A method, system, device and medium for quantifying uncertainty in neutron calculations

Abstract

The present application relates to a kind of neutronics calculation uncertainty quantification method, system, equipment and medium, belong to neutronics calculation field.The method includes: based on the covariance information in nuclear data evaluation library making nuclear data covariance library;Using direct numerical perturbation method calculates the sensitivity coefficient of nuclear reaction cross section to spallation neutron source;According to the nuclear data covariance library and the sensitivity coefficient, the uncertainty of spallation neutron source is calculated;According to the nuclear data covariance library and the uncertainty of spallation neutron source, using Latin hypercube sampling method is sampled to generate calculation sample;Based on the calculation sample, the ADS neutronics calculation result is obtained by executing ADS neutronics calculation;The uncertainty of the ADS neutronics calculation result is determined by performing mathematical statistics analysis to the ADS neutronics calculation result.The present application improves the calculation efficiency and calculation accuracy of the uncertainty of ADS neutronics calculation.

Description

Technical Field

[0001] This invention relates to the field of neutronics computation in accelerator-driven subcritical systems, and in particular to a method, system, device, and medium for quantifying uncertainty in neutronics computation. Background Technology

[0002] With the continuous development of nuclear power in China, nuclear waste disposal has become an indispensable part of the sustainable development of China's nuclear power industry. Accelerator-Driven Subcritical Systems (ADS) are powerful nuclear waste treatment devices and a promising new technology approach for the sustainable development of nuclear fission energy in China. Currently, numerical simulation technology is the fundamental research tool for ADS design, optimization, and safety analysis. However, due to the complexity of ADS and the limitations of human cognition, uncertainties inevitably exist in the numerical simulation process, affecting the assessment of the safety and economics of nuclear reactor systems. Neutronics calculations are the most fundamental and critical issue in ADS design, and the reliability of the calculation results directly affects subsequent thermal-hydraulic calculations and safety analyses. There are two main reasons affecting the reliability of neutronics calculation results: the calculation method and the input parameters, especially nuclear data. In recent years, with the continuous development of computer technology and calculation methods, the deviations caused by calculation methods have become smaller, while the deviations caused by the reliability of nuclear data have become increasingly significant. Therefore, it is essential to study the uncertainties in neutronics calculation results caused by nuclear data.

[0003] Compared to neutronics calculations in traditional critical nuclear reactors, ADS neutronics calculations are unique, encompassing two processes: spallation neutron source calculations and subcritical reactor neutronics calculations. Generally, a spallation neutron source is first obtained by simulating the bombardment of a spallation target by a proton source, and then used as an external neutron source for subcritical reactor neutronics calculations. Uncertainties in nuclear data can directly affect subcritical reactor neutronics calculations, and indirectly through spallation neutron sources. The propagation of nuclear data uncertainty in ADS neutronics calculations is more complex. Current techniques for uncertainty quantification in ADS neutronics calculations only consider the impact of nuclear data uncertainty on subcritical reactor neutronics calculations, neglecting the impact on spallation neutron source calculations. This affects the reliability of the uncertainty quantification results. Therefore, deterministic quantitative analysis of the entire process from spallation neutron source to subcritical reactor neutronics calculations is necessary.

[0004] Methods for quantifying uncertainties in reactor neutronics calculations can be categorized into sensitivity analysis and random sampling statistical methods. Sensitivity analysis is further divided into perturbation theory and direct numerical perturbation methods. Perturbation theory is complex, requiring solutions to conjugate neutron transport methods and typically employing first-order approximations. This method is unsuitable for systems with highly nonlinear output responses with respect to input parameters, such as multi-physics coupled calculations. In contrast, direct numerical perturbation and random sampling statistical methods treat the simulation program as a "black box," requiring only processing at the input parameters and output response levels, making them highly applicable. However, these methods require repeated calls to the numerical simulation program, resulting in low computational efficiency. For problems with few inputs, direct numerical perturbation methods offer relatively better computational efficiency, while for problems with many inputs, random sampling statistical methods are more efficient. In ADS neutronics calculations, spallation neutron source calculations involve fewer input parameters, while subcritical reactor neutronics calculations involve more. Therefore, the existing single uncertainty quantification method is not applicable to ADS, and a new coupled computation method needs to be constructed to improve the computational efficiency of uncertainty quantification methods in neutronics computation. Summary of the Invention

[0005] The purpose of this invention is to provide a method, system, device, and medium for quantifying uncertainty in neutronics computation, in order to solve the problems of low computational accuracy and efficiency of the existing ADS neutronics computation uncertainty quantification method.

