Simulation method, system and storage medium for microstructure evolution of small and large defect cluster coexistence system

Abstract

The application discloses a kind of small and large defect cluster coexistence system microstructure evolution simulation method, system and storage medium, the main steps of the simulation method include: S1, set small cluster and large cluster component demarcation parameter, the maximum component of large cluster and the grid number of each dimension of component space;S2, store the structure, energy and dynamics properties of small cluster and component discrete large cluster of defect number continuous change;S3, calculate the action rate table of small cluster inside, large cluster inside and between them;S4, randomly select an event in the rate table to be executed;S5, execute the corresponding event of selection, update defect component and quantity.The simulation method in the application can represent defect cluster in larger size range with limited storage unit, so that the microstructure evolution process of small defect cluster and large cluster coexistence system can be simulated.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of nuclear material irradiation damage simulation, and particularly relates to a simulation method and system for microstructure evolution of a small defect cluster coexisting system with a large defect cluster, and a storage medium. BACKGROUND

[0002] When nuclear materials are exposed to a high radiation level system, the impact of high-energy particles will destroy the original perfect lattice of the material, generating defects such as dislocations, vacancies, and the like, which will seriously affect the macroscopic performance of the material. The irradiation-induced microstructure evolution of the material is a multi-scale process in time and space. In experiments, it is difficult to achieve precise control of the experiment on the atomic spatial scale and the microsecond time scale to study the defect evolution mechanism. Computer simulation can make up for the shortcomings of experiments and can be used to explain the mechanism of microstructure evolution. It is an important method for nuclear material irradiation damage simulation and can provide important scientific basis for irradiation damage mechanism research, performance prediction, and anti-irradiation material research and development.

[0003] Previous studies have shown that when high-energy particles irradiate materials, point defects and small defect clusters are generated in the material. These defects further diffuse and aggregate to form large defect clusters. Small defect clusters and large defect clusters coexist in the material under continuous cumulative irradiation. Understanding the microstructure evolution process (such as the nucleation, growth of small clusters, and interaction between defect clusters of different sizes) is crucial for understanding the performance degradation of materials.

[0004] Currently, when using stochastic cluster dynamics (SCD) to simulate microstructure evolution, the number of point defects constituting the defect cluster is often used to map the storage address of the defect cluster. For example, for a cluster containing vacancies V, helium He, and hydrogen H, let the number of V, He, and H be p1, p2, and p3, respectively. A function about p1, p2, and p3 is constructed to calculate the address of the cluster V p1 He p2 H p3 , and the structure, energetics, and dynamics properties of the defect object are pre-stored. When calculating the defect action rate table, the storage address of the defect cluster can be obtained directly through its composition (the number of point defects constituting the cluster), and then the relevant attributes can be called to quickly calculate the action rate. However, this method is fast in calculation but occupies a large amount of memory space, and is only suitable for simulating the microstructure evolution of a system containing small defect clusters under low irradiation dose. It is ineffective for a system containing large defect clusters under high irradiation dose (for example, a spherical void with a radius of 50 nanometers contains defects in the order of millions). SUMMARY

[0005] Therefore, the present application provides a simulation method for microstructure evolution of small and large defect cluster coexistence system.

[0006] In order to achieve the above object, the present application adopts the following technical scheme:

[0007] The present application provides a simulation method for microstructure evolution of small and large defect cluster coexistence system, comprising the following steps:

[0008] S1, setting small cluster V p1 He p2 H p3 and large cluster V p1 He p2 H p3 _mesh component boundary parameters, maximum component of large cluster and grid number of each dimension of component space, wherein p1, p2 and p3 respectively represent the number of vacancies V, helium He and hydrogen H in the cluster;

[0009] S2, storing small cluster with continuous change of defect number and large cluster with discrete component structure, energy and dynamics properties;

[0010] S3, calculating the action rate table of small cluster, large cluster and the interaction between them;

[0011] S4, randomly selecting an event in the rate table to be executed;

[0012] S5, executing the selected corresponding event and updating the defect component and quantity.

