A method and system for generating tungsten irradiation defects based on MD cascade configuration analysis

Abstract

The present invention provides a tungsten irradiation defect generation method and system based on MD cascade configuration analysis, comprising: using a random sampling method to obtain a single PKA energy E pka ; Obtain the corresponding cascade energy from the PKA energy; Perform subsequent sampling operations according to the number of defects and the number of defect clusters; Obtain defect cluster size distribution parameters through the cascade energy; Obtain the corresponding size distribution form through the defect cluster size distribution parameters, and obtain the size of gap clusters or vacancy clusters by random sampling according to the defect cluster size distribution form; Obtain defect cluster spatial distribution parameters through the cascade energy; Obtain the defect cluster spatial distribution form through the defect cluster spatial distribution parameters, and obtain the position of gap clusters or vacancy clusters by random sampling according to the radial and angular distribution forms of defect clusters, and then obtain the entire cascade defect configuration. The present invention solves the technical problem that the efficiency and accuracy of defect initial distribution generation in the multi-scale simulation framework of irradiation defects cannot be taken into account at the same time.

Description

Technical Field

[0001] The present invention relates to the field of multi-scale simulation of material radiation damage, and in particular to a method and system for generating tungsten radiation defects based on MD cascade configuration analysis. Background Art

[0002] When tungsten materials facing the plasma of nuclear fusion devices are irradiated by high-energy particles (ions or neutrons), large-sized defect clusters will form inside them, such as voids, Burgers vectors of 1 / 2 <111> or <100> Defects such as dislocation loops. Different types of irradiation defects have different geometric structures, diffusion properties and reaction types, and have different influences on the physical properties of materials, which ultimately lead to the degradation of the mechanical properties of materials. Therefore, it is very important to obtain the initial distribution of defects with reliable size distribution and spatial distribution. At present, the evaluation of neutron irradiation behavior of tungsten materials mainly adopts irradiation experimental simulation and multi-scale computational simulation, among which irradiation experimental simulation includes fission neutron and ion irradiation experimental simulation. The evaluation of the service behavior of tungsten materials based on fission neutron experimental simulation is difficult, risky, expensive and has a long cycle, resulting in a lack of relevant system data, which seriously restricts the development of its failure behavior, laws and performance evaluation technology. In recent years, with the improvement of supercomputer computing power and the further development of the algorithm itself, the use of multi-scale schemes based on physics and parameter transfer, such as atomic scale (molecular dynamics / first principles method)-mesoscopic scale (object dynamics Monte Carlo / cluster dynamics method)-macroscopic scale (dislocation dynamics / finite element method) to simulate the radiation damage effect of nuclear materials has made rapid progress and has become an indispensable and powerful tool for the study of nuclear material radiation damage.

[0003] The invention patent application number CN201910724904.1, "A multi-scale coupling simulation method for irradiation damage of nuclear reactor materials", includes the following steps: (1) initializing system settings and atomic information, including the ensemble, temperature, pressure, the incident direction and energy of the first lattice atom PKA that is displaced by a neutron impact, the initial coordinates of the atom, the initial velocity, and neighboring atoms; (2) using molecular dynamics methods to simulate a cascade collision process caused by PKA, repeating the simulation multiple times under the same initial conditions, performing initial defect statistical analysis on the atomic information obtained from the molecular dynamics simulation, and obtaining an initial defect distribution; (3) using the initial defect distribution as the initial defect distribution of the dynamic Monte Carlo simulation. Input information, simulate the defect annealing process with the atomic dynamics Monte Carlo method, perform defect statistics on the gap and vacancy distribution obtained by the dynamics Monte Carlo simulation, and obtain the size and number density distribution of defect clusters; (4) For each cascade collision process, repeat steps (2) to (3) to obtain the size and number density distribution of defect clusters of different PKAs; (5) Use the size and number density distribution information of the defect clusters as input information for cluster dynamics simulation, simulate the long-term evolution process of the defect clusters by cluster dynamics, and use the cluster dynamics method to take into account the spatial information of the defects to obtain the microstructure type and spatial distribution that can be characterized, thereby providing information for the performance prediction of nuclear materials. The atomic-scale (~nanometer) molecular dynamics (MD) method in the multi-scale simulation scheme of radiation damage has become the most widely used method for simulating radiation damage of materials. It can provide reliable spatial distribution and size distribution of cascade damage defects. These key parameters are used as input conditions for subsequent models to simulate defect evolution with larger sizes (micrometers-millimeters / meters) and longer times (seconds-hours), and play a key role in the accurate prediction of simulation results. However, MD cascade simulation is limited by computational efficiency. Usually, only some discrete cascade energy points can be simulated, and the number of simulations is effective (for example, 10 to 50 times) in order to form a primary damage defect distribution database. The initial defect distribution of the multi-scale simulation scheme of defect evolution of neutron-irradiated tungsten materials is selected from the database based on the cascade energy approximation, and cannot be directly used for the simulation of real nuclear reactor irradiation environment with continuous PKA energy spectrum. In addition, although tools based on kinetic Monte Carlo models, such as SRIM, have efficient calculation speed, the initial defect distribution generated has obvious deviations from the more accurate MD method in terms of size distribution and spatial distribution. The research results also show that despite the same irradiation dose, irradiation sources with different neutron energy spectra have obvious differences in the irradiation effects on tungsten materials. It can be seen that reliable primary damage defect distribution plays a key role in the defect evolution and accurate evaluation of mechanical properties of tungsten irradiation damage multi-scale simulation framework.