[0006] To achieve the above objectives, the present invention provides the following solution:

[0007] A method for quantifying uncertainty in neutronics computation, comprising:

[0008] A kernel data covariance library was created based on the covariance information in the kernel data evaluation library.

[0009] The sensitivity coefficient of spallation neutron sources to nuclear reaction cross sections was calculated using the direct numerical perturbation method.

[0010] The uncertainty of the spallation neutron source is calculated based on the nuclear data covariance library and the sensitivity coefficient.

[0011] Based on the nuclear data covariance library and the spallation neutron source uncertainty, the Latin hypercube sampling method is used to generate computational samples.

[0012] Based on the computational sample, ADS neutronics calculations are performed to obtain ADS neutronics calculation results; the ADS neutronics calculation results include the effective multiplication factor, power distribution, and nuclear density;

[0013] Mathematical statistical analysis is performed on the ADS neutronics calculation results to determine the uncertainty of the ADS neutronics calculation results, which is used to identify the main sources of uncertainty in the accelerator-driven subcritical system.

[0014] Optionally, the nuclear data evaluation library is the ENDF / B-VIII.0 evaluation library.

[0015] Optionally, the calculation of the sensitivity coefficient of the spallation neutron source to the nuclear reaction cross section using the direct numerical perturbation method specifically includes:

[0016] Acquire spallation neutron sources and establish a geometric model of the spallation target area;

[0017] The nuclear reaction cross section is determined based on the aforementioned spallation target region geometric model;

[0018] The nuclear reaction cross section is perturbed to determine the perturbed nuclear reaction cross section;

[0019] Calculate the spallation neutron source strength based on the perturbed nuclear reaction cross section;

[0020] Based on the spallation neutron source strength, using the formula Calculate the sensitivity coefficient; where σ is the nuclear reaction cross section; δσ is the nuclear reaction cross section perturbation; Q is the spallation neutron source intensity; and S is the sensitivity coefficient of the spallation neutron source intensity to the nuclear reaction cross section.

[0021] Optionally, calculating the spallation neutron source uncertainty based on the nuclear data covariance library and the sensitivity coefficient specifically includes:

[0022] Based on the aforementioned kernel data covariance library and the aforementioned sensitivity coefficients, the formula SD(Q) = (S × COV) is used. σ ×S T ) 1 / 2 Calculate the spallation neutron source uncertainty; where SD(Q) is the spallation neutron source uncertainty; S is the sensitivity coefficient vector composed of the sensitivity coefficients; S T COV is the transpose of the sensitivity coefficient vector; σ This refers to the covariance matrix in the kernel data covariance library.

[0023] Optionally, the step of generating computational samples using the Latin hypercube sampling method based on the nuclear data covariance library and the spallation neutron source uncertainty specifically includes:

[0024] The probability density distribution functions of the nuclear reaction cross section and the spallation neutron source intensity are determined; the probability density distribution function of the nuclear reaction cross section is a Gaussian distribution function; the probability density distribution function of the spallation neutron source intensity is a uniform distribution function.

[0025] Based on the Gaussian distribution function and the nuclear reaction section covariance in the nuclear data covariance library, a calculated sample of the nuclear reaction section is generated using the Latin hypercube sampling method.

[0026] Based on the uniform distribution function and the uncertainty of the spallation neutron source, a calculated sample of the spallation neutron source strength is generated using the Latin hypercube sampling method.

[0027] Optionally, the step of performing mathematical statistical analysis on the ADS neutronics calculation results to determine the uncertainty of the ADS neutronics calculation results specifically includes:

[0028] Using formula Determine the uncertainty of the neutronics calculation results in ADS; where N is the number of calculation samples; R i,n The response of the nth computational sample is the result of the i-th ADS neutronics computation; Let be the expected value of the neutronics calculation result for the i-th ADS.

