Construction method of critical experiment process database of critical experiment device

Abstract

The embodiment of the invention relates to the field of electric digital data processing methods, in particular to a construction method of a critical experiment process database of a critical experiment device. According to the construction method of the critical experiment process database of the critical experiment device, by determining the working condition of the critical experiment device under the current condition, the critical experiment process data of multiple critical states can be accurately measured and collected in real time, the scale of the critical experiment process database formed by the data is expanded, and the accuracy of the critical experiment process database is improved. The data dimension is expanded, and the data acquisition cost is reduced; by comparing the calculated value of the reactor core reactivity with the measured value of the reactor core reactivity, the automatic screening of the critical experiment process data of the critical experiment device can be realized, the data screening cost is reduced, and the data screening standard is defined, so that the database can verify the state of the reactor under the subcritical condition.

Description

Technical Field

[0001] The embodiments of this application relate to the field of electronic digital data processing methods, and in particular to a method for constructing a critical experimental process database for a critical experimental apparatus. Background Technology

[0002] The statements herein are provided merely as background information in connection with this application and do not necessarily constitute prior art.

[0003] The criticality experiment database contains critical or subcritical experimental data from various critical devices and performs benchmarking analysis and evaluation of the experimental data. The criticality experiment database can assess the accuracy and validity of reactor simulation programs and nuclear data, and is typically used for macroscopic verification of nuclear data, validation of reactor simulation programs, and nuclear criticality safety analysis. Summary of the Invention

[0004] A brief overview of this application is provided below to offer a basic understanding of certain aspects thereof. It should be understood that this overview is not an exhaustive summary of the application. It is not intended to identify key or essential parts of the application, nor is it intended to limit its scope. Its purpose is merely to present certain concepts in a simplified form as a prelude to the more detailed description that follows.

[0005] An embodiment of this application provides a method for constructing a critical experimental process database for a critical experimental device, comprising the following steps: S10: determining the current operating conditions of the critical experimental device, the effective neutron multiplication factor of the core of the critical experimental device, and the neutron flux at the detector, wherein the detector is arranged near the core; S20: determining the calculated value of the core reactivity based on the effective neutron multiplication factor of the core; S30: determining the measured value of the core reactivity based on the neutron flux determined by the detector; S40: determining the core reactivity value for constructing the database based on the calculated value and the measured value; S50: constructing the critical experimental process database, the database including the core reactivity value determined in step S40 and the current operating conditions of the critical experimental device.

[0006] The method for constructing a critical experimental process database for a critical experimental device provided in the embodiments of this application can accurately measure and collect critical experimental process data of multiple critical states in real time by determining the operating conditions of the critical experimental device under the current conditions. This expands the scale of the critical experimental process database composed of these data, extends the data dimensions, and reduces the data acquisition cost. By comparing the calculated value of core reactivity with the measured value of core reactivity, the critical experimental process data of the critical experimental device can be automatically filtered, reducing the data filtering cost and clarifying the data filtering criteria, so that the database can verify the state under subcritical conditions of the reactor. Attached Figure Description

[0007] To further illustrate the above and other advantages and features of this application, the specific embodiments of this application will be described in more detail below with reference to the accompanying drawings. The drawings, together with the following detailed description, are included in and form a part of this specification. Elements having the same function and structure are indicated by the same reference numerals. It should be understood that these drawings only depict typical examples of this application and should not be considered as limiting the scope of this application.

[0008] Figure 1 This is a flowchart illustrating the method for constructing a critical experimental process database of the critical experimental apparatus provided in the embodiments of this application. Detailed Implementation

[0009] Exemplary embodiments of this application will be described below with reference to the accompanying drawings. For clarity and brevity, not all features of actual implementations are described in the specification. However, it should be understood that many implementation-specific decisions must be made in the development of any such actual embodiment to achieve the developer's specific goals, such as complying with constraints related to the system and business, and these constraints may vary depending on the implementation. Furthermore, it should be understood that while development work can be very complex and time-consuming, such development work is merely a routine task for those skilled in the art who benefit from the content of this application.

[0010] It should also be noted that, in order to avoid obscuring this application with unnecessary details, only the equipment structure and / or processing steps closely related to the solution according to this application are shown in the accompanying drawings, while other details that are not closely related to this application are omitted.

[0011] The following disclosure provides several different implementations or examples for carrying out this application. To simplify the disclosure of this application, specific examples of components and methods are described below. Of course, these are merely examples and are not intended to limit this application. In the description of the embodiments of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0012] Currently, compared to the international database of criticality safety test assessment projects, my country has conducted relatively few criticality experiments. Furthermore, because the current criticality benchmark test database only includes experimental data from critical or near-critical states, typically only one set of benchmark data can be obtained from a single criticality experiment. This results in a limited data dimension and a small sample size. The data size of the criticality test database directly affects the determination of subcritical limits during criticality safety assessments, thus limiting further increases in the scale of spent fuel reprocessing and nuclear material production.