[0013] Further scheme, in the step S1, small cluster V p1 He p2 H p3 The grid number of each direction is M i , M j , M k , and the corresponding p1, p2 and p3 change range is 0-M i -1, 0-M j -1, 0-M k -1;

[0014] Large cluster V p1 He p2 H p3 _mesh The grid number of each component direction is M i_mesh , M j_mesh , M k_mesh ; the minimum radius of large cluster is R min_mesh , and the maximum radius is Rmax_mesh ; the minimum values of the three components of the large cluster are p1 min_mesh , p2 min_mesh , p3 min_mesh , and the maximum values are p1 max_mesh , p2 max_mesh , p3 max_mesh , respectively;

[0015] In view of the size connection relationship between the small cluster and the large cluster, p1 min_mesh = M i ; according to the vacancy-type defect cluster spherical approximation, p2 , p3 a = 0.0, p3 min_mesh = 0.0, where V min_mesh is the volume of an atom;

[0016] Preferably, the values of the parameters M i , M j , and M k are constrained by the maximum array length Lmax allowed in the computer, satisfying M i M j M k < Lmax.

[0017] Further, for the small cluster having a very discrete property, M i is set to the order of 100; if the model does not contain He and / or H, M i may be as large as possible under the condition of memory constraint; R max_mesh may be dynamically changed according to the program simulation of different irradiation dose levels, and the value is greater than the size of the largest cluster in the system at a certain stage.

[0018] The parameters p2 max_mesh , p3 max_mesh are set according to the irradiation conditions, and at a certain irradiation dose, the values are greater than the maximum number of He / H in the cluster.

[0019] Further, the step S2 specifically comprises:

[0020] S21, establishing a mapping relationship between the defect components and their storage addresses;

[0021] S22, through the mapping relationship, recording the radius, binding energy, and diffusion coefficient properties of the representative cluster of each node stored.

[0022] Further, the step S21 specifically comprises:

[0023] For the small cluster, V p1 He p2 Hp3 Its storage address is index = nint(p3) + [nint(p2) + nint(p1)M j ]·M k Where nint is the rounding function;

[0024] For large clusters V p1 He p2 H p3 _mesh, whose node storage address is index = indz + [indy + indxM j_mesh ]·M k_mesh Indx, indy, and indz are the coordinates of discrete grid nodes in the three directions of the cluster, respectively; the coordinates of the storage space nodes in the V direction are 0 - indx - (M i_mesh -1) and log10(R) min_mesh -log10(R)-log10(R) max Perform a linear mapping, and then we have Correspondingly In the He direction 0-indy-(M j_mesh -1) and log10(p2) min_mesh +1)-log10(p2+1)-log10(p2 max +1) Linear mapping, which leads to There is a mapping relationship between nodal coordinates and components in the H direction.

[0025] In a further embodiment, step S3 specifically includes:

[0026] Calculate the interaction rates between two small clusters, between two large clusters, and between a small cluster and a large cluster. During the calculation, only valid nodes with non-zero defect counts are considered, and the interaction rates of all events are stored uniformly. The event type indicates different events.

[0027] In a further embodiment, in step S4, the event to be executed is randomly selected using a roulette wheel method.

[0028] In a further embodiment, in step S5, let the newly generated clusters be pq1, pq2, and pq3, and their number be n. p The amount of reactants is reduced by one:

[0029] For reactions between small clusters, if pq1 <M i If the new cluster belongs to a small cluster, then its storage address is index = nint(pq3) + [nint(pq2) + nint(pq1)M j ]·M k, let the number and component of the index node before reaction be N, p old , then mix in V pq1 He pq2 H pq3 , the average component after _mesh is The number of the node after reaction increases n p ; if pq1≥M i , then the new cluster belongs to large cluster, its storage address index=indz+[indy+indxM j_mesh ]·M k_mesh , wherein, indx, indy, indz are the node coordinates of the cluster in three component directions, and have is the radius corresponding to pq1, let the number and component of the index node before reaction be N, p old , then mix in V pq1 He pq2 H pq3 , the average component after _mesh is The number of the node after reaction increases n p ;

[0030] For the reaction between small cluster-large cluster and large cluster, if pq1<M i , that is, the new cluster belongs to small cluster, then its storage address index=nint(pq3)+[nint(pq2)+nint(pq1)M j ]·M k , let the number and component of the index node before reaction be N, p old , then mix in V pq1 He pq2 H pq3 , the average component after _mesh is The number of the node after reaction increases n p ; if pq1≥M i , then the new cluster belongs to large cluster, its storage address index=indz+[indy+indxM j_mesh ]·M k_mesh , wherein, indx, indy, indz are the node coordinates of the cluster in three component directions, and have is the radius corresponding to pq1; let the number and component of the index node before reaction be N, p old , if index is not one of the two reaction nodes, then mix in V pq1 He pq2 H pq3The average component after _mesh is If index is one of the two reaction nodes, mix in V pq1 He pq2 H pq3 The average component after _mesh is The number of nodes after reaction increases n p Wherein, n c Is the number of index nodes consumed after reaction.