[0029] Optionally, after performing mathematical statistical analysis on the ADS neutronics calculation results to determine the uncertainty of the ADS neutronics calculation results, the method further includes:

[0030] Based on the uncertainty of the ADS neutronics calculation results, determine the standard deviation of the uncertainty of the ADS neutronics calculation results;

[0031] Based on the standard deviation, determine whether the confidence level of the uncertainty of the ADS neutronics calculation result reaches the set threshold to obtain the judgment result;

[0032] If the judgment result indicates that the confidence level of the uncertainty of the ADS neutronics calculation result does not reach the set threshold, then the number of calculation samples is increased until the confidence level of the uncertainty of the ADS neutronics calculation result reaches the set threshold.

[0033] A neutronics computation uncertainty quantification system, comprising:

[0034] The database creation module is used to create a kernel data covariance library based on the covariance information in the kernel data evaluation library.

[0035] The first calculation module is used to calculate the sensitivity coefficient of the spallation neutron source to the nuclear reaction cross section using the direct numerical perturbation method;

[0036] The second calculation module is used to calculate the uncertainty of the spallation neutron source based on the nuclear data covariance library and the sensitivity coefficient;

[0037] The sample generation module is used to generate computational samples by using the Latin hypercube sampling method based on the nuclear data covariance library and the spallation neutron source uncertainty.

[0038] The result determination module is used to perform ADS neutronics calculations based on the calculation samples to obtain ADS neutronics calculation results; the ADS neutronics calculation results include the effective multiplication factor, power distribution, and nuclear density;

[0039] The uncertainty determination module is used to perform mathematical and statistical analysis on the ADS neutronics calculation results to determine the uncertainty of the ADS neutronics calculation results, and to identify the main sources of uncertainty in the accelerator-driven subcritical system.

[0040] An electronic device includes a memory and a processor, wherein the memory stores a computer program, and the processor runs the computer program to cause the electronic device to perform the above-described neutronics computation uncertainty quantification method.

[0041] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for quantifying uncertainty in neutronics computation.

[0042] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0043] The method of this invention involves: creating a nuclear data covariance library based on covariance information in a nuclear data evaluation library; calculating the sensitivity coefficient of a spallation neutron source to nuclear data using the direct numerical perturbation method; calculating the uncertainty of the spallation neutron source based on the nuclear data covariance library and the sensitivity coefficient; generating calculation samples using the Latin hypercube sampling method based on the nuclear data covariance library and the uncertainty of the spallation neutron source; performing ADS neutronics calculations based on the calculation samples to obtain ADS neutronics calculation results; and performing mathematical statistical analysis on the ADS neutronics calculation results to determine the uncertainty of the ADS neutronics calculation results, which is used to identify the main sources of uncertainty in accelerator-driven subcritical systems. This invention combines the direct numerical perturbation method with random sampling statistics to quantify the uncertainty of neutronics calculations in accelerator-driven subcritical systems, improving the calculation accuracy and efficiency of ADS neutronics uncertainty calculations. Furthermore, this invention considers the impact on spallation neutron source calculations, quantifying the uncertainty of the entire process from spallation neutron source to subcritical reactor neutronics calculations, thus improving the accuracy of the calculation results. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 A flowchart of the neutronics computation uncertainty quantification method provided by this invention;

[0046] Figure 2 This is a flowchart illustrating the creation of the nuclear data covariance library of this invention.

[0047] Figure 3 The flowchart for calculating the uncertainty of spallation neutron sources provided by this invention;

[0048] Figure 4 The flowchart for quantifying the uncertainty of ADS subcritical reactor neutronics calculations provided by this invention;

[0049] Figure 5 For subcritical system k s Uncertainty curves at different burn depths;

[0050] Figure 6 Let k be the initial time. s right 237 Sensitivity coefficient curve of Np(n,f) reaction;

[0051] Figure 7 The graph shows the uncertainty curves of the nuclear density of major nuclides at different burn-out depths. Detailed Implementation

[0052] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] The purpose of this invention is to provide a method, system, device, and medium for quantifying uncertainty in neutronics computation, in order to solve the problems of low computational accuracy and efficiency of the existing ADS neutronics computation uncertainty quantification method.

[0054] ADS: Accelerator-driven subcritical system. A high-energy, high-current proton beam generated by a proton accelerator is introduced into the reactor through a proton beam duct and bombards the spallation target, producing a large number of spallation neutrons, which drive the subcritical reactor core and release energy.

