A method, device and storage medium for simulating second phase precipitation process

Abstract

The present invention discloses a method, apparatus, and storage medium for simulating the second-phase precipitation process, belonging to the field of computational materials science. By establishing a cellular automaton model of second-phase precipitation, the method can accurately and effectively simulate the evolution of second-phase particles with non-granular morphology precipitated at grain boundaries. This method accurately simulates the changes in the morphological characteristics and transformation dynamics of the second-phase during the precipitation process, a feasibility not achieved by traditional CA models. This invention addresses a gap in the existing technology, provides a theoretical basis for practical engineering applications, and has promising application prospects.

Description

Technical Field

[0001] The present invention belongs to the technical field of computational materials science, and in particular relates to a method, a device and a storage medium for simulating a second phase precipitation process. Background Art

[0002] Second-phase particles, which are either coherent or incoherent with the matrix, significantly hinder dislocation motion, increasing the material's strength. They have become a crucial strengthening method in industrial production. Therefore, rationally and precisely controlling their precipitation process is crucial for improving the material's microstructure and mechanical properties. However, the precipitation of second-phase particles is a strongly coupled process involving multiple physical fields and variables, making it challenging to control. Accurate prediction or control is difficult through experiments or mathematical models alone, necessitating the development of effective research methods to accurately predict or control their behavior.

[0003] The cellular automaton (CA) method has been used since the 1980s and 1990s. Thanks to its good computational flexibility and ability to better describe actual physical metallurgical phenomena, the CA method has gradually been applied in the field of materials science, mainly focusing on the prediction of recrystallization and solidification processes. As an interdisciplinary subject, the CA method requires researchers to have knowledge of mathematics, materials science, and computers. With the continuous development of computer technology, the upgrading of computing resources, and the continuous improvement of thermodynamic and kinetic data in the physical metallurgy of materials, the CA method has gradually been widely used in more aspects of the field of materials science. In addition to its application in recrystallization and solidification processes, it has also been applied to the prediction of microstructures in solid-state phase transformation processes.

[0004] Currently, CA method studies on phase transformation processes mainly focus on the transformation between austenite and ferrite in steel materials. However, there is currently a lack of effective methods for the precipitation of second-phase particles. Summary of the Invention

[0005] In order to solve the above problems, the present invention provides a method, device and storage medium for simulating the second phase precipitation process, which can accurately simulate the precipitation process of second phase particles, fill the gaps in the existing technology, and provide a theoretical basis for engineering practice.

[0006] The present invention is achieved through the following technical solutions:

[0007] The present invention discloses a method for simulating a second phase precipitation process, comprising:

[0008] S1: Establish a cellular automaton model for the second phase precipitation, and input the single-phase grain structure as the initial grain structure of the second phase precipitation into the cellular automaton model;

[0009] S2: After the current initial grain structure meets the nucleation incubation period conditions, randomly select a cell at the grain boundary and assign a random number between 0 and 1 to the cell. Calculate the number of second-phase nuclei in the current time step and obtain the nucleation probability of the current time step. Compare the random number of the current cell with the nucleation probability. If the random number of the current cell is less than the nucleation probability, the current cell transforms from matrix to second phase, the phase state transforms to the second phase state, the phase state variable transforms from 0 to 1, and the precipitation angle variable is assigned. Otherwise, the current cell does not meet the conditions and the phase state variable remains unchanged.

[0010] S3: Traverse all cells in the current computational domain, determine whether the state of each cell meets the state transition condition, and perform cell phase state transition and second phase boundary migration on cells that meet the state transition condition to achieve second phase growth;

[0011] S4: Determine whether the current calculation time reaches the set time for simulating the second phase precipitation; if not, return to S2; if the set time for the second phase precipitation simulation is reached, terminate the calculation and output the simulation results.

[0012] Preferably, in S1, the cells of the cellular automaton model are square cells; each cell has five state variables, namely:

[0013] grain orientation variable, indicating the orientation of the grains;

[0014] Grain boundary variable, distinguishing whether the cell is at the grain boundary or inside the grain, 0 means the cell is inside the grain, 1 means the cell is at the grain boundary;

[0015] Phase state variable, distinguishing the secondary phase from the parent phase, 0 indicates the parent phase state, and 1 indicates the secondary phase state;

[0016] Controlling the concentration variable of the second phase element, the calculation process controls the growth rate of the second phase by controlling the difference of the concentration variable of the second phase element at different positions;

[0017] The phase transition fraction variable represents the proportion of cells at the front end of the second phase growth interface that undergo the second phase transition.