[0031] The application further provides a simulation system for microstructure evolution of a small and large defect cluster coexistence system, comprising:

[0032] An acquisition module is configured to acquire small clusters V p1 He p2 H p3 And large clusters V p1 He p2 H p3 _mesh component boundary parameters, maximum components of large clusters and the number of grids in each dimension of the component space, wherein p1, p2 and p3 represent the number of vacancies V, helium He and hydrogen H in the cluster respectively.

[0033] A storage module is configured to store the structure, energy and dynamics properties of small clusters with continuous defect number and large clusters with discrete components.

[0034] A calculation module is configured to calculate the action rate table of small clusters, large clusters and the interaction between the small clusters and the large clusters.

[0035] A selection module is configured to randomly select an event in the rate table to be executed.

[0036] An execution module is configured to execute the corresponding event and update the defect components and the number of defects.

[0037] The application further provides a computer readable storage medium storing a computer program, wherein the computer program is executed by a processor to make the processor execute the steps of the simulation method.

[0038] Compared with the prior art, the application has the following beneficial effects:

[0039] The simulation method in the application divides discrete grids in the component space of large defect clusters, proposes an approximate defect cluster grouping method, ensures material conservation in the reaction process, and uses limited storage units to represent defect clusters in a large size range; at the same time, the small clusters are processed with continuous components, so that the microstructure evolution process of a small and large defect cluster coexistence system can be simulated.

[0040] Compared with the existing simulation method based on continuous change of the number of cluster center point defects, the large clusters of different sizes are grouped and discretely approximated in the present application, thereby saving memory space and being suitable for simulating microstructure evolution process of a system containing large clusters (such as under high irradiation dose). BRIEF DESCRIPTION OF DRAWINGS

[0041] Figure 1 Flow chart of main steps of the microstructure evolution simulation method for the small and large defect cluster coexisting system proposed in an exemplary embodiment of the present application;

[0042] Figure 2 Flow chart of algorithm of the microstructure evolution simulation method for the small and large defect cluster coexisting system proposed in an exemplary embodiment of the present application;

[0043] Figure 3 Curve of average size and density of vacancy clusters in the vacancy aggregation and growth process of the body-centered cubic iron bulk simulated by the simulation method in the present application in Example 1. DETAILED DESCRIPTION

[0044] Embodiments of the present application are described in detail below, and the embodiments described below are exemplary and are only used to explain the present application, and cannot be understood as limiting the present application.

[0045] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs. The terms used in the specification of the present application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application.

[0046] An exemplary embodiment of the present application proposes a simulation method for microstructure evolution of a small and large defect cluster coexisting system, as shown in Figure 1 which mainly includes the following steps:

[0047] S1, setting small clusters V p1 He p2 H p3 and large clusters V p1 He p2 H p3 _mesh components, boundary parameters, maximum components of large clusters, and the number of grid in each dimension of component space, wherein p1, p2, and p3 represent the number of vacancies V, helium He, and hydrogen H in the cluster, respectively;

[0048] S2, storing small cluster structures with continuous change of defect number and discrete large cluster structures with continuous change of defect number, and energy and dynamics properties;

[0049] S3, calculating the action rate table of small clusters, large clusters, and between them;

[0050] S4, randomly selecting a certain event in the rate table to be executed;

[0051] S5, executing the corresponding event, updating the defect group component and quantity.

[0052] By dividing the component space of large defect clusters into discrete grids, an approximate defect cluster grouping method is proposed. In the reaction process, the material conservation is ensured, and the clusters in a large defect component range can be represented by limited storage units, so as to realize the simulation of microstructure evolution of a system with coexistence of small and large defect clusters.

