, the mutual coupling relationship between the physical processes needs to be considered.

[0003] The multi-physics coupling simulation of a nuclear reactor has extremely high computational complexity. Currently, a numerical reactor is obtained by simulating multiple physical fields in the reactor, and the dynamic evolution process of the nuclear reactor system is identified and tracked through the numerical reactor. In some application scenarios with high real-time requirements, a separate coupling method is generally used to improve the calculation speed. However, in the case of fast calculation by the separate coupling method, the inconsistency between different physical fields also increases, which will cause the accuracy of the numerical reactor at subsequent time to diverge. SUMMARY

[0004] The present application aims to at least solve one of the technical problems existing in the prior art. To this end, the present application provides a multi-physics coupling fast simulation method and system, which can improve the calculation speed while reducing the inconsistency between different physical fields.

[0005] To solve the above technical problems, the present application proposes the following technical solutions:

[0006] The first aspect embodiment of the present application provides a multi-physics coupling fast simulation method, comprising:

[0007] Collecting state information of each physical field of the nuclear reactor under a transient process at an interval of a first preset interval time to obtain physical field snapshots of multiple physical fields, wherein the transient process is simulated by using a reactor simulation model corresponding to the nuclear reactor;

[0008] Splicing the physical field snapshots of the multiple physical fields to obtain a full series physical field;

[0009] Constructing a matrix based on the full series physical field;

[0010] Performing order reduction processing on the matrix to construct a time series model corresponding to the full series physical field;

[0011] The time sequence model corresponding to the full series physical field is decomposed to obtain time sequence models corresponding to the plurality of physical fields.

[0012] The multi-physical field coupling fast simulation method according to the first aspect of the present application has the following beneficial effects: the state information of each physical field of the nuclear reactor under the transient process is collected, the physical field snapshots of the plurality of physical fields are determined, and the physical field snapshots of the plurality of physical fields are spliced to obtain the full series physical field, the plurality of physical fields are spliced and solved simultaneously, and the multi-physical field coupling is realized. After that, the matrix is constructed based on the full series physical field, the full series physical field of the nonlinear system is approximately decomposed into a low-dimensional linear dynamic system through the order reduction processing of the matrix, the calculation difficulty is simplified, and the calculation speed is improved. The time sequence model corresponding to the full series physical field, that is, the amount of change of the state information of each physical field in the full series physical field with time, is constructed based on the matrix subjected to the order reduction processing, and the time sequence model corresponding to the full series physical field is decomposed to obtain the time sequence models corresponding to the plurality of physical fields. Since the plurality of physical fields are solved and reduced simultaneously after being spliced, the consistency is guaranteed, and the inconsistency between different physical fields is reduced.

[0013] According to some embodiments of the first aspect of the present application, the matrix includes a first historical matrix and a second historical matrix, and the constructing the matrix based on the full series physical field includes:

[0014] determining the physical field snapshots of each physical field from the first time to the second time based on the full series physical field;

[0015] constructing the first historical matrix based on the physical field snapshots of each physical field from the first time to the second time;

[0016] determining the physical field snapshots of each physical field from the third time to the fourth time based on the full series physical field, wherein the third time is faster than the first time by a first preset interval time, and the fourth time is faster than the second time by a first preset interval time;

[0017] constructing the second historical matrix based on the physical field snapshots of each physical field from the third time to the fourth time.

[0018] According to some embodiments of the first aspect of the present application, the order reduction processing of the matrix to construct the time sequence model corresponding to the full series physical field includes:

[0019] performing order reduction calculation on the first historical matrix to obtain an order reduction left singular vector, an order reduction right singular vector, and an order reduction singular value matrix;

[0020] construct a time series model corresponding to the full-series physical field based on the reduced left singular vector, the reduced right singular vector, the reduced singular value matrix, and the second historical matrix.

[0021] According to some embodiments of the first aspect of the present application, the reduced left singular vector, the reduced right singular vector, and the reduced singular value matrix are obtained by performing a singular value decomposition on the first historical matrix to obtain a left singular vector, a right singular vector, and a singular value matrix, and then performing a reduction on the left singular vector, the right singular vector, and the singular value matrix based on a preset singular value truncation value.

[0022] performing a singular value decomposition on the first historical matrix to obtain a left singular vector, a right singular vector, and a singular value matrix;

[0023] performing a reduction on the left singular vector, the right singular vector, and the singular value matrix based on a preset singular value truncation value to obtain the reduced left singular vector, the reduced right singular vector, and the reduced singular value matrix.

[0024] According to some embodiments of the first aspect of the present application, the time series model corresponding to the full-series physical field is constructed based on the reduced left singular vector, the reduced right singular vector, the reduced singular value matrix, and the second historical matrix.

[0025] constructing a linear transformation matrix of the first historical matrix and the second historical matrix;

[0026] performing a calculation on the linear transformation matrix based on the reduced left singular vector, the reduced right singular vector, and the reduced singular value matrix to determine eigenvalues and eigenvectors of the linear transformation matrix;

[0027] performing a reconstruction prediction on the full-series physical field based on the eigenvalues of the linear transformation matrix to obtain the time series model corresponding to the full-series physical field.

[0028] According to some embodiments of the first aspect of the present application, the calculation on the linear transformation matrix based on the reduced left singular vector, the reduced right singular vector, and the reduced singular value matrix to determine eigenvalues and eigenvectors of the linear transformation matrix comprises:

[0029] performing a reduction on the linear transformation matrix based on the reduced left singular vector, the reduced right singular vector, and the reduced singular value matrix to obtain a low-order linear transformation matrix;

[0030] performing an eigenvalue decomposition on the low-order linear transformation matrix to obtain eigenvectors of the low-order linear transformation matrix;

[0031] performing a calculation on the linear transformation matrix based on the eigenvectors of the low-order linear transformation matrix to obtain eigenvalues and eigenvectors of the linear transformation matrix.

[0032] According to some embodiments of the first aspect of the present application, the reconstructing and predicting the full series physical field based on the eigenvalue of the linear conversion matrix comprises:

[0033] mapping the eigenvalue of the linear conversion matrix to a first preset interval time to obtain a time series coefficient;

[0034] constructing a time series model corresponding to the full series physical field based on the time series coefficient and the full series physical field at an initial time.