[0018] Preferably, in S1, the cell transition rule of the cellular automaton model is:

[0019] 1) The cell growth rate at the front end of the second phase growth is greater than 0;

[0020] 2) The cell is located at the phase boundary between the second phase and the parent phase. The cell itself is not a second phase cell, and there are second phase cells in the neighboring cells in the precipitation angle direction;

[0021] 3) The second phase transition fraction variable of the cell is ≥1. The second phase transition fraction variable can be expressed as:

[0022]

[0023] Where, is the fraction of the second phase transition at time t, starting from the moment when the current calculation cell becomes the interface between the second phase and the matrix;

[0024] 4) The cells that meet the above conditions 1), 2) and 3) undergo a phase transition from the matrix to the second phase.

[0025] Preferably, in S2, the number of second phase nuclei in the current time step is calculated by a nucleation rate model, which is as follows:

[0026]

[0027] Where, is the second phase precipitation density of the material body; Z is the Zeldovich factor; β * is the coefficient related to the critical nucleation atoms of the second phase; ΔG * is the nucleation barrier of the second phase; τ sec It is the second phase nucleation incubation period.

[0028] Further preferably, the nucleation probability P sec_nuc Use the following formula to calculate:

[0029]

[0030] Where S GB is the grain boundary area; N GB is the number of grain boundary cells; N GB S GB That is, the number of second phase nucleations.

[0031] Further preferably, the Zeldovich factor is calculated using the following formula:

[0032] Where, is the atomic volume of the second phase; x and y are the atomic coefficients in the chemical composition of the second phase particles; Γ is the interface energy between the second phase and the matrix; q is a related parameter characterizing the axial ratio of the second phase; is the critical nucleation radius of the second phase;

[0033] Critical nucleation radius of the second phase Use the following formula to calculate:

[0034]

[0035] Where Δg is the chemical driving force for the nucleation of the second phase.

[0036] Further preferably, the phase nucleation barrier ΔG * Use the following formula to calculate:

[0037]

[0038] Where Δg is the chemical driving force for the nucleation of the second phase; The coefficient introduced to describe the activation energy of the second phase nucleation at different axial ratios;

[0039] The coefficient β related to the critical nucleation atoms of the second phase * for:

[0040]

[0041] Where a * is the average lattice parameter of the second phase and the matrix; X sec D is the atomic volume fraction of the element that controls the precipitation of the second phase before the second phase precipitates; sec The diffusion coefficient of the elements that precipitate in the second phase is controlled by temperature.

[0042] Further preferably, the diffusion coefficient D of the second phase precipitated element is controlled in relation to temperature. sec for:

[0043]

[0044] Where Q sec To control the diffusion activation energy of the second phase precipitation elements.

[0045] The present invention discloses a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for simulating the second phase precipitation process are implemented.

[0046] The present invention discloses a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for simulating the second phase precipitation process are implemented.

[0047] Compared with the prior art, the present invention has the following beneficial technical effects:

[0048] The method for simulating the second phase precipitation process disclosed in the present invention can accurately and effectively simulate the evolution process of the second phase particles with non-granular morphology precipitated at the grain boundaries, and can accurately simulate the changes in the morphological characteristics and transformation dynamics of the second phase during the precipitation of the second phase particles, which is not achievable with the traditional CA model. For the CA method to simulate physical metallurgical phenomena, its core is the relevant, reliable, and computer-implemented physical metallurgical model and the formulation of reasonable cellular transformation rules. However, at present, the traditional CA model cannot be used to simulate the precipitation process of the second phase particles with non-granular morphology due to the lack of a reliable physical metallurgical model and specific cellular transformation rules. The present invention fills the gaps in the existing technology, provides a theoretical basis for engineering practice, and has good application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 Schematic diagram of the method flow of the present invention;

[0050] Figure 2 Schematic diagram of the δ phase precipitation grain boundary in the embodiment;