[0055] Sensitivity coefficient: The relative change in the feature response parameter R caused by a relative change in the input parameter α is defined as the relative sensitivity coefficient of the feature parameter R with respect to the input parameter α.

[0056] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0057] Figure 1 A flowchart of the neutronics computation uncertainty quantification method provided by this invention; as follows: Figure 1 As shown, the method includes:

[0058] Step 101: Create a kernel data covariance library based on the covariance information in the kernel data evaluation library.

[0059] In practical applications, based on the nuclear data evaluation library (ENDF / B-VIII.0 evaluation library), the nuclear data processing software NJOY is used to generate the covariance of nuclear reaction cross sections, such as... Figure 2 As shown, the decimal file of the ENDF / B evaluation library is converted to binary; the point cross-section is reconstructed, and the cross-sectional description is converted into linear interpolation data; the Doppler broadening cross-section at a specified temperature point is calculated; the indistinguishable resonance region is processed into a probability table; thermal scattering is calculated; the variance and covariance data of each reaction cross-section of the nuclide are calculated, and the nuclear reaction cross-section σ i The variance is:

[0060] Var(σ i )=E[(σ i -E(σ i ))(σ i -E(σ i In the formula: E(σ) i )——Nuclear reaction cross section σ i The expected value.

[0061] Nuclear reaction cross section σ i and σ j The covariance is: COV(σ) i ,σ j )=E[(σ i -E(σ i ))(σ j -E(σ j ))).

[0062] Step 102: Calculate the sensitivity coefficient of the spallation neutron source to the nuclear reaction cross section using the direct numerical perturbation method.

[0063] In practical applications, step 102 specifically includes:

[0064] Acquire spallation neutron sources and establish a geometric model of the spallation target area.

[0065] Based on the described spallation target region geometric model, the nuclear reaction cross section is determined. In practical applications, the spallation target region is geometrically modeled, and the reaction cross section of each nuclide is read.

[0066] The nuclear reaction cross section is perturbed to determine the perturbed nuclear reaction cross section. In practical applications, the perturbed nuclear reaction cross section is perturbed by a perturbation amount of δσ, using a two-way perturbation method, that is, the input nuclear reaction cross section σ is directly perturbed to σ+δσ and σ-δσ.

[0067] The spallation neutron source intensity is calculated based on the perturbed nuclear reaction cross section. In practical applications, the spallation neutron source intensity is calculated using the MCNPX program based on the perturbed nuclear reaction cross section.

[0068] Based on the spallation neutron source strength, using the formula The sensitivity coefficient is calculated, where σ is the nuclear reaction cross section; δσ is the perturbation of the nuclear reaction cross section; Q is the spallation neutron source intensity; and S is the sensitivity coefficient of the spallation neutron source intensity to the nuclear reaction cross section. In practical applications, the relative sensitivity coefficient S of the spallation neutron source intensity Q to the nuclear reaction cross section is calculated using the calculation results of the spallation neutron source intensity.

[0069] Step 103: Calculate the spallation neutron source uncertainty based on the nuclear data covariance library and the sensitivity coefficients. In practical applications, based on the nuclear reaction cross-section covariance data obtained in step 101 (nuclear data covariance library) and the sensitivity coefficients obtained in step 102, calculate the uncertainty of the spallation neutron source intensity (the uncertainty of the spallation neutron source intensity), and its calculation expression is:

[0070] SD(Q)=(S×COV σ ×S T ) 1 / 2

[0071] In the formula: SD(Q) represents the standard deviation of the spallation neutron source strength Q, which is the uncertainty of the spallation neutron source strength; S represents the sensitivity coefficient vector of the spallation neutron source strength to the nuclear reaction cross section σ. S T COV represents the transpose of vector S; σ Let represent the covariance matrix of the nuclear reaction cross section σ, where the element in the i-th row and j-th column is represented as:

[0072]

[0073] Where: COV(σ) i ,σ j ) represents the nuclear reaction cross section σ i and σ j covariance; Var(σ)i ) represents the nuclear reaction cross section σ i The variance.

[0074] Step 104: Based on the nuclear data covariance library and the spallation neutron source uncertainty, a Latin hypercube sampling method is used to generate calculation samples. The calculation samples include calculation samples of nuclear reaction cross sections and calculation samples of spallation neutron source intensity.