[0035] According to some embodiments of the first aspect of the present application, the reactor simulation model is determined by the following method:

[0036] obtaining a structure parameter corresponding to the nuclear reactor;

[0037] constructing a reactor three-dimensional model based on the structure parameter of the nuclear reactor;

[0038] performing grid discretization on the reactor three-dimensional model to obtain the reactor simulation model.

[0039] The second aspect of the present application provides a multi-physical field coupling fast simulation system, comprising:

[0040] at least one memory;

[0041] at least one processor;

[0042] at least one program;

[0043] The program is stored in the memory, and the processor executes at least one of the programs to achieve:

[0044] The multi-physical field coupling fast simulation method as described in any one of the first aspect of the present application.

[0045] The third aspect of the present application provides a computer readable storage medium, which stores computer executable signals, and the computer executable signals are used to execute:

[0046] The multi-physical field coupling fast simulation method as described in any one of the first aspect of the present application.

[0047] Additional aspects and advantages of the present application will be given in part in the following description, part will become apparent from the following description, or will be understood by those skilled in the art through practice of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0048] The accompanying drawings are used to provide further understanding of the technical solutions of the present application and constitute a part of the specification, and are used to explain the technical solutions of the present application together with the embodiments of the present application, and do not constitute a limitation on the technical solutions of the present application.

[0049] Fig. 1 is a system architecture diagram of a system to which a multi-physical field coupling fast simulation method is applied according to an embodiment of the present application;

[0050] Fig. 2 is a main flowchart of a multi-physical field coupling fast simulation method according to an embodiment of the present application;

[0051] Fig. 3 is a flowchart of determining a reactor simulation model according to an embodiment of the present application;

[0052] Fig. 4 is a flowchart of constructing a first history matrix and a second history matrix based on full series physical fields according to an embodiment of the present application;

[0053] Fig. 5 is a flowchart of performing a reduction process on a matrix to construct a time series model corresponding to full series physical fields according to an embodiment of the present application;

[0054] Fig. 6 is a flowchart of performing a reduction calculation on a first history matrix according to an embodiment of the present application;

[0055] Fig. 7 is a flowchart of constructing a time series model corresponding to full series physical fields based on a reduced left singular vector, a reduced right singular vector, a reduced singular value matrix and a second history matrix according to an embodiment of the present application;

[0056] Fig. 8 is a flowchart of determining eigenvalues and eigenvectors of a linear transformation matrix according to an embodiment of the present application;

[0057] Fig. 9 is a flowchart of reconstructing a prediction of full series physical fields based on eigenvalues of a linear transformation matrix according to an embodiment of the present application;

[0058] Fig. 10 is a module block diagram of a multi-physical field coupling fast simulation system according to an embodiment of the present application. DETAILED DESCRIPTION

[0059] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0060] Before the embodiments of the present application are further described in detail, the terms and phrases involved in the embodiments of the present application are explained, and the terms and phrases involved in the embodiments of the present application are applicable to the following explanations:

[0061] Nuclear reactor: The nuclear reactor is the heart of a nuclear power plant. Its working principle is as follows: an atom is composed of a nucleus and electrons outside the nucleus. The atomic nucleus is composed of protons and neutrons. When the atomic nucleus of uranium 235 is hit by an external neutron, one atomic nucleus will absorb a neutron and split into two smaller atomic nuclei, while emitting 2-3 neutrons. The neutrons produced by this fission will hit other uranium 235 atomic nuclei, causing new fission. This continues to be a chain reaction of fission. The chain reaction produces a large amount of heat energy. The heat generated by the nuclear reactor can be removed by circulating water (or other substances) to prevent the reactor from overheating and burning out. The heat generated by the nuclear reactor can make the water into water vapor to drive the steam turbine to generate electricity.

[0062] Model reduction: The principle of model reduction is to simplify the model under the premise of ensuring accuracy, in order to reduce the amount of calculation and improve the efficiency of calculation. The essence of dimensionality reduction is to describe the multi-dimensional physical process changing with time in a low-dimensional approximation, and to optimize in the sense of capturing system energy, so as to reduce the calculation dimension, reduce the amount of calculation, save the calculation time and CPU load.

[0063] Data inconsistency: refers to the contradictory and incompatible of various data. Data is a description of the real world and should conform to certain semantic rules and logical common sense, but in actual application, due to various reasons, some data violates such semantic rules, showing abnormal, incomplete or contradictory data values. For relational data, there are also entity abnormalities (multiple records corresponding to the same entity) and inclusion abnormalities (records in multiple tables do not meet the inclusion relationship), etc. All these abnormal data are collectively referred to as inconsistent data. Data inconsistency refers to the contradictory and incompatible of data.

[0064] Nuclear reactor is a highly complex system, which involves multiple physical processes occurring and multiple physical existences being coupled with each other, i.e., their states and behaviors affecting each other. In order to accurately describe and predict the behavior of the reactor, the mutual coupling relationship between these physical processes needs to be considered. The multi-physical field coupling simulation of the nuclear reactor has extremely high computational complexity, and currently, the numerical reactor is generally obtained by simulating multiple physical fields in the reactor, and the dynamic evolution process of the nuclear reactor system is identified and tracked through the numerical reactor. In some application scenarios with high real-time requirements, the separated coupling method is generally used to improve the calculation speed. However, in the case of fast calculation by the separated coupling method, the inconsistency between different physical fields also increases, which will lead to the divergence of the accuracy of the numerical reactor at subsequent time.

[0065] Based on this, the embodiment of the present application provides a multi-physical field coupling fast simulation method, system and storage medium. It can approximately decompose the matrix of a nonlinear system based on full series physical fields into a low-dimensional linear dynamic system, reduce the calculation amount, and improve the calculation speed. Moreover, the embodiment of the present application constructs a time sequence model corresponding to the full series physical fields based on the low-dimensional linear system, decomposes the time sequence model corresponding to the full series physical fields, and obtains time sequence models corresponding to multiple physical fields. Because the multiple physical fields are simultaneously reduced in dimension, the inconsistency between different physical fields is reduced.

[0066] System architecture and scenario to which the embodiment of the present application is applied

[0067] FIG. 1 is a system architecture diagram to which a multi-physical field coupling fast simulation method according to the embodiment of the present application is applied. It includes a terminal 140, an Internet 130, a gateway 120, a server 110, and the like.

