A simulation method for the sintering process of all-ceramic microencapsulated dispersed fuel

Abstract

This invention discloses a simulation method for the sintering process of all-ceramic micro-encapsulated dispersed fuel, comprising the following steps: S1, preparing all-ceramic micro-encapsulated dispersed fuel pellets, measuring the density of the fuel pellets, and determining the microstructure and grain size of the fuel pellets; S2, obtaining the density distribution of the green body after initial pressing before sintering based on the powder plastic yield criterion model, and calculating the plastic strain rate; S3, establishing a thermoviscoelastic constitutive phenomenological model describing the sintering process of all-ceramic micro-encapsulated dispersed fuel based on elastic strain rate, plastic strain rate, thermal strain rate, and sintering strain rate; S4, establishing a finite element model; S5, substituting the thermoviscoelastic constitutive phenomenological model constructed in step S3 into the finite element model to predict density and stress field. This invention not only simulates the microstructure and stress changes during hot pressing sintering, but also fully considers all factors affecting strain, providing simulation accuracy.

Description

Technical Field

[0001] This invention relates to the field of all-ceramic micro-encapsulated dispersed fuel preparation technology, specifically to a simulation method for the sintering process of all-ceramic micro-encapsulated dispersed fuel. Background Technology

[0002] All-ceramic microencapsulated dispersed fuel is a fuel in which multilayer coated fuel microspheres are dispersed in a SiC ceramic matrix. It has excellent properties such as high thermal conductivity, high radiation stability and excellent fission product containment capacity. At the same time, it has good compatibility with coolants and high accident tolerance and safety reliability.

[0003] Sintering densification is crucial for the development of SiC-based microencapsulated dispersion fuels, and its suitability directly determines the microstructure and properties of all-ceramic SiC-based microencapsulated dispersion fuels. Currently, hot pressing is the primary method for sintering densification of all-ceramic SiC-based microencapsulated dispersion fuels. This process is time-consuming and involves numerous influencing factors, making experimental optimization of the sintering process extremely costly in terms of manpower, resources, and time. Therefore, it is necessary to develop a numerical simulation method for the sintering process of all-ceramic microencapsulated dispersion fuels.

[0004] In recent years, computer simulation of the hot pressing process of ceramic powder systems using finite element method (FEM) technology has become an effective method for ceramic material process design. Simulating the sintering process of all-ceramic SiC-based micro-encapsulated dispersed fuels using FEM technology can yield data on the geometry, stress-strain field, and density distribution of the powder blank during sintering. This data can then be used to analyze the causes of quality defects in the fuel pellets and improve the manufacturing process. Therefore, applying FEM numerical simulation to simulate the hot pressing and sintering process of all-ceramic SiC-based micro-encapsulated dispersed fuels will become a highly efficient method for process research and optimization.

[0005] However, to date, there is no integrated model for all-ceramic SiC-based microencapsulated dispersed fuels that combines the densification process, material dynamics, and mechanical evolution. Furthermore, existing simulations of the sintering process of ceramic SiC-based microencapsulated dispersed fuels do not consider the pre-sintering pressing and pre-sintering plastic deformation. Therefore, the simulated stress increments are biased. This bias has a smaller impact on single-phase SiC ceramics, but a greater impact on dispersed fuels containing ceramic microspheres, because the stress distribution non-uniformity within the latter is much greater than in the former, leading to a lack of accuracy in the finite element simulations. At the same time, existing ceramic sintering models generally lack experimental verification, making it difficult for researchers to provide clear guidance for correctly understanding and designing the sintering process of all-ceramic SiC-based microencapsulated dispersed fuels. Summary of the Invention

[0006] The purpose of this invention is to provide a simulation method for the sintering process of all-ceramic micro-encapsulated dispersed fuel, which not only simulates the microstructure and stress changes during hot pressing sintering, but also fully considers all factors affecting strain, thus providing simulation accuracy.

[0007] This invention is achieved through the following technical solution:

[0008] A simulation method for a fully ceramic microencapsulated dispersion fuel sintering process includes the following steps:

[0009] S1. Prepare all-ceramic micro-encapsulated dispersed fuel pellets based on hot pressing sintering technology, determine the density of the all-ceramic micro-encapsulated dispersed fuel pellets, and obtain the microstructure and grain size of the all-ceramic micro-encapsulated dispersed fuel pellets.

