Critical parameter prediction method, readable storage medium and device

Abstract

The invention discloses a critical parameter prediction method, a readable storage medium and a device, and belongs to the technical field of nuclear systems, and the prediction method comprises the steps: determining a to-be-predicted new nuclear system and a calculated value of an existing critical experiment; calculating sensitivity vectors of the new nuclear system and the critical experiments to the nuclear data; on the basis of the calculated value and the sensitivity vector, in combination with the relationship between the experimental value of the existing experiment, the experimental uncertainty and the critical experimental coefficient, constructing a calculation formula of the critical parameter predicted value and the predicted uncertainty of the new nuclear system; solving a coefficient of a critical experiment enabling the uncertainty of the prediction parameters to be minimum; and substituting the coefficient into the two calculation formulas to obtain a predicted value and the uncertainty thereof. The method is stored in the readable storage medium. The device comprises a terminal program for executing the steps of the method. According to the method, the information of a plurality of critical experiments can be effectively integrated, the critical parameters can be more reliably and accurately predicted under the condition of lack of very similar critical experiments, and the uncertainty of the prediction parameters can be more effectively compressed.

Description

Technical Field

[0001] This application belongs to the field of nuclear system technology, and in particular relates to a method for predicting critical parameters, a readable storage medium, and an apparatus. Background Technology

[0002] In the processes of neutronics design and criticality safety assessment of nuclear systems, it is necessary to predict key neutronics parameters of the yet-to-be-established nuclear system. The prediction uncertainty directly determines the reliability of the nuclear system design and the safety of fissile material handling. Calculation parameters (such as k) provided solely through numerical simulations are insufficient. eff While the accuracy of the effective multiplication factor has gradually improved, the reliability of predictions based solely on numerical simulations cannot match that of predictions based on experimental corrections. Furthermore, the accuracy of existing theoretical calculations still lags significantly behind experimental accuracy. Predicting critical parameters in new nuclear systems and fissile material operations based on critical experiments—verifying theoretical calculation deviations while reducing the uncertainty of predicted parameters—remains the most reliable and accurate method.

[0003] Currently, there are two main types of critical parameter prediction methods based on critical experiments: one is prediction through trend analysis, and the other is prediction based on sensitivity analysis.

[0004] Critical parameter prediction methods based on trend analysis extract key characteristic parameters of nuclear systems (such as the types and amounts of fissile nuclides, and the ratio of fissile material to moderator material). These parameters are then analyzed to examine the computational biases of critical experiments with similar characteristic parameters and their trends, leading to the prediction of critical parameters for the nuclear system. However, this approach suffers from the inherent differences between critical experiments and new nuclear systems. The relationship between computational biases and characteristic parameters is not entirely consistent across different critical systems, making the prediction of critical parameters based on trend analysis unreliable and difficult to quantify. Only when critical experiments and new nuclear systems are sufficiently similar can this method achieve good results.

[0005] A sensitivity-based critical parameter prediction and uncertainty reduction method can provide the uncertainty of the predicted parameters by establishing a quantitative method for predicting critical parameters and their uncertainties. If the critical experiment used to reduce the uncertainty of critical parameters is sufficiently similar to the new nuclear system, the prediction uncertainty of critical parameters can be significantly reduced. However, this method can only predict and reduce the uncertainty of critical parameters of a nuclear system through a single critical experiment, and the prediction accuracy directly depends on the similarity between the critical experiment and the new nuclear system. When a critical experiment that is very similar to the new nuclear system is lacking, the parameter prediction accuracy is generally low, and the uncertainty of the predicted parameters cannot be effectively reduced. Summary of the Invention

[0006] This application aims to address the technical problem of effectively predicting key neutronics parameters of nuclear systems based on critical experiments in the absence of similar experiments. To this end, this application provides a method, readable storage medium, and device for predicting critical parameters, which can effectively integrate information from multiple critical experiments and predict critical parameters more reliably and accurately in the absence of very similar critical experiments, while also more effectively reducing the uncertainty of the predicted parameters.