[0004] In summary, in the prior art, there is a technical problem that the initial distribution of defects in the multi-scale simulation framework for defect evolution of tungsten, a plasma-facing material for nuclear fusion devices, irradiated with high-energy neutrons, cannot balance the production efficiency and accuracy. Summary of the invention

[0005] The technical problem to be solved by the present invention is how to solve the technical problem that the efficiency and accuracy of the initial distribution of defects in the multi-scale simulation framework of irradiation defect evolution in the prior art cannot be taken into account at the same time.

[0006] The present invention adopts the following technical solution to solve the above technical problem: A method for generating tungsten irradiation defects based on MD cascade configuration analysis includes:

[0007] S1. The PKA energy spectrum is randomly sampled to obtain a single PKA energy E. pka ;

[0008] S2. Obtain the corresponding cascade energy E according to the single PKA energy processing md =f(E pka )·E pka ;

[0009] S3, according to the cascade energy E md =f(E pka )·E pka The number of defects and the number of defect clusters are obtained by processing, and sampling operations are performed using two modes;

[0010] S4, according to the cascade energy E md =f(E pka )·E pka Obtaining defect cluster size distribution parameters, fitting the size distribution parameters of different cascade energies to obtain the relationship between the defect cluster size distribution parameters and the cascade energy;

[0011] S5, analyzing a preset number of MD cascade simulation defect configurations and processing the defect cluster size distribution parameters to obtain a corresponding defect cluster size distribution form, and then randomly sampling to obtain a size distribution of gap clusters or vacancy clusters;

[0012] S6, obtaining the spatial distribution parameter of the defect cluster according to the cascade energy, and obtaining the relationship between the radial distribution parameter of the defect cluster and the cascade energy by fitting the radial distribution parameters of different cascade energies;

[0013] S7. The spatial distribution form of defect clusters is obtained according to the spatial distribution parameters of the defect clusters, and the positions of gap clusters or vacancy clusters are obtained by random sampling according to the radial and angular distribution forms of the defect clusters, thereby obtaining the entire cascade defect configuration.

[0014] The present invention uses two modes to perform sampling operations, obtains the corresponding cascade energy from the PKA energy, obtains the defect cluster size distribution parameters through the cascade energy, obtains the size distribution form of the gap cluster or vacancy cluster through the defect cluster size distribution parameters, obtains the defect cluster size through random sampling through the defect cluster size distribution form, obtains the defect cluster spatial distribution form through the defect cluster spatial distribution parameters, and obtains the position of the gap cluster or vacancy cluster by random sampling according to the radial and angular distribution forms of the defect cluster, thereby obtaining the entire cascade defect configuration. It can quickly generate high-precision tungsten primary damage defect distribution of arbitrary cascade energy, thereby directly providing reliable primary damage defect distribution for multi-scale simulation of irradiation damage evolution of tungsten materials under actual working conditions of nuclear fusion reactors with continuous PKA energy spectrum. This patent has important application value for the neutron irradiation performance degradation assessment of tungsten plasma-facing materials in future CFETR and ITER.

[0015] In a more specific technical solution, step S1 includes:

[0016] S11. Let P(E pka ) is the cumulative probability of PKA spectrum;

[0017] S12, generating a random number ξ∈[0,1) according to the cumulative probability of the PKA energy spectrum;

[0018] S13, when P(E c )≥ξ, E c Select the PKA energy E pka .