[0010] S2. Based on the powder plastic yield criterion model, the density distribution of the green body after initial pressing before sintering is obtained, and the plastic strain rate is calculated.

[0011] S3. Based on elastic strain rate, plastic strain rate, thermal strain rate and sintering strain rate, establish a thermoviscoelastic constitutive phenomenological model describing the sintering process of all-ceramic micro-encapsulated dispersed fuel. The sintering strain rate is based on the density, microstructure and grain size obtained in step S1 and is optimized using a neural network genetic algorithm.

[0012] S4. Based on the structural characteristics of ceramic micro-encapsulated dispersed fuel, a finite element model is established;

[0013] S5. Substitute the thermoviscoelastic constitutive phenomenological model constructed in step S3 into the finite element model to predict the density and stress field.

[0014] In constructing the thermoviscoelastic constitutive phenomenological model, this invention fully considers the factors affecting the total strain increment (elastic strain rate, plastic strain rate, thermal strain rate, and sintering strain rate), and optimizes the sintering strain rate based on the experimental data obtained from the prepared pellet test. This enables the thermoviscoelastic constitutive phenomenological model to accurately simulate the density prediction and stress field prediction of the sintering process of the all-ceramic micro-encapsulated dispersed fuel pellet when it is incorporated into the finite element model.

[0015] The miniaturized method described in this invention simulates the microstructure and stress changes during hot-pressing sintering to predict the deformation, defect formation, and distribution of all-ceramic microencapsulated dispersed fuels. Based on the simulation results, reasonable process parameters and targeted process measures can be formulated to minimize or avoid density inhomogeneity, deformation inconsistency, and defect generation after sintering, providing guidance for the process design and sintering process optimization of all-ceramic microencapsulated dispersed fuels.

[0016] Furthermore, in step S2, the powder plastic yield criterion model is as follows:

[0017]

[0018] In the formula: f is the yield function; σ m The hydrostatic pressure is σ1, σ2, and σ3, which are the three principal stresses. s γ represents the yield strength of the same dense material, which is a fitting parameter estimated at 800 MPa; γ represents the ratio of the apparent stress of the porous body to the effective stress of the matrix; β determines the influence of the hydrostatic stress component, both estimated as functions of the relative density ρ.

[0019] γ=p 2.5

[0020]

[0021] Furthermore, in step S3, the thermoviscoelastic constitutive phenomenological model is as follows:

[0022]

[0023] In the formula, and ε sint These are the total strain increment, elastic strain rate, plastic strain rate, thermal strain rate, and sintering strain rate, respectively.

[0024] Furthermore, the calculation model for thermal strain rate is as follows:

[0025]

[0026] In the formula, α and These are the coefficient of thermal expansion and the heating / cooling rate, respectively.

[0027] Furthermore, in step S3, the calculation model for the sintering strain rate is as follows;

[0028]

[0029] In the formula, σ′ is the deviatoric stress tensor, G is the shear modulus, and σ m Where σ is the hydrostatic pressure, K is the volumetric viscosity; sint This refers to sintering stress or sintering driving force; δ ij For Kronecker notation, when i = j, δ ij =1; when i≠j, δ ij =1.

[0030] Furthermore, based on the liquid specific surface energy γ1, the current porosity por, and the macropore volume fraction por t Initial grain size R g0 Current grain size R g Calculate the liquid phase pressure σ using empirical parameters B and n. lBased on solid-liquid specific surface energy γ sl Current matrix density and current grain size R g Calculation of solid-liquid interfacial energy σ sl Liquid phase pressure σ l Solid-liquid interfacial energy σ sl Summing to obtain the sintering stress σ sint .

[0031] Furthermore, the liquid phase pressure σ l The calculation model is as follows:

[0032]

[0033] In the formula, γ1, por, por c R g0 These represent the liquid specific surface energy, current porosity, macropore volume fraction, and initial grain size, respectively; B and n are empirical parameters, and R... g The calculation model is as follows:

[0034]

[0035] In the formula, R g0 ρ represents the initial grain size, and ρ represents the relative density.

[0036] Furthermore, the solid-liquid interface energy σ sl The calculation model is as follows:

[0037]

[0038] In the formula, ρ SiC For density; γ sl R is the specific surface energy of the solid and liquid; g The calculation model is as follows:

[0039]

[0040] In the formula, R g0 ρ represents the initial grain size, and ρ represents the relative density.