[0007] In a first aspect, embodiments of this application provide a method for predicting critical parameters, comprising:

[0008] Calculate the critical parameters of the new nuclear system H to be predicted, and calculate the critical parameters of several existing critical experiments; the calculated value of the new nuclear system H is denoted as C. H Number the multiple existing critical experiments 1-N; denot the calculated value corresponding to critical experiment i as C. i , i∈[1,N];

[0009] Calculate the critical parameters of the novel nuclear system H to be predicted and the sensitivity vectors of multiple existing critical experiments to nuclear data; where the sensitivity vector corresponding to the novel nuclear system H is S. H The sensitivity vector corresponding to the critical experiment i is S. i ;

[0010] Based on the calculated value C corresponding to each critical experiment i i Sensitivity vector S i And the calculated value C corresponding to the new core system. H Sensitivity vector S H By combining existing experimental values, experimental uncertainties, and coefficients from critical experiments, predicted values ​​Y for the critical parameters of the new nuclear system H are constructed. H The calculation formula and the uncertainty of the predicted value u(Y) H The calculation formula for )

[0011] Based on the predicted value Y H and the uncertainty of the predicted value u(Y) H The formula for calculating the coefficients of the critical experiment that minimizes the uncertainty of the prediction parameters is used.

[0012] Substitute the obtained coefficients of the critical experiment into the predicted value Y. H The calculation formula and the uncertainty of the predicted value u(Y) H The formula for calculating Y is used to obtain the predicted value Y. H and the uncertainty of the predicted value u(Y) H ).

[0013] In some implementations, the predicted value Y H The formula for calculation is:

[0014]

[0015] Among them, a i E is the coefficient of the critical experiment, dimensionless; i This is the experimental value of critical experiment i.

[0016] In some implementations, the uncertainty of the predicted value u(Y) H The formula for calculating ) is:

[0017]

[0018] Among them, a i E is the coefficient of the critical experiment, dimensionless; i The experimental value of critical experiment i; u(E) i ) represents the experimental uncertainty of critical experiment i; ∑ represents the covariance matrix of the nuclear reaction cross section.

[0019] In some implementations, the experimental values ​​of the critical experiment are E1, E2, ... E N And experimental uncertainty u(E1), u(E2), ... u(E N The results were obtained through an integral experiment database and publicly available literature.

[0020] In some implementations, the calculation of parameters for the new nuclear system and existing critical experiments is based on the same nuclear reaction cross-section library, and the calculations are performed using a Monte Carlo-based program, with the calculation uncertainty controlled to be one order of magnitude or more less than the experimental uncertainty.

[0021] In some implementations, the coefficients of the critical experiment that minimizes the uncertainty of the prediction parameters are calculated using the following mathematical methods: partial derivative method, gradient descent method, Newton's method, quasi-Newton method, or machine learning-based optimization method.

[0022] In some implementations, the critical parameter to be predicted includes the effective proliferation factor k. eff The value of the transient neutron decay constant or the sample reactivity.

[0023] In some implementations, the critical experiment includes measuring k. eff The critical experiments, as well as the neutron integral experiments for measuring the transient neutron decay constant, reaction rate ratio, shielding, and reactive perturbation.

[0024] Secondly, embodiments of this application provide a readable storage medium storing a computer program, which, when executed, performs the critical parameter prediction method described above.

[0025] Thirdly, embodiments of this application provide a critical parameter prediction device, including a memory, a processor, and a terminal program stored in the memory and executable in the processor, wherein the terminal program includes steps corresponding to the critical parameter prediction method described above.

[0026] As can be seen from the above technical solution, the beneficial effects of this application are as follows:

[0027] 1. The method of this application can provide predictive parameters for designers or critical safety analysts of new nuclear systems, enabling them to obtain high-precision prediction results and reliable uncertainty assessments even in the absence of highly similar critical experiments. Specifically, based on the new nuclear system and existing critical experiments, calculated values ​​are determined, and then a sensitivity vector for nuclear data is established. This effectively integrates information from multiple critical experiments, using sensitivity vectors to overcome the limitations of traditional methods in quantitatively assessing differences in nuclear system characteristics, improving prediction specificity and basic accuracy, achieving comprehensive utilization of a large amount of experimental information, and more effectively reducing the uncertainty of prediction parameters. By solving for the coefficients of the critical experiment that minimizes the uncertainty of prediction parameters, the uncertainty of prediction parameters is compressed to the greatest extent, achieving reliable prediction of critical parameters for new nuclear systems. In the absence of highly similar critical experiments, critical parameters are predicted more reliably and accurately, while the uncertainty of prediction parameters is more effectively compressed, providing strong support for the safety of nuclear system design and the feasibility of engineering implementation, enhancing confidence in the critical design of new nuclear systems, and more effectively ensuring critical safety.