[0051] Figure 3 Schematic diagram of the growth of the δ phase in the embodiment;

[0052] Figure 4 Comparison of the simulated and experimental results of the δ-phase precipitation process in the examples. Comparison of CA-based simulation and experimental results of the δ-phase precipitation microstructures at different holding times at T = 930°C: (a) Initial microstructure; (b) Simulation result at 0 min; (c) Simulation result at 120 min; (d) Simulation result at 240 min; (e) Simulation result at 360 min; (f) Experimental result at 0 min; (g) Experimental result at 120 min; (h) Experimental result at 240 min; (i) Experimental result at 360 min. DETAILED DESCRIPTION

[0053] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments, which are intended to explain the present invention rather than to limit it.

[0054] like Figure 1 The method for simulating the second phase precipitation process of the present invention comprises:

[0055] S1: Establish a cellular automaton model for the second phase precipitation, and input the single-phase grain structure as the initial grain structure of the second phase precipitation into the cellular automaton model;

[0056] S2: After the current initial grain structure meets the nucleation incubation period conditions, randomly select a cell at the grain boundary and assign a random number between 0 and 1 to the cell. Calculate the number of second-phase nuclei in the current time step and obtain the nucleation probability of the current time step. Compare the random number of the current cell with the nucleation probability. If the random number of the current cell is less than the nucleation probability, the current cell transforms from matrix to second phase, the phase state transforms to the second phase state, the phase state variable transforms from 0 to 1, and the precipitation angle variable is assigned. Otherwise, the current cell does not meet the conditions and the phase state variable remains unchanged.

[0057] S3: Traverse all cells in the current computational domain, determine whether the state of each cell meets the state transition condition, and perform cell phase state transition and second phase boundary migration on cells that meet the state transition condition to achieve second phase growth;

[0058] S4: Determine whether the current calculation time reaches the set time for simulating the second phase precipitation; if not, return to S2; if the set time for the second phase precipitation simulation is reached, terminate the calculation and output the simulation results.

[0059] In a preferred embodiment of the present invention, in S1, the cells of the cellular automaton model are square cells; each cell has five state variables, namely:

[0060] grain orientation variable, indicating the orientation of the grains;

[0061] Grain boundary variable, distinguishing whether the cell is at the grain boundary or inside the grain, 0 means the cell is inside the grain, 1 means the cell is at the grain boundary;

[0062] Phase state variable, distinguishing the secondary phase from the parent phase, 0 indicates the parent phase state, and 1 indicates the secondary phase state;

[0063] Controlling the concentration variable of the second phase element, the calculation process controls the growth rate of the second phase by controlling the difference of the concentration variable of the second phase element at different positions;

[0064] The phase transition fraction variable represents the proportion of cells at the front end of the second phase growth interface that undergo the second phase transition.

[0065] In a preferred embodiment of the present invention, in S1, the cell transition rule of the cellular automaton model is:

[0066] 1) The cell growth rate at the front end of the second phase growth is greater than 0;

[0067] 2) The cell is located at the phase boundary between the second phase and the parent phase. The cell itself is not a second phase cell, and there are second phase cells in the neighboring cells in the precipitation angle direction;

[0068] 3) The second phase transition fraction variable of the cell is ≥1. The second phase transition fraction variable can be expressed as:

[0069]

[0070] Where, is the fraction of the second phase transition at time t, starting from the moment when the current calculation cell becomes the interface between the second phase and the matrix;

[0071] 4) The cells that meet the above conditions 1), 2) and 3) undergo a phase transition from the matrix to the second phase.

[0072] In a preferred embodiment of the present invention, in S2, the number of second phase nuclei in the current time step is calculated by a nucleation rate model, and the nucleation rate model is as follows:

[0073]

[0074] Where, is the second phase precipitation density of the material body; Z is the Zeldovich factor; β * is the coefficient related to the critical nucleation atoms of the second phase; ΔG * is the nucleation barrier of the second phase; τ sec It is the second phase nucleation incubation period.

[0075] Nucleation probability P sec_nuc Use the following formula to calculate:

[0076]

[0077] Where S GB is the grain boundary area; N GB is the number of grain boundary cells; N GB S GB That is, the number of second phase nucleations.

