CN114547988A - Neutron transport solving method for reactor with uniformly distributed materials - Google Patents
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- OOAWCECZEHPMBX-UHFFFAOYSA-N oxygen(2-);uranium(4+) Chemical compound [O-2].[O-2].[U+4] OOAWCECZEHPMBX-UHFFFAOYSA-N 0.000 description 2
- 229910001220 stainless steel Inorganic materials 0.000 description 2
- 239000010935 stainless steel Substances 0.000 description 2
- FCTBKIHDJGHPPO-UHFFFAOYSA-N uranium dioxide Inorganic materials O=[U]=O FCTBKIHDJGHPPO-UHFFFAOYSA-N 0.000 description 2
- ZIMRZUAJVYACHE-UHFFFAOYSA-N uranium;hydrate Chemical compound O.[U] ZIMRZUAJVYACHE-UHFFFAOYSA-N 0.000 description 2
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Abstract
A neutron transport solution method for a reactor with uniformly distributed materials combines machine learning and a Monte Carlo method. It is first necessary to construct a series of reactors with a uniform distribution of materials, each reactor containing a different material composition or density. And then solving the reactors by using a Monte Carlo method, and counting the scattering and fission probability of neutrons and the characteristic distribution of the neutrons after the simulation is finished. And training to obtain a fully-connected neural network model with neutron energy and material composition as inputs and characteristic distribution of scattered and fission neutrons as outputs by using the probability and characteristic distribution data. And finally, obtaining next generation fission neutrons by using neutrons and materials generated by fission as input by using the obtained fully-connected neural network model, and performing iterative computation until convergence. Compared with the existing Monte Carlo method, the method does not need a large number of sampling processes, has higher calculation speed, and can quickly finish neutron transport calculation aiming at the reactor with uniformly distributed materials.
Description
Technical Field
The invention relates to the field of nuclear reactor core design and safety, in particular to a neutron transport solving method for a reactor with uniformly distributed materials.
Background
The numerical simulation of the reactor is closely related to the design, operation and safety of the reactor, and the continuous development of the nuclear industry puts higher requirements on the precision and efficiency of the numerical simulation of the reactor core. The solution of the neutron transport equation is the core of the numerical simulation of the reactor, and the current main solution methods are divided into a deterministic theory and a Monte Carlo method (Monte Carlo method for short).
The determinism method solves a simplified neutron transport equation after energy, space and angle are dispersed. The method is developed more mature, but the calculation deviation is inevitably introduced in the discrete process of each variable, and the method is not good at processing complex geometric problems. The Monte Carr method is to use pseudo-random numbers to simulate particles to solve neutron transport. Compared with the determinism method, the Monte Carlo method has the following advantages: the geometric universality is high, and various complex geometric structures can be described; various complex energy spectrums can be processed by using point sections of continuous energy, and complicated group combination and resonance processing processes are avoided; as long as enough particles and simulation times are ensured, good calculation precision can be obtained; although the monte carlo method has many advantages, a large number of particles need to be repeatedly sampled in the simulation process, even if parallel computing is adopted, time is consumed, meanwhile, a large number of particle data can be generated in the sampling process, the data occupy a large amount of memory, the utilization rate is low, and the improvement of the computing efficiency is also influenced.
Disclosure of Invention
In order to solve the problems of the existing Monte Carlo method in neutron transport solution, the invention provides a neutron transport solution method for a reactor with uniformly distributed materials, which fully utilizes mass particle data generated in the Monte Carlo calculation process to save the operation of repeatedly sampling particles.
In order to achieve the purpose, the invention adopts the following technical scheme to implement:
a neutron transport solving method for a reactor with uniformly distributed materials combines a Monte Carlo method and machine learning, and comprises the following steps:
step 1: constructing a series of reactors with uniformly distributed materials, and setting boundary conditions of the reactors as total reflection, wherein each reactor contains different material compositions or densities;
step 2: calculating a series of reactors with uniformly distributed materials constructed in the step 1 by using a Monte Carlo method, counting and outputting the energy in the calculation process
The neutron scattering disappearance probability, the fission disappearance probability, the trajectory length distribution of the scattering disappearance, the trajectory length distribution of the fission disappearance, the energy distribution of the neutron initiating the fission, the number distribution of the neutrons generated by the fission, the energy distribution of the neutrons generated by the fission, the different start and end distance distributions in the neutron transport process, and the areas of neutron passing areas at different start and end distances;
and step 3: based on the statistical output data obtained in the step 2, four fully-connected neural network models are established and trained, wherein the four fully-connected neural network models are respectively as follows: the system comprises a scattering disappearance-fission disappearance probability model, a scattering and fission distribution characteristic model, a fission neutron distribution characteristic model and a flux area model, wherein the input and output relations of the four models are as follows:
(a) scattering extinction-fissionEnergy of neutron in probability of disappearance model
And material components are used as input, and scattering disappearance probability and fission disappearance probability are used as output;
(b) scattering and fission profile modeling by neutron energy
And material components are used as input, and the length distribution of the paths where the neutrons scatter and disappear, the length distribution of the paths where the neutrons fission and the neutrons initiate the fission, and the different starting and ending distance distribution in the neutron transport process are used as output;
(c) the fission neutron distribution characteristic model takes the neutron energy and material components for initiating fission as input, and takes the number distribution of neutrons generated by fission and the energy distribution of neutrons generated by fission as output;
(d) flux area modeling with trace length of neutrons
Different start and end distances in neutron transport process
And material composition as input, with different initial and final distances, neutron passing area
Is an output;
and 4, step 4: aiming at a nuclear reactor with uniformly distributed materials, establishing a neutron transport calculation process based on the four models obtained after training in the step 3;
(a) first, give
Initial energy of neutron
,
；
(b) At an initial energy
And the material components are input, and the scattering disappearance probability and the fission disappearance probability are obtained through a scattering disappearance-fission disappearance probability model; at an initial energy