[0068] The terminal 140 includes desktop computers, laptop computers, PDAs (personal digital assistants), mobile phones, vehicle-mounted terminals, home theater terminals, special-purpose terminals, and the like in various forms. In addition, it can be a single device or a collection of multiple devices. The terminal 140 can communicate with the Internet 130 in a wired or wireless manner and exchange data. Moreover, the multi-physical field coupling fast simulation method according to the embodiment of the present application can be implemented on the terminal 140, and the terminal 140 can show the user the calculation process of the multi-physical field coupling fast simulation method.

[0069] The server 110 refers to a computer system capable of providing certain services to the terminal 140. The multi-physical field coupling fast simulation method according to the embodiment of the present application can also be implemented on the server 110. Compared with ordinary terminals 140, the server 110 has higher requirements in stability, security, performance, and the like. The server 110 can be a high-performance computer in a network platform, a cluster of multiple high-performance computers, a part of a high-performance computer (such as a virtual machine), a combination of parts of multiple high-performance computers (such as virtual machines), or the like.

[0070] The gateway 120 is also known as an inter-network connector or a protocol converter. The gateway realizes network interconnection at the transport layer and is a computer system or device that acts as a conversion role. In the case of two systems that use different communication protocols, data formats or languages, or even have completely different architectures, the gateway is a translator. At the same time, the gateway can also provide filtering and security functions. Messages sent by the terminal 140 to the server 110 are sent to the corresponding server 110 through the gateway 120. Messages sent by the server 110 to the terminal 140 are also sent to the corresponding terminal 140 through the gateway 120.

[0071] The embodiments of the present application can be applied in the fields of monitoring the operating state of a nuclear reactor, dynamic diagnosis of the operating state of a reactor core, etc. Here, the monitoring of the operating state of a nuclear reactor is taken as an example for illustration: assuming that the embodiments of the present application are implemented on the terminal 140, a user can input the structural parameters of the nuclear reactor into the terminal 140, so that the terminal 140 performs the multi-physics field coupling fast simulation method of the embodiments of the present application based on the user-input structural parameters of the nuclear reactor, and obtains the operating states of various physical fields in the nuclear reactor. Further, the terminal 140, after obtaining the operating states of various physical fields in the nuclear reactor, can also display the operating states of various physical fields in the nuclear reactor to the user, so as to facilitate the user to monitor the operating state of the nuclear reactor. Moreover, the embodiments of the present application can also directly automatically implement real-time monitoring of the operating state of the nuclear reactor through the terminal 140, and alarm when the operating state of the nuclear reactor is abnormal, so as to facilitate the user to know that the operating state of the nuclear reactor is abnormal.

[0072] It should be understood that the above only illustrates the description of part of the application scenarios of the present application. The business scenarios to which the present application can be applied can include but are not limited to the specific embodiments mentioned above.

[0073] General description of embodiments of the present application

[0074] It should be emphasized that the embodiments of the present application can be applied to various application scenarios, such as the monitoring of the operating state of a nuclear reactor, the dynamic diagnosis of the operating state of a reactor core, etc. The multi-physics field coupling simulation of a nuclear reactor has extremely high computational complexity, and currently, a numerical reactor is generally obtained by simulating multiple physical fields in the reactor, and the dynamic evolution process of the nuclear reactor system is identified and tracked through the numerical reactor. In some application scenarios with high real-time requirements, a separate coupling method is generally used to improve the calculation speed. However, in the case of fast calculation through the separate coupling method, the inconsistency between different physical fields also increases, which will cause the accuracy of the numerical reactor at subsequent time to diverge. Some embodiments of the present application provide a multi-physics field coupling fast simulation method, system and storage medium, which can approximately decompose the matrix of a nonlinear system based on full-series physical fields into a low-dimensional linear dynamic system, thereby reducing the amount of calculation and improving the calculation speed. Moreover, the present application also constructs a time sequence model corresponding to the full-series physical fields based on the low-dimensional linear system, and obtains multiple time sequence models corresponding to the physical fields by decomposing the time sequence model corresponding to the full-series physical fields, because the multiple physical fields are simultaneously reduced in dimension, the inconsistency between different physical fields is reduced.

[0075] The multi-physics field coupling fast simulation method of the embodiments of the present application can be executed on the terminal 140 or on the server 110.

[0076] As shown in FIG. 2, according to one embodiment of the present application, the multi-physical field coupling fast simulation method comprises:

[0077] In step 210, state information of each physical field of the nuclear reactor under a transient process is collected at a first preset interval to obtain a plurality of physical field snapshots, wherein the transient process is simulated by using a reactor simulation model corresponding to the nuclear reactor.

[0078] In step 220, the plurality of physical field snapshots are spliced to obtain a full series physical field.

[0079] In step 230, a matrix is constructed based on the full series physical field.

[0080] In step 240, the matrix is reduced to construct a time sequence model corresponding to the full series physical field.

[0081] In step 250, the time sequence model corresponding to the full series physical field is decomposed to obtain a plurality of time sequence models corresponding to the plurality of physical fields.

[0082] The steps 210-250 are briefly described as follows.

[0083] In step 210, state information of each physical field of the nuclear reactor under a transient process is collected at a first preset interval to obtain a plurality of physical field snapshots, wherein the transient process is simulated by using a reactor simulation model corresponding to the nuclear reactor.

[0084] According to an embodiment of the present application, each physical field of the nuclear reactor includes a fluid dynamics field (such as refrigerant flow), a heat transfer field (such as heat conduction of heating), a pressure field (such as interaction between fluid and solid structure), a strength field (such as stress and strain of solid structure), a radiation field (such as propagation of neutron and gamma ray), and a power field (such as energy deposition of fission or other reactions, etc.). When analyzing and monitoring the nuclear reactor, the interaction and complex effects between the plurality of physical fields need to be considered, which increases the difficulty of research. Only through complex numerical simulation, the effects of multi-physical field coupling in the reactor can be accurately described. Based on this, the reactor simulation model corresponding to the nuclear reactor needs to be constructed first, and the transient process of each physical field of the nuclear reactor is simulated by using the reactor simulation model corresponding to the nuclear reactor. The reactor simulation model can provide valuable information for reactor design, operation and safety analysis.

