Reactor core power reconstruction method, reactor core power reconstruction device, micro reactor core monitoring method and micro reactor core monitoring system

Abstract

The invention discloses a reactor core power reconstruction method and device, and a micro reactor core monitoring method and system. The reactor core power reconstruction method comprises the following steps: acquiring power distribution reference data of a micro reactor under different working conditions and real-time readings of an out-of-reactor detector under the current working condition; according to the power distribution reference data, extracting power distribution characteristics of adjacent segments of the micro-reactor, and obtaining coupling coefficients of the segments of the micro-reactor under different working conditions; and reconstructing the reactor core power of the micro reactor according to the real-time reading of the out-of-reactor detector, the coupling coefficient and the response matrix of the out-of-reactor detector to obtain the reactor core power distribution data of the micro reactor. Therefore, accurate monitoring of the reactor core power can be realized only through a small number of detection signals of the out-of-reactor detector.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of nuclear power, and particularly relates to a reactor core power reconstruction method and device, a micro reactor core monitoring method and system. BACKGROUND

[0002] The reactor core power monitoring is of great significance to the safe operation, optimal control and economy of a nuclear power plant. By monitoring the reactor core power level and distribution in real time, abnormal conditions can be found in time, and the abnormal event diagnosis can be performed, which is an important guarantee for ensuring the reliable, safe and economic operation of the nuclear power plant.

[0003] However, the micro nuclear reactor has a compact structure and a small core volume, and the pressure vessel is not convenient for setting a in-core detector hole. The advanced micro nuclear reactor has a high operating temperature, and the in-core instrument has a poor service environment, which is high temperature, high pressure and high radiation, and may also have corrosion, small space and other problems. Therefore, it is difficult to find a suitable in-core detector type, and in addition, the maintenance and repair are also difficult.

[0004] Therefore, the existing technology has errors in monitoring the reactor core power of the micro nuclear energy device by relying on a small amount of nuclear measurement signals of the out-of-core detector. SUMMARY

[0005] The present application aims to solve the above-mentioned problems in the prior art, and provides a reactor core power reconstruction method, device, micro reactor core monitoring method and system, which can accurately monitor the reactor core power only by a small amount of detection signals of the out-of-core detector.

[0006] In a first aspect, the present application provides a reactor core power reconstruction method applied to a micro reactor, and the method comprises the following steps:

[0007] Obtaining power distribution reference data of the micro reactor under different working conditions and real-time readings of an out-of-core detector under a current working condition;

[0008] According to the power distribution reference data, the power distribution characteristics of adjacent segments of the micro reactor are extracted to obtain the coupling coefficients of each segment of the micro reactor under different working conditions, and the coupling coefficients are used to represent the spatial power correlation between adjacent segments of the reactor core;

[0009] According to the real-time readings of the out-of-core detector, the coupling coefficients and an out-of-core detector response matrix, the reactor core power of the micro reactor is reconstructed to obtain the reactor core power distribution data of the micro reactor,

[0010] The out-of-core detector response matrix is a mathematical matrix representing the linear relationship between the power distribution of each segment of the reactor core and the readings of the detector.

[0011] Optionally, the power distribution reference data includes the power of each segment of the micro-relay under different operating conditions.

[0012] The step of extracting the node power distribution characteristics of the micro-relay based on the power distribution reference data to obtain the coupling coefficient of the micro-relay under different operating conditions specifically includes:

[0013] For each operating condition, the power of each segment corresponding to that condition is substituted into the following formula (1) to obtain the coupling coefficient of each segment under each operating condition:

[0014]

[0015] Where, N i C represents the total number of radially i-th nodes and their adjacent nodes in the n-th layer. in Let P be the coupling coefficient of the i-th node. in P is the power of the i-th radial section of the n-th layer. jn Let j be the power of the block adjacent to the i-th block.

[0016] Optionally, the method further includes:

[0017] The off-pile detector response matrix is ​​constructed using the following steps:

[0018] Based on the setting parameters of the external detector of the micro-pile, the response coefficient matrix of the external detector is obtained through Monte Carlo simulation;

[0019] Based on the response coefficient matrix and the power distribution baseline data under different operating conditions, the detector simulation readings corresponding to the power distribution of each section of the reactor core under different operating conditions are obtained.

[0020] The off-core detector response matrix includes the detector simulation readings corresponding to the power distribution of each core segment under different operating conditions.

[0021] Optionally, the micro-stacking system includes 12 off-pile detectors and 90 nodes.

[0022] The core power reconfiguration equations include the following formulas (3) and (4):

[0023]

[0024] Where f(x)·P=D, f(x) is the detector response function, P is the power distribution reference data, D is the detector analog reading matrix, and P1 to P2 are the reference values ​​for the detector analog reading matrix. 90 For reconstructed core power distribution data;

[0025]

[0026] Among them, c1 to c90 The coupling coefficients for each segment are N1 to N. 90 This represents the total number of adjacent nodes for each node.

[0027] Secondly, embodiments of the present invention also provide a method for monitoring the core of a microreactor, the method comprising:

[0028] The core power reconfiguration method described in the first aspect is used to obtain the core power distribution data of the microreactor under the target operating conditions.