[0019] In a more specific technical solution, step S2 includes:

[0020] S21, using the dynamic Monte Carlo simulation program IM3D to calculate the ratio of the cascade energy to the PKA energy f(E pka );

[0021] S22, according to the ratio of the cascade energy to the PKA energy f(E pka ) to obtain the corresponding cascade energy E md =f(E pka )·E pka .

[0022] The present invention provides a reliable defect initial distribution generation tool for the radiation damage multi-scale simulation framework, avoiding the problem that the traditional MD method and the dynamic Monte Carlo method SRIM cannot take into account both high generation efficiency and accuracy, and can provide a reliable defect initial distribution for the radiation degradation simulation problem of materials in a real nuclear reactor with a continuous PKA energy spectrum.

[0023] In a more specific technical solution, the total number of vacancies and gaps in the sampling operation in step S3 is the same.

[0024] In a more specific technical solution, step S3 includes:

[0025] S31. The relationship between the number of point defects and the cascade energy is expressed by the following logic:

[0026] ,

[0027] Among them, E d is the threshold delocalization energy, E0 is the turning point of the piecewise function, where

[0028] S32. The relationship between the number of defect clusters and the cascade energy is expressed by the following logic:

[0029] N(E)=a·E b +c,

[0030] Wherein, a, b, c are fitting parameters, and N(E) is the number of defect clusters.

[0031] In a more specific technical solution, in step S4, for the vacancy cluster: Interstitial Clusters: Take the average value as the S2 value, where It is the transition of vacancy cluster.

[0032] In a more specific technical solution, step S5 includes:

[0033] S51. Defect cluster size distribution is expressed by the following logic:

[0034]

[0035]

[0036] k(N)=g(N)·exp[-(NN max ) 2 / 2σ 2 ],N≥N max ,

[0037] Where N0 is the turning point of the piecewise function; N max is the average maximum cluster size, σ is the variance of the maximum cluster size;

[0038] S52, when N ≥ N max When , the product of the second segment function and the Gaussian distribution form in the above logic is taken to represent the Gaussian function decay of the defect cluster size near the average maximum cluster size.

[0039] The number of defect clusters of the present invention shows a translational power exponential law with cascade energy, the size distribution of defect clusters shows a segmented power exponential relationship, the spatial distribution of interstitial clusters and vacancy clusters conforms to radial Poisson distribution and radial exponential decay distribution respectively, and the defect cluster size distribution and spatial distribution fitting parameters show a power exponential law with cascade energy, so as to provide a reliable primary damage defect distribution for multi-scale simulation of irradiation damage evolution of tungsten material under actual working conditions of a nuclear fusion reactor with a continuous PKA energy spectrum.

[0040] In a more specific technical solution, the relationship between the radial spatial distribution fitting parameter and the cascade energy in step S6 satisfies the following formula:

[0041] ,

[0042] Among them, μ and σ are the fitting parameters for interstitial clusters, and λ represents the vacancy cluster.

[0043] In a more specific technical solution, in step S7, by displaying the defect configuration after the MD cascade simulation, it is found that the center of the cascade volume is a vacancy cluster and the surrounding is a gap cluster, so that the defect configuration is approximated as a sphere with uniform angular distribution. Step S7 includes:

[0044] S71. The radial distribution of cascade energy deposition is obtained by IM3D calculation:

[0045] dE(r)~exp(-r / λ)·4πr 2 ·sinθ·dθ·dψ,

[0046] Among them, r, θ, ψ are the three coordinate distance, polar angle and azimuth angle of the spherical coordinate system, E is the cascade energy, λ is the fitting attenuation constant, and ΔV is the volume element;

[0047] S72. Assume that the number of defect clusters generated is proportional to the energy deposition, according to the following logic:

[0048] The radial distribution parameter is: dE(r)~exp(-r / λ)·4πr 2 ·sinθ·dθ·dψ;

[0049] S73, according to the cascade defect angular uniform distribution data: dN(r,θ,ψ)~dE(r,θ,ψ) with the following logic: dN(r,θ,ψ)~dE(r), where N(r,θ,ψ) is the number of defect clusters generated,

[0050] dN(r,θ,ψ)=k·r 2 ·exp(-r / λ)·dr·sinθ·dθ·dψ

[0051] The polar angle distribution of defect clusters is obtained by processing: Azimuth distribution: Radial distribution: The cascade defect configuration is obtained accordingly.