[0041] Furthermore, based on the viscous Poisson's ratio ν vp The ratio of shear modulus G to bulk viscosity K is calculated using the following formula:

[0042]

[0043] In the formula, v vp =0.5exp(6.8×(ρ-1));

[0044]

[0045] In the formula,

[0046] Z = 12ρ

[0047]

[0048] Where, k LΩ and E LΩ These are the model fitting parameters.

[0049] Furthermore, k is obtained based on a neural network genetic algorithm. LΩ and E LΩ Yes, that's the range.

[0050] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0051] 1. In constructing the thermoviscoelastic constitutive phenomenological model, this invention fully considers the factors affecting the total strain increment (elastic strain rate, plastic strain rate, thermal strain rate, and sintering strain rate), which can improve the accuracy of the simulation.

[0052] 2. The simulation method of the present invention can simulate the microstructure and stress changes during the hot pressing sintering process, and can formulate reasonable process parameters and targeted process measures based on the simulation results to avoid or reduce density inhomogeneity, deformation inconsistency and defect generation.

[0053] 3. This invention determines model parameters based on a small number of experiments and combined with machine learning programs, reducing reliance on human experience and accelerating the efficiency of process optimization. Attached Figure Description

[0054] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings:

[0055] Figure 1 This is a logic diagram of the simulation method of the present invention;

[0056] Figure 2 This is a schematic diagram of the sintering process;

[0057] Figure 3 The relationship between SiC matrix grain size and relative density;

[0058] Figure 4 A schematic diagram of the geometry, boundary conditions, and finite element mesh of the sample and mold;

[0059] Figure 5 The algorithm flow for extreme value optimization in neural network genetic algorithms;

[0060] Figure 6 This is a comparison chart of predicted density and experimental results. Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0062] Example 1:

[0063] like Figure 1 As shown, a simulation method for the sintering process of all-ceramic microencapsulated dispersed fuel is illustrated in this embodiment using ceramic SiC-based microencapsulated dispersed fuel, including:

[0064] Step 1: Cubic silicon carbide (β-SiC) nanoparticles with an average particle size of approximately 40 nm, a mixture of nano-alumina (Al₂O₃) and nano-yttrium oxide (Y₂O₃) powders with an average particle size of 30 nm were used as sintering aids. The mass ratio of the sintering aids was 2 wt%–10 wt%, with a molar ratio of Al₂O₃ to Y₂O₃ of 5:3. Multilayer coated fuel microspheres were prepared using ZrO₂ spheres as the simulated fuel core, with a diameter of approximately 800 μm. The multilayer coated fuel microspheres were uniformly mixed with the β-SiC powder and sintering aids, and then hot-pressed and sintered to form a core sample with a diameter of 30 mm and a thickness of 25 mm. The sintering process used was as follows: Figure 2 As shown. After preparation, the sample density was determined using the Archimedes method; the microstructure and grain size of the sample were analyzed using SEM and XRD; and model parameters were determined using typical experimental data.

[0065] Step 2: Construct a thermoviscoelastic constitutive phenomenological model

[0066] The all-ceramic SiC-based micro-encapsulated dispersed fuel pellet blank is composed of powder and pores. The macroscopic mechanical model proposed in this patent is based on the principles of continuum mechanics, using a viscoplastic constitutive relation to describe the sintering densification behavior of the powder. Simultaneously considering the elastic and thermal expansion effects of the material, a thermoviscoelastic constitutive phenomenological model describing the sintering process of the all-ceramic SiC-based micro-encapsulated dispersed fuel is established. Its total strain increment can be divided into three parts:

[0067]

[0068] In the formula and ε sint These represent the total strain increment, elastic strain rate, plastic strain rate, thermal strain rate, and strain rate caused by sintering densification, respectively. Assuming the material is linearly elastic and isotropic, then the stress increment... To solve for Equation (1), we convert it into the rate form of Hooke's Law:

[0069]

[0070] Where D is the elastic stiffness matrix.