[0029] Core monitoring is performed on the microreactor based on the core power distribution data.

[0030] Thirdly, embodiments of the present invention also provide a core power reconfiguration device for use in microreactors, the device comprising:

[0031] The acquisition module is used to acquire the power distribution reference data of the micro-recharge under different operating conditions and the real-time readings of the external detector under the current operating condition;

[0032] The calculation module, connected to the acquisition module, is used to extract the power distribution characteristics of adjacent nodes of the micro-recharge based on the power distribution reference data, and obtain the coupling coefficient of each node under different operating conditions of the micro-recharge. The coupling coefficient is used to characterize the spatial power correlation between adjacent nodes of the core.

[0033] The reconstruction module, connected to the calculation module, is used to reconstruct the core power of the microreactor based on the real-time readings of the external detector, the coupling coefficient, and the response matrix of the external detector, thereby obtaining the core power distribution data of the microreactor.

[0034] The off-core detector response matrix is ​​a mathematical matrix that characterizes the linear relationship between the power distribution of each node in the core and the detector readings.

[0035] Fourthly, embodiments of the present invention also provide a micro reactor core monitoring system, the system comprising:

[0036] The core power reconfiguration device described in the third aspect is used to obtain core power distribution data of the microreactor under target operating conditions.

[0037] The monitoring module is used to monitor the core of the microreactor based on the core power distribution data.

[0038] The core power reconfiguration method of the present invention extracts the coupling coefficient of the power distribution characteristics between nodes under different operating conditions of the microreactor and performs joint constraint reconfiguration by combining real-time readings from external detectors. This ensures a high degree of consistency between the reconfigured power distribution and the actual core power distribution, thereby significantly improving the core power reconfiguration accuracy under the condition of a limited number of detectors. Attached Figure Description

[0039] Figure 1 This is a flowchart of a core power reconfiguration method according to Embodiment 1 of the present invention;

[0040] Figure 2 : This is a flowchart of another core power reconfiguration method according to Embodiment 1 of the present invention;

[0041] Figure 3 : This is a typical coupling coefficient distribution diagram under a temperature of 800K in Embodiment 1 of the present invention;

[0042] Figure 4 : This is a distribution diagram of the response function of the gas-cooled microreactor detector under typical operating conditions in Embodiment 1 of the present invention;

[0043] Figure 5 : A schematic diagram of the relative deviation of the power distribution of the segment-level core reconfiguration under different temperature conditions in Embodiment 1 of the present invention;

[0044] Figure 6 This is a schematic diagram illustrating the relative deviation of the power distribution of the segment-level core reconfiguration under different burn-out depth conditions in Embodiment 1 of the present invention.

[0045] Figure 7 This is a schematic diagram of the relative deviation of the core reconfiguration power at the segment level under the condition that the control rod position is 0cm in Embodiment 1 of the present invention.

[0046] Figure 8 : A schematic diagram of the relative deviation of the reconfiguration power of the segment-level core under the condition that the control rod position is 50cm in Embodiment 1 of the present invention;

[0047] Figure 9 This is a schematic diagram of the relative deviation of the core reconfiguration power at the segment level under the condition of a control rod position of 100cm in Embodiment 1 of the present invention.

[0048] Figure 10 This is a schematic diagram showing the segment-level core reconfiguration power and relative deviation under different xenon concentration conditions in Embodiment 1 of the present invention.

[0049] Figure 11 : This is a structural diagram of a core power reconfiguration device according to Embodiment 3 of the present invention. Detailed Implementation

[0050] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0051] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0052] Currently, after long-term development, core online monitoring technology has reached a relatively mature application in commercial pressurized water reactors. Representative systems in this field include Westinghouse's BEACON system, Areva's MAGLAN system, Siemens' POWERPLEX system, Shanghai Nuclear Engineering Research and Design Institute's SOMPAS system, and CGN's SOPHORA system. These systems primarily employ mapping fitting and rapid model calculation methods. For example, the BEACON system uses spline fitting, while the MAGLAN system utilizes detector correction. Regarding other reactor types, a research team at Tsinghua University applied the harmonic synthesis method to large pebble bed high-temperature gas-cooled reactors, proposing a generalized harmonic synthesis method to reconstruct power distribution. Researchers at Xi'an Jiaotong University verified the accuracy of this method through its application in pressurized water reactors. Researchers at the China Nuclear Power Research and Design Institute have also conducted research on data-driven methods such as model order reduction and orthogonal decomposition in this field, achieving significant results in pressurized water reactor monitoring. However, unlike conventional pressurized water reactors, microreactor systems have a compact layout, typically lacking space for a sufficient number of in-reactor detectors. Furthermore, the in-reactor environment is often harsh, characterized by high temperature, high pressure, and high radiation, which existing in-reactor detector designs cannot meet. Therefore, the online monitoring system for microreactor systems differs significantly from that of conventional pressurized water reactor systems, relying solely on a limited number of external detectors to measure signals for core monitoring. The accuracy of conventional methods is insufficient.