[0052] The present invention adopts the EAM empirical potential to simulate the cascade defect generation process with cascade energy ranging from 50eV to 150keV, and systematically analyzes a large amount of tungsten MD cascade damage data to explore the number, size and spatial distribution characteristics of defects or clusters. It is found that the number of point defects shows a segmented power exponential relationship with the cascade energy.

[0053] In a more specific technical solution, a tungsten irradiation defect generation system based on MD cascade configuration analysis includes:

[0054] A single PKA module is used to obtain a single PKA energy E by random sampling of the PKA energy spectrum. pka ;

[0055] Corresponding cascade energy module, used to obtain corresponding cascade energy E according to the single PKA energy processing md =f(E pka )·E pka , the corresponding cascade energy module is connected to the single PKA module;

[0056] The defect cluster number module is used to calculate the defect cluster number according to the cascade energy E md =f(E pka )·E pka Processing to obtain the number of defects and the number of defect clusters, and performing sampling operations using two modes, wherein the defect cluster number module is connected to the corresponding cascade energy module;

[0057] The size distribution fitting module is used to fit the cascade energy E md =f(E pka )·E pka Obtaining defect cluster size distribution parameters, fitting the size distribution parameters of different cascade energies to obtain the relationship between defect cluster size distribution parameters and cascade energy, wherein the size distribution fitting module is connected to the defect cluster number module;

[0058] A gap cluster vacancy cluster size distribution module is used to obtain a corresponding defect cluster size distribution form by analyzing a preset number of MD cascade simulation defect configurations and processing according to the defect cluster size distribution parameters, and to obtain the size distribution of gap clusters or vacancy clusters by random sampling, and the gap cluster vacancy cluster size distribution module is connected to the size distribution fitting module;

[0059] A radial distribution fitting module, used to obtain the spatial distribution parameters of the defect cluster according to the cascade energy, and to obtain the relationship between the radial distribution parameters of the defect cluster and the cascade energy by fitting the radial distribution parameters of different cascade energies. The radial distribution fitting module is connected to the defect cluster number module;

[0060] The overall cascade configuration acquisition module is used to obtain the spatial distribution form of defect clusters according to the defect cluster spatial distribution parameter processing, and randomly sample the positions of gap clusters or vacancy clusters according to the radial and angular distribution forms of defect clusters, so as to obtain the entire cascade defect configuration. The overall cascade configuration acquisition module is connected to the radial distribution fitting module.

[0061] Compared with the prior art, the present invention has the following advantages:

[0062] The present invention uses two modes to perform sampling operations, obtains the corresponding cascade energy from the PKA energy, obtains the defect cluster size distribution parameters through the cascade energy, obtains the size distribution form of the gap cluster or vacancy cluster through the defect cluster size distribution parameters, obtains the defect cluster size through random sampling through the defect cluster size distribution form, obtains the defect cluster spatial distribution form through the defect cluster spatial distribution parameters, and obtains the position of the gap cluster or vacancy cluster by random sampling according to the radial and angular distribution forms of the defect cluster, thereby obtaining the entire cascade defect configuration. It can quickly generate high-precision tungsten primary damage defect distribution of arbitrary cascade energy, thereby directly providing reliable primary damage defect distribution for multi-scale simulation of irradiation damage evolution of tungsten materials under actual working conditions of nuclear fusion reactors with continuous PKA energy spectrum. This patent has important application value for the neutron irradiation performance degradation assessment of tungsten plasma-facing materials in future CFETR and ITER.

[0063] The present invention provides a reliable defect initial distribution generation tool for the multi-scale simulation framework of radiation damage, avoiding the problem that the traditional MD method and the dynamic Monte Carlo method SRIM cannot take into account both high generation efficiency and accuracy, and can provide a reliable defect initial distribution for the radiation degradation simulation problem of materials in a real nuclear reactor with a continuous PKA energy spectrum. The number of defect clusters of the present invention shows a translational power exponential law with the cascade energy, the size distribution of defect clusters shows a segmented power exponential relationship, the spatial distribution of interstitial clusters and vacancy clusters conforms to the radial Poisson distribution and the radial exponential decay distribution respectively, and the defect cluster size distribution and spatial distribution fitting parameters show a power exponential law with the cascade energy, so as to provide a reliable primary damage defect distribution for the multi-scale simulation of radiation damage evolution of tungsten materials under the actual working conditions of a nuclear fusion reactor with a continuous PKA energy spectrum.