[0013] To address the aforementioned problems, embodiments of this application provide a method for constructing a critical experimental process database for a critical experimental apparatus. Figure 1 This is a flowchart illustrating the method for constructing a critical experimental process database for a critical experimental apparatus provided in an embodiment of this application. Figure 1 As shown, it includes the following steps: S10: Determine the operating conditions of the critical experimental setup under the current conditions, the effective neutron multiplication factor of the core of the critical experimental setup, and the neutron flux at the detector, which is located near the core; S20: Determine the calculated value of the core reactivity based on the effective neutron multiplication factor of the core; S30: Determine the measured value of the core reactivity based on the neutron flux determined by the detector; S40: Determine the core reactivity value used to construct the database based on the calculated value and the measured value; S50: Construct a critical experimental process database, which includes the core reactivity value determined in step S40 and the operating conditions of the critical experimental setup under the current conditions.

[0014] The method for constructing a critical experimental process database for a critical experimental device provided in the embodiments of this application can accurately measure and collect critical experimental process data of multiple critical states in real time by determining the operating conditions of the critical experimental device under the current conditions. This expands the scale of the critical experimental process database composed of these data, extends the data dimensions, and reduces the data acquisition cost. By comparing the calculated value of core reactivity with the measured value of core reactivity, the critical experimental process data of the critical experimental device can be automatically filtered, reducing the data filtering cost and clarifying the data filtering criteria, so that the database can verify the state under subcritical conditions of the reactor.

[0015] In some embodiments, in step S10, image recognition methods can be used to determine the operating conditions, including the number and arrangement of fuel elements in the reactor core, and the positions of control rods and external neutron sources can be obtained through position sensor signals. Using image recognition and other methods can reduce human error and lower the cost of database construction.

[0016] In some embodiments, in step S10, a neural network method can be used to determine in real time the effective neutron multiplication factor of the reactor core, the average neutron flux of the reactor core, and the neutron flux at the detector under the current operating conditions. Using a neural network method can reduce the time required to calculate parameters such as the effective neutron multiplication factor of the reactor.

[0017] In some embodiments, step S30 further includes the following steps: S31: determining the detector's response correction factor using a neural network method; S32: determining the detector's neutron count, and determining the core's neutron flux based on the neutron count.

[0018] Traditional methods assume a uniform neutron flux distribution in the reactor core, directly equating the detector signal to the global core flux without considering local neutron flux differences, leading to significant errors introduced by space effects. This application, however, determines the detector's response correction factor through neural network simulation, enabling the correction of neutron flux data obtained from multiple states and locations. It considers local neutron flux differences caused by core space variations, achieving accurate correction of the neutron flux data.

[0019] In some embodiments, in step S31, the response correction factor is determined as follows: ;in, Let be the response correction factor for the j-th detector at time t; The average neutron flux in the reactor core obtained from neural network simulation; Let be the neutron flux of the j-th detector at time t obtained from neural network simulation. This can reflect the mapping relationship between the neutron flux value at the detector location and the total reactor power, thereby enabling a more accurate determination of the true reactor core power.

[0020] In some embodiments, in step S32, the neutron flux of the reactor core is determined as follows: ,in, Let be the core neutron flux obtained from the count of the j-th detector; C is a constant characterizing the detector's detection efficiency, which is related to the properties of the detector itself. Let be the response correction factor for the j-th detector at time t; This is the count for the j-th detector. This allows for real-time correction of spatial effects caused by factors such as control rod movement, thereby reducing restrictions on the target core configuration and the requirements for detector placement.

[0021] In some embodiments, the core neutron fluxes calculated by different detectors can be compared using the Grubbs test. The mean and standard deviation of the core neutron fluxes from multiple detectors are used as a benchmark, with a significance level of α=0.01. Data with core neutron fluxes significantly different from the mean are discarded. The abnormal group is defined as follows: G is the Grubbs critical value, n is the number of detectors, and S represents the sample standard deviation of the average core neutron flux values ​​obtained from all detector locations. The average value of the remaining core neutron flux after removal is then calculated, and this average value represents the measured core reactivity.

[0022] In some embodiments, step S40 further includes: S41: determining the deviation between the calculated value and the measured value; S42: determining a threshold for comparing the deviation and a critical state for core processing; S43: determining a value for core reactivity used to construct the database based on the threshold and the critical state. By determining the threshold for comparing the deviation, data with excessive deviation can be eliminated, making the database data source more reliable.

[0023] In some embodiments, step S43 further includes: determining a threshold of 0.3%, determining that the reactor core is near a critical state, and determining the measured value as a numerical value for constructing the reactor core reactivity in the database. When the reactor core is near a critical state, the reactivity measurement results are generally more accurate; that is, the deviation between the measured and calculated values ​​of the reactor core reactivity data is usually small when the reactor core is near a critical state. Therefore, in this state, the threshold range needs to be set to the minimum to make the retained measurement data more accurate.