[0053] Example 1:

[0054] Based on the above research, in order to solve the aforementioned technical problems, such as Figure 1 As shown, this embodiment provides a core power reconfiguration method that can be applied to microreactors.

[0055] Specifically, it includes the following steps 101 to 104.

[0056] Step 101: Obtain the power distribution baseline data of the micro-recharger under different operating conditions and the real-time readings of the external detector under the current operating condition.

[0057] Specifically, the method for obtaining power distribution reference data may include using a Monte Carlo program to establish a refined three-dimensional core model, including the complete structure of TRISO fuel particle distribution, fuel rod structure, fuel assemblies and internal structure, control rods, reflector layer, etc.; dividing the core into multiple power statistical blocks (e.g., 3 axial layers × 30 radial blocks); simulating the core operation under different operating conditions; and calculating the power value of each block under each operating condition using a Monte Carlo program to establish a power distribution database (i.e., power distribution reference data).

[0058] Real-time readings from external detectors can include: neutron flux / neutron count rate; gamma dose rate / gamma count rate; reactor power (absolute or relative); nuclear instrument channel data (such as fast / slow power); ionization current, instantaneous power changes, etc. The required readings can be selected based on the specific application.

[0059] Step 102: Based on the power distribution benchmark data, extract the power distribution characteristics of adjacent segments of the micro-relay to obtain the coupling coefficient of each segment under different operating conditions of the micro-relay.

[0060] The coupling coefficient is used to characterize the spatial power correlation between adjacent nodes in the reactor core.

[0061] Specifically, typical operating conditions (such as 5 sets of operating conditions with different control rod positions) can be selected from the power distribution benchmark data; the power distribution relationship between each segment and its adjacent segments can be analyzed to extract spatial distribution characteristics; based on the power distribution characteristics, the coupling coefficient of each segment under different operating conditions of the microreactor can be determined; the coupling coefficient of each segment can be calculated to generate a coupling coefficient matrix; the coupling coefficients of different operating conditions can be summarized and analyzed to establish a coupling coefficient database.

[0062] Step 103: Based on the real-time readings of the external detectors, the coupling coefficient, and the response matrix of the external detectors, the core power of the microreactor is reconstructed to obtain the core power distribution data of the microreactor.

[0063] Among them, the external detector response matrix is ​​a mathematical matrix that characterizes the linear relationship between the power distribution of each section of the reactor core and the detector readings.

[0064] Specifically, the detector response matrix can be constructed using the Monte Carlo forward calculation method; a mathematical model of the core power distribution can be established using the real-time readings of the external detectors that have been collected, as well as the pre-obtained coupling coefficient and the external detector response matrix; and the model can be solved to reconstruct the power distribution data of each segment of the microreactor core under the current operating conditions.

[0065] By using mathematical mapping, the block power is derived into the possible readings of the detector, and then the power distribution of the reactor core is inferred from the actual data of the detector through reverse calculation.

[0066] In this embodiment, by combining real-time signals from external detectors with a reactor reference database, spatial coupling characteristics between adjacent node power, and detector response matrices, the core power distribution can be accurately reconstructed through a mathematical model. This achieves efficient, real-time, and accurate monitoring of the microreactor core power without increasing in-reactor equipment or being limited by detector placement.

[0067] Optionally, the power distribution baseline data includes the power of each segment of the microreactor under different operating conditions.

[0068] Step 102 above may specifically include the following steps:

[0069] For each operating condition, the power of each segment corresponding to that condition is substituted into the following formula (1) to obtain the coupling coefficient of each segment under each operating condition:

[0070]

[0071] Where, N i C represents the total number of radially i-th nodes and their adjacent nodes in the n-th layer. in Let P be the coupling coefficient of the i-th node. in P is the power of the i-th radial section of the n-th layer. jn Let j be the power of the block adjacent to the i-th block.

[0072] Specifically, by using the power distribution data of the nodes under different operating conditions, the power distribution characteristics between each node and its adjacent nodes are extracted; for each operating condition, the power of each node corresponding to that operating condition is substituted into formula (1) to obtain the coupling coefficient corresponding to each operating condition and each node.

[0073] In this embodiment, by utilizing the power distribution data of each segment under different operating conditions, the spatial power distribution characteristics of adjacent segments are quantitatively extracted, yielding a parameter characterizing the correlation of core spatial power, namely the coupling coefficient. Introducing the coupling coefficient allows the subsequent power distribution reconstruction process to more fully reflect the spatial continuity and mutual influence of actual power throughout the entire core, thereby improving the accuracy and stability of microreactor core power distribution reconstruction.

[0074] Optionally, the above method further includes:

[0075] The following steps are used to construct the response matrix of the off-chip detector:

[0076] Based on the configuration parameters of the external detector of the micro-recharge stack, the response coefficient matrix of the external detector is obtained through Monte Carlo simulation.

[0077] Based on the response coefficient matrix and the power distribution baseline data under different operating conditions, the detector simulation readings corresponding to the power distribution of each section of the reactor core under different operating conditions are obtained.