[0078] The Zeldovich factor is calculated using the following formula:

[0079]

[0080] Where, is the atomic volume of the second phase; x and y are the atomic coefficients in the chemical composition of the second phase particles; Γ is the interface energy between the second phase and the matrix; q is a related parameter characterizing the axial ratio of the second phase; is the critical nucleation radius of the second phase;

[0081] Critical nucleation radius of the second phase Use the following formula to calculate:

[0082]

[0083] Where Δg is the chemical driving force for the nucleation of the second phase.

[0084] Phase nucleation barrier ΔG * Use the following formula to calculate:

[0085]

[0086] Where Δg is the chemical driving force for the nucleation of the second phase; The coefficient introduced to describe the activation energy of the second phase nucleation at different axial ratios;

[0087] The coefficient β related to the critical nucleation atoms of the second phase * for:

[0088]

[0089] Where a * is the average lattice parameter of the second phase and the matrix; X sec D is the atomic volume fraction of the element that controls the precipitation of the second phase before the second phase precipitates; sce The diffusion coefficient of the elements that precipitate in the second phase is controlled by temperature.

[0090] Temperature-dependent control of the diffusion coefficient D of the second phase precipitated elements sec for:

[0091]

[0092] Where Q sec To control the diffusion activation energy of the second phase precipitation elements.

[0093] The present invention will be further explained below with reference to a specific embodiment:

[0094] This example simulates the CA method for δ-phase precipitation in GH4169 alloy at a temperature of 930°C and a heating and holding time of 360 minutes. The original austenite is a uniform structure with an average grain size of 75 μm. This method consists of three main stages: initialization, time-step-by-time simulation, and output of simulation results.

[0095] (1) Cell: Select a square cell.

[0096] (2) Cell state: Each cell in the computation process has five state variables, which are as follows:

[0097] ① Grain orientation variable: used to indicate the orientation of grains.

[0098] ② Grain boundary variable: used to distinguish whether the cell is located at the grain boundary or within the grain. 0 means the cell is within the grain, and 1 means the cell is at the grain boundary.

[0099] ③ Phase state variable: used to distinguish the δ phase from the parent phase. 0 indicates the parent phase state, and 1 indicates the δ phase state.

[0100] ④Nb concentration variable: Because the Nb concentrations in the austenite matrix, δ phase, and the interface between the δ phase and the matrix are different, and the δ phase is controlled by the Nb concentration gradient, the calculation process controls the δ phase growth rate through the difference in Nb concentration variables at different positions.

[0101] ⑤ Phase transition fraction variable: represents the proportion of cells at the front end of the δ phase growth interface that undergo δ phase transition.

[0102] Specifically, the following steps are included:

[0103] Step 1: Input the single-phase grain structure as the initial grain structure for the precipitation of the second phase particles.

[0104] Step 2: After the nucleation incubation period conditions are met, randomly select a cell at the grain boundary and assign a random number r between 0 and 1 to the current calculation cell. rand , the number of second phase nuclei N in the current time step is calculated by the nucleation rate model δ_tstep , and at the same time obtain the nucleation probability P of the current time step δ_nuc The nucleation rate model is as follows:

[0105]

[0106] Where, is the δ phase precipitation density of the material body; Z is the Zeldovich factor; β * is the coefficient related to the critical nucleation atom of the δ phase; ΔG * is the nucleation barrier of the δ phase; τ δ is the δ phase nucleation incubation period. The Zeldovich factor can be calculated using the following formula:

[0107]

[0108] Where, The atomic volume of the δ phase is 1.2312×10 -29 m 3 The δ phase particle composition is Ni3Nb, and the values ​​of x and y are 3 and 1 respectively; Γ is the interface energy between the δ phase and the matrix, which is 0.1 J / m 2 ; q is a parameter related to the δ phase axial ratio. is the critical nucleation radius of the δ phase, which can be expressed as:

[0109]

[0110] Nucleation barrier ΔG * The following formula can be used for calculation:

[0111]

[0112] Where Δg is the chemical driving force for the nucleation of the δ phase; The coefficient introduced to describe the activation energy of the nucleation of the δ phase with different axial ratios. In formula (3-1), β * It can be expressed as:

[0113]

[0114] Where a * is the average lattice parameter of the δ phase and the matrix, which is 3.6077×10 -10 m;X Nb D is the atomic volume fraction of the element that controls the second phase precipitation before the δ phase precipitation; Nb To control the diffusion coefficient of the δ phase precipitation element, for the δ phase of GH4169 alloy, Nb is used. The temperature-dependent diffusion coefficient of the second phase precipitation element can be expressed as:

[0115]

[0116] Where, is the diffusion coefficient of Nb element at standard temperature, which is 8.8×10 -5 m 2 / s,Q Nb is the diffusion activation energy of Nb element, which is 143 kJ / mol.

