A reference experiment sequence recommendation method and system

Abstract

The present application relates to a kind of benchmark experiment sequence recommendation method, comprising the following steps: establishing sensitivity database;The sensitivity nuclide of target device is obtained;The similarity of sensitivity spectrum corresponding to the sensitivity nuclide of target device in sensitivity database is calculated;According to the calculation result of sensitivity spectrum similarity, find out the benchmark experiment for verifying each sensitivity nuclide, take the union and obtain the test sequence of entire multi-group constant library.The present application also provides a kind of benchmark experiment sequence recommendation system, using the benchmark experiment sequence recommendation method and system described in the present application can realize the full automation of calculation, selection, drawing and other processes, save a lot of manual input parameter, extract result and analysis time, greatly improve the efficiency of macroscopic test.

Description

Technical Field

[0001] This invention belongs to the field of sequence recommendation technology, specifically relating to a method and system for recommending benchmark experimental sequences. Background Technology

[0002] After constructing a corresponding multi-group constant library for a target device, macroscopic verification is required to validate its accuracy and reliability. This involves modeling, calculating, and analyzing a sufficient number of suitable benchmark experiments to assess the quality of the multi-group constant library. Regarding the selection of benchmark experiments to form a verification sequence, thousands of benchmark experiments have been established internationally, including the International Critical Safety Benchmark Evaluation Manual (ICSBEP), which categorizes benchmark experiments according to information such as fissile materials, physical models, and energy spectra. However, applying a large number of benchmark experiments for verification is costly and unnecessary; typically, benchmark experiments are selected based on the specific type of the target device.

[0003] Traditional selection methods rely on a series of characteristics of the target device, such as nuclide composition, chemical and physical structure, moderation level, and EALF value. This method heavily depends on expert experience and introduces personal bias. Because it relies on these characteristics, the selected benchmark experiments have significant limitations. For example, for a multigroup constant library built for fast reactors, thermal spectrometers are generally not selected for verification, and the selection process cannot be automated. To overcome the shortcomings of traditional methods, similarity analysis methods have been established internationally for benchmark experiment selection. These include the TSUNAMI-IP similarity analysis program developed by Oak Ridge National Laboratory (ORNL) in the United States, which recommends verification sequences by calculating similarity indices; Uppsala University in Sweden's proposed benchmark device selection method based on random sampling, using the Total Monte Carlo method to calculate the Pearson coefficient to characterize the similarity between the target device and the benchmark experiment, as well as among the benchmark experiments themselves; and Argonne National Laboratory (ANL) in the United States using the Representative Factor to screen similar devices and validate ABTR. These methods calculate the similarity between devices by processing the sensitivity of the effective multiplication factor to nuclear data and the covariance data of nuclear data, providing a quantitative standard for benchmark experiment selection.

[0004] However, the covariance data of the introduced kernel data is not mature enough, so the reliability of the calculated similarity will be reduced, and the selected benchmark experiments still have certain limitations. In addition, the above method does not realize the automated selection process, and the efficiency of selecting benchmark experiments by calculating similarity comparison one by one is low. Summary of the Invention

[0005] To address the shortcomings of existing designs, the purpose of this invention is to provide a benchmark experimental sequence recommendation method and system to overcome the limitations imposed by covariance data maturity and to quickly and efficiently generate recommended test sequences.

[0006] To achieve the above objectives, the technical solution adopted by this invention is: a method for recommending benchmark experimental sequences, comprising the steps of: establishing a sensitivity database; acquiring sensitive nuclides of the target device; calculating the sensitivity spectrum similarity of the corresponding sensitive nuclides of the target device in the sensitivity database; and, based on the calculation results of the sensitivity spectrum similarity, finding benchmark experiments for verifying each sensitive nuclide, and taking the union of the results to obtain the test sequence of the entire multi-group constant library.

[0007] Furthermore, the acquisition of sensitive nuclides of the target device includes: acquiring the sensitivity of all nuclides, reaction types, and energy groups of the target device; sorting the sensitivity of all nuclides, reaction types, and energy groups according to their contribution to the total sensitivity of all sensitivities; and determining the sensitive nuclides of the target device.

[0008] Furthermore, the establishment of the sensitivity database includes: calculating the sensitivity of benchmark experiments; and establishing a sensitivity database that can be linked and retrieved.

[0009] Furthermore, the calculation of the sensitivity spectrum similarity of the corresponding sensitive nuclide of the target device in the sensitivity database includes: searching the sensitivity database using the sensitive nuclide to obtain the corresponding benchmark experiment; and calculating the sensitivity spectrum similarity of the corresponding benchmark experiment.