[0053] Specifically, in step S1, the cluster refers to a cluster containing vacancies V, helium He and hydrogen H. The small cluster in the present application refers to a cluster with less than 100 orders of magnitude of point defects, and the large cluster refers to a cluster with more than 100 orders of magnitude of point defects. The small cluster is represented by V p1 He p2 H p3 , and the large cluster is represented by V p1 He p2 H p3 _mesh, wherein p1, p2 and p3 represent the number of vacancies V, helium He and hydrogen H in the cluster, respectively. p1 He p2 H p3 The small cluster V p1 He p2 H p3 and the large cluster V j _mesh refer to the number of point defects V, He and H constituting the cluster.

[0054] The small cluster V p1 He p2 H p3 has M i , M j and M k grids in each direction, and the corresponding p1, p2 and p3 change in the range of 0-M i -1, 0-M j -1 and 0-M k -1, respectively.

[0055] The large cluster V p1 He p2 H p3 _mesh has M i_mesh , M j_mesh and M k_mesh grids in each component direction; the minimum radius of the large cluster is R min_mesh , and the maximum radius is R max_mesh; The minimum values of the three component directions of the large cluster are p1 min_mesh , p2 min_mesh , p3 min_mesh , and the maximum values are: p1 max_mesh , p2 max_mesh , p3 max_mesh ; Considering the size connection relationship between the small cluster and the large cluster, p1 min_mesh = M i ; According to the spherical approximation of the vacancy-type defect cluster, there is where V a is the volume of one atom. For example, for a body-centered cubic material, V a = 0.5a 3 , where a is the lattice constant; for the dimensions p2 min_mesh where He and H are located in the cluster, p2 min_mesh = 0.0, p3

[0056] Further, in step S1, the values of the parameters M i , M j and M k are constrained by the maximum array length Lmax allowed in the computer, satisfying M i M j M k < Lmax. For example, for the windows system, Lmax is on the order of 5 million. In addition, due to the very discrete nature of the small cluster, M i is set in the order of 100; if the model does not contain He and / or H, M i can take a relatively large value as much as possible under the condition of satisfying the memory constraint; R max_mesh can vary dynamically according to different irradiation dose levels simulated by the program, and its value should be greater than the size of the largest cluster in the system at a certain stage. In an exemplary embodiment, R max_mesh can be set to 2 nm during the cluster nucleation stage, and it can be set between 10 - 50 nm during the growth stage; while the parameters p2 max_mesh , p3 max_mesh are set similarly according to the irradiation conditions, that is, at a certain irradiation dose, their values are greater than the maximum number of He / H in the cluster. It should be noted that when this condition is satisfied, the values should be taken as small as possible to make the grid resolution higher.

[0057] Further solution, step S2 specifically further includes the following steps:

[0058] S21. Establish a mapping relationship between the defect components and their storage addresses:

[0059] For the small cluster V p1 He p2 H p3(contains the number of vacancies p1 i ; p1, p2, p3 are floating-point numbers), the storage address index = nint (p3) + [nint (p2) + nint (p1) M j ]·M k , wherein nint is the rounding function;

[0060] For large clusters V p1 He p2 H p3 _mesh, the node storage address index = indz + [indy + indxM j_mesh ]·M k_mesh , indx, indy, indz are the coordinates of the three directions of the cluster discrete grid nodes respectively; its storage space node in the V direction coordinate 0-indx-(M i_mesh -1) and log10(R min_mesh )-log10(R)-log10(R max ) linear mapping, and then Correspondingly In the He direction 0-indy-(M j_mesh -1) and log10(p2 min_mesh +1)-log10(p2+1)-log10(p2 max +1) linear mapping, and then Similarly, in the H direction, there is a node coordinate and component mapping relationship

[0061] S22, through the mapping relationship, record the radius (structure attribute), binding energy (energy attribute) and diffusion coefficient (kinetics attribute) of each node representing the cluster: specifically, through the conversion relationship of step S21, the relationship between the components and the storage address of small and large defect clusters is obtained, and then for each node, record the radius, binding energy and diffusion coefficient of the cluster it represents.

[0062] Further scheme, the step S3 is specifically:

[0063] The action rate between two small clusters, between two large clusters, and between a small cluster and a large cluster is calculated respectively, and the specific calculation method is calculated by using the conventional rate theory method in the art; since for the node with zero defect number, since its action rate is zero, only the effective node with non-zero defect number is considered during calculation, and the action rate of all events is uniformly stored, so that the event to be executed is selected from different events in proportion to its rate, wherein the event type indicates different events, so as to indicate which event to execute when executing the event.