[0085] It can be understood that high-resolution computational fluid simulation software and neutron Monte Carlo simulation software can be used to simulate the operating conditions and transient processes of each physical field of the nuclear reactor.

[0086] Further, when simulating the transient process of each physical field of the nuclear reactor, the state information of each physical field of the nuclear reactor under the transient process can be collected at intervals of a first preset interval time to obtain a plurality of physical field snapshots of the physical fields. The first preset interval time refers to the interval time of sampling the state information of each physical field of the nuclear reactor under the transient process. The physical field snapshot refers to an unmodifiable fast data copy storage of the state information of the physical field at a certain time. For a certain physical field in the reactor, the running process of the physical field can be simulated by the reactor simulation model, and the state information in the running process of the physical field can be sampled at intervals of a first preset interval time in a preset time period to obtain a plurality of physical field snapshots of the physical field at different times.

[0087] Further, in order to guarantee the consistency of the plurality of physical fields, the plurality of physical field snapshots obtained by the present application not only need to ensure that the interval time (i.e. the first preset interval time) of sampling the physical field snapshot of each physical field is the same, but also need to ensure that the state of the reactor in the preset time period corresponding to the plurality of snapshots obtained by each physical field is the same.

[0088] For example, it is assumed that the nuclear reactor is in a steady state at time t0, and then the transient process of the nuclear reactor is introduced (for example, the parameter change of the control rod of the reactor, or the power transient of the reactor, or the change of the heat exchange condition). Further, for the first time period after the transient state of the nuclear reactor (the first time period can be 1 minute or 2 minutes), the running process of the plurality of physical fields in the first time period after the transient state of the nuclear reactor is simulated based on the simulated time step Δt1. Further, the state information of the plurality of physical fields in the reactor can be sampled at intervals of a preset first preset interval time Δt2 to determine the physical field snapshots of the plurality of physical fields at different times. The simulated time step Δt1 can be 0.001 seconds, the first preset interval time Δt2 can be 0.02 seconds, and the first preset interval time Δt2 is greater than the simulated time step Δt1.

[0089] In step 220, the plurality of physical field snapshots are spliced to obtain a full series physical field.

[0090] According to the embodiment of the present application, the full-series physical field refers to a matrix obtained by splicing physical field snapshots of multiple physical fields at a certain moment, wherein the certain moment can refer to any moment. Assuming that there are physical field snapshots of a power field Power(N), a pressure field Press(N), a temperature field Temp(N), a critical mass field Mass(N), a thermal group neutron flux field Flux1(N), Flux2(N), and the like at a certain moment, wherein N represents the overall grid number of the reactor simulation model corresponding to the physical field, because of spatial anisotropy and the complexity of the nuclear reactor geometry, the order of magnitude of N can be very large (for example, the overall grid number N of the reactor simulation model corresponding to a typical pressurized water reactor core can be 1*10 5 In order to guarantee the consistency of the multiple physical fields, the present application can splice the physical field snapshots of the multiple physical fields at a certain moment to construct a full-series physical field P(M), which can be represented as:

[0091] wherein M=m*N; m represents the number of types of physical fields in the reactor; N represents the overall grid number of the reactor simulation model corresponding to the physical field; P(M) i represents the i-th data in the full-series physical field P(M).

[0092] Further, because the order of magnitude of N is large and the dimension of the full-series physical field P(M) is M=m*N, it can be determined that the dimension of the full-series physical field P(M) is also very large, which means that the calculation of the full-series physical field P(M) will bring huge calculation amount.

[0093] In step 230, a matrix is constructed based on the full-series physical field.

[0094] According to the embodiment of the present application, the full-series physical field refers to a matrix obtained by splicing physical field snapshots of multiple physical fields at a certain moment, and a matrix can be constructed based on the full-series physical field, which contains physical field snapshots of multiple physical fields at continuous multiple moments.

[0095] For example, assuming that the matrix contains physical field snapshots of multiple physical fields at continuous n moments, because the dimension of the full-series physical field is M=m*N, the dimension of the matrix can be represented as M*n.

[0096] The specific method of “constructing a matrix based on the full-series physical field” will be described in detail below.

[0097] In step 240, the matrix is reduced in order to construct a time series model corresponding to the full-series physical field.

[0098] According to the embodiment of the present application, because the dimension of the matrix is M*n, where M is a large number, if the matrix is directly calculated, heavy calculation will be brought. Therefore, the matrix needs to be calculated by reduction, and the dimension of the reduced matrix is greatly reduced, which simplifies the calculation amount brought by subsequent construction of the time sequence model corresponding to the full series physical field based on the reduced matrix, and improves the calculation efficiency.

[0099] Further, the time sequence model corresponding to the full series physical field can also be constructed based on the reduced matrix. The time sequence model corresponding to the full series physical field refers to the running process of the multiple physical fields in the reactor at different times. Based on the time sequence model corresponding to the full series physical field, the state information of the multiple physical fields in the reactor at any time can be determined.

[0100] In step 250, the time sequence model corresponding to the full series physical field is decomposed to obtain the time sequence model corresponding to the multiple physical fields.

[0101] According to the embodiment of the present application, the time sequence model corresponding to the full series physical field includes the running process of the multiple physical fields in the reactor at different times. After the time sequence model corresponding to the full series physical field is decomposed, the time sequence model corresponding to the different physical fields can be obtained. Because the present application is to simultaneously reduce and solve the multiple physical fields in the reactor by connecting and splicing the snapshots of the multiple physical fields, the consistency of the multiple physical fields is ensured.

[0102] For example, it is assumed that the physical field snapshot Power(N) of the power field, the physical field snapshot Press(N) of the pressure field, the physical field snapshot Temp(N) of the temperature field, the physical field snapshot Mass(N) of the critical mass field, the physical field snapshot Flux1(N) and Flux2(N) of the thermal group neutron flux field, etc. exist at a certain time. After the time sequence model corresponding to the full series physical field obtained by simultaneously connecting the multiple physical fields is decomposed, the time sequence model corresponding to the multiple physical fields is: Power i = P i ; Press i = P i-N ; Temp i = P i-2N ; Flux1 i = P i-3N ; Flux2 i = P i-3N ;...

[0103] Wherein, P i represents the i th data in the time sequence model corresponding to the full series physical field.