And the material components are input, and the trace length distribution of neutron scattering disappearance, the trace length distribution of fission disappearance, the energy distribution of fission-initiating neutrons and different starting and ending distance distributions in the neutron transport process are obtained through a scattering and fission distribution characteristic model;
(c) neutron energy and material components for initiating fission are input into the fission neutron distribution characteristic model to obtain the number distribution of neutrons generated by fission;
(d) according to the first
Directly sampling the trace length distribution of scattered or fission lost neutrons
Track length of individual neutrons
(ii) a According to the first
Different initial and final distances are distributed in the neutron transport process, and the first distance is sampled
Starting and ending distances in the process of transporting neutrons
(ii) a Will be first
Track length of individual neutrons
Distance between start and end of transport
And the material composition is an input flux area model, obtaining
Area of neutron transit region;
and 5: processing the outputs of the four models in the step 4 to obtain effective multiplication factors and neutron flux;
wherein,
Wherein,
step 6: taking the generated fission neutron as input information of next generation simulation, repeating the processes of the step 5 and the step 6, and setting the generation number of one simulation neutron until all simulation generations are completed; and outputting the final effective multiplication factor and neutron flux to complete the transport calculation of the reactor with uniform material distribution.
Compared with the traditional Monte Carlo method, the massive neutron data generated in the Monte Carlo simulation process are utilized through machine learning, and the neutron data are used as a machine learning data set through mining various distribution characteristics of neutrons during transportation and collision in materials, scattering and fission death and generating the probability of different neutron numbers, so that a machine learning model replacing the transportation and collision process is trained. The repeated sampling operation of the neutrons in the transportation and collision processes is omitted, so that the calculation speed is higher, meanwhile, the intermediate data generated in the calculation process can be greatly reduced, the memory consumption is reduced, and the calculation efficiency is improved.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific examples.
The specific steps are shown in figure 1. The invention relates to a neutron transport solving method for a reactor with uniformly distributed materials, which combines a Monte Carlo method with machine learning, takes a reactor with uniformly distributed materials as an example, supposes that the materials in the reactor only contain uranium dioxide, water and stainless steel, and comprises the following concrete steps of completing the transport calculation of the reactor:
step 1: constructing a series of reactors with uniformly distributed uranium dioxide, water and stainless steel as materials, setting boundary conditions of the reactors to be total reflection, wherein each reactor contains different material components or densities, but the material composition and the density of the reactors are not completely the same as those of a target reactor to be solved;
step 2: calculating a series of reactors with uniformly distributed materials constructed in the step 1 by using a Monte Carlo method, counting and outputting the energy in the calculation process
The neutron scattering disappearance probability, the fission disappearance probability, the trajectory length distribution of the scattering disappearance, the trajectory length distribution of the fission disappearance, the energy distribution of the neutron initiating the fission, the number distribution of the neutrons generated by the fission, the energy distribution of the neutrons generated by the fission, the different start and end distance distributions in the neutron transport process, and the areas of neutron passing areas at different start and end distances;
and step 3: based on the statistical output data obtained in the step 2, four fully-connected neural network models are established and trained, wherein the four fully-connected neural network models are respectively as follows: the system comprises a scattering disappearance-fission disappearance probability model, a scattering and fission distribution characteristic model, a fission neutron distribution characteristic model and a flux area model, wherein the input and output relations of the four models are as follows:
(a) scattering extinction-fission extinction probability model by neutron energy
And material components are used as input, and scattering disappearance probability and fission disappearance probability are used as output;
(b) scattering and fission profile modeling by neutron energy
And material components are used as input, and the length distribution of the paths where the neutrons scatter and disappear, the length distribution of the paths where the neutrons fission and the neutrons initiate the fission, and the different starting and ending distance distribution in the neutron transport process are used as output;
(c) the fission neutron distribution characteristic model takes the neutron energy and material components for initiating fission as input, and takes the number distribution of neutrons generated by fission and the energy distribution of neutrons generated by fission as output;
(d) flux area modeling with trace length of neutrons
Different start and end distances in neutron transport process
And material composition as input, with different initial and final distances, neutron passing area
Is an output;
and 4, step 4: aiming at a nuclear reactor with uniformly distributed materials, establishing a neutron transport calculation process based on the four models obtained after training in the step 3;
(a) first, give
Initial energy of neutron
,
；
(b) At an initial energy
And the material components are input, and the scattering disappearance probability and the fission disappearance probability are obtained through a scattering disappearance-fission disappearance probability model; at an initial energy
And the material components are input, and the trace length distribution of neutron scattering disappearance, the trace length distribution of fission disappearance, the energy distribution of fission-initiating neutrons and different starting and ending distance distributions in the neutron transport process are obtained through a scattering and fission distribution characteristic model;
(c) neutron energy and material components for initiating fission are input into the fission neutron distribution characteristic model to obtain the number distribution of neutrons generated by fission;
(d) according to the first
Directly sampling the trace length distribution of scattered or fission lost neutrons
Track length of individual neutrons
(ii) a According to the first
Different initial and final distances are distributed in the neutron transport process, and the first distance is sampled
Transport distance of individual neutrons during transit
Is prepared from
Track length of individual neutrons
Distance between start and end of transport
And the material composition is an input flux area model, obtaining
Area of neutron transit region;
and 5: processing the outputs of the four models in the step 4 to obtain effective multiplication factors and neutron flux;
wherein,
Wherein,
step 6: taking the generated fission neutron as input information of next generation simulation, repeating the processes of the step 5 and the step 6, and setting the generation number of one simulation neutron until all simulation generations are completed; and outputting the final effective multiplication factor and neutron flux to finish the transport calculation of the reactor with uniform material distribution.