[0075] In practical applications, the number of input parameters (such as nuclear reaction cross section and spallation neutron source intensity) is determined according to the actual problem, and the probability density distribution function of each input parameter is determined in combination with the physical characteristics of each input parameter. For example, the nuclear reaction cross section adopts a Gaussian distribution, and the spallation neutron source intensity adopts a uniform distribution.

[0076] The minimum required sample size is determined based on Wilks' formula, which is:

[0077] 1-a N ≥b

[0078] (1-a N )-N(1-a)a N-1 ≥b

[0079] Where a represents the output response parameter; b represents the confidence level of the output response; and N represents the sample size.

[0080] Based on the probability density distribution function and covariance information of the input parameters, a calculation sample is generated, as follows:

[0081] Based on the Gaussian distribution function and the covariance of the nuclear reaction cross section in step 101, the Latin hypercube sampling method is used to generate a calculated sample of the nuclear reaction cross section.

[0082] Based on the uniform distribution function and the uncertainty of the spallation neutron source intensity in step 103, a calculated sample of the spallation neutron source intensity is generated by Latin hypercube sampling.

[0083] Step 105: Perform ADS neutronics calculations based on the calculated samples to obtain the ADS neutronics calculation results. Specifically, as follows... Figure 4 As shown, based on the nuclear data covariance library and the spallation neutron source uncertainty, a Latin hypercube sampling method is used to generate calculation samples. The calculation samples of the nuclear reaction cross section and the spallation neutron source intensity generated in step 104 are read. For each calculation sample, the MCNP+ORIGEN command in the ADS neutronics calculation software is executed to obtain the ADS neutronics calculation results. The calculation results of the effective multiplication factor, power distribution, and nuclear density calculated by the neutronics calculation software are statistically analyzed.

[0084] Step 106: Perform mathematical statistical analysis on the ADS neutronics calculation results to determine the uncertainty of the ADS neutronics calculation results, which is used to identify the main sources of uncertainty in the accelerator-driven subcritical system and guide the improvement of nuclear data accuracy.

[0085] Further, step 106 specifically includes:

[0086] Statistical analysis of the calculation results using mathematical statistics methods yields the uncertainty of the neutronics calculation results, expressed as:

[0087] Where N is the number of samples to be calculated, i.e., the sample size calculated in step 104; R i,n The response of the nth computational sample is the result of the i-th ADS neutronics computation; Let be the expected value of the quantum computing result for the i-th ADS.

[0088] Based on the uncertainty of the ADS neutronics calculation results, the standard deviation of the uncertainty of the ADS neutronics calculation results is determined. Under a specific sample size condition, a resampling technique is used to quantify the statistical fluctuations of the calculation response uncertainty. Let R be the calculated response obtained from the nth (n = 1, 2, ..., N) resampling. i The uncertainty is The standard deviation of its uncertainty is then expressed as:

[0089]

[0090] in, This indicates that the response R is calculated under all resampling conditions. i The expected value of the uncertainty is calculated using the following formula:

[0091] Based on the standard deviation, determine whether the confidence level of the uncertainty of the quantum computing result in the ADS reaches a set threshold. If not, increase the number of calculation samples until the confidence level of the uncertainty of the quantum computing result in the ADS reaches the set threshold. Determine whether the confidence level (confidence degree) of the calculation response uncertainty (uncertainty of the quantum computing result in the ADS) reaches 95%. If not, increase the number of samples and repeat steps 104 and 105 until the confidence level of the calculation response uncertainty reaches 95%.

[0092] This invention employs a combination of direct numerical perturbation and random sampling statistics to quantify the uncertainties in quantum computation in accelerator-driven subcritical systems. For example... Figure 5 , Figure 6 and Figure 7 As shown, Figure 5Give the effective multiplication coefficient k of the subcritical system s Uncertainty at different burn depths: As burn depth increases, uncertainty propagates and accumulates. s Uncertainty is gradually increasing; Figure 6 Given the initial time k s right 237 Np(n,f) is the sensitivity coefficient of the reaction, k s It is mainly determined by the fission cross section. 237 The fission cross section of Np is significantly higher in the high-energy region than in the low-energy region, therefore k s right 237 The sensitivity coefficient of the Np(n,f) reaction is significantly greater in the high-energy region than in the low-energy region; Figure 7 Give different fuel consumption depths 237 Np, 238 Pu、 241 Pu、 242m Am、 242 The uncertainty of the nuclear density of nuclides such as Cm, among which 242 The uncertainty of the Cm atomic nucleus density is the largest, with a maximum value of 7.47%. Numerical analysis results show that the present invention has reliable accuracy and good efficiency, and is suitable for the calculation requirements in practical engineering.