[0010] Furthermore, in the process of finding benchmark experiments to verify each sensitive nuclide based on the calculation results of sensitivity spectrum similarity, and taking the union of the results to obtain the test sequence of the entire multigroup constant library, the top 20 benchmark experiments with similarity ranking and similarity greater than 0.6 are selected as the recommended sequence.

[0011] Furthermore, the sorting method based on the contribution of each sensitivity to the total of all sensitivities is as follows: sorting is based on the absolute value of each sensitivity.

[0012] Furthermore, the method for calculating the sensitivity spectrum similarity of the corresponding benchmark experiment is as follows:

[0013]

[0014] Where vector T represents the sensitive nuclide in the target device as a vector; vector B represents the device containing the reaction corresponding to the sensitive nuclide in the target device from all basic experiments in the sensitivity database as a vector; and E... k The closer the similarity is to 1, the higher the similarity between the devices.

[0015] Furthermore, when establishing the sensitivity database, the benchmark experiments in the International Critical Safety Benchmark Evaluation Experiment Manual are modeled and their sensitivity is calculated.

[0016] Furthermore, the benchmark experiments in the International Critical Safety Benchmark Evaluation Experiment Manual were modeled and sensitivity calculations were performed using the Monte Carlo program.

[0017] This invention also provides a benchmark experiment sequence recommendation system, comprising: a database establishment unit for establishing a sensitivity database; a sensitive nuclide acquisition unit for acquiring sensitive nuclides of a target device; a similarity calculation unit for calculating the sensitivity spectrum similarity of the corresponding sensitive nuclides of the target device in the sensitivity database; and a recommendation unit for finding benchmark experiments to verify each sensitive nuclide based on the sensitivity spectrum similarity calculation results, and taking the union of the results to obtain the test sequence of the entire multi-group constant library.

[0018] The advantages of this invention are as follows: Sensitive nuclides are identified through sensitivity analysis of the target device. By searching the sensitivity database, calculating the similarity of sensitivity spectra, and plotting sensitivity spectrum curves, the similarity between the selected benchmark experiments and the sensitive nuclides of the target device can be intuitively observed, automatically generating a benchmark experiment sequence for macroscopic verification of multi-group constant libraries. This not only solves the shortcomings of traditional methods in lacking quantitative standards but also avoids the limitations introduced by nuclear data covariance. Furthermore, it automates the calculation, selection, and plotting processes, saving significant time spent on manual parameter input, result extraction, and analysis, thus greatly improving the efficiency of macroscopic verification. Attached Figure Description

[0019] Figure 1 A flowchart illustrating the steps of a benchmark experiment sequence recommendation method provided by this invention;

[0020] Figure 2 This is a schematic diagram of a module for a recommended method in benchmark experiments;

[0021] Figure 3 This is a comparison chart of the sensitivity spectra of the benchmark experiments selected for CEFR-U-235-n, gamma. Detailed Implementation

[0022] The present invention will now be further described with reference to the accompanying drawings and specific embodiments.

[0023] like Figure 1-2 As shown, the present invention provides a method for recommending benchmark experimental sequences, comprising the following steps:

[0024] S1, Establish a sensitivity database;

[0025] Specifically, the benchmark tests in the critical safety benchmark evaluation test manual are modeled and their sensitivity is calculated to obtain the corresponding sensitivity and uncertainty. The data are then correlated and stored in a unified database to form a sensitivity database.

[0026] In this embodiment, the Monte Carlo program is used to model and calculate the sensitivity of benchmark experiments in the International Critical Safety Benchmark Evaluation Manual (ICSBEP), obtaining the sensitivity and uncertainty of the effective proliferation factor for each "nuclide-reaction type-energy group". These data are then correlated and stored in a unified database. This allows for the retrieval of benchmark experiments by searching different keywords, such as nuclide, reaction type, and energy group, to find sensitivity data for different benchmark experiments, which can then be used for subsequent calculations and comparisons of benchmark experiment priorities.

[0027] S2, acquire the sensitive nuclide of the target device;

[0028] Specifically, the target device is modeled and its sensitivity analyzed to obtain the sensitivities of all nuclides, reaction types, and energy groups. By sorting all sensitivities according to their contribution to the total sensitivities, the most sensitive nuclides and reaction types can be identified, i.e., the sensitive nuclides, which can be used as criteria for searching in the sensitivity database.