[0064] The present invention uses EAM empirical potential to simulate the cascade defect generation process with cascade energy ranging from 50eV to 150keV, and systematically analyzes a large amount of tungsten MD cascade damage data, explores the number, size and spatial distribution characteristics of its defects or clusters, and finds that the number of point defects is in a segmented power exponential relationship with the cascade energy. The present invention solves the technical problem that the efficiency and accuracy of the initial distribution of defects in the multi-scale simulation framework of irradiation defect evolution in the prior art cannot be taken into account. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 It is a schematic flow chart of a method for generating tungsten irradiation defects based on MD cascade configuration analysis;

[0066] Figure 2 Schematic diagram of the relationship between the number of point defects and the cascade energy;

[0067] Figure 3 Schematic diagram of the relationship between the number of interstitial or vacancy clusters and the cascade energy;

[0068] Figure 4 Schematic diagram of defect cluster size distribution;

[0069] Figure 5 It is a schematic diagram of a typical cascade configuration;

[0070] Figure 6 It is a schematic diagram of an abstract cascade configuration;

[0071] Figure 7 Schematic diagram of radial spatial distribution of defect clusters;

[0072] Figure 8 is a schematic diagram of azimuth distribution;

[0073] Fig. 9 is a schematic diagram of polar angle distribution;

[0074] Fig.10 Schematic diagram of the relationship between defect cluster size distribution fitting parameters and cascade energy;

[0075] Fig.11 The relationship between the fitting parameters of the radial spatial distribution of defect clusters and the cascade energy. DETAILED DESCRIPTION

[0076] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in combination with the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0077] Example 1

[0078] like Figure 1 As shown in S1: The PKA energy spectrum is randomly sampled to obtain a single PKA energy E pka .

[0079] Specifically, let P(E pka ) is the cumulative probability of the PKA energy spectrum, generating a random number ξ∈[0,1). When P(E c )≥ξ, then E c The selected E pka ;

[0080] S2: Obtain the corresponding cascade energy from PKA energy.

[0081] The ratio of the cascade energy to the PKA energy was calculated by the dynamic Monte Carlo simulation program IM3D as f(E pka ), therefore, the corresponding cascade energy E md =f(E pka )·E pka .

[0082] There are two modes: S3, subsequent sampling operation according to the number of defects and S4, subsequent sampling operation according to the number of defect clusters. The subsequent sampling operation is exactly the same, just make sure that the total number of vacancies and gaps is the same.

[0083] S5: Defect cluster size distribution parameters are obtained by cascade energy. The relationship between defect cluster size distribution parameters and cascade energy is obtained by fitting size distribution parameters of different cascade energies, which is in the form of a power exponential function.

[0084] S6: The corresponding size distribution form can be obtained through the defect cluster size distribution parameters, and the size distribution of interstitial clusters or vacancy clusters can be obtained by random sampling according to the defect cluster size distribution form. The defect cluster size distribution form is obtained by analyzing a large number of MD cascade simulation defect configurations, showing a segmented power exponential distribution.

[0085] S7: The spatial distribution parameters of defect clusters are obtained by cascade energy. The relationship between the radial distribution parameters of defect clusters and the cascade energy is obtained by fitting the radial distribution parameters of different cascade energies, which is in the form of a power exponential function. The azimuth and polar angles of defect clusters are uniformly distributed.

[0086] S8: The spatial distribution form of defect clusters is obtained through the spatial distribution parameters of defect clusters, and the positions of interstitial clusters or vacancy clusters are randomly sampled according to the radial and angular distribution forms of defect clusters, and then the entire cascade defect configuration is obtained. The radial distribution form of defect clusters is obtained by analyzing a large number of MD cascade simulation defect configurations, showing a Gaussian distribution or E exponential decay law.

[0087] Example 2

[0088] 1.1 Number of point defects and defect clusters

[0089] 1.1.1 Relationship between the number of point defects and cascade energy

[0090] like Figure 2 As shown, the relationship between the number of point defects and the cascade energy satisfies the following formula:

[0091]

[0092] Where E d is the threshold delocalization energy, which is 0.085keV, and E0 is the turning point of the piecewise function. a1=2.09243, s1=0.700659, a2=0.080481, s2=1.71097.