[0104] The steps 210 to 250 above determine the physical field snapshots of the plurality of physical fields by collecting the state information of each physical field of the nuclear reactor under the transient process, and splice the physical field snapshots of the plurality of physical fields to obtain the full series physical field, and realize the coupling of the plurality of physical fields by splicing the plurality of physical fields simultaneously. After that, a matrix will be constructed based on the full series physical field, and the full series physical field of the nonlinear system is approximately decomposed into a low-dimensional linear dynamic system by the order reduction processing of the matrix, which simplifies the calculation difficulty and improves the calculation speed. The application will also construct the time sequence model corresponding to the full series physical field based on the matrix after order reduction, that is, the amount of change of the state information of each physical field in the full series physical field with time, and obtain the time sequence model corresponding to the plurality of physical fields by decomposing the time sequence model corresponding to the full series physical field. Since the plurality of physical fields are solved and reduced simultaneously after being spliced, the consistency is guaranteed, and the inconsistency between different physical fields is reduced.

[0105] The above is a general description of steps 210 to 250. Since steps 220 and 250 have been described in sufficient detail above, only the specific implementation process of steps 210, 230 and 240 will be described in detail below.

[0106] Detailed description of step 210

[0107] In step 210, the state information of each physical field of the nuclear reactor under the transient process is collected at intervals of a first preset interval time to obtain the physical field snapshots of the plurality of physical fields, wherein the transient process is simulated by using the reactor simulation model corresponding to the nuclear reactor.

[0108] In an embodiment, referring to FIG. 3, the reactor simulation model of step 210 is determined by the following method:

[0109] Step 310, obtain the structure parameters corresponding to the reactor;

[0110] Step 320, construct a three-dimensional model of the reactor based on the structure parameters of the reactor;

[0111] Step 330, discretize the three-dimensional model of the reactor by mesh to obtain the reactor simulation model.

[0112] The steps 310 to 330 will be described in detail below.

[0113] According to an embodiment of the present application, the process of multi-physics coupling simulation involves modeling and solving the physical processes in a nuclear reactor, and a typical multi-physics coupling simulation includes multiple physical processes such as heat conduction, fluid mechanics, neutron transport, etc. Further, in order to accurately simulate these complex physical phenomena involved in multi-physics coupling, it is first necessary to obtain a structure parameter matrix for indicating the structural characteristics of the reactor, and then a three-dimensional model of the reactor is constructed according to the structure parameter matrix. Then, the reactor three-dimensional model is discretized by grid to obtain a reactor simulation model. Specifically, discretizing the reactor three-dimensional model by grid can discretize the continuous solving domain into a plurality of finite sub-regions, and each sub-region is adjacent and continuous and coordinated, so as to achieve the coordination and continuity of the entire reactor simulation model.

[0114] Detailed description of step 230

[0115] In an embodiment, the matrix includes a first history matrix and a second history matrix, with reference to FIG. 4, step 230 includes:

[0116] Step 410, determining the physical field snapshots of each physical field from the first time to the second time based on the full series of physical fields;

[0117] Step 420, constructing a first history matrix based on the physical field snapshots of each physical field from the first time to the second time;

[0118] Step 430, determining the physical field snapshots of each physical field from the third time to the fourth time based on the full series of physical fields, wherein the third time is faster than the first time by a first preset interval time, and the fourth time is faster than the second time by a first preset interval time;

[0119] Step 440, constructing a second history matrix based on the physical field snapshots of each physical field from the third time to the fourth time.

[0120] The steps 410 to 440 are described in detail as follows.

[0121] According to an embodiment of the present application, the first history matrix and the second history matrix both refer to the physical field snapshots of multiple physical fields at consecutive times, i.e. the combination of the full series of physical fields at consecutive times. Moreover, because the dimension of the full series of physical fields is M=m×N, the dimension of the first history matrix and the second history matrix can be represented as M×n, wherein n represents the number of sampled times in the first history matrix / second history matrix, i.e. the first history matrix / second history matrix contains the physical field snapshots of multiple physical fields at consecutive n times.

[0122] It should be noted that although the first history matrix and the second history matrix are not exactly the same, they have a time-related relationship.

[0123] It can be understood that, in order to realize short-term prediction of a period of time after the t0 moment based on the physical field snapshots of the plurality of physical fields at the initial t0 moment, so as to determine the state information of each physical field in the reactor at different moments, it is necessary to first construct a first historical matrix based on the physical field snapshots of each physical field from the first moment to the second moment, and construct a second historical matrix based on the physical field snapshots of each physical field from the third moment to the fourth moment. Here, the process of determining the first historical matrix and the second historical matrix is described in combination with the formula:

[0124] First, the physical field snapshots of each physical field from the first moment T-n to the second moment T-1 are determined based on the full-series physical field P(M), and the physical field snapshots of each physical field from the first moment T-n to the second moment T-1 can be understood as the full-series physical field P(M) from the first moment T-n to the second moment T-1. Based on this, the physical field snapshots of each physical field from the first moment T-n to the second moment T-1 can be represented as {P T-n , P T-n+1 ...P T-1}, wherein n represents the number of physical field snapshots of each physical field from the first moment T-n to the second moment T-1; P T-n represents the full-series physical field P(M) corresponding to the first moment T-n. Then, the first historical matrix X1 can be constructed based on the physical field snapshots {P T-n , P T-n+1 ...P T-1} of each physical field from the first moment T-n to the second moment T-1, and the dimension of the first historical matrix X1 is Mx n. The first historical matrix X1 can be represented as:

[0125] At the same time, the physical field snapshots of each physical field from the third moment T-n+1 to the fourth moment T can be determined based on the full-series physical field P(M), and the third moment T-n+1 is faster than the first moment T-n by a first preset interval time, and the fourth moment T is faster than the second moment T-1 by a first preset interval time. The physical field snapshots of each physical field from the third moment T-n+1 to the fourth moment T can be understood as the full-series physical field P(M) from the third moment T-n+1 to the fourth moment T. Based on this, the physical field snapshots of each physical field from the third moment T-n+1 to the fourth moment T can be represented as {P T-n+1 , P T-n+2 ...P T}, wherein n represents the number of physical field snapshots of each physical field from the third moment T-n+1 to the fourth moment T; P T-n+1representing the full series physical field P(M) corresponding to the third time T-n+1. Then, the second history matrix X2 can be constructed based on the physical field snapshots {P T-n+1 T-n+2 ...P T} of each physical field from the third time T-n+1 to the fourth time T, the second history matrix X2 having a dimension of Mxn, and the second history matrix X2 can be represented as:

[0126] Detailed description of step 240

[0127] In an embodiment, referring to FIG. 5, step 240 includes:

[0128] Step 510, performing a reduced-rank calculation on the first history matrix to obtain a reduced-rank left singular vector, a reduced-rank right singular vector, and a reduced-rank singular value matrix;

[0129] Step 520, constructing a time series model corresponding to the full series physical field based on the reduced-rank left singular vector, the reduced-rank right singular vector, the reduced-rank singular value matrix, and the second history matrix.