[0078] The external detector response matrix includes simulated detector readings corresponding to the power distribution of each core segment under different operating conditions. It reflects the theoretical response value of each detector under known core power distribution conditions.

[0079] Here, the detector simulated readings refer to the theoretical readings that each off-site detector should display under various operating conditions.

[0080] Specifically, firstly, based on the configuration parameters of the external detectors of the microreactor, the response coefficient matrix of the external detectors is obtained using the Monte Carlo simulation method. This response coefficient matrix reflects the influence of power variations in each core segment on the readings of the external detectors.

[0081] Then, by combining the obtained response coefficient matrix and the power distribution baseline data under different operating conditions, the detector simulation readings corresponding to the power distribution of each section of the reactor core under each operating condition are determined.

[0082] Finally, by integrating the power distribution of each core segment under different operating conditions with the corresponding simulated detector readings, a complete database of external detector response matrices was established. This resulted in an external detector response matrix that contains the correspondence between the power distribution of each core segment and detector readings under different operating conditions.

[0083] In this embodiment, by establishing an external detector response matrix covering different operating conditions, the response of the external detector can be simulated or predicted when the power distribution of the nodes is known, thereby improving the mapping accuracy between each detection signal and the actual state of the core during power reconfiguration.

[0084] In one example, the response matrix of the off-pile detector includes the following formula (2):

[0085] f(x)·P=D (2)

[0086] Where f(x) is the detector response function, P is the power distribution data calculated in advance by the Monte Carlo program, and D is the constructed detector reading matrix.

[0087] Optionally, S103 above may specifically include the following steps:

[0088] Based on the coupling coefficient and the response matrix of the external detector, the core power reconfiguration equation is constructed;

[0089] Substituting the real-time readings of the external detectors under the current operating conditions into the core power reconstruction equation, the core power distribution data of the microreactor is obtained.

[0090] Specifically, firstly, the coupling coefficient can be used to construct the spatial constraint equation, that is, based on the obtained coupling coefficient of each segment, a mathematical equation characterizing the spatial correlation of the power of each segment in the core is established (as in the above formula (1)), forming a set of power constraint conditions based on spatial correlation; then, the detector response equation is constructed using the external detector response matrix, that is, the linear equation between the power of each segment in the core and the external detector reading is established using the external detector response matrix, forming a set of power constraint conditions based on the detector response; the spatial constraint equation based on the coupling coefficient and the detector response equation based on the response matrix are combined to form a power reconstruction equation that comprehensively considers spatial correlation and detector response; then, the real-time reading of the external detector collected under the current operating condition is substituted into the core power reconstruction equation, and the core power reconstruction equation containing spatial constraints and detector response is solved by numerical calculation method (such as the least squares method) to obtain the power value of each segment that satisfies all constraint conditions.

[0091] Finally, by integrating the calculation results, complete core power distribution data under the current operating conditions of the microreactor is obtained.

[0092] In this embodiment, a core power reconfiguration equation is established using the obtained coupling coefficient matrix and the external detector response matrix. The spatial power correlation between core segments (represented by the coupling coefficients) is combined with the influence of the power distribution of each segment on the detector readings (represented by the response matrix). During core operation, real-time readings of the external detectors under the current operating conditions are collected. These readings are substituted into the aforementioned core power reconfiguration equation, and the equation is solved to obtain the power distribution data of each segment of the current microreactor core.

[0093] In one example, the core power reconfiguration equation can be obtained by combining the above equations (1) and (2).

[0094] Optionally, the microreactor includes 12 off-pile detectors and 90 nodes.

[0095] The core power reconfiguration equations include the following formulas (3) and (4):

[0096]

[0097] Where f(x)·P=D, f(x) is the detector response function, P is the power distribution reference data, and D1 to D 12 These are simulated or real-time readings from 12 external detectors, P1 to P2. 90 For reconstructed core power distribution data;

[0098]

[0099] Among them, c1 to c 90 The coupling coefficients for each segment are N1 to N. 90 This represents the total number of adjacent nodes for each node.

[0100] Specifically, the actual obtained detector data D, response matrix f(x), coupling coefficients c1 to c90 and adjacent number of blocks N1 to N90 are substituted into the above formulas (3) and (4); the equations are solved by numerical calculation method to obtain the power distribution data of the 90 blocks of the reconstructed core.

[0101] In practical applications, by collecting the real-time readings D of the detector and using the two formulas mentioned above to solve for P, the power distribution of all nodes of the reactor under specific operating conditions can be obtained.

[0102] In this embodiment, by incorporating the detector response relationship and the inter-node coupling relationship into the power reconstruction equation, quantitative and systematic reconstruction of the core power distribution is achieved. This significantly improves the accuracy and reliability of microreactor core power distribution reconstruction.

[0103] Example 2: This example provides a method for monitoring the core of a microreactor, specifically including:

[0104] The core power reconfiguration method provided in any of the above embodiments is used to obtain the core power distribution data of the microreactor under the target operating condition.

[0105] Core monitoring of microreactors is performed based on core power distribution data.