[0093] 1.1.2 Relationship between the number of defect clusters and cascade energy

[0094] like Figure 3 As shown, the following formula is satisfied:

[0095] N(E)=a·E b +c

[0096] Where a, b, c are fitting parameters, for interstitial clusters ai = 0.761029, bi = 0.726393, ci = 1.75919; for vacancy clusters av = 0.389685, bv = 1.25708, cv = 1.30993.

[0097] 1.2 Defect cluster size distribution and radial spatial distribution

[0098] 1.2.1 Defect cluster size distribution

[0099] like Figure 4 As shown, the following formula is satisfied:

[0100]

[0101]

[0102] k(N)=g(N)·exp[-(NN max ) 2 / 2σ 2 ],N≥N max

[0103] Where N0 is the turning point of the piecewise function. After analyzing a large number of MD primary damage defect configurations, it is found that the value of N0 is around 10, so N0 is fixed at 10; N max is the average maximum cluster size, and σ is the variance of the maximum cluster size. max When , the product of the second segment function and the Gaussian distribution form is taken, indicating that the Gaussian function decays around the average maximum cluster size of the defect cluster size.

[0104] 1.2.2 Radial distribution of defect clusters and angular uniform distribution of defect clusters

[0105] like Figure 5 and Figure 6 As shown in the figure, by displaying the defect configuration after MD cascade simulation, it is found that the center of the cascade volume is a vacancy cluster, and the surrounding is a gap cluster, which can be approximated as a sphere with uniform angular distribution. The radial distribution form of the cascade energy deposition calculated by IM3D is: That is, dE(r)~exp(-r / λ)·4πr 2 ·sinθ·dθ·dψ, assuming that the number of defect clusters generated is proportional to the energy deposition, that is Since the cascade defects are uniformly distributed in the angular direction, then therefore Finally, the polar angle distribution of defect clusters is obtained: Azimuth distribution: Radial distribution:

[0106] like Figure 7 , Figure 8 and Fig. 9 As shown, the radial distribution of defect clusters satisfies the following formula:

[0107]

[0108] Among them A i and A v is a normalization constant, and μ, σ, and λ are fitting parameters for the radial spatial distribution of interstitial or vacancy clusters.

[0109] 1.3 Defect cluster size distribution and radial spatial distribution fitting parameters

[0110] 1.3.1 Defect cluster size distribution fitting parameters

[0111] like Fig.10 As shown, for vacancy clusters: Interstitial Clusters As can be seen from the figure, the standard error of S2 is large, but the average value is not much different, so the average value is taken as the S2 value. The parameters used are: for vacancy cluster S1: a1 = 2.274, s1 = -0.195, a2 = 5.996, s2 = 0.1409; for interstitial cluster S1: a1 = 2.425, s1 = 0.0241. Vacancy cluster S2 = 1.662; interstitial cluster S2 = 0.993; vacancy cluster transition When the cascade energy is less than 1keV, the relationship between the defect cluster size distribution fitting parameter S1 and the cascade energy no longer conforms to the overall power law, especially for interstitial clusters. This problem can be solved by directly inputting the cascade defect size distribution into the MD simulation. Therefore, we conducted and analyzed a large number of cascade simulations with cascade energies ranging from 50eV to 1keV and intervals of 1eV.

[0112] 1.3.2 Fitting parameters of radial spatial distribution of defect clusters

[0113] like Fig.11 As shown, the relationship between the radial spatial distribution fitting parameters and the cascade energy satisfies the following formula: Among them, the fitting parameters for interstitial clusters are: μ: a = 1.05682, s = -0.398905; σ: a = 0.459835, s = -0.415145; vacancy clusters λ: a = 0.267698, s = -0.356524.

[0114] In summary, the present invention uses two modes to perform sampling operations, obtains the corresponding cascade energy from the PKA energy, obtains the defect cluster size distribution parameters through the cascade energy, obtains the size distribution form of the gap cluster or vacancy cluster through the defect cluster size distribution parameters, obtains the defect cluster size through random sampling of the defect cluster size distribution form, obtains the defect cluster spatial distribution form through the defect cluster spatial distribution parameters, and obtains the position of the gap cluster or vacancy cluster according to the radial and angular distribution form of the defect cluster by random sampling, thereby obtaining the entire cascade defect configuration. It can quickly generate high-precision tungsten primary damage defect distribution of arbitrary cascade energy, thereby directly providing reliable primary damage defect distribution for multi-scale simulation of irradiation damage evolution of tungsten materials under actual working conditions of nuclear fusion reactors with continuous PKA energy spectrum. This patent has important application value for the neutron irradiation performance degradation assessment of tungsten plasma-facing materials for future CFETR and ITER.