[0064] In a further embodiment, in step S4, an event to be executed is randomly selected from the action rate table calculated in step S3. There is no particular limitation on the specific selection method. In an exemplary embodiment of the present invention, a general roulette wheel method is used to randomly select an event to be executed.

[0065] In a further embodiment, in step S5, the events selected in step S4 are executed, and the newly generated clusters are pq1, pq2, and pq3, with a quantity of n. p The amount of reactants is reduced by one:

[0066] For reactions between small clusters, if pq1 <M i If the new cluster belongs to a small cluster, then its storage address is index = nint(pq3) + [nint(pq2) + nint(pq1)M j ]·M k Let the quantity and composition of the index node before the reaction be N and p, respectively. old (Taking the V direction as an example), then mixing in V pq1 He pq2 H pq3 The subsequent average composition is The number of nodes increases by n after the reaction. p If pq1≥M i The new cluster belongs to a large cluster, and its storage address is index = indz + [indy + indxM]. j_mesh ]·M k_mesh Where indx, indy, and indz are the node coordinates of the cluster in the three component directions, respectively, and have Let pq1 be the radius, and let the quantity and composition of the index node before the reaction be N and p, respectively. old (Taking the V direction as an example), then mixing in V pq1 He pq2 H pq3 The average composition after _mesh is The number of nodes increases by n after the reaction. p ;

[0067] For reactions between small clusters and large clusters, and between large clusters, if pq1 <M i If the new cluster belongs to a small cluster, then its storage address is index = nint(pq3) + [nint(pq2) + nint(pq1)M j ]·M k, let the number and component of index node before reaction be N, p old (For example, in V direction), then mix V pq1 He pq2 H pq3 The average component after _mesh is The number of this node increases n p after reaction; if pq1≥M i , then the new cluster belongs to large cluster, and its storage address index=indexz+[indy+indxM j_mesh ]·M k_mesh , wherein indx, indy, indz are the node coordinates of the cluster in three component directions respectively, and have is the radius corresponding to pq1; let the number and component of index node before reaction be N, p old (For example, in V direction), if index is not one of the two reaction nodes, then mix V pq1 He pq2 H pq3 The average component after _mesh is If index is one of the two reaction nodes, then mix V pq1 He pq2 H pq3 The average component after _mesh is The number of this node increases n p after reaction, wherein n c is the consumed number of index node after reaction.

[0068] In a typical embodiment of the present application, as shown in Figure 2 , the specific process of the above simulation method is as follows:

[0069] S100, start, enter S200;

[0070] S200, read in the component division parameter of small cluster and large cluster, the maximum component of large cluster, and the grid number of each dimension of component space, enter S300;

[0071] S300, store the structure, energy and dynamics properties of large cluster corresponding to small cluster and discrete node, enter S400;

[0072] S400, calculate the action rate inside and between small cluster and large cluster, construct rate table, enter S500;

[0073] S500, randomly select an event in the rate table, enter S600;

[0074] S600, execute the event selected in S500, enter S700;

[0075] S700, update the defect group component and quantity, enter S800;

[0076] S800, judge whether the simulated event or irradiation dose reaches the set value, if yes, enter S900; if no, return to S400, and iterate S400-S700;

[0077] S900, end, stop.

[0078] The present application represents the defect cluster in a large size range by dividing discrete mesh in the component space with limited storage, and at the same time, uses the component continuity to process small cluster, so as to simulate the microstructure evolution process of small and large defect cluster coexistence system, and save the memory space.

[0079] In another typical embodiment of the present application, a simulation system of microstructure evolution of small and large defect cluster coexistence system is provided, the simulation system comprises: an acquisition module, configured to acquire small cluster V p1 He p2 H p3 and large cluster V p1 He p2 H p3 _mesh component boundary parameters, maximum component of large cluster and mesh number of each dimension of component space, wherein, p1, p2 and p3 respectively represent the number of vacancy V, helium He and hydrogen H in the cluster; a storage module, configured to store the structure, energy and dynamics attribute of small cluster with continuous change of defect number and large cluster with discrete component; a calculation module, configured to calculate the action rate table of small cluster, large cluster and between them; a selection module, configured to randomly select an event in the rate table to be executed; and an execution module, configured to execute the corresponding event and update the defect component and quantity.

[0080] In another typical embodiment of the present application, a computer readable storage medium is provided, which stores a computer program, and the computer program is executed by a processor to make the processor execute the steps of the simulation method as described above.