[0029] In this embodiment, step S2 includes the following steps:

[0030] S21, obtain all nuclides, reaction types, and energy group sensitivity of the target device;

[0031] S22, sort all nuclides, reaction types and energy groups according to their contribution to the total sensitivity of all sensitivities;

[0032] S23, a sensitive nuclide for target positioning devices;

[0033] It should be noted that the method of obtaining the sensitivity of all nuclides, reaction types and energy groups of the target device in step S21 is the same as the method of modeling and sensitivity calculation of the benchmark test in the critical safety benchmark evaluation test manual in step S1 to obtain the corresponding sensitivity and uncertainty, and will not be repeated here.

[0034] In step S22, the sorting method based on the contribution of each sensitivity to the total of all sensitivities is as follows: sorting is done according to the absolute value of each sensitivity.

[0035] In step S23, the sensitive nuclides of the target device can be determined according to actual needs. That is, the number of nuclides selected according to actual needs can be used as the sensitive nuclides.

[0036] S3, calculate the sensitivity spectrum similarity of the corresponding sensitive nuclides of the target device in the sensitivity database;

[0037] Specifically, in any device, the curve showing the sensitivity of each nuclide and reaction type as a function of energy groups is called the sensitivity spectrum. After determining the sensitive nuclide of the target device through step S2, a macroscopic verification of the multi-group constant library is performed. This requires selecting several devices from numerous benchmark experiments that best verify the sensitive nuclide of the target device, i.e., identifying several devices with the most similar sensitivity spectra. Therefore, a specific sensitive nuclide of the target device is used as a selection criterion, such as "Fe-56-inelastic". The sensitivity of this nuclide-reaction is taken as a vector T. A device containing this reaction in all benchmark experiments in the sensitivity database is taken as another vector B. By calculating the cosine of vector T and vector B, the similarity between the target device and the benchmark experiment is determined.

[0038] The calculation method is as follows:

[0039]

[0040] Among them, E k The closer the similarity is to 1, the higher the similarity between the devices.

[0041] S4. Based on the calculation results of sensitivity spectrum similarity, find the benchmark experiment used to verify each sensitive nuclide, and take the union to obtain the test sequence of the entire multigroup constant library.

[0042] Specifically, using the sensitive nuclides of the target device obtained in step S2 as search criteria, a search is performed in the sensitivity database. By repeatedly executing the sensitivity spectrum similarity calculation in step S3, several benchmark experiments for verifying each sensitive nuclide are identified. Finally, the union of these experiments is used to obtain the test sequence of the entire multigroup constant library.

[0043] Furthermore, step S1 also includes the step of:

[0044] S11, Calculation of sensitivity in benchmark experiments;

[0045] Specifically, the sensitivity calculation was performed using the Monte Carlo program, and the sensitivity calculation settings were added by modifying the input card parameters.

[0046] In this embodiment, the energy group structures of 14 typical reaction types and 33 groups were calculated, as shown in Tables 1-2.

[0047]

[0048]

[0049] Table 1 Calculation of Reaction Type

[0050]

[0051] Table 2. Neutron energy group structure of group 33

[0052] S12, Establish a sensitivity database that can be linked and retrieved;

[0053] Specifically, the sensitivity data is stored using a relational database MySQL. Two forms were designed. The first form specifies the sensitivity of each subgroup and includes six fields: baseline experiment name (bname), nuclide name (iso), reaction type (mt), energy group (ng), sensitivity value (sens), and uncertainty value (uncty). The second form specifies the sensitivity of each reaction type and includes four fields: baseline experiment name (bname), nuclide name (iso), reaction type (mt), and sensitivity value (sumSens).

[0054] Furthermore, step S3 also includes the following steps:

[0055] S31, use sensitive nuclides to search in the sensitivity database to obtain the corresponding benchmark experiment;

[0056] Specifically, after performing sensitivity calculations on the target device and obtaining the sensitive nuclides, the MySQL database is searched to find all benchmark experiments containing these "nuclide-reaction types," and the results are output to a file for similarity calculation.

[0057] The process is as follows: First, generate a query statement based on the required search conditions. Then, connect to the database, execute the query statement, and output the results to the file.

[0058] S32, calculate the sensitivity spectrum similarity of the corresponding benchmark experiment;

[0059] In this embodiment, the similarity between the target device and the benchmark experiment is visually illustrated through drawings, such as... Figure 3 As shown, the plotting is achieved by calling the third-party library matplotlib.

[0060] Furthermore, in step S4, the top 20 benchmark experiments with similarity ranking and similarity greater than 0.6 are selected as recommended sequences.