[0115] The present invention provides a reliable defect initial distribution generation tool for the radiation damage multi-scale simulation framework, avoiding the problem that the traditional MD method and the dynamic Monte Carlo method SRIM cannot take into account both high generation efficiency and accuracy, and can provide a reliable defect initial distribution for the radiation degradation simulation problem of materials in a real nuclear reactor with a continuous PKA energy spectrum.

[0116] The number of defect clusters of the present invention shows a translational power exponential law with cascade energy, the size distribution of defect clusters shows a segmented power exponential relationship, the spatial distribution of interstitial clusters and vacancy clusters conforms to radial Poisson distribution and radial exponential decay distribution respectively, and the defect cluster size distribution and spatial distribution fitting parameters show a power exponential law with cascade energy, so as to provide a reliable primary damage defect distribution for multi-scale simulation of irradiation damage evolution of tungsten material under actual working conditions of a nuclear fusion reactor with a continuous PKA energy spectrum.

[0117] The present invention uses EAM empirical potential to simulate the cascade defect generation process with cascade energy ranging from 50eV to 150keV, and systematically analyzes a large amount of tungsten MD cascade damage data, explores the number, size and spatial distribution characteristics of its defects or clusters, and finds that the number of point defects is in a segmented power exponential relationship with the cascade energy. The present invention solves the technical problem that the efficiency and accuracy of the initial distribution of defects in the multi-scale simulation framework of irradiation defect evolution in the prior art cannot be taken into account.

[0118] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for generating tungsten irradiation defects based on MD cascade configuration analysis, characterized in that: The method comprises: S1. Use random sampling method to obtain the single PKA energy E of the PKA energy spectrum. pka ; S2. Obtain the corresponding cascade energy E according to the single PKA energy processing md =f(E pka )·E pka ; S3, according to the cascade energy E md =f(E pka )·E pka The number of defects and the number of defect clusters are obtained by processing, and sampling operations are performed using two modes. S3 includes: S31. The relationship between the number of point defects and the cascade energy is expressed by the following logic: Among them, E d is the threshold delocalization energy, E0 is the turning point of the piecewise function, where S32. The relationship between the number of defect clusters and the cascade energy is expressed by the following logic: N(E)=a·E b +c Wherein, a, b, c are fitting parameters, and N(E) is the number of defect clusters; S4, according to the cascade energy E md =f(E pka )·E pka Obtaining defect cluster size distribution parameters, fitting the size distribution parameters of different cascade energies to obtain the relationship between the defect cluster size distribution parameters and the cascade energy; S5, analyzing a preset number of MD cascade simulation defect configurations and processing the defect cluster size distribution parameters to obtain a corresponding defect cluster size distribution form, and then randomly sampling to obtain a size distribution of gap clusters or vacancy clusters; S6, obtaining the spatial distribution parameter of the defect cluster according to the cascade energy, and obtaining the relationship between the radial distribution parameter of the defect cluster and the cascade energy by fitting the radial distribution parameters of different cascade energies; S7, according to the defect cluster spatial distribution parameter processing to obtain the defect cluster spatial distribution form, according to the defect cluster radial and angular distribution form of random sampling to obtain the gap cluster or vacancy cluster position, and then obtain the entire cascade defect configuration, wherein, by displaying the defect configuration after MD cascade simulation in S7, it is found that the cascade volume center is a vacancy cluster, and the surrounding is a gap cluster, so that the defect configuration is approximated as a sphere with uniform angular distribution, and S7 includes: S71. The radial distribution of cascade energy deposition is obtained by IM3D calculation: That is, dE(r)~exp(-r / λ)·4πr 2 ·sinθ·dθ·dψ, Among them, r, θ, ψ are the three coordinate distance, polar angle and azimuth angle of the spherical coordinate system, E is the cascade energy, λ is the fitting attenuation constant, and ΔV is the volume element; S72. Assume that the number of defect clusters generated is proportional to the energy deposition, according to the following logic: The radial distribution parameter is: dE(r)~exp(-r / λ)·4πr 2 ·sinθ·dθ·dψ; S73. According to the angular uniform distribution data of the cascade defect: With the following logic: Where N(r,θ,ψ) is the number of defect clusters generated, The polar angle distribution of defect clusters is obtained by processing: Azimuth distribution: Radial distribution: The cascade defect configuration is obtained accordingly.