[0028] 2. The readable storage medium of this application stores the prediction method of critical parameters in the form of a program, providing a storage medium to facilitate the application of the method. This enables its application in various scenarios and electronic devices, thereby achieving the prediction of critical parameters and the rapid determination of critical parameters and uncertainties.

[0029] 3. The device of this application solidifies the critical parameter prediction method into an executable terminal program. The terminal program executes the steps corresponding to the critical parameter prediction method. It is integrated into hardware along with the memory and processor. The hardware device facilitates the actual operation and execution of the method. It can quickly predict critical parameters based on physical equipment and intuitively obtain the prediction calculation results of the critical parameters of the new core system, providing a reliable basis for accurately and effectively predicting the predicted value and uncertainty of the new core system. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced one by one below. Obviously, the accompanying drawings described below are some embodiments of this application. For those skilled in the art, other embodiments and drawings can be obtained based on these drawings without creative effort. The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be decomposed, while some operations / steps can be combined or partially combined. Therefore, the actual execution order may change according to the actual situation.

[0031] Figure 1 A schematic flowchart of an embodiment of the critical parameter prediction method of the present invention is shown. Detailed Implementation

[0032] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. The detailed description of the embodiments of this application provided in the drawings is not intended to limit the scope of the claimed application. The described embodiments are only a part of the embodiments of this application, not all of them. Based on the embodiments in this application, they can be arranged and designed in various different configurations. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0033] This application is described below with reference to the accompanying drawings and specific embodiments:

[0034] A first aspect of this application provides a method for predicting critical parameters, comprising:

[0035] S1. Determine the calculated values ​​of the critical parameters of the new nuclear system H to be predicted, and the calculated values ​​of the critical parameters of multiple existing critical experiments; the calculated value of the new nuclear system H is denoted as C. H Number the multiple existing critical experiments 1-N; denot the calculated value corresponding to critical experiment i as C. i , i∈[1,N].

[0036] S2. Calculate the sensitivity vectors of the new nuclear system H to be predicted and multiple existing critical experiments to nuclear data; where the sensitivity vector corresponding to the new nuclear system H is S. H The sensitivity vector corresponding to the critical experiment i is S. i The sensitivity vectors for multiple critical experiments are: S1, S2, ... S N Sensitivity analysis can be performed based on perturbation theory or sampling statistics methods.

[0037] S3, Calculated value C based on each critical experiment ii (C1,C2,…C N Sensitivity vector S i (S1,S2,…S N ), and the calculated value C corresponding to the new core system. H Sensitivity vector S H By combining existing experimental values, experimental uncertainties, and coefficients from critical experiments, predicted values ​​Y for the critical parameters of the new nuclear system H are constructed. H The calculation formula and the uncertainty of the predicted value u(Y) H The calculation formula for ).

[0038] S4, Based on the predicted value Y H and the uncertainty of the predicted value u(Y) H The formula for calculating the coefficients of the critical experiment that minimizes the prediction uncertainty is used.

[0039] S5. Substitute the obtained coefficients of the critical experiment into the predicted value Y. H The calculation formula and the uncertainty of the predicted value u(Y) H The formula for calculating Y is used to obtain the predicted value Y. H and the uncertainty of the predicted value u(Y) H ).

[0040] Existing techniques, whether trend-based or sensitivity-based combined with single critical experiments, cannot reliably predict critical parameters of nuclear systems and reduce prediction uncertainties in the absence of highly similar critical experiments. The computational biases in existing theoretical calculations can originate from both the program (method) and the input parameters (nuclear data). By evaluating these computational biases, the reliability of critical parameter predictions can be improved, while simultaneously reducing uncertainty. With the gradual improvement of programs, especially the development of high-precision particle transport calculation programs based on the Monte Carlo method, the computational biases introduced by the program are negligible compared to those introduced by the nuclear data. Therefore, it can be approximated that the critical calculation bias is mainly introduced by the nuclear data; thus, the focus should be on reducing the uncertainty in critical parameter predictions introduced by the nuclear data. The computational bias ΔC of the new nuclear system H... H It can be approximately expressed as equation (1):

[0041] ΔC H ≈S H ·Δσ(1)

[0042] Where Δσ is the cross-sectional deviation.