[0093] Example 2

[0094] To implement the method corresponding to Embodiment 1 above and achieve the corresponding functions and technical effects, a neutronics computation uncertainty quantification system is provided below, comprising:

[0095] The database creation module is used to create a kernel data covariance library based on the covariance information in the kernel data evaluation library.

[0096] The first calculation module is used to calculate the sensitivity coefficient of the spallation neutron source to the nuclear reaction cross section using the direct numerical perturbation method.

[0097] The second calculation module is used to calculate the uncertainty of the spallation neutron source based on the nuclear data covariance library and the sensitivity coefficient.

[0098] The sample generation module is used to generate computational samples by using the Latin hypercube sampling method based on the nuclear data covariance library and the spallation neutron source uncertainty.

[0099] The result determination module is used to execute the ADS neutronics calculation algorithm based on the calculation sample to obtain the ADS neutronics calculation results; the ADS neutronics calculation results include the effective multiplication coefficient, power distribution, and atomic nucleus density.

[0100] The uncertainty determination module is used to perform mathematical and statistical analysis on the ADS neutronics calculation results to determine the uncertainty of the ADS neutronics calculation results, and to identify the main sources of uncertainty in the accelerator-driven subcritical system.

[0101] Example 3

[0102] The present invention also provides an electronic device, including: a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to perform the neutronics computation uncertainty quantification method of Embodiment 1.

[0103] Example 4

[0104] The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the neutronics computation uncertainty quantification method of Embodiment 1.

[0105] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0106] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for quantifying uncertainty in neutronics computation, characterized in that, include: A kernel data covariance library was created based on the covariance information in the kernel data evaluation library. The sensitivity coefficient of spallation neutron sources to nuclear reaction cross sections was calculated using the direct numerical perturbation method. The uncertainty of the spallation neutron source is calculated based on the nuclear data covariance library and the sensitivity coefficient. Based on the nuclear data covariance library and the spallation neutron source uncertainty, the Latin hypercube sampling method is used to generate computational samples. Based on the computational sample, ADS neutronics calculations are performed to obtain ADS neutronics calculation results; the ADS neutronics calculation results include the effective multiplication factor, power distribution, and nuclear density; Mathematical statistical analysis is performed on the ADS neutronics calculation results to determine the uncertainty of the ADS neutronics calculation results, which is used to identify the main sources of uncertainty in the accelerator-driven subcritical system; The sensitivity coefficient of spallation neutron sources to nuclear reaction cross sections is calculated using the direct numerical perturbation method, specifically including: Acquire spallation neutron sources and establish a geometric model of the spallation target area; The nuclear reaction cross section is determined based on the aforementioned spallation target region geometric model; The nuclear reaction cross section is perturbed to determine the perturbed nuclear reaction cross section; Calculate the spallation neutron source strength based on the perturbed nuclear reaction cross section; Based on the spallation neutron source strength, using the formula Calculate the sensitivity coefficient; where, For nuclear reaction cross section; denoted as , where is the perturbation of the nuclear reaction cross section; Q is the spallation neutron source intensity; and S is the sensitivity coefficient of the spallation neutron source intensity to the nuclear reaction cross section. Based on the nuclear data covariance library and the sensitivity coefficients, the uncertainty of the spallation neutron source is calculated, specifically including: Based on the kernel data covariance library and the sensitivity coefficients, using the formula Calculate the uncertainty of spallation neutron sources; where, Uncertainty regarding spallation neutron sources; The sensitivity coefficient vector formed by the aforementioned sensitivity coefficients; This is the transpose of the sensitivity coefficient vector; This refers to the covariance matrix in the kernel data covariance library.

2. The method for quantifying uncertainty in neutronics computation according to claim 1, characterized in that, The nuclear data evaluation library is the ENDF / B-VIII.0 evaluation library.