2. The method for generating tungsten irradiation defects based on MD cascade configuration analysis according to claim 1, characterized in that: The S1 includes: S11. Let P(E pka ) is the cumulative probability of PKA spectrum; S12, generating a random number ξ∈[0,1) according to the cumulative probability of the PKA energy spectrum; S13, when P(E c )≥ξ, E c Select the PKA energy E pka .

3. The method for generating tungsten irradiation defects based on MD cascade configuration analysis according to claim 1, characterized in that: The S2 includes: S21, using the dynamic Monte Carlo simulation program IM3D to calculate the cascade energy and the PKA energy E pka The ratio f(E pka ); S22, according to the ratio of the cascade energy to the PKA energy f(E pka ) to obtain the corresponding cascade energy E md =f(E pka )·E pka .

4. The method for generating tungsten irradiation defects based on MD cascade configuration analysis according to claim 1, characterized in that: The total number of vacancies and gaps in the sampling operation in S3 is the same.

5. The method for generating tungsten irradiation defects based on MD cascade configuration analysis according to claim 1, characterized in that: In S4, for vacancy clusters: Interstitial Clusters: Take the average value as the S2 value, where It is the transition of vacancy cluster.

6. The method for generating tungsten irradiation defects based on MD cascade configuration analysis according to claim 1, characterized in that: The S5 includes: S51. Defect cluster size distribution is expressed by the following logic: k(N)=g(N)·exp[-(N-N max ) 2 / 2σ 2 ],N≥N max , Where N0 is the turning point of the piecewise function; N max is the average maximum cluster size, σ is the variance of the maximum cluster size; S52, when N ≥ N max When , the product of the second segment function and the Gaussian distribution form in the above logic is taken to represent the attenuation of the Gaussian function of the defect cluster size near the average maximum cluster size.

7. The method for generating tungsten irradiation defects based on MD cascade configuration analysis according to claim 1, characterized in that: The relationship between the radial spatial distribution fitting parameters and the cascade energy in S6 satisfies the following formula: Among them, μ and σ are the fitting parameters for interstitial clusters, and λ represents the vacancy cluster.

8. A tungsten irradiation defect generation system based on MD cascade configuration analysis, used to execute a tungsten irradiation defect generation method based on MD cascade configuration analysis as claimed in any one of claims 1 to 7, characterized in that: The system comprises: A single PKA module is used to obtain a single PKA energy E by random sampling of the PKA energy spectrum. pka ; Corresponding cascade energy module, used to obtain corresponding cascade energy E according to the single PKA energy processing md =f(E pka )·E pka , the corresponding cascade energy module is connected to the single PKA module; The defect cluster number module is used to calculate the defect cluster number according to the cascade energy E md =f(E pka )·E pka Processing to obtain the number of defects and the number of defect clusters, and performing sampling operations using two modes, wherein the defect cluster number module is connected to the corresponding cascade energy module; The size distribution fitting module is used to fit the cascade energy E md =f(E pka )·E pka Obtaining defect cluster size distribution parameters, fitting the size distribution parameters of different cascade energies to obtain the relationship between defect cluster size distribution parameters and cascade energy, wherein the size distribution fitting module is connected to the defect cluster number module; A gap cluster vacancy cluster size distribution module is used to obtain a corresponding defect cluster size distribution form by analyzing a preset number of MD cascade simulation defect configurations and processing according to the defect cluster size distribution parameters, and to obtain the size distribution of gap clusters or vacancy clusters by random sampling, and the gap cluster vacancy cluster size distribution module is connected to the size distribution fitting module; A radial distribution fitting module, used to obtain the spatial distribution parameters of the defect cluster according to the cascade energy, and to obtain the relationship between the radial distribution parameters of the defect cluster and the cascade energy by fitting the radial distribution parameters of different cascade energies. The radial distribution fitting module is connected to the defect cluster number module; The overall cascade configuration acquisition module is used to obtain the spatial distribution form of defect clusters according to the defect cluster spatial distribution parameter processing, and randomly sample the positions of gap clusters or vacancy clusters according to the radial and angular distribution forms of defect clusters, so as to obtain the entire cascade defect configuration. The overall cascade configuration acquisition module is connected to the radial distribution fitting module.

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