[0043] This application addresses the issue of reducing prediction uncertainty introduced by nuclear data. It provides predictive parameters for designers of new nuclear systems or critical safety analysts, enabling high-precision predictions and reliable uncertainty assessments even in the absence of highly similar critical experiments. Specifically, it calculates the critical parameters of the new nuclear system and the calculated values ​​from existing critical experiments, then establishes a sensitivity vector for the nuclear data. This effectively integrates information from multiple critical experiments, overcoming the limitations of traditional methods in quantitatively assessing differences in nuclear system characteristics. This improves the specificity and accuracy of predictions, enabling comprehensive utilization of a large amount of experimental information and more effectively reducing the uncertainty of prediction parameters. By solving for the coefficients of the critical experiment that minimizes the uncertainty of prediction parameters, it maximizes the reduction of prediction parameter uncertainty, achieving reliable prediction of the critical parameters of the new nuclear system. Even in the absence of highly similar critical experiments, it provides more reliable and accurate predictions of critical parameters, while more effectively reducing the uncertainty of prediction parameters. This enhances confidence in the critical design of the new nuclear system and more effectively ensures critical safety, providing strong support for the safety of nuclear system design and the feasibility of engineering implementation.

[0044] In some implementations, the predicted value Y H The formula for calculation is:

[0045]

[0046] Among them, a i E is the coefficient of the critical experiment, dimensionless; i This is the experimental value of critical experiment i.

[0047] In some implementations, the uncertainty of the predicted value u(Y) H The formula for calculating ) is:

[0048]

[0049] Among them, a i E is the coefficient of the critical experiment, dimensionless; i The experimental value of critical experiment i; u(E) i ) represents the experimental uncertainty of critical experiment i; ∑ represents the covariance matrix of the nuclear reaction cross section.

[0050] In some implementations, the experimental values ​​of the critical experiment are E1, E2, ... E N And experimental uncertainty u(E1), u(E2), ... u(E NThe data were obtained through integral experimental databases such as the criticality experimental database ICSBEP (International Criticality Safety Benchmark Evaluation Project) manual, the reactor physics experimental database IRPhEP (International Reactor Physics Experiment Evaluation Project) manual, and the SINBAD shielding experimental database. Publicly available literature included industry-related documents such as ENDF-202 (Cross Section Evaluation Working Group Benchmark Specifications).

[0051] In some implementations, the parameters and calculations for both the new nuclear system and existing critical experiments are based on the same nuclear reaction cross-section library. The Monte Carlo particle transport program must be used for calculations. This program simulates the motion, fission, scattering, and other interactions of particles such as neutrons within the nuclear system (including the new nuclear system H to be predicted and the nuclear system corresponding to the existing critical experiment), thereby calculating the critical parameters of the nuclear system (such as keff, the transient neutron decay constant, etc.).

[0052] The assumption of this application is based on the same nuclear reaction cross-section library. In the prediction of critical parameters of nuclear systems, theoretical calculation errors mainly originate from two aspects: the program (method) and the input parameters (nuclear data). The calculation error introduced by the program is much smaller than that introduced by the nuclear data. Therefore, the critical calculation error is mainly introduced by the nuclear data (such as nuclear reaction cross-sections). In some implementations, the calculation needs to be performed using the Monte Carlo method, and the calculation uncertainty (excluding the uncertainty introduced by the nuclear data) needs to be controlled to be one order of magnitude or more less than the experimental uncertainty. For this purpose, a sufficient number of particles needs to be used to control the statistical uncertainty, laying the foundation for the subsequent critical parameter prediction steps.

[0053] In some implementations, in formulas (2) and (3) above, it is necessary to determine the coefficient 'a' for each critical experiment. i Let u(Y H To minimize the uncertainty of prediction parameters, a convenient and cost-efficient calculation method is introduced below.

[0054] Solve for the uncertainty of the prediction parameters u(Y) H When the coefficients of the critical experiment reach their minimum value, first establish the uncertainty of the prediction parameter (u(Y)). H The square of the coefficient a with respect to the critical experiment iThe function g(A) is given, and the coefficients a that minimize it are found using classical differential methods (partial derivative method). i The uncertainty of the predicted value u(Y) H The function under the square root of formula (3) is denoted as g(A), and the specific function g(A) is:

[0055]

[0056] Where A is a i The vector formed; E i The experimental value of critical experiment i; u(E) i ) represents the experimental uncertainty of critical experiment i; ∑ represents the covariance matrix of the nuclear reaction cross section.