3. The method for quantifying uncertainty in neutronics computation according to claim 1, characterized in that, The step of generating computational samples using the Latin hypercube sampling method based on the nuclear data covariance library and the spallation neutron source uncertainty specifically includes: The probability density distribution functions of the nuclear reaction cross section and the spallation neutron source intensity are determined; the probability density distribution function of the nuclear reaction cross section is a Gaussian distribution function; the probability density distribution function of the spallation neutron source intensity is a uniform distribution function. Based on the Gaussian distribution function and the nuclear reaction section covariance in the nuclear data covariance library, a calculated sample of the nuclear reaction section is generated using the Latin hypercube sampling method. Based on the uniform distribution function and the uncertainty of the spallation neutron source, a calculated sample of the spallation neutron source strength is generated using the Latin hypercube sampling method.

4. The method for quantifying uncertainty in neutronics computation according to claim 1, characterized in that, The step of performing mathematical statistical analysis on the ADS neutronics calculation results to determine the uncertainty of the ADS neutronics calculation results specifically includes: Using formula Determine the uncertainties in the neutronics calculation results of ADS; among which, N To calculate the sample size; For the first n The calculated sample response of the first i ADS neutronics calculation results; For the first i The expected value of the neutronics calculation results in ADS.

5. The method for quantifying uncertainty in neutronics computation according to claim 1, characterized in that, After performing mathematical statistical analysis on the ADS neutronics calculation results to determine the uncertainty of the ADS neutronics calculation results, the method further includes: Based on the uncertainty of the ADS neutronics calculation results, determine the standard deviation of the uncertainty of the ADS neutronics calculation results; Based on the standard deviation, determine whether the confidence level of the uncertainty of the ADS neutronics calculation result reaches the set threshold to obtain the judgment result; If the judgment result indicates that the confidence level of the uncertainty of the ADS neutronics calculation result does not reach the set threshold, then the number of calculation samples is increased until the confidence level of the uncertainty of the ADS neutronics calculation result reaches the set threshold.

6. A neutronics computation uncertainty quantification system, characterized in that, include: The database creation module is used to create a kernel data covariance library based on the covariance information in the kernel data evaluation library. The first calculation module is used to calculate the sensitivity coefficient of the spallation neutron source to the nuclear reaction cross section using the direct numerical perturbation method; The sensitivity coefficient of spallation neutron sources to nuclear reaction cross sections is calculated using the direct numerical perturbation method, specifically including: Acquire spallation neutron sources and establish a geometric model of the spallation target area; The nuclear reaction cross section is determined based on the aforementioned spallation target region geometric model; The nuclear reaction cross section is perturbed to determine the perturbed nuclear reaction cross section; Calculate the spallation neutron source strength based on the perturbed nuclear reaction cross section; Based on the spallation neutron source strength, using the formula Calculate the sensitivity coefficient; where, For nuclear reaction cross section; denoted as , where is the perturbation of the nuclear reaction cross section; Q is the spallation neutron source intensity; and S is the sensitivity coefficient of the spallation neutron source intensity to the nuclear reaction cross section. The second calculation module is used to calculate the uncertainty of the spallation neutron source based on the nuclear data covariance library and the sensitivity coefficient; Based on the nuclear data covariance library and the sensitivity coefficients, the uncertainty of the spallation neutron source is calculated, specifically including: Based on the kernel data covariance library and the sensitivity coefficients, using the formula Calculate the uncertainty of spallation neutron sources; where, Uncertainty regarding spallation neutron sources; The sensitivity coefficient vector formed by the aforementioned sensitivity coefficients; This is the transpose of the sensitivity coefficient vector; The covariance matrix in the kernel data covariance library; The sample generation module is used to generate computational samples by using the Latin hypercube sampling method based on the nuclear data covariance library and the spallation neutron source uncertainty. The result determination module is used to perform ADS neutronics calculations based on the calculation samples to obtain ADS neutronics calculation results; the ADS neutronics calculation results include the effective multiplication factor, power distribution, and nuclear density; The uncertainty determination module is used to perform mathematical and statistical analysis on the ADS neutronics calculation results to determine the uncertainty of the ADS neutronics calculation results, and to identify the main sources of uncertainty in the accelerator-driven subcritical system.

7. An electronic device, characterized in that, include: A memory and a processor, the memory being used to store a computer program, the processor running the computer program to cause the electronic device to perform the neutronics computation uncertainty quantification method according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the neutronics computation uncertainty quantification method according to any one of claims 1-5.

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