[0117] The nucleation probability P δ_nuc The following formula can be used for calculation:

[0118]

[0119] Where S GB is the grain boundary area; N GB is the number of grain boundary cells.

[0120] Compare the random number of the current cell with the nucleation probability P δ_nuc The size of r rand <P δ_nuc , the current cell transforms from matrix to δ phase, the phase state transforms to δ phase state, the phase state variable transforms from 0 to 1, and a certain precipitation angle variable is given to it; otherwise, the current cell does not meet the conditions, and the phase state variable remains unchanged.

[0121] When the CA method is used to simulate the δ phase precipitation process, the two layers of cells at the interface between adjacent grains are considered to be cells at the grain boundary. Therefore, the grain boundary in the model is a double-layer cell, but the double-layer cells at the grain boundary have different orientations, such as Figure 2 When the δ phase nucleates at the grain boundary, in order to avoid the unreasonable phenomenon of the δ phase growing across the grain boundary, the orientation of the grain to which the cell belongs needs to be considered.

[0122] Step 3: Traverse all cells in the current computational domain, and determine whether each cell satisfies the aforementioned cell transformation rules. For cells that satisfy all cell transformation rules, perform cell phase state transformation, δ phase boundary migration, and realize δ phase growth, as shown in the following example: Figure 3 shown.

[0123] Step 4: Compare the current calculation time to see if it meets the simulation requirement of δ phase precipitation setting time. If not, return to step 2 to continue the calculation; if it meets the simulation requirement of δ phase precipitation setting time, terminate the calculation and output the required calculation results. The output simulation results are compared with the experimental results. Figure 4 shown.

[0124] The present invention also provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for simulating the second phase precipitation process of the present invention are implemented.

[0125] The method for simulating the second phase precipitation process of the present invention can be implemented in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention can be implemented in the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. If the method for simulating the second phase precipitation process of the present invention is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0126] Based on this understanding, in exemplary embodiments, a computer-readable storage medium is also provided. The present invention implements all or part of the process steps in the above-mentioned method embodiments, and can also be completed by instructing related hardware through a computer program. The computer program can be stored in the computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. Computer-readable storage media include permanent and non-permanent, removable and non-removable media, and can use any method or technology to implement information storage. The information can be computer-readable instructions, data structures, program modules, or other data. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased based on the requirements of legislation and patent practice in a jurisdiction. For example, in some jurisdictions, based on legislation and patent practice, computer-readable media does not include electric carrier signals and telecommunication signals. Among them, the computer storage medium can be any available medium or data storage device that can be accessed by the computer, including but not limited to magnetic storage (such as floppy disks, hard disks, magnetic tapes, magneto-optical disks (MO)), optical storage (such as CDs, DVDs, BDs, HVDs, etc.), and semiconductor storage (such as ROM, EPROM, EEPROM, non-volatile memory (NANDFLASH), solid-state drives (SSDs)), etc.

[0127] In an exemplary embodiment, a computer device is also provided, including a memory, a processor and a computer program stored in the memory and running on the processor, and the processor realizes the step of the method for simulating the second phase precipitation process when executing the computer program. The processor may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc.

[0128] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be embodied in the widest possible manner consistent with the principles and novel features disclosed herein.