[0130] The steps 510 to 520 are described in detail as follows.

[0131] According to the embodiments of the present application, because the dimension of the first history matrix is Mxn, where M is a huge number, if the first history matrix is directly calculated, it will bring heavy calculation. Therefore, the first history matrix needs to be calculated by reducing the rank, and the reduced-rank first history matrix includes a reduced-rank left singular vector, a reduced-rank right singular vector, and a reduced-rank singular value matrix, and the dimension of the reduced-rank first history matrix is greatly reduced, which simplifies the calculation amount in subsequent solving and improves the calculation efficiency.

[0132] Further, a time series model corresponding to the full series physical field can also be constructed based on the reduced-rank left singular vector, the reduced-rank right singular vector, the reduced-rank singular value matrix, and the second history matrix, to represent the running process of the multiple physical fields in the reactor at different times. Based on the time series model corresponding to the full series physical field, the state information of the multiple physical fields in the reactor at any time can be clearly determined, which facilitates the monitoring of the running state of the reactor.

[0133] In an embodiment, referring to FIG. 6, step 510 includes:

[0134] Step 610, performing singular value decomposition on the first history matrix to obtain a left singular vector, a right singular vector, and a singular value matrix;

[0135] ​Step 620: based on the preset singular value truncation value, the left singular vector, the right singular vector and the singular value matrix are reduced in order respectively, to obtain the reduced left singular vector, the reduced right singular vector and the reduced singular value matrix.

[0136] The steps 610 to 620 are described in detail below.

[0137] According to the embodiments of the present application, since the dimension of the first historical matrix X1 is Mxn, the dimension M is very large, in order to reduce the amount of calculation, the first historical matrix X1 needs to be reduced in order. First, the first historical matrix X1 is singular value decomposed, and the decomposed first historical matrix X1 is: T ;

[0138] Wherein, U represents the left singular vector, and the left singular vector is an orthogonal matrix; Σ represents the singular value matrix, and the singular value matrix is a non-negative real diagonal matrix; V represents the right singular vector, and the right singular vector is also an orthogonal matrix.

[0139] Since the dimension of the first historical matrix X1 is large, the dimension M of the first historical matrix X1 can be reduced to r by defining an appropriate singular value truncation value r, so as to realize the reduction of the first historical matrix X1. Since M>>r, the data dimension can be greatly reduced, so as to achieve the purpose of reducing the calculation complexity. Further, the process of reducing the left singular vector, the right singular vector and the singular value matrix based on the preset singular value truncation value can be represented as:

[0140] Wherein, the subscript res represents the m-r singular values truncated and ignored by the singular value truncation value r; represents the reduced left singular vector; represents the reduced singular value matrix; represents the reduced right singular vector.

[0141] It can be understood that the steps 610 to 620 are essentially linearly mapping the first historical matrix X1 to a low-dimensional space, so as to achieve the purpose of reducing the calculation complexity and improving the calculation efficiency.

[0142] In an embodiment, referring to FIG. 7, the step 520 includes:

[0143] Step 710: constructing a linear transformation matrix of the first historical matrix and the second historical matrix;

[0144] Step 720: calculating the linear transformation matrix based on the reduced left singular vector, the reduced right singular vector and the reduced singular value matrix, to determine the eigenvalue and the eigenvector of the linear transformation matrix;

[0145] Step 730, reconstructing and predicting the full-series physical field based on the eigenvalue of the linear transformation matrix to obtain a time sequence model corresponding to the full-series physical field.

[0146] The steps 710 to 730 are described in detail below.

[0147] According to an embodiment of the present application, the linear transformation matrix can be used to indicate the linear relationship of the state information of the multi-physical field at two adjacent time points, and the linear transformation matrix can be expressed as: P t = AP t -1.

[0148] Wherein, A represents the linear transformation matrix; P t represents the state information of the multi-physical field at t time point; P t-1 represents the state information of the multi-physical field at t-1 time point.

[0149] Because the elements in the same row and column of the first historical matrix and the second historical matrix in the present application are also the state information of the multi-physical field at two adjacent time points, the linear transformation matrix can be determined based on the first historical matrix and the second historical matrix, and the linear transformation matrix can be expressed as: X2=AX1; A=X2X1 -1 =X1VΣ -1 U T ;

[0150] Wherein, A represents the linear transformation matrix; X1 represents the first historical matrix; X2 represents the second historical matrix; U T represents the transpose matrix of the left singular vector (the transpose matrix of the orthogonal matrix is the inverse matrix of the orthogonal matrix); Σ -1 represents the inverse matrix of the singular value matrix; V represents the right singular vector.

[0151] Further, in order to reduce the calculation difficulty of the linear transformation matrix, the linear transformation matrix can also be calculated based on the reduced first historical matrix, so as to determine the eigenvalue and eigenvector of the linear transformation matrix.

[0152] The specific method of "calculating the linear transformation matrix based on the reduced left singular vector, the reduced right singular vector and the reduced singular value matrix, and determining the eigenvalue and eigenvector of the linear transformation matrix" will be described in detail below.

[0153] After the eigenvalue and eigenvector of the linear transformation matrix are determined, the eigenvalue of the linear transformation matrix can be mapped to the continuous time eigenvalue, so as to reconstruct and predict the full-series physical field to obtain a time sequence model corresponding to the full-series physical field.

[0154] The specific method of "reconstructing and predicting the full-series physical field based on the eigenvalues of the linear transformation matrix to obtain a time sequence model corresponding to the full-series physical field" will be described in detail below.