[0057] In some implementations, when solving for the function g(A), the derivative of the function g(A) is taken, and the derivative is set to 0 to obtain the coefficient a with respect to the critical experiment. i Solving the expression yields the coefficients 'a' for all critical experiments. i .

[0058] The solution process is as follows:

[0059]

[0060]

[0061] The coefficient a of the critical experiment can be obtained by solving the linear equation system (8). i Substituting it into formula (2) yields the prediction parameter Y. H Substituting into formula (3), we can obtain the uncertainty of the predicted value u(Y). H ).

[0062] The following introduces another method to solve for the uncertainty of the prediction parameters u(Y). H The method for determining the coefficients of the critical experiment that reach their minimum value is the gradient descent method. An initial value A0 is selected for the critical experiment coefficients, and the convergent critical experiment coefficients are obtained through iteration. For iteration step j, the uncertainty u(Y) of the predicted parameters is calculated. H The critical experimental coefficient A obtained in the previous step (j-1 step) j-1 The gradient at the given point is used to update the critical experimental coefficient A for this iteration step. j As shown in equation (9), where η is the learning rate, which can be selected according to the specific problem (affecting whether convergence occurs and the convergence speed). For gradient operators, For in A j-1 at u(Y H The gradient of ) can be obtained after the convergence requirement is met, which makes the uncertainty of the prediction parameter u(Y) known. H The smallest critical experimental coefficient.

[0063]

[0064] In some implementations, the critical parameter to be predicted includes the effective proliferation factor k. eff The transient neutron decay constant or sample reactivity value. In some embodiments, the critical experiment includes measuring the k... eff The criticality experiments, as well as neutron integral experiments measuring the transient neutron decay constant, reactivity ratio, shielding, and reactive perturbation, are described above. These critical parameters and experiments define the scope of application for the critical parameter prediction method of this application. This allows the application to predict a variety of different parameters, thereby providing sufficient data support for the design or use of new nuclear systems.

[0065] A second aspect of this application provides a readable storage medium storing a computer program that, when executed, performs a critical parameter prediction method as described in any of the above embodiments. If the critical parameter prediction method is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, all or part of the processes of the methods described in the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in the readable storage medium. When executed by a processor, the computer program can implement the steps of each method in the above embodiments. When the computer program is executed by the processor, the specific implementation of each step and the resulting technical effects are the same as in the aforementioned method embodiments. For the sake of brevity, any parts not mentioned in this embodiment can be referred to the corresponding content in the aforementioned method embodiments.

[0066] A third aspect of this application provides a critical parameter prediction apparatus, including a memory, a processor, and a terminal program stored in the memory and executable in the processor. The terminal program includes steps corresponding to the critical parameter prediction method described above. When the processor executes the terminal program, it implements the steps in the method embodiments described above, or it implements the functions of each module / unit in the readable storage medium described above. It should be understood that the apparatuses of various embodiments of this application can be implemented based on a memory and a processor. Each memory is used to store a terminal program for executing the methods described above, and the processor executes the terminal program, causing the critical parameter prediction apparatus to implement the methods of the various embodiments described above.

[0067] In some embodiments, the critical parameter prediction device described above can be a desktop computer, laptop, industrial computer, handheld computer, tablet computer, or other mobile terminal, as well as a cloud server or other computer equipment, and is not limited to any particular operating system. Those skilled in the art will understand that the above does not constitute a limitation on the critical parameter prediction device, and it may include more or fewer components, or combinations of certain components, or different components. For example, the critical parameter prediction device may also include input devices, output devices, network access devices, buses, etc., such as a keyboard for inputting parameter data from new core systems and existing critical experiments, and a display screen for displaying calculation results.

[0068] Regarding the specific implementation methods of this application, it should be noted that:

[0069] In the description of this application, the terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, apparatus, or readable storage medium that comprises a list of elements includes not only those elements but also other elements not expressly listed that conform to the concept of this application, or elements inherent to such a process, method, apparatus, or readable storage medium. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of additional elements in the process, method, apparatus, or readable storage medium that includes said element.

[0070] In the description of this application, the use of terms such as "some embodiments," "optional embodiments," "example," "specific example," "optional example," or "optional embodiment," etc., indicates that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application, but does not imply that these embodiments illustrate and describe all possible forms of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Furthermore, those skilled in the art can combine and integrate the different embodiments or examples described in this specification.