Claims (1)
1. A neutron transport solving method for a reactor with uniformly distributed materials combines a Monte Carlo method and machine learning, and is characterized in that: the method comprises the following steps:
step 1: constructing a series of reactors with uniformly distributed materials, and setting the boundary conditions of the reactors to be total reflection, wherein each reactor contains different material compositions or densities;
step 2: calculating a series of reactors with uniformly distributed materials constructed in the step 1 by using a Monte Carlo method, counting and outputting the energy in the calculation process
The neutron scattering disappearance probability, the fission disappearance probability, the trajectory length distribution of the scattering disappearance, the trajectory length distribution of the fission disappearance, the energy distribution of the neutron initiating the fission, the number distribution of the neutrons generated by the fission, the energy distribution of the neutrons generated by the fission, the different start and end distance distributions in the neutron transport process, and the areas of neutron passing areas at different start and end distances;
and 3, step 3: based on the statistical output data obtained in the step 2, four fully-connected neural network models are established and trained, wherein the four fully-connected neural network models are respectively as follows: the system comprises a scattering disappearance-fission disappearance probability model, a scattering and fission distribution characteristic model, a fission neutron distribution characteristic model and a flux area model, wherein the input and output relations of the four models are as follows:
(a) scattering extinction-fission extinction probability model by neutron energy
And material components are used as input, and scattering disappearance probability and fission disappearance probability are used as output;
(b) scattering and fission profile modeling by neutron energy
And material components are used as input, and the length distribution of the paths where the neutrons scatter and disappear, the length distribution of the paths where the neutrons fission and the neutrons initiate the fission, and the different starting and ending distance distribution in the neutron transport process are used as output;
(c) the fission neutron distribution characteristic model takes the neutron energy and material components for initiating fission as input, and takes the number distribution of neutrons generated by fission and the energy distribution of neutrons generated by fission as output;
(d) flux area modeling with trace length of neutrons
Different start and end distances in neutron transport process
And material composition as input, with different initial and final distances, neutron passing area
Is an output;
and 4, step 4: aiming at a reactor with uniformly distributed materials, establishing a neutron transport calculation process based on the four models obtained after training in the step 3;
(a) first, give
Initial energy of neutron
,
；
(b) At an initial energy
And the material components are input, and the scattering disappearance probability and the fission disappearance probability are obtained through a scattering disappearance-fission disappearance probability model; at an initial energy
And the material components are input, and the trace length distribution and fission of the disappearance of the neutron scattering are obtained through a scattering and fission distribution characteristic modelThe length distribution of the disappeared tracks, the energy distribution of neutrons initiating fission, and different starting and ending distance distributions in the neutron transport process;
(c) neutron energy and material components for initiating fission are input into the fission neutron distribution characteristic model to obtain the number distribution of neutrons generated by fission;
(d) according to the first
Directly sampling the trace length distribution of scattered or fission lost neutrons
Track length of individual neutrons
(ii) a According to the first
Different initial and final distances are distributed in the neutron transport process, and the first distance is sampled
Starting and ending distances in neutron transport process
(ii) a Will be first
Track length of individual neutrons
Distance between start and end of transport
And a material composition input flux area model, obtaining
Area of neutron transit region;
and 5: processing the outputs of the four models in the step 4 to obtain effective multiplication factors and neutron flux;
wherein,
wherein,
step 6: taking the generated fission neutron as input information of next generation simulation, repeating the processes of the step 5 and the step 6, and setting the generation number of one simulation neutron until all simulation generations are completed; and outputting the final effective multiplication factor and neutron flux to finish the transport calculation of the reactor with uniform material distribution.
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| CN115935770A (en) * | 2022-12-16 | 2023-04-07 | 西安交通大学 | Neutron transport method based on neural network and designed for nuclear reactor shielding |
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