[0024] In some embodiments, step S43 further includes: determining a threshold of 0.3%-1%, determining that the reactor core is in a deep subcritical state, and determining the measured value as a numerical value for constructing the reactor core reactivity in the database. When the reactor core is in a deep subcritical state, the measured value of the reactor core reactivity data may deviate significantly from the calculated value due to reasons such as low detector counts. Therefore, it is necessary to appropriately widen the threshold range to ensure that the database data standards screened under different reactivity levels are consistent.

[0025] In some embodiments, step S43 further includes: determining a threshold of 3%, determining that the reactor core is in the control rod moving process, and determining the measured value as the core reactivity value used to construct the database. When the reactor core is in the control rod moving process, it can cause large fluctuations in local neutron flux, easily introducing additional systematic errors. Instantaneous changes can easily lead to larger deviations between the measured and calculated values ​​of the core reactivity data. Therefore, in this state, the threshold range needs to be set to its maximum accordingly.

[0026] By determining the deviation between the calculated and measured values ​​of core reactivity, and based on the deviation range, determining the core reactivity values ​​used to construct the database, it is possible to eliminate data introduced by factors such as noise based on different critical states of the core, thereby improving the reliability of the data in the database.

[0027] This application collects data from critical, subcritical, and slow-release supercritical reactors, enabling the acquisition of multiple sets of critical experimental data from a single critical experiment. This improves the efficiency of constructing a critical experimental process database and overcomes the problems of insufficient data scale, single data dimension, and high data acquisition cost in existing databases, providing a more reliable data foundation for the verification and development of reactor simulation programs.

[0028] In some embodiments, in step S50, the core reactivity value determined in step S43 can be marked and distinguished from the data measured under steady-state conditions.

[0029] Regarding the embodiments of this application, it should also be noted that, without conflict, the embodiments of this application and the features in the embodiments can be combined with each other to obtain new embodiments.

[0030] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. The scope of protection of this application shall be determined by the scope of the claims.

Claims

1. A method of constructing a critical experiment process database of a critical experiment device, characterized by, It comprises the following steps: S10: determining the operating condition of the critical experiment device, the effective neutron multiplication factor of the core of the critical experiment device, and the neutron flux at the detector, which is arranged near the core, under the current condition; S20: determining the calculated value of the core reactivity according to the effective neutron multiplication factor of the core; S30: determining the measured value of the core reactivity according to the neutron flux determined by the detector; S40: determining the numerical value of the core reactivity for constructing the database according to the calculated value and the measured value; S50: constructing the critical experiment process database, which comprises the numerical value of the core reactivity determined in step S40 and the operating condition of the critical experiment device under the current condition.

2. The method of claim 1, wherein, In step S10, The operating condition is determined by an image recognition method, and the operating condition comprises the number of fuel elements in the core and the arrangement mode of the fuel elements. The rod position of the control rod and the position of the external neutron source are obtained through the position sensor signal.

3. The method of claim 1, wherein, In step S10, the effective neutron multiplication factor of the core, the average neutron flux of the core, and the neutron flux at the detector under the current operating condition are determined in real time by a neural network method.

4. The method of claim 1, wherein, In step S30, the following steps are further included: S31: determining the response correction factor of the detector by a neural network method; S32: determining the neutron count of the detector, and determining the neutron flux of the core according to the neutron count.

5. The method of claim 4, wherein, In step S31, the response correction factor is determined by the following method: ； wherein, is the response correction factor for the jth detector at time t; core average neutron flux obtained by neural network simulation; The jth detector neutron flux at time t from the neural network simulation.

6. The method of claim 5, wherein, In step S32, the neutron flux of the core is determined by the following method: ； wherein, Pj is the core neutron flux from the count of the jth detector; C is a constant representing the detection efficiency of the detector, which is related to the properties of the detector itself; Rj(t) is the response modifier for the jth detector at time t; Count for the jth detector.

7. The method of claim 1, wherein, In step S40, the following steps are further included: S41: determining the deviation between the calculated value and the measured value, S42: determining the threshold for comparing the deviation and the critical state of the core treatment; S43: determining the numerical value of the core reactivity for constructing the database according to the threshold and the critical state.

8. The method of claim 7, wherein, In step S43, the following steps are further included: The threshold is determined to be 0.3%, the core is determined to be in a near-critical state, and the measured value is determined to be the numerical value of the core reactivity for constructing the database.

9. The method of claim 7, wherein, In step S43, the following steps are further included: The threshold is determined to be 0.3%-1%, the core is determined to be in a deep near-critical state, and the measured value is determined to be the numerical value of the core reactivity for constructing the database.

10. The method of claim 7, wherein, In step S43, the following steps are further included: determining that the threshold value is 3% and determining that the core is in a control rod movement process, determining the measured value as a value for constructing the core reactivity in the database.

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