[0071] In the initial pressing stage, the volume of the preform containing a certain amount of porosity changes continuously during plastic deformation. The study of the properties of powder plastic materials usually treats it as a compressible continuous medium, and describes it using the yield condition, flow law, and hardening law of continuous medium plasticity theory. The following Shima model is adopted for the yield criterion of powder plasticity:

[0072]

[0073] In the above formula: f is the yield function; σ m The hydrostatic pressure is σ1, σ2, and σ3, which are the three principal stresses. s The yield strength of the same dense material is estimated to be 500 MPa based on experimental results. γ represents the ratio of the apparent stress of the porous body to the effective stress of the matrix, and β determines the influence of the hydrostatic stress component, both estimated as functions of the relative density ρ.

[0074] γ=ρ 2.5 (4)

[0075]

[0076] Before sintering, the green body is only subjected to compressive stress. Combining equations (1)-(5), we can ignore the stress. and ε sint The phase distribution, density distribution, and matrix density distribution of the green body before sintering can be obtained. After sintering begins, the plastic strain rate during sintering can be obtained by combining subsequent equations (6)-(22) simultaneously. .

[0077] Thermal strain rate is expressed as:

[0078]

[0079] Where α and These are the coefficient of thermal expansion and the heating / cooling rate, respectively.

[0080] The expression for the strain rate caused by sintering is as follows:

[0081]

[0082] Where σ′ is the stress deviatoric tensor, G is the shear modulus, and σ m σ is the hydrostatic pressure, K is the bulk viscosity. aint Sintering stress or sintering driving force is the liquid phase pressure σ. l and solid-liquid interfacial energy σ sl sum.

[0083] σ sint =σ l +σ sl (8)

[0084] σ l It is the specific surface energy of the liquid γ l Current porosity por, volume fraction of macropores (pore size greater than 2 / 3 of the grain radius) por c Initial grain size R g0 Current grain size R g Functions of empirical parameters B and n:

[0085]

[0086] Where, γ l por c R g0 B and n are each taken as 0.25 J / m 2 , 0.1, 20nm, 0.12 and 1. σ sl It is the solid-liquid specific surface energy γ sl Current SiC packing density ρ SiC and the current grain size R g Functions:

[0087]

[0088] SiC density ρ SiC It satisfies the following relationship with relative density ρ:

[0089] ρ SiC =ρ(1-V1) (11)

[0090] Where V1 is the liquid volume fraction, estimated to be 0.045. For randomly distributed particles, the theoretical ratio of shear modulus G to bulk viscosity K is 0.6, while most experimental values ​​fall between 0.1 and 0.5. Here we use the viscous Poisson's ratio v, which is frequently used in experiments and simulations. vp Estimate:

[0091]

[0092] Among them, v vp =0.5exp(6.8×(ρ-1)) (13)

[0093] Equation (13) describes how the viscous Poisson's ratio approaches 0.5 when an incompressible material gradually becomes denser.

[0094] According to the model by Riedel et al., the rate of change of relative density of SiC The contribution is estimated to be three parts: i) particle rearrangement; ii) melting of sintering aid particles; and iii) contact flattening mechanism. Numerical experiments revealed that particle rearrangement and reaction rate are minor factors in densification, hence the simplified Riedel model yields...

[0095]

[0096]

[0097] Z = 12ρ (16)

[0098] Where δ is the strain of the particle center distance caused by the flattening mechanism, Z is the coordination number, ρ0 is the initial relative density, Ω is the atomic volume of the dissolved substance, and L is the reaction rate constant of the solution or precipitate. Except for LΩ, all values ​​in this model are known values ​​or functions of packing density. LΩ can be expressed as an Arrhenius function:

[0099]

[0100] k LΩ E LΩ These are the model fitting parameters.

[0101] For liquid-phase sintering, the mechanical model based on viscoplastic constitutive and solution redeposition theories involves many parameters related to material properties and processes. Accurate determination of these parameters is crucial to ensuring the correctness of numerical simulation results. In summary, the evolution equations of the sintering and pressing models essentially require only two state and field variables: the current solid relative density ρ and the current particle size R. g Kleebe et al. found that coarsening during SiC liquid-phase sintering is primarily due to solution redeposition, meaning large particles grow at the expense of smaller particles, as the higher curvature of smaller particles provides a greater driving force, thus preferentially dissolving them. Grain size increases with sintering time, and densification depends on grain size. German et al. compared different types of ceramics, showing that at low densities, grain size can be approximated as a linear function of relative density; conversely, at high densities, grain size is inversely proportional to the square root of relative density. Therefore, a simple grain coarsening function in μm was used to fit the measured experimental data.