[0106] Specifically, firstly, the core power distribution reconstruction method described in the above embodiments (such as: block division based on reference data, extraction of coupling coefficients, establishment of response matrix, and solution of equations based on real-time detection signals) is used to acquire the power distribution data of all segments of the micro reactor core under the target operating condition (such as specific temperature, burnup, control rod position, xenon concentration, etc.); based on the core power distribution data, the core is monitored: based on the obtained detailed core power distribution data under the current operating condition, multiple state monitoring of the core can be realized, including but not limited to: abnormal segment power identification, spatial power distribution uniformity analysis, and safety margin judgment under critical operating conditions.

[0107] It can also effectively monitor the reactor core's operating status by comparing power distribution trends or analyzing deviations from baseline data, enabling intelligent core control, fault warning, and safety management.

[0108] In this embodiment, a high-precision core power reconfiguration method enables real-time and accurate monitoring of the microreactor core under target operating conditions. Only a small number of external detector signals are needed to obtain global core distribution information. This allows for timely detection and location of core power anomalies, enhancing the safety and reliability of reactor operation.

[0109] To facilitate understanding of the core power reconfiguration method provided in this embodiment, a practical application description of the above method is provided here.

[0110] A typical gas-cooled microreactor core structure includes the following elements: hexagonal prism-shaped fuel assemblies, control rod assemblies, and a reflector layer within the active zone. Externally, there are boron-carbon bricks, a basket, a pressure vessel, a shielding layer, and a chassis frame. Neutron detectors are arranged radially outside the steel shielding layer, with three detection points arranged asymmetrically. Each detection point has four sets of detectors along its axial direction, forming a total of 12 sets of external detectors. Twelve rows of fuel assemblies are radially distributed within the core, each row containing three layers of fuel assemblies, resulting in a total of 36 fuel assemblies in the entire core. Each fuel assembly contains 18 fuel rods, and each fuel rod has 15 fuel pellets mounted on its axial portion. The control rod system consists of two sets: the first set, located outside the active zone, consists of six sets of control rods and is responsible for core operation control and various shutdown modes (such as cold shutdown, hot shutdown, and emergency shutdown); the second set of control rods, located at the core center, consists of only one set and serves as a backup shutdown method, activated only when the first set of control rods fails to achieve a hot shutdown of the core.

[0111] like Figure 2 As shown, the specific steps are as follows:

[0112] 1. Establish an accurate three-dimensional core model of the gas-cooled microreactor.

[0113] Based on the core loading design scheme of the gas-cooled microreactor, an accurate three-dimensional core model is constructed using the universally applicable and precise Monte Carlo nuclear design program. The model needs to include internal structures such as fuel assemblies, reflectors, control rods, and coolant. It should be noted that steps (1) and the following step (2) are only used to verify the accuracy and reliability of the method described in this invention, and are not necessary in actual applications.

[0114] 2. Construct a database of core power distribution under different operating conditions of the gas-cooled microreactor.

[0115] Based on a 3D core model, the entire core is divided into different statistical blocks according to power distribution statistical requirements. The power of each block must be sufficient to represent the overall power distribution of the entire core (e.g., based on the 3D core model, the entire core is divided into 3 axial layers and 30 radial blocks, totaling 90 core power distribution statistical blocks). After the blocks are divided, models for all possible operating conditions throughout the entire lifespan are established, and the power of each core block under different operating conditions is calculated to construct a power distribution database. The main operating conditions include different temperatures, burn-up depths, control rod positions, and xenon concentrations.

[0116] 3. Construct an external detector response matrix database.

[0117] Calculate the response matrices for all power statistics nodes and all detector locations, constructing an off-chip detector response matrix database containing response matrices for 90 nodes and 12 detector locations. Specific data is as follows: Figure 3 As shown. The Monte Carlo forward calculation method is used:

[0118] 1) Statistical analysis of the fission neutron source intensity distribution at various locations within the reactor;

[0119] 2) Set source terms according to the fission neutron source strength, use the Monte Carlo program to calculate in fixed source mode, solve the problem of deep neutron penetration through the gate element importance setting function, and statistically analyze the spatial response matrix of each detector outside the stack.

[0120] 3) Calculate the response matrix under different operating conditions and construct a response matrix database.

[0121] 4. Construct a database of coupling coefficients under typical operating conditions.

[0122] (1) Construct a set of power equations based on coupling coefficients under typical operating conditions.

[0123] Based on the design characteristics of microreactors, particularly the power distribution variations with different operating parameters (including but not limited to different temperatures, burn-up depths, control rod positions, and xenon concentrations), a set of typical operating conditions are selected to construct a three-dimensional core model under these conditions. The power distribution data of this model is then obtained and converted into coupling coefficients for different nodes. It should be noted that the varying parameters under typical operating conditions can usually be obtained directly or quickly from reactor status information and external detector signals. After calculating the coupling coefficients under typical operating conditions, a set of power equations based on these coupling coefficients is constructed.