[0061] This invention also provides a benchmark experiment sequence recommendation system, comprising:

[0062] The database creation unit is used to create a sensitivity database;

[0063] Sensitive nuclide acquisition unit, used to acquire sensitive nuclides of the target device;

[0064] The similarity calculation unit is used to calculate the sensitivity spectrum similarity of the corresponding sensitive nuclides of the target device in the sensitivity database;

[0065] The recommendation unit is used to find the benchmark experiment for verifying each sensitive nuclide based on the calculation results of the sensitivity spectrum similarity, and take the union to obtain the test sequence of the entire multigroup constant library.

[0066] As can be seen from the above embodiments, this invention determines sensitive nuclides through sensitivity analysis of the target device. By searching the sensitivity database, calculating the sensitivity spectrum similarity, and plotting the sensitivity spectrum curves, the similarity between the selected benchmark experiments and the sensitive nuclides of the target device can be intuitively observed, and a benchmark experiment sequence for macroscopic verification of multi-group constant libraries can be automatically generated. This not only solves the shortcomings of traditional methods in lacking quantitative standards but also avoids the limitations introduced by nuclear data covariance. Furthermore, it automates the calculation, selection, and plotting processes, saving significant time on manual parameter input, result extraction, and analysis, thus greatly improving the efficiency of macroscopic verification.

[0067] The methods and systems described in this invention are not limited to the embodiments described in the specific implementation. Other implementation methods derived by those skilled in the art based on the technical solutions of this invention also fall within the scope of technical innovation of this invention.

Claims

1. A method for recommending benchmark experimental sequences, characterized in that, include: Establish a sensitivity database, including: calculation of baseline experimental sensitivity, and establishment of a sensitivity database that can be linked and searched; Acquire the sensitive nuclides of the target device; Calculating the sensitivity spectrum similarity of the sensitive nuclides corresponding to the target device in the sensitivity database includes: searching the sensitivity database using the sensitive nuclides to obtain the corresponding benchmark experiments; and calculating the sensitivity spectrum similarity of the corresponding benchmark experiments. Based on the calculation results of sensitivity spectrum similarity, the benchmark experiment used to verify each sensitive nuclide is identified, and the union of the results is used to obtain the test sequence for the entire multigroup constant library.

2. The benchmark experiment sequence recommendation method as described in claim 1, characterized in that, The sensitive nuclides used to acquire the target device include: Obtain all nuclides, reaction types, and energy group sensitivities of the target device; The sensitivities of all nuclides, reaction types, and energy groups are ranked according to their contribution to the overall sensitivities. Identify the sensitive nuclides of the target device.

3. The benchmark experiment sequence recommendation method as described in claim 1, characterized in that: Based on the calculation results of sensitivity spectrum similarity, benchmark experiments for verifying each sensitive nuclide are identified, and the union of these experiments is used to obtain the test sequence of the entire multigroup constant library. The top 20 benchmark experiments with similarity ranking and similarity greater than 0.6 are selected as the recommended sequence.

4. The benchmark experiment sequence recommendation method as described in claim 2, characterized in that: The sorting method based on the contribution of each sensitivity to the total of all sensitivities is as follows: sorting is done according to the absolute value of each sensitivity.

5. The benchmark experiment sequence recommendation method as described in claim 1, characterized in that... : The method for calculating the sensitivity spectrum similarity of the corresponding benchmark experiment is as follows: Where vector T represents the sensitive nuclide in the target device as a vector; vector B represents the device containing the reaction corresponding to the sensitive nuclide in the target device from all basic experiments in the sensitivity database as a vector; and E... k The closer the similarity is to 1, the higher the similarity between the devices.

6. The benchmark experiment sequence recommendation method as described in claim 1, characterized in that... : When establishing the sensitivity database, the benchmark experiments in the International Critical Safety Benchmark Evaluation Experiment Manual are modeled and their sensitivity is calculated.

7. The benchmark experiment sequence recommendation method as described in claim 6, characterized in that: Modeling and sensitivity calculations of the benchmark experiments in the International Critical Safety Benchmark Evaluation Experiment Manual were performed using the Monte Carlo program.

8. A benchmark experiment sequence recommendation system, characterized in that, include: The database creation unit is used to create a sensitivity database; Sensitive nuclide acquisition unit, used to acquire sensitive nuclides of the target device; The similarity calculation unit is used to calculate the sensitivity spectrum similarity of the corresponding sensitive nuclides of the target device in the sensitivity database; The recommendation unit is used to find the benchmark experiment for verifying each sensitive nuclide based on the calculation results of the sensitivity spectrum similarity, and take the union to obtain the test sequence of the entire multigroup constant library.

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