[0102] Figure 3 The results show that the fitting results are in good agreement with the experimental results.

[0103] Besides grain size, other model parameters need to be determined to avoid overcomplicating the fitting process. For example, the liquid volume fraction V... l It is estimated to be 0.045 from the weight ratio. Similarly, ρ c γ sl γ lpor c B and n were determined to be 0.152 and 0.25 J / m, respectively. 2 0.25J / m 2 The values ​​of 0.1, 0.12, and 1 are all taken from reported data in reliable literature. The simulation also requires the material properties of the SiC matrix, including elasticity, Poisson's ratio, and coefficient of thermal expansion, which evolve with density and temperature during densification.

[0104] E=[460GPa-0.04GPa / K×T×exp(-962K / T)]exp(-3.57×(1-ρ)) (19)

[0105] v = 0.21 (20)

[0106]

[0107] The elastic constant, Poisson's ratio, and coefficient of thermal expansion of the ZrO2 microspheres are 200 GPa, 0.25, and 1 × 10⁻⁶, respectively. -5 l / K, while the silicon carbide layer coated on the ZrO2 microspheres uses the values ​​of equations (19)-(21) at zero porosity. Finally, there are only two parameters k. LΩ and E LΩ This needs to be determined.

[0108] During the sintering process, the fuel pellets may deform due to friction. Therefore, Coulomb's law of friction is used to estimate the frictional effect:

[0109] σ t =μσ n (twenty two)

[0110] In the formula, σ t σ is the tangential frictional stress along the surface of the component. n Let μ be the contact pressure and μ be the Coulomb friction coefficient. Equilibrium and (22) are applied as boundary conditions to the contact surface between the fuel pellet and the die. For simplicity, a constant friction coefficient of 0.1 is used in this study.

[0111] Step 3: Due to time constraints, simulations can only be performed a limited number of times, making it difficult to find the optimal model parameter values. At this point, based on the known experimental data and the model, the problem of "finding the optimal model parameter values" can be transformed into a "function extremum optimization problem":

[0112]

[0113] stk min ≤k LΩ ≤k max

[0114] E min ≤ELΩ ≤E max

[0115] n is the number of experimental data points, ρ exp and ρ sim These are the experimentally measured density value and the calculated simulated value under the same process, respectively. Here, [k] min k max ] and [E min E max ] represents the possible value range, which is set to

[10] . -10 10 -8 [300(kJ / mol), 500(kJ / mol)].

[0116] For nonlinear functions of unknown form, it is difficult to accurately find the function's extrema solely based on the function's input and output data. Such problems can be solved using a combination of neural networks and genetic algorithms, leveraging the nonlinear fitting ability of neural networks and the nonlinear optimization ability of genetic algorithms to find the function's extrema. LΩ E LΩ From [k] min k max ] and [E min E max Select specific values ​​within the range: k min k min +0.2(k max -k min ), k min +0.5(k max -k min ), k min +0.7(k max -k min ), k max E min E min +0.2(E max -E min E min +0.5(E max -E min E min +0.7(E max -E min E max These values ​​were selected as model parameters, and the average density ρ under the same process conditions (same temperature, pressure, and sintering time) was simulated respectively. sim Then, based on (23), we calculate the corresponding F value. Finally, we establish an input set [k LΩ E LΩThe output is a dataset of [F]. Experiments were conducted at temperatures of 1600℃, 1650℃, 1700℃, and 1750℃, with each temperature measured 15 times at different times, resulting in 4 × 15 data points. For each specific experimental value, data was extracted from [k] according to the principles described above. min k max ] and [E min E max The range of values ​​will generate 5 × 5 = 25 sets of data. Therefore, this dataset will generate a total of 4 × 15 × 5 × 5 = 1500 sets of data.