[0071] The present invention has been described in detail above with reference to specific embodiments and exemplary examples. The above description is exemplary and not exhaustive, and is not limited to the disclosed embodiments; the above description should not be construed as a limitation of the present invention. Technical solutions between various embodiments can be combined with each other, but must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application. Although embodiments of the present application have been shown and described, various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present application. Those skilled in the art will understand that various other specific changes and combinations of embodiments based on the technical teachings disclosed in this application, without departing from the essence of the present application, are still within the scope of protection defined by the claims of the present invention and their equivalent technical solutions.

Claims

1. A method for predicting critical parameters, characterized in that, include: The calculated values ​​of the critical parameters of the new nuclear system H to be predicted, and the calculated values ​​of the critical parameters of multiple existing critical experiments are determined; the calculated value corresponding to the new nuclear system H is denoted as C. H Number the multiple existing critical experiments 1-N; denot the calculated value corresponding to critical experiment i as C. i , i∈[1,N]; Calculate the sensitivity vector S of the novel nuclear system H to be predicted and multiple existing critical experiments to nuclear data. i The sensitivity vector corresponding to the new core system H is S. H The sensitivity vector corresponding to the critical experiment i is S. i ; Based on the calculated value C corresponding to each critical experiment i i Sensitivity vector S i And the calculated value C corresponding to the new core system. H Sensitivity vector S H By combining existing experimental values, experimental uncertainties, and coefficients from critical experiments, predicted values ​​Y for the critical parameters of the new nuclear system H are constructed. H The calculation formula and the uncertainty of the predicted value u(Y) H The calculation formula for ) Based on the predicted value Y H and the uncertainty of the predicted value u(Y) H The formula for calculating the coefficients of the critical experiment that minimizes the uncertainty of the prediction parameters is used. Substitute the obtained coefficients of the critical experiment into the predicted value Y. H The calculation formula and the uncertainty of the predicted value u(Y) H The formula for calculating Y is used to obtain the predicted value Y. H and the uncertainty of the predicted value u(Y) H ).

2. The method for predicting critical parameters according to claim 1, characterized in that, The predicted value Y H The formula for calculation is: Among them, a i E is the coefficient of the critical experiment, dimensionless; i This is the experimental value of critical experiment i.

3. The method for predicting critical parameters according to claim 1, characterized in that, The uncertainty of the predicted value u(Y) H The formula for calculating ) is: Among them, a i E is the coefficient of the critical experiment, dimensionless; i The experimental value of critical experiment i; u(E) i ) represents the experimental uncertainty of critical experiment i; ∑ represents the covariance matrix of the nuclear reaction cross section.

4. The method for predicting critical parameters according to claim 2 or 3, characterized in that, The experimental values ​​of the critical experiment are E1, E2, ... E N And experimental uncertainty u(E1), u(E2), ... u(E N The results were obtained through an integral experiment database and publicly available literature.

5. The method for predicting critical parameters according to claim 1, characterized in that, The calculations of all parameters for the new nuclear system and the existing critical experiments are based on the same nuclear reaction cross-section library, and the calculations must be performed using a program based on the Monte Carlo method, with the calculation uncertainty controlled to be one order of magnitude or more less than the experimental uncertainty.

6. The method for predicting critical parameters according to claim 1, characterized in that, When solving for the coefficients of the critical experiment that minimizes the uncertainty of the prediction parameters, the following mathematical methods are used for calculation: partial derivative method, gradient descent method, Newton's method, quasi-Newton method, or optimization method based on machine learning.

7. The method for predicting critical parameters according to claim 1, characterized in that, The critical parameters to be predicted include the effective proliferation factor k. eff The value of the transient neutron decay constant or the sample reactivity.

8. The method for predicting critical parameters according to claim 7, characterized in that, The existing critical experiment includes measuring k. eff The critical experiments, as well as the neutron integral experiments for measuring the transient neutron decay constant, reaction rate ratio, shielding, and reactive perturbation.

9. A readable storage medium storing a computer program, characterized in that, When the computer program is executed, it performs the method for predicting critical parameters as described in any one of claims 1-8.

10. A critical parameter prediction device, comprising a memory, a processor, and a terminal program stored in the memory and executable in the processor, characterized in that, The terminal program includes steps corresponding to the prediction method for critical parameters as described in any one of claims 1-8.

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