[0081] It can be understood that the system provided by the embodiments of the present application corresponds to the method provided by the embodiments of the present application, and the explanation, examples and beneficial effects of the related content can refer to the corresponding part in the above method.

[0082] Those skilled in the art will appreciate that embodiments of the application can be devised for a method, a system, or a computer program product. Accordingly, the present application can be embodied in the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer readable program code.

[0083] The present application is described in reference to the flowchart and / or block diagrams of the method, apparatus (system) and computer program product according to embodiments of the application. It will be understood that each block of the flowchart and / or block diagrams, and combinations of blocks in the flowchart and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.

[0084] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.

[0085] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.

[0086] Application Example

[0087] The simulation method described in the present application is used to simulate the vacancy aggregation, growth process of vacancy clusters, and the change of the average size and density of vacancy clusters with time in a body-centered cubic iron bulk. The lattice constant a of the body-centered cubic iron bulk is The simulation temperature is 800°C.

[0088] The results are shown in FIG. 1, wherein Figure 3 The results are shown in FIG. 1, whereinFigure 3 The horizontal dotted line in a indicates the maximum size that can be simulated using conventional methods, by Figure 3 As can be seen from the results, the simulation method of the present application can simulate the situation in which clusters of up to 2.5 nm in size are present in the system. Thus, the present application can represent defect clusters in a larger size range using limited storage space.

[0089] The technical features of the above-described embodiments can be combined in any manner. In order to make the description simple, all possible combinations of the technical features in the above-described embodiments are not described, however, as long as the combinations of the technical features do not contradict each other, they should be considered to be within the scope of the present disclosure.

[0090] The above-described embodiments only express several implementation manners of the present application, and the description is relatively specific and detailed, however, it should not be understood as a limitation on the scope of the patent of the present application. It should be pointed out that, for ordinary skilled persons in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

Claims

1. A simulation method for the microstructure evolution of a system in which small and large defect clusters coexist, characterized in that, The method comprises the following steps: S1, set small cluster V p1 He p2 H p3 and large cluster V p1 He p2 H p3 _mesh component boundary parameters, the maximum component of large cluster and the number of grids in each dimension of component space, wherein p1, p2 and p3 represent the number of vacancies V, helium He and hydrogen H in the cluster respectively; S2, storing the structure, energy and dynamic properties of small clusters with continuous change of defect number and large clusters with discrete composition; S3, calculating the rate table of interaction within small clusters, within large clusters and between small and large clusters; S4, randomly selecting an event in the rate table to be executed; S5, executing the selected corresponding event, and updating the defect composition and number; In the step S1, small clusters V p1 He p2 H p3 Each direction grid number is M i , M j , M k , and the corresponding p1, p2, p3 change range is 0-M i -1, 0-M j -1, 0-M k -1; Large cluster V p1 He p2 H p3 _mesh each component direction grid number is M i_mesh , M j_mesh , M k_mesh ; set the minimum radius of large cluster R min_mesh , the maximum radius R max_mesh ; the minimum value of three component directions of large cluster is respectively p1 min_mesh , p2 min_mesh , p3 min_mesh , the maximum value is respectively: p1 max_mesh , p2 max_mesh , p3 max_mesh ; In view of the size relationship between small and large clusters, p1 min_mesh = M i ; according to the vacancy-type defect cluster spherical approximation, p2 where p2 V a is the volume of one atom; for He and H in the cluster, p2 min_mesh = 0.0, p3 min_mesh = 0.0; the values of the parameters M i , M j , and M k are constrained by the maximum array length Lmaxallowed in the computer, satisfying M i M j M k < Lmax; The step S2 specifically comprises: S21, establishing a mapping relationship between defect composition and its storage address; S22, recording the radius, binding energy and diffusion coefficient properties of the representative cluster of each node in storage through the mapping relationship; The step S21 specifically comprises: For small clusters V p1 He p2 H p3 whose storage address is index = nint(p3) + [nint(p2) + nint(p1) M j ] · M k where nint is the rounding function; For large cluster V p1 He p2 H p3 _mesh, whose nodes store address index = indz + [indy + indxM j_mesh ]·M k_mesh , indx, indy, indz are respectively the discrete grid node coordinates in three directions of the cluster; its storage space nodes are linearly mapped in V direction coordinates 0-indx-(M i_mesh -1) and log10(R min_mesh )-log10(R)-log10(R max ); thus , accordingly ; in He direction 0-indy-(M j_mesh -1) and log10(p2 min_mesh +1)-log10(p2+1)-log10(p2 max +1) are linearly mapped, thus ; in H direction, there is a node coordinate and component mapping relationship .