[0117] In this example, since the nonlinear function has two input parameters and one output parameter, the BP neural network structure is determined to be 2-5-1. The process and model parameters are used as input data, and the prediction density is used as output data. 1400 sets of data are randomly selected to train the network, and 100 sets are used to test the network performance. The trained network is then used to predict the output of the nonlinear function. The training is repeated 10,000 times, and the result with the smallest error is selected as the final training model. The genetic algorithm for extreme value optimization uses the prediction results of the trained BP neural network as the individual fitness value; the smaller the individual fitness value, the better the individual. The global optimum of the function and its corresponding input value are found through selection, crossover, and mutation operations. Since the optimization function has only two input parameters, the individual length is 2. The population size is set to 10, the number of evolutions is 50, the crossover probability is set to 0.4, and the mutation probability is set to 0.2. The algorithm flow is as follows: Figure 5 As shown.

[0118] Step 4: Assuming an initial volume fraction of 10% for ZrO2 microspheres, a 3D model of the fuel pellet containing randomly distributed coated particles is generated, with dimensions identical to the actual pellet. Because calculating the entire geometric model is too complex, and considering z-axis symmetry, only 1 / 24 of the model is extracted for modeling, as shown below. Figure 4 As shown. Furthermore, only the side mold is considered, ignoring the upper and lower molds to simplify the model and facilitate convergence. Three boundary conditions are applied: a specified pressure boundary (temperature below 1600℃, pressure below 20MPa) is applied to the upper surface of the sample, while fixed constraints are applied to the lower surface of the sample and the upper and lower surfaces of the mold. A free boundary is applied to the outer surface of the mold, and symmetrical boundaries are applied to the inner surfaces of the sample and mold. A suitable mesh size is determined through parametric analysis. Figure 4 The example has approximately 700,000 grids.

[0119] Step 5: After generating the geometric mesh, it was used as the initial structure and sintered and pressed together with the mold in a simulation. Experimental measurements showed that the initial relative density of the core was approximately 42%. Therefore, the initial relative density and particle size were assumed to be 42% and 0.2 μm, respectively.

[0120] It is generally believed that dynamic elastic motion and heat transfer can reach equilibrium in a shorter time, therefore mechanical motion can be considered quasi-static to reduce unnecessary complexity. During sintering, it is assumed that the temperature is spatially uniform. The time step used in the simulation is 0.1 min, which is sufficient to provide smooth and fine results. The time / density / displacement list is updated by updating the density and displacement, and repeating the above calculations according to the sintering regime. The simulation's starting temperature is set to 1200 °C because the densification rate is very low below 1200 °C, while the sintering aids begin to melt around 1430 K. Although our method can fully simulate the complete thermal cycle, the cooling phase is also ignored to save computation time. Therefore, according to... Figure 2 The sintering regimes were simulated at 1600℃, 1650℃, 1700℃ and 1750℃, with simulation times of 140 min, 157 min, 173 min and 190 min, respectively.

[0121] After obtaining all computational and experimental datasets, an external optimization algorithm (neural genetic optimization algorithm) was used to optimize [k]. min k max ] and [E min E max The range is used to determine the model parameters. Finally, the optimized values ​​are fed back into the model. Figure 6 The simulated density changes were compared with experimental results and were basically consistent.

[0122] This embodiment modifies and combines the powder theory model and the liquid phase sintering model, and uses the finite element method to simulate sintering, which can accurately predict the influence of microstructure and process parameters on deformation and stress during sintering.

[0123] Although the present invention has described the SiC matrix and cylindrical pellets, those skilled in the art, upon learning the basic inventive concept, can modify or equivalently substitute the technical solutions of the present invention. For example, the material system and fuel pellet shape can be changed to apply it to different temperature and pressure conditions, or even to non-nuclear fields. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the essence of the invention; these modifications and combinations are still within the protection scope of this invention.

[0124] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A simulation method for the sintering process of all-ceramic micro-encapsulated dispersed fuel, characterized in that, Includes the following steps: S1. Prepare all-ceramic micro-encapsulated dispersed fuel pellets based on hot pressing sintering technology, determine the density of the all-ceramic micro-encapsulated dispersed fuel pellets, and obtain the microstructure and grain size of the all-ceramic micro-encapsulated dispersed fuel pellets. S2. Based on the powder plastic yield criterion model, the density distribution of the green body after initial pressing before sintering is obtained, and the plastic strain rate is calculated. S3. Based on elastic strain rate, plastic strain rate, thermal strain rate and sintering strain rate, establish a thermoviscoelastic constitutive phenomenological model describing the sintering process of all-ceramic micro-encapsulated dispersed fuel. The sintering strain rate is based on the density, microstructure and grain size obtained in step S1 and is optimized using a neural network genetic algorithm. S4. Based on the structural characteristics of ceramic micro-encapsulated dispersed fuel, a finite element model is established; S5. Substitute the thermoviscoelastic constitutive phenomenological model constructed in step S3 into the finite element model to predict the density and stress field.