[0124] Based on the design characteristics of the gas-cooled microreactor, five typical operating conditions with different control rod positions were selected. A three-dimensional core model under these typical operating conditions was constructed, and the coupling coefficient was calculated. The specific calculation formula is as follows:

[0125]

[0126] Where, N i C represents the total number of radially i-th nodes in the n-th layer and their adjacent nodes; in P represents the coupling coefficient of the i-th node; in and P jn These represent the power of the i-th and j-th radial nodes in the n-th layer and the power of the adjacent nodes, respectively.

[0127] Formula (1) is based on the law of conservation of neutrons. It constructs spatial constraints through the weighted average relationship of power of adjacent nodes, reflecting the spatial continuity characteristics of core power distribution (dominated by neutron leakage effect).

[0128] After calculating the power distribution under typical operating conditions using the Monte Carlo program, the coupling coefficients can be calculated using the above formula, thus completing the construction of the coupling coefficient matrix database for typical operating conditions.

[0129] In one example, after calculating the power distribution under typical operating conditions using a Monte Carlo program, the coupling coefficients are calculated using the formula described above, thus completing the construction of the coupling coefficient matrix database for typical operating conditions. The coupling coefficient distribution diagram for typical operating conditions with a control rod position of 0cm, a temperature of 800K, and a burnup depth of 0MWd / tU is shown below. Figure 4 As shown.

[0130] 5. Real-time acquisition of signals from external detectors and construction of a detector reading matrix database.

[0131] Construct a set of power equations based on detector readings.

[0132] The constructed detector readings are used instead of the actual detector readings. Using the reference power distribution data under different operating conditions in step 2, and combined with the detector response function, the detector readings for the corresponding operating conditions are constructed, and then the system of equations satisfied by the core power is constructed based on the detector readings.

[0133] Since the gas-cooled microreactor is currently in the conceptual design stage, actual detector readings are lacking. This embodiment uses the Monte Carlo program to calculate reference power distribution data under different operating conditions, and combines this with the detector response function to obtain the detector readings for the corresponding operating conditions, thereby constructing a detector reading matrix. The specific calculation formula is as follows:

[0134] f(x)·P=D (2)

[0135] Where f(x) is the detector response function, P is the power distribution data calculated in advance by the Monte Carlo program, and D is the constructed detector reading matrix.

[0136] In one example, taking the power distribution under a typical operating condition as an example, the 12 detector signal values ​​obtained by the above formula are shown in Table 1. The specific detector reading distribution is as follows:Figure 4 As shown.

[0137] Table 1. Signal values ​​of 12 detectors under a certain operating condition.

[0138] Detector number Detector count 1 6.959E-09 2 6.818E-09 3 1.320E-08 4 9.191E-09 5 9.302E-09 6 1.939E-08 7 9.712E-09 8 9.731E-09 9 2.000E-08 10 8.327E-09 11 8.241E-09 12 1.571E-08

[0139] 6. Using the least squares method to solve the detector reading equation and coupling coefficient method, the core power distribution data at the current signal point is reconstructed.

[0140] The power solution equation is constructed based on the detector response function and the detector readings under known operating conditions. The constraint equation is constructed using the coupling coefficient matrix. The core power distribution data under the current detector readings is reconstructed using the least squares method.

[0141] The power reconstruction equation is constructed using formulas (1) and (2), and the specific matrix form is shown in formula (3) and formula (4) for Huanggang City:

[0142]

[0143]

[0144] Where P represents the reconstructed power distribution data, with 3 layers of axial distribution from top to bottom and 30 layers of radial distribution from north to south and from west to east.

[0145] The above equations can be solved using the least squares method, thereby reconstructing the power distribution data for different segments based on the detector readings under the current operating conditions.

[0146] 7. Comparison of core parameters of gas-cooled microreactor reactor.

[0147] Based on the gas-cooled microreactor core power distribution data under different operating conditions (different burn-out depths, temperatures, control rod positions, and xenon concentrations) obtained in step 6, the calculation results are compared with the baseline power distribution data calculated in step 2 to test the accuracy and reliability of the invention.

[0148] This embodiment selected different temperatures (300.0, 400.0, 500.0, 600.0, 700.0, 800.0, 1000.0, 1200.0, 1600.0 K) and burn depths (0.0, 97.0, 324.0, 1620.0, 3241.0, 4861.0, 6481.0, 8102.0, 9722.0, 11343.0, 12963.0, 14583.0, 16204.0). The power was reconstructed using the gas-cooled microreactor core detector reading matrix under xenon concentrations (17824.0 MWd / tU), control rod positions (120.0, 110.0, 100.0, 90.0, 80.0, 70.0, 60.0, 50.0, 40.0, 30.0, 20.0, 10.0, 0.0, -10.0, -20.0, -29.0 cm), and xenon concentrations (xenon balance and xenon transient). The calculated results were then compared with reference values. Specific calculation results are shown below. Figures 5-10 As shown. Among them, Figure 5 The relative deviation of power distribution during reconfiguration of the segment-level core under different temperature conditions; Figure 6 The relative deviation of the power distribution during reconfiguration of the block-level core under different burnup depth conditions; Figure 7 To control the relative deviation of the reconfiguration power of the segment-level core under the condition of 0cm rod position; Figure 8 To control the relative deviation of the reconfiguration power of the segment-level core under the condition of 50cm rod position; Figure 9 To control the relative deviation of the reconfiguration power of the segment-level core under the 100cm rod position condition; Figure 10 The segment-level core reconfiguration power and relative deviation under different xenon concentration conditions.