2. The method of claim 1, wherein the method is used to simulate the evolution of microstructure in a system containing both small and large defect clusters. For small clusters, M i is set to the order of 100; if He and / or H are not included in the model, M i is as large as possible while satisfying the memory constraint; R max_mesh is dynamically changed according to the simulation program and the irradiation dose level, and its value is greater than the size of the largest cluster in the system at a certain stage. Parameter p2 max_mesh , p3 max_mesh Depending on the irradiation conditions setting, its value is greater than the maximum He / H number in the cluster at a certain irradiation dose.

3. The method of claim 1, wherein the method is used to simulate the evolution of microstructure in a system containing both small and large defect clusters. The step S3 specifically comprises: The interaction rates between two small clusters, between two large clusters and between a small cluster and a large cluster are calculated respectively; when calculating, only the effective nodes with non-zero defect number are considered, and the interaction rates of all events are uniformly stored, wherein the event type indicates different events.

4. The method of claim 1, wherein the small and large defect cluster coexistence system microstructure evolution simulation method is characterized by, In the step S4, the roulette method is used to randomly select an event to be executed.

5. The method of claim 1, wherein the method is used to simulate the evolution of microstructure in a system containing both small and large defect clusters. In the step S5, the generated new clusters are respectively pq1, pq2, pq3, and the number thereof is n p , the number of reactants is reduced by one respectively: For the reaction between small clusters, if pq1 < M i , the new cluster belongs to small clusters, its storage address index = nint(pq3) + [nint(pq2) + nint(pq1) M j ]·M k , let the number and component of index node before reaction be N, p old , then the average component after mixing V pq1 He pq2 H pq3 _mesh is , and the number of this node increases n p after reaction; if pq1 ≥ M i , the new cluster belongs to large clusters, its storage address index = indz + [indy + indx M j_mesh ]·M k_mesh , wherein, indx, indy, indz are the node coordinates of the cluster in three component directions, and have , , , , the radius corresponding to pq1, let the number and component of index node before reaction be N, p old , then the average component after mixing V pq1 He pq2 H pq3 _mesh is , and the number of this node increases n p after reaction; For reactions between small clusters and large clusters, and between large clusters, if pq1 <M i If the new cluster belongs to a small cluster, then its storage address is index = nint(pq3) + [nint(pq2) + nint(pq1)M j ]·M k Let the quantity and composition of the index node before the reaction be N and p, respectively. old Then V is mixed in pq1 He pq2 H pq3 The subsequent average composition is After the reaction, the number of nodes increases by n. p If pq1≥M i If the new cluster belongs to a large cluster, then its storage address is index = indz + [indy + indxM]. j_mesh ]·M k_mesh Where indx, indy, and indz are the node coordinates of the cluster in the three component directions, respectively, and have , , , Let pq1 be the radius; let the quantity and composition of the index node before the reaction be N and p, respectively. old If the index is not one of the two reaction nodes, then V is mixed in. pq1 He pq2 H pq3 The average composition after _mesh is If index is one of two reaction nodes, then V is mixed in. pq1 He pq2 H pq3 The average composition after _mesh is After the reaction, the number of nodes increases by n. p , where n c This represents the number of index nodes consumed after the reaction.

6. A system for implementing the simulation method of any one of claims 1-5, characterized by The system comprises: The acquisition module is configured to acquire small clusters V p1 He p2 H p3 and large clusters V p1 He p2 H p3 The _mesh component boundary parameter, the maximum component of the large cluster, and the number of grids in each dimension of the component space, wherein p1, p2, and p3 respectively represent the number of vacancies V, helium He, and hydrogen H in the cluster. A storage module for storing the structure, energy and dynamic properties of small clusters with continuous change of defect number and large clusters with discrete composition; A calculation module for calculating the rate table of interaction within small clusters, within large clusters and between small and large clusters; A selection module for randomly selecting an event in the rate table to be executed; An execution module for executing the corresponding event and updating the defect composition and number.

7. A computer readable storage medium storing a computer program, wherein the computer program is executed by a processor to make the processor execute the steps of the simulation method according to any one of claims 1-5.

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