[0155] In an embodiment, referring to FIG. 8, step 720 includes:

[0156] Step 810, based on the reduced left singular vector, the reduced right singular vector, and the reduced singular value matrix, the linear transformation matrix is reduced to obtain a low-order linear transformation matrix;

[0157] Step 820, the low-order linear transformation matrix is eigenvalue decomposed to obtain the eigenvector of the low-order linear transformation matrix;

[0158] Step 830, based on the eigenvector of the low-order linear transformation matrix, the linear transformation matrix is calculated to obtain the eigenvalue and eigenvector of the linear transformation matrix.

[0159] The steps 810 to 830 are described in detail below.

[0160] According to the embodiments of the present application, in order to reduce the calculation difficulty of the linear transformation matrix, it is necessary to calculate the low-order approximation of the linear transformation matrix based on the reduced first historical matrix, and the low-order linear transformation matrix can be represented as:

[0161] wherein, represents the low-order linear transformation matrix; X2 represents the second historical matrix; represents the transpose matrix of the reduced left singular vector; represents the inverse matrix of the reduced singular value matrix; represents the transpose matrix of the reduced right singular vector.

[0162] Further, the low-order linear transformation matrix can be eigenvalue decomposed to obtain the eigenvector of the low-order linear transformation matrix , and the process of eigenvalue decomposing the low-order linear transformation matrix can be represented as:

[0163] wherein, represents the low-order linear transformation matrix; W represents the eigenvector of the low-order linear transformation matrix ; Λ1 represents a diagonal matrix.

[0164] After the eigenvector W of the low-order linear transformation matrix is calculated, the low-order linear transformation matrix The process of calculating the eigenvalue and eigenvector of the full-order linear transformation matrix A from the eigenvector W of the low-order linear transformation matrix can be represented as: Φ = X2VΣ -1 W;

[0165] Wherein, Φ represents the eigenvector of the linear transformation matrix A, and the corresponding eigenvalue of Φ is Λ2; X2 represents the second historical matrix; W represents the eigenvector of the low-order linear transformation matrix A; -1 Σ represents the inverse matrix of the singular value matrix; V represents the right singular vector.

[0166] It can be understood that the eigenvalue and eigenvector of the full-order linear transformation matrix can be obtained by the eigenvector of the low-order linear transformation matrix, which reduces the calculation difficulty and improves the calculation efficiency.

[0167] In an embodiment, referring to FIG. 9, step 730 includes:

[0168] Step 910, mapping the eigenvalue of the linear transformation matrix to a first preset interval time to obtain a time series coefficient;

[0169] Step 920, constructing a time series model corresponding to the full series physical field based on the time series coefficient and the full series physical field at the initial time.

[0170] The steps 910 to 920 are described in detail below.

[0171] According to the embodiments of the present application, after the eigenvector Φ of the linear transformation matrix A and the eigenvalue Λ2 of the linear transformation matrix A are calculated, the linear relationship of the state information of the multi-physical field at adjacent two time points can be determined. Based on this, the eigenvalue Λ2 of the discrete system can be mapped to the first preset interval time Δt2 (the linear transformation matrix A is constructed based on the first historical matrix and the second historical matrix, and the interval time of the elements in the same row and column in the first historical matrix and the second historical matrix is the first preset interval time Δt2) to determine the time series coefficient, and the process of determining the time series coefficient can be represented as: ω = (log Λ2) / Δt2;

[0172] Wherein, ω represents the time series coefficient; Λ2 represents the eigenvalue of the linear transformation matrix A; and Δt2 represents the first preset interval time.

[0173] After the time series coefficients for indicating the linear relationship of the state information of the multi-physical field at different time points are determined, the state information of the full-series physical field after a plurality of first preset interval times from the initial state (herein, the T-1 time point is assumed as the initial state) can be short-term predicted based on the time series coefficients, i.e., a time series model corresponding to the full-series physical field is constructed based on the time series coefficients and the full-series physical field at the initial time point. The process of constructing the time series model corresponding to the full-series physical field can be represented as: P T-1 = ΦB; P(t) = Φe ωt B;

[0174] wherein, P T-1 represents the state information of the multi-physical field at the T-1 time point (herein, the T-1 time point is assumed as the initial state); Φ represents the eigenvector of the linear transformation matrix A; B represents the coordinates of P T-1 in the eigenvector base Φ; t represents the interval time from the T-1 time point; ω represents the time series coefficient.

[0175] The multi-physical field coupling fast simulation system and the computer readable storage medium of the embodiments of the present application are described

[0176] In a second aspect, referring to FIG. 10, the embodiments of the present application provide a multi-physical field coupling fast simulation system, comprising:

[0177] at least one memory 200;

[0178] at least one processor 100;

[0179] at least one program;

[0180] The program is stored in the memory 200, and the processor 100 executes the at least one program to realize:

[0181] the multi-physical field coupling fast simulation method as any one of the embodiments of the first aspect of the present application.

[0182] The processor 100 and the memory 200 can be connected through a bus or other means.

[0183] The memory 200 as a kind of non-transient readable storage medium, it can be used to store non-transient software instructions and non-transient instructions can be instructed.In addition, the memory 200 can include high-speed random access memory, can also include non-transient memory, for example, at least one disk storage device, flash memory device or other non-transient solid-state memory device.It can be understood that the memory 200 can optionally include the memory 200 remotely arranged relative to the processor 100, and these remote memories 200 can be connected to the processor 100 through network.The above-mentioned network examples include but are not limited to Internet, intranet, local area network, mobile communication network and combination thereof.

[0184] The processor 100 implements the multi-physical field coupling fast simulation method of the first aspect of the present application by running the non-transient software instructions and signals stored in the memory 200, i.e., various functional applications and data processing.

[0185] The non-transient software instructions and signals required to implement the multi-physical field coupling fast simulation method of the above-mentioned embodiments are stored in the memory 200, and when executed by the processor 100, the multi-physical field coupling fast simulation method of the first aspect of the present application is executed, for example, the steps 210 to 250 in FIG. 2, the steps 310 to 330 in FIG. 3, the steps 410 to 440 in FIG. 4, the steps 510 to 520 in FIG. 5, the steps 610 to 620 in FIG. 6, the steps 710 to 730 in FIG. 7, the steps 810 to 830 in FIG. 8, and the steps 910 to 920 in FIG. 9 described above.