[0129] The above description is only a specific embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for simulating the second phase precipitation process, characterized in that: include: S1: Establish a cellular automaton model for the second phase precipitation, and input the single-phase grain structure as the initial grain structure of the second phase precipitation into the cellular automaton model; S2: After the current initial grain structure meets the nucleation incubation period conditions, randomly select a cell at the grain boundary and assign a random number between 0 and 1 to the cell. Calculate the number of second-phase nuclei in the current time step and obtain the nucleation probability of the current time step. Compare the random number of the current cell with the nucleation probability. If the random number of the current cell is less than the nucleation probability, the current cell transforms from matrix to second phase, the phase state transforms to the second phase state, the phase state variable transforms from 0 to 1, and the precipitation angle variable is assigned. Otherwise, the current cell does not meet the conditions and the phase state variable remains unchanged. S3: Traverse all cells in the current computational domain, determine whether the state of each cell meets the state transition condition, and perform cell phase state transition and second phase boundary migration on cells that meet the state transition condition to achieve second phase growth; S4: Determine whether the current calculation time reaches the set time for simulating the second phase precipitation; if not, return to S2; if the set time for the second phase precipitation simulation is reached, terminate the calculation and output the simulation results; In S2, the number of second-phase nuclei in the current time step is calculated using the nucleation rate model, which is as follows: Where, is the second phase precipitation density of the material body; is the Zeldovich factor; is the coefficient related to the critical nucleation atoms of the second phase; is the nucleation barrier of the second phase; It is the second phase nucleation incubation period; Nucleation probability Use the following formula to calculate: Where, is the grain boundary area; is the number of grain boundary cells; That is, the number of second phase nucleations.

2. The method for simulating the second phase precipitation process according to claim 1, characterized in that: In S1, the cells of the cellular automaton model are square cells; each cell has five state variables, namely: grain orientation variable, indicating the orientation of the grains; Grain boundary variable, distinguishing whether the cell is at the grain boundary or inside the grain, 0 means the cell is inside the grain, 1 means the cell is at the grain boundary; Phase state variable, distinguishing the secondary phase from the parent phase, 0 indicates the parent phase state, and 1 indicates the secondary phase state; Controlling the concentration variable of the second phase element, the calculation process controls the growth rate of the second phase by controlling the difference of the concentration variable of the second phase element at different positions; The phase transition fraction variable represents the proportion of cells at the front end of the second phase growth interface that undergo the second phase transition.

3. The method for simulating the second phase precipitation process according to claim 1, characterized in that: In S3, the cell transition rule of the cellular automaton model is: 1) The cell growth rate at the front end of the second phase of growth is greater than 0; 2) The cell is located at the phase boundary between the second phase and the parent phase. The cell itself is not a second phase cell, and there are second phase cells in the neighboring cells in the precipitation angle direction; 3) Second phase transition fraction variable of the cell , the second phase transition fraction variable can be expressed as: Where, The time starts from the moment when the current calculation cell becomes the interface between the second phase and the matrix. The fraction of the second phase transition at time ; 4) Cells that meet the above conditions 1), 2) and 3) undergo phase transition, transforming from matrix to the second phase.

4. The method for simulating the second phase precipitation process according to claim 1, wherein The Zeldovich factor in the S2 nucleation rate model is calculated using the following formula: Where, is the atomic volume of the second phase; and are the atomic coefficients in the chemical composition of the second phase particles, respectively; is the interfacial energy between the second phase and the matrix; is the parameter that characterizes the axial ratio of the second phase; is the critical nucleation radius of the second phase; Critical nucleation radius of the second phase Use the following formula to calculate: Where, It is the chemical driving force for the nucleation of the second phase.

5. The method for simulating the second phase precipitation process according to claim 1, characterized in that: Phase nucleation barrier in the S2 nucleation rate model Use the following formula to calculate: Where, It is the chemical driving force for the nucleation of the second phase; The coefficient introduced to describe the activation energy of the second phase nucleation at different axial ratios; coefficients related to critical nucleation atoms of the second phase for: Where, is the average lattice parameter of the second phase and the matrix; To control the atomic volume fraction of the second phase precipitation element before the second phase precipitation; The diffusion coefficient of the elements that precipitate in the second phase is controlled by temperature.

6. The method for simulating the second phase precipitation process according to claim 5, characterized in that: Temperature-dependent control of the diffusion coefficient of the second phase precipitated elements for: Where, To control the diffusion activation energy of the second phase precipitation elements.

7. A computer device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the method for simulating the second phase precipitation process according to any one of claims 1 to 6 are implemented.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for simulating the second phase precipitation process according to any one of claims 1 to 6 are implemented.

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