[0149] Calculation results show that the online power monitoring software for gas-cooled microreactors based on coupling coefficients proposed in this application has high calculation accuracy and good stability. For different temperatures and burnup depths, selecting intermediate points (800K and 4861MWd / tU) for coupling coefficient calculation can reconstruct all temperature and burnup depth points covered by the overall gas-cooled microreactor operation, and the relative deviation between the reconstructed power distribution and the actual power distribution is within 5%. For different control rod positions, calculation results show that the online power monitoring method developed in this paper can reconstruct the power within ±10cm of the rod position calculated by the coupling coefficient, and the relative deviation between the reconstructed power distribution and the actual power distribution is within 5%. For different xenon concentration conditions, unlike pressurized water reactor systems, the energy spectrum of gas-cooled microreactors is relatively hard, and the xenon concentration change during the control rod insertion process has an impact of no more than 2% on the calculation results. However, the online power monitoring method proposed in this application can still reconstruct the power distribution of the current signal point based on the detector signal of the xenon transient concentration, and the relative deviation with the actual power distribution is within 1%, demonstrating good calculation accuracy.

[0150] Example 3:

[0151] like Figure 11 As shown, this embodiment provides a core power reconfiguration device 1100, applied to a microreactor, which includes:

[0152] The acquisition module 1101 is used to acquire the power distribution baseline data of the micro-recharge under different operating conditions and the real-time readings of the external detector under the current operating condition;

[0153] The calculation module 1102, connected to the acquisition module 1101, is used to extract the power distribution characteristics of adjacent segments of the micro-recharger based on the power distribution reference data, and obtain the coupling coefficient of each segment under different operating conditions of the micro-recharger. The coupling coefficient is used to characterize the spatial power correlation between adjacent segments of the core.

[0154] The reconstruction module 1103, connected to the calculation module 1102, is used to reconstruct the core power of the microreactor based on real-time readings from the external detectors, the coupling coefficient, and the response matrix of the external detectors, thereby obtaining the core power distribution data of the microreactor.

[0155] Among them, the external detector response matrix is ​​a mathematical matrix that characterizes the linear relationship between the power distribution of each section of the reactor core and the detector readings.

[0156] Optionally, the power distribution baseline data includes the power of each segment of the microreactor under different operating conditions.

[0157] The aforementioned calculation module 1102 is specifically used for:

[0158] For each operating condition, the power of each segment corresponding to that condition is substituted into the following formula (1) to obtain the coupling coefficient of each segment under each operating condition:

[0159]

[0160] Where, N i C represents the total number of radially i-th nodes and their adjacent nodes in the n-th layer. in Let P be the coupling coefficient of the i-th node. in P is the power of the i-th radial section of the n-th layer. jn Let j be the power of the block adjacent to the i-th block.

[0161] Optionally, the device further includes:

[0162] Create a module for constructing the response matrix of the off-pile detector;

[0163] Create modules, including:

[0164] The simulation unit is used to obtain the response coefficient matrix of the external detector through Monte Carlo simulation based on the setting parameters of the external detector of the micro-repository.

[0165] A cell is created to obtain the detector simulation readings corresponding to the power distribution of each section of the reactor core under different operating conditions, based on the response coefficient matrix and the power distribution reference data under different operating conditions.

[0166] The off-core detector response matrix includes the detector simulation readings corresponding to the power distribution of each core segment under different operating conditions.

[0167] The core power reconfiguration device in this embodiment accurately reconstructs the core power distribution through a mathematical model, using real-time signals from external detectors, combined with a reactor reference database, spatial coupling characteristics between adjacent node power, and detector response matrices. This achieves efficient, real-time, and precise monitoring of the microreactor core power without increasing in-reactor equipment or being limited by detector placement.

[0168] Example 4:

[0169] This embodiment provides a micro reactor core monitoring system, which includes:

[0170] The core power reconfiguration apparatus provided in any of the above embodiments is used to obtain core power distribution data of a microreactor under target operating conditions;

[0171] The monitoring module is used to monitor the core of the microreactor based on the core power distribution data.

[0172] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0173] The functional blocks shown in the above block diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0174] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0175] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable scheduling apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable scheduling apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0176] The above are merely specific embodiments of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A core power reconfiguration method, characterized in that, Applied to microheaps, the method includes: Acquire the power distribution baseline data of the micro-recharge under different operating conditions and the real-time readings of the external detector under the current operating condition; Based on the power distribution baseline data, the power distribution characteristics of adjacent segments of the micro-recharge are extracted to obtain the coupling coefficient of each segment under different operating conditions of the micro-recharge. The coupling coefficient is used to characterize the spatial power correlation between adjacent segments of the core. Based on the real-time readings of the external detector, the coupling coefficient, and the response matrix of the external detector, the core power of the microreactor is reconstructed to obtain the core power distribution data of the microreactor. The off-core detector response matrix is ​​a mathematical matrix that characterizes the linear relationship between the power distribution of each node in the core and the detector readings.