[0186] In a third aspect, the embodiments of the present application provide a computer readable storage medium, which stores computer executable signals, and the computer executable signals are used to execute:

[0187] The multi-physical field coupling fast simulation method of any one of the first aspect of the present application.

[0188] For example, the steps 210 to 250 in FIG. 2, the steps 310 to 330 in FIG. 3, the steps 410 to 440 in FIG. 4, the steps 510 to 520 in FIG. 5, the steps 610 to 620 in FIG. 6, the steps 710 to 730 in FIG. 7, the steps 810 to 830 in FIG. 8, and the steps 910 to 920 in FIG. 9 described above.

[0189] The device embodiments described above are only schematic, and the units illustrated as separate components can or can not be physically separate, and the components illustrated as units can or can not be physical units, i.e., can be located in one place, or can be distributed on multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the present embodiment.

[0190] From the description of the above embodiments, those skilled in the art can understand that all or some steps in the method disclosed above can be implemented as software, firmware, hardware and appropriate combinations thereof. Some or all physical components can be implemented as software executed by a processor such as a central processing unit, a digital signal processor or a microprocessor, or as hardware, or as an integrated circuit such as an application specific integrated circuit. Such software can be distributed on a readable medium, which can include computer storage media (or non-transitory media) and communication media (or transitory media). As known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable signals, data structures, instruction modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, as known to those skilled in the art, communication media typically includes computer readable signals, data structures, instruction modules or other data in modulated data signals such as carrier waves or other transport mechanisms, and can include any information delivery medium.

[0191] The above detailed description of the embodiments of the present application is made in conjunction with the accompanying drawings, but the present application is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present application.

Claims

1. A multi-physics coupled fast simulation method, characterized in that, The method comprises the following steps: collecting state information of each physical field of a nuclear reactor under a transient process at a first preset interval time, to obtain a plurality of physical field snapshots of the physical fields, wherein the transient process is simulated by using a reactor simulation model corresponding to the nuclear reactor; splicing the plurality of physical field snapshots to obtain a full-series physical field; constructing a matrix based on the full-series physical field; performing order reduction processing on the matrix to construct a time series model corresponding to the full-series physical field; decomposing the time series model corresponding to the full-series physical field to obtain a plurality of time series models corresponding to the physical fields.

2. The multi-physics coupled fast simulation method of claim 1, wherein, The matrix comprises a first historical matrix and a second historical matrix, and the constructing the matrix based on the full-series physical field comprises: determining the physical field snapshots of each physical field from a first time to a second time based on the full-series physical field; constructing a first historical matrix based on the physical field snapshots of each physical field from the first time to the second time; determining the physical field snapshots of each physical field from a third time to a fourth time based on the full-series physical field, wherein the third time is one first preset interval time faster than the first time, and the fourth time is one first preset interval time faster than the second time; constructing a second historical matrix based on the physical field snapshots of each physical field from the third time to the fourth time.

3. The multi-physics coupled fast simulation method of claim 2, wherein, The order reduction processing on the matrix to construct the time series model corresponding to the full-series physical field comprises: performing order reduction calculation on the first historical matrix to obtain an order reduction left singular vector, an order reduction right singular vector, and an order reduction singular value matrix; constructing the time series model corresponding to the full-series physical field based on the order reduction left singular vector, the order reduction right singular vector, the order reduction singular value matrix, and the second historical matrix.

4. The multi-physics coupled fast simulation method of claim 3, wherein, The order reduction calculation on the first historical matrix to obtain the order reduction left singular vector, the order reduction right singular vector, and the order reduction singular value matrix comprises: performing singular value decomposition on the first historical matrix to obtain a left singular vector, a right singular vector, and a singular value matrix; performing order reduction on the left singular vector, the right singular vector, and the singular value matrix based on a preset singular value truncation value to obtain the order reduction left singular vector, the order reduction right singular vector, and the order reduction singular value matrix.

5. The multi-physics coupled fast simulation method of claim 3, wherein, The construction of the time series model corresponding to the full-series physical field based on the order reduction left singular vector, the order reduction right singular vector, the order reduction singular value matrix, and the second historical matrix comprises: constructing a linear transformation matrix of the first historical matrix and the second historical matrix; calculating the linear transformation matrix based on the order reduction left singular vector, the order reduction right singular vector, and the order reduction singular value matrix to determine eigenvalues and eigenvectors of the linear transformation matrix; reconstructing and predicting the full-series physical field based on the eigenvalues of the linear transformation matrix to obtain the time series model corresponding to the full-series physical field.

6. The multi-physics coupled fast simulation method of claim 5, wherein, The calculation of the linear transformation matrix based on the order reduction left singular vector, the order reduction right singular vector, and the order reduction singular value matrix to determine the eigenvalues and eigenvectors of the linear transformation matrix comprises: order the linear transformation matrix based on the reduced left singular vector, the reduced right singular vector, and the reduced singular value matrix to obtain a low-order linear transformation matrix; perform eigen decomposition on the low-order linear transformation matrix to obtain an eigenvector of the low-order linear transformation matrix; perform calculation on the linear transformation matrix based on the eigenvector of the low-order linear transformation matrix to obtain an eigenvalue and an eigenvector of the linear transformation matrix.

7. The multi-physics coupled fast simulation method of claim 5, wherein, The reconstructing prediction of the full-series physical field based on the eigenvalue of the linear transformation matrix to obtain a time series model corresponding to the full-series physical field comprises: mapping the eigenvalue of the linear transformation matrix to a first preset interval time to obtain a time series coefficient; constructing the time series model corresponding to the full-series physical field based on the time series coefficient and the full-series physical field at an initial time.

8. The multi-physics coupled fast simulation method of claim 1, wherein, The reactor simulation model is determined by the following method: obtaining a structure parameter corresponding to a nuclear reactor; constructing a reactor three-dimensional model based on the structure parameter of the nuclear reactor; performing grid discretization on the reactor three-dimensional model to obtain the reactor simulation model.

9. A multi-physics coupled fast simulation system, comprising: comprise: at least one memory; at least one processor; at least one program; The program is stored in the memory, and the processor executes at least one of the programs to implement: The multi-physical field coupling fast simulation method according to any one of claims 1 to 8.

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