2. The simulation method for the sintering process of all-ceramic micro-encapsulated dispersed fuel according to claim 1, characterized in that, In step S2, the powder plastic yield criterion model is as follows: In the formula: f is the yield function; σ m The hydrostatic pressure is σ1, σ2, and σ3, which are the three principal stresses. s γ represents the yield strength of the same dense material, which is a fitting parameter estimated at 800 MPa; γ represents the ratio of the apparent stress of the porous body to the effective stress of the matrix; β determines the influence of the hydrostatic stress component, both estimated as functions of the relative density ρ. c = p 2.5 3. The simulation method for the sintering process of all-ceramic micro-encapsulated dispersed fuel according to claim 1, characterized in that, In step S3, the thermoviscoelastic constitutive phenomenological model is as follows: In the formula, and ε sint These are the total strain increment, elastic strain rate, plastic strain rate, thermal strain rate, and sintering strain rate, respectively.

4. The simulation method for the sintering process of all-ceramic micro-encapsulated dispersed fuel according to claim 3, characterized in that, The calculation model for thermal strain rate is as follows: In the formula, α and These are the coefficient of thermal expansion and the heating / cooling rate, respectively.

5. The simulation method for the sintering process of all-ceramic micro-encapsulated dispersed fuel according to claim 3, characterized in that, In step S3, the calculation model for the sintering strain rate is as follows: In the formula, σ ′ Let G be the stress deviatoric tensor, G be the shear modulus, and σ be the shear modulus. m Where σ is the hydrostatic pressure, K is the volumetric viscosity; sint This refers to sintering stress or sintering driving force; δ ij For Kronecker notation, when i = j, δ ij =1; when i≠j, δ ij =0.

6. The simulation method for the sintering process of all-ceramic micro-encapsulated dispersed fuel according to claim 5, characterized in that, Based on liquid specific surface energy γ l Current porosity por, macropore volume fraction por c Initial grain size R g0 Current grain size R g The liquid phase pressure σ1 is calculated using empirical parameters B and n; the specific surface energy γ of the solid and liquid phases is also used. sl Current matrix density and current grain size R g Calculation of solid-liquid interfacial energy σ sl Liquid phase pressure σ1 and solid-liquid interfacial energy σ sl Summing to obtain the sintering stress σ sint .

7. The simulation method for the sintering process of all-ceramic micro-encapsulated dispersed fuel according to claim 6, characterized in that, The calculation model for liquid phase pressure σ1 is as follows: In the formula, γ1, por, porc, R g0 These represent the liquid specific surface energy, current porosity, macropore volume fraction, and initial grain size, respectively; B and n are empirical parameters, and R... g The calculation model is as follows: In the formula, R g0 ρ represents the initial grain size, and ρ represents the relative density.

8. The simulation method for the sintering process of all-ceramic micro-encapsulated dispersed fuel according to claim 6, characterized in that, Solid-liquid interfacial energy σ sl The calculation model is as follows: In the formula, ρ SiC For density; γ sl R is the specific surface energy of the solid and liquid; g The calculation model is as follows: In the formula, R g0 ρ represents the initial grain size, and ρ represents the relative density.

9. The simulation method for the sintering process of all-ceramic micro-encapsulated dispersed fuel according to claim 5, characterized in that, Based on viscous Poisson's ratio ν vP The ratio of shear modulus G to bulk viscosity K is calculated using the following formula: In the formula, ν vp =0.5exp(6.8×(ρ-1)), where ρ is the relative density; In the formula, ρ0 is the initial relative density; V1 is the liquid phase volume percentage; Z = 12ρ Where, k LΩ and E LΩ These are the model fitting parameters, R is the molar gas constant, and T is the absolute temperature.

10. The simulation method for the sintering process of all-ceramic micro-encapsulated dispersed fuel according to claim 9, characterized in that, k is obtained based on neural network genetic algorithm. LΩ and E LΩ The range.

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