2. The method according to claim 1, characterized in that, The power distribution baseline data includes the power of each section of the micro-relay under different operating conditions. The step of extracting the nodal power distribution characteristics of the micro-relay based on the power distribution reference data to obtain the coupling coefficient of the micro-relay under different operating conditions specifically includes: For each operating condition, the power of each segment corresponding to that condition is substituted into the following formula (1) to obtain the coupling coefficient of each segment under each operating condition: Where, N i C represents the total number of radially i-th nodes and their adjacent nodes in the n-th layer. in Let P be the coupling coefficient of the i-th node. in P is the power of the i-th radial section of the n-th layer. jn Let j be the power of the block adjacent to the i-th block.

3. The method according to claim 1, characterized in that, The method further includes: The off-pile detector response matrix is ​​constructed using the following steps: Based on the setting parameters of the external detector of the micro-pile, the response coefficient matrix of the external detector is obtained through Monte Carlo simulation; Based on the response coefficient matrix and the power distribution reference data under different operating conditions, the detector simulation readings corresponding to the power distribution of each section of the reactor core under different operating conditions are obtained. The off-core detector response matrix includes simulated detector readings corresponding to the power distribution of each core segment under different operating conditions.

4. The method according to claim 3, characterized in that, The process of reconstructing the core power of the microreactor based on real-time readings from the external detector, the coupling coefficient, and the external detector response matrix to obtain the core power distribution data of the microreactor specifically includes: Based on the coupling coefficient and the response matrix of the external detector, the core power reconfiguration equation is constructed; The real-time readings of the external detector under the current operating conditions are substituted into the core power reconstruction equation for solution to obtain the core power distribution data of the microreactor.

5. The method according to claim 4, characterized in that, The micro-stacking system comprises 12 external detectors and 90 nodes. The core power reconfiguration equations include the following formulas (3) and (4): Where f(x)·P=D, f(x) is the detector response function, P is the power distribution reference data, D is the detector analog reading matrix, and P1 to P2 are the reference values ​​for the detector's analog reading matrix. 90 For reconstructed core power distribution data; Among them, c1 to c 90 The coupling coefficients for each segment are N1 to N. 90 This represents the total number of adjacent nodes for each node.

6. A method for monitoring the core of a microreactor, characterized in that, The method includes: The core power reconfiguration method according to any one of claims 1 to 5 is used to obtain the core power distribution data of the microreactor under the target operating conditions. Core monitoring is performed on the microreactor based on the core power distribution data.

7. A core power reconfiguration device, characterized in that, Applied to micro-stacking, the device includes: The acquisition module is used to acquire the power distribution reference data of the micro-recharge under different operating conditions and the real-time readings of the external detector under the current operating condition; The calculation module, connected to the acquisition module, is used to extract the power distribution characteristics of adjacent nodes of the micro-recharge based on the power distribution reference data, and obtain the coupling coefficient of each node under different operating conditions of the micro-recharge. The coupling coefficient is used to characterize the spatial power correlation between adjacent nodes of the core. The reconstruction module, connected to the calculation module, is used to reconstruct the core power of the microreactor based on the real-time readings of the external detector, the coupling coefficient, and the response matrix of the external detector, thereby obtaining the core power distribution data of the microreactor. The off-core detector response matrix is ​​a mathematical matrix that characterizes the linear relationship between the power distribution of each node in the core and the detector readings.

8. The apparatus according to claim 7, characterized in that, The power distribution baseline data includes the power of each section of the micro-relay under different operating conditions. The calculation module is specifically used for: For each operating condition, the power of each segment corresponding to that condition is substituted into the following formula (1) to obtain the coupling coefficient of each segment under each operating condition: Where, N i C represents the total number of radially i-th nodes and their adjacent nodes in the n-th layer. in Let P be the coupling coefficient of the i-th node. in P is the power of the i-th radial section of the n-th layer. jn Let j be the power of the block adjacent to the i-th block.

9. The apparatus according to claim 7, characterized in that, The device includes: Create a module to construct the response matrix of the off-pile detector; The creation module includes: The simulation unit is used to obtain the response coefficient matrix of the external detector through Monte Carlo simulation based on the setting parameters of the external detector of the micro-pile. A creation unit is used to obtain the detector simulation readings corresponding to the power distribution of each section of the reactor core under different operating conditions, based on the response coefficient matrix and the power distribution reference data under different operating conditions. The off-core detector response matrix includes simulated detector readings corresponding to the power distribution of each core segment under different operating conditions.

10. A micro reactor core monitoring system, characterized in that, The system includes: The core power reconfiguration apparatus according to any one of claims 7 to 9 is used to obtain core power distribution data of a microreactor under target operating conditions; The monitoring module is used to monitor the core of the microreactor based on the core power distribution data.

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